ArticlePublisher preview available

Amazonia as a carbon source linked to deforestation and climate change

July 2021
Nature 595(7867):388-393

DOI:10.1038/s41586-021-03629-6

Authors:

Luciana Vanni Gatti

National Institute for Space Research

Luana Santamaria Basso

Max Planck Institute for Biogeochemistry

John B. Miller

National Oceanic and Atmospheric Administration

Manuel Gloor

University of Leeds

Show all 19 authorsHide

Amazonia hosts the Earth’s largest tropical forests and has been shown to be an important carbon sink over recent decades1,2,3. This carbon sink seems to be in decline, however, as a result of factors such as deforestation and climate change1,2,3. Here we investigate Amazonia’s carbon budget and the main drivers responsible for its change into a carbon source. We performed 590 aircraft vertical profiling measurements of lower-tropospheric concentrations of carbon dioxide and carbon monoxide at four sites in Amazonia from 2010 to 2018⁴. We find that total carbon emissions are greater in eastern Amazonia than in the western part, mostly as a result of spatial differences in carbon-monoxide-derived fire emissions. Southeastern Amazonia, in particular, acts as a net carbon source (total carbon flux minus fire emissions) to the atmosphere. Over the past 40 years, eastern Amazonia has been subjected to more deforestation, warming and moisture stress than the western part, especially during the dry season, with the southeast experiencing the strongest trends5,6,7,8,9. We explore the effect of climate change and deforestation trends on carbon emissions at our study sites, and find that the intensification of the dry season and an increase in deforestation seem to promote ecosystem stress, increase in fire occurrence, and higher carbon emissions in the eastern Amazon. This is in line with recent studies that indicate an increase in tree mortality and a reduction in photosynthesis as a result of climatic changes across Amazonia1,10.

VPs, time series and annual mean CO2 concentrations a, Time series of mean VPs of the CO2 mole fractions of the flasks below 1.5 km a.s.l. (red circles) and above 3.8 km a.s.l. (blue circles) for sites SAN, ALF, RBA and TAB_TEF (590 VPs) and the background sites RPB, ASC and CPT. b, Annual mean VPs for the four sites (annual mean per height; see Methods). c, Annual mean ΔVP (see Methods) for each site and year. d, Annual mean differences between mean CO2 mole fractions below 1.5 km a.s.l. and means above 3.8 km a.sl. for each site and year (see Methods). e, Partial column annual means plotted against annual mean fluxes, by site. Source data

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Regions of influence a, Mean quarterly regions of influence for the ALF, SAN, RBA, TEF and TAB sites, averaged between 2010 and 2018, calculated using the density of back-trajectories (see Methods). b, Deforestation inside quarterly regions of influence and the Amazon mask (purple line) using data from PRODES³² (see Methods). c, Annual mean regions of influence (trajectory densities) averaged between 2010 and 2018.

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Seasonal carbon flux and driver variables Average monthly means of potential flux driver variables at sites TAB_TEF, SAN, RBA and ALF in 2010–2018. Grey bands denote the standard deviation of the monthly mean.

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Time series of carbon flux and driver variables As in Extended Data Fig. 3, but showing the full time series of monthly means from 2010 to 2018 for SAN, ALF, TAB_TEF and RBA. Grey bands as in Extended Data Fig. 3, showing the 2010–2018 standard deviation for each month.

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ALF NBE drivers a, ALF annual mean NBE (NBE = total C flux − fire C flux, in g C m⁻² d⁻¹) from 2010 to 2018. Error bars are uncertainties related to the background, travel time trajectories, emission ratios CO/CO2 and natural CO flux (see Methods). b, Annual mean FCNBE, annual mean temperature and GRACE (equivalent water thickness) satellite soil water storage anomalies.

…

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388 | Nature | Vol 595 | 15 July 2021

Article

Amazonia as a carbon source linked to

deforestation and climate change

Luciana V. Gatti1,2 ✉, Luana S. Basso1, John B. Miller3, Manuel Gloor4,

Lucas Gatti Domingues1,2,5, Henrique L. G. Cassol1, Graciela Tejada1, Luiz E. O. C. Aragão1,6,

Carlos Nobre7, Wouter Peters8,9, Luciano Marani1, Egidio Arai1, Alber H. Sanches1,

Sergio M. Corrêa1,10 , Liana Anderson11, Celso Von Randow1, Caio S. C. Correia1,2,

Stephane P. Crispim1 & Raiane A. L. Neves1

Amazonia hosts the Earth’s largest tropical forests and has been shown to be an

important carbon sink over recent decades1–3. This carbon sink seems to be in decline,

however, as a result of factors such as deforestation and climate change1–3. Here we

investigate Amazonia’s carbon budget and the main drivers responsible for its change

into a carbon source. We performed 590 aircraft vertical proling measurements of

lower-tropospheric concentrations of carbon dioxide and carbon monoxide at four

sites in Amazonia from 2010 to 20184. We nd that total carbon emissions are greater

in eastern Amazonia than in the western part, mostly as a result of spatial dierences

in carbon-monoxide-derived re emissions. Southeastern Amazonia, in particular,

acts as a net carbon source (total carbon ux minus re emissions) to the atmosphere.

Over the past 40 years, eastern Amazonia has been subjected to more deforestation,

warming and moisture stress than the western part, especially during the dry season,

with the southeast experiencing the strongest trends5–9. We explore the eect of

climate change and deforestation trends on carbon emissions at our study sites, and

nd that the intensication of the dry season and an increase in deforestation seem to

promote ecosystem stress, increase in re occurrence, and higher carbon emissions in

the eastern Amazon. This is in line with recent studies that indicate an increase in tree

mortality and a reduction in photosynthesis as a result of climatic changes across

Amazonia1,10.

The Amazon forest contains about 123±23 petagrams carbon (Pg C)

of above- and belowground biomass

, which can be released rapidly

and may thus result in a sizeable positive feedback on global climate

Additionally, deforestation and forest degradation reduce Amazo

nia’s capacity to act as carbon sink. Hydrologically, Amazonia is one

of the three major air upwelling regions in the tropics, and the rain-

forest receives basin-wide rainfall averaging around 2,200mmyr

−1

Amazonia exhibits complex relationships between ecosystem carbon

and water fluxes and climate

13,14

. For example, evapotranspiration has

been estimated by several studies to be responsible for 25% to 35%

of total rainfall14–16. Large-scale human disturbance of these ecosys-

tems can be expected to alter these ecosystem–climate interactions.

Over the past 40 to 50 years, human impact has increasingly affected

Amazonia, resulting in a forest loss of around 17%, of which 14% has

been converted mostly to agricultural land (89% pasture and 10%

crops)

. Removal of forests causesan increase in temperature

13,18–20

and reduces evapotranspiration, and has been shown to reduce pre-

cipitation downwind of deforested areas

6,14,21

. Furthermore, regional

deforestation and selective logging lead tothe degradation of adjacent

forests, which increases their vulnerability to fires, promoting further

degradation4,13,22. These effects are further enhanced by temperature

increases caused by a decrease in forest cover

6,7

and are superimposed

on the backdrop of global warming.

Atmospheric carbon vertical profiles

A large-scale integrating indicator of the state of an ecosystem is its green-

house gas balance, mainly the carbon balance. Here, we report CO

fluxes

between 2010 and 2018 using almost 600 CO

(Extended Data Fig.1a) and

CO aircraft vertical profiles (VPs) that provide the responses of Amazo-

nian ecosystems to direct human impact and regional climate change.

Figure1 shows the regions of influence and the location of four vertical

profiling sites. Profiles extend from near the surface to approximately

4.5km above sea level and are collectively sensitive to surface fluxes

from a large fraction of Amazonia. The air arriving at our sampling sites

comes predominantly from the east, with the north–south component

https://doi.org/10.1038/s41586-021-03629-6

Received: 11 September 2020

Accepted: 10 May 2021

Published online: 14 July 2021

Check for updates

1General Coordination of Earth Science (CGCT), National Institute for Space Research (INPE), São José dos Campos, Brazil. 2Nuclear and Energy Research Institute (IPEN), São Paulo, Brazil.

3Global Monitoring Laboratory, National Oceanic and Atmospheric Administration (NOAA), Boulder, CO, USA. 4School of Geography, University of Leeds, Leeds, UK. 5National Isotope Centre,

GNS Science, Lower Hutt, New Zealand. 6College of Life and Environmental Sciences, University of Exeter, Exeter, UK. 7Institute of Advanced Studies (IEA), University of São Paulo (USP),

São Paulo, Brazil. 8Department of Meteorology and Air Quality, Wageningen University, Wageningen, The Netherlands. 9Centre for Isotope Research, University of Groningen, Groningen,

The Netherlands. 10Rio de Janeiro State University (UERJ), Resende, Brazil. 11National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), São José dos Campos, Brazil.

✉e-mail: luciana.gatti@inpe.br

Content courtesy of Springer Nature, terms of use apply. Rights reserved

Spatiotemporal modeling of vegetation dynamics in a changing environment: combining earth observation and machine learning

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Carmelo Bonannella

Vegetation, ranging from dense forests to open grasslands, plays a vital role in sustaining Earth's environmental health. These diverse ecosystems are crucial, offering services like carbon sequestration, soil stabilization, and water regulation, while providing habitats for countless species. Forests, in particular, stand as ecological powerhouses, encompassing nearly a third of the planet's land area and harboring around 80% of terrestrial biodiversity. They are instrumental in regulating freshwater flow and precipitation, crucial for agriculture, and are key players in atmospheric carbon absorption. Supporting the livelihoods of over 1.6 billion people globally, forests' ecological and socio-economic roles are intertwined. Consequently, their protection and judicious management are essential to ensure the ongoing provision of these vital ecosystem services and the sustenance of life on Earth. Traditional forest monitoring methods, including field surveys and National Forest Inventories (NFI) campaigns, are pivotal for understanding forest composition, structure, and health. Established in the early 20th century, these approaches are crucial for forestry management and environmental assessments. NFIs in particular, provide comprehensive, country-specific data, supporting national forestry policies and international reporting. Yet, these traditional methods encounter limitations in scalability, frequency, and logistics, especially for monitoring large, remote, or inaccessible areas. With the accelerating pace of climate change, these methods fall short in monitoring dynamic ecological variables. They struggle to track shifts in species composition, alterations in species ranges, biodiversity or forest disturbances such as wildfires, pest outbreaks, droughts, and heatwaves. 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The integration of Earth observation data has initiated a transformative era in forest and vegetation monitoring, significantly enhancing our capacity to assess and track vegetation dynamics. Among the different Earth observation tools, satellites, especially those from Landsat and Sentinel missions, offer a vast spatial coverage and a detailed enough spatial resolution that is crucial for observing large forested areas and detecting changes over time. The Global Forest Watch (GFW), an initiative utilizing mainly Landsat satellite imagery to monitor forests globally, exemplifies the application of Earth observation in large-scale forest monitoring. These satellite-based observations provide vital data on forest cover changes, deforestation rates, and reforestation efforts, contributing to a more nuanced understanding of global forest ecosystems and integrating traditional international reports on the state of the forests like the FAO's Forest Resource Assessment (FRA). Machine learning technologies, particularly in the realm of image processing, have transformed the way we analyze data from remotely sensed sources. Machine learning applications have proven effective in processing complex datasets from satellite missions, including the intricacies of hyperspectral data, which present challenges due to their high dimensionality. These machine learning methods are adept at identifying patterns and changes in forest landscapes, such as variations in tree species composition, signs of forest degradation, and impacts of climate change. The ability of machine learning algorithms to process and analyze large collections of satellite imagery has opened new possibilities for comprehensive and dynamic forest monitoring. The integration of Earth observation and machine learning technologies with traditional ground-based methods, such as NFIs, is pivotal in creating a robust forest monitoring system. This combination allows for the validation and enhancement of satellite-based observations with detailed ground-truth data. By merging the broad spatial coverage of satellite data with the accuracy and specificity of field data, we can achieve a more accurate, timely, and holistic view of forest ecosystems. This integrated approach is instrumental in addressing the limitations of traditional methods and fulfilling the need for rapid and responsive forest monitoring in the face of global environmental changes. It represents a significant step forward in our ability to manage and conserve forest resources effectively, ensuring their sustainability for future generations. Thus, the objective of this thesis is to integrate Earth observation and machine learning technologies with field data to enhance our understanding of ongoing vegetation dynamics and the overall monitoring and management of forest ecosystems. This work mainly contributes to this goal by developing and applying novel methods at different spatial and temporal scales for vegetation modeling. More specifically, in order to accomplish this objective, four research questions have been formulated and addressed in this work: (1) What is the impact of climate change on potential biomes distribution based on Earth observation and machine learning methods, and what are the projected shifts in vegetation under various climate change scenarios? (2) What combination of Earth observation and machine learning methods allows to map and analyze the distribution of forest tree species at high resolution? (3) How can these methods be applied to capture trends and disturbance impacts on forest tree species distributions and how do these reflect the ongoing changes in forest ecosystems? (4) What is the effect of coordinate precision of NFI data on the accuracy of high-resolution tree species classification models?

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Climate change is predicted to intensify extreme weather events, leading to prolonged dry seasons and reduced rainfall in Amazônia. This, coupled with human-induced ignition, can increase wildfires' frequency across the region. We investigated the spatial and temporal changes in the dry season extent and intensity across Amazonia from 2000 to 2023. Using precipitation and multiple evapotranspiration datasets, Maximum Cumulative Water Deficit (MCWD) and dry season length were calculated. Highlights include increasing frequency, severity and spatial extent of droughts since 2010, with 2023 marking the largest affected area (68%), and 2016 with largest water deficit. Overall, the increasing frequency of extreme droughts in Amazônia raises concerns about the rainforest's long-term resilience and heightened fire risk.

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Aircraft atmospheric profiling is a valuable technique for determining greenhouse gas fluxes at regional scales (10⁴–10⁶ km²). Here, we describe a new, simple method for estimating the surface influence of air samples that uses backward trajectories based on the Lagrangian model Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT). We determined “regions of influence” on a quarterly basis between 2010 and 2018 for four aircraft vertical profile sites: SAN and ALF in the eastern Amazon, and RBA and TAB or TEF in the western Amazon. We evaluated regions of influence in terms of their relative sensitivity to areas inside and outside the Amazon and their total area inside the Amazon. Regions of influence varied by quarter and less so by year. In the first and fourth quarters, the contribution of the region of influence inside the Amazon was 83–93% for all sites, while in the second and third quarters, it was 57–75%. The interquarter differences are more evident in the eastern than in the western Amazon. Our analysis indicates that atmospheric profiles from the western sites are sensitive to 42–52.2% of the Amazon. In contrast, eastern Amazon sites are sensitive to only 10.9–25.3%. These results may help to spatially resolve the response of greenhouse gas emissions to climate variability over Amazon.

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The large amount of carbon stored in trees and soils of the Amazon rain forest is under pressure from land use as well as climate change. Therefore, various efforts to monitor greenhouse gas exchange between the Amazon forest and the atmosphere are now ongoing, including regular vertical profile (surface to 4.5 km) greenhouse gas measurements across the Amazon. These profile measurements can be used to calculate fluxes to and from the rain forest to the atmosphere at large spatial scales by considering the enhancement or depletion relative to the mole fraction of air entering the Amazon basin from the Atlantic, providing an important diagnostic of the state, changes and sensitivities of the forests. Previous studies have estimated greenhouse gas mole fractions of incoming air (‘background’) as a weighted mean of mole fractions measured at two background sites, Barbados (Northern Hemisphere) and Ascension (Southern hemisphere) in the Tropical Atlantic, where the weights were based on sulphur hexafluoride (SF6) measured locally (in the Amazon vertical profiles) and at the two background sites. However, this method requires the accuracy and precision of SF6 measurements to be significantly better than 0.1 parts per trillion (picomole mole⁻¹), which is near the limit for the best SF6 measurements and assumes that there are no SF6 sources in the Amazon basin. We therefore present here an alternative method. Instead of using SF6, we use the geographical position of each air-mass back-trajectory when it intersects the limit connecting these two sites to estimate contributions from Barbados versus Ascension. We furthermore extend the approach to include an observation site further south, Cape Point, South Africa. We evaluate our method using CO2 vertical profile measurements at a coastal site in Brazil comparing with values obtained using this method where we find a high correlation (r² = 0.77). Similarly, we obtain good agreement for CO2 background when comparing our results with those based on SF6, for the period 2010–2011 when the SF6 measurements had excellent precision and accuracy. We also found high correspondence between the methods for background values of CO, N2O and CH4. Finally, flux estimates based on our new method agree well with the CO2 flux estimates for 2010 and 2011 estimated using the SF6-based method. Together, our findings suggest that our trajectory-based method is a robust new way to derive background air concentrations for the purpose of greenhouse gas flux estimation using vertical profile data.

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Structurally intact tropical forests sequestered about half of the global terrestrial carbon uptake over the 1990s and early 2000s, removing about 15 per cent of anthropogenic carbon dioxide emissions. Climate-driven vegetation models typically predict that this tropical forest ‘carbon sink’ will continue for decades. Here we assess trends in the carbon sink using 244 structurally intact African tropical forests spanning 11 countries, compare them with 321 published plots from Amazonia and investigate the underlying drivers of the trends. The carbon sink in live aboveground biomass in intact African tropical forests has been stable for the three decades to 2015, at 0.66 tonnes of carbon per hectare per year (95 per cent confidence interval 0.53–0.79), in contrast to the long-term decline in Amazonian forests. Therefore the carbon sink responses of Earth’s two largest expanses of tropical forest have diverged. The difference is largely driven by carbon losses from tree mortality, with no detectable multi-decadal trend in Africa and a long-term increase in Amazonia. Both continents show increasing tree growth, consistent with the expected net effect of rising atmospheric carbon dioxide and air temperature. Despite the past stability of the African carbon sink, our most intensively monitored plots suggest a post-2010 increase in carbon losses, delayed compared to Amazonia, indicating asynchronous carbon sink saturation on the two continents. A statistical model including carbon dioxide, temperature, drought and forest dynamics accounts for the observed trends and indicates a long-term future decline in the African sink, whereas the Amazonian sink continues to weaken rapidly. Overall, the uptake of carbon into Earth’s intact tropical forests peaked in the 1990s. Given that the global terrestrial carbon sink is increasing in size, independent observations indicating greater recent carbon uptake into the Northern Hemisphere landmass reinforce our conclusion that the intact tropical forest carbon sink has already peaked. This saturation and ongoing decline of the tropical forest carbon sink has consequences for policies intended to stabilize Earth’s climate.

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INT J CLIMATOL

Past studies presented evidence that deforestation may affect the precipitation seasonality in southern Amazon. This study uses daily rainfall data from Tropical Rainfall Measurement Mission 3B42 product and a recent yearly 1‐km land use dataset to evaluate the quantitative effects of deforestation on the onset, demise and length of the rainy season in southern Amazon for a period of 15 years (1998–2012). Additionally, we use the Niño4 index, zonal wind data and deforestation data to explain and predict the interannual variability of the onset of the rainy season. During this period, onset has delayed ~0.38 ± 0.05 days per year (5.7 ± 0.75 days in 15 years), demise has advanced 1.34 ± 0.76 days per year (20 ± 11.4 days in 15 years) and the rainy season has shortened by 1.81 ± 0.97 days per year (27 ± 14.5 days in 15 years). Onset, demise and length also present meridional and zonal gradients linked to large‐scale climate mechanisms. After removing the effects related to geographical position and year, we verified a relationship between onset, demise and length and deforestation: Onset delays ~0.4 ± 0.12 day, demise advances ~1.0 ± 0.22 day and length decreases ~0.9 ± 0.34 day per each 10% deforestation increase relative to existing forested area. We also present empirical evidence of the interaction between large‐scale and local‐scale processes, with interannual variation of the onset in the region explained by Niño4 sea surface temperature anomalies, Southern Hemisphere subtropical jet position, deforestation and their interactions (r² = 69%, p < .001, mean absolute error = 2.7 days).

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Full-text available

Aug 2019

Tropical forests have an important regulating influence on local and regional climate, through modulating the exchange of moisture and energy between the land and the atmosphere. Deforestation disrupts this exchange, though the climatic consequences of progressive, patch-scale deforestation of formerly intact forested landscapes have not previously been assessed. Remote sensing datasets of land surface and atmospheric variables were used to compare the climate responses of Amazon evergreen broadleaf forests that lost their intact status between 2000 and 2013. Clear gradients in environmental change with increasing disturbance were observed. Leaf area index (LAI) showed progressively stronger reductions as forest loss increased, with evapotranspiration (ET) showing a comparative decline. These changes in LAI and ET were related to changes in temperature (T), with increased warming as deforestation increased. Severe deforestation of intact Amazon forest, defined as areas where canopy cover was reduced below 70 %, was shown to have increased daytime land surface T by 0.44 °C over the study period. Differences between intact and disturbed forests were most pronounced during the dry season, with severely deforested areas warming as much as 1.5 °C. Maintenance of canopy cover was identified as an important factor in minimising the impacts of disturbance. Overall, the results highlight the climate benefits provided by intact tropical forests, providing further evidence that protecting intact forests is of utmost importance.

Effects of Deforestation on the Onset of the Rainy Season and the Duration of Dry Spells in Southern Amazonia

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May 2019

Amazonian deforestation is causing notable changes in the hydrological cycle by altering important precipitation characteristics. This study uses daily rainfall time series data from 112 rain gauges and a recent yearly 1‐km land use data set covering the period from 1974 to 2012 to evaluate the effects of the extent of deforestation at different spatial scales on the onset of the rainy season and on the duration of dry spells in southern Amazonia. Correlation analyses indicate a delay in the onset of 0.12–0.17 days per percent increase in deforestation. Analysis of cumulative probability density functions emphasizes that the likelihood of rainy season onset occurring earlier than normal decreases as the local deforestation fraction increases. In addition, the probability of occurrence of dry spells in the early and late rainy season is higher in areas with greater deforestation. The delayed onset and longer dry spell events in highly deforested areas increase the climate risk to agriculture in the region.

Changes in Climate and Land Use Over the Amazon Region: Current and Future Variability and Trends

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Full-text available

Dec 2018

This paper shows recent progress in our understanding of climate variability and trends in the Amazon region, and how these interact with land use change. The review includes an overview of up-to-date information on climate and hydrological variability, and on warming trends in Amazonia, which reached 0.6–0.7°C over the last 40 years, with 2016 as the warmest year since at least 1950 (0.9°C + 0.3°C). We focus on local and remote drivers of climate variability and change. We review the impacts of these drivers on the length of dry season, the role of the forest in climate and carbon cycles, the resilience of the forest, the risk of fires and biomass burning, and the potential “die back” of the Amazon forests if surpassing a “tipping point”. The role of the Amazon in moisture recycling and transport is also investigated, and a review of model development for climate change projections in the region is included. In sum, future sustainability of the Amazonian forests and its many services requires management strategies that consider the likelihood of multi-year droughts superimposed on a continued warming trend. Science has assembled enough knowledge to underline the global and regional importance of an intact Amazon region that can support policymaking and to keep this sensitive ecosystem functioning. This major challenge requires substantial resources and strategic cross-national planning, and a unique blend of expertise and capacities established in Amazon countries and from international collaboration. This also highlights the role of deforestation control in support of policy for mitigation options as established in the Paris Agreement of 2015.