How climate change and deforestation interact in the transformation of the Amazon rainforest

Domain: earth_science
Published: 2025-09-02
Editorial outcome: minor_revision

Abstract
The Amazon rainforest is one of Earth’s most diverse ecosystems, playing a key role in maintaining regional and global climate stability. However, recent changes in land use, vegetation, and the climate have disrupted biosphere-atmosphere interactions, leading to significant alterations in the water, energy, and carbon cycles. These disturbances have far-reaching consequences for the entire Earth system. Here, we quantify the relative contributions of deforestation and global climate change to observed shifts in key Amazonian climate parameters. We analyzed long-term atmospheric and land cover change data across 29 areas in the Brazilian Legal Amazon from 1985 to 2020, using parametric statistical models to disentangle the effects of forest loss and alterations of temperature, precipitation, and greenhouse gas mixing ratios. While the rise in atmospheric methane (CH4) and carbon dioxide (CO2) mixing ratios is primarily driven by global emissions (>99%), deforestation has significantly increased surface air temperatures and reduced precipitation during the Amazonian dry season. Over the past 35 years, deforestation has accounted for approximately 74% of the ~21 mm dry season−1 decline and 16.5% of the 2°C rise in maximum surface air temperature. Understanding the interplay between global climate change and deforestation is essential for developing effective mitigation and adaptation strategies to preserve this vital ecosystem.

Introduction
The Amazon rainforest plays a key role in the Earth’s hydrological, energy, and carbon cycles1–3. It is a highly biodiverse environment4, and influences weather patterns and climate conditions on a continental scale5–7. As the largest tropical forest on Earth, the biome stores between 150 and 200 billion tons of carbon above and below the ground8,9, and it has acted as a major sink for carbon dioxide (CO2)10,11. Over the last decades, however, parts of the forest have become major carbon sources rather than carbon sinks, and droughts appear to be a key driver in this transformation10,12–14. Accordingly, the impact of climate change and deforestation on the water cycling in the Amazon has been carefully investigated15–18. Recent studies have further highlighted how ocean–atmosphere teleconnections modulate carbon assimilation and evapotranspiration dynamics across South America’s biomes, including the Amazon, at seasonal to interannual scales19,20. These findings emphasize the need to contextualize Amazonian carbon and water fluxes not only in terms of local land-use changes but also in large-scale climate drivers. Of the nine processes identified as critical for Earth system stability21, five with the most critical status, namely climate change, biosphere integrity, land system change, freshwater change, and biogeochemical flows, are significantly influenced by the Amazonian Basin, particularly at regional to continental scales.

The increase in CO2 and other anthropogenic greenhouse gases (GHG) in the Earth’s atmosphere is warming the planet and generating feedbacks that alter the global climate. This effect will henceforth be referred to as Global Climate Change. A temperature increase of about 0.15°C per decade has been observed in Amazonia since 1950, along with an increase in dry season length by 6.5 days per decade22. Highly deforested areas experience a dry season five weeks longer compared to 197923. Further increases in dry season length might trigger positive feedback between enhanced flammability of the vegetation, resulting in an intensified fire regime, a perturbed hydrological cycle, as well as an increased vulnerability of the forest to droughts and fires24–26. Recent studies have also evaluated how climate and land use changes jointly affect other environmental variables such as hydrology. For instance, Cavalcante et al.27 assessed the Itacaiúnas River Basin and found that climate variability and deforestation have opposite effects on streamflow. Similarly, Panday et al.28 demonstrated similar patterns in the Xingu River Basin using a combination of satellite observations and modeling approaches. These findings highlight the complexity and scale dependence of interactions between climate change, deforestation, and hydrological responses in the Amazon.

Deforestation, forest degradation, and wildfires significantly alter the Amazon’s climate, which has far-reaching consequences for the global climate system29,30. The Amazon Basin covers about 59% of the territory of Brazil, spanning around 5 million km2. From 1985 to 2020, there was a significant drop in forest cover, from 89.1% to 78.7%, while pasture land increased from 4.2% to 14.8%31. Deforestation directly affects precipitation rates and spatial patterns, and the forest’s ability to remove CO2 32–38. Together, human activities associated with land use change and global climate change are disrupting the natural balance of the ecosystem, leading to a range of environmental impacts that affect both the region and the whole planet15–17,39. The impact of climate change on the Amazon rainforest and its potential for carbon release raises concerns for the global climate system feedback loop18. Particularly, deforestation has a substantial impact on the regional climate of Amazonia5,22,32,40,41. Although previous studies have investigated the combined effects of deforestation and climate change in specific subregions or watersheds27, the relative roles of deforestation and global climate change and their interaction in driving the transformation of the Amazon remain poorly understood and have yet to be disentangled25,42.

In this study, we use observational/reanalysis and remote sensing data to quantify the effects of 35 years of deforestation and global climate change on the regional climate in Brazil’s Legal Amazon (BLA). The datasets used, along with their spatial and temporal coverage, are summarized in Table 1. The BLA was chosen because it corresponds to the official jurisdiction used in Brazil’s environmental policy and monitoring frameworks (e.g., MapBiomas43), and because high-resolution, long-term land use data are reliably available for this territory. Moreover, the BLA encompasses the majority of the Amazon biome within Brazil, where deforestation has been most intense and extensively monitored. A comprehensive assessment of the long-term trends of key climatic variables allowed us to quantify changes and determine the relative contributions from the driving forces.

Results
Quantifying global and regional effects across Brazilian Amazon areas
To disentangle the regional effects of deforestation from the global effects of climate change in the Brazilian Amazon, data on 29 areas of ~300×300 km2 between 1985 and 2020 were considered. Deforestation is defined here based on the MapBiomas land cover classification as any loss of native forest vegetation, regardless of the resulting land use class. The particular area size was selected for two reasons. First, they encompass a sufficiently large area to represent the observed path length of deforestation, as indicated by previous studies38, and have recently been adopted in similar spatial analyses of Amazonian land cover dynamics44. This ensures that the effects of deforestation within a given region are adequately captured and analyzed. Second, the dimension of ~90,000 km2 is within the mesoscale range, encompassing atmospheric phenomena like thunderstorms, squall lines, and deep convection, which are characterized by radii between 75 and 150 km45. The 29 areas were chosen to maximize spatial coverage of the BLA while excluding areas dominated by water bodies or permanent wetlands, ensuring reliable surface climate and land cover analysis. To assess the robustness of our findings across different spatial scales, we conducted a sensitivity analysis using smaller grid sizes (50×50, 100×100, and 200×200 km2). The results, shown in Supplementary Fig. S1, indicate that the long-term trends in temperature and precipitation remain consistent across scales, reinforcing the suitability of the 300×300 km2 resolution adopted in this study. However, greater variability during the dry season suggests a slight scale dependency for the precipitation in the analysis.

Figure 1 presents the 29 areas analyzed within the BLA. The land-use classification (see Methods for a detailed description) for 1985 (a) and 2020 (b) is overlaid on the areas, providing a clear representation of the deforestation that has occurred in each area over time. The northwest areas of the Amazonian region stand out due to their high fraction of natural forest cover. In contrast, the southern and eastern areas face alarming levels of deforestation, commonly referred to as the arc of deforestation. The deforestation fraction for each area (Supplementary Fig. S2) varies significantly across the 29 areas. Upon analyzing the variation in forest cover across areas, it was determined that the 75th and 25th quantiles of vegetation loss corresponded to 19% and 0.6%, respectively. These findings underscore the substantial spatial variation in the extent of deforestation across different regions.

Deforestation and global climate change effects
We collected time series data for average methane (CH4) and CO2 mixing ratios, maximum surface air temperature (Tmax), and average cumulative monthly rainfall for each area. The maximum surface air temperature was used because it provides the clearest and most statistically significant indication of the impact of deforestation compared with the minimum and mean air temperatures. A summary of datasets used, including their resolution and coverage, is provided in Table 1. The deforestation dataset spans the period from 1985 to 2020; however, each variable dataset covers a different time interval, as described in Table 1. This analysis is based on long-term remote sensing and reanalysis datasets, including MapBiomas (land cover), ERA5 (temperature), GPM (precipitation), AIRS (CH4), and OCO-2 (CO2). A detailed description is provided in the Section “Methods”. Each variable dataset shows a distinct pattern of change over the years, as shown in Supplementary Fig. S3.

Deforestation and global climate change influence rainfall, temperature, and GHG mixing ratios in distinct ways across the wet and dry seasons14,46,47. However, several studies have shown that the regional climatic impacts of deforestation are more pronounced and meaningful during the dry season, when the forest is more vulnerable to moisture deficits and surface–atmosphere interactions are intensified. In our analysis, the dry season consistently exhibited stronger correlations between deforestation and key climate variables, including maximum temperature and precipitation (see Supplementary Fig. S4), while the wet season showed weaker and often statistically insignificant relationships (see Supplementary Fig. S5). Therefore, our study focuses exclusively on the dry season to isolate and quantify the climatic effects of deforestation with greater precision. A detailed explanation and supporting statistical evidence are provided in the “Methods”, subsection “Definition of wet and dry season, and data processing”.

Our analysis indicates that the relationship between the variable and time is linear, while the relationship with deforestation follows a logarithmic pattern. For example, the analysis of the maximum daily temperature (Fig. 2) shows that during the dry season, these two distinct processes exhibit a nearly linear temporal trend across all deforestation extensions, which is attributed to global climate change, as evidenced by the gradual increase in maximum temperature over the years. Additionally, a logarithmic effect of deforestation is observed, irrespective of the year, whereby an extension in the deforestation area corresponds to a rise in daily maximum temperature. The other atmospheric variables considered in this study (CO2, CH4, and precipitation) also exhibited meaningful variability associated with both long-term global trends and/or regional deforestation (see Supplementary Fig. S4). Regional deforestation patterns strongly modulate dry season precipitation, and the mixing ratios of GHG (CO2 and CH4) exhibit clear long-term trends, primarily driven by global changes. In general, variables more strongly influenced by global climate change exhibited smoother temporal increases, while those sensitive to land cover change responded more directly to deforestation. Three distinct patterns emerge: variables with a clear long-term trend and a weaker influence of deforestation (CH4 and CO2 mixing ratios), a variable sensitive to deforestation and long-term trends (maximum surface air temperature), and a variable with a strong dependence on deforestation and a weak long-term trend (rainfall).

We assessed the impacts of regional and global contributions of these parameters during the dry season by fitting linear and logarithmic equations to the dataset. More details are presented in the Section “Methods”. These results contrast with a recent study, which reported a linear response of temperature to deforestation48. However, their analysis does not disentangle climate change’s contribution from deforestation, which our findings indicate is essential for capturing the nonlinear response. By isolating the effects of deforestation, we demonstrate that temperature increases with forest loss logarithmically, emphasizing the distinct role of land-use changes in modifying regional climate. This interplay is explored in detail in the following sections, where we disentangle the respective contributions of time and forest loss to the observed patterns.

Modeling the evolution of atmospheric variables
To assess the effect of deforestation and global climate change on GHG mixing ratios and weather variables, we developed an approach that integrates linear temporal trends with the exponential trends associated with deforestation. These effects interact non-linearly due to the interdependence of deforestation (D) and time (t). As detailed in section Methods, we obtained a parametric equation (Eq. (4)) that incorporates the linear time dependence, the logarithmic dependence on deforestation, and their nonlinear interactions. The parameters of Eq. (4) were obtained by fitting observational data to separately capture the contributions of deforestation and global influence to observed changes in climate variables.

Using the parameterized equation, we effectively isolated the individual effects of global emissions and deforestation on changes in GHG mixing ratios and weather parameters. This was achieved by differentiating Equation (4) with respect to time and deforestation, holding either deforestation or time constant, allowing us to distinguish the unique contributions of global climate forcing and deforestation dynamics. Integrating the derived equations for time and deforestation enabled us to quantify the specific contributions of deforestation and global climate change to the observed variations over the 35 years. To account for regional variation, we applied the parameterized equation individually to all 29 studied areas, each covering 300×300 km2. While our primary results present Amazon-wide averages, we also present regional distributions in a box plot, including the statistics for all areas. Further details on the equation formulation and parameter definitions can be found in the “Methods”, subsection “Derivation of parametric fits and calculations of deforestation and global contributions”.

Supplementary Figure S6 presents the fitted three-dimensional surfaces that depict how each climate variable responds to both time (year) and the fraction of deforestation, as modeled by Eq. (4). These visualizations offer complementary insight into the nonlinear interactions captured by our approach. The parametric formulations used to construct these surfaces are provided in Table 2 and serve as the foundation for disentangling the respective contributions of global climate change and regional deforestation. To assess the model’s performance, we evaluated the quality of the fit for each variable, which is also shown in Table 2. In addition, Supplementary Figure S7 presents the correlation coefficients individually for the linear and logarithmic contributions, showing that the dry season fits were statistically significant, with confidence levels above 95%, unlike those observed for the wet season. All fits were statistically robust (p<0.05) and showed strong correlation coefficients (R≥0.69), confirming the reliability of our approach.

Disentangling the influence of deforestation and climate change
Although each variable dataset covers a different period, as shown in Table 1, the fitted equations enable us to extrapolate all analyzed variables to the same period, from 1985 to 2020, which is the period covered by the land use dataset. This extrapolation serves as a reference for understanding the variation of GHG mixing ratio, temperature, and precipitation over the same range of years. Figure 3 presents the overall changes calculated over the 35-year period, as well as the specific contributions from global climate change and deforestation. The boxplots, based on data from all 29 regions, offer a robust statistical summary across the Amazon biome. Median percentage contributions are shown next to the bars, while the deltas indicate the average change observed for each variable. Notably, the relatively short lengths of the boxplots for GHG mixing ratios reflect much lower variability in their contributions compared to those of the meteorological variables. For the gas mixing ratios, CO2 and CH4 exhibited notable increases of approximately 87 ppm and 173 ppb, respectively. Additionally, the maximum surface air temperature experienced a rise of ~2°C, while the total precipitation during the dry season decreased by about 21 mm dry season−1, on average. By using our parametric equations, we estimated the individual contributions of deforestation and climate change to the observed variabilities. The modulation of the maximum surface air temperature is intricately shaped by the dual influence of global climate change, which exhibits a discernible linear rise over successive years, and the impact of land use transformations resulting from the conversion of forests into pasture and agricultural areas. Separating the distinct influences of regional and global factors reveals that deforestation in the Amazon region resulted in a rise of 0.39°C in the average daily maximum temperature over 35 years, corresponding to ~16.5% of the total contribution. While this value represents the mean across all study areas, the variation in maximum temperature in the study area with the highest percentage of deforestation (28.5%) reaches values as high as 1.25°C. In contrast, the more comprehensive effects of global influences contributed to a temperature increase of 1.63°C, corresponding to 83.5% of the 2.0°C observed temperature increase. These results highlight that the observed increase in maximum temperature during the dry season cannot be attributed solely to global climate change or deforestation. Instead, it reflects a synergistic interaction in which long-term global warming amplifies the sensitivity of local climate to land-use change, while deforestation intensifies regional heating and reduces evapotranspiration, further exacerbating the warming trend.

These results align with previous observational studies. For instance, Gatti et al.14 reported that regions in the eastern and southeastern Amazon, where deforestation is most intense, exhibited stronger warming trends and carbon release compared to more preserved western regions. This spatial differentiation, which is supported by Supplementary Fig. S8, reinforces our findings that deforestation contributes disproportionately to regional temperature increases. While prior work did not explicitly separate regional and global contributions, our study builds on these patterns by providing a first-order quantification of their respective roles in shaping temperature and rainfall trends across the Amazon basin.

Related to precipitation, our findings reveal that the reduction in forest cover results in a 15.8 mm decrease in precipitation per dry season in the Amazon region, constituting 74.5% of the overall effect. On the other hand, global climate change contributes to a reduction of 5.2 mm in precipitation per dry season, representing 25.6% of the total effect. These results highlight the substantial impact of deforestation on the rainfall regime in the Amazon, with the primary effect occurring during the dry season. Recent studies estimated a 3 mm yr−1 reduction in rainfall for every percentage point decrease in forest cover38. Based on this rate, the decrease in forest cover from 89.1% to 78.7% reported in our study, during the 35 years, would correspond to a rainfall reduction of approximately 30 mm, during the whole year, not far from our calculation only for the dry season. As previously stated, these numbers represent the average for the entire Amazon region. However, when considering the study area with the highest percentage of deforestation (28.5%), there is a discernible rainfall reduction that could reach values of around 50.5 mm during the dry season. Numerous studies have examined the complex changes in rainfall patterns resulting from deforestation, global climate change, or the combined effects of both factors49. In addition, previous research showed the different seasonal impacts of deforestation on cloud cover50 while highlighting the distinct influences of deforestation and climate change on the rainfall regime51. Shallow clouds predominate in deforested areas, while deep convection is favored in forested surfaces52.

Considering the GHG mixing ratios, CO2 has global effects that drive the overall variability and contribute to an approximately 87 ppm increase in mixing ratio over the 35 years modeled. For methane, the mean contributions of the effects of deforestation and global change were of the same order, 0.1% and 99.9%, respectively. Considering the total background mixing ratio, on average, the regional influence resulted in a small increase in the total methane mixing ratio, while the global influence contributed to a change of ~173 ppb. When we apply the model to describe the regional effect over the highest deforestation fraction (28.5%), the mixing ratio changed by around 0.83 ppm, giving a maximum regional change of around 6.9%, which is greater than the average effect. The same reasoning can be applied to analyzing the maximum regional fluctuation in the CH4 mixing ratio. After subtracting the background mixing ratio, methane levels changed by approximately 75 ppb over the 35-year data period, and by around 0.80 ppb between the two hotspots of forest and deforestation. On average, methane varies by around 0.12%, but locally it can reach values of up to 1.06%.

Since GHGs have a long residence time in the atmosphere, their mixing ratios vary slowly and smoothly in space and time in all areas. The fact that the GHG observational data refer to the troposphere also contributes to the smooth variation in mixing ratio. As highlighted in the Introduction section, certain regions within the Amazonian basin may no longer act as carbon sinks but have likely become carbon sources due to deforestation, affecting the net ecosystem exchange (NEE) and eddy covariance fluxes. However, it is worth noting that GHG mixing ratios are disconnected from NEE53. The changes in CO2 mixing ratios are relatively modest, ranging from 2 to 4 ppm14,53, which translates to less than 1% of the total CO2 mixing ratio. Removing the background mixing ratio, the change in mixing ratio over the 6 years analyzed with satellite data changed by about 12%.

Methods
Land use and land cover data
The land use and land cover (LULC) data were obtained from the MapBiomas Project43, an initiative dedicated to monitoring and mapping changes in LULC throughout Brazil. The MapBiomas platform effectively collects and processes extensive geospatial data from various sources, including satellite imagery captured by different sensors. By implementing advanced classification and analysis algorithms, it identifies and maps alterations in land cover over time. MapBiomas has generated Brazil’s most detailed, up-to-date, and longest time series of LULC mapping. We used Collection 6.0 from MapBiomas for this study. This collection comprises maps with an annual temporal resolution covering the period from 1985 to 2020. The spatial resolution of the data is 30 m based on Landsat satellite images. The global accuracy of the MapBiomas Collection 6 land-use and land-cover maps stands at 87.4%. These datasets are available on Google Earth Engine as a data asset and can be freely accessed through the platform’s website at https://brasil.mapbiomas.org (accessed June 19, 2022).

In our analysis, deforestation is defined as the conversion of areas classified as “forest" in MapBiomas to any other land cover class, including agriculture, pasture, urban areas, water bodies, or natural non-forest formations. This inclusive definition encompasses all transitions that lead to forest loss, regardless of the subsequent land use. Our estimates of deforestation do not consider specific transitions (e.g., forest-to-agriculture or forest-to-mining). Deforestation fractions were calculated as annual averages within each of the 29 study areas, based on the percentage of forest cover loss relative to the initial forest area in 1985. We emphasize that the analysis focuses on the Brazilian Legal Amazon (BLA), rather than the full Amazon biome, due to the availability and consistency of spatially explicit deforestation and land cover data provided by MapBiomas, which is optimized and validated within Brazilian territory. While the Amazon biome extends beyond Brazil’s borders, the BLA encompasses the vast majority of its forest area under national jurisdiction and includes the regions experiencing the highest rates of deforestation. As such, it provides a policy-relevant and analytically consistent domain for our regional climate assessment.

Definition of wet and dry seasons, and data processing
Our analysis evaluated the dry and wet seasons separately to assess the seasonal impact of deforestation on each variable. Given the spatial heterogeneity of seasonal cycles across the Amazonian region, we categorized the dry and wet season months based on the 25th and 75th percentiles of the total integrated rainfall (see Supplementary Fig. S9). The wet and dry seasons were determined by analyzing the precipitation patterns specific to each area within the Amazon region. A comprehensive analysis of the entire time series for each area was conducted to derive an average precipitation annual cycle. To study the whole period, we used data from ERA5 from 1985 to 2020. The objective was not to quantify rainfall amounts, but to describe the distinct seasonal variations in precipitation patterns, which are accurately depicted by the ERA5 dataset. The 75th quartile, representing months with the highest annual rainfall, was identified as the wet season. In contrast, the 25th quartile, representing months with the lowest total rainfall, was designated as the dry season. It is worth noting that the composition of each quartile may vary across the Amazon. Based on quartiles, this methodology ensures an objective approach to defining the seasons. Thus, the dry and wet season data were computed for each time series and obtained based on the 25th and 75th quartiles, respectively, and their annual averages for each area. This was necessary to ensure accurate comparisons regarding the change in forest cover.

We evaluate the sensitivity of deforestation to rainfall, temperature, and trace gas mixing ratios by applying wet and dry season classifications across the 29 study regions. Statistically significant trends emerge predominantly during the dry season. In this period, the relationship between deforestation and climatic variables is particularly strong, with median correlation coefficients of R ~0.75 for maximum temperature and R ~0.70 for rainfall. In contrast, during the wet season, these correlations decline substantially to R~0.50 and R~0.30, respectively, and most trends are not statistically significant. Supplementary Figures S4 and S5, along with Fig. 2 (which illustrates the evolution of dry-season temperature), presents scatter plots for both seasons, highlighting the seasonal contrasts. Deforestation disrupts the natural modulation of the water and energy cycles, leading to reduced rainfall and elevated surface temperatures, particularly during the dry season, in line with previous findings47. The linear component of our model, representing the influence of large-scale climatic drivers, shows consistently high correlation coefficients for CO2 and CH4 (R>0.90) in both seasons, suggesting that variations in GHG mixing ratios are primarily governed by global-scale processes and are relatively insensitive to local deforestation. In contrast, the logarithmic component, capturing the regional-scale effects of deforestation, exhibits marked seasonal variability. These findings confirm that the climatic impacts of deforestation are most pronounced during the dry season, offering a more distinct and statistically robust signal of its regional influence. Conversely, the wet season appears less responsive to local land cover changes, likely due to the overriding influence of large-scale atmospheric dynamics. Accordingly, our analysis emphasizes the dry season, where the signal of deforestation can be more accurately isolated and quantified.

Weather and greenhouse gases data
This study utilized time series data for methane (CH4), carbon dioxide (CO2), daily surface maximum air temperature, and total precipitation during the dry season for each of the 29 study areas. The datasets are summarized in Table 1. These datasets were retrieved from Google Earth Engine (GEE)58 and NASA’s Giovanni platform. Meteorological data were obtained using custom JavaScript code within GEE. Specifically, we extracted the following products:
• Daily surface maximum air temperature (2 m above ground level) was obtained from the ERA5 Daily Aggregates dataset, provided by the European Center for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The dataset is available on GEE under the asset ECMWF/ERA5/DAILY and provides global coverage at a spatial resolution of 0.25°×0.25°, spanning the period from 1979 to September 2020. Daily maximum air temperature values are derived from hourly ERA5 data and represent the highest temperature recorded at 2 m above ground for each day.
• Total monthly rainfall data derived from the integrated multi-satellite retrievals for GPM (IMERG), Final Run version 6 product, available as the NASA/GPM_L3/IMERG_MONTHLY_V06 asset on GEE. The data provide monthly estimates of precipitation rate (in mm/h) at a spatial resolution of approximately 0.1° (about 11 km at the equator), covering the period from 2000 to 2020. We converted the precipitation rate to total monthly rainfall (in mm/month) by multiplying the average rate by the number of hours in each month.

Greenhouse gas mixing ratios were obtained from NASA’s Giovanni platform:
• CH4 mixing ratios were obtained from the Atmospheric Infrared Sounder (AIRS) instrument aboard NASA’s Aqua satellite. We used daily Level 3 data (AIRS3STD) corresponding to the 400 hPa pressure level (upper troposphere), processed using the Standard Physical Retrieval algorithm (version 7). The data provide global coverage at a spatial resolution of 1°×1° and include both ascending and descending orbits. Daily average CH4 values were extracted for each study region for the period 2002–202059.
• CO2 mixing ratios were obtained from the Gridded OCO-2 product, which provides daily global maps of column-averaged CO2 dry air mole fraction (XCO2) derived from the Orbiting Carbon Observatory-2 (OCO-2) satellite. Individual retrievals (Level 2) from the high-resolution spectrometer aboard OCO-2 are assimilated into the GEOS modeling system (GMAO/CoDAS) every 6 h, producing Level 3 gridded fields that smooth orbital gaps and ensure spatiotemporal consistency. The dataset is available at a spatial resolution of 0.5°×0.625°, covering the period from 2015 to 202060.

All datasets were first re-aggregated into monthly means for each of the 29 study regions. Based on the long-term climatological patterns, each region’s time series was then classified into wet and dry seasons, following a percentile-based threshold applied to the monthly precipitation data. This classification allowed us to capture the distinct seasonal responses of atmospheric variables to forest loss. For each season, we subsequently computed annual means, enabling a robust comparison of dry- and wet-season dynamics and a consistent assessment of long-term trends across datasets with different temporal coverage. Given the varying temporal coverage of the datasets (Tmax: 1985–2020; rainfall: 2000–2020; CH4: 2002–2020; CO2: 2015–2020), we applied our obtained parametric modeling approach (see Eqs. (7) and (8)) to reconstruct and extend the time series across the full analysis period (1985–2020). This approach allowed us to capture long-term trends and ensure temporal consistency across variables. By harmonizing datasets with different native resolutions and timespans, this framework enabled robust and consistent comparisons across variables and study regions, facilitating an integrated analysis of the impacts of deforestation and climate change.

Derivation of the parametric fits and calculations of the deforestation and global contributions
The atmospheric variables investigated in this study show a dependency on both the time and the deforestation fraction. To unravel the contributions of time and deforestation, two-dimensional functions were fit to each atmospheric variable. In the following equations, the variable y represents an atmospheric variable (CO2, CH4, the maximum surface temperature, or the rainfall), the dependent variable in the model setup. The two independent variables are time (t, in years) and the deforestation fraction (D, representing the percentage of the area that has been deforested). The influence of deforestation was modeled considering a logarithmic dependence between y and D, as exemplified in Fig. 2c. The dependent variable y, which represents any of the four atmospheric variables investigated in this study, was initially modeled with a logarithmic dependency on deforestation (D) plus a linear dependency on time (t).

y(t,D) = AlnD + Bt + C, (1)

where A, B, and C are coefficients fitted to the observations representing the contribution of a regional forcing related to deforestation, a global forcing related to global changes and long-term trends, and an intercept. Our analysis revealed that the coefficients A and B were not independent, as the deforestation fraction depends on time (see Fig. S4). To accurately incorporate these dependencies and account for their non-linear effects, we have extended Eq. (1) into a more comprehensive form, including, for instance, a linear variation of coefficient A and a non-linear variation of coefficient B as follows.

A = a1t + a2, (2)

B = b1lnD + b2, (3)

Rearranging the terms, we obtained a model for y(D,t) based on four parameters.

y(t,D) = (γt + a2)lnD + b2t + C, (4)

where γ = a1 + b1. This equation is referred to as Eq. (4) in the main text. Thus, four parameters are required to define Eq. (4), which was used to fit the data, considering the fraction of deforested areas and the years. The optimal fits were obtained through the least squares method. This approach resulted in a parametric surface that accurately captures the data behaviors and statistical parameters such as the p-value and correlation coefficient. These statistical measures were used to assess the quality of the data fitting. To obtain the rates of global and regional change, we differentiate Eq. (4) while holding deforestation and year as constants. This approach allows us to isolate the distinct contributions from global influences and deforestation dynamics. To achieve this, we differentiate Eq. (4) with respect to the year, while maintaining deforestation as a constant. In addition, we differentiate Eq. (4) with respect to the logarithm of the deforested area fraction while holding the year constant. This procedure leads to the following expressions:

(∂y(t,D) / ∂t) |D=cte = γlnD + b2, (5)

(∂y(t,D) / ∂lnD) |t=cte = γt + a2, (6)

Then, Eqs. (5) and (6) were integrated in relation to the year and to the natural logarithm of the fraction of deforested areas, where for the first case, we kept the constant deforestation as the average value of deforestation in each one of the 29 areas from 1985 to 2020 (D), and for the second case, we used the average year (t) which is 2002. Then, the contribution exclusively due to the global changes is

yglobal = ∫ 2020 1985 (γlnD + b2)dt = (γlnD + b2) × (2020 − 1985), (7)

On the other hand, the contribution exclusively due to deforestation is

yregional = ∫ D2020 D1985 (γt + a2)dlnD = (γt + a2) × (lnD2020 − lnD1985), (8)

Table 2 presents the fitted equations for each variable obtained for the dry season.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "- What are the noteworthy results?\nIn their article, the authors disentangle the effects of regional deforestation and global climate processes on climate\nparameters during the past 35 years in the Amazon basin. While in respect of temperature change global processes play the\ndominant role, the authors identify deforestation as the dominant driver of in tendency decreasing precipitation. In contrast,\nthe effect of deforestation on the atmospheric concentrations of the greenhouse gases CO2 and CH4 is negligible.\n- Will the work be of significance to the field and related fields? How does it compare to the established literature? If the work\nis not original, please provide relevant references.\nThere are numerous studies investigating (1) the effects of deforestation in the Amazon on global climate, (2) the impacts of\nglobal climate change on the structure and stability of the Amazon biome and (3) regional climate effects from deforestation.\nMany of these studies focus on future developments (scenario analyses) and apply dynamic climate models.\nTo my knowledge, this article is the first attempt to differentiate between regional and global drivers and their respective\nimpacts on regional climate. From this perspective, I regard the research as highly original and relevant for a better\nunderstanding of climate change drivers in the Amazon. In particular, since the authors put a strong emphasis on\nretrospective analyses, their work is an important addition to the aforementioned simulation-based analyses.\n- Does the work support the conclusions and claims, or is additional evidence needed?\nWhile to the most part, the conclusions and claims are well supported by the study results, from my point of view, it is not\nvalid to draw conclusions related to non-linear effects such as tipping points or a potential dieback of the Amazon biome (or\nbiome shift). In this sense, I ask the authors to add a critical reflection of the limitations of their analytical approach – either in\nthe Preserving Amazonia’s forest…” or the “Conclusion” section.\n- Are there any flaws in the data analysis, interpretation and conclusions? - Do these prohibit publication or require revision?\nI think a careful revision is required. My three arguments:\n(1) As pointed out, I am sceptical about using the introduced methodology for future projections as it does not replicate\ndynamic processes but in its core just extrapolates historic trends. The authors should also avoid the term simulation (page\n6/22, line 199) and include a critical reflection in the manuscript (see above).\n(2) My second question is why the authors only present results for the dry seasons and neglect wet seasons (except figures\n4 and 6 in the supplement).\n(3) Thirdly, the analysis as presented is conducted for fixed 300 km cells (areas). In order to further explore the behaviour of\nthe presented methodology, it would be interesting to investigate how sensitive it is is to scale, e.g. by systematically\nmodifying the size and position of the cells.\n- Is the methodology sound? Does the work meet the expected standards in your field?\nThe authors combine statistical analysis with methods from (real) analysis. To my understanding, the presented method is\nsimple (in a positive sense), sound and robust. As I am not a mathematician by training, I would ask the editors to get\nanother opinion from that side.\n- Is there enough detail provided in the methods for the work to be reproduced?\nThe authors use a wide variety of datasets with different temporal coverage and spatial resolution to (if I understood\ncorrectly) generate the aggregated data for the 29 cells/areas they use for their analysis. Here a more detailed description of\nthe data aggregation and fusion processes is required. In addition, I suggest providing the source codes of the scripts used\nfor data analyses/aggregation.", "reviewer_2": "Review of: How climate change and deforestation interact in the transformation of the Amazon rainforest\nBroad thoughts:\nThis is an important and timely piece of research which aims to disentangle the effects of land cover change and climate\nchange in the Brazilian Legal Amazon. The research is of good quality, and I believe should be published. Currently I find\nthe structure of the article hard to follow as a story and the figures and results are not well described nor put in context. As\ndetailed below, the reliance on supplementary figures and references to details contained within the methods make reading\nthis article quite challenging and present significant barriers to comprehension, in my opinion.\nThe main novelties are separating the climate change and land cove responses, and finding that temperature has a non-\nlinear response, contrary to what other works have shown. I recommend perhaps considering a different order of figures to\ncommunicate this story more effectively. The messages within the current\nsub-headings don’t seem focussed on the heading, i.e. in “Preserving Amazonia’s Forest Cover: Understanding its Importance and Anticipating Future Impacts”, two\nparagraphs pass, before there is a mention of future changes. Therein, there is insightful discussion of results in context,\nhence I believe, with a reorder, this could be a good an effective manuscript.\nIf these amendments cannot be achieved within the constraints of this journal, I would encourage to authors to think about\nwhether this is more possible elsewhere.\nWhy BLA not amazon biome\nHow were boxes chosen? Add briefly to L51 etc (i.e. maximum number of cells within the domain, not including water?)\nWhat forest data?\nWhy including other land covers?\nWhy say tipping point? Do you reference this/ make an assessment?\nWhy just dry season?\nAdd Data summary table for referencing on L57 and throughout\nSpecific thoughts:\nIn abstract: clarify whether you mean deforestation has impacted amazonian dry season precip and temperatures. Currently\nit could read like these are impacting global temperatures and precip.\nL29: Perhaps in the intro somewhere you could explain why you use maximum (daytime?) dry season temperature as your\nindicator in the analysis. I think this is an important point, that you’re choosing not to use average daily temperature.\nL46: “Our study reveals that changes in temperature and precipitation in the dry season are critical for the fate of the Amazon\nunder the combined pressure of climate change and deforestation.” I think this is more for the conclusions/ abstract rather\nthan the introduction in my opinion.\nF1 caption: I find it unclear using a satellite basemap as you overlay your own green colour for forest. Where is the forest\ndata from, would be good to cite the data here so readers can understand what they’re looking at at a glance i.e. Hansen et\nal., 2013. Similarly, I’d like a bit more specific info about the land covers shown in the figure (and source(s)), rather than just\nvarious. If they’re not crucial to the story, consider removing them from this plot. Figure 1 needs to set the scene and at the\nmoment, a reader glancing through the figures isn’t sure what to take from this.\nF1: At present, I’m not sure if this is a main text figure. A suggestion could be to combine the panels into one forest cover\nchange map, overlaid with the boxes, since the other land covers don’t seem to matter in this analysis? I would also suggest\nremoving the satellite land cover background as it distracts from the important elements you’re trying to convey, i.e. that\nforest is being lost, particularly in the southeast.\nL68: I’d prefer citations or names of datasets here, you can then point to the section, and or data table where the reader can\nlook for more info. The data choices are very important to the results, hence should be a main feature of the text.\nL70: which variables? Would be better to be a little more specific here, otherwise the reader hasn’t gained anything.\nL72: Really interesting and engaging way of conveying this result.\nL78: I appreciate the need to be concise on words, however I do think you could briefly summarise these choices here and\ncondense: “For a complete understanding of our approach and rationale, we refer readers to the section Data and Methods,\nsubsection Definition of Wet and Dry Season, and 80 Data Processing, where a detailed explanation and justification are\nprovided” to just “(See methods)”. I really think this crucial info should be in the main text, rather than only in methods. As you\nsay, the effects are different in the wet and dry season, the reader wants then to know why and how.\nF2: can you say whether these linear and log fits are significant? Panel a) has a lot of data spread and it isn’t immediately\nclear whether this would be significant. Consider printing r2 and p values on the plot or in caption text to add robustness.\nL92: Far too vague, must include some insight in the main text. If they’re important enough results to mention in the main text,\nthey should either be in a main text figure, or explicitly described in text.\nL113: I wonder whether a journal with a greater number of figures would be more suitable here as in my view supplementary\nfigures shouldn’t be referenced as main figures in the text. They seem here to be used to tell parts of the main story, so\nshould probably be main text figures. Either this or reframe the sentences so that these figures are merely adding nuance\nand robustness to the story.\nL129: what do you mean by saying “we have accurately quantified”?\nL133: Figure 4 is mentioned before Figure 3, this needs reordering, either of figures or describing figure 3 earlier. Indeed\nwhen Figure 3 is mentioned, I don’t believe the results are fully described and put in context. Figure 3 has some really nice\nresults and clearly shows the contributions to each change. I believe this should be more thoroughly described and\nexplained. As far as I can tell, Figure 4 is repeating the information from Figure 3? It is a really nice synthesis of the\ninformation in a good looking figure, but I wonder if both figures are needed in the main text.\nL139: How do these increases compare to previous work?\nL143: It would be helpful to be more explicit when you say “These results highlight the synergistic interaction”.\nL184: To me, the following seems like a conclusion, rather than an opening for a discussion on the future impacts. Perhaps it\ncould be moved to the conclusions, or reordered in this section. “This study shows the impact of global and land-use\nchanges on the variables examined. Deforestation mainly drives the meteorological variables during the dry season. When\nconsidering all study areas across the Brazilian Amazonian region between 1985 and 2020, deforestation is associated\nwith a daily maximum temperature increase of approximately 16.5%, equivalent to a\ntemperature rise of 0.39 °C. Deforestation also has a dominant influence on the reduction in total annual rainfall. It is\nresponsible for 74.5% of the total decrease in precipitation, resulting in a net 15.8 mm reduction in annual rainfall during the\n35 years. On the other hand, deforestation had a smaller impact on GHG concentrations, which were driven predominantly by\nglobal changes.”\nL196: This is an important result and should be highlighted better in the paragraph, better still, within the section where you\ntalk about the figure it relates to “within the first 10%-40% of coverage loss”. I’m not sure how most of this paragraph relates\nto the subheading title “Preserving Amazonia’s Forest Cover: Understanding its Importance and Anticipating Future\nImpacts”.\nL206: To me, this discussion of tipping points isn’t central enough to the analysis for it to be referenced in the abstract.\nRather I would focus on your good and important results.\nL245: I think this is still a proposed tipping point, rather than a certainty?\nL275: Previous analysis has found there to be strong discrepancy between precipitation datasets in particular, but also\ntemperature datasets with respect to their changes due to deforestation. How did you choose these datasets and (also why a\nnear surface, not a land surface temperature) and is there any way the choice of dataset might impact your result. Perhaps\nsomething worth mentioning in the text somewhere.\nL290: Reading this section makes me think that some version of Supplementary Figure 5 really should be in the main text,\nperhaps in lieu of removing Figure 1? The results are really interesting an important. In addition to this, I’m still quite unclear\nas to why you are only presenting the dry-season in the main text. Is there a justification for this?\nTable 1: might be useful to display some of these stats on the relevant figures, so readers can view this at the same time as\nseeing the results.\nFigure S1: Would be nice to see this in map form as well, perhaps as a multi panel plot. You could take Figure 1 and\ncombine with this as a new Figure S1.\nFigure S5: small point, the panels are not in the same order as other plots. It would be visually helpful to be consistent, so it\nis easier to flick between plots.\nEditorial comments:\nL83: processes for should be processes form?\nL103: I’m unclear about what you mean by exchange effects. Perhaps this is obvious to others, but if not it could be worth\nelaborating what you mean.\nL111: consider adding numbers to these data subsections, to reduce main text word count. i.e. see methods section 1.x.\nL127: Possibly could condense the following into the subsequent sentence as this\nhas already been said before: “The central question we sought to address is the\nextent to which the observed changes can be attributed to global climate change\nversus the effects of deforestation.”", "reviewer_3": "How Climate Change and Deforestation Interact in the Transformation of the Amazon Rainforest\nReviewer summary:\nAbstract:\nPlease provide a short explanation of the methods in the abstract.\nAre 21 millimeters in the dry season and over 35 years statistically significant?\nThe statement on non-linear behavior and tipping points without a short explanation of methods looks confusing.\nIntroduction:\nAbout the carbon and water fluxes: Please consider using some new references: http://dx.doi.org/10.1088/2515-\n7620/adc06a and http://dx.doi.org/10.1038/s41598-025-85272-z\nLine 25: I'm not sure about the statement that the Amazon basin controls all these globally critical issues. For instance,\ncritical freshwater is about \"blue\" and \"green\" water availability pre- and post-industrial times. However, the authors showed\ntransgressions in these attributes even during pre-industrial events. This makes sense, even nowadays, several studies\nhave shown some resilience in hydrological processes in the Amazon in recent events.\nLine 36: You should check the Amazon basin area. It is about 6 mi km², and if you consider Tocantins, it reaches 7 mi km².\nBut, if your study area is Legal Amazon, this number drops slightly.\nLine 43: Some studies assessed the combined effect of deforestation and climate in the Amazon's watersheds. For instance,\nhttps://doi.org/10.1029/2019WR025083.\nLine 30: I missed more studies that assessed the effects of climate change in different variables (e.g. hydrology, biodiversity,\netc), even in small basins. I suggest a more detailed review of these papers.\nQuantifying global and regional effects across Brazilian Amazon areas:\nYou could explain better how the assessment was in each area. Did you evaluate the fluxes pixel by pixel? The mean,\nmedian...?\nFigure S1: You should categorize the bars by region using different colors (e.g., pristine areas, the arc of deforestation, and\nso on).\nEvaluation of the deforestation and global climate change effects\nLine 77: \"Our study....\". This phrase isn't in the right place. You could use it in the abstract or introduction, but not in this\nsection.\nLine 81: Is this section about results? You began to explain some methods (method), and after that, you showed results (\"our\nanalysis reveals...\"). You should replace some parts of the method section.\nFigure 2: I didn't understand the relationship between the temperature increase (in Figure 2) and climate change. I can see a\nnoticeable rise in temperature with an increase in deforestation. How did you isolate climate change using Temp = f (D,\ntime)?\nModeling the evolution of atmospheric variables\nLine 106: I understand equations 6 and 7, but it is not clear that\nEquation 1: Please explain how \"B\" is a parameter related to global forces/global changes.\nDisentangling the Influence of Deforestation and Global Climate Change on the Amazonian Atmosphere\nHow did you use the equations in Table 1 to find 87 ppm and 173 ppm (CO2 and CH4)?\nThis is because your data set analysis focused only on the study area. So, the global influence is not clear."}, "author_response": "General Statement of Changes\nIn response to the reviews, we made the following major revisions:\n● Restructured the figures, supplementary figures, and main text to improve\nnarrative flow and support the progression of the results, ensuring that visual\nelements are better aligned with the storyline.\n● Refined the discussion of non-linear dynamics and tipping points, removing\nspeculative language and clearly stating the limitations of our empirical\napproach to extrapolate the results to the near future. We now emphasize that\nour findings are indicative of emerging trends but do not demonstrate\ncausality or threshold behavior.\n● Clarified the methodology, particularly in the Abstract and Methods sections,\nby explicitly describing the model structure, the data sources, the rationale\nbehind selecting dry season data, and the spatial aggregation procedures.\n● Added a detailed justification for focusing on the dry season, supported by\nnew statistical comparisons showing the stronger and more robust climatic\nsignals associated with deforestation during this period.\n● Improved the contextual framing of the study by incorporating additional\nreferences and explanations to situate our findings within the existing\nliterature better.\nIn addition to these structural and conceptual revisions, we introduced new\nsupplementary materials and analyses, including:\n● A sensitivity analysis on spatial scale, assessing the robustness of results\nacross different grid sizes.\n● Additional supplementary figures illustrating seasonal comparisons, variable\ntrends, and model performance across the 29 study regions.\nThese revisions collectively strengthen the manuscript's scientific contribution and\nensure clearer communication of our findings and their implications.\nReviewer #1\nWe thank Reviewer #1 for the thoughtful and constructive review of our manuscript.\nWe appreciate the recognition of the novelty and significance of our approach in\ndisentangling the effects of deforestation and global climate change on Amazonian\nclimate dynamics. The reviewer’s insightful comments helped us refine the scope of\nour interpretations, clarify the methodological framework, and improve the overall\ntransparency of the analysis. We address each point below in detail.\n- What are the noteworthy results?\nIn their article, the authors disentangle the effects of regional deforestation and\nglobal climate processes on climate parameters during the past 35 years in the\nAmazon basin. While in respect of temperature change global processes play the\ndominant role, the authors identify deforestation as the dominant driver of in\ntendency decreasing precipitation. In contrast, the effect of deforestation on the\natmospheric concentrations of the greenhouse gases CO2 and CH4 is negligible.\n- Will the work be of significance to the field and related fields? How does it compare\nto the established literature? If the work is not original, please provide relevant\nreferences.\nThere are numerous studies investigating (1) the effects of deforestation in the\nAmazon on global climate, (2) the impacts of global climate change on the structure\nand stability of the Amazon biome and (3) regional climate effects from deforestation.\nMany of these studies focus on future developments (scenario analyses) and apply\ndynamic climate models.\nTo my knowledge, this article is the first attempt to differentiate between regional and\nglobal drivers and their respective impacts on regional climate. From this\nperspective, I regard the research as highly original and relevant for a better\nunderstanding of climate change drivers in the Amazon. In particular, since the\nauthors put a strong emphasis on retrospective analyses, their work is an important\naddition to the aforementioned simulation-based analyses.\n- Does the work support the conclusions and claims, or is additional evidence\nneeded?\nWhile to the most part, the conclusions and claims are well supported by the study\nresults, from my point of view, it is not valid to draw conclusions related to non-linear\neffects such as tipping points or a potential dieback of the Amazon biome (or\nbiome shift). In this sense, I ask the authors to add a critical reflection of the limitations of\ntheir analytical approach – either in the Preserving Amazonia’s forest…” or the\n“Conclusion” section.\n- Are there any flaws in the data analysis, interpretation and conclusions? - Do these\nprohibit publication or require revision?\nI think a careful revision is required. My three arguments:\n(1) As pointed out, I am sceptical about using the introduced methodology for future\nprojections as it does not replicate the dynamic processes, feedbacks, or potential\nthreshold mechanisms that would be required to robustly assess non-linear\ntransitions such as biome shifts or ecosystem tipping points. Our methodology was\ndesigned to disentangle and quantify the relative historical contributions of global\nclimate change and regional deforestation to observed trends in temperature, rainfall,\nand greenhouse gas concentrations. We aimed to explore the equations to gain\ninsight into potential future changes. However, as the reviewer rightly notes, this\napproach is fundamentally empirical and trend-based, and thus inadequate for\nsimulating future system dynamics or capturing critical feedback mechanisms.\nTo address these points, we have revised the manuscript as follows:\n● We removed the term \"simulation\" from line 199 and replaced it with\n“extrapolation” to better reflect the nature of our calculations.\n● We added a paragraph to the Preserving Amazonia's Forest Cover section\n(and referenced it earlier in the Discussion) to explicitly acknowledge the\nlimitations of our analytical approach. In this paragraph, we clarify that while\nour findings highlight trends consistent with potential tipping behavior, they do\nnot demonstrate causality or confirm thresholds; instead, they should be\ninterpreted as indicative, not predictive, of biome-level change. The new\nparagraph reads as follows:\nWhile our findings suggest trends consistent with early signs of biome\ndestabilization, we acknowledge that our analytical approach based on\nstatistical extrapolation has limitations in its ability to capture dynamic\nfeedbacks, threshold responses, or ecological resilience mechanisms.\nHowever, by disentangling historical contributions of deforestation and global\nclimate change, our method offers a valuable first-order assessment of likely\ntrajectories if current trends persist. While limited, this approach provides a\npreliminary indication of the risks associated with ongoing deforestation and\nclimate stressors in the Amazon.\n● In addition, we have revised the manuscript (see, for instance, the Abstract\nand the Significance Statement) to avoid using the term “tipping point” in\nrelation to our findings, to better reflect the scope and limitations of our\napproach.\nAll the suggestions discussed here were incorporated into the conclusion section,\nwhich was restructured to accommodate them. Notably, the last two paragraphs\nwere improved, which now reads as follows:\nOverall, these findings highlight the intricate relationship between deforestation and\nglobal climate change in influencing rainfall patterns across the Amazon. This\ninteraction contributes to nonlinear trends in precipitation, amplifying the impacts of\nongoing climate change. The observed reduction in total precipitation during the dry\nseason, combined with a significant increase in maximum surface temperature linked\nto deforestation, underscores the crucial role of forest cover in maintaining a stable\nregional water cycle and mitigating climate stress. If deforestation continues\nunabated, the extrapolation of our results suggests a further decline in total\nprecipitation during the dry season and an increase in maximum surface\ntemperature, factors that could push the Amazon ecosystem toward increasingly\nunstable states. Emerging evidence suggests that deforestation in the Amazon is\nalready altering South American monsoon patterns, resulting in drier conditions that\nmay compromise the long-term resilience of the rainforest47,54.\nIn addition, the results of this study suggest that Amazon is moving through a critical\ntransition, potentially exacerbated by extreme events such as the unprecedented\n2023 drought55,56. However, we remain cautious about defining a precise\ndeforestation threshold that could trigger biome-scale dieback, particularly because\nour extrapolation does not fully account for the substantial uncertainties inherent in\nclimate–vegetation interactions, which themselves remain subject to significant\nuncertainty57. Together, deforestation and climate change are driving substantial\ntransformations across the region during the dry season, resulting in reduced rainfall,\nelevated temperatures, and increased atmospheric GHG levels. These findings\nunderscore the importance of maintaining and restoring forest cover in the Amazon\nas a crucial strategy for mitigating climate change and ensuring the stability of\necosystems.\n(2) My second question is why the authors only present results for the dry seasons\nand neglect wet seasons (except figures 4 and 6 in the supplement).\nThank you for this important observation. We agree that the manuscript should have\nmore clearly explained the rationale behind focusing our analysis on the dry season,\nand we appreciate the opportunity to clarify this. We conducted a series of sensitivity\nanalyses to evaluate the seasonal variability in the response of climate variables to\ndeforestation and global climate change. These analyses revealed that the effects of\ndeforestation are much more pronounced and statistically robust during the dry\nseason, whereas during the wet season, the signal of land-use change is\nsignificantly weaker or even negligible. This pattern aligns with findings from recent\nstudies (e.g., Liu et al., 2025, DOI: https://doi.org/10.1029/2025AV001670), which\nemphasise the increased sensitivity of rainfall and temperature to deforestation in the\nAmazon under dry conditions.\nFigure 1: Boxplots obtained with the correlation coefficients (R) considering only the linear and the\nlogarithmic components of the fits in our model for (a) CH , (b) CO , (c) T , and (d) annual rainfall\n4 2 max\nduring the wet and dry seasons. Red stars represent the limit at which p-value < 0.05. The absence of\nstars means that all fits were statistically significant.\nTo support this, we have highlighted more of the Supplementary Figure S7 (Figure 1\nin this response letter) in the manuscript, which compares the performance of both\nlinear and logarithmic model fits across wet and dry seasons. In both seasons, as\nshown in Figure 1, the linear component, associated with long-term global trends,\nyields high correlation coefficients for CH₄ and CO₂ (R > 0.90; p-value < 0.05),\nindicating a dominant influence from global emissions. However, the logarithmic\ncomponent, which captures the regional effect of deforestation, shows much\nstronger correlations during the dry season: R ~ 0.75 for T and R ~ 0.70 for\nmax\nrainfall, compared to only R ~ 0.50 and R ~ 0.30, respectively, during the wet\nseason, with many of these wet-season fits lacking statistical significance. In\naddition, a similar trend for the logarithmic component is also observed for CO and\n4\nthe dry season, whereas R ~ 0.60 and R ~ 0.30 without statistical significance,\nrespectively, for the wet season.\nFigure 2: Relationship between wet season precipitation (a, b), maximum surface temperature (c,d),\nCO concentrations (e, f), and CH concentrations (g, h) for each year (dots are colored by\n2 4\ndeforestation percentage), and deforestation percentage (dots are colored by year), respectively, for\nall 29 areas.\nTo further illustrate this point, we now include the new Supplementary Figure S5 in\nthe revised manuscript, which is also shown in this response letter as Figure 2. This\nfigure illustrates the temporal and spatial relationships between deforestation and\neach variable during the wet season, similar to those shown in the manuscript for the\ndry season (Supplementary Figure S4). It confirms the limited influence of local land\ncover changes during this period. In addition, it is worth mentioning that when our\nmodel (represented by Equation 4 in the manuscript) is applied to the different\nvariables during the wet season, the model correlation with the actual data is very\nlow (T and rainfall with R ~ 0.35 and ~0,20, respectively. CO and CH have R >\nmax 2 4\n0.90 due to the reasons highlighted previously) when compared with the correlations\nfor the dry season (Table 1 in the manuscript), although both when considering the\ncombination of linear and logarithmic effects have p-value <0.05.\nThese results underscore the importance of focusing on the dry season in our\nanalysis. This is when deforestation-driven changes to the surface climate are most\ndetectable, statistically significant, and physically interpretable.\nWe have revised the manuscript to include a detailed justification for performing the\ncalculations using only the dry season data. In the updated manuscript, under the\nsection Evaluation of the Deforestation and Global Climate Change Effects , we have\nadded the following paragraph:\nDeforestation and global climate change influence rainfall, temperature, and GHG\nmixing ratios in distinct ways across the wet and dry seasons14, 46, 47. However,\nseveral studies have shown that the regional climatic impacts of deforestation are\nmore pronounced and meaningful during the dry season, when the forest is more\nvulnerable to moisture deficits and surface-atmosphere interactions are intensified.\nIn our analysis, the dry season consistently exhibited stronger correlations between\ndeforestation and key climate variables, including maximum temperature and\nprecipitation (See Supplementary Figure S4), while the wet season showed weaker\nand often statistically insignificant relationships (See Supplementary Figure S5).\nTherefore, our study focuses exclusively on the dry season to isolate and quantify\nthe climatic effects of deforestation with greater precision. A detailed explanation and\nsupporting statistical evidence are provided in the Data and Methods, subsection\nDefinition of Wet and Dry Season, and Data Processing.\nWe also improved Section Data and Methods, Subsection Definition of Wet and Dry\nSeason, and Data Processing, and referenced the relevant figures accordingly. The\nnew text reads as follows:\nOur analysis evaluated the dry and wet seasons separately to assess the seasonal\nimpact of deforestation on each variable. Given the spatial heterogeneity of seasonal\ncycles across the Amazonian region, we categorized the dry and wet season months\nbased on the 25th and 75th percentiles of rainfall (see Supplementary Figure S9).\nThe wet and dry seasons were determined by analyzing the precipitation patterns\nspecific to each area within the Amazon region. A comprehensive analysis of the\nentire time series for each area was conducted to derive an average precipitation\nannual cycle from 2000 to 2020. The 75th quartile, representing months with the\nhighest annual rainfall, was identified as the wet season. In contrast, the 25th\nquartile, representing months with the lowest total rainfall, was designated as the dry\nseason. It is worth noting that the composition of each quartile may vary across\nAmazon. Based on quartiles, this methodology ensures an objective approach to\ndefining the seasons. Thus, the dry and wet season data were computed for each\ntime series and obtained based on the 25th and 75th quartiles, respectively, and\ntheir annual averages for each area. This was necessary to ensure accurate\ncomparisons regarding the change in forest cover.\nUsing the wet and dry classifications of each of the 29 studied areas, we evaluate\nthe sensitivity of deforestation to rainfall, temperature, and greenhouse gases. The\ntrends and significance are only significant for the dry season. During the dry\nseason, the relationship between deforestation and climatic variables is statistically\nrobust, with median correlation coefficients of R ∼ 0.75 for maximum temperature\nand R ∼ 0.70 for rainfall. However, in the wet season, these correlations decrease\nsubstantially to R ∼ 0.50 and R ∼ 0.30, respectively, with most of the trends lacking\nstatistical significance. Supplementary Figures S4 and S5 (the temperature evolution\nfor the dry season is presented in Figure 2) present the scattering plots for dry and\nwet seasons, clarifying this aspect.\nWe evaluate the sensitivity of deforestation to rainfall, temperature, and trace gas\nmixing ratios by applying wet and dry season classifications across the 29 study\nregions. Statistically significant trends emerge predominantly during the dry season.\n10/26 In this period, the relationship between deforestation and climatic variables is\nparticularly strong, with median correlation coefficients of R ∼ 0.75 for maximum\ntemperature and R ∼ 0.70 for rainfall. In contrast, during the wet season, these\ncorrelations decline substantially to R ∼ 0.50 and R ∼ 0.30, respectively, and most\ntrends are not statistically significant. Supplementary Figures S4 and S5, along with\nFigure 2 (which illustrates the evolution of dry-season temperature), present scatter\nplots for both seasons, highlighting the seasonal contrasts.\nDeforestation disrupts the natural modulation of the water and energy cycles, leading\nto reduced rainfall and elevated surface temperatures, particularly during the dry\nseason, in line with previous findings47. The linear component of our model,\nrepresenting the influence of large-scale climatic drivers, shows consistently high\ncorrelation coefficients for CO and CH (R > 0.90) in both seasons, suggesting that\n2 4\nvariations in GHG mixing ratios are primarily governed by global-scale processes\nand are relatively insensitive to local deforestation. In contrast, the logarithmic\ncomponent, capturing the regional-scale effects of deforestation, exhibits marked\nseasonal variability.\nThese findings confirm that the climatic impacts of deforestation are most\npronounced during the dry season, offering a more distinct and statistically robust\nsignal of its regional influence. Conversely, the wet season appears less responsive\nto local land cover changes, likely due to the overriding influence of large-scale\natmospheric dynamics. Accordingly, our analysis emphasizes the dry season, where\nthe signal of deforestation can be more accurately isolated and quantified.\n(3) Thirdly, the analysis as presented is conducted for fixed 300 km cells (areas). In\norder to further explore the behaviour of the presented methodology, it would be\ninteresting to investigate how sensitive it is to scale, e.g. by systematically modifying\nthe size and position of the cells.\nWe thank the reviewer for this insightful suggestion regarding scale sensitivity. As\nnoted, our analysis was conducted using fixed ~ 300 × 300 km² grid cells, and this\nchoice was carefully justified in the manuscript. Specifically, this scale was selected\nbased on two main criteria: (1) it encompasses the typical spatial extent of\ndeforestation paths in the Amazon, ensuring that aggregated land use change\nsignals are representative of regional trends (Smith, C., Baker, J. & Spracklen, D.,\n2023; DOI: https://doi.org/10.1038/s41586-022-05690-1); and (2) it aligns with the\nspatial scale of mesoscale atmospheric processes, such as deep convection and\nsquall lines, whose average horizontal dimensions are in the range of 75–150 km in\nradius (Machado et al., 2018; DOI: https://doi.org/10.5194/acp-18-6461-2018). This\nensures that climate and land cover interactions are meaningfully captured within\neach cell.\nFurthermore, this approach has been independently validated and applied in a\nrecent peer-reviewed study (Teixeira et al., 2025; DOI:\nhttps://doi.org/10.1016/j.foreco.2025.122662), which adopts the same spatial\nresolution (∼300 x 300 km²) to characterize the spatial evolution and morphology of\ndeforestation patches in the Amazon. We also added this new citation to the\nmanuscript and the Data and Methods section to enforce our choice of the area size.\nTo further address the reviewer’s suggestion, we conducted a targeted sensitivity\nanalysis to evaluate the robustness of our results with respect to the spatial scale\nused in the study. Specifically, we examined how different cell sizes—50 × 50 km²,\n100 × 100 km², 200 × 200 km², and 300 × 300 km²—affect the time series trends of\nmaximum temperature and precipitation during the dry season. These tests were\ncentered on two representative regions: one with the highest observed forest loss\n(almost 35%) and one with the lowest (~1%) over the analysed 35 years, based on\nour 29-area domain (sectors 3 and 9 from Figure S2 and Figure 1 of the revised\nmanuscript).\nThe results, presented now in the new Supplementary Figure S1 (Figure 3 of this\nletter), show that while minor variations in absolute values are observed across\ndifferent spatial resolutions, the trends in temperature increase and precipitation\ndecrease remain consistent among the different sizes of the regions. This reinforces\nthe stability and robustness of the parametric approach employed in our study\nacross different spatial scales.\nFigure 3: Sensitivity analysis of grid size on climate variable trends in areas with maximum (left\npanels) and minimum (right panels) forest loss over the analysed 35 years. Top panels: Time series of\ndry-season maximum surface temperature (T(cid:0)ₐₓ) for different grid sizes (50 × 50 km², 100 × 100 km²,\n200 × 200 km², and 300 × 300 km²). Bottom panels: Corresponding time series of dry-season GPM\nprecipitation for the same grid sizes. Each curve represents the spatial average over a grid centered\nin the target area. Despite minor differences in absolute values, the overall trends are consistent\nacross scales, supporting the robustness of the methodology regardless of grid size.\nTherefore, we maintained the use of 300 × 300 km² grid cells as our standard for\nthree main reasons: (i) this resolution aligns with the average extent of deforestation\npathways and disturbance fronts in the Amazon, thus allowing for a regionally\nmeaningful signal (Smith et al., 2023); (ii) it matches the scale of atmospheric\nsystems that dominate convective and moisture recycling processes in the region\n(Machado et al., 2018); and (iii) it has been successfully applied in recent\nAmazon-wide studies, such as Teixeira et al. (2025), which used this resolution to\ncharacterize the dynamics of deforestation morphology and spatial evolution.\nTogether, the theoretical rationale and empirical sensitivity tests indicate that our\nconclusions are robust across spatial scales within the mesoscale range.\nWe have revised the manuscript to incorporate the suggested sensitivity analysis.\nThe updated paragraph that justifies the choice of the 300 × 300 km² analysis areas\nnow reads as follows:\nTo disentangle the regional effects of deforestation from the global effects of climate\nchange in the Brazilian Amazon, data on 29 areas of ∼ 300 × 300 km2 between 1985\nand 2020 were considered. Deforestation is defined here based on the MapBiomas\nland cover classification as any loss of native forest vegetation, regardless of the\nresulting land use class. The particular area size was selected for two reasons. First,\nthey encompass a sufficiently large area to represent the observed path length of\ndeforestation, as indicated by previous studies38, and have recently been adopted in\nsimilar spatial analyses of Amazonian land cover dynamics44. This ensures that the\neffects of deforestation within a given region are adequately captured and analyzed.\nSecond, the dimension of ∼90,000 km2 is within the mesoscale range, encompassing\natmospheric phenomena like thunderstorms, squall lines, and deep convection,\nwhich are characterized by radii between 75 and 150 km. The 29 areas were chosen\nto maximize spatial coverage of the BLA while excluding areas dominated by water\nbodies or permanent wetlands, ensuring reliable surface climate and land cover\nanalysis. To assess the robustness of our findings across different spatial scales, we\nconducted a sensitivity analysis using smaller grid sizes (50×50, 100×100, and\n200×200 km2). The results, shown in Supplementary Figure S1, indicate that the\nlong-term trends in temperature and precipitation remain consistent across scales,\nreinforcing the suitability of the 300×300 km2 resolution adopted in this study.\n- Is the methodology sound? Does the work meet the expected standards in your\nfield?\nThe authors combine statistical analysis with methods from (real) analysis. To my\nunderstanding, the presented method is simple (in a positive sense), sound and\nrobust. As I am not a mathematician by training, I would ask the editors to get\nanother opinion from that side.\n- Is there enough detail provided in the methods for the work to be reproduced?\nThe authors use a wide variety of datasets with different temporal coverage and\nspatial resolution to (if I understood correctly) generate the aggregated data for the\n29 cells/areas they use for their analysis. Here a more detailed description of\nthe data aggregation and fusion processes is required. In addition, I suggest providing\nthe source codes of the scripts used for data analyses/aggregation.\nWe thank the reviewer for this helpful and constructive suggestion. In the revised\nmanuscript, we have improved the Methods section to provide a clearer and more\ndetailed description of the spatial and temporal aggregation procedures used to\nharmonize the datasets across the 29 fixed grid cells in the Amazonian region.\nSpecifically, we now clarify the spatial resolution of each dataset, the temporal\ncoverage, the aggregation steps applied (e.g., monthly or annual averages), and the\nselection of these 29 areas.\nGiven the diversity in spatial and temporal resolutions across datasets (e.g.,\nLandsat-based LULC at 30 m, ERA5 at 0.25° x 0.25°, AIRS at 1° x 1°, and OCO-2 at\n0.5° × 0.625°), we employed a consistent spatial averaging procedure to compute\nmean values of each variable within each of the 29 areas. All variables were initially\naggregated to monthly values per grid cell to ensure temporal comparability. To\naddress differences in temporal coverage (e.g., GPM from 2000, CH₄ from 2002,\nCO₂ from 2015), we applied a parametric modeling approach (described in Equations\n8 and 9 of the manuscript) to reconstruct continuous time series for each variable.\nThis enabled us to extrapolate trends backward, ensuring that all variables could be\nanalyzed over a common time period from 1985 to 2020. This information is now\nclearly described in the revised manuscript, on the subsection “Land Use and Land\nCover Data” and “Greenhouse Gases and Weather Data.” Particularly, the new\nparagraph describing the calculations and aggregations reads as:\nAll datasets were first re-aggregated into monthly means for each of the 29 study\nregions. Based on the long-term climatological patterns, each region’s time series\nwas then classified into wet and dry seasons, following a percentile-based threshold\napplied to the monthly precipitation data. This classification allowed us to capture the\ndistinct seasonal responses of atmospheric variables to forest loss. For each\nseason, we subsequently computed annual means, enabling a robust comparison of\ndry- and wet-season dynamics and a consistent assessment of long-term trends\nacross datasets with different temporal coverage. Given the varying temporal\ncoverage of the datasets (Tmax: 1985–2020; rainfall: 2000 - 2020; CH4: 2002–2020;\nCO2: 2015–2020), we applied our obtained parametric modeling approach (see\nEquations 7 and 8) to reconstruct and extend the time series across the full analysis\nperiod (1985–2020). This approach allowed us to capture long-term trends and\nensure temporal consistency across variables. By harmonizing datasets with\ndifferent native resolutions and time spans, this framework enabled robust and\nconsistent comparisons across variables and study regions, facilitating an integrated\nanalysis of the impacts of deforestation and climate change.\nAdditionally, the source code used for data aggregation, modeling, and figure\ngeneration has been provided in a public GitHub repository\n(https://github.com/marcomac27/ClimateChangeDeforestationPaper), in line with\nNature Communications’ reproducibility requirements.\nReviewer #2:\nWe thank Reviewer #2 for the thoughtful and constructive review of our manuscript.\nWe appreciate the recognition of the novelty and significance of our approach in\ndisentangling the effects of deforestation and global climate change on Amazonian\nclimate dynamics. The reviewer’s insightful comments helped us refine the scope of\nour interpretations, clarify the methodological framework, and improve the overall\ntransparency of the analysis. We address each point below in detail.\nReview of: How climate change and deforestation interact in the transformation of\nthe Amazon rainforest\nBroad thoughts:\nThis is an important and timely piece of research which aims to disentangle the\neffects of land cover change and climate change in the Brazilian Legal Amazon. The\nresearch is of good quality, and I believe should be published. Currently I find the\nstructure of the article hard to follow as a story and the figures and results are not\nwell described nor put in context. As detailed below, the reliance on supplementary\nfigures and references to details contained within the methods make reading this\narticle quite challenging and present significant barriers to comprehension, in my\nopinion.\nThe main novelties are separating the climate change and land cove responses, and\nfinding that temperature has a non-linear response, contrary to what other works\nhave shown. I recommend perhaps considering a different order of figures to\ncommunicate this story more effectively. The messages within the current\nsub-headings don’t seem focussed on the heading, i.e. in “Preserving Amazonia’s\nForest Cover: Understanding its Importance and Anticipating Future Impacts”, two\nparagraphs pass, before there is a mention of future changes. Therein, there is\ninsightful discussion of results in context, hence I believe, with a reorder, this could\nbe a good an effective manuscript.\nIf these amendments cannot be achieved within the constraints of this journal, I\nwould encourage to authors to think about whether this is more possible elsewhere.\nWe sincerely thank Reviewer #2 for their thoughtful and encouraging feedback on\nthe originality and relevance of our study. We are particularly grateful for the\nconstructive suggestions aimed at improving the manuscript’s structure, clarity, and\nflow. We fully agree that a clear and coherent narrative is essential for effectively\ncommunicating our findings, especially given the complexity of the topic. In\nresponse, we undertook a careful revision of the manuscript with a strong focus on\nimproving the overall readability and logical progression of the text. We improved the\npresentation of figures and reorganized the results sections to better align visual\nelements with the narrative. We also refined how key findings are introduced and\ndiscussed in the main text to ensure that the most relevant results are highlighted in\nthe appropriate sections of the manuscript. Finally, we revised the language\nthroughout to enhance clarity, eliminate redundancies, and address ambiguities in\nwording and phrasing. These improvements collectively aim to deliver a clearer and\nmore compelling account of our methodology and results, thereby supporting the\nstudy's main messages more effectively.\nBelow are the one-by-one responses to the reviewer’s questions.\nWhy BLA not amazon biome\nWe thank the reviewer for this pertinent question. We chose to focus our analysis on\nthe Brazilian Legal Amazon (BLA) rather than the whole Amazon biome mainly\nbecause the key land cover datasets used in this study (e.g., MapBiomas Collection\n6.0) are produced and explicitly validated within the BLA, with higher spatial detail\nand classification accuracy in Brazilian territory. This ensures greater consistency\nand reliability in deforestation estimates and land use trajectories. The BLA region\nrepresents more than 60% of the entire Amazon and encompasses the majority of\nthe heavily deforested areas. In addition, the BLA is the main administrative region\nused in national and international climate and deforestation policy frameworks (e.g.,\nBrazil’s Forest Code, Amazon Fund, and monitoring by INPE/PRODES). As such,\nour results are directly relevant to ongoing efforts in governance, restoration, and\nmitigation.\nThe following modifications were implemented to improve the clarity of the\nmanuscript regarding the aspect of using BLA instead of the whole Amazon biome:\n● Last paragraph of the Introduction section:\nIn this study, we use observational remote sensing data to quantify the effects\nof 35 years of deforestation and global climate change on the regional climate\nin Brazil’s Legal Amazon (BLA). The datasets used, along with their spatial\nand temporal coverage, are summarized in Table 1. The BLA was chosen\nbecause it corresponds to the official jurisdiction used in Brazil’s\nenvironmental policy and monitoring frameworks (e.g., MapBiomas43), and\nbecause high-resolution, long-term land use data are reliably available for this\nterritory. Moreover, the BLA encompasses the majority of the Amazon biome\nwithin Brazil, where deforestation has been most intense and extensively\nmonitored. A comprehensive assessment of the long-term trends of key\nclimatic variables allowed us to quantify changes and determine the relative\ncontributions from the driving forces.\n● Subsection Land Use and Land Cover Data:\nThe land use and land cover (LULC) data were obtained from the MapBiomas\nProject43, an initiative dedicated to monitoring and mapping changes in LULC\nthroughout Brazil. The MapBiomas platform effectively collects and processes\nextensive geospatial data from various sources, including satellite imagery\ncaptured by different sensors. By implementing advanced classification and\nanalysis algorithms, it identifies and maps alterations in land cover over time.\nMapBiomas has generated Brazil’s most detailed, up-to-date, and longest\ntime series of LULC mapping. We used Collection 6.0 from MapBiomas for\nthis study. This collection comprises maps with an annual temporal resolution\ncovering the period from 1985 to 2020. The spatial resolution of the data is 30\nmeters based on Landsat satellite images. The global accuracy of the\nMapBiomas Collection 6 land-use and land-cover maps stands at 87.4%.\nThese datasets are available on Google Earth Engine as a data asset and\ncan be freely accessed through the platform’s website at\nhttps://brasil.mapbiomas.org (accessed June 19, 2022).\nIn our analysis, deforestation is defined as the conversion of areas classified\nas \"forest\" in MapBiomas to any other land cover class, including agriculture,\npasture, urban areas, water bodies, or natural non-forest formations. This\ninclusive definition encompasses all transitions that lead to forest loss,\nregardless of the subsequent land use. Our estimates of deforestation do not\nconsider specific transitions (e.g., forest-to-agriculture or forest-to-mining).\nDeforestation fractions were calculated as annual averages within each of the\n29 study areas, based on the percentage of forest cover loss relative to the\ninitial forest area in 1985. We emphasize that the analysis focuses on the\nBrazilian Legal Amazon (BLA), rather than the full Amazon biome, due to the\navailability and consistency of spatially explicit deforestation and land cover\ndata provided by MapBiomas, which is optimized and validated within\nBrazilian territory. While the Amazon biome extends beyond Brazil’s borders,\nthe BLA encompasses the vast majority of its forest area under national\njurisdiction and includes the regions experiencing the highest rates of\ndeforestation. As such, it provides a policy-relevant and analytically consistent\ndomain for our regional climate assessment.\nWe acknowledge the distinction between the BLA and the Amazon biome and now\nclarify this choice more explicitly in the Methods section of the manuscript. We have\nalso revised wording throughout the text to ensure that this distinction is transparent\nto readers.\nHow were boxes chosen? Add briefly to L51 etc (i.e. maximum number of cells within\nthe domain, not including water?)\nWe thank the reviewer for this important question. The 29 fixed grid boxes used in\nour analysis were selected using a spatial tiling approach over the BLA, with each\ngrid cell measuring approximately 300 × 300 km² (∼90,000 km²). This spatial scale\nwas chosen based on two main scientific criteria:\n1. It is large enough to encompass the typical spatial extent of deforestation\nfronts in the Amazon, allowing us to capture coherent land cover\ntransformation patterns and their regional climate signals. This criterion is\nsupported by recent findings by Smith et al. (2023; DOI:\nhttps://doi.org/10.1038/s41586-022-05690-1).\n2. It corresponds to the mesoscale dimension of key atmospheric processes in\nthe Amazon, such as squall lines, deep convection, and mesoscale\nconvective systems, which typically have horizontal dimensions of 75–150 km\nin radius (Machado et al., 2018; DOI:\nhttps://doi.org/10.5194/acp-18-6461-2018). This ensures that\nland-atmosphere interactions are properly captured within each cell.\nThis spatial resolution has been independently applied and validated in a recent\nstudy by Teixeira et al. (2025; DOI: https://doi.org/10.1016/j.foreco.2025.122662),\nwhich analyzed the spatial evolution and morphology of deforestation patches using\na comparable grid-based approach. The final set of 29 cells was selected to\nmaximize land coverage within the BLA while excluding cells dominated by surface\nwater bodies or permanent inundation zones, which could bias land cover analysis or\naffect surface climate signals. In addition, we conducted a sensitivity analysis to\nassess the robustness of our methodology with respect to spatial scale. For this, we\nre-evaluated the trends in maximum temperature and precipitation during the dry\nseason using alternative grid sizes of 50×50 km², 100×100 km², and 200×200 km²,\ncentered on the same locations as the original 300×300 km² grid cells. We focused\non areas with the highest and lowest deforestation fractions across the study period\n(1985–2020) to capture the full range of land cover gradients.\nThe results, summarized in the new Supplementary Figure 1 (also shown as Figure\n2 in this letter), indicate that while there are minor variations in magnitude due to\nscale, the temporal trends and general patterns in temperature increase and\nprecipitation reduction remain consistent across all grid sizes. This confirms that our\nfindings are not strongly dependent on the chosen spatial resolution and that the\n300×300 km² resolution offers a reliable and representative balance between\ncapturing regional climate dynamics and deforestation processes. Given the\nconsistency across scales and the precedent in the literature, we maintained the 300\nkm2 resolution for the main analysis.\nWe improved the manuscript by adding text to clarify the question discussed here.\nFor instance, the main modifications were made in section Quantifying global and\nregional effects across Brazilian Amazon areas. It's the first paragraph now reads as:\nTo disentangle the regional effects of deforestation from the global effects of climate\nchange in the Brazilian Amazon, data on 29 areas of ∼ 300 × 300 km2 between 1985\nand 2020 were considered. Deforestation is defined here based on the MapBiomas\nland cover classification as any loss of native forest vegetation, regardless of the\nresulting land use class. The particular area size was selected for two reasons. First,\nthey encompass a sufficiently large area to represent the observed path length of\ndeforestation, as indicated by previous studies38, and have recently been adopted in\nsimilar spatial analyses of Amazonian land cover dynamics44. This ensures that the\neffects of deforestation within a given region are adequately captured and analyzed.\nSecond, the dimension of ∼90,000 km2 is within the mesoscale range, encompassing\natmospheric phenomena like thunderstorms, squall lines, and deep convection,\nwhich are characterized by radii between 75 and 150 km. It is worth mentioning that\nthe 29 areas were chosen to maximize spatial coverage of the BLA while excluding\nareas dominated by water bodies or permanent wetlands, ensuring reliable surface\nclimate and land cover analysis. To assess the robustness of our findings across\ndifferent spatial scales, we conducted a sensitivity analysis using smaller grid sizes\n(50×50, 100×100, and 200×200 km2). The results, shown in Supplementary Figure\nS1, indicate that the long-term trends in temperature and precipitation remain\nconsistent across scales, reinforcing the suitability of the 300×300 km2 resolution\nadopted in this study.\nWhat forest data? Why including other land covers?\nWe thank the reviewer for raising this point. In our study, land use cover data were\nobtained from MapBiomas Collection 6.0, which provides annual land use and land\ncover maps for Brazil from 1985 to 2020, at 30-meter spatial resolution. This dataset\nis widely used and validated across Brazilian biomes, providing the most consistent\nlong-term forest classification for the BLA. To define deforestation, we considered\nany transition from land cover classes labeled as \"forest\" by MapBiomas to any other\nclass, including agriculture, pasture, urban areas, non-forest natural formations, or\nwater. This inclusive approach enables us to capture all significant forest loss events,\nregardless of the final land use, thereby providing a more comprehensive\nassessment of landscape transformation and its impact on regional climate\nvariables.\nWe chose this broader definition to:\n1. Avoid underestimating forest degradation that does not fall neatly into\n\"agriculture\" or \"pasture\" categories.\n2. Account for complex and mixed post-deforestation land uses that may not be\nresolved in annual classifications.\n3. Remain consistent with national-level assessments of total forest loss used in\nBrazil's environmental monitoring programs.\nWe have now clarified this definition and rationale in the Land Use and Land Cover\nData subsection (Data and Methods). We revised the section Quantifying global and\nregional effects across Brazilian Amazon areas to briefly state how forest data were\ndefined and why all non-forest transitions were included in the deforestation metric.\nWhy say tipping point? Do you reference this/ make an assessment?\nWe thank the reviewer for raising this important point. We agree that our analytical\nframework, which is based on empirical trend analysis and statistical extrapolation,\ndoes not provide a process-based assessment of ecosystem tipping points or critical\nthresholds. Our methodology is designed to disentangle and quantify the historical\ncontributions of deforestation and global climate change to observed changes in\nsurface climate and atmospheric composition in the BLA. However, it does not\nsimulate the internal feedbacks, nonlinear transitions, or resilience mechanisms that\nwould be required to formally identify or predict tipping point behavior. In response to\nthis comment, and consistent with feedback from another reviewer, we have revised\nthe manuscript to avoid using the term “tipping point” in relation to our findings.\nInstead, we refer to the emergence of destabilizing trends or to the risk of critical\ntransitions in a more cautious and qualified manner. We have also added a\nparagraph in the Preserving Amazonia’s Forest Cover section to explicitly\nacknowledge the limitations of our approach in assessing biome-scale regime shifts.\nThe revised Conclusion section reflects this updated framing. It emphasizes that,\nwhile our results reveal trends that may be consistent with increasing system\nvulnerability, they should be interpreted as indicative early warnings, not as definitive\nevidence of an approaching tipping point.\nWhy just dry season?\nWe thank the reviewer for this important question. We recognize that the rationale for\nfocusing on the dry season must be clearly stated, and we have revised the\nmanuscript accordingly to provide a full justification, supported by both literature and\nstatistical evidence. As detailed now in both the Introduction and the Data and\nMethods section, our decision to focus on the dry season is based on the fact that\nthe regional climatic impacts of deforestation are significantly more pronounced and\nstatistically detectable during this period. This aligns with recent studies (e.g., Liu et\nal., 2025) showing that land-atmosphere feedbacks in the Amazon are particularly\nsensitive to surface changes during the dry season, when the forest is more\nvulnerable to water stress and convective dynamics are more locally driven. In our\nown analysis, we performed a comparative seasonal evaluation using the parametric\nmodel described in the manuscript. The logarithmic component, which isolates the\nlocal effects of deforestation, showed consistently stronger correlations during the\ndry season (e.g., R ~ 0.75 for T and R ~ 0.70 for rainfall),\nmax\nwhile these relationships\ndeclined sharply during the wet season (R ~ 0.50 and R ~ 0.30, respectively), with\nmany fits losing statistical significance. The full results, including a new\nSupplementary Figure (see Supplement Figures S4 and S5 on the revised\nmanuscript), now clarify this difference and are referenced in the main text. We have\nalso added a detailed explanation of how dry and wet seasons were objectively\ndefined for each region based on long-term precipitation quantiles (25th and 75th\npercentiles), ensuring a robust and regionally adaptive seasonal classification. These\nresults indicate that the dry season provides a more reliable window for isolating the\nregional climatic signal of deforestation. Therefore, our analysis focuses on this\nperiod to maximize interpretability and statistical rigor. We have revised the\nmanuscript to highlight this rationale and to guide readers to the relevant sections.\nAdd Data summary table for referencing on L57 and throughout\nWe thank the reviewer for this helpful suggestion. We have created a summary table\n(now Table 1) that lists all datasets used in the study, including variable names,\nspatial resolution, temporal coverage, and data source or platform. This table\nprovides a quick reference to support the reader’s understanding of our\nmethodological framework. We also refer to this table at the first mention of datasets\nin the Evaluation of the Deforestation and Global Climate Change Effects section\nand the Methods section.\nTable 1: Summary of datasets used in the analysis, including spatial and temporal\ncoverage and data sources.\nVariable Dataset/Sour Spatial Temporal Access\nce Resolution Coverage Platform\nLand cover / MapBiomas 30 m 1985–2020 Google Earth\ndeforestation Collection Engine\n6.045\nRainfall GPM ~11 km 2000–2020 GEE\nTmax (2m) ERA5 0.25° × 0.25° 1985–2020 GEE\nReanalysis59\nCH₄ (400 hPa) AIRS (Aqua 1° 2002–2020 NASA\nsatellite)60 Giovanni\nCO₂ OCO-2 0.5° × 0.625° 2015–2020 NASA\nGridded61 Giovanni\nSpecific thoughts:\nIn abstract: clarify whether you mean deforestation has impacted amazonian dry\nseason precip and temperatures. Currently, it could read like these are impacting\nglobal temperatures and precip.\nWe thank the reviewer for this helpful observation. We agree that the original\nwording in the abstract could be misinterpreted to suggest that deforestation has\ninfluenced global precipitation and temperature patterns. To address this, we revised\nthe sentence to clearly specify that the observed changes are regional and refer\nspecifically to the Amazonian dry season. The revised sentence now reads:\nThis study quantifies the relative contributions of deforestation and global climate\nchange to observed shifts in key Amazonian climate parameters. We analyzed\nlong-term atmospheric and land cover change data across 29 areas in the Brazilian\nLegal Amazon from 1985 to 2020, using parametric statistical models to disentangle\nthe effects of forest loss and global forcing on temperature, precipitation, and\ngreenhouse gas mixing ratios. While the rise in atmospheric methane (CH ) and\n4\ncarbon dioxide (CO ) mixing ratios is primarily driven by global emissions (>99%),\n2\ndeforestation has significantly increased surface air temperatures and reduced\nprecipitation during the Amazonian dry season.\nL29: Perhaps in the intro somewhere you could explain why you use maximum\n(daytime?) dry season temperature as your indicator in the analysis. I think this is an\nimportant point, that you’re choosing not to use average daily temperature.\nWe thank the reviewer for this thoughtful comment. We agree that it is important to\nclarify our choice of maximum daily surface air temperature (T ) as a key climate\nmax\nindicator in the analysis. Our preliminary assessments evaluated the model's\nperformance using maximum, average, and minimum daily temperatures, as shown\nin Figure 4 below, considering only the dry season data. The results revealed that\nT exhibits the strongest and most statistically robust relationship with forest cover\nmax\nloss, with R = 0.63. While the average temperature (T in Figure 4, below) also\nmean\nshowed a detectable signal (R = 0.43), it was notably weaker. In contrast, the\nminimum temperature (Tmin in Figure 4) showed a negligible correlation (R = 0.16)\nwith deforestation, and the model fits did not work properly. Based on this evaluation,\nwe selected T as the most suitable temperature variable to represent the effects of\nmax\nclimate change induced by deforestation. We have now added this clarification to the\nIntroduction section of the revised manuscript.\nFigure 4: Relationship between maximum (left), average (middle), and minimum\n(right) daily temperatures with respect to deforestation for the dry season.\nFor clarification, we improved the manuscript and added the following sentence to\nthe first paragraph of Section Evaluation of the deforestation and global climate\nchange effects:\nThe maximum surface air temperature was used because it provides the clearest\nand most statistically significant indication of the impact of deforestation compared\nwith the minimum and mean air temperatures.\nL46: “Our study reveals that changes in temperature and precipitation in the dry\nseason are critical for the fate of the Amazon under the combined pressure of\nclimate change and deforestation.” I think this is more for the conclusions/ abstract\nrather than the introduction in my opinion.\nWe agree with the reviewer that the sentence at line 46 conveys the result of our\nstudy rather than setting up its motivation, and it is more appropriate for the\nConclusion or Abstract. In response, we have removed it from the Introduction and\nensured that the message is clearly articulated in the concluding paragraphs of the\nmanuscript, where the implications of our findings are discussed.\nF1 caption: I find it unclear using a satellite basemap as you overlay your own green\ncolour for forest. Where is the forest data from, would be good to cite the data here\nso readers can understand what they’re looking at at a glance i.e. Hansen et al.,\n2013. Similarly, I’d like a bit more specific info about the land covers shown in the\nfigure (and source(s)), rather than just various. If they’re not crucial to the story,\nconsider removing them from this plot. Figure 1 needs to set the scene and at the\nmoment, a reader glancing through the figures isn’t sure what to take from this.\nF1: At present, I’m not sure if this is a main text figure. A suggestion could be to\ncombine the panels into one forest cover change map, overlaid with the boxes, since\nthe other land covers don’t seem to matter in this analysis? I would also suggest\nremoving the satellite land cover background as it distracts from the important\nelements you’re trying to convey, i.e. that forest is being lost, particularly in the\nsoutheast.\nWe thank the reviewer for this thoughtful and constructive feedback. We agree that\nFigure 1 plays a key role in setting the spatial context of our analysis and appreciate\nthe opportunity to clarify its composition and purpose more explicitly. The figure is\nnot based on a generic satellite image or color basemap. Instead, both the\nbackground and the forest cover overlay are derived directly from the MapBiomas\nCollection 6.0 land use and land cover (LULC) classification, which is based on\nLandsat imagery and provides annual maps at 30-meter spatial resolution from 1985\nto 2020 (Souza Jr, C. M. et al., 2020, DOI: https://doi.org/10.3390/rs12172735). The\nforest areas shown in green correspond to MapBiomas classes labeled as “Forest,”\nwhile non-forest land covers (including agriculture, pasture, natural non-forest\nformations, and urban areas) appear in light yellow or beige tones. Water bodies are\nshown in blue.\nIn response to the reviewer’s concern, we have revised the caption of Figure 1 to:\n● Clearly state that the map is based on MapBiomas data, not a satellite image,\n● Identify the meaning of the colors used (forest vs. non-forest),\n● Explain the grid boxes and their role in the analysis,\n● Highlight the central message of the figure: the extent and regional\nconcentration of forest loss between 1985 and 2020.\nWe recognize the suggestion to simplify the figure by removing other land cover\ntypes or replacing the background entirely. However, we decided to retain the current\nversion because it serves an important communication function: it shows the\nlandscape transformation over time, not only forest loss, but also how other land\nuses have expanded across space. This helps the reader understand the\nheterogeneity in land use patterns among the 29 study areas. We agree that the\noriginal caption was insufficiently descriptive. We believe the revised version now\nclarifies what is shown and how it supports the objectives of the paper. Additionally,\nwe now cite MapBiomas explicitly in the figure caption to increase transparency\nregarding data sources.\nThe new Figure 1 caption now reads as:\nSpatial distribution of forest and non-forest land cover in the Brazilian Legal Amazon\n(BLA) in 1985 (left) and 2020 (right), based on classifications from MapBiomas\nCollection 6.045. The green areas represent forest cover as defined by MapBiomas,\ncorresponding to regions with intact native vegetation. Non-forest land covers are\nshown in yellow and beige tones and include pasture, agriculture, urban areas, and\nnatural non-forest formations. Water bodies are displayed in blue. The black line\ndelineates the boundary of the BLA, and the black squares represent the 29 fixed\ngrid cells (each ~ 300 × 300 km²) selected for our analysis. These regions were\nselected to assess the relative impact of deforestation and global climate change on\ndry season temperatures, precipitation, and greenhouse gas concentrations. The\nfigure illustrates widespread forest loss over the past 35 years, particularly in the\nsoutheastern portion of the Amazon.\nL68: I’d prefer citations or names of datasets here, you can then point to the section,\nand or data table where the reader can look for more info. The data choices are very\nimportant to the results, hence should be a main feature of the text.\nWe thank the reviewer for emphasizing the importance of dataset transparency. In\nresponse, we revised the sentence at line 68 to include the names of the main\ndatasets used in the analysis (e.g., MapBiomas, ERA5, GPM, AIRS, OCO-2), as\nthese are central to the credibility and interpretation of our findings. We also added a\nreference to Table 1, which summarizes the spatial resolution, temporal coverage,\nand sources of all datasets. These changes have improved the readability and\ncomprehension of the data used in the study. Thank you again for your suggestion.\nThe new sentence now reads as:\nThis analysis is based on long-term remote sensing and reanalysis datasets,\nincluding MapBiomas (land cover), ERA5 (temperature), GPM (precipitation), AIRS\n(CH ), and OCO-2 (CO ). A detailed description is provided in the section Data and\n4 2\nMethods, and Table 1 presents a summary of dataset specifications.\nL70: which variables? Would be better to be a little more specific here, otherwise the\nreader hasn’t gained anything.\nWe thank the reviewer for the comment. We agree that the sentence was overly\nbroad, and we have since rewritten it to explicitly state the particular variables that\nare impacted by global drivers (e.g., CO₂ and CH₄) and those that are more sensitive\nto regional deforestation (e.g., maximum temperature and precipitation). This change\nimproves clarity and better frames the rationale behind the modeling approach\npresented in the following sections.\nL72: Really interesting and engaging way of conveying this result.\nWe thank the reviewer for this encouraging comment and are glad to hear that our\napproach to communicating this result was clear and engaging. This section was\ndesigned to effectively illustrate the differentiated influence of deforestation and\nglobal climate change on regional climate variables, and we appreciate the positive\nfeedback.\nL78: I appreciate the need to be concise on words, however I do think you could\nbriefly summarise these choices here and condense: “For a complete understanding\nof our approach and rationale, we refer readers to the section Data and Methods,\nsubsection Definition of Wet and Dry Season, and 80 Data Processing, where a\ndetailed explanation and justification are provided” to just “(See methods)”. I really\nthink this crucial info should be in the main text, rather than only in methods. As you\nsay, the effects are different in the wet and dry season, the reader wants then to\nknow why and how.\nWe thank the reviewer for this helpful suggestion. We fully agree that the rationale\nfor focusing on the dry season and its definition should be clear to the reader early in\nthe manuscript. In the revised version, we have already added a new paragraph that\nexplicitly addresses this point. It summarizes the seasonal differences observed in\nour analysis, provides scientific justification for focusing on the dry season, and\nrefers to the full methodological details presented later in the Data and Methods\nsection. We hope this addition resolves the concern and improves the clarity and\ntransparency of our approach.\nThe new paragraph now reads as:\nDeforestation and global climate change influence rainfall, temperature, and GHG\nmixing ratios in distinct ways across the wet and dry seasons14, 46, 47. However,\nseveral studies have shown that the regional climatic impacts of deforestation are\nmore pronounced and meaningful during the dry season, when the forest is more\nvulnerable to moisture deficits and surface-atmosphere interactions are intensified.\nIn our analysis, the dry season consistently exhibited stronger correlations between\ndeforestation and key climate variables, including maximum temperature and\nprecipitation (See Supplementary Figure S4), while the wet season showed weaker\nand often statistically insignificant relationships (See Supplementary Figure S5).\nTherefore, our study focuses exclusively on the dry season to isolate and quantify\nthe climatic effects of deforestation with greater precision. A detailed explanation and\nsupporting statistical evidence are provided in the Data and Methods, subsection\nDefinition of Wet and Dry Season, and Data Processing.\nF2: can you say whether these linear and log fits are significant? Panel a) has a lot\nof data spread and it isn’t immediately clear whether this would be significant.\nConsider printing r2 and p values on the plot or in caption text to add robustness.\nWe thank the reviewer for this valuable suggestion. In response, we have updated\nthe manuscript’s Figure 2 to include the Pearson correlation coefficient (R) and\np-value for both the linear (panel a) and logarithmic (panel b) fits, as shown below\n(Figure 5 of this response letter and Figure 2 of the revised manuscript). As now\nshown in the figure, both fits are statistically significant (p < 0.05; in fact, both\np-values are virtually zero), with R = 0.47 for the linear fit and R = 0.63 for the\nlogarithmic fit. We also revised the figure caption to explicitly report the statistical\nsignificance and provide a clearer guide for the reader to interpret the results.\nFigure 5: Relationship between dry season daily maximum surface temperature\n(Tmax) and (a) year, and (b) deforestation percentage, for all 29 areas. In panel (a),\ndata points are colored by deforestation percentage, and a linear fit is applied (R =\n0.47, p < 0.05). In panel (b), data points are colored by year, and a logarithmic fit is\napplied (R = 0.63, p < 0.05). Both relationships are statistically significant. The yellow\nlines represent the fitted linear (a) and logarithmic (b) models.\nL92: Far too vague, must include some insight in the main text. If they’re important\nenough results to mention in the main text, they should either be in a main text\nfigure, or explicitly described in text.\nWe thank the reviewer for this valuable observation. Our intention with this\nparagraph was to provide a general introductory synthesis of the broader\natmospheric responses, while reserving detailed statistical results for the subsequent\nsections, where each variable is discussed more thoroughly. However, we\nacknowledge that the original wording was too vague and could give the impression\nof a lack of clarity or specificity. To address this, we have revised the paragraph to\noffer a clearer conceptual summary of the patterns observed across variables,\nwithout introducing numerical detail prematurely. The new version clarifies the\ncontrasting responses of different variables to temporal (global) and spatial\n(regional/deforestation) drivers, while pointing forward to the in-depth analyses\npresented in the main text. The revised paragraph now reads:\nWe assessed the impacts of regional and global contributions of these parameters\nduring the dry season by fitting linear and logarithmic equations to the dataset. More\ndetails are presented in section Data and Methods. These results contrast with a\nrecent study, which reported a linear response of temperature to deforestation48.\nHowever, their analysis does not disentangle climate change’s contribution from\ndeforestation, which our findings indicate is essential for capturing the nonlinear\nresponse. By isolating the effects of deforestation, we demonstrate that temperature\nincreases with forest loss logarithmically, emphasizing the distinct role of land-use\nchanges in modifying regional climate. This interplay is explored in detail in the\nfollowing sections, where we disentangle the respective contributions of time and\nforest loss to the observed patterns.\nL113: I wonder whether a journal with a greater number of figures would be more\nsuitable here as in my view supplementary figures shouldn’t be referenced as main\nfigures in the text. They seem here to be used to tell parts of the main story, so\nshould probably be main text figures. Either this or reframe the sentences so that\nthese figures are merely adding nuance and robustness to the story.\nWe thank the reviewer for this thoughtful suggestion. We agree that the old\nSupplementary Figure S3 (now Supplementary Figure S6 in the revised manuscript)\ncontributes valuable insight by visualizing the fitted surfaces from our model in\nthree-dimensional space. However, to maintain clarity and conciseness in the main\ntext and in accordance with the journal's figure limits, we have decided to keep this\nfigure in the Supplementary Information. In response to the reviewer’s concern, we\nhave revised the corresponding sentence in the main text to better frame this figure\nas providing complementary detail rather than being central to the core results. The\nupdated sentence embedded in the following paragraph now reads:\nSupplementary Figure S6 presents the fitted three-dimensional surfaces that depict\nhow each climate variable responds to both time (year) and the fraction of\ndeforestation, as modeled by Equation 4. These visualizations offer complementary\ninsight into the nonlinear interactions captured by our approach. The parametric\nformulations used to construct these surfaces are provided in Table 2 and serve as\nthe foundation for disentangling the respective contributions of global climate change\nand regional deforestation. To assess the model’s performance, we evaluated the\nquality of the fit for each variable, which is also shown in Table 2. In addition,\nSupplementary Figure S7 presents the correlation coefficients individually for the\nlinear and logarithmic contributions, showing that the dry season fits were statistically\nsignificant, with confidence levels above 95%, unlike those observed for the wet\nseason. All fits were statistically robust (p < 0.05) and showed strong correlation\ncoefficients (R ≥ 0.69), confirming the reliability of our approach.\nL129: what do you mean by saying “we have accurately quantified”?\nWe thank the reviewer for highlighting this point. To avoid overstating the precision of\nour analysis, we have revised the sentence to remove the word “accurately” and\ninstead emphasize the statistical robustness of the modeling approach. The updated\nsentence now reads:\nUsing the parameterized equation, we successfully isolated the individual effects of\nglobal emissions and deforestation on changes in GHG mixing ratios and weather\nparameters.\nL133: Figure 4 is mentioned before Figure 3, this needs reordering, either of figures\nor describing figure 3 earlier. Indeed when Figure 3 is mentioned, I don’t believe the\nresults are fully described and put in context. Figure 3 has some really nice results\nand clearly shows the contributions to each change. I believe this should be more\nthoroughly described and explained. As far as I can tell, Figure 4 is repeating the\ninformation from Figure 3? It is a really nice synthesis of the information in a good\nlooking figure, but I wonder if both figures are needed in the main text.\nWe thank the reviewer for this insightful comment. We agree that the previous order\nof figures and the limited contextualization of the manuscript’s Figure 3 may have\nhindered the narrative flow and clarity. To address this:\n1. We have reorganized the text so that the manuscript’s Figure 3 is now\nintroduced and discussed before Figure 4. This reordering ensures that the\ncore quantitative results, i.e., the separate contributions of deforestation and\nglobal climate change to each variable, are properly contextualized and\npresented before we introduce a synthesized visualization.\n2. We have expanded the discussion of the manuscript’s Figure 3, especially to\nhighlight the observed variability across the 29 regions as shown in the\nboxplots. This figure now serves as the main vehicle for communicating our\nquantitative findings in the section “Disentangling the Influence of\nDeforestation and Global Climate Change on the Amazonian Atmosphere.”\n3. Figure 4 has been retained in the main text because it provides a\ncomplementary and integrated visualization of the findings in Figure 3. Figure\n3 provides a more detailed statistical breakdown, whereas Figure 4\nsummarises this information in a concise visual format that makes it easier to\ninterpret and compare. This figure is now referenced later in the section\n“Preserving Amazonia’s Forest Cover: Understanding its Importance and Anticipating Future Impacts”, where its synthetic character supports the\nbroader discussion of ecological and policy implications.\nWe believe that this restructuring enhances the manuscript’s readability and more\neffectively conveys the progression from quantitative analysis to integrated\ninterpretation.\nL139: How do these increases compare to previous work?\nWe thank the reviewer for this important comment. Our study is, to our knowledge,\nthe first to quantitatively separate the contributions of global climate change and\nregional deforestation to the observed changes in temperature and precipitation\nacross the Amazon. While no prior work has directly partitioned these drivers,\nseveral recent studies support our findings. For example, Gatti et al. (2021) observed\nstronger warming and carbon fluxes in the eastern and southeastern Amazon,\nregions with strong deforestation, compared to more preserved western areas.\nThese spatial gradients are consistent with our results, in which the largest\ntemperature increases are associated with the most heavily deforested regions. We\nhave now included this comparison in the main text to provide additional context and\nreinforce the robustness of our findings.\nL143: It would be helpful to be more explicit when you say “These results highlight\nthe synergistic interaction”.\nWe thank the reviewer for this helpful suggestion. We agree that the phrase\n“synergistic interaction” was too general and have revised the sentence to clarify the\nphysical meaning. The updated version now explains that the observed increase in\ntemperature is the result of an interaction in which global climate change amplifies\nthe regional climatic response to deforestation, while deforestation itself contributes\nto local warming through reduced evapotranspiration and surface energy balance\nchanges. This revised sentence reads:\nThese results highlight that the observed increase in maximum temperature during\nthe dry season cannot be attributed solely to global climate change or deforestation.\nInstead, it reflects a synergistic interaction in which long-term global warming\namplifies the sensitivity of local climate to land-use change, while deforestation\nintensifies regional heating and reduces evapotranspiration, further exacerbating the\nwarming trend.\nL184: To me, the following seems like a conclusion, rather than an opening for a\ndiscussion on the future impacts. Perhaps it could be moved to the conclusions, or\nreordered in this section. “This study shows the impact of global and land-use\nchanges on the variables examined. Deforestation mainly drives the meteorological\nvariables during the dry season. When considering all study areas across the\nBrazilian Amazonian region between 1985 and 2020, deforestation is associated\nwith a daily maximum temperature increase of approximately 16.5%, equivalent to a\ntemperature rise of 0.39 °C. Deforestation also has a dominant influence on the\nreduction in total annual rainfall. It is responsible for 74.5% of the total decrease in\nprecipitation, resulting in a net 15.8 mm reduction in annual rainfall during the 35\nyears. On the other hand, deforestation had a smaller impact on GHG\nconcentrations, which were driven predominantly by global changes.”\nWe thank the reviewer for this helpful observation. We agree that the paragraph at\nline 184 was written as a summary of findings and could be misread as a conclusion\nrather than a prelude to the discussion of future impacts. To address this, we have\nrevised the paragraph to clarify that it serves as a transition into the analysis of future\nscenarios. The revised version briefly recaps the main findings and introduces their\nrelevance for understanding the projected trajectory of deforestation-driven changes.\nWe believe this restructuring improves the logical flow and strengthens the narrative\nleading into the forward-looking discussion.\nL196: This is an important result and should be highlighted better in the paragraph,\nbetter still, within the section where you talk about the figure it relates to “within the\nfirst 10%-40% of coverage loss”. I’m not sure how most of this paragraph relates to\nthe subheading title “Preserving Amazonia’s Forest Cover: Understanding its\nImportance and Anticipating Future Impacts”.\nWe thank the reviewer for this valuable observation. We acknowledge that the\noriginal paragraph did not sufficiently emphasise the key finding that the most\nsignificant climate impacts occur during the initial stages of deforestation.. In\nresponse, we have rewritten the paragraph to emphasize this finding more clearly,\nboth in its phrasing and placement. The revised text now highlights the 10–40%\nthreshold more explicitly and frames it as a core scientific insight with strong\nimplications for conservation and climate resilience. This adjustment also helps the\nparagraph better align with the subheading “Preserving Amazonia’s Forest Cover:\nUnderstanding its Importance and Anticipating Future Impacts.”\nThe new paragraph now reads as:\nOur findings show that the most pronounced climate impacts occur early in the\ndeforestation process, within the first 10–40% of forest cover loss. This nonlinear\nresponse suggests a steep initial degradation of local climate regulation functions.\nWhile further deforestation continues to alter the climate, the marginal impact\nbecomes less dramatic after this threshold. It underscores the disproportionate\nimportance of preserving existing forest areas before they cross these critical initial\nloss intervals. Comparisons between the least and most deforested areas further\nreinforce this aspect: regions that have retained more forest cover show greater\nclimate stability, while those with extensive land cover conversion exhibit intensified\nwarming and drying trends (see Supplementary Figure S8). These results\nemphasize that avoiding even early-stage deforestation is essential for safeguarding\nthe biome’s meteorological resilience and minimizing future disruptions to rainfall and\ntemperature patterns.\nL206: To me, this discussion of tipping points isn’t central enough to the analysis for\nit to be referenced in the abstract. Rather I would focus on your good and important\nresults.\nWe appreciate the reviewer’s observation and fully agree that the original framing of\na potential “tipping point” was not adequately supported by the methodology or\ncentral to the analytical framework of the study. In response, we have removed\nreferences to “tipping points” from the abstract and throughout the main text, where\nappropriate. The manuscript has been restructured to emphasize the core results,\nincluding the quantified contributions of deforestation and global climate change to\nobserved changes in temperature, precipitation, and greenhouse gas concentrations.\nThis ensures that the focus remains on our most robust and well-supported findings.\nL245: I think this is still a proposed tipping point, rather than a certainty?\nWe thank the reviewer for this important observation. We fully agree that our results\nshould not be interpreted as confirming a tipping point in the Amazon, but rather as\nsuggesting potential early signs of ecological destabilization under ongoing\npressures from deforestation and climate change. To address this, we revised the\nparagraph in question to explicitly avoid deterministic language and better reflect the\nlimitations of our analysis. Specifically, we now state that the Amazon may be\napproaching a critical ecological transition. However, we emphasise that we remain\ncautious in referring to this as a definitive tipping point. This is partly due to structural\nuncertainties in current climate and vegetation models, and partly due to our\nextrapolation based on historical data, which does not account for the effects of other\nfactors, such as the response of vegetation to these changes. This revision clarifies\nthat our findings are consistent with potential transition signals but do not constitute\npredictive evidence of a biome-scale threshold being crossed.\nL275: Previous analysis has found there to be strong discrepancy between\nprecipitation datasets in particular, but also temperature datasets with respect to their\nchanges due to deforestation. How did you choose these datasets and (also why a\nnear surface, not a land surface temperature) and is there any way the choice of\ndataset might impact your result. Perhaps something worth mentioning in the text\nsomewhere.\nWe appreciate the reviewer’s thoughtful observation regarding discrepancies across\nclimate datasets, particularly for precipitation and temperature in regions such as the\nAmazon. These differences are indeed critical when evaluating the impacts of\ndeforestation. In our study, we selected datasets based on three main criteria: (1)\nspatial resolution and domain coverage over the Brazilian Legal Amazon, (2)\ntemporal coverage matching our 1985–2020 analysis window, and (3) scientific\nvalidation and widespread use in peer-reviewed literature. Yes, the choice of dataset\ncan have some influence on the estimated trends, particularly in regions like the\nAmazon, where observational coverage is sparse and environmental variability is\nhigh. To minimize uncertainties, we deliberately selected datasets that are widely\nused and validated in the scientific literature. In addition, we restructured and\nexpanded the subsections Greenhouse Gases and Weather Data and Land Use and\nLand Cover Data to clearly present our dataset choices, including their sources,\nspatial and temporal resolutions, and relevance to our research objectives.\nL290: Reading this section makes me think that some version of Supplementary\nFigure 5 really should be in the main text, perhaps in lieu of removing Figure 1? The\nresults are really interesting an important. In addition to this, I’m still quite unclear\nas to why you are only presenting the dry-season in the main text. Is there a justification\nfor this?\nWe appreciate the reviewer’s comment and agree that the results shown in the old\nSupplementary Figure 5 are valuable. However, we respectfully chose to keep this\nfigure in the Supplementary Material because the variability in the impacts of\ndeforestation across regions is already robustly illustrated in the main text via the\nboxplots in Figure 3, which display the distribution of deforestation and global climate\nchange contributions for all 29 areas. These boxplots convey the spatial variability\nacross the Amazon and the statistical range of responses in a compact and\nintegrative way, which we consider suitable for the main text. Including\nSupplementary Figure 5 would be partially redundant and may divert focus from the\nprimary narrative centered on the parametric attribution analysis.\nRegarding the exclusive focus on the dry season, as already discussed, we have\nexpanded our justification in the revised manuscript and the subsection Definition of\nWet and Dry Season, and Data Processing in Data and Methods. Briefly, we\nobserved that the climatic effects of deforestation are significantly more pronounced\nand statistically robust during the dry season, when vegetation is most stressed and\nsurface-atmosphere interactions are enhanced. The wet season presented weaker\nor statistically insignificant correlations between deforestation and key variables, as\nsupported by our statistical tests. Therefore, we focused on the dry season to ensure\nclarity and accuracy in quantifying the effects under conditions when they are most\nrelevant.\nTable 1: might be useful to display some of these stats on the relevant figures, so\nreaders can view this at the same time as seeing the results.\nWe thank the reviewer for this helpful suggestion. In response, we have incorporated\nkey statistical values from Table 2 (previous Table 1) directly into the relevant figures,\nallowing readers to more easily interpret and evaluate the robustness of our results\nin context. This is now implemented in the revised Figure 2, which presents the\nscatter plots of the different contributions (global and regional) to the temperature.\nWe believe this integration improves the clarity and accessibility of the results and\nappreciate the reviewer’s recommendation.\nFigure S1: Would be nice to see this in map form as well, perhaps as a multi panel\nplot. You could take Figure 1 and combine with this as a new Figure S1.\nWe appreciate the reviewer’s suggestion to include a map-based or multi-panel\nvisualization for the old Supplementary Figure S1. While we agree that geographic\nrepresentations can enhance visual understanding, in this particular case, our\nobjective with Supplementary Figure S1 (now Supplementary Figure S2 in the\nrevised manuscript) was to provide a concise overview of the temporal evolution of\ndeforestation and climate variables across all 29 study areas. The current format\nenables direct comparison of trends year by year across all sites, a task that is more\nchallenging to achieve in map-based plots, particularly when considering the large\nnumber of regions. Given space constraints and the analytical focus of the study, we\nhave opted to keep Supplementary Figure S1 in its current format, which efficiently\nsummarizes the temporal behavior of the variables. However, we have revised the\nfigure caption to better highlight its comparative purpose and ease of interpretation.\nThe new caption reads as:\nTemporal evolution of forest cover loss for each of the 29 study areas across the\nBrazilian Legal Amazon, calculated as the change in forest fraction between 1985\nand 2020. This panel-based format allows a direct comparison of deforestation\ntrends across all regions, supporting the identification of spatial heterogeneity in\ndeforestation trajectories.\nFigure S5: small point, the panels are not in the same order as other plots. It would\nbe visually helpful to be consistent, so it is easier to flick between plots.\nWe appreciate the reviewer’s attention to visual consistency. The panels in old\nSupplementary Figure S5 (now Supplementary Figure S8) were arranged to follow\nthe same geographic order as presented in Figure 1, allowing for a direct spatial\ncomparison between forest cover and variable response across the study areas. We\nunderstand the importance of consistent figure layout for ease of interpretation and\nhave clarified this in the figure caption. If deemed preferable, we are happy to\nreorder the panels to match the sequence used in other figures. The new caption\nreads as:\nPie charts showing the relative contributions of global (blue) and\nregional/deforestation-driven (green) factors to changes in CO₂, CH₄, precipitation,\nand maximum surface temperature during the dry season across the 29 study areas.\nThe modeled values used to compute these contributions are derived from the\nparametric fits in Table 1. The panels are organized following the geographic\narrangement presented in Figure 1, facilitating spatial comparison.\nEditorial comments:\nL83: processes for should be processes form?\nWe thank the reviewer for identifying this editorial issue. We have revised the\nsentence for clarity and grammatical correctness. The phrase “processes for” has\nbeen corrected to “processes exhibit,” which more accurately conveys the intended\nmeaning. The revised sentence now reads:\nAs an exemplification, the analysis of the maximum daily temperature (Figure 2)\nshows that during the dry season, these two distinct processes exhibit a nearly linear\ntemporal trend across all deforestation extensions, which is attributed to global\nclimate change, as evidenced by the gradual increase in maximum temperature over\nthe years.\nL103: I’m unclear about what you mean by exchange effects. Perhaps this is obvious\nto others, but if not it could be worth elaborating what you mean.\nWe thank the reviewer for pointing out the ambiguity in our phrasing. We agree that\nthe term \"exchange effects\" was unclear and have revised the sentence to more\nclearly reflect the intended meaning. The updated sentence now reads:\nThe parameters of Equation 4 were obtained by fitting observational data to\nseparately capture the contributions of deforestation and global influence to\nobserved changes in climate variables.\nL111: consider adding numbers to these data subsections, to reduce main text word\ncount. i.e. see methods section 1.x.\nWe appreciate the reviewer’s suggestion to number the sections and subsections in\nthe Methods to facilitate easier cross-referencing from the main text. However,\nNature Communications follows a formatting style that does not allow numbered\nsection headings in the manuscript. As an alternative, we have revised the main text\nto refer more concisely and precisely to the relevant subsection titles in the Methods\n(e.g., “Greenhouse Gases and Weather Data”), allowing the reader to locate\nsupporting information efficiently without departing from journal guidelines.\nL127: Possibly could condense the following into the subsequent sentence as this\nhas already been said before: “The central question we sought to address is the\nextent to which the observed changes can be attributed to global climate change\nversus the effects of deforestation.”\nWe appreciate the reviewer’s suggestion. Indeed, the sentence as originally written\nreiterates information that was already implied in previous lines. We have revised the\nparagraph to improve clarity and conciseness by merging the idea into it, which now\nreads as:\nTo assess the effects of deforestation and global climate change on GHG mixing\nratios and weather variables, we developed a novel approach that integrates linear\ntemporal trends with the exponential trends associated with deforestation. These\neffects interact non-linearly due to the interdependence of deforestation (D) and time\n(t). As detailed in section Data and Methods, we obtained a parametric equation\n(Equation 4) that incorporates the linear time dependence, the logarithmic\ndependence on deforestation, and their nonlinear interactions. The parameters of\nEquation 4 were obtained by fitting observational data to separately capture the\ncontributions of deforestation and global influence to observed changes in climate\nvariables.\nReviewer #3 (Remarks to the Author):\nHow Climate Change and Deforestation Interact in the Transformation of the\nAmazon Rainforest\nWe thank Reviewer #3 for the thoughtful and constructive review of our manuscript.\nYour suggestions led us to improve the clarity and contextualization of the study,\nparticularly by revising the Abstract to include a concise summary of the\nmethodological approach and by incorporating new references that strengthen the\nframing of our analysis. We are grateful for your input, which contributed to refining\nthe manuscript and enhancing its scientific communication..\nReviewer summary:\nAbstract:\nPlease provide a short explanation of the methods in the abstract.\nWe thank the reviewer for this valuable suggestion. We agree that including a brief\nexplanation of the methodological approach helps clarify how our findings were\nobtained. Thus, we have revised the abstract to include a concise description of the\nmethods. Specifically, we now state that the study is based on long-term\natmospheric and land cover data across 29 areas in the Brazilian Legal Amazon\nfrom 1985 to 2020, and that we used parametric statistical models to disentangle the\ncontributions of deforestation and global climate change to changes in temperature,\nprecipitation, and greenhouse gas concentrations. The new abstract reads as\nfollows:\nThe Amazon rainforest is one of Earth’s most diverse ecosystems, playing a key role\nin maintaining regional and global climate stability. However, recent changes in land\nuse, vegetation, and the climate have disrupted biosphere-atmosphere interactions,\nleading to significant alterations in the water, energy, and carbon cycles. These\ndisturbances have far-reaching consequences for the entire Earth system. This study\nquantifies the relative contributions of deforestation and global climate change to\nobserved shifts in key Amazonian climate parameters. We analyzed long-term\natmospheric and land cover change data across 29 areas in the Brazilian Legal\nAmazon from 1985 to 2020, using parametric statistical models to disentangle the\neffects of forest loss and alterations of temperature, precipitation, and greenhouse\ngas mixing ratios. While the rise in atmospheric methane (CH ) and carbon dioxide\n4\n(CO ) mixing ratios is primarily driven by global emissions (>99%), deforestation has\n2\nsignificantly increased surface air temperatures and reduced precipitation during the\nAmazonian dry season. Over the past 35 years, deforestation has accounted for\napproximately 74% of the ∼21 mm dry season−1 decline and 16.5% of the 2◦C rise in\nmaximum surface air temperature. Understanding the interplay between global\nclimate change and deforestation is essential for developing effective mitigation and\nadaptation strategies to preserve this vital ecosystem.\nAre 21 millimeters in the dry season and over 35 years statistically significant?\nWe thank the reviewer for bringing this important point to our attention. First, we\nclarify that the value reported in the abstract was misrepresented: the correct value\nis a decline of approximately 21 mm during the dry season, not over the entire\n35-year period, but only during the dry season. This has been corrected in the\nrevised abstract for clarity and accuracy. Regarding statistical significance: yes, the\nobserved trend of ~21 mm in dry season precipitation associated with deforestation\nis statistically significant. This result is supported by the model fit, which is performed\nsimilarly to what is shown in Figure 2b, where the logarithmic regression between\ndeforestation and precipitation yields a p-value < 0.01, indicating strong significance.\nAdditionally, the confidence intervals presented in the boxplots of Figure 3,\ncalculated with the parameters from Table 1 confirm the robustness of this trend\nacross the 29 study regions. Particularly for precipitation (and for all the variables),\nthe p-value is nearly zero. To further clarify this point, we have updated the abstract\nand reinforced the statistical evidence in the relevant Results sections.\nThe statement on non-linear behavior and tipping points without a short explanation\nof methods looks confusing.\nWe appreciate the reviewer’s observation and agree that the abstract and the overall\nmanuscript must avoid unintended implications. Our methodology is based on\nretrospective statistical modeling and does not simulate ecological feedbacks or\nnonlinear tipping dynamics. While we observe nonlinear trends in key climate\nvariables, such as rapid changes in temperature and precipitation associated with\ninitial forest cover loss, we recognize that these trends do not imply confirmation of a\ntipping point in a mechanistic or predictive sense. To prevent confusion, we revised\nthe abstract and the whole manuscript to remove references to tipping points and\ninstead focus on the observed magnitude and trends of change. We also added a\nconcise statement of our methodology to contextualize the findings (see revised\nabstract). These changes are consistent with the framing used throughout the\nrevised manuscript, especially in the sections where we discuss the results, where\nwe acknowledge the limitations of our approach in capturing critical ecological\nthresholds.\nIntroduction:\nAbout the carbon and water fluxes: Please consider using some new references:\nhttp://dx.doi.org/10.1088/2515-7620/adc06a and\nhttp://dx.doi.org/10.1038/s41598-025-85272-z\nWe thank the reviewer for suggesting the inclusion of recent literature on carbon and\nwater fluxes. We agree that both papers provide valuable context regarding\nlarge-scale drivers of carbon assimilation and evapotranspiration dynamics across\nSouth America. In response, we have incorporated both references into the\nIntroduction section of the revised manuscript. These studies strengthen our framing\nby emphasizing the role of ocean-atmosphere teleconnections and regional climate\nvariability in shaping biosphere-atmosphere interactions in the Amazon. Specifically,\nwe added the following sentence:\nRecent studies have further highlighted how ocean-atmosphere teleconnections\nmodulate carbon assimilation and evapotranspiration dynamics across South\nAmerica’s major biomes, including the Amazon, at seasonal to interannual scales19,\n20. These findings emphasize the need to contextualize Amazonian carbon and water\nfluxes not only in terms of local land-use changes but also in large-scale climate\ndrivers.\nBrazilian biomes, Environmental Research Communications.\ncontrols across South America, Scientific Reports.\nLine 25: I'm not sure about the statement that the Amazon basin controls all these\nglobally critical issues. For instance, critical freshwater is about \"blue\" and \"green\"\nwater availability pre- and post-industrial times. However, the authors showed\ntransgressions in these attributes even during pre-industrial events. This makes\nsense, even nowadays, several studies have shown some resilience in hydrological\nprocesses in the Amazon in recent events.\nWe appreciate the reviewer’s thoughtful observation regarding the framing of the\nAmazon Basin’s role in regulating planetary boundaries, particularly with respect to\nfreshwater change. Indeed, the Planetary Boundaries framework addresses blue and\ngreen water availability in terms of long-term, global-scale alterations from\npre-industrial baselines. Freshwater boundary transgressions may indeed occur\nindependently of Amazonian dynamics. In our revised manuscript, we have clarified\nthat while Amazon has a strong regional influence on continental-scale freshwater\ndynamics and contributes substantially to the global hydrological cycle (e.g., through\nevapotranspiration and rainfall recycling), it is one of several regions influencing\nglobal freshwater change. We have modified the sentence accordingly:\nOf the nine processes identified as critical for Earth system stability21, five with the\nmost critical status, namely climate change, biosphere integrity, land system change,\nfreshwater change, and biogeochemical flows, are significantly influenced by the\nAmazonian Basin, particularly at regional to continental scales.\nLine 36: You should check the Amazon basin area. It is about 6 mi km², and if you\nconsider Tocantins, it reaches 7 mi km². But, if your study area is Legal Amazon, this\nnumber drops slightly.\nWe thank the reviewer for this important observation. Indeed, the total area of the\nAmazon Basin can reach approximately 6 to 7 million km² when considering\nhydrological boundaries, including the Tocantins-Araguaia basin. However, as\nclarified in our study, our analysis focuses specifically on Brazil’s Legal Amazon\n(Amazônia Legal), which is a well-defined socio-political and administrative region\nused in national environmental policy frameworks. According to the Brazilian Institute\nof Geography and Statistics (IBGE), the area of the Legal Amazon is approximately\n5,015,146 km², representing around 58.9% of the Brazilian territory, consistent with\nthe most recent official delimitation.\nLine 43: Some studies assessed the combined effect of deforestation and climate in\nthe Amazon's watersheds. For instance, https://doi.org/10.1029/2019WR025083.\nWe thank the reviewer for this insightful comment and for pointing to the study by\nCavalcante et al. (2019), which indeed provides important insights into the joint\neffects of land use and climate change on hydrological processes in Amazonian\nwatersheds. We have now acknowledged this in our revised manuscript and clarified\nthat, while several studies have examined the combined effects at local or watershed\nscales, our study expands on this by providing a biome-wide, long-term\nquantification of the separate contributions of deforestation and global climate\nchange across key climatic variables using observational data and a parametric\nmodeling framework. The improved text now reads as:\nParticularly, deforestation has a substantial impact on the regional climate of\nAmazonia5, 22, 32, 40, 41. Although previous studies have investigated the combined\neffects of deforestation and climate change in specific subregions or watersheds27,\nthe relative roles of deforestation and global climate change and their interaction in\ndriving the transformation of the Amazon remain poorly understood and have yet to\nbe disentangled25, 42.\nLine 30: I missed more studies that assessed the effects of climate change in\ndifferent variables (e.g. hydrology, biodiversity, etc), even in small basins. I suggest a\nmore detailed review of these papers.\nThank you for this suggestion. In response, we have included a more detailed review\nof studies that evaluated the effects of climate change on different variables,\nincluding hydrology, in both large and small Amazonian basins. Notably, studies such\nas Panday et al. (2015) (DOI: https://doi.org/10.1016/j.jhydrol.2015.02.018) and\nCavalcante et al. (2019) (DOI: https://doi.org/10.1029/2019WR025083) have\nassessed the interplay between climate variability and land-use change, particularly\nin the Xingu and Itacaiúnas River basins, respectively. We have now revised the\nmanuscript to incorporate these references and contextualize our findings within this\nbroader literature.\nQuantifying global and regional effects across Brazilian Amazon areas:\nYou could explain better how the assessment was in each area. Did you evaluate the\nfluxes pixel by pixel? The mean, median...?\nWe thank the reviewer for this important question. We evaluated each variable\n(temperature, rainfall, CH₄, and CO₂) by aggregating the data within each of the 29\nfixed ∼300 × 300 km² areas across the Brazilian Legal Amazon. Specifically, we\ncalculated area-averaged (mean) values for each variable within each grid cell at\nmonthly and annual time steps. These spatial means were used to construct\nconsistent time series for each region, which were then analyzed using our\nparametric model (Equation 4 in the manuscript) to disentangle the effects of\ndeforestation and global climate change. This averaging approach ensures that\nlocal-scale variability is accounted for within each cell while preserving regional-scale\ntrends. The choice of mean (as opposed to median or other statistics) was motivated\nby its consistency across datasets and its compatibility with the linear and\nlogarithmic regression framework applied. We have revised the manuscript\naccordingly to clarify this methodology, particularly in the Sections Quantifying global\nand regional effects across Brazilian Amazon areas, Evaluation of the deforestation\nand global climate change effects, and Data and Methods, subsection Greenhouse\nGases and Weather Data.\nFigure S1: You should categorize the bars by region using different colors (e.g.,\npristine areas, the arc of deforestation, and so on).\nWe thank the reviewer for the suggestion to categorize the bars in Figure S1 by\nregion (e.g., pristine areas, the arc of deforestation). While we recognize the value of\nsuch stratification, we have chosen to maintain the current single-color format to\npreserve clarity and visual emphasis on the magnitude of forest cover loss in each of\nthe 29 study areas between 1985 and 2020. Our goal with this figure is to highlight\nthe heterogeneity in deforestation intensity across the study domain without\nintroducing additional visual complexity that might detract from the primary message.\nMoreover, detailed regional classifications, such as the arc of deforestation or\npristine zones, are already provided in previous studies, including Teixeira et al.\n(2025) (DOI: https://doi.org/10.1016/j.foreco.2025.122662), which contains a\ncomprehensive map of these spatial distinctions (see, for example, their Figure 1).\nWe now reference this complementary source in the revised caption of Figure S1 to\nguide readers seeking a regional breakdown of the study areas.\nEvaluation of the deforestation and global climate change effects\nLine 77: \"Our study....\". This phrase isn't in the right place. You could use it in the\nabstract or introduction, but not in this section.\nWe appreciate the reviewer’s attention to the clarity and structure of the manuscript.\nThe sentence in question was placed at the section Evaluation of the deforestation\nand global climate change effects with the intent of introducing the specific\nmethodological approach adopted in this part of the study. While it refers to the\noverall study, it also serves to guide the reader into the technical discussion that\nfollows. To better align with the reviewer’s suggestion, we have revised the whole\nparagraph to reflect its role as a lead-in to the analytical evaluation rather than a\nrestatement of the study’s broader aims. We hope this adjustment clarifies the\npurpose of the sentence in its current location.\nDeforestation and global climate change influence rainfall, temperature, and GHG\nmixing ratios in distinct ways across the wet and dry seasons14, 46, 47. However,\nseveral studies have shown that the regional climatic impacts of deforestation are\nmore pronounced and meaningful during the dry season, when the forest is more\nvulnerable to moisture deficits and surface-atmosphere interactions are intensified.\nIn our analysis, the dry season consistently exhibited stronger correlations between\ndeforestation and key climate variables, including maximum temperature and\nprecipitation (See Supplementary Figure S4), while the wet season showed weaker\nand often statistically insignificant relationships (See Supplementary Figure S5).\nTherefore, our study focuses exclusively on the dry season to isolate and quantify\nthe climatic effects of deforestation with greater precision. A detailed explanation and\nsupporting statistical evidence are provided in the Data and Methods, subsection\nDefinition of Wet and Dry Season, and Data Processing.\nLine 81: Is this section about results? You began to explain some methods (method),\nand after that, you showed results (\"our analysis reveals...\"). You should replace\nsome parts of the method section.\nWe thank the reviewer for their insightful comment regarding the structure of the\nsection “Evaluation of the deforestation and global climate change effects.” We\nunderstand the concern about blending methodological descriptions with the\npresentation of results, and we would like to clarify our intention with this section.\nThis portion of the manuscript was designed to provide a conceptual and analytical\noverview of the approach we use to distinguish between the effects of deforestation\nand global climate change. Specifically, it introduces the rationale for our choice of\nmodeling strategy by fitting linear models to represent temporal trends (as a proxy\nfor global climate change) and logarithmic models to represent spatial deforestation\nimpacts. These relationships form the analytical foundation for subsequent sections\nof the manuscript, where the disaggregated effects are further explored in detail.\nAlthough the section references some findings (e.g., the linear trend with time and\nlogarithmic response to deforestation), we believe this discussion is essential to\nguide the reader through the logic behind our analytical framework. To that end, we\nclarify in the text that a more detailed description of datasets and statistical fitting\nprocedures is provided in the Data and Methods section. The sentence beginning\nwith “Our analysis reveals…” marks the transition from the description of data\ncharacteristics into the modeling insights that justify our separation of effects. We\nrespectfully argue that this integrated structure enhances the narrative coherence of\nthe manuscript, helping readers understand why specific models were selected and\nhow the relationships were empirically validated. Additionally, the figure referenced\nhere plays a central role in visually introducing the dual-model structure, bridging\nmethods, and initial findings. Nevertheless, we carefully reviewed this section to\nensure that any methodological elements presented here are confined to high-level\njustifications, while technical implementation details remain in the Methods section.\nWe hope that with this clarification, the reviewer agrees that the structure serves the\nmanuscript’s clarity and purpose.\nFigure 2: I didn't understand the relationship between the temperature increase (in\nFigure 2) and climate change. I can see a noticeable rise in temperature with an\nincrease in deforestation. How did you isolate climate change using Temp = f (D,\ntime)?\nWe appreciate the reviewer’s question regarding how we isolate the effects of global\nclimate change from those of deforestation in Figure 2. Indeed, Figure 2 was\ndesigned as an initial diagnostic visualization to highlight the distinct relationships of\nmaximum dry-season temperature with time and deforestation, treated separately.\nPanel (a) presents the temporal trend of temperature across all study regions,\nrevealing a statistically significant linear increase over the 35-year period. This trend\nis interpreted as the effect of global climate change, which progresses with time and\nis consistent across regions regardless of their specific deforestation histories. Panel\n(b), on the other hand, shows a logarithmic relationship between temperature and\nthe deforestation fraction, irrespective of year, emphasizing the localized and\nnonlinear influence of land-use change. These two panels help establish that both\ntime and deforestation are important drivers of temperature change, but they do not\nyet account for the potential interactions between the two.\nTo move beyond this bivariate representation, the section \"Derivation of the\nParametric Fits and Calculations of the Deforestation and Global Contributions\"\nintroduces a more robust and integrated analytical approach. We model temperature\n(and other variables) as a function of both time and deforestation fraction\nsimultaneously. The initial model assumes additive linear (for time) and logarithmic\n(for deforestation) contributions (Eq. 2), but this proved insufficient because\ndeforestation itself evolves over time, introducing potential interaction effects. To\ncapture this interdependency, we allow the regression coefficients to vary with the\nother variable (Eqs. 3–4), leading to a more flexible and physically interpretable\nfour-parameter model (Eq. 5).\nThis parametric surface (Eq. 5) enables us to analyze the joint and interacting\ninfluences of deforestation and time (i.e., global climate change). It is adjusted using\nleast squares fitting to observed data, and its partial derivatives (Eqs. 6–7) allow us\nto isolate the marginal effects of time (holding deforestation constant) and of\ndeforestation (holding time constant). We then integrate these expressions over time\nand over the observed range of deforestation for each region, producing the absolute\ncontributions of global and regional drivers (Eqs. 8–9). Thus, while Figure 2 provides\na visual justification for modeling temperature as a function of both time and\ndeforestation (each showing distinct and statistically significant patterns), the full\nattribution of changes to global or regional processes is achieved through the\nanalytical framework outlined in the subsequent section. We revised the sections to\nensure a better connection between these two parts of the text, thereby improving\nclarity for the reader.\nModeling the evolution of atmospheric variables\nLine 106: I understand equations 6 and 7, but it is not clear that\nEquation 1: Please explain how \"B\" is a parameter related to global forces/global changes.\nWe thank the reviewer for highlighting the need to clarify the attribution of the\nparameter B in Equation 1 as being related to global forces. In our framework,\nEquation 1 is given by:\n𝑦(𝑡,𝐷) = 𝐴ln𝐷 + 𝐵𝑡 + 𝐶,\nwhere y represents an atmospheric variable (e.g., temperature), D is the\ndeforestation fraction, and t is time. The rationale for interpreting B as the\ncontribution of global climate change stems from both theoretical expectations and\nempirical evidence. As shown in Figure 2a, we observe a clear, nearly linear\nincrease in temperature over time across all regions, including those with minimal\ndeforestation. This trend reflects the ubiquitous and regionally homogeneous\ninfluence of global climate change (e.g., rising greenhouse gas concentrations and\nradiative forcing), which progresses over time and is not strongly modulated by local\nland-use changes. Therefore, we associate the linear-in-time component (Bt) with\nthe global forcing. But as mentioned above, in the previous response, the\nrelationship between global and regional contributions are non-linear, so that we\ndefine B as\n𝐵 = 𝑏 ln𝐷 + 𝑏 .\n1 2\nThis is further elaborated and justified in the section Derivation of the Parametric Fits\nand Calculations of the Deforestation and Global Contributions. In that section, we\nrefine this initial model by allowing both A and B to vary as functions of the other\nindependent variable (Equations 3 and 4), acknowledging that deforestation and time\nare not fully independent. However, the interpretation of B as the global component\nremains valid because it governs the evolution of y with time when deforestation is\nheld constant. This logic is explicitly formalized in Equation 6, where we compute the\npartial derivative of y(t,D) with respect to t while keeping D constant. The resulting\nexpression\n∂𝑦\n| = γln𝐷 + 𝑏\n∂𝑡 𝐷 = 𝑐𝑜𝑛𝑠𝑡 2\nisolates the temporal effect, where b₂ is directly associated with the time-only (global)\ncomponent of the trend. This decomposition allows us to quantify the distinct\ncontributions from global climate change and regional deforestation, as shown in\nEquations 8 and 9.\nDisentangling the Influence of Deforestation and Global Climate Change on the\nAmazonian Atmosphere\nHow did you use the equations in Table 1 to find 87 ppm and 173 ppm (CO2 and\nCH4)? This is because your data set analysis focused only on the study area. So,\nthe global influence is not clear.\nWe appreciate the reviewer’s careful attention to this point. The global contributions\nfor CO₂ (87 ppm) and CH₄ (173 ppb) presented in our manuscript were computed\nbased on Equation 4 plus the parameters from Table 2 (previously Table 1), as\nshown in the section Derivation of the Parametric Fits and Calculations of the\nDeforestation and Global Contributions. Our analysis focuses on the Brazilian Legal\nAmazon, and the global component we refer to in this context represents the\nlong-term atmospheric trend that cannot be explained by local or regional land-use\nchanges (i.e., deforestation), but especifically for the Legal Amazon. Equation 4\ncaptures this by fitting each variable as a function of both time and deforestation\nfraction, allowing us to statistically distinguish the signal associated with global-scale\nprocesses (represented by the linear term in time) from the regional deforestation\ninfluence (represented by the logarithmic term in deforested fraction).\nSpecifically, we compute the global component by integrating the time-dependent\npart of Equation 4 while holding the deforestation fraction constant at its mean value\nacross the full study period (see Eq. 7). This procedure yields the total change in the\nvariable that can be attributed to temporal trends (i.e., global influences such as\nfossil fuel emissions, global methane sources, etc.) within the BLA during the\nanalysis period. Although our spatial domain is regional, the time-dependent signal\nextracted from the statistical model reflects processes consistent with globally\nobserved trends in greenhouse gas concentrations. Therefore, the values of 87 ppm\n(CO₂) and 173 ppb (CH₄) correspond to the integrated global contributions over the\n35-year period (1985–2020) within the BLA, derived from the partial derivative of\nEquation 4 with respect to time, as shown in Equation 7.\nResponses to Reviewers\nWe address the remaining comments below using the following color code strategy:\n● In black are the reviewer’s comments.\n● In blue are the author’s responses.\n● In blue and italics are the text modifications we made in the\nmanuscript or section names when referred to.\nWe trust that the revisions satisfactorily address the concerns raised and hope the\nmanuscript is now suitable for publication in Nature Communications.\nSincerely,\nMarco Aurélio de Menezes Franco, Luiz A. T. Machado, and all co-authors\nReviewer #1 (Remarks to the Author):\nOverall I think the authors comprehensively addressed the reviewers' comments and\nsignificantly improved the clarity of their manuscript.\nAll that is left from my side is minor remark regarding the sensitivity analysis related to the\nsize of their study plots in Figure S1. Especially the variety between the different\nprecipitation results shown in the lower part of the panel are not \"minor\". The authors did\njustify very well why they have chosen the 300 km resolution but I think they should also\nmention the scale dependency of their results. Please also add \"dry season\" to the upper\nparts of the panel.\nWe thank the reviewer for their positive and constructive feedback. We have now\nmentioned the scale dependency in the manuscript, which reads as follows:\nThe results, shown in Supplementary Figure S1, indicate that the long-term trends in\ntemperature and precipitation remain consistent across scales, reinforcing the\nsuitability of the 300×300 km2 resolution adopted in this study. However, greater\nvariability during the dry season suggests a slight scale dependency for the\nprecipitation in the analysis.\nAdditionally, we have revised Figure S1 to include the label \"dry season\" in the upper\nparts of the panel, as requested (Figure S1 of the manuscript, shown below).\nWe appreciate the reviewer’s careful attention to detail, which has helped improve\nthe clarity of our manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Overall I think the authors comprehensively addressed the reviewers' comments and significantly improved the clarity of their\nmanuscript.\nAll that is left from my side is minor remark regarding the sensitivity analysis related to the size of their study plots in Figure\nS1. Especially the variety between the different precipitation results shown in the lower part of the panel are not \"minor\". The\nauthors did justify very well why they have chosen the 300 km resolution but I think they should also mention the scale\ndependency of their results. Please also add \"dry season\" to the upper parts of the panel.", "reviewer_2": "I believe the authors have been thorough in addressing both my comments and the comments of other reviewers. To my\nknowledge this response is carefully considered and has improved the manuscript greatly. I would be happy to see this\nnovel and interesting research published."}, "author_response": "We thank the reviewer for their positive and constructive feedback. We have now\nmentioned the scale dependency in the manuscript, which reads as follows:\nThe results, shown in Supplementary Figure S1, indicate that the long-term trends in\ntemperature and precipitation remain consistent across scales, reinforcing the\nsuitability of the 300×300 km2 resolution adopted in this study. However, greater\nvariability during the dry season suggests a slight scale dependency for the\nprecipitation in the analysis.\nAdditionally, we have revised Figure S1 to include the label \"dry season\" in the upper\nparts of the panel, as requested (Figure S1 of the manuscript, shown below).\nWe appreciate the reviewer’s careful attention to detail, which has helped improve\nthe clarity of our manuscript."}]

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