what are the effects of deforestation on climate change essay

• ENVIRONMENT

Why deforestation matters—and what we can do to stop it

Large scale destruction of trees—deforestation—affects ecosystems, climate, and even increases risk for zoonotic diseases spreading to humans.

As the world seeks to slow the pace of climate change , preserve wildlife, and support more than eight billion people , trees inevitably hold a major part of the answer. Yet the mass destruction of trees—deforestation—continues, sacrificing the long-term benefits of standing trees for short-term gain of fuel, and materials for manufacturing and construction.

We need trees for a variety of reasons, not least of which is that they absorb the carbon dioxide we exhale and the heat-trapping greenhouse gases that human activities emit. As those gases enter the atmosphere, global warming increases, a trend scientists now prefer to call climate change.

There is also the imminent danger of disease caused by deforestation. An estimated 60 percent of emerging infectious diseases come from animals, and a major cause of viruses’ jump from wildlife to humans is habitat loss, often through deforestation.

But we can still save our forests. Aggressive efforts to rewild and reforest are already showing success. Tropical tree cover alone can provide 23 percent of the climate mitigation needed to meet goals set in the Paris Agreement in 2015, according to one estimate .

Causes of deforestation

Forests still cover about 30 percent of the world’s land area, but they are disappearing at an alarming rate. Since 1990, the world has lost more than 420 million hectares or about a billion acres of forest, according to the Food and Agriculture Organization of the United Nations —mainly in Africa and South America. About 17 percent of the Amazonian rainforest has been destroyed over the past 50 years, and losses recently have been on the rise . The organization Amazon Conservation reports that destruction rose by 21 percent in 2020 , a loss the size of Israel.

Farming, grazing of livestock, mining, and drilling combined account for more than half of all deforestation . Forestry practices, wildfires and, in small part, urbanization account for the rest. In Malaysia and Indonesia, forests are cut down to make way for producing palm oil , which can be found in everything from shampoo to saltine crackers. In the Amazon, cattle ranching and farms—particularly soy plantations—are key culprits .

For Hungry Minds

Logging operations, which provide the world’s wood and paper products, also fell countless trees each year. Loggers, some of them acting illegally , also build roads to access more and more remote forests—which leads to further deforestation. Forests are also cut as a result of growing urban sprawl as land is developed for homes.

Not all deforestation is intentional. Some is caused by a combination of human and natural factors like wildfires and overgrazing, which may prevent the growth of young trees.

Why it matters

There are some 250 million people who live in forest and savannah areas and depend on them for subsistence and income—many of them among the world’s rural poor.

Eighty percent of Earth’s land animals and plants live in forests , and deforestation threatens species including the orangutan , Sumatran tiger , and many species of birds. Removing trees deprives the forest of portions of its canopy, which blocks the sun’s rays during the day and retains heat at night. That disruption leads to more extreme temperature swings that can be harmful to plants and animals.

With wild habitats destroyed and human life ever expanding, the line between animal and human areas blurs, opening the door to zoonotic diseases . In 2014, for example, the Ebola virus killed over 11,000 people in West Africa after fruit bats transmitted the disease to a toddler who was playing near trees where bats were roosting.

( How deforestation is leading to more infectious diseases in humans .)

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Some scientists believe there could be as many as 1.7 million currently “undiscovered” viruses in mammals and birds, of which up to 827,000 could have the ability to infect people, according to a 2018 study .

Deforestation’s effects reach far beyond the people and animals where trees are cut. The South American rainforest, for example, influences regional and perhaps even global water cycles, and it's key to the water supply in Brazilian cities and neighboring countries. The Amazon actually helps furnish water to some of the soy farmers and beef ranchers who are clearing the forest. The loss of clean water and biodiversity from all forests could have many other effects we can’t foresee, touching even your morning cup of coffee .

In terms of climate change, cutting trees both adds carbon dioxide to the air and removes the ability to absorb existing carbon dioxide. If tropical deforestation were a country, according to the World Resources Institute , it would rank third in carbon dioxide-equivalent emissions, behind China and the U.S.

What can be done

The numbers are grim, but many conservationists see reasons for hope . A movement is under way to preserve existing forest ecosystems and restore lost tree cover by first reforesting (replanting trees) and ultimately rewilding (a more comprehensive mission to restore entire ecosystems).

( Which nation could be the first to be rewilded ?)

Organizations and activists are working to fight illegal mining and logging—National Geographic Explorer Topher White, for example, has come up with a way to use recycled cell phones to monitor for chainsaws . In Tanzania, the residents of Kokota have planted more than 2 million trees on their small island over a decade, aiming to repair previous damage. And in Brazil, conservationists are rallying in the face of ominous signals that the government may roll back forest protections.

( Which tree planting projects should you support ?)

Stopping deforestation before it reaches a critical point will play a key role in avoiding the next zoonotic pandemic. A November 2022 study showed that when bats struggle to find suitable habitat, they travel closer to human communities where diseases are more likely to spillover. Inversely, when bats’ native habitats were left intact, they stayed away from humans. This research is the first to show how we can predict and avoid spillovers through monitoring and maintaining wildlife habitats.

For consumers, it makes sense to examine the products and meats you buy, looking for sustainably produced sources when you can. Nonprofit groups such as the Forest Stewardship Council and the Rainforest Alliance certify products they consider sustainable, while the World Wildlife Fund has a palm oil scorecard for consumer brands.

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Original research article, the unseen effects of deforestation: biophysical effects on climate.

• 1 Department of Environmental Sciences, University of Virginia, Charlottesville, VA, United States
• 2 The Woodwell Climate Research Center, Falmouth, MA, United States
• 3 The Alliance of Bioversity International and the International Center for Tropical Agriculture, Cali, Colombia

Climate policy has thus far focused solely on carbon stocks and sequestration to evaluate the potential of forests to mitigate global warming. These factors are used to assess the impacts of different drivers of deforestation and forest degradation as well as alternative forest management. However, when forest cover, structure and composition change, shifts in biophysical processes (the water and energy balances) may enhance or diminish the climate effects of carbon released from forest aboveground biomass. The net climate impact of carbon effects and biophysical effects determines outcomes for forest and agricultural species as well as the humans who depend on them. Evaluating the net impact is complicated by the disparate spatio-temporal scales at which they operate. Here we review the biophysical mechanisms by which forests influence climate and synthesize recent work on the biophysical climate forcing of forests across latitudes. We then combine published data on the biophysical effects of deforestation on climate by latitude with a new analysis of the climate impact of the CO 2 in forest aboveground biomass by latitude to quantitatively assess how these processes combine to shape local and global climate. We find that tropical deforestation leads to strong net global warming as a result of both CO 2 and biophysical effects. From the tropics to a point between 30°N and 40°N, biophysical cooling by standing forests is both local and global, adding to the global cooling effect of CO 2 sequestered by forests. In the mid-latitudes up to 50°N, deforestation leads to modest net global warming as warming from released forest carbon outweighs a small opposing biophysical cooling. Beyond 50°N large scale deforestation leads to a net global cooling due to the dominance of biophysical processes (particularly increased albedo) over warming from CO 2 released. Locally at all latitudes, forest biophysical impacts far outweigh CO 2 effects, promoting local climate stability by reducing extreme temperatures in all seasons and times of day. The importance of forests for both global climate change mitigation and local adaptation by human and non-human species is not adequately captured by current carbon-centric metrics, particularly in the context of future climate warming.

Introduction

Failure to stabilize climate is in itself a large threat to biodiversity already at risk from deforestation. Protection, expansion, and improved management of the world’s forests represent some of the most promising natural solutions to the problem of keeping global warming below 1.5–2 degrees ( Griscom et al., 2017 ; Roe et al., 2019 ). Forests sequester large quantities of carbon; of the 450–650 Pg of carbon stored in vegetation ( IPCC, 2013 ), over 360 Pg is in forest vegetation ( Pan et al., 2013 ). Adding the carbon in soils, forests contain over 800 PgC, almost as much as is currently stored in the atmosphere ( Pan et al., 2013 ). In addition, forests are responsible for much of the carbon removal by terrestrial ecosystems which together remove 29% of annual CO 2 emissions (∼11.5 PgC; Friedlingstein et al., 2019 ). Globally, forest loss not only releases a large amount of carbon to the atmosphere, but it also significantly diminishes a major pathway for carbon removal long into the future ( Houghton and Nassikas, 2018 ). Tropical forests, which hold the greatest amount of aboveground biomass and have one of the fastest carbon sequestration rates per unit land area ( Harris et al., 2021 ), face the greatest deforestation pressure ( FAO, 2020 ). Given the long half-life and homogenous nature of atmospheric CO 2 , current forest management decisions will have an enduring impact on global climate through effects on CO 2 alone. However, forests also impact climate directly through controls on three main biophysical mechanisms: albedo, evapotranspiration (ET) and canopy roughness.

The direct biophysical effects of forests moderate local climate conditions. As a result of relatively low albedo, forests absorb a larger fraction of incoming sunlight than brighter surfaces such as bare soil, agricultural fields, or snow. Changes in albedo can impact the radiation balance at the top of the atmosphere and thus global temperature. The local climate, however, is not only impacted by albedo changes but also by how forests partition incoming solar radiation between latent and sensible heat. Deep roots and high leaf area make forests very efficient at moving water from the land surface to the atmosphere via ET, producing latent heat. Thus, beneath the forest canopy, the sensible heat flux and associated surface temperature are relatively low, especially during the growing season when ET is high ( Davin and de Noblet-Ducoudré, 2010 ; Mildrexler et al., 2011 ; Alkama and Cescatti, 2016 ). This cooling is enhanced by the relatively high roughness of the canopy, which strengthens vertical mixing and draws heat and water vapor away from the surface. Higher in the atmosphere, as water vapor condenses, the latent heat is converted to sensible heat. As a result, warming that began with sunlight striking the canopy is felt higher in the atmosphere rather than in the air near the land surface. These non-radiative processes stabilize local climate by reducing both the diurnal temperature range and seasonal temperature extremes ( Lee et al., 2011 ; Zhang et al., 2014 ; Alkama and Cescatti, 2016 ; Findell et al., 2017 ; Forzieri et al., 2017 ; Hirsch et al., 2018 ; Lejeune et al., 2018 ). Their impact on global climate, however, is less clear.

Despite high spatial variability, forest biophysical impacts do follow predictable latitudinal patterns. In the tropics, higher incoming solar radiation and moisture availability provide more energy to drive ET and convection, which in combination with roughness overcome the warming effect of low albedo, and result in year round cooling by forests. At higher latitudes, where incoming solar radiation is highly seasonal, the impacts of ET and surface roughness are diminished ( Anderson et al., 2011 ; Li et al., 2015 ) and albedo is the dominant biophysical determinant of the climate response. In boreal forests, relatively low albedo and low ET cause strong winter and spring warming. In the summer, higher incoming radiation and somewhat higher ET result in mild cooling by boreal forests ( Alkama and Cescatti, 2016 ). In the mid-latitudes, forest cover results in mild biophysical evaporative cooling in the summer months and mild albedo warming in the winter months ( Davin and de Noblet-Ducoudré, 2010 ; Li et al., 2015 ; Schultz et al., 2017 ). The latitude of zero net biophysical effect, the point at which the annual effect of the forest shifts from local cooling to local warming, ranges from 30 to 56°N in the literature ( Figure 1 ). These generalized latitudinal trends can be modified by aridity, elevation, species composition, and other characteristics, which vary across a range of spatial scales ( Anderson-Teixeira et al., 2012 ; Williams et al., 2021 ).

Figure 1. Latitude of net zero biophysical effect of forests on local temperature varies from 30 to 56°N. Above the line, forest cover causes local warming; below the line, forest cover causes local cooling. The thickness of the line indicates the number of studies that show forest cooling up to that threshold. Data sources as indicated.

Various mechanisms can amplify or dampen a forest’s direct effects on the energy and water balance, with climate impacts in the immediate vicinity, in remote locations, or both ( Bonan, 2008 ). Indirect biophysical effects are particularly important in the boreal region where snow-forest albedo interactions are prevalent. Low albedo forests typically mask high albedo snow, resulting in local radiative warming ( Jiao et al., 2017 ). At the larger scale this forest-induced warming is transferred to the oceans and further amplified by interactions with sea ice ( Brovkin et al., 2004 ; Bala et al., 2007 ; Davin and de Noblet-Ducoudré, 2010 ; Laguë and Swann, 2016 ). In fact, indirect biophysical feedbacks appear to dominate the global temperature response to deforestation in the boreal region ( Devaraju et al., 2018 ). Future climate warming may alter the strength of such feedbacks, depending on the rate at which forests expand northward and the extent and persistence of spring snow cover in a warmer world.

In the tropics, where ET and roughness are the dominant biophysical drivers, forests cool the lower atmosphere, but also provide the water vapor to support cloud formation ( Teuling et al., 2017 ). Clouds whiten the atmosphere over forests and thus increase albedo, at least partially offsetting the inherently low albedo of the forest below ( Heald and Spracklen, 2015 ; Fisher et al., 2017 ). However, the water vapor in clouds also absorbs and re-radiates heat, counteracting some of the cloud albedo-induced cooling ( Swann et al., 2012 ). In the Amazon basin, evidence suggests that deep clouds may occur more frequently over forested areas as a result of greater humidity and consequently greater convective available potential energy ( Wang et al., 2009 ). The impact of tropical deforestation on cloud formation is modified by biomass burning aerosols ( Liu et al., 2020 ) and the net impact on global climate is unclear. Quantifying these indirect biophysical feedback effects is an ongoing challenge for the modeling community particularly in the context of constraining future climate scenarios.

Forest production of biogenic volatile organic compounds (BVOC), which affect both biogeochemical and biophysical processes, further complicate quantification of the net climate impact of forests. BVOC and their oxidation products regulate secondary organic aerosols (SOA), which are highly reflective and result in biophysical cooling. SOA also act as cloud condensation nuclei, enhancing droplet concentrations and thereby increasing cloud albedo, which leads to additional biophysical cooling ( Topping et al., 2013 ). On the other hand, SOA can also cause latent heat release in deep convective cloud systems resulting in strong radiative warming of the atmosphere ( Fan et al., 2012 , 2013 ). Furthermore, through impacts on the oxidative capacity of the atmosphere, BVOC increase the lifetime of methane and lead to the formation of tropospheric ozone in the presence of nitrogen oxides ( Arneth et al., 2011 ; McFiggans et al., 2019 ). The persistence of ozone and methane (both greenhouse gases) results in a biogeochemical warming effect. The net effect of forest BVOC at both local and global scales remains uncertain. Current evidence, from modeling forest loss since 1850, suggests that BVOC result in a small net cooling, if indirect cloud effects are included ( Scott et al., 2018 ). The strongest effect is in the tropics, where BVOC production is highest ( Messina et al., 2016 ).

An improved understanding of the combined effects of forest carbon and biophysical controls on both local and global climate is necessary to guide policy decisions that support global climate mitigation, local adaptation and biodiversity conservation. The relative importance of forest carbon storage and biophysical effects on climate depend in large part on the spatial and temporal scale of interest. Local surface or air temperature may not be sensitive to the incremental impact of atmospheric CO 2 removed by forests growing in a particular landscape or watershed. In contrast, local temperature is sensitive to biophysical changes in albedo, ET and roughness. At regional and global scales, where the cumulative effects of forests on atmospheric CO 2 become apparent in the temperature response, we can usefully compare these impacts. Estimates of the relative impact of biophysical and biogeochemical (e.g., carbon cycle) processes on global or zonal climate have been provided primarily by model simulations of large-scale deforestation or afforestation ( Table 1 ). These studies generally show that CO 2 effects on global temperature are many times greater than the biophysical effects of forest cover or forest loss. In models depicting global or zonal deforestation outside the tropics, however, global warming from CO 2 release offsets only 10–90% of the global biophysical cooling. The global CO 2 effects of total deforestation in the tropics greatly outweigh the global biophysical effects ( Table 1 ). With the exception of Davin and de Noblet-Ducoudré (2010) , these studies have estimated the net contribution of biophysical processes, without isolating the individual biophysical components. Here, we provide a new analysis of CO 2 -induced warming from deforestation by 10° latitudinal increments ( Supplementary Information 1 ). We then compare the CO 2 effect with the only published determination of biophysical effects by latitude ( Davin and de Noblet-Ducoudré, 2010) to clarify the potential net impact of forest loss in a particular region on local and global climate.

Table 1. Forest effects on global temperature in modeling experiments from biogeochemical (CO 2 ) versus biophysical impacts (albedo, evapotranspiration and roughness as well as changes in atmospheric and ocean circulation, snow and ice, and clouds).

Materials and Methods

In the scientific literature, biophysical impacts have been quantified using a number of different methods. In situ observational data, including weather station and eddy flux measurements, have shaped our understanding of the direct biophysical impacts of forests on the surface energy balance. With the advantage of high temporal resolution, they allow for process level investigation of forest biophysical impacts and attribution of temperature changes to particular biophysical forcings, both radiative (albedo) and non-radiative (ET and roughness) ( Lee et al., 2011 ; Luyssaert et al., 2014 ; Vanden Broucke et al., 2015 ; Bright et al., 2017 ; Liao et al., 2018 ). Remote sensing techniques have recently been used to extrapolate to larger scales, providing a global map of forest cover effects on local climate ( Li et al., 2015 ; Alkama and Cescatti, 2016 ; Bright et al., 2017 ; Duveiller et al., 2018 ; Prevedello et al., 2019 ). However, in contrast to in situ approaches which generally measure near surface air temperature (generally but not always at 2 m), remote sensing studies have investigated the response of land surface temperature (i.e., skin temperature) which is 0.5–3 times more sensitive to forest cover change ( Alkama and Cescatti, 2016 ; Novick and Katul, 2020 ).

Generally, both in situ and remote sensing analyses have adopted a space-for-time approach where differences in surface climate of neighboring forest and non-forest sites are used as proxies for the climate signal from deforestation/afforestation over time. This approach assumes that neighboring sites share a common background climate and that any temperature differences between them can be attributed solely to differences in forest cover. Consequently, large-scale biophysical feedback effects are ignored. New observation-based methodologies have been devised to investigate impacts from ongoing land use change rather than estimating climate sensitivities to idealized forest change ( Alkama and Cescatti, 2016 ; Bright et al., 2017 ; Prevedello et al., 2019 ), however, they too measure only local biophysical impacts.

Numerical modeling of paired climate simulations with contrasting forest cover is necessary to investigate the net climate response to forest cover change, including both local and non-local impacts. Model simulations have focused on idealized scenarios of large-scale deforestation/afforestation which are more likely to trigger large-scale climate feedbacks than more realistic incremental forest cover change. Discrepancies between observed and modeled results may be due in part to the influence of indirect climate feedbacks that are not captured by observations ( Winckler et al., 2017a , 2019a ; Chen and Dirmeyer, 2020 ). Unfortunately, model resolution is currently too coarse to guide local policy decisions. Modeling results are also plagued by a number of uncertainties associated with the partitioning of energy between latent or sensible heat ( de Noblet-Ducoudré et al., 2012 ). The predicted impacts of similar land cover changes are model specific and can vary in sign, magnitude, and geographical distribution ( Devaraju et al., 2015 ; Lawrence and Vandecar, 2015 ; Garcia et al., 2016 ; Laguë and Swann, 2016 ; Stark et al., 2016 ; Quesada et al., 2017 ; Boysen et al., 2020 ) and therefore must be viewed with caution. In this paper, we synthesize all types of observational data from the literature to illustrate the biophysical impacts of forests on local climate. However, given that local impacts have been extensively explored and summarized in the past ( Anderson et al., 2011 ; Perugini et al., 2017 ), and because we wish to include indirect effects and feedbacks, we rely predominantly on modeling studies and our own calculations to elucidate the role of forests at different latitudes in shaping climate.

Effects on Global Temperature From Deforestation by 10° Latitude Band

We combined published data on biophysical effects of deforestation by latitude with our own analysis of CO 2 effects from deforestation by latitude to compare the relative strength of biophysical factors and CO 2 (the dominant biogeochemical factor) affecting global climate. Most modeling experiments available in the literature involve total deforestation at all latitudes, and the ocean feedbacks prove very strong ( Davin and de Noblet-Ducoudré, 2010) . Here, we consider land-only effects within a given 10° latitudinal band as this scale of impact is more indicative of the effects of regional or more incremental change on global temperature than the combined land/ocean effects. Finer scale, more realistic forest loss scenarios would not trigger massive cooling through albedo effects on the oceans. Area-scaled, land-only biophysical effects from deforestation provide the most realistic comparison with the effects of carbon stored by forests, and released through deforestation, at a given latitude. The biophysical response was derived from the results of Davin and de Noblet-Ducoudré (2010) who simulated total deforestation and decomposed the temperature response, by 10° latitude bands, into the fraction due to albedo, evapotranspiration, roughness and a non-linear response (see Supplementary Table 1 ).

The biogeochemical response was estimated by accounting for the CO 2 effect of deforestation, using existing biomass data and known equilibrium temperature sensitivity to doubled CO 2 . The principal input to our analysis is a 2016 global extension of the 500-m resolution aboveground carbon density (ACD) change (2003–2016) product applied by Walker et al. (2020) to the Amazon basin. It is based on an approach to pantropical ACD change estimation developed by Baccini et al. (2017) . The pantropical product combined field measurements with colocated NASA ICESat GLAS spaceborne light detection and ranging (LiDAR) data to calibrate a machine-learning algorithm that produced estimates of ACD using MODIS satellite imagery. This approach was modified for application to the extratropics, principally the temperate and boreal zones but also extratropical South America, Africa and Australia, using 47 allometric equations compiled from 27 unique literature sources for relating field-based measurements of aboveground biomass to airborne LiDAR metrics ( Chapman et al., 2020 ). These equations were used to predict ACD within the footprints of GLAS LiDAR acquisitions in each region with the result being a pseudo-inventory of LiDAR-based estimates of ACD spanning the extratropics. This dataset was then combined with the pantropical dataset first generated by Baccini et al. (2012) to produce a global database of millions of spatially explicit ACD predictions. This database was used to calibrate six ecoregional MODIS-based models for the purposes of generating a global 500-m resolution map of ACD for the year 2016. Additional details on these methods can be found in Chapman et al. (2020) .

The total aboveground carbon (GtC) was summed for each 10° latitude band and converted to CO 2 (GtC*44/12 = GtCO 2 , Supplementary Information 1 ). The mass of CO 2 was converted to ppm CO 2 in the atmosphere (2.12 Gt/ppm). The derived CO 2 concentration was reduced by 23% to account for ocean uptake ( Global Carbon Project, 2019 ). We assumed that no uptake occurred on land, as the carbon stock in vegetation was completely removed in our experiment to match what occurred in Davin and de Noblet-Ducoudré (2010) . Next, we calculated the global temperature response to the increase in atmospheric CO 2 due to the CO 2 released by completely deforesting each 10° latitudinal band using the equilibrium temperature sensitivity derived from general circulation models. Given the accepted value of 3°C (±1.5°C) for a doubling of atmospheric CO 2 (an increase of 280 ppm) (IPCC, 2013), we determined that temperature sensitivity is equivalent to 0.107°C (±0.054°C) for every 10 ppm increase in atmospheric CO 2 content.

To determine the global temperature response to deforestation of a given band, we calculated the area-weighted values for each biophysical response within each latitude band. The area encompassed by 10° of latitude increases toward the equator. Thus, to determine the contribution of a given band to a global temperature response, scaling by the surface area within the band was essential. We used average temperature responses over the land only to avoid the strong bias associated with ocean feedbacks from global scale implementation of deforestation.

For the global analysis, we also determined the contribution of BVOC to global temperature change for deforestation of each 10° of latitude. Scott et al. (2018) described the warming from deforestation due to BVOC in relation to the amount of cooling due to changes in albedo. For the tropics, the BVOC effect on global temperature was 17% of the albedo effect. For the temperate zone, it was 18% and for the boreal, it was 2% of the albedo effect. We applied these scalars (with an opposite sign) to the albedo figures for each 10° latitude band.

Effects on Regional (Local) Temperature From Deforestation by 10° Latitude Band

To analyze the effect of deforesting 10° of latitude on the temperature within that latitude zone (‘local’ effect), we did not scale by area within the band. Rather we assessed the average temperature change across the band, locally felt, as reported in the original study. The CO 2 effect was calculated as above and then scaled to reflect the sensitivity of a given latitudinal band to a global forcing. Only the CO 2 emitted by the latitudinal band itself was considered when determining the locally felt effects of CO 2 in a given band. Our experimental design involved global deforestation and all emitted CO 2 would have had an effect in a given band, but the point of the analysis was to isolate the temperature change caused by forests in a given latitude. We determined the latitudinal sensitivity to warming in response to added CO 2 from a re-analysis of global 2 m temperature data (CERA-20C) obtained from the European Centre for Medium-Range Weather https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/cera-20c . We compared average temperatures from 1901 to 1910 and 2001–2010, by latitude on land only (inadequate land only data for 50–60S and 80–90N; for those, we do not report a locally felt CO 2 effect). Then we divided the temperature change for each latitude band by the change in global temperature over the same period. We scaled the effect of CO 2 emitted by a given 10° latitude band by this sensitivity to represent the influence of non-linear responses such as polar amplification (see Supplementary Information 1 and Supplementary Table 2 ).

Biophysical Effects of Deforestation on Local Climate: A Broader Context

Our analysis is the first to compare regional scale biophysical and CO 2 impacts from regional scale deforestation but the literature is replete with data on local biophysical impacts. The results for local biophysical effects (100s of m to 100s of km) agree with our results at the regional scale (below). Figures 2 , 3 synthesize local biophysically-driven temperature responses to deforestation, as indicated by forest/no-forest comparisons or forest change over time, from the scientific literature. Satellite and flux tower data indicate that surface temperatures in tropical forests are significantly lower than in cleared areas nearby. On an annual basis, local surface cooling of 0.2–2.4°C has been observed (mean 0.96°C, Figure 2 and Supplementary Information 2 ). In the temperate zone, satellite studies of land surface temperature (which is more sensitive than the temperature of the air at 2 m) have shown biophysical cooling from forest cover, or biophysical warming from deforestation (0.02–1.0°C, mean of 0.4°C; see Figure 2 and Supplementary Information 2 ). Both in situ and satellite data generally indicate an average annual cooling of under 1°C from boreal deforestation ( Figure 2 ). Across latitudinal zones, warming from deforestation is generally greater during the day, and during the dry (hot) season ( Figure 3 ).

Figure 2. Local average annual temperature change in response to deforestation (black symbols) or afforestation (green symbols) as determined by comparing neighboring forested and open land (space for time approach) or measuring forest change over time in the tropics, temperate and boreal zones, by (A) in situ or (B) satellite based land surface temperature measurements (0 m, triangles) or air temperature measurements (2 m, circles). See Supplementary Information 2 for data sources.

Figure 3. Local temperature change in response to deforestation by season and time of day in the various climate zones as determined by comparing neighboring forested and open land (space for time approach) or measuring forest change over time. Warm/dry season response, averaged over the entire diurnal cycle, in red shading and cold/wet season response in blue shading. Daytime response, averaged over the entire annual cycle, in yellow shading and nighttime response in gray shading. See Supplementary Information 3 for data sources.

CO 2 -Induced Warming Versus Biophysical Effects on Regional (Local) Temperature From Deforestation by 10° Latitude Band

As expected, the regionally felt effect of regionally (10° band) produced CO 2 is very small compared to any individual biophysical effect or the sum of all non-CO 2 effects ( Figure 4 ). These results indicate that the net impact of all non-CO 2 effects is negligible between 20 and 30N. Beyond 30N the local biophysical response to deforestation is cooling. In the broader literature, this latitude of net zero biophysical effect on local temperature is generally between 30 and 40N ( Figure 1 ).

Figure 4. Effect of complete deforestation on local annual temperature by climate factor, averaged across the land surface within a 10° latitudinal band. Complete deforestation was implemented globally and analyzed by 10° latitudinal bands ( Davin and de Noblet-Ducoudré, 2010) . The CO 2 effect was determined from total aboveground biomass in each 10° band after Walker et al. (2020) and scaled by CERA-derived sensitivity by latitude. Inset distinguishes the sum of all local biophysical effects from local CO 2 effects.

Biophysical Effects on Global Temperature From Deforestation by 10° Latitude Band

For most latitudinal bands, the strongest biophysical effect of deforestation is cooling from albedo changes. In the tropics, however, the warming effect of lost roughness is comparable to or greater than the albedo effect ( Figure 5A ). Adding the warming from lost evapotranspiration, the net biophysical effect from tropical deforestation is global warming, as much as 0.1°C contributed each by latitudes 0°–10°S and 0°–10°N. The net biophysical effect of intact tropical forest, therefore, is global cooling; slightly more cooling if BVOCs are also considered (see Figure 5B ). Roughness effects are generally greater than evapotranspiration effects across latitudes providing a strong counterbalance to albedo effects ( Davin and de Noblet-Ducoudré, 2010 ; Burakowski et al., 2018 ; Winckler et al., 2019b ; Figure 5A ). Albedo almost balances the combined effect of roughness, evapotranspiration, BVOC and non-linear effects between 20 and 30°N resulting in close to zero net biophysical effect on global temperature ( Figure 5B ). From 30–40°N and northward, albedo dominates, and the net biophysical effect of deforestation is cooling.

Figure 5. Effect of complete deforestation on global temperature by 10° band of latitude. (A) Contribution to global temperature change by climate forcing factor. Biophysical factors are from Davin and de Noblet-Ducoudré, 2010 , area-weighted. BVOC effects are estimated relative to albedo effects based on Scott et al., 2018 . CO 2 effect is based on aboveground live biomass for each 10° latitudinal band following Baccini et al., 2017 and Walker et al., 2020 . (B) Net biophysical and BVOC effect versus CO 2 effect. (C) Cooling or warming effects of deforestation by 10° latitudinal band (BVOC included). “Forests as mountains” map of aboveground biomass carbon in woody vegetation ca. 2016 courtesy of Woodwell Climate Research Center and shaded to indicate where deforestation results in net global warming. See Supplementary Information 1 for details.

CO 2 -Induced Warming Versus Biophysical Effects on Global Temperature From Deforestation by 10° Latitude Band

From 30°S to 30°N, the biophysical effect of deforesting a given 10° latitudinal band is about half as great and in the same direction as the CO 2 effect: global warming. Biophysical warming is around 60% as great as warming from released CO 2 in the outer tropics (20°S–10°S and 10°N–20°N) and about 35% as great in the heart of the tropics (10°S–10°N). Biophysical cooling due to deforestation from 30°N to 40°N offsets about 40% of the warming associated with carbon loss from deforestation; from 40°N to 50°N biophysical effects offset 85% of CO 2 effects ( Figure 5B ). Above 50°N, biophysical global cooling is 3–6 times as great as CO 2 induced global warming. The net impact of deforestation (effects of CO 2 , biophysical processes and BVOC combined) is warming at all latitudes up to 50N ( Figure 5C ). Thus, from 50S to 50N, an area that encompasses approximately 65% of global forests ( FAO, 2020 ), deforestation results in global warming ( Figure 5C ).

All Forests Provide Local Climate Benefits Through Biophysical Effects

Ignoring biophysical effects on local climate means casting aside a powerful inducement to promote global climate goals and advance forest conservation: local self-interest. The biogeochemical effect of forests tends to dominate the biophysical effect at the global scale because physical effects in one region can cancel out effects in another. Nevertheless, biophysical effects are very important, and can be very large, at the local scale (e.g., Anderson-Teixeira et al., 2012 ; Bright et al., 2015 ; Jiao et al., 2017 ; Figures 2 – 4 ). The role of forests in maintaining critical habitat for biodiversity is well known, but new research on extinction confirms the role of forests in maintaining critical climates to support biodiversity. Changes in maximum temperature are driving extinction, not changes in average temperature ( Román-Palacios and Wiens, 2020 ). Deforestation is associated with an increase in the maximum daily temperature throughout the year in the tropics and during the summer in higher latitudes ( Lee et al., 2011 ; Zhang et al., 2014 ). Of course deforestation also increases average daytime temperatures in boreal, mid-latitude and tropical forests ( Figure 3 ). The biophysical effects of forests also moderate local and regional temperature extremes such that extremely hot days are significantly more common following deforestation even in the mid- and high latitudes ( Vogel et al., 2017 ; Stoy, 2018 ). Historical deforestation explains ∼1/3 of the present day increase in the intensity of the hottest days of the year at a given location ( Lejeune et al., 2018 ). It has also increased the frequency and intensity of hot dry summers two to four fold ( Findell et al., 2017 ). Local increases in extreme temperatures due to forest loss are of comparable magnitude to changes caused by 0.5°C of global warming ( Seneviratne et al., 2018 ). Forests provide local cooling during the hottest times of the year anywhere on the planet, improving the resilience of cities, agriculture, and conservation areas. Forests are critical for adapting to a warmer world.

Forests also minimize risks due to drought associated with heat extremes. Deep roots, high water use efficiency, and high surface roughness allow trees to continue transpiring during drought conditions and thus to dissipate heat and convey moisture to the atmosphere. In addition to this direct cooling, forest ET can influence cloud formation ( Stoy, 2018 ), enhancing albedo and potentially promoting rainfall. The production of BVOCs and organic aerosols by forests accelerates with increasing temperatures, enhancing direct or indirect (cloud formation) albedo effects. This negative feedback on temperature has been observed to counter anomalous heat events in the mid-latitudes ( Paasonen et al., 2013 ).

Some Forests Provide Global Climate Benefits Through Biophysical Effects

Disregarding the biophysical effects of specific forests on global climate means under-selling some forest actions and over-selling others. The response to local forest change is not equivalent for similar sized areas in different latitudes. According to Arora and Montenegro (2011) warming reductions per unit reforested area are three times greater in the tropics than in the boreal and northern temperate zone due to a faster carbon sequestration rate magnified by year-round biophysical cooling. Thus, considering biophysical effects significantly enhances both the local and global climate benefits of land-based mitigation projects in the tropics (see Figures 4 , 5 ).

Constraints on Forest Climate Benefits in the Future

Climate change is likely to alter the biophysical effect of forests in a variety of ways. Deforestation in a future (warmer) climate could warm the tropical surface 25% more than deforestation in a present-day climate due to stronger decreases in turbulent heat fluxes ( Winckler et al., 2017b ). In a warmer climate, reduced snow cover in the temperate and boreal regions will lead to a smaller albedo effect and thus less biophysical cooling with high latitude deforestation. In addition to snow cover change, future rainfall regimes will affect the response of climate to changes in forest cover ( Pitman et al., 2011 ) as rainfall limits the supply of moisture available for evaporative cooling. Increases in water use efficiency due to increasing atmospheric CO 2 may reduce evapotranspiration ( Keenan et al., 2013 ), potentially reducing the local cooling effect of forests and altering atmospheric moisture content and dynamics at local to global scales. Future BVOC production may increase due to warming and simultaneously decline due to CO 2 suppression ( Lathière et al., 2010 ; Unger, 2014 ; Hantson et al., 2017 ). The physiological and ecological responses of forests to warming, rising atmospheric CO 2 and changing precipitation contribute to uncertainty in the biophysical effect of future forests on climate.

Forest persistence is essential for maintaining the global benefits of carbon removals from the atmosphere and the local and global benefits of the physical processes described above. Changing disturbance regimes may limit forest growth and regrowth in many parts of the world. Dynamic global vegetation models currently show an increasing terrestrial carbon sink in the future. This sink is thought to be due to the effects of fertilization by rising atmospheric CO 2 and N deposition on plant growth as well as the effects of climate change lengthening the growing season in northern temperate and boreal areas ( Le Quéré et al., 2018 ). Free-air carbon dioxide enrichment (FACE) experiments often show increases in biomass accumulation under high CO 2 but results are highly variable due to nutrient limitations and climatic factors ( Feng et al., 2015 ; Paschalis et al., 2017 ; Terrer et al., 2018 ). Climate change effects on the frequency and intensity of pest outbreaks are poorly studied, but are likely to be significant, particularly at the margins of host ranges. Warmer springs and winters are already increasing insect-related tree mortality in boreal forests through increased stress on the tree hosts and direct effects on insect populations ( Volney and Fleming, 2000 ; Price et al., 2013 ).

Climate also affects fire regimes. In the tropics, fire regimes often follow El Niño cycles ( van der Werf et al., 2017 ). As temperatures increase, however, fire and rainfall are decoupled as the flammability of forests increases even in normal rainfall years ( Fernandes et al., 2017 ; Brando et al., 2019 ). Fire frequency is also increasing in some temperate and boreal forests, with a discernable climate change signal ( Abatzoglou and Williams, 2016 ). Modeling exercises indicate that this trend is expected to continue with increasing damage to forests as temperatures rise and fire intensity increases ( De Groot et al., 2013 ).

In addition to changes induced by warming, continued deforestation could severely stress remaining forests by warming and drying local and regional climates ( Lawrence and Vandecar, 2015 ; Costa et al., 2019 ; Gatti et al., 2021 ). In the tropics, a tipping point may occur, potentially resulting in a shift to shorter, more savannah-like vegetation and altering the impact of vast, previously forested areas on global climate ( Nobre et al., 2016 ; Brando et al., 2019 ). Some of these processes are included in climate models and some are not. The gaps leave considerable uncertainty. Nevertheless, a combination of observations, models, and theory gives us a solid understanding of the biophysical effects of forests on climate at local, regional and global scales. We can use that knowledge to plan forest-based climate mitigation and adaptation.

Mitigation Potential of Forests: Byond the Carbon/Biophysical Divide

If instead of focusing on the contrast between biophysical and biochemical impacts of forests and forest loss, we focus on the potential of forests to cool the planet through both pathways, another picture emerges. By our conservative estimate, through the combined effects on CO 2 , BVOC, roughness and evapotranspiration, forests up to 50°N provide a net global cooling that is enough to offset warming associated with their low albedo. Given the most realistic pathways of forest change in the future (not complete deforestation of a 10° latitudinal band, or an entire biome), global climate stabilization benefits likely extend beyond 50°N. For the 29% of the global land surface that lies beyond 50°N, forests may warm the planet, but only as inferred from assessing the effects of complete zonal deforestation with all the associated, and powerful, land-ocean feedbacks spawned by largescale forest change in the boreal zone. Forests above 50°N, like forests everywhere, provide essential local climate stabilization benefits by reducing surface temperatures during the warm season as well as periods of extreme heat or drought. Indeed, they also reduce extreme cold.

Creating a fair and effective global arena for market-based solutions to climate change requires attention to all the ways that forests affect climate, including the biophysical effects. Future metrics of forest climate impacts should consider the effects of deforestation beyond CO 2 . Only recently have modelers begun to include BVOC. Doing so means that the albedo of intact forests (or the atmosphere above them) is higher due to the creation of SOA and subsequent cloud formation. Modeled deforestation thus results in less of a change in albedo, reducing the biophysical cooling effect. Similarly, accounting for the ozone and methane effects of BVOC reduces the biogeochemical warming from deforestation ( Scott et al., 2018 ). In addition, especially in the tropics, deforestation reduces the strength of the soil CH 4 sink ( Dutaur and Verchot, 2007 ). While a small change relative to the atmospheric pool of CH 4 (the second most important greenhouse gas), the loss of this sink is equivalent to approximately 13% of the current rate of increase in atmospheric CH 4 ( Saunois et al., 2016 ). We already have the data ( Figure 5 ) to begin conceptualizing measures to coarsely scale CO 2 impacts of forest change by latitude. Finer resolution of latitude, background climate (current and future) and forest type would improve any such new, qualifying metric for the climate mitigation value of forests.

The role of forests in addressing climate change extends beyond the traditional concept of CO 2 mitigation which neglects the local climate regulation services they provide. The biophysical effects of forest cover can contribute significantly to solving local adaptation challenges, such as extreme heat and flooding, at any latitude. The carbon benefits of forests at any latitude contribute meaningfully to global climate mitigation. In the tropics, however, where forest carbon stocks and sequestration rates are highest, the biophysical effects of forests amplify the carbon benefits, thus underscoring the critical importance of protecting, expanding, and improving the management of tropical forests. Perhaps it is time to think more broadly about what constitutes global climate mitigation. If climate mitigation means limiting global warming, then clearly the biophysical effects of deforestation must be considered in addition to its effects on atmospheric CO 2 . We may further consider whether mitigation is too narrow a scope for considering the climate benefits provided by forests. Climate policy often separates mitigation from adaptation, but the benefits of forests clearly extend into both realms.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author.

Author Contributions

DL conceived the presented idea. All authors helped perform the computations, discussed the results, and contributed to the final manuscript.

Financial support from the University of Virginia and the Climate and Land Use Alliance grant #G-1810-55876.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

Thanks to Frances Seymour, Michael Wolosin, Billie L. Turner, Ruth DeFries, and the reviewers for feedback on this manuscript and to the University of Virginia and the Climate and Land Use Alliance grant #G-1810-55876 for financial support.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/ffgc.2022.756115/full#supplementary-material

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Keywords : forest, biophysical effects, temperature, climate policy, deforestation/afforestation

Citation: Lawrence D, Coe M, Walker W, Verchot L and Vandecar K (2022) The Unseen Effects of Deforestation: Biophysical Effects on Climate. Front. For. Glob. Change 5:756115. doi: 10.3389/ffgc.2022.756115

Received: 10 August 2021; Accepted: 02 March 2022; Published: 24 March 2022.

Reviewed by:

Copyright © 2022 Lawrence, Coe, Walker, Verchot and Vandecar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Deborah Lawrence, [email protected]

This article is part of the Research Topic

Global Patterns and Drivers of Forest Loss and Degradation Within Protected Areas

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What is the role of deforestation in climate change and how can 'Reducing Emissions from Deforestation and Degradation' (REDD+) help?

What is the scale of deforestation and its role in climate change?

Deforestation refers to the purposeful clearing or thinning of trees and forests. When deforestation occurs, much of the carbon stored by trees is released back into the atmosphere as carbon dioxide, which contributes to climate change.

In the last decade, the largest amounts of deforestation occurred across the humid tropics, mostly in Africa, followed by South America. The UN Food and Agriculture Organisation (FAO) estimates that around 420 million hectares of forest were lost between 1990 and 2020 (or 178 million hectares net, i.e. taking into account afforestation and the natural expansion of forests). The annual rate of deforestation has since slowed but was still 10 million hectares per year between 2015 and 2020 .  The most important driver of deforestation is the global demand for agricultural commodities: agribusinesses clear huge tracts of forest and use the land to plant high-value cash crops like palm oil and soya, and for cattle ranching.

Land use change, principally deforestation, contributes  12–20%  of global greenhouse gas emissions. Forest degradation (changes that negatively affect a forest’s structure or function but that do not decrease its area), and the destruction of tropical peatlands, also contribute to these emissions. As a result of deforestation and degradation, some tropical forests now emit more carbon  than they capture, turning them from a carbon ‘sink’ into a carbon source. For example, the south-eastern part of the Amazon Rainforest is now considered a net carbon source by scientists.

Where does ‘REDD+’ come in?

Scientists have recognised the value of protecting forests in tackling climate change. In response, policymakers have developed a family of policies – collectively known as ‘reducing emissions from deforestation and degradation’ (REDD) – to provide a financial incentive to governments, agribusinesses and communities to maintain and possibly increase, rather than reduce, forest cover. The plus in ‘REDD+’ refers to “the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries”. Under REDD+, incentives for forest protection are offered to countries, communities and individual landowners in exchange for slowing deforestation, and carrying out activities that promote reforestation and sustainable forest management. Where local people are properly involved in the REDD+ process it may also help alleviate rural poverty. The principles of REDD+ were further reinforced in the  Paris Agreement  on climate change.

REDD+ policies operate through a variety of mechanisms, including those administered by the United Nations ( UN-REDD  ) and the World Bank (the  Forest Carbon Partnership Facility  ). REDD+ finance is also considered in the international climate change negotiations, remains a key component of international climate finance discussions, and is often channelled through the voluntary carbon markets and via activities implemented by for- and non-profit organisations.

How fair, effective and efficient is REDD+?

While experts have demonstrated how REDD+ has the potential to reduce CO 2  emissions, it is not without its problems. For example, some question the fairness of a scheme that focuses on reducing emissions caused by some of the world’s poorest people while emissions continue to rise in richer countries. Some developing countries may be wary of foreign interference in their land use policies. Researchers also highlight operational concerns – such as the difficulty in monitoring and measuring deforestation rates, or attributing changes in deforestation to REDD+ finance. Variations in local circumstances and institutional capacities mean that not all countries that have tropical forests possess the capabilities to address these challenges.

How much REDD+ finance has been pledged?

Estimates of the global cost of REDD+ vary greatly, but at least  US$15 billion  would be needed annually to address tropical deforestation across the world. Current funding remains far off this mark: according to a 2020 review , between 2015 and 2019 an average of US$220 million a year of funding was approved. The Amazon Fund, with US$720 million of approved projects, remains the largest dedicated REDD+ fund.

Without sufficient finance, it can be difficult to protect forests, as alternative land uses (such as for palm oil) can offer more immediate and guaranteed cash returns. Consequently, many experts have called for a scaling up of commitments and finance flows , although some have argued that even if large-scale REDD+ finance does materialise it may still struggle to compete with other land uses – especially as and when commodity prices rise.

Whatever becomes of REDD+ in the future, experts agree it should focus first on areas that can most efficiently provide CO 2  reductions (such as tropical peat swamp forests) while also offering the potential for biodiversity conservation and poverty alleviation.

This Explainer was updated in February 2023 by Charles Palmer, Natalie Pearson and Georgina Kyriacou. The original was reproduced from:  What’s REDD and will it help tackle climate change? ,  a collaboration between the Grantham Research Institute and the Guardian, © The Guardian 2012, used under a  Creative Commons No Derivative Works licence .

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Home » Insights » What is the Relationship Between Deforestation And Climate Change?

What is the Relationship Between Deforestation And Climate Change?

Filed Under: Insights   |  Tagged: Deforestation , Climate Last updated August 12, 2018

What, exactly, is the relationship between deforestation and climate change? The Rainforest Alliance breaks down the numbers for you—and explains our innovative approach to keeping forests standing.

Among the many gifts forests give us is one we desperately need: help with slowing climate change. Trees capture greenhouse gases (GHGs) like carbon dioxide, preventing them from accumulating in the atmosphere and warming our planet.

When we clear forests, we’re not only knocking out our best ally in capturing the staggering amount of GHGs we humans create (which we do primarily by burning fossil fuels at energy facilities, and of course, in cars, planes, and trains). We’re also creating emissions by cutting down trees: when trees are felled, they release into the atmosphere all the carbon they’ve been storing. What the deforesters do with the felled trees—either leaving them to rot on the forest floor or burning them—creates further emissions. All told, deforestation on its own causes about 10 percent of worldwide emissions.

Healthy forests and vibrant communities are an essential part of the global climate solution. Sign up to learn more about our growing alliance.

Knowing that deforestation robs us of a crucial weapon in the battle against climate change—and creates further emissions—why on Earth would anyone clear a forest? The main reason is agriculture. The world’s exploding population has made it profitable for big business to raze forests so it can plant mega crops like soy and oil palm; meanwhile, on a much, much smaller scale, subsistence farmers often clear trees so they can plant crops to feed their families and bring in small amounts of cash.

But there’s a tragic irony to clearing rainforests for agriculture: their underlying soils are extremely poor. All the nutrient-richness is locked up in the forests themselves, so once they are burned and the nutrients from their ashes are used up, farmers are left with utterly useless soil. So on they go to the next patch of forest: raze, plant, deplete, repeat. All told, agriculture is responsible for at least 80 percent of tropical deforestation .

Not surprisingly, agriculture causes emissions, too—in fact, farm emissions are second only to those of the energy sector in the dubious contest for the emissions title. In 2011, farms were responsible for about 13 percent of total global emissions. Most farm-related emissions come in the form of methane (cattle belching) and nitrous oxide (from fertilizers and the like).

All told, deforestation causes a triple-whammy of global warming:

• We lose a crucial ally in keeping excess carbon out of the atmosphere (and in slowing global warming),
• Even more emissions are created when felled trees release the carbon they’d been storing, and rot or burn on the forest floor, and
• What most often replaces the now-vanished forest, livestock and crops, generate massive amounts of even more greenhouse gases. Taken together, these emissions account for a quarter of all emissions worldwide.

Our accounting of the ugly impacts of deforestation only considers emissions and doesn’t even touched on how the lives and traditions of forest communities are ruined when forests are razed, or how many species of plants and animals are lost, upsetting the delicate balance of ecosystems. The uptick in mosquito-borne diseases, for example, or the rapid spread of roya, an insidious plant disease that threatens our supply of coffee are all indirect consequences of deforestation and global warming.

There’s no doubt about it: the best thing we can do to fight climate change is keep forests standing. Yet the need to feed a rapidly growing global population—projected to reach 9 billion by 2050—is urgent. That’s why the Rainforest Alliance works with farmers to advance a variety of strategies , such as crop intensification (growing more food on less land), and with traditional forest-dwellers to develop livelihoods that don’t hurt forests or ecosystems . We stand more of a chance in this fight with forests standing strong.

2022 Was One Of The Hottest Years On Record. Will 2023 Be Worse?

November 13, 2012

Deforestation and Its Extreme Effect on Global Warming

From logging, agricultural production and other economic activities, deforestation adds more atmospheric CO2 than the sum total of cars and trucks on the world's roads

Land cleared of forest by timber industry.

Nazar Abbas Getty Images

Dear EarthTalk : Is it true that cutting and burning trees adds more global warming pollution to the atmosphere than all the cars and trucks in the world combined? — Mitchell Vale, Houston

By most accounts, deforestation in tropical rainforests adds more carbon dioxide to the atmosphere than the sum total of cars and trucks on the world’s roads. According to the World Carfree Network (WCN), cars and trucks account for about 14 percent of global carbon emissions, while most analysts attribute upwards of 15 percent to deforestation.

The reason that logging is so bad for the climate is that when trees are felled they release the carbon they are storing into the atmosphere, where it mingles with greenhouse gases from other sources and contributes to global warming accordingly. The upshot is that we should be doing as much to prevent deforestation as we are to increase fuel efficiency and reduce automobile usage.

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According to the Environmental Defense Fund (EDF), a leading green group, 32 million acres of tropical rainforest were cut down each year between 2000 and 2009—and the pace of deforestation is only increasing. “Unless we change the present system that rewards forest destruction, forest clearing will put another 200 billion tons of carbon into the atmosphere in coming decades…,” says EDF.

“Any realistic plan to reduce global warming pollution sufficiently—and in time—to avoid dangerous consequences must rely in part on preserving tropical forests,” reports EDF. But it’s hard to convince the poor residents of the Amazon basin and other tropical regions of the world to stop cutting down trees when the forests are still worth more dead than alive. “Conservation costs money, while profits from timber, charcoal, pasture and cropland drive people to cut down forests,” adds EDF. Exacerbating global warming isn’t the only negative impact of tropical deforestation. It also wipes out biodiversity: More than half of the world’s plant and animal species live in tropical rainforests.

One way some tropical countries are reducing deforestation is through participation in the United Nations’ Reducing Emissions from Deforestation and Forest Degradation (REDD) program. REDD essentially works to establish incentives for the people who care for the forest to manage it sustainably while still being able to benefit economically. Examples include using less land (and therefore cutting fewer trees) for activities such as coffee growing and meat and milk production. Participating nations can then accrue and sell carbon pollution credits when they can prove they have lowered deforestation below a baseline. The REDD program has channeled over $117 million in direct financial aid and educational support into national deforestation reduction efforts in 44 developing countries across Africa, Asia and Latin America since its 2008 inception.

Brazil is among the countries embracing REDD among other efforts to reduce carbon emissions. Thanks to the program, Brazil has slowed deforestation within its borders by 40 percent since 2008 and is on track to achieve an 80 percent reduction by 2020. Environmentalists are optimistic that the initial success of REDD in Brazil bodes well for reducing deforestation in other parts of the tropics as well.

CONTACTS : WCN, www.worldcarfree.net; EDF, www.edf.org; REDD, www.un-redd.org .

EarthTalk® is written and edited by Roddy Scheer and Doug Moss and is a registered trademark of E - The Environmental Magazine (www.emagazine.com). Send questions to: [email protected] . Subscribe : www.emagazine.com/subscribe . Free Trial Issue : www.emagazine.com/trial .

How to tackle the global deforestation crisis

Imagine if France, Germany, and Spain were completely blanketed in forests — and then all those trees were quickly chopped down. That’s nearly the amount of deforestation that occurred globally between 2001 and 2020, with profound consequences.

Deforestation is a major contributor to climate change, producing between 6 and 17 percent of global greenhouse gas emissions, according to a 2009 study. Meanwhile, because trees also absorb carbon dioxide, removing it from the atmosphere, they help keep the Earth cooler. And climate change aside, forests protect biodiversity.

“Climate change and biodiversity make this a global problem, not a local problem,” says MIT economist Ben Olken. “Deciding to cut down trees or not has huge implications for the world.”

But deforestation is often financially profitable, so it continues at a rapid rate. Researchers can now measure this trend closely: In the last quarter-century, satellite-based technology has led to a paradigm change in charting deforestation. New deforestation datasets, based on the Landsat satellites, for instance, track forest change since 2000 with resolution at 30 meters, while many other products now offer frequent imaging at close resolution.

“Part of this revolution in measurement is accuracy, and the other part is coverage,” says Clare Balboni, an assistant professor of economics at the London School of Economics (LSE). “On-site observation is very expensive and logistically challenging, and you’re talking about case studies. These satellite-based data sets just open up opportunities to see deforestation at scale, systematically, across the globe.”

Balboni and Olken have now helped write a new paper providing a road map for thinking about this crisis. The open-access article, “ The Economics of Tropical Deforestation ,” appears this month in the Annual Review of Economics . The co-authors are Balboni, a former MIT faculty member; Aaron Berman, a PhD candidate in MIT’s Department of Economics; Robin Burgess, an LSE professor; and Olken, MIT’s Jane Berkowitz Carlton and Dennis William Carlton Professor of Microeconomics. Balboni and Olken have also conducted primary research in this area, along with Burgess.

So, how can the world tackle deforestation? It starts with understanding the problem.

Replacing forests with farms

Several decades ago, some thinkers, including the famous MIT economist Paul Samuelson in the 1970s, built models to study forests as a renewable resource; Samuelson calculated the “maximum sustained yield” at which a forest could be cleared while being regrown. These frameworks were designed to think about tree farms or the U.S. national forest system, where a fraction of trees would be cut each year, and then new trees would be grown over time to take their place.

But deforestation today, particularly in tropical areas, often looks very different, and forest regeneration is not common.

Indeed, as Balboni and Olken emphasize, deforestation is now rampant partly because the profits from chopping down trees come not just from timber, but from replacing forests with agriculture. In Brazil, deforestation has increased along with agricultural prices; in Indonesia, clearing trees accelerated as the global price of palm oil went up, leading companies to replace forests with palm tree orchards.

All this tree-clearing creates a familiar situation: The globally shared costs of climate change from deforestation are “externalities,” as economists say, imposed on everyone else by the people removing forest land. It is akin to a company that pollutes into a river, affecting the water quality of residents.

“Economics has changed the way it thinks about this over the last 50 years, and two things are central,” Olken says. “The relevance of global externalities is very important, and the conceptualization of alternate land uses is very important.” This also means traditional forest-management guidance about regrowth is not enough. With the economic dynamics in mind, which policies might work, and why?

The search for solutions

As Balboni and Olken note, economists often recommend “Pigouvian” taxes (named after the British economist Arthur Pigou) in these cases, levied against people imposing externalities on others. And yet, it can be hard to identify who is doing the deforesting.

Instead of taxing people for clearing forests, governments can pay people to keep forests intact. The UN uses Payments for Environmental Services (PES) as part of its REDD+ (Reducing Emissions from Deforestation and forest Degradation) program. However, it is similarly tough to identify the optimal landowners to subsidize, and these payments may not match the quick cash-in of deforestation. A 2017 study in Uganda showed PES reduced deforestation somewhat; a 2022 study in Indonesia found no reduction; another 2022 study, in Brazil, showed again that some forest protection resulted.

“There’s mixed evidence from many of these [studies],” Balboni says. These policies, she notes, must reach people who would otherwise clear forests, and a key question is, “How can we assess their success compared to what would have happened anyway?”

Some places have tried cash transfer programs for larger populations. In Indonesia, a 2020 study found such subsidies reduced deforestation near villages by 30 percent. But in Mexico, a similar program meant more people could afford milk and meat, again creating demand for more agriculture and thus leading to more forest-clearing.

At this point, it might seem that laws simply banning deforestation in key areas would work best — indeed, about 16 percent of the world’s land overall is protected in some way. Yet the dynamics of protection are tricky. Even with protected areas in place, there is still “leakage” of deforestation into other regions. 

Still more approaches exist, including “nonstate agreements,” such as the Amazon Soy Moratorium in Brazil, in which grain traders pledged not to buy soy from deforested lands, and reduced deforestation without “leakage.”

Also, intriguingly, a 2008 policy change in the Brazilian Amazon made agricultural credit harder to obtain by requiring recipients to comply with environmental and land registration rules. The result? Deforestation dropped by up to 60 percent over nearly a decade. 

Politics and pulp

Overall, Balboni and Olken observe, beyond “externalities,” two major challenges exist. One, it is often unclear who holds property rights in forests. In these circumstances, deforestation seems to increase. Two, deforestation is subject to political battles.

For instance, as economist Bard Harstad of Stanford University has observed, environmental lobbying is asymmetric. Balboni and Olken write: “The conservationist lobby must pay the government in perpetuity … while the deforestation-oriented lobby need pay only once to deforest in the present.” And political instability leads to more deforestation because “the current administration places lower value on future conservation payments.”

Even so, national political measures can work. In the Amazon from 2001 to 2005, Brazilian deforestation rates were three to four times higher than on similar land across the border, but that imbalance vanished once the country passed conservation measures in 2006. However, deforestation ramped up again after a 2014 change in government. Looking at particular monitoring approaches, a study of Brazil’s satellite-based Real-Time System for Detection of Deforestation (DETER), launched in 2004, suggests that a 50 percent annual increase in its use in municipalities created a 25 percent reduction in deforestation from 2006 to 2016.

How precisely politics matters may depend on the context. In a 2021 paper, Balboni and Olken (with three colleagues) found that deforestation actually decreased around elections in Indonesia. Conversely, in Brazil, one study found that deforestation rates were 8 to 10 percent higher where mayors were running for re-election between 2002 and 2012, suggesting incumbents had deforestation industry support.

“The research there is aiming to understand what the political economy drivers are,” Olken says, “with the idea that if you understand those things, reform in those countries is more likely.”

Looking ahead, Balboni and Olken also suggest that new research estimating the value of intact forest land intact could influence public debates. And while many scholars have studied deforestation in Brazil and Indonesia, fewer have examined the Democratic Republic of Congo, another deforestation leader, and sub-Saharan Africa.

Deforestation is an ongoing crisis. But thanks to satellites and many recent studies, experts know vastly more about the problem than they did a decade or two ago, and with an economics toolkit, can evaluate the incentives and dynamics at play.

“To the extent that there’s ambuiguity across different contexts with different findings, part of the point of our review piece is to draw out common themes — the important considerations in determining which policy levers can [work] in different circumstances,” Balboni says. “That’s a fast-evolving area. We don’t have all the answers, but part of the process is bringing together growing evidence about [everything] that affects how successful those choices can be.”

New to Climate Change?

Forests and climate change.

Forests cover about 30% of the Earth’s land surface. As forests grow, their trees take in carbon from the air and store it in wood, plant matter, and under the soil . If not for forests, much of this carbon would remain in the atmosphere in the form of carbon dioxide (CO 2 ), the most important greenhouse gas driving climate change.

Each year since 2000, forests are estimated to have removed an average of 2 billion metric tons of carbon from the atmosphere. 1 This “carbon sink function” of forests is slowing climate change by reducing the rate at which CO 2 , mainly from fossil fuel burning, builds up in the atmosphere. Careful forest management can therefore be an important strategy to help address climate change in the future. Healthy forests also provide a host of other benefits, from clean water to habitat for plants and animals that can live nowhere else.

Deforestation, and our options to reverse it

Over the past 8,000 years, humans have cleared up to half of the forests on our planet, mostly to make room for agriculture . 2 Cutting down or burning forests releases the carbon stored in their trees and soil, and prevents them from absorbing more CO 2 in the future. Since 1850, about 30% of all CO 2 emissions have come from deforestation. 3 Deforestation can also have more local climate impacts. Because trees release moisture that cools the air around them, scientists have found that deforestation has led to more intense heat waves in North America and Eurasia. 4

There are three ways to reverse these losses: afforestation, reforestation, and the natural regeneration of forest ecosystems. Afforestation refers to planting forests where there were none before, or where forests have been missing for a long time—50 years or more. Reforestation is planting trees where forests have been recently cleared. Natural regeneration, on the other hand, does not involve tree-planting. 5 Instead, forest managers help damaged forests regrow by letting trees naturally re-seed, and through techniques like coppicing, in which trees are cut down to stumps so new shoots can grow.

Forests as a climate solution

There is no doubt that these strategies can help remove CO 2 from the atmosphere, but their impact is hard to measure. Even for China, which has done more afforestation and reforestation than the rest of the world combined, there are still large uncertainties about how much carbon these projects are storing. 6

Looking at China also shows some of the unintended consequences of large-scale tree-planting projects. In the dry northern part of the country, people have planted trees to fight desert expansion. But because the tree species that were planted were ill-suited to a dry climate, this effort has depleted water supplies and degraded soils. In the south of China, reforestation with monocultures—that is, just one species of tree—has led to loss of biodiversity. 7

Natural regeneration of forests, on the other hand, has few unintended consequences and large potential to store carbon over the coming decades. If done worldwide, natural regeneration of forests could capture up to 70 billion tons of carbon in plants and soils between now and 2050 8 —an amount equal to around seven years of current industrial emissions. Combining natural regeneration with thoughtful afforestation and reforestation is an important option for combating climate change.

Updated October 7, 2021.

1 Harris, N.L., D.A. Gibbs, A. Baccini, R.A. Birdsey, S. de Bruin, et al. (2021). " Global maps of twenty-first century forest carbon fluxes ." Nature Climate Change 11, 234–240. doi:10.1038/s41558-020-00976-6 2 Ahrends, Q. P.M. Hollingworth, P. Beckschafer, H. Chen, R.J. Zomer, L. Zhang, M. Wang, J. Xu. 2017. " China's fight to halt tree cover loss ." Proceedings of the Royal Society Biological Sciences 284, May 2017. doi:10.1098/rspb.2016.2559 3 Le Quéré, Corrine, et al. “ Global Carbon Budget 2016 .” Earth Systems Science Data, vol. 8, no. 2, 2018. doi:10.5194/essd-8-605-2016 4 Lejeune, Q., Davin, E.L., Gudmundsson, L. et al. (2018). " Historical deforestation locally increased the intensity of hot days in northern mid-latitudes ." Nature Climate Change 8, April 2018. doi:10.1038/s41558-018-0131-z 5 IPCC, 2019: Summary for Policymakers . In: "Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems." [P.R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.- O. Pörtner, D. C. Roberts, P. Zhai, R. Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E. Huntley, K. Kissick, M. Belkacemi, J. Malley, (eds.)]. 6 Wang, J., L. Feng, P.I. Palmer, Y. Liu, S. Fang, H. Bosch, C. W. O’Dell, X. Tang, D. Yang, L. Liu, C. Xia. " Large Chinese land carbon sink estimated from atmospheric carbon dioxide data ." Nature 586, October 2020. doi:10.1038/s41586-020-2849-9 7 Hua, F., X. Wang, X. Zheng, B. Fisher, L. Wang, J. Zhu, et al. " Opportunities for biodiversity gains under the world’s largest reforestation programme ." Nature Communications, 7(1), Sept 2016. doi: 10.1038/ncomms12717 8 Cook-Patton S.C., S.M. Leavitt, D. Gibbs, N.L. Harris, et al. " Mapping carbon accumulation potential from global natural forest regrowth ." Nature 585: 545-550, Sept 2020. doi:10.1038/s41586-020-2686-x

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Robert McSweeney

“The effects of tropical deforestation on climate go well beyond carbon,” says Professor Deborah Lawrence, “[it] causes warming locally, regionally, and globally, and it changes rainfall by altering the movement of heat and water.”

These are the conclusions of a worldwide study into the deforestation of tropical rainforests, which shows that cutting down trees can have immediate impacts on the climate and put agricultural productivity at risk.

Rainforests are more than just a carbon store

Deforestation and land use change account for approximately  11 per cent  of global carbon dioxide emissions. But the new research finds that cutting down trees doesn’t only affect the  carbon  they lock up.

The research, published in  Nature Climate Change , reviews academic studies on deforestation of  tropical rainforests  in the Amazon basin, central Africa, and southeast Asia. Many of the studies use climate models to simulate what happens if you remove these forests completely, and they suggest that deforestation in the tropics can affect the climate on the other side of the world.

The map below shows how far-reaching some of these potential impacts are. The triangles show areas where rainfall is expected to decrease because of tropical deforestation, and the circles show areas of increase. The colours indicate the link to where the deforestation occurs.

So the models suggest deforestation in the Amazon, for example, can reduce rainfall over the US Midwest and even in northeast China. Deforestation in central Africa can cause a drop in rainfall in southern Europe, and loss of trees in southeast Asian can bring wetter conditions in southern Europe and the Arabian Peninsula.

Global impact of tropical deforestation on rainfall. Projected increases (circles) and decreases (triangles) in rainfall due to complete deforestation of either the Amazon (red), central Africa (yellow) or southeast Asia (blue). Boxes indicate the area where the forest was removed in the models. Numbers show the study the results relate to. Source: Lawrence & Vandecar ( 2014 )

Lead author Professor Deborah Lawrence tells us in an email:

“These are physical effects from removing trees that are not simply related to the loss of carbon dioxide stored inside them. Tropical deforestation results in immediate climate impacts independent of, and in addition to, its contribution to the greenhouse effect.”

Tropical deforestation is a global problem

So how does it work? How can cutting trees down in the Amazon affect rainfall in China?

First you have to bear in mind that rainforests cool the air above them by turning water from the soil into moisture in the air. Chop the trees down, and you remove the cooling effect from this additional moisture. The effect is so pronounced, the study finds, that if all the trees in the tropics were cut down global temperature could increase by as much as 0.7 degrees.

With the trees gone the air warms up, creating large, rising masses of warm air. When these air masses hit the upper reaches of the atmosphere, they create ripples called teleconnections that flow towards the mid- and higher latitudes.

Lawrence compares it to boiling water:

“Imagine steam rising off a pot of boiling water, hitting the ceiling in your kitchen and flowing outward, along the ceiling, out the door to your hallway.”

So these changes to the atmosphere in the tropics can flow out to the atmosphere of temperate regions and alter their climate, Lawrence says.

Worst-case scenario

It seems unlikely we’ll ever cut down an entire rainforest. So why do scientists run these experiments? Lawrence explains:

“We want to understand just how important rainforests are to sustaining the life support system on earth, and we start with a worst case scenario. Large-scale tropical deforestation is the outcome of business as usual economic development, it is the path we took in the US and Europe during the course of our development.”

Scientists also use more realistic scenarios, Lawrence tells us, which help identify potential tipping points if deforestation continues at current rates. For example, some studies show that clearing 30 to 50 per cent of the Amazon would trigger a drop in rainfall that could cause a significant decline in how the rainforests functions as an ecosystem.

The study also looks at changes that have already happened as forests have been cleared. In Brazil, for example, the rainy season starts 11 days later in deforested areas, and scientists think that the loss of trees in central Africa may have caused a more than 20 per cent decline in rainfall from the Congo basin to the east coast.

Implications for agriculture

Scientists have also modelled the impact climate changes from deforestation could have on farming in the tropics.

Warmer and drier conditions caused by deforestation could put agricultural productivity at risk, the study finds. Yields of soy in the Amazon, for example, are projected to drop by up to 60 per cent if more trees are cut down, and cattle production may not be viable in some areas as the quality of pasture declines. Adaptation measures might reduce the impact of these effects to some extent, of course.

Deforestation can also cause longer dry seasons and delays to the start of the rainy season, the study suggests. Because forests help moderate high daytime and low night-time temperatures, cleared land is more susceptible to temperature extremes, which some crops may not tolerate.

Limiting deforestation is therefore important for farming as well as tackling climate change, Lawrence argues:

“Agriculture and forestry need to be considered together. Maintaining large tracts of tropical forest is essential for maintaining a climate that sustains tropical agriculture. Forest conservation is an essential aspect of planning for agricultural development.”

And alongside climate change, cutting down trees will make growing crops and raising livestock even harder:

“We are already anticipating worldwide challenges to food security because of what we are doing to the atmosphere. Now we have to worry about additional climate challenges because of what we are doing to the surface of the earth.”

Main image: Deforestation in Thailand. Credit: think4photop/ Shutterstock.com .

Lawrence, D. and Vandecar, K. (2014) Effects of tropical deforestation on climate and agriculture, Nature Climate Change, http://dx.doi.org/10.1038/nclimate2430

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Open Access

Peer-reviewed

Research Article

Deforestation Induced Climate Change: Effects of Spatial Scale

Affiliations Climate & Atmospheric Sciences Institute (CASI), St. Francis Xavier University, Antigonish, NS, Canada, Department of Earth Sciences, St. Francis Xavier University, Antigonish, NS, Canada

* E-mail: [email protected]

Affiliations Department of Geography, Ohio State University, Columbus, OH, United States of America, Climate & Atmospheric Sciences Institute (CASI), St. Francis Xavier University, Antigonish, NS, Canada, Câmpus do Litoral Paulista Univ Estadual Paulista, São Vicente, SP, Brazil

Affiliation School of Earth and Ocean Sciences, University of Victoria, Victoria, BC, Canada

• Patrick Longobardi, 
• Alvaro Montenegro, 
• Hugo Beltrami, 
• Michael Eby

• Published: April 21, 2016
• https://doi.org/10.1371/journal.pone.0153357
• Reader Comments

Deforestation is associated with increased atmospheric CO 2 and alterations to the surface energy and mass balances that can lead to local and global climate changes. Previous modelling studies show that the global surface air temperature (SAT) response to deforestation depends on latitude, with most simulations showing that high latitude deforestation results in cooling, low latitude deforestation causes warming and that the mid latitude response is mixed. These earlier conclusions are based on simulated large scal land cover change, with complete removal of trees from whole latitude bands. Using a global climate model we examine the effects of removing fractions of 5% to 100% of forested areas in the high, mid and low latitudes. All high latitude deforestation scenarios reduce mean global SAT, the opposite occurring for low latitude deforestation, although a decrease in SAT is simulated over low latitude deforested areas. Mid latitude SAT response is mixed. In all simulations deforested areas tend to become drier and have lower SAT, although soil temperatures increase over deforested mid and low latitude grid cells. For high latitude deforestation fractions of 45% and above, larger net primary productivity, in conjunction with colder and drier conditions after deforestation cause an increase in soil carbon large enough to produce a net decrease of atmospheric CO 2 . Our results reveal the complex interactions between soil carbon dynamics and other climate subsystems in the energy partition responses to land cover change.

Citation: Longobardi P, Montenegro A, Beltrami H, Eby M (2016) Deforestation Induced Climate Change: Effects of Spatial Scale. PLoS ONE 11(4): e0153357. https://doi.org/10.1371/journal.pone.0153357

Editor: Juan A. Añel, Universidade de Vigo, SPAIN

Received: June 18, 2015; Accepted: March 29, 2016; Published: April 21, 2016

Copyright: © 2016 Longobardi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data are deposited into Figshare (DOIs: https://dx.doi.org/10.6084/m9.figshare.3146257 , https://dx.doi.org/10.6084/m9.figshare.3146263 , https://dx.doi.org/10.6084/m9.figshare.3146260 , https://dx.doi.org/10.6084/m9.figshare.3146254 , https://dx.doi.org/10.6084/m9.figshare.3146251 , https://dx.doi.org/10.6084/m9.figshare.3146212 ).

Funding: This work was supported by NSERC ( http://www.nserc-crsng.gc.ca/ ).

Competing interests: The authors have declared that no competing interests exist.

Introduction

Agricultural lands occupy approximately 38% of the Earth’s land surface [ 1 ]. These croplands and pastures presently cover about 10%, 45% and 27% of the areas originally occupied by boreal, temperate, and tropical forests respectively [ 1 – 4 ]. Population growth and the associated expansion of agricultural lands is the primary cause of present day deforestation [ 4 , 5 ]. Although rates of deforestation have decreased over the last decade, the loss of forested areas is expected to continue during the present century [ 6 , 7 ]. Forested area in the Amazon Basin, where the largest rainforest on Earth is found, could be reduced in approximately 50% by 2050. [ 6 – 8 ].

While most deforestation occurs in the tropics, non-tropical forests are likely to suffer new deforestation pressures as the climate warms and areas which were previously too cold become suitable for agriculture [ 9 , 10 ].

Assuming recent rates of human population growth are maintained until the end of the century, the Earth’s population will approach 10 billion around 2100. With current population to agriculture density of ∼ 147 people per km 2 , to meet the same quantity of food availability as present day, with no increases in productivity through technological advances, by 2100 agricultural areas would have to be increased by 43% [ 1 ].

Deforestation can impact climate on local and global scales by changes in the energy, mass and momentum fluxes between climate subsystems energy reservoirs. Deforestation is also associated with CO 2 emissions, as crops and marginal lands that usually replace trees after land clearing tend to hold less carbon per unit area than forests [ 11 , 12 ]. The radiative forcing associated with an increase in atmospheric CO 2 is, from a climatic perspective, the most important biogeochemical impact of deforestation. Increases in CO 2 also have the potential to affect climate by altering transpiration rates, due to CO 2 increased water use efficiency reducing stomatal conductance and increasing plant growth [ 13 – 15 ].

The biogeophysical impacts of deforestation most pertinent to climate are changes to surface albedo, evapotranspiration (ET) and surface roughness length [ 16 ]. Croplands and pastures tend to have higher albedo than forests, which causes them to absorb a smaller fraction of the incoming solar radiation. Trees tend to have deeper rooting depth than crops and grasses such that tree removal implies a decreased ET and associated reduction in latent heat flux [ 12 , 14 , 17 ], ET can also be reduced through the reduction in canopy capture following deforestation, as well as from reduced turbulence associated with a lower aerodynamic roughness length and colder temperatures. For large-scale land cover change the alterations in ET could influence cloud formation potentially impacting atmospheric albedo and atmospheric longwave absorption [ 12 ].

In previous modelling efforts, the net temperature response to deforestation, to a large extent, is determined by the magnitudes of these opposing warming (higher atmospheric CO 2 and lower latent heat flux) and cooling (increased albedo) effects (for some examples: [ 11 , 12 , 18 – 21 ]). The albedo-related cooling is particularly important at mid to high latitudes, where the presence of snow exacerbates the differences in reflectivity between forests and fields [ 11 , 12 ], while the warming due to decreases in latent heat flux has a greater impact at low latitudes where the absolute changes in ET are larger [ 12 , 22 , 23 ].

Most modelling studies so far have analyzed the response to large-scale land cover change. In some, deforestation was global or performed over whole latitude bands [ 12 , 18 , 20 , 24 , 25 ] while others simulated global historical anthropogenic deforestation [ 21 , 26 , 27 ]. In general terms, these past simulations show that the temperature response of high latitude deforestation is still dominated by the albedo effect, resulting in a cooler climate. That is, while deforestation causes atmospheric CO 2 concentrations to increase, the increment in albedo is enough to counteract greenhouse gas warming and yield a reduced surface air temperature (SAT). This cooling is global, and centered over the deforested areas [ 12 , 22 , 24 ]. Global temperature changes associated with mid latitude deforestation follow the same trend as for the high latitudes but with temperature changes of smaller magnitude [ 12 ]. Contrary to the cooling seen in the mid and high latitudes, simulated low latitude deforestation yields a warmer climate, with the increase in temperature attributed to the reduction in ET, and increased atmospheric CO 2 , which dominates the temperature signal [ 25 , 28 ]. Some studies have noted that a reduction in cloud cover, and hence, reduced atmospheric albedo over deforested regions was an important contributor to the modelled warming [ 12 ]. There have been indications from satellite based observations [ 29 ] and modeling [ 23 , 30 ] efforts that the temperature response is dependent on the scale and location of land cover change. According to these studies, in many high latitude and mid latitude areas deforestation would result not in cooling but in net warming or no significant change as the CO 2 and ET induced energy gain overtakes the albedo induced losses.

Here we use a global climate model of intermediate complexity, with a coupled carbon cycle model, to determine to what degree the scale of deforestation may influence the climate system’s response to high, mid and low latitude deforestation. This is done by a series of experiments, where deforestation fractions range from 5%-100% of the tree covered area over these distinct latitude bands. The simulations are conducted from 2011 to 2100 with CO 2 emissions based on the IPCC A2 scenario [ 31 ].

1 Model Description

The University of Victoria Earth System Climate Model (UVic ESCM) version 2.9 is an intermediate complexity climate model with horizontal resolution of 1.8° (meridional) X 3.6° (zonal). It is composed of a vertically integrated energy-moisture balance atmospheric model, a dynamic-thermodynamic sea-ice model, a continental ice dynamics model, and version 2.2 of the Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model (MOM2). The MOM2 is a general circulation ocean model with 19 vertical layers. The terrestrial carbon model is a modified version of the MOSES2 land surface model and the TRIFFID dynamic vegetation model [ 32 , 33 ]. Ocean inorganic carbon is based on the OCMIP abiotic protocol. Ocean biology is simulated by an ecosystem model of nitrogen cycling [ 34 , 35 ]. Water, heat and carbon are conserved between model components with no flux adjustments. Cloud cover is set at a constant in the UVic ESCM. It has been shown that that large-scale deforestation may influence cloud cover and have an effect on the climate [ 12 ], however uncertainties exist in the change of cloud cover due to deforestation [ 29 , 36 , 37 ]. Precipitation is a function of relative humidity and not influenced by the fixed cloud cover. A full description of the atmospheric, oceanic, and sea ice models are in [ 38 ], while the land surface scheme and dynamic vegetation model are described in [ 32 , 33 ].

1.1 Vegetation Model and Land Surface Scheme

TRIFFID defines the state of the terrestrial biosphere in terms of soil carbon, and the structure and coverage of five plant functional types (PFT), broadleaf trees, needleleaf trees, C 3 grasses, C 4 grasses and shrubs within each grid cell [ 32 ]. Using a carbon balance approach, TRIFFID determines the change in areal coverage, leaf area index and canopy height, as a result of net carbon fluxes calculated by the MOSES 2 land surface scheme. MOSES 2 recognizes the five PFTs used by TRIFFID, plus four non-vegetation types, bare soil, urban areas, land ice and inland water. Based on a photosynthesis-stomatal conductance model, plant respiration and photosynthesis are dependent upon climate and atmospheric CO 2 [ 39 ]. Through this, the response of vegetation to climate occurs via climate-induced changes in the vegetation to atmospheric fluxes of carbon [ 32 ]. In each 1.8° X 3.6° grid cell, changes to land coverage through time is determined by a dynamic competition between the different PFTs. This is based on the Lotka-Volterra approach and a tree-shrub-grass dominance hierarchy. TRIFFID also allows agricultural areas to exist. These areas are defined as croplands and are treated as grass PFTs for determining their biogeochemical and biophysical behaviors.

Due to the non-linear character of the Lotka-Volterra equations used for the competition algorithm, there exists a possibility for rapid loss of vegetation species if the land-use disturbance is large enough to trigger the requisite scenario. This scenario can produce rapid increases or decreases in the abundance of a species [ 40 ].

Soil carbon pools are increased through litterfall, and reduced by heterotrophic respiration Litterfall is calculated as an area weighted sum from each PFT, and is dependent upon the degree of the land disturbance and competition between PFTs. Respiration is determined by the soil carbon content, a q 10 soil temperature equation, and a piecewise linear soil moisture function [ 32 ]. In general terms this means that higher NPP results in an increase in the rate with which carbon is added to the soil with higher soil temperatures and moisture resulting in increases in respiration and hence in the rate of soil carbon decrease. Soil temperature, moisture and carbon content represent values from the model’s single one meter deep layer.

2 Experiments

For all experiments, the model is integrated from equilibrium at year 1800 to year 2000 forced by historical CO 2 emissions from combustion of fossil fuels and land-use change [ 41 , 42 ]. For the period between 2001 and 2100 simulations are forced by CO 2 emissions from the IPCC A2 scenario [ 31 ].

Deforestation experiments cover the period between 2010 and 2100. Deforestation is simulated separately in three bands: the area northward of 40°N (high latitudes), the areas between 20°N to 40°N, and 20°S to 40°S (mid latitudes) and the area between 20°S to 20°N (low latitudes) ( Fig 1 ). While the adoption of latitudinal bands is our best attempt to represent impacts of deforestation on different environments, it must be noted that, as TRIFFID only contains five PFTS, the type of vegetation cover present in each latitude band is a very simple and incomplete representations of existing biomes, which are not only more complex but also distributed over the landscape space in complex patterns dictated by many other factors in addition to latitude.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

Coverage is represented as fractional amount of broadleaf and needleleaf trees in each grid cell. Latitude bands designated by black lines. 40°N and above for the high latitudes, 20-40°N and 20-40°S for the mid latitudes and 20°S to 20°N for the low latitudes.

https://doi.org/10.1371/journal.pone.0153357.g001

At the start of 2010, all experiments have the same crop area distribution based on [ 2 ]. The vegetation is specified by a land cover data set [ 43 ]. All results are compared to a control run where the crop area fraction remained fixed at the 2010 distribution. In the deforestation experiments crop area fraction is increased by different amounts in order to generate arbitrary deforestation ranging from 5% to 100% of the total forested area of the three different latitudes at 2010. The land cover change is performed in a single step at the start of 2011 by substituting trees with crops.

In all but the 100% deforestation scenario only grid cells that contain both crops and forests are defined as eligible for deforestation ( Fig 2 ). In these simulations deforestation is performed by reducing the forest cover by a fixed amount in all eligible grid cells. The rationale is that experiments should simulate, as well as the coarse spatial scale of the model allows, land cover change resulting from an expansion in agricultural areas. In the 100% scenario, any grid cell with forests was deemed eligible for deforestation. There is no 75% deforestation simulation for the high latitudes, as the requirements for deforestation did not allow sufficient grid cells to be used to reach the required forest loss.

Grid cells eligible for deforestation in the high (top row), mid (middle row) and low latitudes (bottom row) in the non 100% simulations (left column) and 100% simulations (right column).

https://doi.org/10.1371/journal.pone.0153357.g002

The expansion of croplands in the model follows a hierarchy where grasslands are converted to crops, before shrubs and trees. The result is that eligible grid cells that contain grasslands and shrubs prior to deforestation have these fractions converted to crops as well at the start of 2011. It should be noted that in TRIFFID crops and grasslands have identical biogeochemical and biogeophysical characteristics. The only difference is that grasslands can be outcompeted by other plant functional types while the crop distribution is prescribed. This means that all areas converted to crops in 2011 remain as such until the end of the experiments.

After the initial disturbance the remaining forest cover is free to change according to the vegetation model’s response to climate. In presenting and discussing our results, experiments are classified according to their initial arbitrary deforestation fraction. For example, the 5% experiment refers to the simulation in which 5% of the forest cover was removed instantaneously at the start of 2011. Average SAT, soil temperature and P-E values for the 2090-2100 decade and their 95% confidence intervals are presented in Tables 1 and 2 .

Average (final) and 95% confidence margin of error (err) for last 10 years of the experiments from the high- (H), mid- (M) and low-latitude (L) deforestation scenarios. Lines labeled 5-100 refer to initial deforestation fraction. Errors are estimated based on a t confidence interval for the mean.

https://doi.org/10.1371/journal.pone.0153357.t001

https://doi.org/10.1371/journal.pone.0153357.t002

3.1 Further forest loss

With the exception of the 100% simulations, all deforestation scenarios, regardless of location, experience further loss of forests after the initial disturbance ( Fig 3 ). In all experiments, the fraction of forest loss of the 25%-75% simulations tends to converge to around 50% in the high latitudes, 70% in the mid latitudes and 80% in the low latitudes. The 5% and 15% scenarios, continue to loose forests up to the end of the simulations. Pre-deforestation forest cover is identical for all experiments. The initial and continued forest loss percentages are based on this original, pre-deforestation value.

Deforestation percentage is relative to the control run at the same time step.

https://doi.org/10.1371/journal.pone.0153357.g003

In all cases, the post-deforestation dieback is caused by further forest loss in the deforested bins, which in the more extensive deforestation scenarios tend to lose all of their trees. It is this loss of trees in the deforested bins which produces the converging trend observed in the 25%-75% simulations, as their forest coverage in the deforested bins is near identical. Some regrowth occurs in the non-deforested bins, where the forested fraction increases in relationship to the control for all simulations. This further loss of trees is not related to the dynamic vegetation model responding to climatic changes brought by deforestation but occurs because of the response of the competition algorithms adopted by TRIFFID to the large and rapid land cover change implemented at 2011. Following the large land-use change, and subsequent changes to climate, TRIFFID’s competition algorithm produced a continual loss of forests in the disturbed bins, with these forests being primarily replaced by shrubs. In that sense, the observed continuos loss of forest cover after deforestation are more akin to an external forcing to the simulations than to a response of the vegetation model to environmental change. Since our goal is to evaluate the climatic response to vegetation change and not vice-versa we feel that the continuous loss of forest resulting from this limitation of the competition algorithms do not invalidate our analysis and results.

3.2 Temperature and Moisture Response

The modeled temperature and carbon cycle responses to deforestation are intrinsically linked, complicating our choice of what to present and discuss first. Some readers might wish to come back to the temperature findings after reading the carbon cycle results (Section 3.3).

3.2.1 High Latitudes.

For all simulations deforestation causes a reduction in global SAT, with the cooling being proportional to deforested percentage ( Fig 4 ). The reduction in temperature is magnified at higher latitudes due to an increase in snow and ice cover and consequent increase in albedo. In the 100% scenario the average temperature change from 20°N to 40°N was -0.42K and the temperature change from 40°N and above was -0.78K. Lower atmospheric CO 2 values are also responsible for some of the larger cooling seen in the 45% and 100% deforestation simulations (see Section 4). Deforestation also causes a decrease in global and local soil temperature ( Fig 5 —The term local refers to area weighted averages over the initially deforested grid cells), however soil temperatures become warmer in areas with large forest cover prior to deforestation ( Fig 1 ). This warming is due to, similarly to what is observed more commonly in the mid- and low-latitude experiments, a decrease in sensible heat flux following deforestation. Soil temperatures show a similar trend to the SAT response

Annually averaged global air surface temperature anomalies for high latitude deforestation (Top). Annually averaged air surface temperature anomalies over deforested areas for high latitude deforestation (Middle). Air surface temperature anomalies at 2100 for the 100% high latitude deforestation simulation (Bottom). All anomalies are shown in K. Here, and in all other cases, anomalies are calculating by subtracting the annual value from the control simulation from the annual experiment value.

https://doi.org/10.1371/journal.pone.0153357.g004

Annually averaged global soil temperature anomalies for high latitude deforestation (Top). Annually averaged soil temperature anomalies over deforested areas for high latitude deforestation (Middle). Soil temperature anomalies at 2100 for the 100% high latitude deforestation simulation (Bottom). All anomalies are shown in K.

https://doi.org/10.1371/journal.pone.0153357.g005

Although the averaged global and local precipitation minus evaporation (P-E) point to drying, local drying is an order of magnitude larger than the global averages ( Fig 6 , top two panels). The areas with the largest drying occur in regions of increased soil temperatures. In these areas both ET and precipitation increase, however the increase in ET is larger than the increase in precipitation due to the enhanced soil temperatures. In the areas where conditions become wetter, there is also an increase of both precipitation and ET, however the increase in precipitation is larger. For all scenarios deforestation results in an overall drier climate over deforested areas ( Fig 6 , top two panels).

Annually averaged global precipitation minus evapotranspiration (ET) anomalies over land for high-latitude deforestation (Top). Annually averaged precipitation minus ET anomalies over deforested areas for high latitude deforestation (Middle). Precipitation minus ET anomalies at 2100 for the 100% high latitude deforestation simulation (Bottom). All anomalies are shown in kg m −2 s −1 . Note that the global anomalies are adjusted by 10 −7 while local ones are multiplied by 10 −6 .

https://doi.org/10.1371/journal.pone.0153357.g006

3.2.2 Mid Latitudes.

Mean global SAT anomalies, while statistically significant by year 2100 ( Table 1 ), are small and straddle zero K. At the end of the century the general tendency is cooling in the lower deforestation fraction with slight warming in the 15% and 25% scenarios. The larger input of CO 2 into the atmosphere results in initial warming for deforestation of and above 45% (see Carbon Cycle section below). By mid-century albedo effects overcome the initial increase in greenhouse gas concentration and at the end of the simulation these scenarios exhibit very small SAT change or some cooling ( Fig 7 ). The increase in local albedo dominates the temperature response over deforested areas which remain colder than the control even during periods where higher atmospheric CO 2 cause mean global positive SAT anomalies ( Fig 7 ).

Annually averaged global air surface temperature anomalies for mid latitude deforestation (Top). Annually averaged air surface temperature anomalies over deforested areas for mid latitude deforestation (Middle). Air surface temperature anomalies at 2100 for the 100% mid latitude deforestation simulation (Bottom). All anomalies are shown in K.

https://doi.org/10.1371/journal.pone.0153357.g007

The globally averaged time series of soil temperature response shows a similar pattern to the SATs ( Fig 8 ). In all simulations, average local soil temperatures over deforested bins are warmer than those of the control, with cooling occurring outside of the deforested areas ( Fig 8 ). The initial local soil temperature response is proportional to the amount of deforestation. In most cases positive anomalies continue to increase, the exceptions being the 75% and 100% simulations which, due to reduced rates of energy absorption, resulting from increased albedo and outgoing latent heat flux, have a decreasing trend following deforestation.

Annually averaged global soil temperature anomalies for mid latitude deforestation (Top). Annually averaged soil temperature anomalies over deforested areas for mid latitude deforestation (Middle). Soil temperature anomalies at 2100 for the 100% mid latitude deforestation simulation (Bottom). All anomalies are shown in K.

https://doi.org/10.1371/journal.pone.0153357.g008

In all scenarios, deforestation leads to mean drying over land, with the decrease in moisture driven predominately by change over deforested bins ( Fig 9 ). Drying over deforested areas tends to be proportional to the deforestation fraction, the exception being the 100% simulation which, although drier than the control, has slightly higher P-E values than the 75% scenario.

Annually averaged global precipitation minus evapotranspiration (ET) anomalies over land for mid latitude deforestation (Top). Annually averaged precipitation minus ET anomalies over deforested areas for mid latitude deforestation (Middle). Precipitation minus ET anomalies at 2100 for the 100% mid latitude deforestation simulation (Bottom). All anomalies are shown in kg m −2 s −1 .

https://doi.org/10.1371/journal.pone.0153357.g009

Mean ET and precipitation both increase over deforested areas, however in all simulations ET increases more than precipitation. Differing from the mean response, conditions are wetter in a significant number of deforested bins over Eastern Asia ( Fig 9 ). While precipitation does increase, the positive P-E anomaly over these deforested bins is caused by a decrease in ET. The change in soil temperature over deforested areas in Eastern Asia also tends to be different from that of other areas. While the general response is warming, the temperature increase tends to be smaller and many bins in the area show cooler soil conditions after deforestation ( Fig 8 ).

3.2.3 Low Latitudes.

The global SAT response to deforestation of 15% and higher is a general increase followed by cooling ( Fig 10 ). The magnitude and rate of initial warming tends to be proportional to the deforested area fraction and higher deforestation fractions tend to reach their peak positive anomaly sooner. Similar to the mid-latitude experiments, albedo effects start to overcome the initial CO 2 driven warming but not to the point of generating negative anomalies ( Fig 10 ). For the 5% scenario there is relatively constant warming associated with the progressive loss of forested area and addition of CO 2 to the atmosphere seen in this experiment (see Carbon Cycle results below). SAT change over deforested bins is eventually dominated by the local increase in albedo and differs significantly from what is seen globally. At the end of the simulation all scenarions show negative local anomalies. As in the global case, the relative importance of albedo effects tend to be increase with deforested area ( Fig 10 ).

Annually averaged global air surface temperature anomalies for low latitude deforestation (Top). Annually averaged air surface temperature anomalies over deforested areas for low latitude deforestation (Middle). Air surface temperature anomalies at 2100 for the 100% low latitude deforestation simulation (Bottom). All anomalies are shown in K.

https://doi.org/10.1371/journal.pone.0153357.g010

The globally averaged time series of soil temperature response to deforestation exhibit a pattern similar to that of the global SAT but anomalies remain positive ( Fig 11 ). The pattern of local soil temperature response over deforested bins is similar to the global response for the 5%-45% simulations. The 75% and 100% scenarios, similarly to what is observed at mid latitudes, show positive anomalies that decrease during to 21 st century resulting in soil temperatures that are still higher than the control but lower than those observed in the 15%-45% simulations.

Annually averaged global soil temperature anomalies for low latitude deforestation (Top). Annually averaged soil temperature anomalies over deforested areas for low latitude deforestation (Middle). Soil temperature anomalies at 2100 for the 100% low latitude deforestation simulation (Bottom). All anomalies are shown in K.

https://doi.org/10.1371/journal.pone.0153357.g011

The global moisture response over land over the low latitudes is less consistent between simulations than that of the high and mid latitude simulations, with most deforestation fractions changing between drier and wetter conditions during the experiment ( Fig 12 ). In all experiments, conditions become drier over deforested bins and the P-E anomalies over these areas are an order of magnitude larger than those registered in the global response ( Fig 12 ). Both ET and precipitation increase over deforested bins and drying occurs because the increase in ET overtakes the increase in precipitation. It is interesting to note that the local drying is usually more intense in the mid latitudes than in the low latitude simulations ( Fig 12 ).

Annually averaged global precipitation minus evapotranspiration (ET) anomalies over land for low latitude deforestation (Top). Annually averaged precipitation minus ET anomalies over deforested areas for low latitude deforestation (Middle). Precipitation minus ET anomalies at 2100 for the 100% low latitude deforestation simulation (Bottom). All anomalies are shown in kg m −2 s −1 .

https://doi.org/10.1371/journal.pone.0153357.g012

While the mean local response is drying, some areas in equatorial Africa and the Amazon become wetter after deforestation. Our simulations indicate that contrary to the areas that become wetter in the mid latitudes simulations, the positive P-E anomalies in the low latitude deforested bins are caused by an increase in precipitation and not decreased ET.

Compared to the mid latitudes, where the wetter conditions occur in areas of mixed warming and cooling soil temperatures, low latitude bins with positive P-E show no significant variation in soil temperature ( Fig 11 ). From Fig 12 (bottom) it can be seen that there is an increase in P-E outside the deforested areas. This increase is not exclusive to the 100% deforestation shown, being seen in all scenarios. It is this increase in moisture in non-deforested areas, as well as the less pronounced decrease in moisture in the deforested areas for the 5%-45% simulations, that leads to an average global increase of moisture over land at various times in the simulations for the 15%-45% scenarios.

3.3 Carbon Cycle

3.3.1 high latitudes..

All deforestation simulations show an initial increase in atmospheric CO 2 relative to the control. The increase is proportional to deforested area and ranges from 3 to 50 ppmv ( Fig 13 , top). This expected increase in CO 2 concentration is due to the release of carbon stored in the forests [ 44 , 45 ]. Although the relative difference between the simulations and the control decreases in the first 10 years after deforestation, atmospheric CO 2 values for the 15% and 25% deforestation experiments remain above those of the control during the whole simulation due to continued, competition algorithm induced, loss of forests leading to increased CO 2 emissions. For the 45%-100% deforestation experiments however, the post-deforestation pulse reduction in CO 2 is such that from about year 2040 onward these scenarios’ atmospheric CO 2 concentrations are lower than those of the control ( Fig 13 , top).

Annually averaged global CO 2 concentration anomalies for high (top), mid (middle), and low latitude (bottom) deforestation. All anomalies shown in ppm.

https://doi.org/10.1371/journal.pone.0153357.g013

In all simulations the behaviour of ocean carbon is similar to atmospheric CO 2 and all simulations with a relative loss of atmospheric CO 2 also show a relative reduction in ocean carbon ( Fig 14 , top row).

Temporal evolution of annually averaged carbon stock anomalies from different model components for high (top row), mid (middle row), and low latitude (bottom row) simulations for the 25% (left column) and 45% deforestation (right column) scenarios. All anomalies shown in Pg C.

https://doi.org/10.1371/journal.pone.0153357.g014

Atmospheric carbon closely mirrors the changes to land carbon—the sum of soil and vegetation carbon. CO 2 levels are higher in experiments where deforestation leads to net loss of carbon by the land reservoir and lower in simulations where the removal of trees cause an increase in land carbon stocks ( Fig 14 ). Deforestation always results in an decrease in vegetation carbon and an increase in the soil carbon pool. Some of this increase takes place over non-deforested areas where, for high- mid- and low-latitude deforestation, NPP increases due to changes in climate and the CO 2 fertilization effect. The net change in the land carbon pool, and consequently the atmospheric CO 2 response, is determined by the relative magnitude of these soil and vegetation carbon changes.

Ignoring the small drawdowns of the 5% experiment, deforestation of up to 25% results in losses in the land carbon pool and increase in atmospheric carbon ( Fig 14 ). For the 45%-100% deforestation scenarios the increase in soil carbon overcomes the losses from vegetation carbon. In these simulations the land gains carbon at the expense of the atmosphere, where CO 2 concentrations decrease ( Fig 14 ).

The increase in soil carbon is related to a reduction in soil heterotrophic respiration due to colder and drier conditions and also to an increase in modelled net primary productivity (NPP) over deforested areas (not shown and caused by the higher modeled NPP of grasslands compared to forests in these latitudes). In the 5%-25% simulations the increase in soil carbon was larger in the non-deforested areas than in the deforested areas. We interpret this as an indication that climate played a larger role than alterations in NPP due to land cover change in the increase of soil carbon in these experiments. Deforested areas contribute to approximately 58%-70% of the increase in soil carbon in the 45%-100% deforestation scenarios, showing that in these experiments the higher NPP of croplands also played a role in accumulation of land carbon. Although croplands are usually associated with reductions in NPP, observations have found an increase in NPP after forest loss in high latitudes [ 46 ], and modeling efforts have found an increase due to higher grassland productivity and CO 2 -fertilization despite colder temperatures [ 24 ].

3.3.2 Mid Latitudes.

In all scenarios, deforestation produces a rapid increase in atmospheric CO 2 (2 to 47 ppmv) as the carbon lost by vegetation due to deforestation makes its way into the atmosphere (Figs 13 and 14 ). This is followed again, in all scenarios, by a decrease in CO 2 values. After this initial pulse, the behaviour of the simulations differs. The 5%-25% scenarios show increases relative to the initial post pulse value up to year 2100. CO 2 concentrations remain near the post-pulse value for 45% experiment while the 75% and 100% scenarios, in similar fashion to the high latitude case, exhibit continuos reductions in atmospheric CO 2 after the initial peak. Contrary to the high latitudes simulations these reductions in atmospheric CO 2 are not large enough to generate negative anomalies by the end of the century.

All simulations experience a global increase in soil carbon that offsets a portion of the losses in land carbon caused by deforestation and dieback. Following a brief (about four years) initial decrease in soil carbon, all experiments show a relatively rapid increase of carbon in this pool up to about 2040 to 2060. The magnitude of the increase is dependent on the deforestation fraction and in all experiments this is accompanied by a reduction in atmospheric carbon ( Fig 14 ).

For all experiments, both forested and non-forested bins contribute to the initial increase in soil carbon. Due to changes in climate and enhanced NPP caused by increased atmospheric CO 2 [ 47 ], non-deforested bins maintain positive soil carbon anomalies over the duration of all experiments. As simulations progress, some scenarios experience eventual reductions in soil carbon, associated with loss of carbon in deforested bins. These losses are larger for the 15%-25% scenarios, ( Fig 14 ) and are associated with increased respiration, due to warmer soil temperatures ( Fig 8 ) and wetter conditions over their deforested bins when compared to those of the other scenarios (Figs 6 , 9 and 12 ). The increase in NPP, as well as the enhanced drying and cooler temperatures over the deforested bins of the 75% and 100% experiments causes these areas to gain soil carbon continuously during the experiments. The large increase of soil carbon in the higher deforestation fraction experiments leads to the larger atmospheric CO 2 drawdown seen in these scenarios.

Ocean carbon stocks are responding to atmospheric CO 2 ( Fig 14 ) as ocean carbon increases and decreases with the fluctuating CO 2 values.

3.3.3 Low Latitudes.

As in the other latitudinal bands, the carbon lost by vegetation due to tree removal over the low latitudes enters the atmosphere, generating a rapid increase in CO 2 concentrations proportional to the extent of deforestation (7 to 127 ppmv). Due to the larger quantity of trees removed in the low latitudes, this initial CO 2 increase is higher than what is seen in the high and mid latitudes combined ( Fig 13 ).

This increase is followed by a period of diminishing CO 2 , with the rate of decrease proportional to the deforestation fraction caused mostly by oceanic absorption of CO 2 . Atmospheric CO 2 values in the 75% and 100% scenarios fall relatively quickly, and for the duration of the experiments, remain below those of the initial peak. This is not the case for the other scenarios, where by no later than the mid 21 st century, the atmosphere holds more carbon than it had at the peak of the post deforestation pulse ( Fig 13 ).

The low latitudes had the same initial soil carbon response to deforestation as the high and mid latitudes, with a small initial decrease over the first few years followed by a rapid increase ( Fig 14 ). As with the mid latitudes, the changes in soil carbon are explained by continuous increase in carbon density over non-deforested areas and a mixed response in the deforested bins. The largest soil carbon losses occur in the intermediary deforestation fractions, where soils are warmer and not as dry. Different from the high and mid latitudes, these losses resulted in periods where some intermediary fractions, like the 25% scenario, exhibited global changes soil carbon near zero. Again as seen in the high and mid latitudes, the experiments with the smaller soil carbon anomalies are the ones which show the largest concentrations of atmospheric CO 2 . Increases in NPP, relatively colder temperatures and drier conditions lead to large soil carbon gains by deforested bins in the higher fraction deforestation scenarios. Similarly to the mid latitudes, the deforested bins of the 75% and 100% simulations hold the majority of the global soil carbon by the end of the experiments.

The ocean carbon response to low latitude deforestation is very similar to the high and mid latitudes, where ocean carbon to a certain degree mirrors atmospheric carbon. In the low latitudes we do not see the same level of post initial pulse reduction in atmospheric carbon as was observed in the high and mid latitudes, and the ocean carbon also shows this, as there is less variation in total ocean carbon between the simulations, due to the oceans slower response to atmospheric carbon change ( Fig 14 ).

4 Discussion

The use of a model of intermediate complexity in the study of deforestation has some drawbacks, among the more evident, the lack of a cloud response, which is expected to play a larger role in low-latitude deforestation. Still due to computational and time constraints, the large number of experiments required by the project could only be conducted with this kind of model.

4.1 High Latitudes

The lower temperatures observed in our 5%-25% deforestation experiments are in agreement with previous modelling studies where the albedo effect outweighs the increase in CO 2 for high latitude deforestation [ 12 , 22 , 24 ]. Differently from these earlier results, our cooling is magnified in the 45%-100% scenarios by a decrease in atmospheric CO 2 caused largely by an accumulation of soil carbon over deforested areas. By 2100 our 100% simulation produced cooling of approximately 0.4 K, with an average cooling over the duration of the simulation of 0.33 K. The average cooling is similar to what was found by previous experiments where a cooling of 0.25 K is observed [ 24 ], but at 2100 we obtained cooling that was only about half of what is modeled by other simulations [ 12 ]. In terms of albedo, we detected high-latitude increases ranging from 2.02% to 12.74% by 2100, with larger deforestation events producing higher increases in albedo. This is higher than the 10.7% increase of high latitude albedo at 2100 obtained by earlier efforts [ 12 ]. When global albedo is considered, our average increases of 0.01 to 0.04 are smaller than what was observed by other experiments that reported a global average increase of 0.07 [ 24 ]. It is likely that the discrepancies between our results and these past efforts are at least partly due to differences in the selection of the latitudinal ranges used by the different experiments [ 12 , 24 ].

Earlier simulations of complete deforestation in areas above 45°N caused an increase in soil carbon over deforested areas [ 24 ], but in these experiments the extra soil carbon was not enough to compensate for the loss of biomass and litter carbon, leading to an overall loss of land carbon and a ≤ 5 ppm increase in atmospheric CO 2 [ 24 ]. Another set of previous experiments, while not reporting changes in soil carbon, find a similar (5 ppm at 2100) increase in atmospheric CO 2 in their 100% high latitude deforestation simulations [ 12 ]. It must be noted that one of these experiments did not account for anthropogenic emissions [ 24 ], which are considered by the other [ 12 ].

In a managed forest, clear cutting has been shown to reduce soil carbon [ 48 ]. At particular temperate zone sites conversion of forests to cropland resulted in about a 32% ±20% decrease in soil carbon and the conversion of grassland to forest caused soil carbon reduction of about 7%± 23% [ 49 ]. Even our low-end deforestation scenarios result in land cover change at much larger spatial scale than those analyzed by these observational studies [ 48 , 49 ] and comparison with our results should be made with caution.

The initial surface air and soil cooling of the 45%-100% experiments are markedly larger (Figs 4 and 5 ) than those of the other simulations. It is this rapid albedo-related cooling, and the associated slow down of soil respiration, which lead to the larger retention of carbon over land in these scenarios. As the surface albedo and atmospheric CO 2 begin to change, they influence the surface energy balance, which in turn effects the soil temperatures. In the case of the high latitudes, the resultant magnitude of the changes to the energy balance that lead to the decrease in soil temperatures.

Removal of trees shortens the roughness length and causes a reduction in outgoing sensible heat flux, a warming effect, over deforested bins. At the same time, deforested areas—due to an increase in modeled evaporation—experience increases in the outgoing latent heat flux and a decrease in shortwave absorption. The final result is a reduction in net incoming energy and soil cooling ( Fig 15 ). In a similar way, high-latitude deforestation has been observed to cause local cooling despite a decreased outgoing sensible heat flux [ 50 ]

Annually averaged outgoing surface energy flux anomalies over deforested areas for high latitude deforestation. Energy fluxes shown as latent heat (Top), sensible heat (Middle), and net surface radiation (Bottom) All anomalies shown in W m −2 .

https://doi.org/10.1371/journal.pone.0153357.g015

Where R n is the net surface radiation, LE is latent heat, H is sensible heat, and G is the ground heat flux. By increasing the surface albedo, and latent heat, the decreased surface skin temperature contributes to the reduced soil temperatures.

The reduction in soil moisture, while less important in explaining the initial accumulation of soil carbon, is likely exerting a larger influence after about 2060, when soil carbon continues to increase (albeit at slower rates) in spite of increasing soil temperature over deforested areas (Figs 5 and 14 ). In Fig 6 , an interesting result is shown where the drying in the 100% simulation isn’t the largest, compared to the temperature and carbon response, where the 100% scenario produces the most extreme changes. The increased moisture is likely due to the larger area used for 100% deforestation, with more snow covered areas and more cooling resulting in less ET. This result can also be observed in ( Fig 15 ) where the latent heat flux of the 100% simulation is less than the 45% and 50% scenarios for the majority of the simulation.

Evidently, we don’t make the claim that our findings justify large scale high latitude deforestation as a means of carbon sequestration. Nevertheless, our results point to the complex interaction between soil carbon dynamics and climate and the significant role this interaction plays on the modelled climatic response to land cover change.

4.2 Mid Latitudes

The SAT cooling seen in the 100% mid latitude deforestation scenario is in general agreement with previous studies [ 12 , 25 ], albeit the 0.077 K reduction in temperature at 2100 recorded by our experiments is larger than the 0.04 K cooling produced by comparable simulations [ 12 ]. Our experiments generated albedo increases of 1% to 5%, within the range range of earlier efforts that found increases of 4.7% to 5% [ 12 ].

By 2100 the 75% scenario has near identical forest loss as the 25% and 45%. However, the 75% and 100% experiments show higher initial albedo increase, as well as reduced atmospheric CO 2 through increased soil carbon, driving further global cooling. The rapid increase in albedo in the 75% and 100% scenarios effect the soil temperatures as well, which decrease over the first 60 years, before the temperature anomalies begin to trend upwards again. This points to the importance of the magnitude of the initial disturbance on modelled climates. Although the 25%-75% scenarios all reach similar forest loss by 2100, and display converging albedo values, the larger initial land use change results in higher initial albedo values which allow the 45%-75% simulations to reach lower temperatures despite having final land use change fractions similar to the 25% simulations.

In the mid latitudes, the local response to deforestation differs compared to the high latitudes. The increase in ET leads to more drying than what is observed in the high latitudes (Figs 6 , 9 , 15 and 16 ). This is also accompanied by increased soil temperatures in the mid latitude deforested bins, opposed to the local response to high latitude deforestation (Figs 5 and 8 ). Soil temperatures increase, despite the added cooling from increased modelled outgoing latent heat and albedo, primarily due to the decreased roughness length following deforestation. Decreased roughness lengths lead to less outgoing sensible heat, which becomes the dominant driver in local soil temperatures for these experiments. Following deforestation the outgoing sensible heat flux ( Fig 16 ) decreases more than at the high latitudes, resulting in a higher retention of heat in the soils, and thus higher soil temperatures.

Annually averaged outgoing surface energy flux anomalies over deforested areas for mid latitude deforestation. Energy fluxes shown as latent heat (Top), sensible heat (Middle), and net surface radiation (Bottom) All anomalies shown in W m −2 .

https://doi.org/10.1371/journal.pone.0153357.g016

Where k is the Von Karman constant, z is a reference height and z 0 is the aerodynamic roughness length [ 21 , 51 ].

In the simulations, deforestation reduces C D because the grassland z 0 is smaller than the forest z 0 . This change in C D is what inevitably reduces SH , as the other terms in the equation act towards increasing SH . Decreasing roughness length over the mid latitude deforested bins reduces the surface’s ability to lose sensible heat, resulting in a net soil temperature increase over these areas.

The accumulation of global soil carbon is driven by the increase in NPP and a decrease in soil respiration. The change in soil carbon accumulation is intrinsically tied to the change in soil temperature and moisture. Globally, all deforestation fractions led to drier conditions over land, and global soil temperatures tend to decrease, the exception being the 15% and 25% scenarios (Figs 9 and 8 ).

The opposing effects of warming and drying trends determines the exchange of carbon between soil and atmosphere. The 15% and 25% scenarios experience the largest local warming, however do not experience the same level of drying as is seen in the 45%-100% scenarios. The 45%-100% scenarios also experience less warming then the 15% and 25% scenarios, leading to reduced respiration in the 45%-100% cases and larger drawdown of atmospheric CO 2 . This results in larger soil carbon quantities and less atmospheric CO 2 (Figs 13 and 14 ). The increase in CO 2 seen in the 5%-25% simulations is linked to the less rapid loss of forests than was seen in the 45%-100% scenarios, resulting in less albedo cooling, and a slower release of CO 2 over time. The warmer global soil temperatures of the 15% and 25% scenarios are due to the higher surface air temperatures, where increased CO 2 concentrations, as well as lower surface albedos than the 45%-100% scenarios, highlighting the complex interactions which take place in determining local and global temperatures.

Similar to the high latitudes, some mid latitude deforestation simulations differed from observational studies where deforestation led to a decrease in soil carbon due to increased respiration [ 48 , 52 ]. In our experiments the 45%-100% simulations resulted in local soil carbon increase, which is in agreement with observations that indicate that the conversion of grassland to forest causes a soil carbon reduction [ 49 ].

The response to deforestation in the mid latitudes shows the transition between high and low latitude deforestation where albedo change becomes a less dominant driver of temperature and changes to CO 2 concentrations and the sensible heat flux play a larger role in local and global temperature response. The high latitudes experience cooling in every simulation almost instantaneously ( Fig 4 ), whereas the presence of higher CO 2 , and decreased outgoing sensible heat flux overcome the albedo change and increased latent heat, resulting in longer lasting warmer conditions in the mid latitudes ( Fig 7 ).

4.3 Low Latitudes

The initial air surface warming which occurs due to low latitude deforestation is in agreement with earlier studies [ 12 , 24 , 53 ]. The magnitude of the temperature change is not consistent with previous studies due to differences in the location and magnitude of deforested areas and CO 2 concentrations. Our 100% simulation resulted in a 0.04 K global warming by 2100 while previous studies show values ranging from 0.4 K [ 24 ] to 0.7 K [ 12 ].

Our 3% to 10.8% albedo increase was larger than the 4.1% recorded for some experiments [ 12 ] while our average albedo increase of 0.01 to 0.02 was smaller than the increase of 0.04 seen in other simulations [ 24 ]. The local cooling observed in our study is seen, although not to the same extent, in some [ 12 , 22 ] but not all [ 24 ] comparable experiments.

The difference between these studies is likely due to the different carbon and albedo responses, as well as some of the inherent differences in the models used. By 2100 our 100% scenario had an increased CO 2 concentration of ∼ 66.5 ppm, while comparable experiments resulted in an increase of 199 ppm [ 12 ]. Another difference is that previous studies [ 12 , 24 ] produced a reduction in ET, as well as a decreased atmospheric albedo from reduced cloud cover, both of which contribute to increased temperatures.

Satellite based studies show that that depending upon the scale of deforestation, cloud cover may not change and may even increase over disturbed areas [ 29 , 36 , 37 ]. This contradicts the above mentioned results [ 12 , 24 ] and may lead to added cooling not accounted for in these modelling studies. One of these previous efforts [ 12 ] found that the net albedo change over the deforested regions was negligible as the increase in surface albedo was compensated by the decrease in atmospheric albedo, and suggests that cloud cover may play a major role in tropical climates.

Clouds cover is prescribed in the UVic ESCM and in our experiments the post-deforestation surface albedo increase is not compensated by a decrease in atmospheric albedo due to a reduction in cloud cover over deforested areas. This helps explains the local cooling registered by our simulations and lends support to the argument that a reduction in cloud cover is an important component of the modelled temperature response to deforestation in the tropics see in previous experiments.

Low latitude deforestation has been associated with a decrease in modelled total land carbon [ 24 ], and the same occurs in all of our simulations ( Fig 14 ). The same authors [ 24 ] also see a reduction in soil carbon in the deforested areas, and increases in the non-deforested areas, which is observed in our 5%-45% scenarios, but is in disagreement with our 50%-100% experiments where we record an increase in soil carbon in all locations, regardless of land cover change. Soil temperatures were larger in the low latitudes, and conditions were wetter than the mid latitudes. It is likely this difference in climate that causes the larger reduction in local soil carbon, in the 5%-45% scenarios than the mid latitude cases. During the simulations the 5%-25% scenarios become wetter, relative to their initial drying following deforestation ( Fig 12 ). This change in behaviour, as well as warmer soil temperatures ( Fig 11 ), can account for the increased respiration relative to carbon drawdown, and hence the decreased local soil carbon in these experiments.

Local soil warming occurs due to the same energy balance modifications seen in the mid latitudes, with higher temperatures related to a decrease in outgoing sensible heat flux that overcomes the increase in outgoing net radiative and latent heat fluxes ( Fig 17 ). The higher local soil temperature anomalies present in the low latitudes, occur due to the larger reductions in outgoing sensible heat, as well as increased rates of change in the energy budget of the soils, than what is seen in the mid latitudes (Figs 16 and 17 ). The increase in outgoing latent heat flux and reduction in sensible heat flux, also explains the lower SATs over deforested areas in the low latitudes ( Fig 10 ).

Annually averaged outgoing surface energy flux anomalies over deforested areas for low latitude deforestation. Energy fluxes shown as latent heat (Top), sensible heat (Middle), and net surface radiation (Bottom) All anomalies shown in W m −2 .

https://doi.org/10.1371/journal.pone.0153357.g017

The direction of the soil carbon response of our 5- 45% low latitude simulations are in agreement with some observations [ 48 , 52 ] but differ from what has been described by other [ 49 ]. Although NPP increases due to the forest-to-cropland conversion, this is not enough to overcome the changes in respiration, where consistently higher soil temperatures, as well as less drying than is observed in the mid latitudes (Figs 8 , 11 , 9 and 12 ), lead to a larger reduction in local soil carbon. However our 50%-100% simulations present the same response as our high and mid latitude results, where local soil carbon increases. This highlights the importance of the initial magnitude of land use change, and resultant climate change, on the soil carbon response to deforestation in the UVic ESCM. The 15%-75% simulations all have converging levels of forest loss by 2100 ( Fig 3 ), thus the rate of change of land cover can also play a role in soil carbon retention. This further enhances the argument that the change in soil carbon arises through a multitude of pathways, which influence the influx and outflux of carbon.

The increase in atmospheric CO 2 , and warmer temperatures seen in our low latitude simulations, are climatic effects more often associated with modelled deforestation. Even then, due mostly to the soil carbon response, CO 2 and temperature increases are not proportional to deforested area. Seeing as how the location and scale of deforestation can lead to either an increase or decrease to local and global soil carbon, our results call attention to the subtleties of this response and toward a need for a better understanding the complexity of soil carbon dynamics [ 54 ].

5 Summary and Conclusions

• Global SAT response . High-latitude deforestation leads to cooling proportional to deforested area. Removing trees over the low latitudes causes warming but due to an increase in albedo and increase in the soil carbon pool (see below) this warming is reduced in experiments with large initial deforestation fraction. The SAT response of mid-latitude deforestation is small compared to the other two bands with intermediary deforestation fractions causing warming and larger fractions cooling.
• SAT response over deforested areas . Albedo driven cooling is observed for all latitude bands and all initial deforestation fractions.
• The importance of soil carbon . Atmospheric CO 2 concentration, and consequently the global temperature response to deforestation, are greatly influenced by post-disturbance changes in soil carbon pools inside and outside deforested areas. It is in large part due to an increase in soil carbon over deforested areas that, irrespective of latitude band, larger initial deforestation scenarios show lower final atmospheric CO 2 concentrations than intermediate scenarios. Given the large uncertainties associated with the modelled terrestrial carbon cycle [ 55 ], our results also point to the need for greater understanding of how organic matter behaves in soils [ 54 ] and for the adoption of this new knowledge by terrestrial models.
• Pattern and drivers of soil carbon change . Larger NPP, due mostly to CO 2 fertilization, is the cause of soil carbon increase over non-deforested areas. In the high-latitude experiments non-deforested areas also gain carbon due to colder and dryer conditions. In the mid- and low-latitude experiments deforested area soils become warmer and dryer, with moisture effects overcoming the opposing temperature impacts and leading to net soil carbon gain, particularly for the higher deforestation fraction scenarios, where local soil temperatures do not increase as much. Among experiments that end up with similar total deforested area, those with larger initial deforestation fractions tend to show larger soil carbon gains and consequently relatively cooler temperatures, an indication that the rate of land cover change is an important determinant of the model’s response to deforestation.
• Drivers of soil temperature change over deforested areas . At high latitudes the tendency is for cooling due to the increase in albedo. In both mid and low latitudes the response is warming caused by a post-deforestation reduction in sensible heat flux.
• Clouds and low-latitude deforestation . The local cooling recorded by our experiments is not seen in simulations of low-latitude deforestation conducted by models that incorporate cloud dynamics. Reliable predictions of the effects of deforestation require understanding the cloud response to land cover change, a problem with yet many uncertainties.

Acknowledgments

This work was supported by grants from the Natural Sciences and Engineering Research Council of Canada Discovery Grant (NSERC DG 140576948), The Canada Research Program (c) to H. Beltrami. Computational facilities provided by the Atlantic Computational Excellence Network (ACEnet-Compute Canada) with support from the Canadian Foundation for Innovation. H. Beltrami holds a Canada Research Chair in Climate Dynamics. P.L was funded by graduate fellowships from a NSERC-CREATE Training Program in Climate Sciences based at St. Francis Xavier University and by ACENET. AM was partially supported by the Univ Estadual Paulista International Visiting Professor Fellowship.

Author Contributions

Conceived and designed the experiments: PL AM HB ME. Performed the experiments: PL. Analyzed the data: PL AM HB. Contributed reagents/materials/analysis tools: AM HB ME. Wrote the paper: PL AM HB ME.

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Amazon Deforestation and Climate Change

Join Gisele Bundchen when she meets with one of Brazil’s top climate scientists to discuss the complexity of the Amazon rainforest and its connection to Earth’s atmosphere.

Anthropology, Geography

High on a tower overlooking the lush Amazon canopy, Gisele Bundchen and Brazilian climate scientist Antonio Nobre talk about the importance of the rainforest and the impact of cutting down its trees.

As Nobre explains, the rainforest is not only home to an incredible diversity of species, it also has a critical cooling effect on the planet because its trees channel heat high into the atmosphere. In addition, forests absorb and store carbon dioxide (CO 2 ) from the atmosphere—CO 2 that is released back into the atmosphere when trees are cut and burned.

Nobre warns that if deforestation continues at current levels, we are headed for disaster. The Amazon region could become drier and drier, unable to support healthy habitats or croplands.

Find more of this story in the “Fueling the Fire” episode of the National Geographic Channel’s Years of Living Dangerously series.

Transcript (English)

- Growing up in Southern Brazil, my five sisters and I ate meat pretty much every day. It's just part of the culture here. Per capita, Brazilians are one of the top consumers of beef on the planet. Now, with the world's growing appetite for beef, Brazil has also become a major exporter and is aiming to increase its market share, partly by selling to the US, the world's biggest consumer of beef, and to China, where demand for beef has grown 25% in just 10 years. I understand the need to develop and grow, but does that have to come at the expense of the rainforest and the climate? The Amazon Rainforest is about the same size as the continental United States. One-fifth of the world's fresh water runs through it, and it is home to more species of animals and plants than anywhere on Earth. The Amazon represents more than half of the remaining rainforests on the planet. This forest is so vast, but it is not indestructible. To find out what's at stake, I'm going to talk to one of Brazil's top climate scientist, Dr. Antonio Nobre. So Antonio, tell us a little bit about this amazing green carpet of heaven over here.

- Well, most people don't have the opportunity to come from the top of the forest. If you see all this many shades of green as you see here, it's because biodiversity is the essence of this type of forest. Every species of trees has thousands of species of bugs, and also if you get a leaf of one of the species, and you look to the microbes that is sitting on the top of leaf, you find millions of species, millions, and this is all below our radar screen, so to speak, because we don't realize, it's invisible. And the trees are shooting water from the ground, groundwater up high in the sky, and this goes up into the atmosphere and releases the heat out there, and this radiates to space. And this is very important as a mechanism to cool the planet. They're like air conditioners. Open air conditioning, that's what the forest is.

- So in other words, if we lose all these trees, we are losing the air conditioning that cools off the whole planet.

- Not only that.

- Not only that?

- No. The trees are soaking up carbon, you know the pollution that we produce, like carbon dioxide? Yeah, yeah, yeah.

- Burning gasoline in our cars, you release carbon dioxide in the air, or burning coal, and the trees use carbon dioxide as a raw material.

- So the trees are storing all this carbon, so if you come and cut it down and burn it out, does that mean that all that carbon goes up in the air?

- Absolutely. Yeah.

- What would happen if this forest was gone?

- When the forest is destroyed, climate changes, and then forest that's left is damaged as well. And then the forest grows drier and drier and eventually catch fire. So in the extreme, the whole area becomes a desert. And that's what is in store if we deforest. So we have to quit deforestation yesterday, not 2020 or '30. And there is no plan C. You know, you have plan A. Plan A is business as usual. Keep plundering with all the resources and using as if it were infinite. Plan B is what many people are attempting, changing the matrix of energy and using clean sources, stop eating too much meat, and replanting forests If that doesn't work, then we go to plan C. What's plan C? I have no idea.

- Going to another planet.

- But we can't do that.

- We don't have another planet, so either we work with plan B or we're-

- Basically, yeah. We're done, and so plan B has to work. It has to work.

- People have to take accountability, 'cause it can't just be like, I'm leaving over here and whatever happens over there, who cares?

- It's not my problem.

- It's not my problem, because it is everyone's problem.

- Yes. People should wake up. It's like when you're in the midst of an unfolding disaster, what do you do? You panic? No. You move it. Move, move, move, move. That's what we need to do.

Transcripción (Español)

- El año en que vivimos en peligro.

- Cuando era niña en el sur de Brasil, mis cinco hermanas y yo comíamos carne casi todos los días. Es parte de la cultura aquí. Per cápita, los brasileños son uno de los mayores consumidores de carne de res en el planeta. Ahora, con el creciente apetito mundial por la carne de res, Brasil también se ha convertido en un importante exportador y está buscando aumentar su participación en el mercado, en parte vendiendo a los Estados Unidos, el mayor consumidor de carne de res del mundo, y a China, donde la demanda de carne de res ha crecido un 25 % en tan solo 10 años. Entiendo la necesidad de desarrollarse y crecer, pero ¿tiene que ser a expensas de la selva tropical y el clima? La selva amazónica tiene casi el mismo tamaño que los Estados Unidos continentales. Una quinta parte del agua dulce del mundo fluye a través de ella. Y es hogar de más especies de animales y plantas que cualquier otro lugar en la Tierra. El Amazonas representa más de la mitad de las selvas tropicales restantes en el planeta. Estado Mato Grosso, Brasil Esta selva es tan vasta, pero no es indestructible. Para descubrir lo que está en juego, voy a hablar con uno de los principales científicos climáticos de Brasil, el Dr. Antonio Nobre. Antonio, cuéntanos un poco acerca de esta increíble alfombra verde de cielo que tenemos aquí.

- Bueno, la mayoría de las personas no tienen la oportunidad de venir hasta la cima de la selva. Si ves todos los diferentes tonos de verde como estos aquí, es porque la biodiversidad es la esencia de este tipo de selva. Cada especie de árboles tiene miles de especies de insectos, y también si tomas una hoja de una de las especies, y miras a los microbios en la parte superior de la hoja, encuentras millones de especies, millones, y todo esto queda por debajo de nuestro radar, porque no nos damos cuenta, es invisible. Y los árboles están extrayendo agua del subsuelo, hasta lo alto en el cielo, y esto sube a la atmósfera y libera el calor allí, y esto se irradia al espacio. Este es un mecanismo muy importante para enfriar el planeta. Son como aires acondicionados. Aire acondicionado al aire libre, eso es el bosque.

- En otras palabras, si perdemos todos estos árboles, estamos perdiendo el aire acondicionado que enfría todo el planeta.

- No solo eso.

- ¿No solo eso?

- No. Los árboles están absorbiendo carbono, ¿la contaminación que producimos, como el dióxido de carbono?

- Al quemar gasolina en los autos, se libera dióxido de carbono al aire, o quemando carbón, y los árboles usan el dióxido de carbono como materia prima.

- Entonces los árboles están almacenando todo este carbono, así que si lo cortas y lo quemas, ¿eso significa que todo ese carbono sube al aire?

- Absolutamente. Sí.

- ¿Qué pasaría si este bosque desapareciera?

- Cuando el bosque es destruido, el clima cambia, y luego el bosque que queda también se daña. Luego el bosque se vuelve cada vez más seco y eventualmente se incendia. En caso extremo, toda el área se convierte en un desierto. Eso es lo que nos espera si deforestamos. Así que tenemos que dejar de deforestar desde ayer, no en 2020 o 2030. No hay un plan C. Tienes un plan A. El plan A es seguir como siempre. Continuar saqueando todos los recursos y usarlos como si fueran infinitos. El plan B es lo que muchos están intentando, cambiar la matriz de energía y usar fuentes limpias, dejar de comer demasiada carne y reforestar bosques. Si eso no funciona, entonces pasamos al plan C. ¿Cuál es el plan C?

- No tengo idea.

- Ir a otro planeta.

- Pero no podemos hacer eso.

- No tenemos otro planeta, así que o trabajamos con el plan B o estamos-

- Acabados.

- Básicamente, sí. Estamos acabados, así que el plan B tiene que funcionar. Tiene que funcionar.

- Las personas deben asumir responsabilidad, porque no puedes nada más pensar, yo vivo aquí y lo que suceda por allá, ¿a quién le importa?

- A mí qué.

- No es mi problema, porque es un problema de todos.

- Sí. La gente debería despertar. Es como cuando estás en medio de un desastre en desarrollo, ¿qué haces? ¿Entrar en pánico? No. Lo mueves. Que se mueva. Eso es lo que necesitamos hacer.

The Amazon rain forest absorbs one-fourth of the CO2 absorbed by all the land on Earth. The amount absorbed today, however, is 30% less than it was in the 1990s because of deforestation. A major motive for deforestation is cattle ranching. China, the United States, and other countries have created a consumer demand for beef, so clearing land for cattle ranching can be profitable—even if it’s illegal. The demand for pastureland, as well as cropland for food such as soybeans, makes it difficult to protect forest resources.

Many countries are making progress in the effort to stop deforestation. Countries in South America and Southeast Asia, as well as China, have taken steps that have helped reduce greenhouse gas emissions from the destruction of forests by one-fourth over the past 15 years.

Brazil continues to make impressive strides in reducing its impact on climate change. In the past two decades, its CO2 emissions have dropped more than any other country. Destruction of the rain forest in Brazil has decreased from about 19,943 square kilometers (7,700 square miles) per year in the late 1990s to about 5,180 square kilometers (2,000 square miles) per year now. Moving forward, the major challenge will be fighting illegal deforestation.

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Climate change and its impact on biodiversity and human welfare

K. r. shivanna.

Ashoka Trust for Research in Ecology and the Environment, Srirampura, Jakkur Post, Bengaluru, 560064 India

Climate change refers to the long-term changes in temperature and weather due to human activities. Increase in average global temperature and extreme and unpredictable weather are the most common manifestations of climate change. In recent years, it has acquired the importance of global emergency and affecting not only the wellbeing of humans but also the sustainability of other lifeforms. Enormous increase in the emission of greenhouse gases (CO 2 , methane and nitrous oxide) in recent decades largely due to burning of coal and fossil fuels, and deforestation are the main drivers of climate change. Marked increase in the frequency and intensity of natural disasters, rise in sea level, decrease in crop productivity and loss of biodiversity are the main consequences of climate change. Obvious mitigation measures include significant reduction in the emission of greenhouse gases and increase in the forest cover of the landmass. Conference of Parties (COP 21), held in Paris in 2015 adapted, as a legally binding treaty, to limit global warming to well below 2 °C, preferably to 1.5 °C by 2100, compared to pre-industrial levels. However, under the present emission scenario, the world is heading for a 3–4 °C warming by the end of the century. This was discussed further in COP 26 held in Glasgow in November 2021; many countries pledged to reach net zero carbon emission by 2050 and to end deforestation, essential requirements to keep 1.5 °C target. However, even with implementation of these pledges, the rise is expected to be around 2.4 °C. Additional measures are urgently needed to realize the goal of limiting temperature rise to 1.5 °C and to sustain biodiversity and human welfare.

Introduction

Climate change refers to long-term changes in local, global or regional temperature and weather due to human activities. For 1000s of years, the relationship between lifeforms and the weather have been in a delicate balance conducive for the existence of all lifeforms on this Planet. After the industrial revolution (1850) this balance is gradually changing and the change has become apparent from the middle of the twentieth century. Now it has become a major threat to the wellbeing of humans and the sustainability of biodiversity. An increase in average global temperature, and extreme and unpredictable weather are the most common manifestations of climate change. It has now acquired the importance of global emergency. According to the report of the latest Intergovernmental Panel for Climate Change (AR6 Climate Change 2021 ), human-induced climate change as is prevalent now is unprecedented at least in the last 2000 years and is intensifying in every region across the globe. In this review the drivers of climate change, its impact on human wellbeing and biodiversity, and mitigation measures being taken at global level are briefly discussed.

Drivers of climate change

Emission of green-house gases.

Steady increase in the emission of greenhouse gases (GHGs) due to human activities has been the primary driver for climate change. The principal greenhouse gases are carbon dioxide (76%), methane (16%), and to a limited extent nitrous oxide (2%). Until recent decades, the temperature of the atmosphere was maintained within a reasonable range as some of the sunlight that hits the earth was reflected back into the space while the rest becomes heat that keeps the earth and the atmosphere warm enough for the sustenance of life forms. Accumulation of greenhouse gases combine with water vapour to form a transparent layer in the atmosphere that traps infrared radiation (net heat energy) emitted from the Earth’s surface and reradiates it back to Earth’s surface, thus contributing to the increasing temperature (greenhouse effect). Methane is 25 times and nitrous oxide 300 times more potent than CO 2 in trapping heat. Until 2019, the US, UK, European Union, Canada, Australia, Japan and Russia were the major CO 2 producers and were responsible for 61% of world’s emissions. Now, China produces the maximum amount of CO 2 (27%) followed by USA (11%) and India (6.6%); on per capita basis, however, India stands ninth.

The emission of GHGs is largely due to the burning of fossil fuels (coal, oil and natural gas) for automobiles and industries which result in carbon emissions during their extraction as well as consumption. The amount of CO 2 in the atmosphere before the industrial revolution used to be around 280 ppm and now it has increased to 412 ppm (as of 2019). Increase in the atmospheric temperature also leads to an increase in the temperature of the ocean. The oceans play an important role in the global carbon cycle and remove about 25% of the carbon dioxide emitted by human activities. Further, some CO 2 dissolves in the ocean water releasing carbonic acid which increases the acidity of the sea water. Rising ocean temperatures and acidification not only reduce their capacity to act as carbon sinks but also affect ocean ecosystems and the populations that relay on them.

Increasing demand for meat and milk has led to a significant increase in the population of livestock and conversion of enormous amount of the land to pasture and farm land to raise livestock. Ruminant animals (largely cows, buffaloes and sheep) produce large amounts of methane when they digest food (through enteric fermentation by microbes), adding to the greenhouse gases in the atmosphere (Sejiyan et al. 2016 ). To produce 1 kg of meat it requires 7 kg of grain and between 5000 and 20,000 L of water whereas to produce 1 kg of wheat it requires between 500 and 4000 L of water (Pimentel and Pimentel 2003 ). Anaerobic fermentation of livestock manure also produces methane. According to Patrick Brown, our animal farming industry needs to be changed; using readily available plant ingredients, the nutritional value of any type of meat can be matched with about one twentieth of the cost (See Leeming 2021 ).

The main natural source of nitrous oxide released to the atmosphere (60%) comes from the activity of microbes on nitrogen-based organic material from uncultivated soil and waste water. The remaining nitrous oxide comes from human activities, particularly agriculture. Application of nitrogenous fertilizers to crop plants is a routine practice to increase the yield; many of the farmers tend to apply more than the required amount. However, it results in nitrous oxide emissions from the soil through nitrification and denitrification processes by microbes. Both synthetic and organic fertilizers increase the amount of nitrogen available in the soil to microbial action leading to the release of nitrous oxide. Organic fertilizers, however, release nitrogen more slowly than synthetic ones so that most of it gets absorbed by the plants as they become available. Synthetic fertilizers release nitrogen rapidly which cannot be used by plants right away, thus making the excess nitrogen available to microbes to convert to nitrous oxide. Presently CO 2 concentration in the atmosphere is higher than at any time in at least 2 million years, and methane and nitrous oxide are higher than at any time in the last 800,000 years (AR6 Climate Change 2021 ).

Permafrost (permanently frozen soil), widespread in Arctic regions of Siberia, Canada, Greenland, Alaska, and Tibetan plateau contains large quantities of organic carbon in the top soil leftover from dead plants that could not be decomposed or rot away due to the cold. Global warming-induced thawing of permafrost facilitates decomposition of this material by microbes thus releasing additional amount of carbon dioxide and methane to the atmosphere.

Deforestation

Limited deforestation in early part of human civilization was the result of subsistence farming; farmers used to cut down trees to grow crops for consumption of their families and local population. In preindustrial period also, there was a balance between the amount of CO 2 emitted through various processes and the amount absorbed by the plants. Forests are the main sinks of atmospheric CO 2 . After the industrial revolution, the trend began to change; increasing proportion of deforestation is being driven by the demands of urbanization, industrial activities and large-scale agriculture. A new satellite map has indicated that field crops have been extended to one million additional km 2 of land over the last two decades and about half of this newly extended land has replaced forests and other ecosystems (Potapov et al. 2021 ).

In recent decades the demands on forest to grow plantation crops such as oil palm, coffee, tea and rubber, and for cattle ranching and mining have increased enormously thus reducing the forest cover. According to the World Wildlife Fund (WWF), over 43 million hectares of forest was lost between 2004 and 2017 out of 377 million hectares monitored around the world (Pacheco et al. 2021 ). Amazon Rain Forest is the largest tropical rain forest of the world and covers over 5 million km 2 . It is undergoing extensive degradation and has reached its highest point in recent years. According to National Geographic, about 17% of Amazon rain forest has been destroyed over the past 50 years and is increasing in recent years; during the last 1 year it has lost over 10,000 km 2 . In most of the countries the forest cover is less than 33%, considered necessary. For example, India’s forest and tree cover is only about 24.56% of the geographical area (Indian State Forest Report 2019 ).

Impacts of climate change

Increase in atmospheric temperature has serious consequences on biodiversity and ecosystems, and human wellbeing. The most important evidences of climate change is the long term data available on the CO 2 levels, global temperature and weather patterns. The impacts of climate change in the coming decades are based on published models on the basis of the analysis of the available data. Comparison of the performance of climate models published between 1970 and 2007 in projecting global mean surface temperature and associated changes with actual observations have shown that the models were consistent in predicting global warming in the years after publication (Hausfather et al. 2019 ). This correlation between predicted models and actual data indicates that the models are indeed reliable in accurately predicting the global warming and its impacts on weather pattern in the coming decades and their consequences on biodiversity and human welfare.

Weather pattern and natural disasters

One of the obvious changes observed in recent years is the extreme and unpredictable weather, and an increase in the frequency and intensity of natural disasters. Brazil’s south central region saw one of the worst droughts in 2021with the result many major reservoirs reached < 20% capacity, seriously affecting farming and energy generation (Getirana et al. 2021 ). In earlier decades, it was possible to predict with reasonable certainty annual weather pattern including the beginning and ending of monsoon rains; farmers could plan sowing periods of their crops in synchrony with the prevailing weather. Now the weather pattern is changing almost every year and the farmers are suffering huge losses. Similarly the extent of annual rainfall and the locations associated with heavy and scanty rainfall are no more predictable with certainty. Many areas which were associated with scanty rainfall have started getting much heavier rains and the extent of rainfall is getting reduced in areas traditionally associated with heavy rainfall. Similarly the period and the extent of snowfall in temperate regions have also become highly variable.

Increase in the frequency and intensity of natural disasters such as floods and droughts, cyclones, hurricanes and typhoons, and wildfires have become very obvious. Top five countries affected by climate change in 2021 include Japan, Philippines, Germany, Madagascar and India. Apart from causing death of a large number of humans and other animals, economic losses suffered by both urban and rural populations have been enormous. Deadly floods and landslides during 2020 forced about 12 million people leave their homes in India, Nepal and Bangladesh. According to World Meteorological Organization’s comprehensive report published in August 2021 (WMO-No.1267), climate change related disasters have increased by a factor of five over the last 50 years; however, the number of deaths and economic losses were reduced to 2 million and US$ 3.64 trillion respectively, due to improved warning and disaster management. More than 91% of these deaths happened in developing countries. Largest human losses were brought about by droughts, storms, floods and extreme temperatures. The report highlights that the number of weather, climate and water-extremes will become more frequent and severe as a result of climate change.

Global warming enhances the drying of organic matter in forests, thus increasing the risks of wildfires. Wildfires have become very common in recent years, particularly is some countries such as Western United States, Southern Europe and Australia, and are becoming more frequent and widespread. They have become frequent in India also and a large number of them have been recorded in several states. According to European Space Agency, fire affected an estimated four million km 2 of Earth’s land each year. Wildfires also release large amounts of carbon dioxide, carbon monoxide, and fine particulate matter into the atmosphere causing air pollution and consequent health problems. In 2021, wildfires around the world, emitted 1.76 billion tonnes of carbon (European Union’s Copernicus Atmospheric Monitoring Service). In Australia, more than a billion native animals reported to have been killed during 2020 fires, and some species and ecosystems may never recover (OXFAM International 2021 ).

Sea level rise

Global warming is causing mean sea level to rise in two ways. On one hand, the melting of the glaciers, the polar ice cap and the Atlantic ice shelf are adding water to the ocean and on the other hand the volume of the ocean is expanding as the water warms. Incomplete combustion of fossil fuels, biofuels and biomass releases tiny particles of carbon (< 2.5 µm), referred to as black carbon. While suspended in the air (before they settle down on earth’s surface) black carbon particles absorb sun’s heat 1000s of times more effectively than CO 2 thus contributing to global warming. When black particles get deposited over snow, glaciers or ice caps, they enhance their melting further adding to the rise in sea level. Global mean sea level has risen faster since 1900 than over any preceding century in at least the last 3000 years. Between 2006 and 2016, the rate of sea-level rise was 2.5 times faster than it was for almost the whole of the twentieth century (OXFAM International 2021 ). Precise data gathered from satellite radar measurements reveal an accelerating rise of 7.5 cm from 1993 to 2017, an average of 31 mm per decade (WCRP Global Sea Level Budget Group 2018 ).

Snow accounts for almost all current precipitation in the Arctic region. However, it continues to warm four times faster than the rest of the world as the melting ice uncovers darker land or ocean beneath, which absorbs more sunlight causing more heating. The latest projections indicate more rapid warming and sea ice loss in the Arctic region by the end of the century than predicted in previous projections (McCrystall et al. 2021 ). It also indicates that the transition from snow to rain-dominated Arctic in the summer and autumn is likely to occur decades earlier than estimated. In fact this transition has already begun; rain fell at Greenland’s highest summit (3216 m) on 14 August 2021 for several hours for the first time on record and air temperature remained above freezing for about 9 h (National Snow and Ice Sheet Centre Today, August 18, 2021).

In the annual meeting of the American Geophysical Union (13 December 2021) researchers warned that rapid melting and deterioration of one of western Antarctica’s biggest glaciers, roughly the size of Florida, Thwaites (often called as Doomsday Glacier), could lead to ice shelf’s complete collapse in just a few years. It holds enough water to raise sea level over 65 cm. Thwaites glacier is holding the entire West Antarctic ice sheet and is being undermined from underneath by warm water linked to the climate change. Melting of Thwaites could eventually lead to the loss of the entire West Antarctic Ice Sheet, which locks up 3.3 m of global sea level rise. Such doomsday may be coming sooner than expected (see Voosen 2021 ). If this happens, its consequences on human tragedy and biodiversity loss are beyond imagination.

The Himalayan mountain range is considered to hold the world’s third largest amount of glacier ice after Arctic and Antarctic regions. It is considered as Asian water tower (Immerzeel et al. 2020 ); the meltwater from the Himalayan glaciers provide the source of fresh water to nearly 2 billion people living along the mountain valleys and lowlands around the Himalayas. These glaciers are melting at unprecedented rates. Recently King et al. ( 2021 ) studied 79 glaciers close to Mt. Everest by analysing mass-change measurements from satellite archives and reported that the rate of ice loss from glaciers consistently increased since the early 1960s. This loss is likely to increase in the coming years due to further warming. In another study, a tenfold acceleration in ice loss was observed across the Himalayas than the average rate in recent decades over the past centuries (Lee et al. 2021 ). Melting of glaciers also results in drying up of perennial rivers in summer leading to the water scarcity for billions of humans and animals, and food and energy production downstream. See level rise and melting of glaciers feeding the rivers could lead to migration of huge population, creating additional problems. Even when the increase in global temperature rise is limited to 1.5 °C (discussed later), it generates a global sea-level rise between 1.7 and 3.2 feet by 2100. If it increases to 2 °C, the result could be more catastrophic leading to the submergence of a large number of islands, and flooding and submergence of vast coastal areas, saltwater intrusion into surface waters and groundwater, and increased soil erosion. A number of islands of Maldives for example, would get submerged as 80% of its land area is located less than one meter above the sea level. The biodiversity in such islands and coastal areas becomes extinct. China, Vietnam, Fiji, Japan, Indonesia, India and Bangladesh are considered to be the most at risk. Sundarbans National Park (UNESCO world heritage Site), the world’s largest Mangrove Forest spread over 140,000 hectares across India and Bangladesh, is the habitat for Royal Bengal Tiger and several other animal species. The area has already lost 12% of its shoreline in the last four decades by rising see level; it is likely to be completely submerged. Jakarta in Indonesia is the fastest sinking city in the world; the city has already sunk 2.5 m in the last 10 years and by 2050, most of it would be submerged. In Europe also, about three quarters of all cities will be affected by rising sea levels, especially in the Netherlands, Spain, Belgium, Greece and Italy. The entire city of Venice may get submerged (Anonymous 2018 ). In USA, New York City and Miami would be particularly vulnerable.

Crop productivity and human health

Many studies have indicated that climate change is driving increasing losses in crop productivity (Zhu et al. 2021 ). The models on global yield loss for wheat, maize and rice indicate an increase in yield losses by 10 to 25% per degree Celsius warming (Deutsch et al. 2018 ). Bras et al. ( 2021 ) reported that heatwave and drought roughly tripled crop losses over the last 50 years, from − 2.2% (1964–1990) to − 7.3% (1991–2015). Overall, the loss in crop production from climate-driven abiotic stresses may exceed US$ 170 billion year –1  and represents a major threat to global food security (Razaaq et al. 2021 ). Analysis of annual field trials of common wheat in California from 1985 to 2019 (35 years), during which the global atmospheric CO 2  concentration increased by 19%, revealed that the yield declined by 13% (Bloom and Plant 2021 ). Apart from crop yield, climate change is reported to result in the decline of nutritional value of food grains (Jagermeyr et al. 2021 ). For example, rising atmospheric CO 2 concentration reduces the amounts of proteins, minerals and vitamins in rice (Zhu et al. 2018 ). This may be true in other cereal crops also. As rice supplies 25% of all global calories, this would greatly affect the food and nutritional security of predominantly rice growing countries. Climate change would also increase the prevalence of insect pests adding to the yield loss of crops. The prevailing floods and droughts also affect food production significantly. Global warming also affects crop productivity through its impact on pollinators. Insect pollinators contribute to crop production in 75% of the leading food crops (Rader et al. 2013 ). Climate change contributes significantly to the decline in density and diversity of pollinators (Shivanna 2020 ; Shivanna et al. 2020 ). Under high as well as low temperatures, bees spend less time in foraging (Heinrich 1979 ) adding additional constraints to pollination efficiency of crop species.

The IPCC Third Assessment Report (Climate change 2001: The scientific basis – IPCC) concluded that the poorest countries would be hardest hit with reductions in crop yields in most tropical and sub-tropical regions due to increased temperature, decreased water availability and new or changed insect pest incidence. Rising ocean temperatures and ocean acidification affect marine ecosystems. Loss of fish habitats is modifying the distribution and productivity of both marine and freshwater species thus affecting the sustainability of fisheries and populations dependent on them (Salvatteci 2022 ).

Air pollution is considered as the major environmental risk of climate change due to its impact on public health causing increasing morbidity and mortality (Manisalidis 2020 ). Particulate matter, carbon monoxide, nitrogen oxide, and sulphur dioxide are the major air pollutants. They cause respiratory problems such as asthma and bronchiolitis and lung cancer. Recent studies have indicated that exposure to air pollution is linked to methylation of immunoregulatory genes, altered immune cell profiles and increased blood pressure in children (Prunicki et al. 2021 ). In another study wildfire smoke has been reported to be more harmful to humans than automobiles emissions (Aquilera et al. 2021 ). Stubble burning (intentional incineration of stubbles by farmers after crop harvest) has been a common practice in some parts of South Asia particularly in India; it releases large amount of toxic gases such as carbon monoxide and methane and causes serious damage to the environment and health (Abdurrahman et al. 2020 ). It also affects soil fertility by destroying the nutrients and microbes of the soil. Attempts are being made to use alternative methods to prevent this practice.

A number of diseases such as zika fever, dengue and chikungunya are transmitted by Aedes mosquitoes and are now largely restricted to the monsoon season. Global warming facilitates their spread in time and space thus exposing new populations and regions for extended period to these diseases. Lyme disease caused by a bacterium is transmitted through the bite of the infected blacklegged ticks. It is one of the most common disease in the US. The cases of Lyme disease have tripled in the past two decades. Recent studies have suggested that variable winter conditions due to climate change could increase tick’s activity thus increasing the infections (see Pennisi 2022 ).

Biodiversity

Biodiversity and associated ecoservices are the basic requirements for human livelihood and for maintenance of ecological balance in Nature. Documentation of biodiversity, and its accelerating loss and urgent need for its conservation have become the main concern for humanity since several decades (Wilson and Peter 1988 ; Wilson 2016 ; Heywood 2017 ; IPBES 2019 ; Genes and Dirzo 2021 ; Shivanna and Sanjappa 2021 ). It is difficult to analyse the loss of biodiversity exclusively due to climate change as other human-induced environmental changes such as habitat loss and degradation, overexploitation of bioresources and introduction of alien species also interact with climate change and affect biodiversity and ecosystems. In recent decades there has been a massive loss of biodiversity leading to initiation of the sixth mass extinction crisis due to human-induced environmental changes. These details are not discussed here; they are dealt in detail in many other reviews (Leech and Crick 2007 ; Sodhi and Ehrlich 2010 ; Lenzen et al. 2012 ; Dirzo and Raven 2003 ; Raven 2020 ; Ceballos et al. 2015 ; Beckman et al. 2020 ; Shivanna 2020 ; Negrutiu et al. 2020 ; Soroye et al. 2020 ; Wagner 2020 ,  2021 ; Anonymous 2021 ; Zattara and Aizen 2021 ).

Terrestrial species

There are several effects on biodiversity caused largely by climate change. Maxwell et al. ( 2019 ) reviewed 519 studies on ecological responses to extreme climate events (cyclones, droughts, floods, cold waves and heat waves) between 1941 and 2015 covering amphibians, birds, fish, invertebrates, mammals, reptiles and plants. Negative ecological responses have been reported for 57% of all documented groups including 31 cases of local extirpations and 25% of population decline.

Increase in temperature impacts two aspects of growth and development in plants and animals. One of them is a shift in distributional range of species and the other is the shift in phenological events. Plant and animal species have adapted to their native habitat over 1000s of years. As the temperature gets warmer in their native habitat, species tend to move to higher altitudes and towards the poles in search of suitable temperature and other environmental conditions. There are a number of reports on climate change-induced shifts in the distributional range of both plant and animal species (Grabherr et al. 1994 ; Cleland et al. 2007 ; Parmesan and Yohe 2003 ; Beckage et al. 2008 ; Pimm 2009 ; Miller-Rushing et al. 2010 ; Lovejoy and Hannah 2005 ; Lobell et al. 2011 ). Many species may not be able to keep pace with the changing weather conditions and thus lag behind leading to their eventual extinction. Long-term observations extending for over 100 years have shown that many species of bumblebees in North-America and Europe are not keeping up with the changing climate and are disappearing from the southern portions of their range (Kerr et al. 2015 ). Most of the flowering plants depend on animals for seed dispersal (Beckman et al. 2020 ). Defaunation induced by climate change and other environmental disturbances has reduced long-distance seed dispersal. Prediction of dispersal function for fleshly-fruited species has already reduced the capacity of plants to track climate change by 60%, thus severely affecting their range shifts (Fricke et al. 2022 ).

Climate change induced shifts in species would threaten their sustenance even in protected areas as they hold a large number of species with small distributional range (Velasquez-Tibata et al. 2013 ). Pautasso ( 2012 ) has highlighted the sensitivity of European birds to the impacts of climate change in their phenology (breeding time), migration patterns, species distribution and abundance. Metasequoia glyptostroboides is one of the critically endangered species with extremely small populations distributed in South-Central China. Zhao et al. ( 2020 ) analysed detailed meteorological and phenological data from 1960 to 2016 and confirmed that climate warming has altered the phenology and compressed the climatically suitable habitat of this species. Their studies revealed that the temperature during the last 57 years has increased significantly with the expansion of the length of growing season of this species. Climatically suitable area of the species has contracted at the rate of 370.8 km 2 per decade and the lower and upper elevation limits shrunk by 27 m over the last 57 years.

The other impact of climate change on plant and animal species has been in their phenological shift. Phenology is the timing of recurring seasonal events; it is a sort of Nature’s calendar for plants and animals. In flowering plants, various reproductive events such as the timing of flowering, fruiting, their intensity, and longevity are important phenological events, and in animals some of the phenological events include building of nests in birds, migration of animal species, timing of egg laying and development of the larva, pupa and adult in insects. Phenological events of both plants and animals are generally fixed in specific time of the year as they are based on environmental cues such as temperature, light, precipitation and snow melt. Phenological timings of species are the results of adaptations over 100 s of years to the prevailing environment. Wherever there is a mutualism between plants and animals, there is a synchrony between the two partners. For example in flowering plants, flowering is associated with the availability of pollinators and fruiting is associated with the availability of seed dispersers and optimal conditions for seed germination and seedling establishment. In animals also, phenological events are adapted to suit normal growth and reproduction. In temperate regions, melting of ice initiates leafing in plants; this is followed by the flowering in the spring. Similarly, warming of the climate before the spring induces hatching of the hibernating insects which feed on newly developed foliage. Insects emerge and ready to pollinate the flowers by the time the plants bloom.

The dates of celebration of the cherry blossom festival, an important cultural event in Japan that coincides with the peak of flowering period of this species and for which > 1000 years of historical records are available, has shown advances in the dates of the festival in recent decades (Primack et al. 2009 ). The records between 1971 and 2000 showed that the trees flowered an average of 7 days earlier than all the earlier years (Allen et al. 2013 ). These advances were correlated with increasing temperature over the years. Spring temperatures in the Red River valley, North Dakota, USA have extended the period of the growing season of plants significantly over the years. Flowering times, for which data are available from1910 to1961, have been shown to be sensitive to at least one variable related to temperature or precipitation for 75% of the 178 species investigated (Dunnell and Traverse 2011 ). The first flowering time has been significantly shifted earlier or later over the last 4 years of their study in 5–15% of the observed species relative to the previous century. Rhododendron arboretum , one of the central Himalayan tree species, flowers from early February to mid-March. Generalized additive model using real-time field observations (2009–2011) and herbarium records (1893–2003) indicated 88–97 days of early flowering in this species over the last 100 years (Gaira et al. 2014 ). This early flowering was correlated with an increase in the temperature.

One of the consequences of a shift in the distributional range of species and phenological timings is the possible uncoupling of synchronization between the time of flowering of plant species and availability of its pollinators (see Gerard et al. 2020 ). When a plant species migrates, its pollinator may not be able to migrate; similarly when a pollinator migrates, the plant species on which it depends for sustenance may not migrate. Memmott et al. ( 2007 ) explored potential disruption of pollination services due to climate change using a network of 1420 pollinators and 429 plant species by simulating consequences of phenological shifts that can be expected with doubling of atmospheric CO 2 . They reported phenological shifts which reduced available floral resources to 17–50% of all pollinator species. A long-term study since the mid-1970s in the Mediterranean Basin has indicated that unlike the synchrony present in the earlier decades between the flowering of plant species and their pollinators, insect phenoevents during the last decade showed a steeper advance than those of plants (Gordo and Sanz 2005 ). Similar asynchrony has been reported between the flowering of Lathyrus and one of its pollinators, Hoplitis fulgida (Forrest and Thomson 2011 ). Asynchrony between flowering and appearance of pollinator has also been reported in a few other cases (Kudo and Ida 2013 ; Kudo 2014 ). Such asynchrony could affect the sustenance of plant and/or pollinator species in the new environment.

Marine species

Amongst the marine species, corals are the most affected groups due to the rise in temperature and acidity of oceans. Corals live in a symbiotic relationship with algae which provide colour and photosynthates to the corals. Corals are extremely sensitive to heat and acidity; even an increase of 2–3°F of ocean water above normal results in expulsion of the symbiotic algae from their tissues leading to their bleaching (Hoegh-Guldberg et al. 2017 ). When this bleached condition continues for several weeks, corals die. Nearly one-third of the Great Barrier Reef, the world’s largest coral reef system that sustains huge Australian tourism industry, has died as a result of global warming (Hughes et al. 2018 ). According to the experts the reef will be unrecognizable in another 50 years if greenhouse gas emissions continue at the current rate.

According to UNESCO, coral reefs in all 29 reef-containing World Heritage sites would cease to exist as functioning ecosystems by the end of this century if greenhouse gas emissions continue to be emitted at the present rate (Elena et al. 2020 ). Recent assessment of the risk of ecosystem collapse to coral reefs of the Western Indian Ocean, covering about 5% of the global total, range from critically endangered to vulnerable (Obura et al. 2021 ). Coral reefs provide suitable habitat for thousands of other species, including sharks, turtles and whales. If corals die, the whole ecosystem will get disrupted.

Melting of ice in Arctic region due to global warming is threatening the survival of native animals such as polar bear, Arctic fox and Arctic wolf. Rising of sea level also leads to the extinction of a large number of endangered and endemic plant and animal species in submerged coastal areas and islands. Over 180,000 islands around the globe contain 20% of the world’s biodiversity. Bellard et al. ( 2013 ) assessed consequences of sea level rise of 1–6 m for 10 insular biodiversity hotspots and their endemic species at the risk of potential extinction. Their study revealed that 6 to19% of the 4447 islands would be entirely submerged depending on the rise of sea level; three of them, the Caribbean islands, the Philippines and Sundaland, displayed the most significant hotspots representing a potential threat for 300 endemic species. According to the Centre for Biological Diversity ( 2013 ) 233 federally protected threatened and endangered species in 23 coastal states are threatened if rising sea is unchecked. Recently more than 100 Aquatic Science Societies representing over 80,000 scientists from seven continents sounded climate alarm (Bonar 2021 ). They have highlighted the effects of climate change on marine and aquatic ecosystems and have called on the world leaders and public to undertake mitigation measures to protect and sustain aquatic systems and theirs services.

Mitigation measures

The principal mitigation measures against climate change are obvious; they include significant reduction in greenhouse gas emission, prevention of deforestation and increase in the forest cover. To reduce greenhouse gas emission, use of coal and fossil fuels needs to be reduced markedly. As climate change is a global challenge, local solutions confined to one or a few countries do not work; we need global efforts. Many attempts are being made to achieve these objectives at the global level since many decades. Mitigation measures are largely at the level of diplomatic negotiations involving states and international organizations, Governments and some nongovernmental organizations. The Intergovernmental Panel on Climate Change (IPCC) was established by the United Nations Environment Programme (UNEP) and the World Meteorological Organization (WMO) in 1988. Its mandate was to provide political leaders with periodic scientific assessments concerning climate change, its implications and risks, and also to put forward adaptation and mitigation strategies. In 1992 more than 1700 World scientists, including the majority of living Nobel laureates gave the first Warning to Humanity about climate change and associated problems. They expressed concern about potential damage to the Planet Earth by human-induced environmental changes such as climate change, continued human population growth, forest loss, biodiversity loss and ozone depletion. Conference of Parties (COP) of the UN Convention on Climate Change was established in 1992 under the United Nations Framework Convention on Climate Change (UNFCCC) to discuss global response to climate change. Its first meeting (COP 1) was held in Berlin in March 1995 and is being held every year since then. The Fifth Assessment Report of the IPCC, released in November 2014, projected an increase in the mean global temperature of 3.7 to 4.8 °C by 2100, relative to preindustrial levels (1850), in the absence of new policies to mitigate climate change; it highlighted that such an increase would have serious consequences. This prediction compelled the participating countries at the COP 21 held in Paris in December 2015 to negotiate effective ways and means of reducing carbon emissions. In this meeting the goal to limit global warming to well below 2 °C, preferably to 1.5 °C, compared to preindustrial levels was adapted by 196 participating countries as a legally binding treaty on climate change. It also mandated review of progress every 5 years and the development of a fund containing $100 billion by 2020, which would be replenished annually, to help developing countries to adopt non-greenhouse-gas-producing technologies.

In 2017, after 25 years after the first warning, 15,354 world scientists from 184 countries gave ‘second warning to humanity’ (Ripple et al. 2017 ). They emphasized that with the exception of stabilizing the stratospheric ozone layer, humanity has failed to make sufficient progress in solving these environmental challenges, and alarmingly, most of them are getting far worse. Analysis of Warren et al. ( 2018 ) on a global scale on the effects of climate change on the distribution of insects, vertebrates and plants indicated that even with 2 °C temperature increase, approximately 18% of insects, 16% of plants and 8% of vertebrates species are projected to loose > 50% geographic range; this falls to 6% for insects, 8% for plants and 4% for vertebrates when temperature increase is reduced to 1.5 °C.

UN Report on climate change (prepared by > 90 authors from 40 countries after examining 6000 scientific publications) released in October 2018 in South Korea also gave serious warning to the world. Some of the salient features of this report were:

• Overshooting 1.5 °C will be disastrous. It will have devastating effects on ecosystems, communities and economies. By 2040 there could be global food shortages, the inundation of coastal cities and a refugee crisis unlike the world has ever seen.
• Even 1.5 °C warming would rise sea levels by 26–77 cm by 2100; 2 °C would add another 10 cm which would affect another 10 million people living in coastal regions.
• Coral reefs are projected to decline 70–90% even at 1.5 °C. At 2 °C, 99% of the reefs would be ravaged.
• Storms, floods, droughts and forest fires would increase in intensity and frequency.
• The world has already warmed by about 1 °C since preindustrial times. We are currently heading for about 3–4 °C of warming by 2100.
• Unless rapid and deep reductions in CO 2 and other greenhouse gas emissions occur in the coming decades, achieving the goals of the 2015 Paris Agreement will be beyond reach.
• To keep 1.5 °C target, coal’s share of global electricity generation must be cut from the present 37% to no more than 2% by 2050. Renewable power must be greatly expanded. Net CO 2  emissions must come down by 45% (from 2010 levels) by 2030 and reach net zero (emissions of greenhouse gases no more than the amount removed from the atmosphere) around 2050.

This report awakened the world Governments about the seriousness of the climate change. The COP 26 meeting which was to be held in 2020 had to be postponed due to Covid-19 pandemic. The first part of the sixth report of IPCC was released in August 2021 (AR6 Climate Change 2021 ), just before the postponed COP 26 meeting was to be held; it highlighted that the threshold warming of 1.5 °C (the target of keeping the warming by the end of the century) would reach in the next 20 years itself and if the present trends continue, it would reach 2.7 °C by the end of the century.

Under this predicted climate emergency (see Ripple et al. 2020 ), COP 26 meeting was held in Glasgow, Scotland between October 31 and November 12, 2021. Nearly 200 countries participated in this meeting. The main aim of the COP 26 was finalization of the rules and procedures for implementation of the Paris agreement to keep the temperature increase to 1.5 °C. A number of countries including USA and European Union pledged to reach net zero carbon emission by 2050. China pledged to reach net zero emissions by 2060 and India by 2070. India also committed to reduce the use of fossil fuels by 40% by 2030. More than 100 countries committed to reduce worldwide methane emissions by 30% (of 2020 levels) by 2030 and to end deforestation by 2030. The average atmospheric concentration of methane reached a record 1900 ppb in September 2021; it was 1638 ppb in 1983 (US National Oceanic and Atmospheric Administration), highlighting the importance of acting on pledges made at the COP 26.

One of the limitations of COP meetings has been nonadherence of the commitment made by developed countries at Paris meeting to transfer US $100 billion annually to developing and poor countries to support climate mitigation and loss of damage, through 2025; only Germany, Norway and Sweden are paying their share. Several experts feel that the adoption of the Glasgow Climate Pact was weaker than expected. According to the assessment of Climate Action Tracker, a non-profit independent global analysis platform, emission reduction commitments by countries still lead to 2.4 °C warming by 2100. However, a positive outcome of the meeting was that it has kept alive the hopes of achieving the 1.5 °C goal by opening the options for further discussion in the coming COP meetings. Apart from implementation of mitigation pledges made by countries, it is also important to pay attention to climate adaptation since the negative effects of climate change will continue for decades or longer (AR6 Climate Change 2021 ). Investment in early warning is an important means of climate adaptation, which is lacking in many parts of Africa and Latin America.

Conclusions

Climate change has now become the fastest growing global threat to human welfare. The world has realized the responsibility of the present generation as it is considered to be the last generation capable of taking effective measures to reverse its impact. If it fails, human civilization is likely to be doomed beyond recovery. As emphasized by many organizations, the climate crisis is inherently unfair; poorer countries will suffer its consequences more than others. India is one amongst the nine countries identified to be seriously affected by climate change. According to a WHO analysis ( 2016 ) India could face more than 25% of all global climate-related deaths by 2050 due to decreasing food availability. China is expected to face the highest number of per capita food insecurity deaths. Bhutan, a small Himalayan kingdom with 60% forest cover, is the most net negative carbon emission country; its GHG emission is less than the amount removed from the atmosphere. Other countries should aim to emulate Bhutan as early as possible.

A number of other options have been suggested to trap atmospheric carbon dioxide (Climate change mitigation—Wikipedia). Carbon storage through sequestration of organic carbon by deep-rooted grasses has been one such approach (Fisher et al. 1994 ). Several studies from Africa have indicated that introduction of Brachiaria grasses in semi-arid tropics can help to increase not only carbon stock in the soil but also yield greater economic returns (Gichangi et al. 2017 ). Recently a new seed bank, ‘Future Seeds’ was dedicated at Palmira, Columbia to store world’s largest collection of beans, cassava, and tropical forage grasses for the use of breeders to create better performing and climate-resistant crops (Stokstad 2022 ). Brachiaria humidicola is one of the tropical forage grass stored in this seed bank for its potential benefit in carbon sequestration. Lavania and Lavania ( 2009 ) have suggested vetiver ( Vetiveria zizanioides ), a C 4 perennial grass, with massive fibrous root system that can grow up to 3 m into the soil in 1 year, as a potential species for this purpose. Vetiver is estimated to produce 20–30 tonnes of root dry matter per hectare annually and holds the potential of adding 1 kg atmospheric CO 2 annually to the soil carbon pool per m 2 surface area. Carbon dioxide capture and storage is another such potential approach. At present it is too expensive and this approach may have to wait until improvement of the technology, reduction in the cost and feasibility of transfer of the technology to developing countries (IPCC Special Report on carbon dioxide capture and storage 2005 ).

There has been some discussion on the role of climate change on speciation (Levin 2019 ; Gao et al. 2020 ). Some evolutionary biologists have observed that the rate of speciation has accelerated in the recent past due to climate change and would continue to increase in the coming decades (Thomas 2015 ; Levin 2019 ; Gao et al. 2020 ). They propose that auto- and allo-polyploidy are going to be the primary modes of speciation in the next 500 years (Levin 2019 , see also Gao 2019 , Villa et al. 2022 ). However, extinction of species imposed by climate change may excel positive impact on plant speciation via polyploidy (Gao et al. 2020 ). The question is will climate change induce higher level of polyploidy and other genetic changes in crop species also that would promote evolution of new genotypes to sustain productivity and quality of food grains? If so, it would ameliorate, to some extent, food and nutritional insecurity of humans especially in the developing world.

Effective implementation of the pledges made by different countries in COP 26 and actions to be taken in the coming COP meetings are going to be crucial and determine humanity’s success or failure in tackling climate change emergency. COP 26 climate pact to cut greenhouse gas emissions, end of deforestation and shift to sustainable transport is certainly more ambitious then earlier COPs. There are also many other positive signals for reducing fossil fuels. Scientists have started using more precise monitoring equipment to collect more reliable environmental data, and more options are being developed by researchers on renewable and alternate energy sources, and to capture carbon from industries or from the air (Chandler D, MIT News 24 Oct 2019, Swain F, BBC Future Planet, 12 March 2021). Scotland has become coal-free and Costa Rica has achieved 99% renewable energy. India has reduced the use of fossil fuel by 40% of it installed capacity, 8 years ahead of its commitment at the COP 26.

Further, people are becoming more conscious to reduce carbon emission by following climate-friendly technologies. Human sufferings associated with an increase in natural disasters throughout the world have focussed public attention on climate change as never before. They also realise the benefits of improved air quality by reducing consumption of coal and fossil fuels on health and ecosystems. The demand for electric vehicles is steadily growing. Reforestation is being carried out in a large scale in many countries. Recent studies across a range of tree plantations and native forests in 53 countries have revealed that carbon storage, soil erosion control, water conservation and biodiversity benefits are delivered better from native forests compared to monoculture tree plantations, although the latter yielded more wood (Hua et al. 2022 ). This has to be kept in mind in reforestation programmes. Hopefully the world will be able to realize the goal of limiting the temperature rise to 1.5 °C by the end of the century and humanity would learn to live in harmony with Nature.

Declarations

The author declares no conflict of interest.

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What is climate change mitigation and why is it urgent?

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• Climate change mitigation involves actions to reduce or prevent greenhouse gas emissions from human activities.
• Mitigation efforts include transitioning to renewable energy sources, enhancing energy efficiency, adopting regenerative agricultural practices and protecting and restoring forests and critical ecosystems.
• Effective mitigation requires a whole-of-society approach and structural transformations to reduce emissions and limit global warming to 1.5°C above pre-industrial levels.
• International cooperation, for example through the Paris Agreement, is crucial in guiding and achieving global and national mitigation goals.
• Mitigation efforts face challenges such as the world's deep-rooted dependency on fossil fuels, the increased demand for new mineral resources and the difficulties in revamping our food systems.
• These challenges also offer opportunities to improve resilience and contribute to sustainable development.

What is climate change mitigation?

Climate change mitigation refers to any action taken by governments, businesses or people to reduce or prevent greenhouse gases, or to enhance carbon sinks that remove them from the atmosphere. These gases trap heat from the sun in our planet’s atmosphere, keeping it warm. 

Since the industrial era began, human activities have led to the release of dangerous levels of greenhouse gases, causing global warming and climate change. However, despite unequivocal research about the impact of our activities on the planet’s climate and growing awareness of the severe danger climate change poses to our societies, greenhouse gas emissions keep rising. If we can slow down the rise in greenhouse gases, we can slow down the pace of climate change and avoid its worst consequences.

Reducing greenhouse gases can be achieved by:

• Shifting away from fossil fuels : Fossil fuels are the biggest source of greenhouse gases, so transitioning to modern renewable energy sources like solar, wind and geothermal power, and advancing sustainable modes of transportation, is crucial.
• Improving energy efficiency : Using less energy overall – in buildings, industries, public and private spaces, energy generation and transmission, and transportation – helps reduce emissions. This can be achieved by using thermal comfort standards, better insulation and energy efficient appliances, and by improving building design, energy transmission systems and vehicles.
• Changing agricultural practices : Certain farming methods release high amounts of methane and nitrous oxide, which are potent greenhouse gases. Regenerative agricultural practices – including enhancing soil health, reducing livestock-related emissions, direct seeding techniques and using cover crops – support mitigation, improve resilience and decrease the cost burden on farmers.
• The sustainable management and conservation of forests : Forests act as carbon sinks , absorbing carbon dioxide and reducing the overall concentration of greenhouse gases in the atmosphere. Measures to reduce deforestation and forest degradation are key for climate mitigation and generate multiple additional benefits such as biodiversity conservation and improved water cycles.
• Restoring and conserving critical ecosystems : In addition to forests, ecosystems such as wetlands, peatlands, and grasslands, as well as coastal biomes such as mangrove forests, also contribute significantly to carbon sequestration, while supporting biodiversity and enhancing climate resilience.
• Creating a supportive environment : Investments, policies and regulations that encourage emission reductions, such as incentives, carbon pricing and limits on emissions from key sectors are crucial to driving climate change mitigation.

Photo: Stephane Bellerose/UNDP Mauritius

Photo: La Incre and Lizeth Jurado/PROAmazonia

What is the 1.5°C goal and why do we need to stick to it?

In 2015, 196 Parties to the UN Climate Convention in Paris adopted the Paris Agreement , a landmark international treaty, aimed at curbing global warming and addressing the effects of climate change. Its core ambition is to cap the rise in global average temperatures to well below 2°C above levels observed prior to the industrial era, while pursuing efforts to limit the increase to 1.5°C.

The 1.5°C goal is extremely important, especially for vulnerable communities already experiencing severe climate change impacts. Limiting warming below 1.5°C will translate into less extreme weather events and sea level rise, less stress on food production and water access, less biodiversity and ecosystem loss, and a lower chance of irreversible climate consequences.

To limit global warming to the critical threshold of 1.5°C, it is imperative for the world to undertake significant mitigation action. This requires a reduction in greenhouse gas emissions by 45 percent before 2030 and achieving net-zero emissions by mid-century.

What are the policy instruments that countries can use to drive mitigation?

Everyone has a role to play in climate change mitigation, from individuals adopting sustainable habits and advocating for change to governments implementing regulations, providing incentives and facilitating investments. The private sector, particularly those businesses and companies responsible for causing high emissions, should take a leading role in innovating, funding and driving climate change mitigation solutions. 

International collaboration and technology transfer is also crucial given the global nature and size of the challenge. As the main platform for international cooperation on climate action, the Paris Agreement has set forth a series of responsibilities and policy tools for its signatories. One of the primary instruments for achieving the goals of the treaty is Nationally Determined Contributions (NDCs) . These are the national climate pledges that each Party is required to develop and update every five years. NDCs articulate how each country will contribute to reducing greenhouse gas emissions and enhance climate resilience.   While NDCs include short- to medium-term targets, long-term low emission development strategies (LT-LEDS) are policy tools under the Paris Agreement through which countries must show how they plan to achieve carbon neutrality by mid-century. These strategies define a long-term vision that gives coherence and direction to shorter-term national climate targets.

Photo: Mucyo Serge/UNDP Rwanda

Photo: William Seal/UNDP Sudan

At the same time, the call for climate change mitigation has evolved into a call for reparative action, where high-income countries are urged to rectify past and ongoing contributions to the climate crisis. This approach reflects the UN Framework Convention on Climate Change (UNFCCC) which advocates for climate justice, recognizing the unequal historical responsibility for the climate crisis, emphasizing that wealthier countries, having profited from high-emission activities, bear a greater obligation to lead in mitigating these impacts. This includes not only reducing their own emissions, but also supporting vulnerable countries in their transition to low-emission development pathways.

Another critical aspect is ensuring a just transition for workers and communities that depend on the fossil fuel industry and its many connected industries. This process must prioritize social equity and create alternative employment opportunities as part of the shift towards renewable energy and more sustainable practices.

For emerging economies, innovation and advancements in technology have now demonstrated that robust economic growth can be achieved with clean, sustainable energy sources. By integrating renewable energy technologies such as solar, wind and geothermal power into their growth strategies, these economies can reduce their emissions, enhance energy security and create new economic opportunities and jobs. This shift not only contributes to global mitigation efforts but also sets a precedent for sustainable development.

What are some of the challenges slowing down climate change mitigation efforts?

Mitigating climate change is fraught with complexities, including the global economy's deep-rooted dependency on fossil fuels and the accompanying challenge of eliminating fossil fuel subsidies. This reliance – and the vested interests that have a stake in maintaining it – presents a significant barrier to transitioning to sustainable energy sources.

The shift towards decarbonization and renewable energy is driving increased demand for critical minerals such as copper, lithium, nickel, cobalt, and rare earth metals. Since new mining projects can take up to 15 years to yield output, mineral supply chains could become a bottleneck for decarbonization efforts. In addition, these minerals are predominantly found in a few, mostly low-income countries, which could heighten supply chain vulnerabilities and geopolitical tensions.

Furthermore, due to the significant demand for these minerals and the urgency of the energy transition, the scaled-up investment in the sector has the potential to exacerbate environmental degradation, economic and governance risks, and social inequalities, affecting the rights of Indigenous Peoples, local communities, and workers. Addressing these concerns necessitates implementing social and environmental safeguards, embracing circular economy principles, and establishing and enforcing responsible policies and regulations .

Agriculture is currently the largest driver of deforestation worldwide. A transformation in our food systems to reverse the impact that agriculture has on forests and biodiversity is undoubtedly a complex challenge. But it is also an important opportunity. The latest IPCC report highlights that adaptation and mitigation options related to land, water and food offer the greatest potential in responding to the climate crisis. Shifting to regenerative agricultural practices will not only ensure a healthy, fair and stable food supply for the world’s population, but also help to significantly reduce greenhouse gas emissions.  

Photo: UNDP India

Photo: Nino Zedginidze/UNDP Georgia

What are some examples of climate change mitigation?

In Mauritius , UNDP, with funding from the Green Climate Fund, has supported the government to install battery energy storage capacity that has enabled 50 MW of intermittent renewable energy to be connected to the grid, helping to avoid 81,000 tonnes of carbon dioxide annually. 

In Indonesia , UNDP has been working with the government for over a decade to support sustainable palm oil production. In 2019, the country adopted a National Action Plan on Sustainable Palm Oil, which was collaboratively developed by government, industry and civil society representatives. The plan increased the adoption of practices to minimize the adverse social and environmental effects of palm oil production and to protect forests. Since 2015, 37 million tonnes of direct greenhouse gas emissions have been avoided and 824,000 hectares of land with high conservation value have been protected.

In Moldova and Paraguay , UNDP has helped set up Green City Labs that are helping build more sustainable cities. This is achieved by implementing urban land use and mobility planning, prioritizing energy efficiency in residential buildings, introducing low-carbon public transport, implementing resource-efficient waste management, and switching to renewable energy sources. 

UNDP has supported the governments of Brazil, Costa Rica, Ecuador and Indonesia to implement results-based payments through the REDD+ (Reducing emissions from deforestation and forest degradation in developing countries) framework. These include payments for environmental services and community forest management programmes that channel international climate finance resources to local actors on the ground, specifically forest communities and Indigenous Peoples. 

UNDP is also supporting small island developing states like the Comoros to invest in renewable energy and sustainable infrastructure. Through the Africa Minigrids Program , solar minigrids will be installed in two priority communities, Grand Comore and Moheli, providing energy access through distributed renewable energy solutions to those hardest to reach.

And in South Africa , a UNDP initative to boost energy efficiency awareness among the general population and improve labelling standards has taken over commercial shopping malls.

What is UNDP’s role in supporting climate change mitigation?

UNDP aims to assist countries with their climate change mitigation efforts, guiding them towards sustainable, low-carbon and climate-resilient development. This support is in line with achieving the Sustainable Development Goals (SDGs), particularly those related to affordable and clean energy (SDG7), sustainable cities and communities (SDG11), and climate action (SDG13). Specifically, UNDP’s offer of support includes developing and improving legislation and policy, standards and regulations, capacity building, knowledge dissemination, and financial mobilization for countries to pilot and scale-up mitigation solutions such as renewable energy projects, energy efficiency initiatives and sustainable land-use practices. 

With financial support from the Global Environment Facility and the Green Climate Fund, UNDP has an active portfolio of 94 climate change mitigation projects in 69 countries. These initiatives are not only aimed at reducing greenhouse gas emissions, but also at contributing to sustainable and resilient development pathways.

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Biodiversity Loss Increases the Risk of Disease Outbreaks, Analysis Suggests

Researchers found that human-caused environmental changes are driving the severity and prevalence of disease, putting people, animals and plants at risk

Christian Thorsberg

Daily Correspondent

Human-driven changes to the planet are bringing widespread and sometimes surprising effects—including shifting the Earth’s rotation , hiding meteorites in Antarctic ice and, potentially, supporting locust swarms .

Now, a large-scale analysis of nearly 1,000 scientific studies has shown just how closely human activity is tied to public health. Published last week in the journal Nature ,   the findings suggest anthropogenic environmental changes are making the risk of infectious disease outbreaks all the more likely.

The biodiversity crisis—which has left some one million plant and animal species at risk of extinction —is a leading driver of disease spread, the researchers found.

“It could mean that by modifying the environment, we increase the risks of future pandemics,” Jason Rohr , a co-author of the study and a biologist at the University of Notre Dame, tells the Washington Post ’s Scott Dance.

The analysis centered on earlier studies that investigated at least one of five “global change drivers” affecting wildlife and landscapes on Earth: biodiversity change, climate change, habitat change or loss, chemical pollution and the introduction of non-native species to new areas. Based on the previous studies’ findings, they collected nearly 3,000 data points related to how each of these factors might impact the severity or prevalence of infectious disease outbreaks.

Researchers aimed to avoid a human-centric approach to their analysis, considering also how plants and animals would be at risk from pathogens. Their conclusions showed that four of the examined factors—climate change, chemical pollution, the introduction of non-native species to new areas and biodiversity loss—all increased the likelihood of spreading disease, with the latter having the most significant impact.

Disease and mortality were nearly nine times higher in areas of the world where human activity has decreased biodiversity, compared to the levels expected by Earth’s natural variation in biodiversity, per the Washington Post .

Scientists hypothesize this finding could be explained by the “dilution effect”: the idea that pathogens and parasites evolve to thrive in the most common species, so the loss of rarer creatures makes infection more likely.

“That means that the species that remain are the competent ones, the ones that are really good at transmitting disease,” Rohr tells the New York Times ’   Emily Anthes.

For example, white-footed mice, the main carriers of Lyme disease, have become one of the most dominant species in their habitat as other, rarer animals have disappeared—a change that might have played a role, among other factors, in driving rising rates of Lyme disease in the United States.

One global change factor, however, actually decreased the likelihood of disease outbreaks: habitat loss and change. But here, context is key. Most habitat loss is linked to creating a single type of environment—urban ecosystems—which generally have good sanitation systems and less wildlife, reducing opportunities for disease spillover.

“In urban areas with lots of concrete, there is a much smaller number of species that can thrive in that environment,” Rohr tells the Guardian ’s Phoebe Weston. “From a human disease perspective, there is often greater sanitation and health infrastructure than in rural environments.”

Deforestation, another type of habitat loss, has been shown to increase the likelihood of disease. The incidence of malaria and Ebola , for example, worsens in such instances.

The new work adds to past research on how human activity can prompt the spread of disease. For instance, climate change-induced permafrost melt may release pathogens from the Arctic , a concern that’s been well-documented in recent years. And both habitat loss and climate change may force some animals to move closer together—and closer to humans — increasing the potential for transmitting disease .

Additionally, the research signals the need for public health officials to remain vigilant as the effects of human-caused climate change play out, experts say.

“It’s a big step forward in the science,” Colin Carlson , a global change biologist at Georgetown University who was not an author of the new analysis, tells the New York Times. “This paper is one of the strongest pieces of evidence that I think has been published that shows how important it is health systems start getting ready to exist in a world with climate change, with biodiversity loss.”

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Christian Thorsberg is an environmental writer and photographer from Chicago. His work, which often centers on freshwater issues, climate change and subsistence, has appeared in Circle of Blue , Sierra  magazine, Discover  magazine and Alaska Sporting Journal .

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Environmental Changes Are Fueling Human, Animal and Plant Diseases, Study Finds

Biodiversity loss, global warming, pollution and the spread of invasive species are making infectious diseases more dangerous to organisms around the world.

By Emily Anthes

Several large-scale, human-driven changes to the planet — including climate change, the loss of biodiversity and the spread of invasive species — are making infectious diseases more dangerous to people, animals and plants, according to a new study.

Scientists have documented these effects before in more targeted studies that have focused on specific diseases and ecosystems. For instance, they have found that a warming climate may be helping malaria expand in Africa and that a decline in wildlife diversity may be boosting Lyme disease cases in North America.

But the new research, a meta-analysis of nearly 1,000 previous studies, suggests that these patterns are relatively consistent around the globe and across the tree of life.

“It’s a big step forward in the science,” said Colin Carlson, a biologist at Georgetown University, who was not an author of the new analysis. “This paper is one of the strongest pieces of evidence that I think has been published that shows how important it is health systems start getting ready to exist in a world with climate change, with biodiversity loss.”

In what is likely to come as a more surprising finding, the researchers also found that urbanization decreased the risk of infectious disease.

The new analysis, which was published in Nature on Wednesday, focused on five “global change drivers” that are altering ecosystems across the planet: biodiversity change, climate change, chemical pollution, the introduction of nonnative species and habitat loss or change.

The researchers compiled data from scientific papers that examined how at least one of these factors affected various infectious-disease outcomes, such as severity or prevalence. The final data set included nearly 3,000 observations on disease risks for humans, animals and plants on every continent except for Antarctica.

The researchers found that, across the board, four of the five trends they studied — biodiversity change, the introduction of new species, climate change and chemical pollution — tended to increase disease risk.

“It means that we’re likely picking up general biological patterns,” said Jason Rohr, an infectious disease ecologist at the University of Notre Dame and senior author of the study. “It suggests that there are similar sorts of mechanisms and processes that are likely occurring in plants, animals and humans.”

The loss of biodiversity played an especially large role in driving up disease risk, the researchers found. Many scientists have posited that biodiversity can protect against disease through a phenomenon known as the dilution effect.

The theory holds that parasites and pathogens, which rely on having abundant hosts in order to survive, will evolve to favor species that are common, rather than those that are rare, Dr. Rohr said. And as biodiversity declines, rare species tend to disappear first. “That means that the species that remain are the competent ones, the ones that are really good at transmitting disease,” he said.

Lyme disease is one oft-cited example. White-footed mice, which are the primary reservoir for the disease, have become more dominant on the landscape, as other rarer mammals have disappeared, Dr. Rohr said. That shift may partly explain why Lyme disease rates have risen in the United States. (The extent to which the dilution effect contributes to Lyme disease risk has been the subject of debate, and other factors, including climate change, are likely to be at play as well.)

Other environmental changes could amplify disease risks in a wide variety of ways. For instance, introduced species can bring new pathogens with them, and chemical pollution can stress organisms’ immune systems. Climate change can alter animal movements and habitats, bringing new species into contact and allowing them to swap pathogens .

Notably, the fifth global environmental change that the researchers studied — habitat loss or change — appeared to reduce disease risk. At first glance, the findings might appear to be at odds with previous studies, which have shown that deforestation can increase the risk of diseases ranging from malaria to Ebola. But the overall trend toward reduced risk was driven by one specific type of habitat change: increasing urbanization.

The reason may be that urban areas often have better sanitation and public health infrastructure than rural ones — or simply because there are fewer plants and animals to serve as disease hosts in urban areas. The lack of plant and animal life is “not a good thing,” Dr. Carlson said. “And it also doesn’t mean that the animals that are in the cities are healthier.”

And the new study does not negate the idea that forest loss can fuel disease; instead, deforestation increases risk in some circumstances and reduces it in others, Dr. Rohr said.

Indeed, although this kind of meta-analysis is valuable for revealing broad patterns, it can obscure some of the nuances and exceptions that are important for managing specific diseases and ecosystems, Dr. Carlson noted.

Moreover, most of the studies included in the analysis examined just a single global change drive. But, in the real world, organisms are contending with many of these stressors simultaneously. “The next step is to better understand the connections among them,” Dr. Rohr said.

Emily Anthes is a science reporter, writing primarily about animal health and science. She also covered the coronavirus pandemic. More about Emily Anthes

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Tackling deforestation risk in financial portfolios

Sponsored: The Accountability Framework supports practical steps for financial institutions to manage deforestation and related risks. Here’s how.

By Jeff Milder

May 14, 2024

Financial institutions can use the Accountability Framework to manage deforestation and human rights risks related to soft commodities. Image courtesy of Adobe Stock.

This article is sponsored by  Accountability Framework initiative.  

Even as the dual crises of climate change and biodiversity loss become ever more urgent, banks continue to pump $30 billion to $50 billion a year into activities that drive the majority of tropical deforestation. And that’s not counting capital from asset managers, pension funds, institutional investors and other financial institutions.

While financing of deforestation has been business-as-usual for decades, there are signs of change. Leading financial institutions are beginning to chart a way forward, while laggards ignore escalating risk at their peril.  

This risk ties back to negative impacts on the ground. When exposed to environmental and social harms in their operations or supply chains, companies can face legal, market, operational, regulatory and reputational risks that threaten financial performance. For financial institutions, having risky companies in their portfolios can translate into negative returns on investments, defaults on loans or losses due to stranded assets.

And while deforestation is but one of many ESG issues on companies’ radar, it is among the most salient for financial institutions to understand and manage. That’s because, in addition to being a major risk in its own right, deforestation is a leading driver of greenhouse gas emissions and contributor to human rights abuses in the soft commodities sector.

To address these risks, financial institutions must engage with the companies in their portfolios. Fortunately, there is no need for them to reinvent the wheel. Instead, they can adapt approaches that have already worked for companies in forest-risk commodity sectors. Financial institutions can build from these companies’ experiences and look to the Accountability Framework as a guide for effective action.  

Setting a strong policy

At the core of a financial institution’s approach to tackling deforestation risk should be a strong and credible policy that commits the financial institution to eliminating deforestation from its portfolio. This includes avoiding loans and other capital flows that support deforestation, while managing deforestation risks and impact across all types of investments. 

To align with the Accountability Framework, no-deforestation policies should use accepted definitions and appropriate timeframes and targets. They should also include commitments to respect the rights of Indigenous peoples, local communities and workers, for whom human rights violations often accompany deforestation-linked commodity production. 

More financial institutions are recognizing the value in setting no-deforestation policies. According to Global Canopy’s latest Forest 500 assessment , the proportion of 150 leading financial institutions adopting at least one such policy has grown from 18 to 45 percent over the past seven years. Yet wide gaps remain, with only 15 percent of these financial institutions having policies that cover all commodities with material deforestation risk.

By setting a strong no-deforestation policy, financial institutions can position themselves favourably relative to peers and demonstrate leadership towards broader initiatives to reorient finance to combat climate change, safeguard nature and transition to a green economy. Examples of such initiatives include the Finance Sector Deforestation Action Initiative and the Glasgow Financial Alliance for Net Zero . 

Managing portfolio risk

To implement no-deforestation policies, financial institutions must take steps to assess exposure, establish effective implementation systems and engage business partners across their portfolios. ForestIQ , whose methods and metrics are aligned with the Accountability Framework, can support an initial portfolio assessment process through its data on deforestation risk exposure and materiality for more than 2,000 companies.

Additional implementation steps are outlined in the Deforestation Free Finance Roadmap , which provides an action pathway for financial institutions, adapted from the Accountability Framework. Central to this approach is for financial institutions to communicate no-deforestation expectations to the companies in their portfolios. Financial institutions should also ask clients and other portfolio companies to set their own no-deforestation policies ; conduct annual monitoring of all clients and holdings; and engage clients and holdings to support or require improvements where needed.

A final important step is for financial institutions to report at least annually on progress toward achieving their no-deforestation policies. The rising tide of mandatory disclosure requirements will position such reporting much more centrally for both financial institutions and their clients and business partners over the next couple of years.

Clear pathways for action

While commodity-linked deforestation is a complex issue, there are clear steps for financial institutions to address it across their portfolio. Doing so can mitigate key risks today and even more so in the future, as climate, nature, and human rights regulation, market expectations, and disclosure requirements continue to grow. To get started managing portfolio risks with the Accountability Framework, visit our page for financial institutions .

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• Published: 01 March 2023

Tropical deforestation causes large reductions in observed precipitation

• C. Smith   ORCID: orcid.org/0000-0002-2705-8398 1 ,
• J. C. A. Baker   ORCID: orcid.org/0000-0002-3720-4758 1 &
• D. V. Spracklen   ORCID: orcid.org/0000-0002-7551-4597 1  

Nature volume  615 ,  pages 270–275 ( 2023 ) Cite this article

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• Climate change
• Tropical ecology

Tropical forests play a critical role in the hydrological cycle and can influence local and regional precipitation 1 . Previous work has assessed the impacts of tropical deforestation on precipitation, but these efforts have been largely limited to case studies 2 . A wider analysis of interactions between deforestation and precipitation—and especially how any such interactions might vary across spatial scales—is lacking. Here we show reduced precipitation over deforested regions across the tropics. Our results arise from a pan-tropical assessment of the impacts of 2003–2017 forest loss on precipitation using satellite, station-based and reanalysis datasets. The effect of deforestation on precipitation increased at larger scales, with satellite datasets showing that forest loss caused robust reductions in precipitation at scales greater than 50 km. The greatest declines in precipitation occurred at 200 km, the largest scale we explored, for which 1 percentage point of forest loss reduced precipitation by 0.25 ± 0.1 mm per month. Reanalysis and station-based products disagree on the direction of precipitation responses to forest loss, which we attribute to sparse in situ tropical measurements. We estimate that future deforestation in the Congo will reduce local precipitation by 8–10% in 2100. Our findings provide a compelling argument for tropical forest conservation to support regional climate resilience.

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Tropical forests play an important role in moderating local, regional and global climate through their impact on energy, water and carbon cycles 3 . Crucially, tropical forests control local-to-regional rainfall patterns 1 , 2 . Evapotranspiration from tropical forests is a strong driver of regional precipitation 4 , 5 contributing up to 41% of basin mean rainfall over the Amazon and up to 50% over the Congo 6 . Evergreen tropical forests are dependent on high annual rainfall for their survival and productivity 7 , and forest–rainfall feedbacks have been highlighted as an important determinant of tropical forest stability 4 , 5 , 8 , amid concerns that the exacerbating impacts of droughts and deforestation could threaten their viability 9 .

Rapid loss of forests is occurring across the tropics 10 . Tropical deforestation warms the climate at local-to-global scales by changing the surface energy balance and through emissions of carbon dioxide 3 . The impact of tropical deforestation on precipitation is less certain with a range of processes operating at different scales. Small-scale deforestation over the southern Amazon has been shown to increase precipitation frequency 11 , 12 owing to thermally 13 and dynamically 12 induced circulations. At larger scales, deforestation reduces precipitation recycling leading to a reduction in precipitation 1 , 14 . Over Indonesia, deforestation has been linked to declining precipitation 15 , and exacerbation of El Niño impacts 16 . Global and regional climate models predict annual precipitation declines of 8.1 ± 1.4% for large-scale Amazonian deforestation by 2050 (ref.  17 ), but an observational study of the impacts of tropical deforestation on precipitation across spatial scales is lacking.

Here we present a pan-tropical assessment of the impact of forest loss on precipitation based on measurements. We use a satellite dataset of forest cover change over the period 2003–2017 to identify areas of forest loss, with a focus on evergreen broadleaf forests of the Amazon, Congo and Southeast Asia (SEA; Fig. 1 ). To provide a robust assessment of the impacts of deforestation on precipitation, we analysed 18 different precipitation datasets, including satellite ( n  = 10), station-based ( n  = 4) and reanalysis ( n  = 4) products (Extended Data Table 1 ). We compared the precipitation change over pixels experiencing forest loss with neighbouring pixels that had experienced less forest loss ( Methods ). This comparison against neighbouring pixels that will have experienced similar climate change focuses our analysis on the changes due to forest loss. To explore the impact of forest loss across scales, we analysed the impacts of forest loss on coincident precipitation at a series of spatial resolutions ranging from roughly 5 km to 200 km (0.05°, 0.1°, 0.25°, 0.5°, 1.0° and 2.0°).

a – f , Forest cover change at 0.05° ( a ), 0.1° ( b ), 0.25° ( c ), 0.5° ( d ), 1.0° ( e ) and 2.0° ( f ) resolution. The Amazon Basin, Congo Basin and SEA regions used in this study are outlined in purple. Maps of the different regions generated using Cartopy and Natural Earth 51 . Forest loss data from ref.  10 .

Source Data

Precipitation response to forest loss

Observed precipitation responses to tropical forest loss across multiple spatial scales and precipitation products are presented in Fig. 2 . Satellite-based precipitation datasets suggest that tropical forest loss causes statistically significant ( P  < 0.05) declines in median annual mean precipitation at all scales analysed. At larger scales (>0.5°), reductions exceed 0.03 mm per month for each percentage point loss of forest cover (Fig. 2d–f ). The largest changes are observed at the 2.0° scale (approximately 220 km at the Equator; Fig. 2f ), for which each percentage point reduction in forest cover causes 0.25 ± 0.1 mm per month reduction in annual precipitation.

a – r , Bars indicate the median absolute change in annual precipitation (millimetres per month) per percentage point of forest loss over 2003 to 2017 in each region (tropics ( a – f ), Amazon ( g – l ), Congo ( m – r ), SEA ( s – x )) for each precipitation dataset category (satellite, station and reanalysis). Results are shown for forest loss scales of 0.05° ( a , g , m , s ), 0.1° ( b , h , n , t ), 0.25° ( c , i , o , u ), 0.5° ( d , j , p , v ), 1.0° ( e , k , q , w ) and 2.0° ( f , l , r , x ). Statistically significant (* P  < 0.05; ** P  < 0.01) and nonsignificant (NS) differences in changes in mean precipitation (calculated as a multi-annual mean over 2003–2007 compared with 2013–2017) over deforested regions compared with control regions are indicated. Error bars show ±1 standard error from the mean. Datasets used in this analysis are detailed in Extended Data Table 1 . Δ P , precipitation change.

Analysis of precipitation change as a function of forest loss confirms larger reductions in precipitation for larger reductions in forest cover (Extended Data Fig. 1 ), although with considerable variability, as seen in the modelled response 18 . Observed reductions in precipitation are consistent across satellite datasets, with all ten satellite precipitation products agreeing on the sign of the rainfall response at 2° over the tropics (Extended Data Fig. 2 ). At the 2° scale, significant ( P  < 0.05) reductions in annual mean precipitation with forest loss were observed across all tropical regions (Fig. 2 ). Reductions in precipitation at 2° based on satellite datasets ranged from 0.48 ± 0.36 mm per month in SEA to 0.23 ± 0.12 mm per month in the Amazon, and 0.21 ± 0.19 mm per month in the Congo for each percentage point loss in forest cover, with at least 8 out of 10 satellite datasets agreeing on the sign of the response within each region (Extended Data Fig. 2 ). In SEA, it has been suggested that proximity to the ocean and the replacement of tropical forest with plantations as opposed to pasture or cropland may cause reduced sensitivity of precipitation to deforestation 1 . Our analysis suggests that forest loss in SEA causes reductions in precipitation consistent with or greater than reductions in precipitation in the Amazon and Congo.

Station-based datasets and reanalysis products exhibit contrasting annual mean precipitation responses to deforestation at 2.0° (Fig. 2 ). Across the tropics, station-based and reanalysis datasets showed no statistically significant changes in annual mean precipitation due to forest loss (Fig. 2f ), and there was little agreement with satellite datasets at the regional scale (Fig. 2l,r,x ), with some non-satellite precipitation products showing small increases in annual mean precipitation due to forest loss. Sparse in situ measurements across the tropics, particularly over regions of forest loss, mean that station-based datasets provide a weak constraint on precipitation changes. A comparison of station-based precipitation datasets revealed higher levels of uncertainty in the tropics, including the Amazon 19 . In regions of sparse data such as tropical forests 20 , interpolation methods may mask precipitation changes driven by forest loss. Reanalysis products, which are numerical models constrained by empirical data, are also expected to be less reliable in regions where in situ data are limited 21 . Our results indicate that precipitation data based on satellite remote-sensing measurements may have an advantage over tropical forest regions where in situ measurements are sparse or unavailable. For these reasons, we focus our analysis on satellite-based datasets and identify where agreement between datasets exists.

Our results are robust (Extended Data Fig. 3 ) to a range of methodological assumptions including the length of analysis period, the choice of start and end period and the spatial extent of control pixels ( Methods ). Our analysis period includes the 2015–2016 El Niño that resulted in negative precipitation anomalies over many tropical land regions (Supplementary Fig. 1 ). We found that the precipitation response to forest loss was robustly negative during both El Niño and non-El Niño years (Extended Data Fig. 3 ). Over the Amazon and SEA, we see a stronger reduction in precipitation over regions of forest loss during El Niño years. The relative impact of El Niño on precipitation is smaller in the Congo 22 , and correspondingly we do not see a stronger reduction here. A stronger precipitation response to forest loss in regions and periods impacted by El Niño is probably due to higher transpiration rates observed in tropical forests during El Niño years 23 and because rainfall is more sensitive to reductions in moisture recycling during drought years 5 , 24 . Climate change is expected to lead to increased droughts over many tropical regions 25 , which may be further exacerbated by ongoing deforestation.

Seasonal precipitation reductions

Changes in precipitation due to forest loss during the dry, wet and transition seasons are nearly consistently negative (Fig. 3 ). For the tropics, absolute changes in precipitation with forest loss are greatest in the wet season (Fig. 3a , up to −0.6 mm per month per percentage point forest loss) whereas relative changes of precipitation with forest loss are similar (−0.2% per percentage point) across dry, wet and transition seasons (Supplementary Fig. 2 ). In the Amazon, deforestation causes the largest reductions in precipitation during the transition season (Fig. 3b ) as has been found previously 18 , 26 , 27 .

a – d , Bars indicate the median change in precipitation (millimetres per month) per percentage point forest cover loss for satellite datasets during 2003–2017 for tropics ( a ), Amazon ( b ), Congo ( c ) and SEA ( d ). Error bars indicate ±1 standard error from the mean. Statistically significant (* P  < 0.05; ** P  < 0.01) and nonsignificant (NS) differences in changes in mean precipitation over deforested regions compared with controls are indicated. Results are shown for the wettest 3 months (wet), the driest 3 months (dry) and the transition months (remaining 6 months). Datasets used in this analysis detailed in Extended Data Table 1 .

Previous case studies have indicated that dry-season precipitation can increase over deforestation in the Amazon 11 , 28 , 29 . We observed a nonsignificant increase in dry-season precipitation due to forest loss in the Amazon at 2° as well as increases in the Congo at 1° and 2° (Fig. 3 ). In SEA, forest loss causes reductions in dry-season precipitation across all scales (Fig. 3d ). The mechanism through which forest loss impacts precipitation is likely to change with both season and spatial scale. At the smallest scales (5 km), thermally driven impacts are likely to dominate, shifting to dynamically driven impacts through reductions to surface roughness, then to reductions in moisture fluxes and precipitation recycling at the largest scales 12 , 30 . Our observation of greater reductions in precipitation due to deforestation at larger spatial scales is consistent with a reduction in moisture recycling emerging as the dominant mechanism 1 .

Comparison with climate models

A meta-analysis of climate model studies (predominantly global models with >2° resolution) found that forest loss in the Amazon resulted in a mean reduction in annual mean precipitation of 0.16 ± 0.13% per percentage point 17 , overlapping with our value of 0.25% per percentage point (Supplementary Fig. 2 ). Fewer simulations have been conducted for the Congo, with models predicting a reduction in precipitation of 0.16 ± 0.17% per percentage point 2 , similar to our reduction of 0.15% per percentage point (Supplementary Fig. 2 ). The large range of model estimates highlights the substantial uncertainty in model predictions. Our observationally derived analysis provides support for models that predict reductions in precipitation under regional deforestation at global climate model scales.

Our observational analysis documents the impacts of deforestation on precipitation across the tropics. Applying linear scaling to the reductions in precipitation observed in our analysis would suggest that complete deforestation could result in reductions in annual precipitation of 10–20%. Previous estimates of the impact of complete deforestation on precipitation range from a 16% (ref.  17 ) to 55–70% (ref.  31 ) reduction in the Amazon and an 18% (ref.  2 ) to 50% (ref.  32 ) reduction in the Congo.

Impacts of future deforestation

To further explore how future deforestation might modify precipitation, we combined our observationally derived estimates of precipitation responses to forest cover loss with future projections of land cover change from a high-deforestation scenario ( Methods ). We estimate that forest loss from 2015 to 2100 (Fig. 4a ) could lead to reductions of annual mean precipitation of up to 16.5 ± 6.2 mm per month in the Congo (Fig. 4b ), equivalent to precipitation declines of 8–10%. Forest loss is projected to be greatest in the western and southern Congo (Fig. 4c ), which will also experience the strongest reductions in precipitation (Fig. 4d ).

a , Mean forest cover loss over 2015–2100 under Shared Socioeconomic Pathway 3–Representative Concentration Pathway 4.5 for the tropics, Amazon, Congo and SEA. b , Impact of projected forest cover loss on precipitation ( P ; ±1 standard error from the mean). c , Spatial pattern of forest cover loss. d , Predicted P change (∆ P ) in 2100 due to forest cover loss. Results are shown for 2.0° resolution. Maps of the different regions generated using Cartopy and Natural Earth 51 .

The sensitivity of precipitation to the extent of forest loss is an uncertainty in our analysis, a result of the relatively short observational record, compounded by large spatial and temporal variability in precipitation. The response of precipitation to forest loss greater than 30%, a threshold beyond which large reductions in precipitation have been postulated 1 , is one such uncertainty. Restricting our analysis to the forest losses of 0–30% that are well sampled in our observational dataset (Supplementary Fig. 3 ), through capping the impacts of greater forest loss at that of 30%, results in projected annual mean precipitation reductions of 6.5 ± 2.6 mm per month in the Congo and 6.2 ± 2.5 mm per month in SEA (Supplementary Fig. 4 ). However, restricting our analysis in this way is likely to underestimate the precipitation impacts over regions projected to experience the most extensive deforestation, including the Congo, where mean forest cover is projected to decline by 40 percentage points between 2015 and 2100 (Fig. 4a ).

Previous studies have identified both linear 9 , 33 and nonlinear 1 , 31 responses of precipitation to forest loss. Such nonlinear interactions and feedbacks have the potential to further amplify or moderate the responses predicted here 14 , 34 . Our analysis shows large reductions in precipitation for relatively small amounts of forest loss and evidence for reduced sensitivity of precipitation to additional amounts of forest loss (Extended Data Fig. 1 ). Assuming a nonlinear relationship between forest loss and precipitation ( Methods ) reduces our projected reductions in precipitation by around a factor of 2 (Supplementary Fig. 5 ). Our observationally based approach will miss tipping points in the climate system that might be reached as deforestation extent progresses further 1 . Such tipping points have been postulated for the Amazon under future global change 25 , 35 . Thus, the substantial declines in precipitation projected in our analysis should be viewed as a conservative estimate of the potential precipitation response to future deforestation. Nevertheless, our analysis suggests that deforestation can drive local and regional precipitation changes that may match or exceed those predicted due to climate change over the same period 36 , 37 .

Implications of precipitation reductions

Reductions in precipitation induced by forest loss have important implications for society and the sustainability of remaining tropical forest. Deforestation-induced reductions in precipitation affect agriculture 1 , 14 and hydropower generation 38 . On average, crop yields decline by 0.5% for each percentage point reduction in precipitation 39 . Our results indicate that forest-loss-induced changes to annual precipitation (Supplementary Fig. 2 ) could cause crop yields to decline by 1.25% for each 10-percentage-point loss of forest cover, potentially exacerbating the impacts of climate change and future drought events. The maintenance of regional rainfall patterns due to forests in the Amazon has been valued at up to US$9 ha −1  yr −1 and US$1.84 ha −1  yr −1 through sustaining agricultural yields and hydropower generation, respectively 40 . Global cropland area increased by 9% in the past two decades, with even higher increases in South America and tropical Africa 41 largely at the expense of natural ecosystems. Further agricultural expansion in tropical forest regions may lead to overall reductions in production if declines in yield due to deforestation-induced reductions in rainfall outweigh increased production from expanded agricultural area 14 .

Furthermore, reductions in rainfall over remaining areas of tropical forest are expected to lead to additional forest loss 9 as well as impacting species composition 22 , carbon sequestration 42 and fire frequency 43 . Reductions in dry-season precipitation pose a particular threat to forest viability by exacerbating seasonal droughts and potentially delaying the onset of the wet season and extending the length of the dry season. Increases in dry-season length over recent decades have previously been reported for the Amazon 44 and the Congo 45 , possibly linked to land cover changes 27 .

Deforestation may also shift precipitation patterns, increasing dry-season rainfall immediately downwind of forest loss and decreasing rainfall in upwind areas 12 . Our approach is restricted to observing deforestation impacts up to scales of 200 km ( Methods ). At larger scales, insufficient pixels experienced forest loss during the relatively short period of satellite observations for a robust analysis. Deforestation is also likely to alter precipitation at these larger scales through reducing moisture recycling leading to reductions in rainfall downwind of forest loss 4 , 5 , 9 , 35 . The length scale of moisture recycling has been estimated at 500–2,000 km in the tropics 46 , with a median value of 600 km in the Amazon 5 . In regions downwind of extensive forests, such as the southwestern Amazon, up to 70% of precipitation could be sourced from upwind evapotranspiration 47 , 48 . Tropical forest loss could therefore have severe implications for precipitation in these regions that are hundreds to thousands of kilometres downwind of the forest loss 5 . Through missing the impacts at these larger scales, our analysis is likely to underestimate the full impacts of deforestation on rainfall.

Our results highlight the importance of remaining tropical forests for sustaining regional precipitation. Despite efforts to reduce deforestation, rates of tropical forest loss have accelerated over the past two decades 49 . Renewed efforts are needed to ensure recent commitments to reduce deforestation, including the New York Declaration on Forests and The Glasgow Leaders’ Declaration on Forests and Land Use made at the 26th UN Climate Change Conference of the Parties, are successful. Global efforts to restore large areas of degraded and deforested land could enhance precipitation 50 , reversing some of the reductions in precipitation due to forest loss observed here.

We used 18 precipitation datasets, listed in Extended Data Table 1 . All datasets were downloaded at the highest available spatial resolution, which for some datasets was 0.04°, or approximately 4 km at the Equator. Data were obtained as monthly means or converted to monthly mean using the Python package xarray 52 . We categorized precipitation datasets as satellite ( n  = 10), station ( n  = 4) and reanalysis ( n  = 4). Satellite datasets are those based primarily on data from satellite sensors and include datasets that have both satellite and station-based data (that is, merged datasets). Station datasets include only ground-based information from weather stations and rain gauges. Reanalysis products are models constrained by surface and satellite data. Precipitation datasets have been compared previously over the Amazon 20 highlighting the limited station data over tropical forest regions. Time series of precipitation (Supplementary Fig. 1 ) reveal variability across the different datasets highlighting the need to analyse impacts of deforestation across multiple datasets.

To analyse the changes in forest canopy cover, we used data from the Global Forest Change (GFC) version 1.9 (ref.  10 ). GFC v1.9 provides forest canopy cover in the year 2000 and subsequent annual forest loss from 2001–2020 at 30-m resolution. We analysed forest cover and precipitation changes over the period 2003 to 2017, which was the period common to all datasets.

Analysis across multiple spatial scales

We analysed the impacts of forest loss across a range of scales (0.05°, 0.1°, 0.25°, 0.5°, 1.0° and 2.0°). Each precipitation dataset was analysed at its native resolution and at all lower resolutions across this range of scales. Spatial regridding was carried out using the Python package xESMF 53 with a bilinear regridding scheme. Two alternative regridding methods (xESMF: conservative-normalized; and iris: area weighted) were tested and had little impact on our results. For GFC data, we calculated forest loss using the original 30-m data and converted the resulting values to each of the six spatial resolutions analysed by taking the sum of all 30-m pixels within each larger pixel. Change in canopy cover from 2003 to 2017 at each resolution is shown in Fig. 1 .

Assessing impact of historical deforestation on precipitation

We used a moving-window nearest-neighbour approach 54 to compare the forest loss and precipitation change of each pixel with that of its immediate neighbours. We tested the sensitivity of the analysis to the size of the moving window and found similar results for 3 × 3 and 5 × 5 (Extended Data Fig. 2 ) moving windows. Results from the 3 × 3 moving-window approach can been seen in the main paper. We calculated the forest loss of each deforested pixel relative to neighbouring control pixels as the forest loss of the deforested pixel minus forest loss of the control. We constrained our analysis to the tropical evergreen broadleaf biome using the Moderate Resolution Imaging Spectroradiometer land cover dataset 55 . To be included in the analysis, deforested pixels must have experienced 0.1% more forest loss over time than their neighbouring control pixels. The number of deforested pixels analysed varied between analysis resolutions as follows: 0.05°, n  = 243,254; 0.1°, n  = 58,660; 0.25°, n  = 9,604; 0.5°, n  = 2,303; 1.0°, n  = 586; 2.0°, n  = 123. We observed similar distributions of canopy change for all spatial resolutions analysed (Supplementary Fig. 6 ).

We calculated the precipitation change of the deforested pixel relative to the precipitation change of the control pixel (Δ P ) as the precipitation change of the deforested pixel over the analysis period (for example, 2003–2017) minus the precipitation change over the control pixel. To reduce the impact of interannual variability in precipitation on our results, we calculated 5-yr means for periods at the start (2003–2007) and end (2013–2017; Extended Data Fig. 5 ) of the analysis period. We then calculated the change in precipitation as the difference between the start and end of these multi-year means. We report precipitation changes (Δ P ) as a function of forest loss by dividing by the difference in forest loss between deforestation and control pixels (units of millimetres per month per percentage point). We also report precipitation change as the percentage change in precipitation (Δ P / P , in units of per cent) as a function of forest loss (in units of per cent per percentage point).

To ensure that control pixels and deforested pixels experience a similar background climate, we conducted a sensitivity test in which we restricted our analysis to pixels for which the pre-deforestation precipitation across the control and deforested pixels differed by less than 10%. Restricting our analysis in this way had little impact on our results (Supplementary Fig. 7 ) showing that our nearest-neighbour approach is effective even at the largest scales analysed here.

To explore the role of the analysis period on our results, we compared the results for 5-yr means to those for shorter 3-yr means (2003–2005 versus 2015–2017) and found consistent results (Extended Data Fig. 3 ). Our analysis period includes the strong 2015/2016 El Niño that resulted in reductions in precipitation over most tropical land regions, particularly in 2015 (Supplementary Fig. 1 ). To explore the potential impacts of the 2015/2016 El Niño on our analysis, we estimated the impact of forest loss on precipitation using 3-yr (2003–2005 versus 2018–2020) and 5-yr (2003–2007 versus 2016–2020) multi-annual means spanning an extended time period. The 3-yr analysis completely excludes the 2015/2016 ENSO, and the 5-yr analysis excludes 2015, which was the driest year (Extended Data Fig. 3 ). Two datasets (TRMM and UDEL) were not available after 2017, so they were removed from this sensitivity analysis.

Statistical analysis

For each category of precipitation data (satellite, station and reanalysis), precipitation change values were grouped together for all deforestation pixels and all control pixels. We found that precipitation changes for deforested pixels and control pixels, and the difference in precipitation change between deforested and control pixels (Extended Data Fig. 4 ), were normally distributed. Error bars (Figs. 2 and 3 ) show ±1 standard error from the mean calculated and displayed using the Python package Seaborn 56 . To test whether mean precipitation changes over regions of deforestation were statistically different from changes over the control areas, we used a Student’s t -test. We also used the Mann–Whitney test to test for significant differences in median precipitation change between control and deforested pixels and found similar results.

Seasonal analysis

For the satellite datasets alone, in addition to calculating precipitation changes at the annual timescale, we calculated changes for the dry season (driest 3 months of each year), wet season (wettest 3 months of each year) and transition season (remaining 6 months). The driest, wettest and transition months were identified for each pixel using each individual precipitation dataset. For each season and dataset, we calculated the median change in precipitation across all of the pixels within the region of interest (Supplementary Figs. 8 – 10 ).

Predicting future precipitation change due to forest loss

We used projections of forest cover change available at 0.05° from the Global Change Analysis Model (GCAM) for 2015–2100 based on the Shared Socioeconomic Pathway 3–Representative Concentration Pathway 4.5 scenario, which represents a high-deforestation future 57 . GCAM includes the impacts of climate and land use on future forest cover. We summed forest cover from all forest categories and calculated forest cover loss in each year compared to a 2015 baseline. Forest cover loss data were regridded to 2°. We estimated the impact of forest loss on future precipitation at the 2° scale through multiplying the projected percentage point forest loss for each pixel by the observed median change in precipitation (millimetres per month) per percentage point forest cover loss across the satellite datasets. To estimate the uncertainty in our predictions, we applied an upper and lower limit on the sensitivity of precipitation to forest loss based on the median value ±1 standard error from the mean (see error bars in Fig. 2 ) and rescaled by forest loss. This provides a range of estimated precipitation impacts of future forest loss. We also tested the impact on our results of capping future forest loss in each pixel at 30%, which is the upper range of forest loss that is well sampled in the observations (Supplementary Fig. 3 ). For each region, we applied the tropical satellite precipitation response to forest loss (Fig. 2f ), meaning that our projected regional precipitation changes are a product of the regional canopy cover change and the median tropical precipitation response. Our approach assumes a linear precipitation response to forest loss, which recent work suggests could provide a conservative estimate of deforestation impacts 31 . We tested the sensitivity of assuming a linear response of precipitation to canopy cover loss. We fitted a nonlinear function to the data presented in Extended Data Fig. 1 through applying the median sensitivity of precipitation to forest cover loss (millimetres per month per percentage point) within each forest cover loss bin. We then scaled by the projected forest cover loss. This approach reduces the projected reduction in precipitation to 2.4 mm per month in SEA and 1.5 mm per month in the Congo (Supplementary Fig. 5 ).

Data availability

Full results for all tested resolutions used in this analysis are available through https://doi.org/10.5281/zenodo.7373832 . The original datasets are freely available to download from the following repositories: CHIRPS from https://data.chc.ucsb.edu/products/?C=M;O=D , CMORPH from https://ftp.cpc.ncep.noaa.gov/precip/CMORPH_RT/GLOBE/data/ , CPC from https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html , CRU from https://crudata.uea.ac.uk/cru/data/hrg/ , ERA5 from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview , GPCC from https://opendata.dwd.de/climate_environment/GPCC/html/download_gate.html , GPCP from https://disc.gsfc.nasa.gov/datasets/GPCPMON_3.1/summary?keywords=GPCPMON , GPM from https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/ , JRA from https://climatedataguide.ucar.edu/climate-data/jra-55 and https://jra.kishou.go.jp/JRA-55/index_en.html , MERRA-2 from https://disc.gsfc.nasa.gov/datasets?project=MERRA-2 , NOAA (PREC/LAND) from https://psl.noaa.gov/data/gridded/data.precl.html , PERSIANN (CCS, CDR, CCS-CDR, PDIR-NOW) from https://chrsdata.eng.uci.edu/ , TRMM from https://disc.gsfc.nasa.gov/datasets/TRMM_3B43_7/summary , UDEL from https://psl.noaa.gov/data/gridded/data.UDel_AirT_Precip.html . The GCAM model output used in this study is available from https://doi.org/10.25584/data.2020-07.1357/1644253 .  Source data are provided with this paper.

Code availability

The code used in this analysis is available through https://doi.org/10.5281/zenodo.7373832 .

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Acknowledgements

The research has been supported by funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (DECAF project, grant agreement no. 771492), and the Newton Fund, through the Met Office Climate Science for Service Partnership Brazil.

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Extended data figures and tables

Extended data fig. 1 annual precipitation change as a function of forest loss..

Results are shown at 2 ° spatial resolution for all satellite precipitation (P) datasets calculated as the change in P over time for deforested data pixels minus change over time for control data pixels. Data is binned according to forest cover change (%) with an equal number of pixels in each bin. Points show the median and error bars show ± 1 standard error from the mean. Details of each data product are provided in Extended Data Table 1 .

Extended Data Fig. 2 Annual precipitation change due to forest loss for individual datasets.

Results are shown for 2003 – 2017 for 5 year averages and 3x3 moving window. Bars show the median absolute change in annual P (mm month −1 ) per percentage point tree cover loss in each region (Tropics (a-f), Amazon (g-l), Congo (m-r), SEA (s-x)). Each P dataset is shown separately and ordered and coloured by category: satellite (orange), station (yellow) and reanalysis (turquoise). The datasets are numbered; 1) CHIRPS, 2) CMORPH, 3) CPC, 4) CRU, 5) ERA5, 6) GPCC, 7) GPCP, 8) GPM, 9) JRA, 10) MERRA-2, 11) NOAA 12) PERSIANN-CCS, 13) PERSIANN-CCSCDR, 14) PERSIANN-CDR, 15) PERSIANN-NOW, 16) PERSIANN, 17) TRMM, 18) UDEL. Results are shown for forest loss scales of 0.05° (a,g,m,s), 0.1° (b,h,n,t), 0.25° (c,i,o,u), 0.5° (d,j,p,v), 1.0° (e,k,q,w), 2.0° (f,l,r,x). Details of each data product are provided in Extended Data Table 1 .

Extended Data Fig. 3 Changes in precipitation due to forest loss for different time periods and nearest neighbour comparisons.

Changes in annual mean precipitation at 2.0° resolution are shown for satellite (orange), station (yellow) and reanalysis (turquoise) datasets for the tropics (a-f), Amazon (g-l), Congo (m-r) and Southeast Asia (SEA, s-x). Columns show the sensitivity of our results to changes in the analysis period, number of years used to compute multi-annual means at start and end of the analysis period, and size of the moving window used for nearest neighbour comparisons: 2003-2017, 3-year averages and 3x3 nearest neighbour (Column 1, a,g,m,s); 2003-2017, 3-year, 5x5 (Column 2; b,g,n,t); 2003-2017, 5-year, 3x3 (Column 3; c,i,o,u); 2003-2017, 5-year, 5x5 (Column 4; d,j,p,v); 2003-2020, 3-year, 3x3 (Column 5; e,k,q,w); 2003-2020, 5-year, 3x3 (Column 6; f,l,r,x). Error bars show ± 1 standard error from the mean. Details of each data product are provided in Extended Data Table 1 . Full results for all tested resolutions are available in an online repository [10.5281/zenodo.7373832].

Extended Data Fig. 4 Change in precipitation over deforested, control and difference between deforested and control pixels.

Change in precipitation over 2003 to 2017 is shown for deforested (a, b), control (c, d) and difference between deforested and control pixels (e, f) for 0.05° (a, c, e) and 2.0° (b, d, f) resolution. Details of each data product are provided in Extended Data Table 1 .

Extended Data Fig. 5 Mean precipitation from satellite, station and reanalysis datasets.

For each class of dataset, satellite (a, d, g), station (b, e, h) and reanalysis (c, f, i), the median value for the 5-year multi-annual mean at the start (2003-2007; a, b, c) and end (2013-2017; d, e, f) of the analysis period as well as the change over the analysis period (end – start; g, h, i) is shown. Mean values across tropical evergreen broadleaf forests are shown in units of mm/month at the top of each panel. Maps of the different regions generated using Cartopy and Natural Earth 51 . Details of each data product are provided in Extended Data Table 1 .

Supplementary information

Supplementary figures.

This file Supplementary Figs. 1–10, which support the results presented in the main paper. Here we also present results from the individual precipitation datasets.

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Smith, C., Baker, J.C.A. & Spracklen, D.V. Tropical deforestation causes large reductions in observed precipitation. Nature 615 , 270–275 (2023). https://doi.org/10.1038/s41586-022-05690-1

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Issue Date : 09 March 2023

DOI : https://doi.org/10.1038/s41586-022-05690-1

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Yale Climate Connections

Climate change is affecting mental health literally everywhere

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Farmers who can’t sleep, worrying they’ll lose everything amid increasing drought. Youth struggling with depression over a future that feels hopeless. Indigenous people grief-stricken over devastated ecosystems. For all these people and more, climate change is taking a clear toll on mental health — in every part of the world.  

Experts shared these examples and others during a recent summit organized by the Connecting Climate Minds network that brought together hundreds of scientists, doctors, community leaders, and other experts from dozens of countries who have spent the past year studying how climate change is harming mental health in their regions. 

Although mental illnesses are often viewed as an individual problem, the experts made clear that climate change is contributing to mental health challenges everywhere. 

The Connecting Climate Minds youth ambassador from Borneo, Jhonatan Yuditya Pratama, said his Indigenous community views nature as a sacred extension of being. Seeing the devastation of climate change on ancestral lands has brought his community “a profound sense of grief and loss,” he said.

“For us, mental health isn’t just about individuals,” he said. “It’s about the collective well-being of our communities and the land itself. When nature suffers, so do we.”  

Extreme weather and air pollution are taking a toll 

In her keynote, Marina Romanello, executive director of the Lancet Countdown and a Connecting Climate Minds advisory board member, explained the key ways that climate change threatens mental health. 

• Extreme heat is associated with increased self-harm and violence as well as more general feelings of negativity. It also leads to feelings of isolation when people feel trapped inside their relatively cooler homes.
• Wildfire or extreme weather stokes anxiety leading up to an event — and afterward — that can lead to PTSD or depression for survivors who have seen cherished places or lives lost.
• Farmers, fisherpeople, and others whose livelihoods are tied to the environment experience chronic stress, worry, and depression over things they can’t control, like extreme weather, habitat loss, and drought.
• Water scarcity increases stress for people in charge of seeking and transporting household water. Water scarcity also makes it hard for people to stay clean, potentially leading to isolation, loneliness, and depression. 
• Air pollution can keep kids out of school, leading to social isolation and, over time, a sense of hopelessness about the future. 

What’s more, people are experiencing the compounding effects of multiple disasters, said Emma Lawrance, who leads the Climate Cares Centre, a U.K.-based team that researches and supports mental health in the face of environmental crises: “With more frequent disasters, people can no longer recover psychologically from one before another occurs,” Lawrance said.  

And these escalating hazards are exacerbating social inequality, said Alaa Abelgawad, the Connecting Climate Minds youth ambassador representing northern Africa and western Asia. “[It’s] manifesting as anxiety, depression, and a profound sense of disempowerment among marginalized populations.”

Who is most vulnerable to climate change and mental health challenges? 

Many Indigenous communities have already been facing intergenerational trauma and a sense of deep disconnect from land and culture. Recurring climate devastation can intensify feelings of grief, stress, and disillusionment about the future, contributing to increased rates of addiction and suicide, participants said. 

Farmers, too, are among the most vulnerable. Changing seasonal norms, increasing drought, and a higher risk of severe weather are directly affecting their livelihoods. 

Sacha Wright, head of research at the youth-focused organization Force of Nature and part of Connecting Climate Minds’s “lived experience” working group, said that in Kenya, many small farmers are struggling with declining harvests and out of desperation have resorted to cutting down trees for charcoal. Though they felt they had no choice, some said cutting down the trees made the whole situation feel even worse. She spoke of high rates of depression, hopelessness, trauma, and a widespread feeling of “not knowing what to do.” 

For young people, climate change can also evoke a sense of hopelessness and powerlessness. In the Yucatan, one young person Wright interviewed said the only choices in life there are to migrate or enter the military. 

“When I see drought, I see my community leaving school and going to the military,” the person interviewed said. 

Mercy Njeru, a member of Connecting Climate Mind’s sub-Saharan Africa working group, said extreme heat is often leading to school closures across the region, setting youth up for failure and a sense of hopelessness. 

“When it’s so hot and you’re so anxious you can’t work, you can’t do anything because you’re feeling anxious or you’re feeling so sad from all the heat around you,” she said. 

In addition to environmental impacts, generational inequity and a sense of moral distress also contribute to anxiety for many youth. Britt Wray, director of Stanford Medicine’s Special Initiative on Climate Change and Mental Health, said she hears from many young people that power holders aren’t taking sufficient action, instead depending entirely on their generation to solve climate change. 

“This offloading of responsibility — without adequate partnership from the elder and more powerful contingents among us — can make burdensome climate anxiety and distress much worse,” she said.

Read: What baby boomers can do about climate change, according to Bill McKibben

What can be done to protect mental health as the climate changes? 

To help address the rising tide of mental health challenges, governments and public health leaders need to know exactly what kinds of impacts people are experiencing in their own communities.

First step: looking at experiences in every region. 

“We will only be successful if we can continue to connect and engage people from very different sectors, from neighborhoods all the way to multilateral organizations,” said Pamela Collins, chair of the department of mental health at the Johns Hopkins Bloomberg School of Public Health. 

Other examples of ways forward include everything from expanding health insurance to include climate-related mental health impacts to ensuring government policy supports people whose work has been affected by climate change to improve their job prospects. Several participants also spoke of the importance of returning to the wisdom of ancestral knowledge to address climate change in general, including mental health impacts. 

Other specific solutions offered by Connecting Climate Minds participants include:

• More public green space. Collins, the Hopkins professor, cited a study highlighting the need for more accessible green space in cities, a move that could have multiple positive outcomes, including on mental health. Forest bathing , AKA spending dedicated time in nature, reduces stress and anxiety, increases serotonin production, and improves mood regulation and overall mental health — all while being low-intensity and low-cost, said Niaya Harper Igarashi, part of Connecting Climate Mind’s eastern and southeastern Asia working group. 
• Focusing on reducing inequity. Making sure everyone has access to nutritious food, clean air and water, and sustainable energy sources is good for the climate and community. 
• Talking helps. In many communities, mental health is a taboo topic. By talking more openly about it on a personal level, in social or spiritual settings, at the dinner table, or in your doctor’s office, individuals can combat stigma and contribute to a growing understanding of these issues. 
• Meeting people where they are. From using vocabulary that makes sense for different communities to meeting people’s basic needs, solutions are most effective when they’re tailored for what real people are actually going through. For example, Wray, the Stanford expert, said meeting kids where they are includes screening for climate distress where many of them are every day: at school.

Lawrance, the Climate Cares lead who helped organize the summit, said it was heartening to see solutions being advanced around the world. 

“The dialogue showed this really strongly: that many solutions do already exist,” she said. “And it’s by learning from each other’s ways of knowing and doing that we can best find the ones that work for our context, and ensure people experiencing the worst climate impacts have a future where they cannot just survive, but thrive.”

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