We thank the referee for the appreciation of our work. 

We agree with the referee on the importance of quantifying the uncertainty associated with estimates of CO₂ emissions from fire-driven degradation. To address this, we have combined the uncertainties of each term in Equation 2.27 of the IPCC (2006) using Monte Carlo simulations. This approach enabled us to incorporate multiple sources of uncertainty:

• pixel-level uncertainty in fuel mass available for combustion from the 2021 ESA-CCI biomass dataset (expressed as the standard deviation in Mg/ha)
• omission and commission errors from JRC-TMF accuracy assessment, as reported by Vancutsem et al. 2021) (Table S5 from the supplementary materials), affecting the estimation of forest degradation from fire
• uncertainty in the combustion factor for primary tropical moist forest (standard deviation reported in IPCC 2006, Table 2.6)
• uncertainty in tropical forest emission factors (standard deviation reported in IPCC 2006, Table 2.5)

As a preprocessing step, we calculated burned forest area for the years 2022, 2023, and 2024 by spatially intersecting JRC-TMF forest degradation data with GWIS fire detections. This approach increases confidence in identifying fire-driven degradation, while providing a conservative estimate. For the available fuel mass, we used the 2021 ESA CCI Aboveground Biomass (AGB) dataset along with its associated standard deviation. In the original version of the manuscript, burnt area and fuel mass were combined at the pixel level using the ‘stratify and multiply’ approach described by Harris et al. (2021). In this revised version, we aggregated the analysis to a 0.5-degree grid across the Pan-Amazon region, computing the total area of fire-driven degradation for 2022–2024, the mean AGB, and the average of AGB standard deviations within each grid cell. The revised paragraph provides a more detailed explanation of how we combined the uncertainties (see below).

Thanks to your suggestion, we have also included in the revised version of the manuscript estimates of deforestation emissions and combined uncertainties from the activity data (deforested area) and the ESA-CCI biomass dataset (see below).

Action taken: We have revised the methodology section related to CO₂ emission estimates (section A4), with corresponding updates made to the Results section. A new figure has been added to the main manuscript, synthesizing Pan-Amazon CO₂ emissions and their associated uncertainties from both burned forest and deforestation for the years 2022, 2023, and 2024. The abstract has been updated to reflect these new analyses, and the limitations section (first paragraph of the Discussion) has been revised to emphasize the varying levels of uncertainty in the analysis and to clarify how our estimates remain conservative.

New version of the methodological section:

A4  Estimation of CO₂ emissions and uncertainty analysis

We estimated carbon dioxide (CO₂) emissions resulting from fire-driven degradation and deforestation in the Amazon basin for the years 2022, 2023, and 2024 using a Monte Carlo simulation framework. Emissions were calculated based on spatially explicit data on change areas and aboveground biomass (AGB) from the 2021 ESA CCI AGB map (Santoro et al., 2024), incorporating uncertainty in all relevant variables, including classification errors in change areas. Burned forest areas from fire-driven degradation were identified through the spatial intersection of TMF forest degradation and GWIS fire detections (as detailed in the “Integration of TMF–GWIS datasets” section), enhancing confidence in the detection of burned forests while yielding a conservative estimate. Areas of deforestation were directly derived from TMF. For computational efficiency and to minimize the influence of spatial autocorrelation in AGB errors, we aggregated change areas (fire-driven degradation and deforestation), AGB, and its associated standard deviation within spatial units of 0.5-degree grid cells.

A4.1 Emissions from forest fires

Emissions from fire-affected areas () were calculated using equation 1, which follows equation 2.27 of the 2006 IPCC guidelines (IPCC 2006) and factors from Table 2.5 and 2.6 of the 2019 IPCC (IPCC 2019):

See Equation 1 in the supplement document attached to the response

where  is the adjusted burned area (ha) in spatial unit , the aboveground biomass (Mg ha^-1) in spatial unit ,  the combustion completeness (adimensional),  the emission factor (g CO₂ kg^-1 dry biomass),  the number of spatial units, and 10^-3 just converts g CO₂ to Mg CO₂. The emission factor  was sampled from a normal distribution with a mean of 1580 g CO₂ kg^-1 dry biomass and a standard deviation of 90 g CO₂ kg^-1 dry biomass, based on Andreae and Merlet (2001). Combustion completeness () was modeled as a normal distribution with a mean of 0.50 and standard deviation of 0.03, consistent with values reported for tropical forest fires (van der Werf et al., 2010).

A4.2 Emissions from deforestation

Emissions from deforestation () were computed with equation 2:

See Equation 2 in the supplement document attached to the response

where  is the adjusted deforested area (ha),  the carbon fraction of dry biomass (fixed at 0.47), 44/12 the molecular weight ratio to convert carbon to CO₂. Unlike fire emissions, no combustion completeness or emission factor is needed for deforestation, as it is assumed that all biomass is eventually emitted (IPCC, 2006).

A4.3 Uncertainty in area estimates

To incorporate classification uncertainty in both burned and deforested area estimates, we applied probabilistic adjustments to the mapped areas using commission and omission error rates derived from the confusion matrix reported in Vancutsem et al. (2021). These error rates were modelled as Beta distributions to reflect their probabilistic nature, enabling their integration into a Monte Carlo simulation framework. This approach follows best practices for area estimation under classification uncertainty (Olofsson et al., 2014). Specifically, commission error was modelled as , and omission error as . These distributions were used to adjust the mapped areas of burned and deforested land in each simulation iteration, allowing uncertainty in classification accuracy to propagate into the final emissions estimates. Adjusted areas ( were computed in each iteration of the simulation using equation 3:

See Equation 3 in the supplement document attached to the response

where  and  are sampled commission and omission errors, respectively.

A4.5 Monte Carlo simulation

We performed 100,000 Monte Carlo iterations per year and source. In each iteration, we simultaneously sampled i) commission and omission errors (affecting area adjustments), ii) AGB values (modeled as normal distributions with mean and standard deviation derived from the data), combustion completeness and emission factor for fire emissions only. Negative samples were truncated at zero. The result was a distribution of total CO₂ emissions for each source (fire, deforestation) and year, from which the average and standard deviation were derived.

Added references to the reference list

Andreae, M. O., & Merlet, P. (2001). Emission of trace gases and aerosols from biomass burning. Global Biogeochemical Cycles, 15(4), 955–966. https://doi.org/10.1029/2000GB001382

IPCC (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Volume 4: Agriculture, Forestry and Other Land Use.

Olofsson, P. et al. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42–57. https://doi.org/10.1016/j.rse.2014.02.015

van der Werf, G. R., et al. (2010). Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmospheric Chemistry and Physics, 10(23), 11707–11735. https://doi.org/10.5194/acp-10-11707-2010

Vancutsem, C. et al. (2021). Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Science Advances, 7, eabel1603. https://doi.org/10.1126/sciadv.abe1603

New version of result section:

Burned forests resulting from fire-driven degradation in the Pan-Amazon region released an estimated 791 ± 86 Mt CO₂ (million tons of carbon dioxide equivalent, ±1 standard deviation) in 2024—approximately seven times higher than the annual average of the previous two years (117 ± 13 Mt CO₂; see Figure 2). Brazil was the largest contributor, accounting for 61% of these emissions, followed by Bolivia at 32%. In contrast, emissions from deforestation declined from 1,044 ± 65 Mt CO₂ in 2022 to 625 ± 38 Mt CO₂ in 2024. Altogether, emissions from deforestation and fire-driven degradation totaled 1,416 ± 108 Mt CO₂ in 2024, with burned forest emerging as the dominant source. Comparatively, the latest publication of the Global Carbon Budget (Friedlingstein et al., 2025) also refers to a massive increase in emissions from deforestation and degradation fires in South America in 2024, from 445 Mt CO₂ in 2023 to 1,227 Mt CO₂ in 2024, mostly driven by the unusual dry conditions linked to El Niño.

See Figure 2 in the supplement document attached to the response

Figure 2: Pan-Amazon emissions from deforestation and fire-driven degradation in 2022-2024. Emissions from small-scale degradation processes (e.g., selective logging) or from disturbances in areas where GWIS thermal anomalies do not overlap with TMF forest degradation are not included in this analysis. Bars represent the mean values, and vertical error bars indicate the standard deviations, both derived from combining uncertainties using Monte Carlo simulation.

New version of the abstract:  

The Amazon rainforest, historically fire-resistant, is experiencing an alarming increase in wildfires due to climate extremes and human activity. The 2023/2024 drought, surpassing previous records, combined with forest fragmentation, has dramatically heightened fire vulnerability. Analysing the Tropical Moist Forest (TMF) and Global Wildfire Information System (GWIS) datasets, we found a 152% surge in forest disturbances from deforestation and degradation in 2024, reaching a two-decade peak of 6.64 million hectares. Forest degradation, particularly large-scale degradation linked to fires, increased by over 400%, largely exceeding deforestation. Brazil and Bolivia experienced the most severe impacts, with Bolivia seeing 9% of its intact forest burned in 2024. Fire-driven forest degradation in the Pan-Amazon released 791 ± 86 Mt CO₂ (million tons of carbon dioxide equivalent, ±1 standard deviation) in 2024—a seven-fold increase compared to the previous two years—surpassing emissions from deforestation. The escalating fire occurrence, driven by climate change and unsustainable land use, threatens to push the Amazon towards a catastrophic tipping point. Urgent, coordinated efforts are crucial to mitigate these drivers and prevent irreversible ecosystem damage.

New version of the limitation section:

Our analysis presents findings with certain inherent limitations that should be considered during interpretation. The TMF dataset employed may have a tendency to underrepresent the extent of small-scale forest degradation (<0.09 ha), particularly that resulting from edge effects, selective logging and low-intensity fire events. These types of non-permanent disturbances can have considerable ecological consequences that may not be fully captured in the data (Bourgoin et al., 2024). Furthermore, differentiating between disturbances caused by degradation processes and those resulting from deforestation posed a challenge, specifically in the context of the 2024 data, as indicated by Vancutsem et al. (2021) due to lack of historical depth in detecting forest recovery following degradation. This overlap in observational characteristics could introduce some level of uncertainty in the precise categorization of forest change. While these limitations suggest a potential for underestimation of the overall impact, our estimates regarding the general scale of the area affected by fires are considered reasonably consistent and remain conservative. The broad magnitude of the impacted area is unlikely to be drastically altered by these factors, suggesting that fire remains a significant driver of landscape change within the study area.

We thank the referee for their positive assessment of our work. We fully agree on the importance of repeated fire-driven degradation, which is often sustained by negative feedback loops and shaped by the interplay between disturbance intensity and recurrence. We also recognize the broader landscape-scale processes—such as fragmentation, drought, and edge effects—that can significantly influence long-term forest recovery. These important aspects are mentioned in the second paragraph of the Discussion section and represent a valuable avenue for future research. The JRC-TMF dataset is particularly well suited for such analyses, as it tracks successive disturbance events and associated recovery dynamics. As the team responsible for maintaining the TMF dataset at the JRC, we plan to explore these dynamics further. However, this lies beyond the scope of the current brief communication, which focuses on reporting new degradation events affecting previously undisturbed forests.

We respond to your specific comments in order:

- Abstract: Correct, we mean disturbed from both deforestation and degradation, we have rephrased this sentence of the abstract accordingly and it now reads as such: “Analysing the Tropical Moist Forest (TMF) and Global Wildfire Information System (GWIS) datasets, we found a 152% surge in forest disturbances from deforestation and degradation in 2024, reaching a two-decade peak of 6.64 million hectares”

- Line 56/57: Thank you, we rephrased it as such: “Large-scale degradation totaled 3.31 Mha in 2024, representing a 1077% increase compared to the annual average for the 2019–2023 period.”

- Line 59: we agree with the referee with the need to homogenize the different terms of disturbances. From the TMF perspective, disturbance is split into permanent change, deforestation, and temporary change, forest degradation. We then separate small-scale from large-scale degradation. Regarding the intersection of GWIS thermal detections with TMF degradation, we decided to name it burned forest area. This change was applied throughout the manuscript and the term is now clearly explained in the section ‘A3 Integration of TMF-GWIS datasets’. Line 59 now reads as such: “This coincided with a 1461% increase in burned forest area, totaling 3.56 Mha, 80% of which overlaps with TMF-large scale degradation (more details on the integration of TMF-GWIS datasets in Section A3).”. Similar change was applied in line 74 and in the revised result section related to forest fire emissions (see our response to referee#1).

- Line 171: We agree with the referee that GWIS thermal detections has coarse resolution, however, in this specific sentence we refer to JRC-TMF dataset only where the spatial and temporal resolution of Landsat remains a limitation for detecting small-scale degradation such as light intensity fires. Hence our result on total magnitude of forest disturbances remain conservative.

- Figures A1 and A2 are cited in the caption of Figure 1 as an extension to the analysis (relative burned forest area and 2001-2024 time series). We now cite Figure A1 directly in the text when presenting results for Bolivia at line 62 and the same for Figure A2 at line 64.  

- Thank you for this recommendation, the caption of Figure A3 has been revised and now reads as such: “(a) Total burned forest area (2012–2024) from GWIS, distributed across JRC-TMF transition classes: undisturbed forest, degraded forest, forest regrowth, deforested land (detected between 1990 and 2024), and other land cover (including areas deforested prior to 1990). (b) Annual GWIS burned area for 2012 and 2024, categorized by TMF annual land cover status, including undisturbed forest, degraded forest, deforested land, forest regrowth, and other land cover types in each respective year.”.