# Reduced Vegetation Uptake During the Extreme 2023 Drought Turns the Amazon Into a Weak Carbon Source

S. Botía, C Q Dias-júnior, S. Komiya, A M van der Woude, M. Terristi, R J de Kok, G. Koren, H. van Asperen, S P Jones, F a F d'Oliveira, et al.

► To cite this version:

S. Botía, C Q Dias-júnior, S. Komiya, A M van der Woude, M. Terristi, et al. Reduced Vegetation Uptake During the Extreme 2023 Drought Turns the Amazon Into a Weak Carbon Source. AGU Advances, 2026, 7 (1), 10.1029/2025av001658. [hal-05526449](https://hal.science/hal-05526449v1)

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# AGU Advances

## RESEARCH ARTICLE

10.1029/2025AV001658

**Peer Review** The peer review history for this article is available as a PDF in the Supporting Information.

**Key Points:**

* Drought turned the biogeographic Amazon in 2023 to a weak carbon source, with a magnitude similar to the drought of 2015 but less than 2016

* Seasonal development of net carbon exchange in 2023 was marked by early uptake that partly compensated the carbon source of the dry season

* The 2023 fire season in the Amazon was extended until November, though annual fire emissions were close to the long-term average

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

S. Botía,
sbotia@bgc-jena.mpg.de

**Citation:**

Botía, S., Dias-Júnior, C. Q., Komiya, S., van der Woude, A. M., Terristi, M., de Kok, R. J., et al. (2026). Reduced vegetation uptake during the extreme 2023 drought turns the Amazon into a weak carbon source. *AGU Advances*, *7*, e2025AV001658. https://doi.org/10.1029/2025AV001658

Received 14 JAN 2025
Accepted 5 JAN 2026

**Author Contributions:**

**Conceptualization:** S. Botía, A. Bastos

**Data curation:** S. Botía, C. Q. Dias-Júnior, S. Komiya, A. M. van der Woude, M. Terristi, R. J. de Kok, G. Koren, H. van Asperen, S. P. Jones, F. A. F. D'Oliveira, U. Weber, E. P. Marques-Filho, I. M. Cely, A. Araujo, J. V. Lavric, D. Walter, X. Li, J. P. Wigneron, B. D. Stocker, J. Gonçalves de Souza, M. O'Sullivan, S. Sitch, P. Ciais, F. Chevallier, W. Li, I. Luijkx, W. Peters, S. Zaehle, A. Bastos

© 2026. The Author(s).
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

<figure><img src="image_2.png" /></figure>

## Reduced Vegetation Uptake During the Extreme 2023 Drought Turns the Amazon Into a Weak Carbon Source

S. Botía¹, C. Q. Dias-Júnior², S. Komiya³, A. M. van der Woude⁴, M. Terristi⁵,⁶, R. J. de Kok⁴,⁷, G. Koren⁸, H. van Asperen³, S. P. Jones³, F. A. F. D'Oliveira², U. Weber⁶, E. P. Marques-Filho⁹, I. M. Cely¹⁰, A. Araujo¹¹, J. V. Lavric¹², D. Walter¹³, X. Li¹⁴, J. P. Wigneron¹⁴, B. D. Stocker¹⁵,¹⁶, J. Gonçalves de Souza¹⁷, M. O'Sullivan¹⁷, S. Sitch¹⁷, P. Ciais¹⁸, F. Chevallier¹⁸, W. Li¹⁹, I. Luijkx⁴, W. Peters⁴,²⁰, C. A. Quesada²¹, S. Zaehle¹, S. Trumbore³, and A. Bastos⁵,⁶

¹Biogeochemical Signals Department, Max Planck Institute for Biogeochemistry, Jena, Germany, ²Departamento de Física, Instituto Federal de Educação, Ciência e Tecnologia do Pará, Belém, Brazil, ³Biogeochemical Processes Department, Max Planck Institute for Biogeochemistry, Jena, Germany, ⁴Meteorology & Air Quality Department, Wageningen University, Wageningen, The Netherlands, ⁵Institute for Earth System Science and Remote Sensing, Leipzig University, Leipzig, Germany, ⁶Biogeochemical Integration Department, Max Planck Institute for Biogeochemistry, Jena, Germany, ⁷ICOS ERIC, Carbon Portal, Geocentrum II, Lund, Sweden, ⁸Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The Netherlands, ⁹Departamento de Física, Universidade Federal da Bahia, Salvador, Brazil, ¹⁰Atmospheric Physics Laboratory, University of São Paulo, São Paulo, Brazil, ¹¹Embrapa Amazônia Oriental, Belém, Brazil, ¹²Acoem GmbH, Hallbergmoos, Germany, ¹³Multiphase Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany, ¹⁴INRAE, UMR1391 ISPA, Villenave d'Ornon, France, ¹⁵Institute of Geography, University of Bern, Bern, Switzerland, ¹⁶Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland, ¹⁷Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK, ¹⁸LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France, ¹⁹Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China, ²⁰University of Groningen, Centre for Isotope Research, Groningen, The Netherlands, ²¹Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Brazil

### Abstract
In 2023, the biogeographic Amazon experienced temperature anomalies of 1.5°C above the 1991–2020 average from September to November. These conditions were driven by high sea surface temperature in the Atlantic and Pacific oceans, together with reduced moisture advection from the Atlantic, causing large vapor pressure and water deficits in the second semester of 2023. Here, we evaluate the response of the Amazon carbon cycle to this extreme event across different spatial scales. We combined atmospheric CO₂ mole fractions and eddy covariance flux data from the Amazon Tall Tower Observatory (ATTO, -2.1441, -58.99), low-latency simulations by Dynamic Global Vegetation Models (DGVMs), an atmospheric inversion, and remote sensing data. We find that in 2023 the Amazon region was, including fires, a net carbon source of 0.01–0.17 PgC. Fire emissions (0.15 [0.13–0.17] PgC) were within typical variability of the 2003–2023 period, thus we attribute the weak carbon source to reduced vegetation uptake during the dry season (August–October). A stronger-than-normal vegetation uptake early in the year (January–April), consistent across data streams and spatial scales, mitigated the total carbon losses by the end of the year. We find a shift from carbon sink to source in May and a peak source in October. Our findings show a reduced vegetation carbon uptake over the Amazon region, leading to a weak carbon source that contributed up to 30% of the net carbon loss in the tropical land in 2023.

### Plain Language Summary
The Amazon rainforest is a fundamental component in the Earth system storing large amounts of carbon in standing biomass. In 2023, unusually high temperatures (1.5°C above typical levels from 1991 to 2020) and atmospheric dryness were recorded. These conditions were caused by warmer water temperature in the Atlantic and Pacific Oceans, causing reduced moisture transport from the Atlantic to South America, leading to drought in the second half of 2023. We studied how these extreme conditions affected the Amazon's ability to absorb carbon. Using carbon dioxide measurements, along with computer simulations, and satellite data, we observed that the Amazon became a carbon source in 2023, releasing 0.01 to 0.17 billion tons of carbon, rather than absorbing it. Fire-related emissions were 0.15 billion tons, within normal levels for the last two decades (2003–2023). We therefore attribute the carbon release in 2023 mostly to weakened vegetation uptake, rather than increased losses from fires. Computer models suggest vegetation absorbed more carbon than usual from January to April, offsetting emissions, but this reversed in May as the Amazon released
**Formal analysis:** S. Botía, S. Komiya, A. M. van der Woude, M. Terristi, R. J. de Kok, G. Koren, H. van Asperen, F. A. F. D'Oliveira, U. Weber, I. M. Cely, A. Araujo, X. Li, J. P. Wigneron, B. D. Stocker, S. Sitch, P. Ciais, W. Peters, A. Bastos

**Funding acquisition:** P. Ciais, C. A. Quesada, S. Zaehle, S. Trumbore, A. Bastos

**Investigation:** S. Botía

**Methodology:** S. Botía, S. Komiya, A. M. van der Woude, B. D. Stocker, J. Gonçalves de Souza, S. Zaehle, A. Bastos

**Project administration:** C. A. Quesada, S. Zaehle, S. Trumbore

**Resources:** C. Q. Dias-Júnior, S. Komiya, S. Sitch, P. Ciais, C. A. Quesada, S. Zaehle, S. Trumbore, A. Bastos

**Software:** J. Gonçalves de Souza, M. O'Sullivan, S. Sitch, P. Ciais, F. Chevallier, W. Li, I. Luijkx, W. Peters, S. Zaehle, A. Bastos

**Supervision:** B. D. Stocker, J. Gonçalves de Souza, A. Bastos

**Visualization:** M. Terristi, R. J. de Kok, A. Bastos

**Writing – original draft:** S. Botía, A. Bastos

**Writing – review & editing:** S. Botía, C. Q. Dias-Júnior, S. Komiya, A. M. van der Woude, M. Terristi, R. J. de Kok, G. Koren, H. van Asperen, S. P. Jones, F. A. F. D'Oliveira, U. Weber, E. P. Marques-Filho, J. V. Lavric, D. Walter, X. Li, J. P. Wigneron, B. D. Stocker, J. Gonçalves de Souza, M. O'Sullivan, S. Sitch, P. Ciais, F. Chevallier, W. Li, I. Luijkx, W. Peters, S. Zaehle, S. Trumbore, A. Bastos

more carbon, peaking in October. Our estimate of carbon emissions from the Amazon accounts for 30% of the net carbon source across tropical land in 2023.

# 1. Introduction

The global atmospheric CO₂ growth rate in 2023 was 2.79 ± 0.08 ppm (Ke et al., 2024; Lan, Mund, et al., 2024), ranking as the third highest since 2000 and the fourth highest since 1959. For comparison, the average growth rate during 2010–2020 was approximately 2.5 ppm (https://gml.noaa.gov/ccgg/trends/gl_gr.html, last accessed 12 December 2024). This unusually high 2023 value cannot be explained solely by the 1.3% increase in anthropogenic CO₂ emissions from 2022 to 2023 (Friedlingstein et al., 2024), suggesting a temporary weakening of global carbon sinks (Ke et al., 2024). Tropical land plays an important role in determining the mean magnitude of the land carbon sink (Schimel et al., 2015), which is controlled by a balance between net carbon sources resulting from deforestation and carbon sinks, where uptake surpasses carbon losses in less disturbed forest vegetation. Net release of carbon and reduction of gross primary productivity in the tropical land has been associated with positive phases of El Niño Southern Oscillation events (Gloor et al., 2018; J. Liu et al., 2017; Palmer et al., 2019; Rödenbeck et al., 2018; Zhang et al., 2019), which in turn is linked to positive surface temperature anomalies globally (Trenberth et al., 2002) and in tropical forests (Jimenez et al., 2018).

Globally, the year 2023 was the first one in which daily global mean temperatures were continuously 1°C above the 1850–1900 reference period, marking an unprecedented record. In about 50% of the days, most of them in the second half of the year, daily global mean temperatures reached values 1.5°C above the 1850–1900 reference period, and from April to December global surface ocean temperatures registered record values (Copernicus, 2024). Associated with these record warming conditions, extreme events were observed both over land and ocean, including major heatwaves and droughts across many regions. For example, in Canada, extreme temperatures combined with low humidity led to unprecedented wildfires which contributed to ca. 25% of total tree cover loss that year (MacCarthy et al., 2024) and released over 0.6 PgC, more than the national fossil fuel emissions (Byrne et al., 2024). Central South America suffered a series of intense heat waves, with some locations reporting temperatures 10°C above the long-term mean (Marengo et al., 2024, 2025). The occurrence of heat wave events amplified the effects of the drought and heat conditions in the Amazon (Marengo et al., 2024, 2025), with temperatures in October larger than 3°C above the 1981–2020 average, and the Port of Manaus reaching the lowest water levels since records began in 1902 (Espinoza et al., 2024).

These extreme conditions and the compound drought-heat events over South America (Marengo et al., 2025), have been linked to a transition from La-Niña conditions in 2022–23 to El-Niño from mid 2023, combined with widespread anomalous warming over the oceans worldwide (Espinoza et al., 2024; Mamani et al., 2025; Marengo et al., 2024). Previous droughts in the Amazon region, such as the ones in 2005 and 2010, and those associated with El-Niño conditions like in 2015–16, have resulted in reduced net carbon uptake by the Amazon forest, due to decreased productivity despite a slight increase in greenness (Bastos et al., 2018; J. Liu et al., 2017; van Schaik et al., 2018; J. Yang et al., 2018), reduced tree growth (Feldpausch et al., 2016; Phillips et al., 2009; H. Yang et al., 2022) and increased mortality (Hartmann et al., 2022; Powers et al., 2020). Hot and dry conditions tend to be associated with high fire activity and increased tree mortality (Brando et al., 2014), resulting in persistent impacts that compound with human-driven disturbances which can accelerate forest degradation (Berenguer et al., 2021; Fawcett et al., 2023; Lapola et al., 2023) and affect agricultural production (Marengo et al., 2022). Furthermore, in previous droughts in 2010 and 2015/2016, intensified fire emissions in the south and east of the Amazon have shifted the region from a carbon sink to a source (Alden et al., 2016; Basso et al., 2023; Gatti et al., 2014; Gloor et al., 2018; Rosan et al., 2024; van der Laan-Luijkx et al., 2015). However, regional differences in drought severity (Aragão et al., 2018; Lapola et al., 2023; Phillips et al., 2009) and thus resistance and resilience to drought (S. Chen et al., 2024) and fires (Berenguer et al., 2018; Esquivel-Muelbert et al., 2020), together with long-term rainfall trends, make impacts of each drought unique (Anderson et al., 2018).

Here, we examine the impacts of the 2023 extreme heat and drought conditions on the Amazon carbon cycle by combining multiple sources of information on ecosystem productivity, changes in biomass growth, fire activity and resulting net carbon fluxes. We first study the evolution of fire activity, contrasting when possible with previous events. Then we compare net land-atmosphere carbon exchanges, gross primary productivity, and ecosystem respiration from global vegetation models (bottom-up approach) and an atmospheric inversion (top-
down approach), analyzing their agreement and differences in magnitude and seasonality during the 2023 extreme event. As a benchmark, we compare the large-scale patterns with in situ measurements of carbon fluxes and atmospheric CO₂ at the Amazon Tall Tower Observatory (ATTO, -2.1441, -58.99), which is located in the central Brazilian Amazon in a well preserved location and is one of the few sites in the region providing carbon cycle in situ data. By combining these multiple lines of evidence, our study aims to provide a robust assessment of the sources and sinks of carbon in 2023 during the extreme drought in the Amazon, and their relevance in the context of the global carbon cycle anomalies in 2023 (Friedlingstein et al., 2024; Ke et al., 2024).

## 2. Materials and Methods

### 2.1. Data Sources and Carbon Cycle Flux Terms at Regional Scale

We used ERA5 reanalysis data (Hersbach et al., 2020), ERA5-Land (Muñoz-Sabater et al., 2021), MSWEP V2.8 (Beck et al., 2019) and GLEAM v4.2a (Miralles et al., 2025) to analyze climatic drivers and anomalies over the Amazon. We refer the reader to Text S1 in Supporting Information S1 for more details on the reanalysis data. To calculate the carbon cycle components (see Figure 1 and Texts S2 and S3 in Supporting Information S1 for more details) we use four land surface models (bottom-up approach): Simple Biosphere model Version 4 (SiB4, Haynes et al., 2019), ORCHIDEE-MICT (Guimberteau et al., 2018; Krinner et al., 2005), O-CN (hereafter referred to as OCN, Zaehle et al., 2010) and JULES (Clark et al., 2011). Three of the land surface models are Dynamic Global Vegetation Models (DGVMs) and one is a process-based model (SiB4), but for practical reasons we will refer to DGVMs throughout the text. As the top-down approach we use one satellite-driven atmospheric inversion (OCO2-Inv) that has a high resolution globally (1 × 1°) and is driven by the OCO-2 satellite atmospheric CO₂ column data. The remote sensing data (Figure 1, Text S4 in Supporting Information S1), like fire counts and emissions, GPP proxies (Sun Induced Fluorescence—SIF and Near Infrared of Vegetation—NIRv) and the Aboveground Carbon (AGC) based on L-band derived Vegetation Optical Depth (L-VOD) are all described in Text S4 in Supporting Information S1. In this study, we focus on the response of carbon cycle components to the 2023 drought, specifically within the biogeographic Amazon. This region encompasses a diverse range of land cover types, including evergreen old-growth forests, grasslands, savannas, and areas that have been heavily degraded or deforested. As such, we aim to assess the total carbon exchange across all land cover classes within the biogeographic Amazon. To do this, we estimate the carbon cycle components for the entire region, defining them as the net land-atmosphere carbon flux ($F_{land}$), which consists of the Net Biome Exchange (NBE) and anthropogenic emissions from fossil fuels ($F_{ff}$).

$$F_{land} = \text{NBE} + F_{ff}$$

NBE is composed by the Net Ecosystem Exchange (NEE) and fire emissions, including deforestation and degradation ($F_{fire}$)

$$\text{NBE} = \text{NEE} + F_{\text{fire}}$$

Note that fires are aggregated across all the biogeographic Amazon. NEE is given by the difference between total ecosystem respiration (TER) and photosynthesis (GPP). A negative sign (−) in $F_{land}$, NBE and NEE indicate a net uptake of carbon over land, whereas a positive (+) sign denotes a net source of carbon over land.

$$\text{NEE} = \text{TER} - \text{GPP}$$

To obtain the bottom-up NBE, we use the ensemble mean of NEE of the DGVMs combined with two different satellite-based global fire emission products: GFAS (Kaiser et al., 2012) and GFED4s (van der Werf et al., 2017). It is important to note that we do not use the simulated fire in the DGVMs (i.e., JULES, OCN, ORCHIDEE). Therefore, to represent total NBE in the bottom-up approach, we add the mean $F_{fire}$ from GFAS and GFED4s to the DGVM ensemble mean.

The top-down approach NBE is obtained using the OCO2-Inv. By subtracting $F_{fire}$ from NBE, we obtain NEE. In Figure 1, we provide a summary of the data streams used to calculate the bottom-up and top-down NBE and NEE, together with the data used as proxies and supporting the main findings of this study.
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Data streams for climate and carbon cycle components used to obtain the carbon budget for the biogeographic Amazon based on top-down and bottom-up approaches. Note that for Sun-induced Fluorescence (SIF), as we describe in Text S4 in Supporting Information S1 we are using two products with different reference periods. The green arrow shows that SiF and Near-Infrared of vegetation (NIRv) are used as GPP proxies. T stands for 2 m air temperature, VPD for vapor pressure deficit, EWH for equivalent water height and L-VOD for L-band vegetation optical depth.</figcaption></figure>

The uncertainty of the $F_{fire}$ is reported in terms of the min-max range [min-max], between GFAS and GFED4s. For the flux components (GPP, TER, NEE and NBE) calculated with the DGVMs we use the standard deviation of the model ensemble mean. For the OCO2-Inv we do not report uncertainty for 2023, but we note that as the global uncertainty ($1-\sigma$) for a single year is $3 \text{ PgC year}^{-1}$ (Chevallier et al., 2006, 2012), the posterior uncertainty for the biogeographic Amazon will be less given the smaller domain and the data constraint, likely within 0.3–0.5 $\text{PgC year}^{-1}$. The inter-annual variability (IAV) is also reported explicitly and is calculated as the standard deviation of the multi-year mean for a given data source (e.g., NEE ± IAV). For the DGVMs, the long-term mean is calculated by first obtaining the ensemble mean for each year and then the average across all years. The IAV is reported to indicate the magnitude of inter-annual fluctuations in each data source and should not be interpreted as an uncertainty estimate.

## 2.2. Local Measurements of Carbon and Climate Variables

The ATTO site (−2.1441°S, −58.999°W, https://www.attoproject.org/) is located 150-km northeast (and upwind) of the city of Manaus. The research site is set within and surrounded by old-growth forest, in the Uatumã Sustainable Reserve. We use several data streams measured at the ATTO site. In situ continuous CO₂ mole fractions covering the period from 2014 to 2023 were used. The ATTO site and most of the measurement systems have been described by Andreae et al. (2015) and the footprint of the CO₂ mole fractions were characterized by Botía et al. (2022) and added in Figure S3 in Supporting Information S1. The half-hourly eddy covariance (EC) data was
measured with a 3D sonic anemometer (CSAT3, Campbell Scientific Inc., Logan, UT, USA) and an open-path infrared gas analyzer (LI-7500A, Li-COR Inc., Lincoln, NE, USA), both mounted at 81 m above ground level on the ATTO 80 m Instant tower. The filtered high-quality and half-hourly turbulent EC-CO₂ fluxes were used for further data analysis: net ecosystem exchange (EC-NEE) calculation, gap-filling and EC-NEE partitioning to Gross Primary Productivity (GPP) and Ecosystem Respiration (R<sub>eco</sub>). The complete details on the processing, partitioning, data quality assessment and calibration of the eddy covariance data and the CO₂ mole fraction data sets can be found in Text S5 in Supporting Information S1.

## 3. Results

### 3.1. Seasonal Evolution and Spatial Distribution of Carbon Cycle Components

#### 3.1.1. Seasonal $F_{fire}$ Anomalies

The fire season in 2023 was extended well into November, different from normal years when the fire season offset is in October (Figures 2b and 2d). This extended fire season was driven by a north-to-south shift, but not necessarily leading to anomalous fire activity and carbon emissions (Figures 2b–2e). Fires were largely concentrated in the Arc of Deforestation, along the southern border of the Amazon (between 70–60°W and 15°S), but also further north in the state of Pará and along the main branch of the Amazon river (Figure 2a). The highest fire counts and emissions were in October and November (Figures 2b and 2d). On average, the fire activity is dominated by fires occurring south of 5°S, with an increasing contribution in October and November (Figures 2f and 2g). In 2023, from July to September the relative contribution of fires north of 5°S was 10% larger than the long-term mean (2003–2023), consistent with the study of Jiménez et al. (2024). Later (November and December), the fire counts in the south (below 5°S) were doubled relative to the 2003–2023 mean. Note that these spatial patterns are consistent with the development of the CWD in the second half of 2023 (Figure S1 in Supporting Information S1). Furthermore, ATTO carbon monoxide data shows larger than average enhancements of CO, especially in September and October, confirming the larger than average contribution of fires north of 5°S (Figure S13 in Supporting Information S1).

Even though there was an extended fire season, total fire emissions in 2023 (0.15 [0.13–0.17] PgC year⁻¹, see Figure 2c) were the same as the long-term (2003–2023) average between the two fire products (0.15 [0.13–0.16] PgC year⁻¹) with an interannual variability of ±0.07 PgC year⁻¹. Individually, over the entire record (2003–2023, Figure S14 in Supporting Information S1), GFED4s emissions and its inter-annual variability (0.16 ± 0.08 PgC year⁻¹) are on average larger than GFAS (0.13 ± 0.07 PgC year⁻¹). Our estimate for the biogeographic Amazon in 2023 is similar to 2015 and 2016 (0.13 and 0.14 PgC year⁻¹), consistent with the total fire counts in 2023 (~180,000), which were marginally higher than 2015 (~163,000) and 2016 (~145,000). Given these findings, we conclude that fires were within the long-term variability and had a close-to-normal contribution to the NBE (i.e., net vegetation exchange + fires) in 2023.

#### 3.1.2. Spatial Distribution of NEE, NBE, and Fires

In 2023, the DGVMs showed consistent spatial patterns of vegetation carbon sources and sinks (Figure 3a), with at least three out of four models agreeing on the sign of NEE (see the spatial anomalies in Figure S25 and ensemble spread in Figure S26 in Supporting Information S1). Vegetation acted as a carbon source south of the main Amazon River and northeast of ATTO. The southern source regions (e.g., between 70°W and 60°W) overlap with fire emissions (Figure 3b), whereas this overlap is not observed in the northeast. Spatial patterns of NEE were consistent with anomalies in temperature, VPD (Figure S2 in Supporting Information S1), and the absolute CWD (Figure S1 in Supporting Information S1), especially across the central, southern, and eastern Amazon. These relationships are supported by strong negative correlations between spatial anomalies in GPP and VPD ($r = -0.69, p < 0.001$), a moderate but significant negative correlation between TER and temperature anomalies ($r = -0.48, p < 0.001$), and significant negative correlations of both GPP ($r = -0.58, p < 0.001$) and TER ($r = -0.55, p < 0.001$) with CWD (Figure S24 in Supporting Information S1). Together, these results indicate that DGVM-based NEE (NEE = GPP-TER) anomalies were closely linked to hydrometeorological anomalies and absolute water deficits.

Combining the vegetation-only NEE from DGVMs with independent fire data sets (Figures 3a and 3b) indicates that carbon losses in 2023 were enhanced by fire activity in the southeast Amazon, while the source signal in the
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Fire counts and carbon emissions from fire in the biogeographic Amazon. Spatial distribution of total Fire counts in 2023 (a) with the latitudinal and longitudinal marginal distributions on y and x. We calculated the mean fire carbon emissions based on the GFED4s (van der Werf et al., 2017) and GFAS (Kaiser et al., 2012) products for each month from 2003 to 2023 (b). The red shading in (b) indicates the maximum and minimum fire emission between GFED4s and GFAS for the months in 2023. The cumulative fire carbon emissions are shown in (c). The seasonal evolution of fire counts (d) and cumulative fire counts from 2003 to 2023 (e). The relative contribution (%) of fire counts north (f) and south (g) of the latitude 5°S to the total fire counts per month for 2023 and the mean for 2003 to 2023.</figcaption></figure>

northeast originated mainly from net vegetation exchange. Note that northeast of ATTO fire emissions were low or absent, therefore the positive NBE (NEE + Fires, Figure 3c) is mainly driven by vegetation. Large negative precipitation anomalies in the southern Amazon from November 2022 to February 2023 (Espinoza et al., 2024) may have preconditioned drier fuels for the 2023 dry season, consistent with evidence that wet-season water storage deficits can increase fire risk months later (Y. Chen et al., 2013). However, the elevated fire activity over the southern Amazon in 2023 is best explained by the combined effects of this previous drying and the persistent warm, dry conditions (see Figures S1 and S2 in Supporting Information S1) from June to November.
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Bottom-up and top-down carbon cycle components for the biogeographic Amazon in 2023. In (a) the mean NEE for the DGVMs, in (b) the mean fire emissions between GFAS and GFED4s, in (c) the NBE calculated as the sum of panels (a, b) (NBE = NEE<sub>DGVMs</sub> + F<sub>fire</sub>) and in (d) the NBE of the OCO2-Inv. In (a) the grid cells in which at least three of the DGVMs coincide in the sign of NEE (i.e., ecosystem respiration—photosynthesis) are highlighted with a marker. Note that we considered the use of the spatial median for (a), but found that the difference with the spatial mean is negligible (see Figure S26 in Supporting Information S1).</figcaption></figure>

The spatial distribution of NBE (i.e., vegetation exchange plus fires) in both DGVMs and the OCO2-Inv shows a broadly consistent east-west sink-to-source gradient across the Amazon (Figures 3c and 3d). The OCO2-Inv indicates a net carbon uptake concentrated over the western basin (west of 70°W), while the DGVMs simulate stronger uptake in the northwest. In contrast, both data sets show carbon sources in the south, where fires are most frequent, but OCO2-Inv captures a stronger NBE source northeast of ATTO. The balance between simulated GPP and TER provides some interesting insights on the described spatial response. The DGVMs simulate a widespread reduction in GPP across most of the basin, particularly in the south and southwest, consistent with drought-related declines in photosynthetic uptake (Figure S22 in Supporting Information S1). TER anomalies were generally negative (Figure S23 in Supporting Information S1), indicating reduced respiration that partly offset the GPP decrease. In contrast, the northeastern Amazon exhibited weakly negative GPP anomalies but slightly positive TER anomalies in most models, suggesting that in this region, increased respiration contributed more strongly to the positive NEE anomaly than reduced GPP.
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Mean seasonal cycles of gross and net carbon fluxes for the biogeographic Amazon. Panels (a, b) show ensemble means for TER and GPP, respectively. The monthly variability is shown as a shaded area (±1 SD) and the ensemble spread for each month as errorbars. Panel (c) shows NEE, and (d) shows NBE for the DGVM ensemble mean. Panels (e, f) compare seasonal and total NBE from OCO2-Inv and DGVMs for 2015–2023. NBE for the DGVM ensemble was computed as the sum of NEE and mean fire emissions (GFED4s, GFAS). The asterisks in (f) indicate ensemble means significantly different from zero (two-sided t-test at 95% CI). The DGVM mean (–0.25 Pg C yr–1, *p* = 0.003) differs from zero, while OCO2-Inv does not (0.06 Pg C yr–1, *p* = 0.66). The 2023 DGVM NBE (+0.01 Pg C yr–1) lies outside the 2015–2023 95% CI (–0.40 to –0.11 Pg C yr–1), indicating an anomalously weak sink.</figcaption></figure>

### 3.1.3. Seasonal Evolution of NEE, NBE and GPP, TER

We compared the calculated seasonal evolution of the gross and net carbon fluxes over the whole basin in 2023 with the long-term mean, both for DGVMs (2003–2023) and the OCO2-Inv (2015–2023) (Figure 4). We use different reference periods as the OCO2-Inv is limited to the OCO2 data coverage. DGVMs simulate positive (slightly above $1\sigma$) TER and GPP anomalies in the beginning of 2023, which result in equally strong negative NEE (increased uptake) until May (Figures 4a–4c). Starting in August, DGVMs indicate a steep decline in GPP, shifting from moderately positive values in August to extremely low ($<-2\sigma$) levels from September to December. This decline is accompanied by a consistent but less pronounced decrease in TER, with the most significant negative anomalies (also below $–2\sigma$) in November. As a result, the NEE over the Amazon shifts from negative (carbon uptake) to positive (carbon source) in September, remaining positive through the end of the year (Figure 4c). Adding fire, the NBE from DGVMs shows a similar trajectory, with more evident contributions from fire since July onwards (Figure 4d). Note that the anomalies given by the DGVMs are based on a reference period
between 2003 and 2023 and represent the largest simulated decline in gross fluxes and the largest increase in net sources (NEE and NBE) during the dry season in the current century.

The OCO2-Inv estimates a strong uptake (NBE) in the first months of 2023, but followed by a switch from sink to source in May and a return to a sink in December (Figure 4e). This is consistent with uptake anomalies (NEE) in DGVMs in the beginning of the year, but the magnitude of uptake in the OCO2-Inv (NBE) relative to the mean of the reference period is larger than in the DGVMs. We find that in the DGVMs and the OCO2-Inv, October is the month in which NEE and NBE reach the seasonal maximum or the largest carbon source in 2023, coinciding with the temperature and VPD anomalies shown in Figure S2 in Supporting Information S1 and with the peak in fire activity at the Amazon region scale. Given the seasonal development, we conclude that the NBE maximum in the second semester was driven by a combination of fires and a positive NEE, but the switch from sink to source in May (only in the OCO2-Inv) is only driven by a positive NEE as fire activity with a late onset began only in August (Figure 2). Nevertheless, the enhanced uptake from January to May, evident in both OCO2-Inv and DGVMs, compensated for the relatively large source in the second part of the year.

Over the whole year (2023), the NBE (i.e., net vegetation exchange + fires) ranges from 0.01 PgC year⁻¹ (DGVMs) to 0.17 PgC year⁻¹ (OCO2-Inv) (Figure 4f). In the top-down estimate the NBE implicitly includes fires, and degradation and deforestation emissions that do not occur through burning (although these were not prescribed in the priors) over the Amazon (Figure 4f). Interestingly, the NBE in DGVMs and the OCO2-Inv during 2023 was similar in magnitude to 2015 (DGVMs, -0.06 PgC year⁻¹ and OCO2-Inv, 0.16 PgC year⁻¹), but contrastingly different than in 2016 (DGVMs, -0.10 PgC year⁻¹ and OCO2-Inv, 0.83 PgC year⁻¹). Without fires (totaling 0.15 [0.13–0.17] PgC year⁻¹ in 2023), the NEE ranges from a sink (DGVMs, -0.13 PgC year⁻¹) to a small source (OCO2-Inv, 0.02 PgC year⁻¹) in 2023. Note that compared to previous years, the DGVMs show the largest difference in NEE relative to the long-term mean, indicating a carbon sink weakened by more than 60%. It is worth noting that the shape of the seasonal cycle differs between the OCO2-Inv and DGVMs, which might be because DGVMs tend to not capture well the seasonality of GPP in the evergreen tropical forests (Restrepo-Coupe et al., 2017; X. Chen et al., 2020) or to missing disturbance sources in the DGVMs. Therefore, even differing in seasonality and in the absolute magnitude of the fluxes, both DGVMs and the OCO2-Inv estimate a large anomaly in 2023 relative to the reference period.

### 3.1.4. GPP Anomaly and Aboveground Biomass Change

To evaluate the negative anomaly in GPP given by the DGVMs, we further analyzed independent GPP proxies for the same region. First, we compared each GPP proxy (OCO2-SiF, Tropomi-SiF, and NIRv) to the local EC-GPP at ATTO (Figure S15 in Supporting Information S1). The OCO2-SiF product is closer to the local GPP ($r = 0.47$; *p*-value << 0.01, *n* = 72), followed by NIRv ($r = 0.32$; *p*-value = 0.01, *n* = 72) and last by Tropomi-SiF ($r = 0.22$, *p*-value = 0.10, *n* = 72). Using the OCO2-SiF product for the Amazon, we found that it is less consistent in the sign of the anomaly compared to the DGVMs in first half of the year (from March to July), while the anomalies in October, November and December coincide in the direction, but less in the magnitude, with the DGVMs (Figure 5a). Earlier in the dry season, in August and September, the OCO2-SiF product and the DGVMs show larger discrepancy in the magnitude with the DGVMs presenting a rather large negative anomaly. Therefore, it is likely that the GPP anomaly in DGVMs had an earlier onset compared to the GPP proxies. However, from October to December, a reduced vegetation uptake, as the DGVMs and the GPP proxies suggest, is likely.

We find that the average Aboveground Carbon (AGC) loss in the Amazon from 2011 to 2023 was -0.39 + -0.15 PgC year⁻¹ (average value over all years with losses) while for 2023 it was -0.61 PgC (Figure 5b). Changes in AGC (ΔAGC) have been associated with tree mortality, deforestation and subsequent degradation at forest edges due to fire and logging, but also to secondary forest growth (Fawcett et al., 2023; Silva Junior et al., 2020), so a ΔAGC represents a net exchange which can have a fraction not emitted directly into the atmosphere. For example, selective logging or tree mortality are processes that can cause a ΔAGC but not an immediate CO₂ source to the atmosphere. The losses in 2023 could have been influenced by the decreasing trend in the L-VOD signal since 2016 (Figure S16 in Supporting Information S1). However, the spatial patterns in the AGC anomalies were found to correlate well with Enhanced Vegetation Index (EVI) and SiF (Wigneron et al., 2020), both proxies of GPP. As a comparison, relative to the long-term mean the reduction in DGVMs-GPP was 0.88 PgC year⁻¹ in 2023. Thus, while the AGC change in 2023 could in part be associated with a reduction in GPP due to deforestation, the mismatch ($GPP_{losses} > AGC_{losses}$) suggests that the reduction in GPP in the DGVMs could be slightly
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Magnitude of the standardized anomalies in OCO2-SiF and the ensemble mean GPP of the DGVMs for the biogeographic Amazon (a) and mean net aboveground carbon loss estimated from L-VOD for the same Amazon region (b). Note that the anomalies in a) were calculated with the OCO2-SiF period as a reference (2015–2013), for the OCO2-SiF and the DGVMs. For (b), we calculated the difference in AGC from year~t+1~ to year~t~ (year~t+1~-year~t~) from 2011 to 2023 and selected the years with losses (negative numbers) and gains (positive numbers), from that we got the mean for the entire record and the individual loss for 2023. The bars in (b) show the interannual variability.</figcaption></figure>

overestimated, like in the beginning of the dry season. This is supported by the GPP reduction of about 0.53 PgC year⁻¹ in 2023 reported by (T. Yang et al., 2025) based on remote sensing data.

## 3.2. ATTO as Independent Benchmark: From Local to Regional Scale

CO₂ mole fraction measurements at ATTO (Figure 6a), representative of a large footprint (Figure 6b and Figure S3 in Supporting Information S1) show a pronounced enhancement over the background (ΔCO₂, Figure 6a), peaking in September and consistent with a larger-than-normal fire activity north of 5°S during this month (Figure S13 in Supporting Information S1). Note that the concentration footprint of the 80-m tower covers the area east of the ATTO site, where some of the largest carbon sources were estimated by both the OCO2-Inv and by the DGVMs-ensemble mean (Figure 3), coinciding with the highest VPD and Temperature anomalies (see Figure S2 in Supporting Information S1). However, at the same location, the local eddy covariance measurement had a different response.

In the absence of large disturbances in the flux footprint, the eddy covariance measurements indicate that the forest close to ATTO was on average a sink of carbon in 2023 (Figure 6c). At ATTO we observe a large (close to but within 1σ) uptake from January to March, interrupted by a positive peak (source) in May and followed by a consistent sink from July to November. The first part of the year is consistent with the seasonal pattern in OCO2-Inv coinciding on a large carbon uptake. The EC-NEE sink is mainly controlled by a large (close to and larger than 1σ) decrease in ecosystem respiration (R<sub>eco</sub>) in the beginning of the year (February–April), and during the dry season (August–October). Note that GPP also attains the lowest values in June and September, but most of the development in 2023 is within the normal variability of the record. Given these findings, the 2023 drought at ATTO had a stronger effect in R<sub>eco</sub> than GPP, leading to a net sink at the local ecosystem scale (i.e., ATTO).

The seasonal development of simulated NEE, Reco and GPP in the DGVMs at the grid cell including ATTO (Figures 6d, 6f, and 6i) is in contrast to the local EC measurements. The DGVMs show a stronger decline in GPP than in R<sub>eco</sub> mainly during the dry season, leading to a positive NEE from September to November in 2023. It is worth noting that the seasonality of the DGVMs is very different to the measured one at ATTO, mainly during the wet season, not being able to reproduce the uptake of carbon from January to March. The EC measurements indicate that such uptake was amplified during 2023, reaching values close to 1σ from January to March in EC-NEE. We find that such mismatch could be associated with the forcing data in the DGVMs, as the measured precipitation and shortwave radiation at ATTO were higher than ERA5 in February and March (Figure S17 in Supporting Information S1). This underestimation of shortwave radiation in ERA5, particularly during the wet season, could contribute to the weaker simulated GPP in the DGVMs, as reduced incoming radiation would directly limit photosynthetic uptake at ATTO.
<figure><img src="image_8.png" /><figcaption>**Figure 6.** Seasonal evolution of the CO₂ mole fractions and eddy covariance fluxes (NEE, GPP, and R<sub>eco</sub>) at ATTO. On the first column (a, c, e, h) the measurements (ΔCO₂, NEE, GPP, and R<sub>eco</sub>) are shown. In the second column (d, f, i) the simulations (NEE, GPP, and R<sub>eco</sub>) are presented. The surface influence of the ΔCO₂ is shown in (b) together with the background atmospheric stations and in (g) a marker is shown indicating the very local influence of the eddy covariance flux data.</figcaption></figure>

The flux response at ATTO deserves more context due to its contrasting development relative to the DGVMs. Local measurements at ATTO show that VPD, air, and soil temperature anomalies in 2023 were consistent with the regional pattern shown in Figure S2 in Supporting Information S1, reaching values larger than 1σ (Figures S18 and S19 in Supporting Information S1). Interestingly, soil moisture in the first 50 cm layer showed slightly positive anomalies during the wet season and remained within the range of normal variability during the dry season (Figure S18 in Supporting Information S1), resulting in a slightly positive annual anomaly but within ±1σ
(Figure S19 in Supporting Information S1). Precipitation was also above average, with approximately 200 mm more than in normal years. These conditions suggest that, despite seasonal fluctuations, soil moisture was generally sufficient to sustain photosynthetic activity and may have buffered the effects of elevated air temperature and VPD locally around ATTO, although this response is likely not representative of the broader region.

# 4. Discussion

## 4.1. The Drivers and Consequences of Drought

An interesting feature of the 2023 drought is that the onset coincided with a transition to an El Niño phase that followed three years of consecutive La Niña conditions (https://psl.noaa.gov/enso/mei/, last accessed 15 August 2024). The transition from La Niña to El Niño was characterized by large precipitation anomalies in the south (negative) and in the north (positive) (Espinoza et al., 2024). We show how elevated SST anomalies in the tropical Atlantic and Pacific oceans and associated changes in atmospheric circulation and surface hydroclimatic conditions, contributed to intensifying widespread soil water deficits across the Amazon during the second half of 2023 (Figure S1 in Supporting Information S1), consistent with the analysis by Espinoza et al. (2024) for the Amazon basin. Compared to the previous El Niño event of 2015/16, 2023 registered higher SST anomalies over the tropical Pacific and especially the tropical Atlantic (Figure S11 in Supporting Information S1), which played an important role in the atmospheric circulation anomalies (Figure S12 in Supporting Information S1), extremes in air temperature and atmospheric (VPD) and surface dryness (CWD). Temperature and precipitation anomalies in the Amazon basin have surpassed the previous record in 2015/16 (Espinoza et al., 2024; Jiménez-Muñoz et al., 2016). Compared with 2015/16 and previous droughts, the cumulative water deficits and extremely high vapor pressure anomalies registered in late 2023 progressed deeper into the humid Amazon forest (Figures S1 and S2 in Supporting Information S1), extending the dry season by several months and thus contributing to increased vegetation stress.

## 4.2. Summary of Responses by Different Data Streams

In this paper, we have reported on the response of the Amazon forest to the 2023 event across various spatial scales using multiple data streams (Figure 7). Tropical carbon emissions including fires (NBE) in 2023 were estimated at $0.56 \pm 0.23 \text{ Pg C yr}^{-1}$ (Ke et al., 2024). Based on this estimate, our results suggest that carbon losses from the biogeographic Amazon accounted for approximately 2% ($0.01 \text{ PgC year}^{-1}$, bottom-up DGVMs) to 30% ($0.17 \text{ PgC year}^{-1}$, top-down OCO2-Inv) of the total tropical carbon emissions. It is worth noting that relative to their respective long-term means, the deviation is larger for the bottom-up than for the top-down estimate. The value we report here for the biogeographic Amazon differs from that reported by Ke et al. (2024) for the Amazon, mainly because their definition of Amazon is larger than ours. Therefore, the definition of the Amazon influences the region-wide fluxes as a large contribution of the fire activity is at the boundary of the Amazon and Cerrado biomes (Botía et al., 2025; T. Yang et al., 2025).

The magnitude of vegetation uptake (NEE) ranged from a small source of $0.02 \text{ PgC year}^{-1}$ (OCO2-Inv) to a sink of $-0.13 \text{ PgC year}^{-1}$ (DGVMs) in 2023. Relative to the long-term mean ($-0.36 \text{ PgC yr}^{-1}$ for 2003–2023), the reduction in the DGVMs-NEE is more than 60%. At the Amazon-wide scale, the DGVMs attribute this decline primarily to a stronger reduction in GPP than in $R_{\text{eco}}$, consistent with findings for the 2015/2016 El Niño event (J. Liu et al., 2017). This interpretation is supported by the timing of the NEE source (Figure 4), which coincides with negative SiF and GPP anomalies during October–December (Figure 5), reduced greenness (Jiménez et al., 2024), and an estimated ~0.5 PgC reduction in GPP (T. Yang et al., 2025). While Yang et al., associate this GPP decline with soil moisture deficits (ERA5-Land), we note that elevated VPD and large CWD also contributed to reduced GPP during the second half of the year (Figure S24 in Supporting Information S1). At the same time, the drivers of the NEE anomaly were not spatially uniform across the basin. In the northeast, for example, respiration anomalies exceeded those of GPP and therefore dominated the DGVM-based NEE anomaly (Figures S22 and S23 in Supporting Information S1).

The annual-scale carbon source reflects the integrated effects of the seasonal variability in 2023. From November 2022 to February 2023, rainfall patterns in the Amazon showed a dipole structure, with negative anomalies in the southwest and positive anomalies in the north (Jiménez et al., 2024). The southwestern deficit was linked to subtropical Atlantic SST anomalies under La Niña conditions (Gutierrez-Villarreal et al., 2024), while later in the year, El Niño-driven drought intensified dry conditions in the northern Amazon (Espinoza et al., 2024). We
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Summary of the drought response across scales and different data streams. In (a) the bottom-up (DGVMs) and top-down (OCO2-Inv) anomalies for the carbon budget components are shown. In panel (b) we present the anomaly for the ΔCO₂ or regional signal at ATTO, which is affected by the areas in scales of gray (i.e., footprint) in the map. The anomaly for the ecosystem level EC-NEE is shown in (c). Note that the footprint of the EC-NEE is less than 100 km².</figcaption></figure>

suggest that this dipole may have influenced NBE regionally: enhanced rainfall in the north may have supported early year carbon uptake (Figure 4), while persistent dryness in the southwest contributed to the positive NBE signal from May onward (Figure 4e). Prior to the onset of the fire season (July/August), the carbon source was likely dominated by respiration (Figure 4), as fire activity remained low (Figure 2). However, as the dry season progressed, limited rainfall likely exacerbated fire activity, leading to a prolonged fire season in the southern Amazon (Figure 2). Based on the OCO2-Inv and the study by Wang et al. (2023), the NBE source was similar to 2015 and lower than 2016. We find that such a response, given the extreme temperature and VPD anomalies, was due to a seasonal compensation with a large carbon uptake in the beginning of 2023, evident regionally in both OCO2-Inv and DGVMs, and locally in the EC-NEE at ATTO. This compensation can be associated with more rainfall during the first months of 2023 in the northern part of the Amazon (Figures S6 and S7 in Supporting Information S1, in line with Espinoza et al. (2024)). It remains to be confirmed whether the findings by Wang et al. (2023) for the 2015/2016 El Niño, in which temperature dominated the total NBE (i.e., net vegetation exchange + fires) flux, but the spatial patterns were controlled by deficits in soil moisture, still hold true for 2023 and the post-El Niño development in 2024.

In the absence of anthropogenic disturbances at the ecosystem scale at ATTO and with no precipitation deficit, the forest remained a carbon sink. This was due to a large reduction in respiratory carbon loss together with a small decline in photosynthetic uptake, resulting in a persistent sink of carbon, presenting a negative anomaly well within the normal variability (Figure 7c, EC-NEE). This finding is consistent with the response to the 2015/2016 El Niño in the Tapajós National Forest, where Restrepo-Coupe et al. (2024) reported a decrease in both ecosystem respiration and photosynthesis, yet with a larger decline in $R_{eco}$. However, it is worth noting that drought effects can manifest with a time-lag, so the 2023-sink at ATTO could be reversed in 2024. Using ATTO data, Botía et al. (2022) showed that the NEE anomaly caused by the 2015/2016 El Niño, was strongest in 2016. Similarly, a recent study found that for the 2023 drought, the most pronounced regional-scale decline in GPP occurred in January 2024 (T. Yang et al., 2025).

Chambers et al. (2004) found that autotrophic respiration contributes a large portion (~70%) of the ecosystem respiration, based on individual measurements of leaf, live wood and soil respiration in the Amazon. Thus, a decline in GPP and in autotrophic respiration due to drought (Doughty et al., 2015) could explain the observed
decline in $R_{eco}$ at ATTO shown in this study. However, an increase in autotrophic respiration and thus an overall increment in ecosystem respiration was found in a rainfall exclusion experiment in the eastern Amazon (Metcalfe et al., 2010), suggesting that there might not be a unidirectional response of $R_{eco}$ to drought in the Amazon given than each drought is unique in its development and preceding conditions (S. Liu et al., 2024). Furthermore, it is worth noting that our estimate of ecosystem respiration ($R_{eco}$) relies on nighttime NEE that inherently assumes that daytime $R_{eco}$ is equal to nighttime $R_{eco}$, leading to a source of uncertainty in our total ecosystem respiration estimates.

The positive $\Delta CO_2$ anomaly (Figure 7b) indicates that the ATTO footprint area was likely a source of carbon with a combined effect of reduced net vegetation uptake, fires and land use change emissions. There are several implications of these findings. At the ecosystem scale, the forest close to ATTO buffered the effects of the large VPD and temperature anomalies with sufficient soil moisture availability (as mentioned above and shown in Figure S19 in Supporting Information S1). Such a localized response is not captured by the DGVMs, presenting a challenge for the bottom-up estimates. However, the atmospheric $CO_2$ signal indicates a larger-than-normal carbon source in the ATTO concentration footprint (which is much larger than the flux footprint). This is consistent with more fire activity in the north of the Amazon (Figure 2, Figure S13 in Supporting Information S1), the NEE source in the DGVMs (Figure 3), and the NBE source in OCO2-Inv (Figure 3) that we report here.

### 4.3. Role of Fire in NBE

Fires and deforestation have been linked to drought (Aragão et al., 2007, 2018), with more fires during years of drought (Aragão et al., 2018; Basso et al., 2023; Gatti et al., 2014; Rosan et al., 2024; Silva Junior et al., 2019; van der Laan-Luijkx et al., 2015). We showed that the fire season was extended until November/December, mainly driven by the progression of surface and atmospheric dryness conditions (Figures S1 and S2 in Supporting Information S1). These resulted in an early onset (July to September) with more (less) fire activity in the north (south) and an offset (November and December) with more (less) fire activity in the south (north), relative to the total fire activity regionally. The north to south dynamic in fire activity reported here, is consistent with the timing of the rainfall anomalies in Espinoza et al. (2024) and in Figure S7 in Supporting Information S1. Furthermore, our results follow the fire anomalies in Jiménez et al. (2024). Even though we find pronounced spatial differences relative to the long-term mean, the total fire counts over the Amazon were similar in magnitude to 2015 and 2016.

Nevertheless, fire emissions in 2023 within the Amazon (0.15 [0.13–0.17] PgC year$^{-1}$, mean between GFAS and GFED4s) were comparable and even lower than non-drought years (e.g., 2022) and similar to the long-term average (see Figure 2 and Figure S14 in Supporting Information S1). The individual estimates for GFED4s and GFAS were equal or lower than their long-term averages, respectively. In both products there were less fires compared to the long-term mean in the southeastern border of the Amazon from July to September (Figure 2), when the rainfall anomalies were not as pronounced as in the following months (October–December). Therefore, our analysis suggests that regardless of an extended fire season, the fire carbon emissions had a smaller contribution to the NBE source in 2023 relative to previous droughts, in which fire changed the sign of NBE from sink to source (Basso et al., 2023; Gatti et al., 2021; van der Laan-Luijkx et al., 2015). We note, though, that the fire inventory products used here (GFAS and GFED4s) tend to underestimate fire emissions, likely due to missing understory fires and small scale fires (Naus et al., 2022; Pessôa et al., 2020). However, the results of Naus et al. (2022) and Botía et al. (2025) suggest that such underestimation is more pronounced outside of the biogeographic Amazon, in the neighboring Cerrado biome.

#### 4.3.1. Remaining Challenges Reconciling Top-Down and Bottom-Up Approaches

The most important differences between top-down and bottom-up NBE are associated with the seasonal timing and the absolute magnitude of NBE. This mismatch is likely due to a misrepresentation of the dry and wet season contrast of NEE, GPP, and $R_{eco}$ in DGVMs (Figures 4 and 6), as well as large uncertainty in the bottom-up NBE driven by disturbances and land use change emissions (Rosan et al., 2024). The response of the DGVMs to the development of the drought in 2023 was more evident in the dry season when there was a larger spread between models (Figures 4 and 6) and larger inter-annual variability, coinciding with the positive anomalies in VPD and temperature and the negative anomalies in terrestrial water storage and large CWD (Figures S1 and S2 in Supporting Information S1). Conversely, during the wet season, the DGVMs showed a flat and more uniform GPP and TER, suggesting a reduced sensitivity to wet season anomalies, notwithstanding potential biases in the
forcing data that would attenuate the severity of the drought. The latter is supported by Figure S17 in Supporting Information S1, where we show that ERA5 overestimates precipitation more consistently during the wet season than during the dry season when compared to local precipitation measurements at ATTO.

Furthermore, due to the lack of explicit drought mortality in DGVMs, the response to drought in 2023 could have been underestimated in terms of biomass change (Sitch et al., 2024). However, the models converge spatially in a positive NEE anomaly in the south of the Amazon (Figure S21 in Supporting Information S1) and three out of four coincide in a positive NEE anomaly in the southeast. The most notable mismatch is in the northwest of the Amazon (Figure S21 in Supporting Information S1), where models disagree on the sign of the NEE anomaly in 2023. Our study thus adds to the body of literature indicating the limitations DGVMs have for tropical regions and specifically the Amazon, lacking processes such as (a) leaf phenology and demography, which affect the seasonality in GPP (X. Chen et al., 2020; Kim et al., 2012; Restrepo-Coupe et al., 2017), (b) low sensitivity of GPP to soil texture (Meunier et al., 2022), (c) lacking a representation of drought induced tree mortality and therefore biomass reductions (de Almeida Castanho et al., 2016; Powell et al., 2013) and (d) not simulating secondary forest regrowth leading to difficulties reproducing gains in biomass and thus hindering the simulated changes in AGC (O'Sullivan et al., 2024; Pugh et al., 2019) in DGVMs.

The missing disturbance sources in bottom-up NBE assessments that are reported in existing literature include low intensity understory fires (Alencar et al., 2022), missing carbon emissions due to edge effects caused by forest fragmentation (Silva Junior et al., 2020), and an enhanced post-logging respiratory flux which is difficult to quantify as it is a function of recovery time and requires long-term observations (Mills et al., 2023). In addition, a remaining challenge to improve the seasonal development of bottom-up NBE is related with non-fire carbon emissions due to forest degradation (Lapola et al., 2023) and how these vary seasonally and inter-annually. Moreover, the role of CO₂ outgassing from river and aquatic surfaces remains as an ignored flux in recent bottom-up and top-down assessments (Basso et al., 2023; Gatti et al., 2021; Rosan et al., 2024), and its role in the Amazon basin seems to result in a weakening of the vegetation (NEE) carbon sink (Hastie et al., 2019). The omission of such disturbance processes in DGVMs suggests that they likely exhibit a bias toward a stronger vegetation sink (i. e., more negative NEE). Therefore, if this bias reflects an overestimation of GPP or an underestimation of TER, then the 2023 ensemble mean of absolute NEE likely underestimates the true NEE magnitude. Nevertheless, because this bias primarily affects absolute fluxes rather than anomalies, the relative changes estimated by DGVMs remain meaningful. Thus, the actual 2023 source is likely closer to the upper end of the estimated NBE range (0.01–0.17 PgC year⁻¹), consistent with the OCO2-Inv results.

To assess the robustness of the OCO2-Inv we have compared our results to the satellite inversions available in Luijkx et al. (2024) and part of the last Global Carbon Budget (GCB2024) (Friedlingstein et al., 2025). We find that seasonal development (Figure S20 in Supporting Information S1) follows a similar pattern in the GCB2024 inversions and the OCO2-Inv. Both show a relatively large uptake from January to March and a peak NBE in October. While the OCO2-Inv has a more pronounced uptake in the beginning of the year and a larger source from May to October, the GCB2024 shows a smaller disagreement with the DGVMs. In terms of the regional budget for 2023, the GCB2024 inversion ensemble (0.19 PgC) is similar to what we report using the OCO2-Inv (0.17 PgC). Note though, that the mean NBE for the reference period (2015–2023) is larger in GCB2024 (0.11 PgC) than in the OCO2-Inv (0.05 PgC), bringing the GCB2024 response in 2023 closer to the long term mean and making the carbon source in 2023 closer to the average. Therefore, we believe that the numbers we reported based on the OCO2-Inv are robust. Here, it is relevant to note that the CAMS-Satellite inversion in GCB2024 (FT24r1) is slightly different from the OCO2-Inv used in this work (FT23r4). The former is the first release that used a horizontal grid of hexagons for the underlying transport model, while the FT23r4 was still on a Cartesian grid.

We acknowledge that cloud cover during the wet season is a further limitation in the OCO2-Inv top-down approach. Clouds can limit the number of successful XCO₂ retrievals (Frankenberg et al., 2024), inducing a dry-season adjustment bias in the inversion (Crowell et al., 2019; Massie et al., 2017). Furthermore, in situ data is scarce in the region hindering evaluation of posterior fluxes in satellite-driven inversions and for inversions assimilating flask or in situ data, leaving no choice but to assimilate most of the available data (Basso et al., 2023; Botía et al., 2025). Additionally, the spatial distribution of the posterior fluxes and their overlap with DGVMs, could be affected by the spatial correlation length scale, which in this study was 500 km. We showed that the strongest carbon sources in the DGVMs overlap to some extent but not entirely, with those in the OCO2-Inv. Therefore, we acknowledge that understanding the causes of the spatial mismatch between top-down and bottom-
up approaches and the processes above mentioned, are the remaining challenges in reconciling top-down and bottom-up processes for regional assessments of the carbon cycle response to drought.

**5. Conclusions**

We studied the Amazon carbon cycle response to the extreme drought in 2023 by using multiple data streams, including land surface models, an atmospheric inversion, remotely sensed vegetation dynamics and aboveground carbon at the regional scale. In addition we used in situ measurements from the Amazon Tall Tower Observatory, including eddy covariance fluxes and a decade long CO₂ mole fraction time series to complement the large scale analysis. At the regional scale, we report that in 2023 the Amazon drought led to a substantial shift in the carbon dynamics of the region, with a net carbon emission (NBE = net vegetation exchange + fires), driven primarily by reduced vegetation uptake. While the fire season extended into November, fire contributions remained near the long-term average, indicating that fires had a smaller influence on the Amazon carbon source compared to vegetation responses. The decline in gross primary productivity (GPP) was particularly strong during October to December coinciding with anomalies in remotely sensed GPP proxies (i.e., Sun-Induced Fluorescence). During these months anomalously high temperatures, vapor pressure deficits and cumulative water deficits were recorded.

Our findings highlight that in early 2023 a relatively large uptake helped to offset the dry-season emissions. The transition from La Niña to El Niño was characterized by high precipitation in the beginning of the year in the northern part of the Amazon, but at the same time a precipitation deficit in the southwest of the Amazon. Based on the OCO2-Inv, such enhanced uptake during the wet season (mainly in the north) appeared to have offset the effect of the precipitation anomaly in the southwest. Interestingly, the early large uptake was consistent in the atmospheric transport inversion and the local eddy flux measurements, but to a lesser extent in the DGVMs. The low sensitivity of DGVMs to the wet season anomalies in precipitation are partly explained by a bias in the forcing data. However, the observed balance between photosynthesis and respiration in the eddy flux measurements was not reproduced by the DGVMs throughout the year. At ATTO, the seasonal development of NEE was mainly controlled by a large (close to and larger than 1σ) decrease in ecosystem respiration (R<sub>eco</sub>) in the beginning of the year (January–March), and during the dry season (August–October). Even though we can not attribute the ATTO-eddy-fluxes to all the Amazon region, we believe the comparison points to processes that should be improved in DGVMs to better represent the inter-annual variability of the carbon dynamics in the Amazon.

Based on our findings, one key implication is that the Amazon may transition to a net carbon source more rapidly than previously projected (Brienen et al., 2015; Hubau et al., 2020). While strong El Niño events, like the one in 2023, can exacerbate this trend, our results and other studies (Espinoza et al., 2024; Mamani et al., 2025; Marengo et al., 2024) also highlight the role of widespread ocean warming beyond the Pacific, suggesting that other factors in addition to ENSO contribute significantly to net carbon exchange in the region. However, a full assessment of future trajectories must also consider the potential for ecosystem recovery, as the literature to date is not conclusive. On one hand, some studies have suggested resilience under certain conditions (M. Liu et al., 2025; Poorter et al., 2016, 2021; Sakschewski et al., 2016; Sanchez-Martinez et al., 2025), while others have suggested a decline in resilience and a potential forest dieback (Boulton et al., 2022; S. Chen et al., 2024; Grodofzig et al., 2024). This reinforces the critical need for continued research and in situ monitoring to clarify the long-term balance between disturbance and recovery processes, which will determine whether the Amazon remains a carbon sink or transitions into a persistent carbon source under future climate conditions.

**As Applicable—Inclusion in Global Research Statement**

The research at ATTO is conducted under a long-standing collaboration between Brazil and Germany. Both countries, with several Institutes and Universities involved have been running the research site with the aim of providing continuous and high precision atmospheric data to understand the carbon cycle in the Amazon. This collaboration has resulted in hundreds of international publications, local and regional capacity building and several unique data sets. We are grateful to the Uatumã Sustainable Development Reserve, where the research site is located. For this manuscript, local researchers contributed with sampling, with data interpretation and processing, and with writing the final manuscript. Furthermore, We thank the Instituto Nacional de Pesquisas da Amazonia (Manaus, Brazil) for their technical support managing and running the ATTO research station. Specifically we thank Roberta Pereira de Souza, Bruno Takeshi, Leonardo Ramos de Oliveira, Nagib Alberto de
Castro Souza, Amauri Rodriguês Pereira, Hermes Braga Xavier, Jailson da Mata, Valmir Ferreira de Lima, Wallace Rabelo Costa, Antonio Huxley Melo Nascimento, Uwe Schultz, Thomas Seifert, Steffen Schmidt, Thomas Disper, and Sipko Bulthuis.

## Conflict of Interest

The authors declare no conflicts of interest relevant to this study.

## Data Availability Statement

The biogeographic Amazon area used for the climate (VPD and 2 m temperature) and carbon cycle (DGVMs, OCO2-Inv, and GPP proxies) analysis can be found here in Botía (2025): https://doi.org/10.17617/3.VFC252. The ERA5 and ERA5-Land data sets are under public access available here: https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=overview. For the CWD calculation we used the following R package in Stocker (2021): https://zenodo.org/records/5359053. All the processed data integrated for the biogeographic Amazon area can be found here: https://doi.org/10.17617/3.OU3KYA (Botia et al., 2026). This includes the DGVM output and fire emissions (GFED4s and GFAS) from 2003 to 2023, the OCO2-Inv prior and posterior from 2015 to 2023 and the fire counts from MODIS (terra and aqua) from 2003 to 2023. Additionally, on the same repository, the monthly totals of NEE, GPP and Reco at ATTO, together with the monthly mean ΔCO₂ can be also found in tabular form. The GRACE data for the Amazon basin used here can be freely downloaded here: https://grace.jpl.nasa.gov/data-analysis-tool/. The ATTO meteorological data is available at https://www.attodata.org/home/Start. The OCO2-Inv gridded files are available via the atmospheric data store: https://ads.atmosphere.copernicus.eu/datasets. TROPOMI SIF data is available through the S5P-PAL Data Portal (https://data-portal.s5p-pal.co.m/products/troposif.html and described in Guanter et al. (2021). MODIS BRDF-corrected surface reflectance (MCD43C4v061, (Schaaf & Wang, 2021)) is available at the Land Processes Distributed Active Archive Center (LP DAAC; https://lpdaac.usgs.gov/products/mcd43c4v061/). The OCO2-SiF data is available from https://oco2.gesdisc.eosdis.nasa.gov/data/OCO2_DATA/OCO2_L2_Lite_SIF.11r/. The L-VOD data (LI & Wigneron, 2025) can be downloaded here: https://zenodo.org/records/15676177.

## Acknowledgments

This work and the ATTO project were funded by the German Federal Ministry of Education and Research (BMBF, contracts 01LB1001A and 01LK1602A). The ATTO project is furthermore funded by the Brazilian Ministério da Ciência, Tecnologia e Inovação (MCTI/FINEP contract 01.11.01248.00) and the Max Planck Society. We acknowledge the Instituto Nacional de Pesquisas da Amazonia as well as the Amazon State University (UEA), FAPEAM, LBA/INPA and SDS/CEUC/RDS-Uatumã for continuous support and logistical management. WP and GK were funded by an ERC-Consolidator Grant (649087) as part of the ASICA (Airborne Stable Isotopes of Carbon from the Amazon) project. This study was supported by funding from the European Space Agency Carbon-RO (4000140982/23/I-EF). AB, PC and SS acknowledge support from the CALIPSO project (Carbon Loss in Plant Soils and Oceans) project, funded through the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Science program. AB acknowledges funding by the European Union (ERC StG, ForExD, grant agreement No. 101039567). CQD acknowledges support from CNPQ (Processes 440170/2022-2, 406884/2022-6, 307530/2022-1). ITL received funding from the Netherlands Organisation for Scientific Research (Grant VI.Vidi.213.143). SiB4 simulations were carried out on the Dutch national e-infrastructure with the support of SURF Cooperative (project number NWO-2023.003). We want to thank the contributors to the satellite inversion ensemble in GCB2024, data that was useful in the peer-review process and added in the Supporting Information S1. Finally, we also want to thank the reviewers who helped improve the manuscript significantly. Open Access funding enabled and organized by Projekt DEAL.

## References

Alden, C. B., Miller, J. B., Gatti, L. V., Gloor, M. M., Guan, K., Michalak, A. M., et al. (2016). Regional atmospheric CO₂ inversion reveals seasonal and geographic differences in Amazon net biome exchange. *Global Change Biology*, 22(10), 3427–3443. https://doi.org/10.1111/gcb.13305

Alencar, A. A. C., Arruda, V. L. S., Silva, W. V., Conciani, D. E., Costa, D. P., Crusco, N., et al. (2022). Long-term Landsat-based monthly burned area dataset for the Brazilian biomes using deep learning. *Remote Sensing*, 14(11), 2510. https://doi.org/10.3390/rs14112510

Anderson, L. O., Ribeiro Neto, G., Cunha, A. P., Fonseca, M. G., Mendes de Moura, Y., Dalagnol, R., et al. (2018). Vulnerability of Amazonian forests to repeated droughts. *Philosophical Transactions of the Royal Society B: Biological Sciences*, 373(1760), 20170411. https://doi.org/10.1098/rstb.2017.0411

Andreae, M. O., Acevedo, O. C., Araújo, A., Artaxo, P., Barbosa, C. G. G., Barbosa, H. M. J., et al. (2015). The Amazon Tall Tower Observatory (ATTO): Overview of pilot measurements on ecosystem ecology, meteorology, trace gases, and aerosols. *Atmospheric Chemistry and Physics*, 15(18), 10723–10776. https://doi.org/10.5194/acp-15-10723-2015

Aragão, L. E. O. C., Anderson, L. O., Fonseca, M. G., Rosan, T. M., Vedovato, L. B., Wagner, F. H., et al. (2018). 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions. *Nature Communications*, 9(1), 1–12. https://doi.org/10.1038/s41467-017-02771-y

Aragão, L. E. O. C., Malhi, Y., Roman-Cuesta, R. M., Saatchi, S., Anderson, L. O., & Shimabukuro, Y. E. (2007). Spatial patterns and fire response of recent Amazonian droughts. *Geophysical Research Letters*, 34(7), L07701. https://doi.org/10.1029/2006GL028946

Basso, L. S., Wilson, C., Chipperfield, M. P., Tejada, G., Cassol, H. L. G., Arai, E., et al. (2023). Atmospheric CO₂ inversion reveals the Amazon as a minor carbon source caused by fire emissions, with forest uptake offsetting about half of these emissions. *Atmospheric Chemistry and Physics*, 23(17), 9685–9723. https://doi.org/10.5194/acp-23-9685-2023

Bastos, A., Friedlingstein, P., Sitch, S., Chen, C., Mialon, A., Wigneron, J.-P., et al. (2018). Impact of the 2015/2016 El Niño on the terrestrial carbon cycle constrained by bottom-up and top-down approaches. *Philosophical Transactions of the Royal Society B: Biological Sciences*, 373(1760), 20170304. https://doi.org/10.1098/rstb.2017.0304

Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., Dijk, A. I. J. M., et al. (2019). MSWEP V2 global 3-hourly 0.1° precipitation: Methodology and quantitative assessment. *Bulletin of the American Meteorological Society*, 100(3), 473–500. https://doi.org/10.1175/BAMS-D-17-0138.1

Berenguer, E., Lennox, G. D., Ferreira, J., Malhi, Y., Aragão, L. E. O. C., Barreto, J. R., et al. (2021). Tracking the impacts of El Niño drought and fire in human-modified Amazonian forests. *Proceedings of the National Academy of Sciences of the United States of America*, 118(30), e2019377118. https://doi.org/10.1073/pnas.2019377118

Berenguer, E., Malhi, Y., Brando, P., Cardoso Nunes Cordeiro, A., Ferreira, J., França, F., et al. (2018). Tree growth and stem carbon accumulation in human-modified Amazonian forests following drought and fire. *Philosophical Transactions of the Royal Society B: Biological Sciences*, 373(1760), 20170308. https://doi.org/10.1098/rstb.2017.0308

Botía, S. (2025). Regional and global mask used for flux integrals in Botía et al., 2025 [Dataset]. Edmond. https://doi.org/10.17617/3.VFC252

Botia, S., Komiya, S., Dias-Junior, C. Q., Lavric, J. V., van Asperen, H., Chevallier, F., et al. (2026). Supporting datasets for the study by Botía et al., (2026)—Carbon cycle response to Amazon drought in 2023 [Dataset]. Edmond. https://doi.org/10.17617/3.OU3KYA
Botía, S., Komiya, S., Marshall, J., Koch, T., Galkowski, M., Lavric, J., et al. (2022). The CO₂ record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter-annual scales. *Global Change Biology*, **28**(2), 588–611. https://doi.org/10.1111/gcb.15905

Botía, S., Munassar, S., Koch, T., Custodio, D., Basso, L. S., Komiya, S., et al. (2025). Combined CO₂ measurement record indicates Amazon forest carbon uptake is offset by savanna carbon release. *Atmospheric Chemistry and Physics*, **25**, 6219–6255. https://doi.org/10.5194/acp-25-6219-2025

Boulton, C. A., Lenton, T. M., & Boers, N. (2022). Pronounced loss of Amazon rainforest resilience since the early 2000s. *Nature Climate Change*, **12**(3), 271–278. https://doi.org/10.1038/s41558-022-01287-8

Brando, P. M., Balch, J. K., Nepstad, D. C., Morton, D. C., Putz, F. E., Coe, M. T., et al. (2014). Abrupt increases in Amazonian tree mortality due to drought–fire interactions. *Proceedings of the National Academy of Sciences*, **111**(17), 6347–6352. https://doi.org/10.1073/pnas.1305499111

Brienen, R. J. W., Phillips, O. L., Feldpausch, T. R., Gloor, E., Baker, T. R., Lloyd, J., et al. (2015). Long-term decline of the Amazon carbon sink. *Nature*, **519**(7543), 344–348. https://doi.org/10.1038/nature14283

Byrne, B., Liu, J., Bowman, K. W., Pascolini-Campbell, M., Chatterjee, A., Pandey, S., et al. (2024). Carbon emissions from the 2023 Canadian wildfires. *Nature*, **633**(8031), 835–839. https://doi.org/10.1038/s41586-024-07878-z

Chambers, J. Q., Tribuzy, E. S., Toledo, L. C., Crispim, B. F., Higuchi, N., Santos, J., et al. (2004). Respiration from a tropical forest ecosystem: Partitioning of sources and low carbon use efficiency. *Ecological Applications*, **14**(sp4), 72–88. https://doi.org/10.1890/01-6012

Chen, S., Stark, S. C., Nobre, A. D., Cuartas, L. A., de Jesus Amore, D., Restrepo-Coupe, N., et al. (2024). Amazon forest biogeography predicts resilience and vulnerability to drought. *Nature*, **631**(8019), 111–117. https://doi.org/10.1038/s41586-024-07568-w

Chen, X., Maignan, F., Viovy, N., Bastos, A., Goll, D., Wu, J., et al. (2020). Novel Representation of leaf phenology improves simulation of Amazonian evergreen forest photosynthesis in a land surface model. *Journal of Advances in Modeling Earth Systems*, **12**(1), e2018MS001565. https://doi.org/10.1029/2018MS001565

Chen, Y., Velicogna, I., Famiglietti, J. S., & Randerson, J. T. (2013). Satellite observations of terrestrial water storage provide early warning information about drought and fire season severity in the Amazon. *Journal of Geophysical Research: Biogeosciences*, **118**(2), 495–504. https://doi.org/10.1002/jgrg.20046

Chevallier, F., Viovy, N., Reichstein, M., & Ciais, P. (2006). On the assignment of prior errors in Bayesian inversions of CO₂ surface fluxes. *Geophysical Research Letters*, **33**(13), L13802. https://doi.org/10.1029/2006GL026496

Chevallier, F., Wang, T., Ciais, P., Maignan, F., Bocquet, M., Altaf Arain, M., et al. (2012). What eddy-covariance measurements tell us about prior land flux errors in CO₂-flux inversion schemes. *Global Biogeochemical Cycles*, **26**, GB1021. https://doi.org/10.1029/2010GB003974

Clark, D., Mercado, L., Sitch, S., Jones, C., Gedney, N., Best, M., et al. (2011). The Joint UK Land Environment Simulator (JULES), model description—Part 2: Carbon fluxes and vegetation dynamics. *Geoscientific Model Development*, **4**(3), 701–722. https://doi.org/10.5194/gmd-4-701-2011

Copernicus. (2024). Copernicus: 2023 is the hottest year on record, with global temperatures close to the 1.5°C limit | Copernicus. Retrieved from https://climate.copernicus.eu/copernicus-2023-hottest-year-record

Crowell, S., Baker, D., Schuh, A., Basu, S., Jacobson, A. R., Chevallier, F., et al. (2019). The 2015–2016 carbon cycle as seen from OCO-2 and the global in situ network. *Atmospheric Chemistry and Physics*, **19**(15), 9797–9831. https://doi.org/10.5194/acp-19-9797-2019

de Almeida Castanho, A. D., Galbraith, D., Zhang, K., Coe, M. T., Costa, M. H., & Moorcroft, P. (2016). Changing Amazon biomass and the role of atmospheric CO₂ concentration, climate, and land use. *Global Biogeochemical Cycles*, **30**(1), 18–39. https://doi.org/10.1002/2015GB005135

Doughty, C. E., Metcalfe, D. B., Girardin, C. A. J., Amézquita, F. F., Cabrera, D. G., Huasco, W. H., et al. (2015). Drought impact on forest carbon dynamics and fluxes in Amazonia. *Nature*, **519**(7541), 78–82. https://doi.org/10.1038/nature14213

Espinoza, J.-C., Jimenez, J. C., Marengo, J. A., Schongart, J., Ronchail, J., Lavado-Casimiro, W., & Ribeiro, J. V. M. (2024). The new record of drought and warmth in the Amazon in 2023 related to regional and global climatic features. *Scientific Reports*, **14**(1), 8107. https://doi.org/10.1038/s41598-024-58782-5

Esquivel-Muelbert, A., Phillips, O. L., Brienen, R. J. W., Fauset, S., Sullivan, M. J. P., Baker, T. R., et al. (2020). Tree mode of death and mortality risk factors across Amazon forests. *Nature Communications*, **11**(1), 5515. https://doi.org/10.1038/s41467-020-18996-3

Fawcett, D., Sitch, S., Ciais, P., Wigneron, J. P., Silva-Junior, C. H. L., Heinrich, V., et al. (2023). Declining Amazon biomass due to deforestation and subsequent degradation losses exceeding gains. *Global Change Biology*, **29**(4), 1106–1118. https://doi.org/10.1111/gcb.16513

Feldpausch, T. R., Phillips, O. L., Brienen, R. J. W., Gloor, E., Lloyd, J., Lopez-Gonzalez, G., et al. (2016). Amazon forest response to repeated droughts. *Global Biogeochemical Cycles*, **30**(7), 964–982. https://doi.org/10.1002/2015GB005133

Frankenberg, C., Bar-On, Y. M., Yin, Y., Wennberg, P. O., Jacob, D. J., & Michalak, A. M. (2024). Data drought in the humid tropics: How to overcome the cloud barrier in greenhouse gas remote sensing. *Geophysical Research Letters*, **51**(8), e2024GL108791. https://doi.org/10.1029/2024GL108791

Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Hauck, J., Landschützer, P., et al. (2024). Global carbon budget 2024. *Earth System Science Data Discussions*, **1–133**. https://doi.org/10.5194/essd-2024-519

Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Hauck, J., Landschützer, P., et al. (2025). Global Carbon Budget 2024. *Earth System Science Data*, **17**(3), 965–1039. https://doi.org/10.5194/essd-17-965-2025

Gatti, L. V., Basso, L. S., Miller, J. B., Gloor, M., Gatti Domingues, L., Cassol, H. L. G., et al. (2021). Amazonia as a carbon source linked to deforestation and climate change. *Nature*, **595**(7867), 388–393. https://doi.org/10.1038/nature12957

Gatti, L. V., Gloor, M., Miller, J. B., Doughty, C. E., Malhi, Y., Domingues, L. G., et al. (2014). Drought sensitivity of Amazonian carbon balance revealed by atmospheric measurements. *Nature*, **506**(7486), 76–80. https://doi.org/10.1038/nature12957

Gloor, M., Wilson, C., Chipperfield, M. P., Chevallier, F., Buermann, W., Boesch, H., et al. (2018). Tropical land carbon cycle responses to 2015/16 El Niño as recorded by atmospheric greenhouse gas and remote sensing data. *Philosophical Transactions of the Royal Society B: Biological Sciences*, **373**(1760), 20170302. https://doi.org/10.1098/rstb.2017.0302

Grodofzig, R., Renoult, M., & Mauritsen, T. (2024). Observation-inferred resilience loss of the Amazon rainforest possibly due to internal climate variability. *Earth System Dynamics*, **15**(4), 913–927. https://doi.org/10.5194/esd-15-913-2024

Guanter, L., Bacour, C., Schneider, A., Aben, I., van Kempen, T. A., Maignan, F., et al. (2021). The TROPOSIF global sun-induced fluorescence dataset from the Sentinel-5P TROPOMI mission. *Earth System Science Data*, **13**(11), 5423–5440. https://doi.org/10.5194/essd-13-5423-2021

Guimberteau, M., Zhu, D., Maignan, F., Huang, Y., Yue, C., Dantec-Nédélec, S., et al. (2018). ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: Model description and validation. *Geoscientific Model Development*, **11**(1), 121–163. https://doi.org/10.5194/gmd-11-121-2018

Gutierrez-Villarreal, R. A., Espinoza, J.-C., Lavado-Casimiro, W., Junquas, C., Molina-Carpio, J., Condom, T., & Marengo, J. A. (2024). The 2022-23 drought in the South American Altiplano: ENSO effects on moisture flux in the western Amazon during the pre-wet season. *Weather and Climate Extremes*, **45**, 100710. https://doi.org/10.1016/j.wace.2024.100710
Hartmann, H., Bastos, A., Das, A. J., Esquivel-Muelbert, A., Hammond, W. M., Martínez-Vilalta, J., et al. (2022). Climate change risks to global forest health: Emergence of unexpected events of elevated tree mortality worldwide. *Annual Review of Plant Biology*, 73(1), 673–702. https://doi.org/10.1146/annurev-arplant-102820-012804

Hastie, A., Lauerwald, R., Ciais, P., & Regnier, P. (2019). Aquatic carbon fluxes dampen the overall variation of net ecosystem productivity in the Amazon basin: An analysis of the interannual variability in the boundless carbon cycle. *Global Change Biology*, 25(6), 2094–2111. https://doi.org/10.1111/gcb.14620

Haynes, K. D., Baker, I. T., Denning, A. S., Stöckli, R., Schaefer, K., Lokupitiya, E. Y., & Haynes, J. M. (2019). Representing grasslands using dynamic prognostic phenology based on biological growth stages: 1. Implementation in the Simple Biosphere Model (SiB4). *Journal of Advances in Modeling Earth Systems*, 11(12), 4423–4439. https://doi.org/10.1029/2018MS001540

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., et al. (2020). The ERA5 global reanalysis. *Quarterly Journal of the Royal Meteorological Society*, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803

Hubau, W., Lewis, S. L., Phillips, O. L., Affum-Baffoe, K., Beeckman, H., Cuní-Sanchez, A., et al. (2020). Asynchronous carbon sink saturation in African and Amazonian tropical forests. *Nature*, 579(7797), 80–87. https://doi.org/10.1038/s41586-020-2035-0

Jimenez, J. C., Barichivich, J., Mattar, C., Takahashi, K., Santamaría-Artigas, A., Sobrino, J. A., & Malhi, Y. (2018). Spatio-temporal patterns of thermal anomalies and drought over tropical forests driven by recent extreme climatic anomalies. *Philosophical Transactions of the Royal Society B: Biological Sciences*, 373(1760), 20170300. https://doi.org/10.1098/rstb.2017.0300

Jiménez, J. C., Miranda, V., Trigo, I., Libonati, R., Albuquerque, R., Peres, L. F., et al. (2024). Vegetation warming and greenness decline across Amazonia during the extreme drought of 2023. *Remote Sensing*, 16(14). https://doi.org/10.3390/rs16142519

Jiménez-Muñoz, J. C., Mattar, C., Barichivich, J., Santamaría-Artigas, A., Takahashi, K., Malhi, Y., et al. (2016). Record-breaking warming and extreme drought in the Amazon rainforest during the course of El Niño 2015–2016. *Scientific Reports*, 6(1). https://doi.org/10.1038/srep33130

Kaiser, J. W., Heil, A., Andreae, M. O., Benedetti, A., Chubarova, N., Jones, L., et al. (2012). Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. *Biogeosciences*, 9(1), 527–554. https://doi.org/10.5194/bg-9-527-2012

Ke, P., Ciais, P., Sitch, S., Li, W., Bastos, A., Liu, Z., et al. (2024). Low latency carbon budget analysis reveals a large decline of the land carbon sink in 2023. *National Science Review*, 11(12), nwae367. https://doi.org/10.1093/nsr/nwae367

Kim, Y., Knox, R. G., Longo, M., Medvigy, D., Hutyra, L. R., Pyle, E. H., et al. (2012). Seasonal carbon dynamics and water fluxes in an Amazon rainforest. *Global Change Biology*, 18(4), 1322–1334. https://doi.org/10.1111/j.1365-2486.2011.02629.x

Krinner, G., Giovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J., Friedlingstein, P., et al. (2005). A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. *Global Biogeochemical Cycles*, 19(1), GB1015. https://doi.org/10.1029/2003GB002199

Lan, X., Mund, J. W., Crotwell, A. M., Thoning, K. W., Moglia, E., Madronich, M., et al. (2024). Atmospheric carbon dioxide dry air mole fractions from the NOAA GML Carbon cycle cooperative global air sampling network, 1968-2023 (version 2024-07-30) [Dataset]. https://doi.org/10.15138/wkgj-f215

Lapola, D. M., Pinho, P., Barlow, J., Aragão, L. E. O. C., Berenguer, E., Carmenta, R., et al. (2023). The drivers and impacts of Amazon forest degradation. *Science*, 379(6630), eabp8622. https://doi.org/10.1126/science.abp8622

Liu, J., Bowman, K. W., Schimel, D. S., Parazoo, N. C., Jiang, Z., Lee, M., et al. (2017). Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Niño. *Science*, 358(6360), eaam5690. https://doi.org/10.1126/science.aam5690

Liu, M., Peñuelas, J., Trugman, A. T., Vargas G., Yang, L., & Anderegg, W. R. L. (2025). Diverging responses of terrestrial ecosystems to water stress after disturbances. *Nature Climate Change*, 15(1), 73–79. https://doi.org/10.1038/s41558-024-02191-z

Liu, S., McVicar, T. R., Wu, X., Cao, X., & Liu, Y. (2024). Assessing the relative importance of dry-season incoming solar radiation and water storage dynamics during the 2005, 2010 and 2015 southern Amazon droughts: Not all droughts are created equal. *Environmental Research Letters*, 19(3), 034027. https://doi.org/10.1088/1748-9326/ad281e

Li, X., & Wigneron, J.-P. (2025). IB-AGC: Annual 25 km global live biomass carbon product from SMOS L-band passive microwave vegetation optical depth [Dataset]. *Zenodo*. https://doi.org/10.5281/zenodo.15676177

Luijkx, I., Peters, W., Lloret, Z., Zhao, M., Niwa, Y., Jacobson, A. R., et al. (2024). Global CO₂ gridded flux fields from 14 atmospheric inversions in GCB2024 (version 1.0) [NetCDF (spatial)]. *ICOS ERIC -- Carbon Portal*. https://doi.org/10.18160/4R5W-VNBV

MacCarthy, J., Tyukavina, A., Weisse, M. J., Harris, N., & Glen, E. (2024). Extreme wildfires in Canada and their contribution to global loss in tree cover and carbon emissions in 2023. *Global Change Biology*, 30(6), e17392. https://doi.org/10.1111/gcb.17392

Mamani, L., Andreoli, R. V., de Souza, I. P., Cevalho, W., Sales, D., Kayano, M. T., et al. (2025). Extreme droughts in the Amazon basin during cyclic ENSO events coupled with Indian Ocean Dipole modes and Tropical North Atlantic warming. *Science of the Total Environment*, 963, 178536. https://doi.org/10.1016/j.scitotenv.2025.178536

Marengo, J. A., Costa, M. C., Cunha, A. P., Espinoza, J.-C., Jimenez, J. C., Libonati, R., et al. (2025). Climatological patterns of heatwaves during winter and spring 2023 and trends for the period 1979–2023 in central South America. *Frontiers in Climate*, 7, 1529082. https://doi.org/10.3389/fclim.2025.1529082

Marengo, J. A., Cunha, A. P., Espinoza, J.-C., Fu, R., Schöngart, J., Jimenez, J. C., et al. (2024). The drought of Amazonia in 2023-2024. *American Journal of Climate Change*, 13(3), 567–597. https://doi.org/10.4236/ajcc.2024.133026

Marengo, J. A., Jimenez, J. C., Espinoza, J.-C., Cunha, A. P., & Aragão, L. E. O. (2022). Increased climate pressure on the agricultural Frontier in the Eastern Amazonia–Cerrado transition zone. *Scientific Reports*, 12(1), 457. https://doi.org/10.1038/s41598-021-04241-4

Massie, S. T., Sebastian Schmidt, K., Elderling, A., & Crisp, D. (2017). Observational evidence of 3-D cloud effects in OCO-2 CO₂ retrievals. *Journal of Geophysical Research: Atmospheres*, 122(13), 7064–7085. https://doi.org/10.1002/2016JD026111

Metcalfe, D. B., Meir, P., Aragão, L. E. O. C., Lobo-do-Vale, R., Galbraith, D., Fisher, R. A., et al. (2010). Shifts in plant respiration and carbon use efficiency at a large-scale drought experiment in the eastern Amazon. *New Phytologist*, 187(3), 608–621. https://doi.org/10.1111/j.1469-8137.2010.03319.x

Meunier, F., Verbruggen, W., Verbeeck, H., & Peaucelle, M. (2022). Low sensitivity of three terrestrial biosphere models to soil texture over the South American tropics. *Geoscientific Model Development*, 15(20), 7573–7591. https://doi.org/10.5194/gmd-15-7573-2022

Mills, M. B., Malhi, Y., Ewers, R. M., Kho, L. K., Teh, Y. A., Both, S., et al. (2023). Tropical forests post-logging are a persistent net carbon source to the atmosphere. *Proceedings of the National Academy of Sciences of the United States of America*, 120(3), e2214462120. https://doi.org/10.1073/pnas.2214462120

Miralles, D. G., Bonte, O., Koppa, A., Baez-Villanueva, O. M., Tronquo, E., Zhong, F., et al. (2025). GLEAM4: Global land evaporation and soil moisture dataset at 0.1° resolution from 1980 to near present. *Scientific Data*, 12(1), 416. https://doi.org/10.1038/s41597-025-04610-y

Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., et al. (2021). ERA5-land: A state-of-the-art global reanalysis dataset for land applications. *Earth System Science Data*, 13(9), 4349–4383. https://doi.org/10.5194/essd-13-4349-2021

Naus, S., Domingues, L. G., Krol, M., Luijkx, I. T., Gatti, L. V., Miller, J. B., et al. (2022). Sixteen years of MOPITT satellite data strongly constrain Amazon CO fire emissions. *Atmospheric Chemistry and Physics*, 22(22), 14735–14750. https://doi.org/10.5194/acp-22-14735-2022
O'Sullivan, M., Sitch, S., Friedlingstein, P., Luijkhx, I. T., Peters, W., Rosan, T. M., et al. (2024). The key role of forest disturbance in reconciling estimates of the northern carbon sink. *Communications Earth & Environment, 5*(1), 1–10. https://doi.org/10.1038/s43247-024-01827-4

Palmer, P. I., Feng, L., Baker, D., Chevallier, F., Bösch, H., & Somkuti, P. (2019). Net carbon emissions from African biosphere dominate pan-tropical atmospheric CO₂ signal. *Nature Communications, 10*(1), 3344. https://doi.org/10.1038/s41467-019-11097-w

Pessóa, A. C. M., Anderson, L. O., Carvalho, N. S., Campanharo, W. A., Junior, C. H. L. S., Rosan, T. M., et al. (2020). Intercomparison of burned area products and its implication for carbon emission estimations in the Amazon. *Remote Sensing, 12*(23). https://doi.org/10.3390/rs1223864

Phillips, O. L., Aragao, L. E. O. C., Lewis, S. L., Fisher, J. B., Lloyd, J., Lopez-Gonzalez, G., et al. (2009). Drought sensitivity of the Amazon rainforest. *Science, 323*(5919), 1344–1347. https://doi.org/10.1126/science.1164033

Poorter, L., Bongers, F., Aide, T. M., Almeyda Zambrano, A. M., Balvanera, P., Becknell, J. M., et al. (2016). Biomass resilience of neotropical secondary forests. *Nature, 530*(7589), 211–214. https://doi.org/10.1038/nature16512

Poorter, L., Craven, D., Jakovac, C. C., van der Sande, M. T., Amissah, L., Bongers, F., et al. (2021). Multidimensional tropical forest recovery. *Science, 374*(6573), 1370–1376. https://doi.org/10.1126/science.abh3629

Powell, T. L., Galbraith, D. R., Christoffersen, B. O., Harper, A., Imbuzeiro, H. M. A., Rowland, L., et al. (2013). Confronting model predictions of carbon fluxes with measurements of Amazon forests subjected to experimental drought. *New Phytologist, 200*(2), 350–365. https://doi.org/10.1111/nph.12390

Powers, J. S., Vargas, G. G., Brodribb, T. J., Schwartz, N. B., Pérez-Aviles, D., Smith-Martin, C. M., et al. (2020). A catastrophic tropical drought kills hydraulically vulnerable tree species. *Global Change Biology, 26*(5), 3122–3133. https://doi.org/10.1111/gcb.15037

Pugh, T. A. M., Lindeskog, M., Smith, B., Poulter, B., Arneth, A., Haverd, V., & Calle, L. (2019). Role of forest regrowth in global carbon sink dynamics. *Proceedings of the National Academy of Sciences of the United States of America, 116*(10), 4382–4387. https://doi.org/10.1073/pnas.1810512116

Restrepo-Coupe, N., Campos, K. S., Alves, L. F., Longo, M., Wiedemann, K. T., de Oliveira, R. C., et al. (2024). Contrasting carbon cycle responses to dry (2015 El Niño) and wet (2008 La Niña) extreme events at an Amazon tropical forest. *Agricultural and Forest Meteorology, 353*, 110037. https://doi.org/10.1016/j.agrformet.2024.110037

Restrepo-Coupe, N., Levine, N. M., Christoffersen, B. O., Albert, L. P., Wu, J., Costa, M. H., et al. (2017). Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison. *Global Change Biology, 23*(1), 191–208. https://doi.org/10.1111/gcb.13442

Rödenbeck, C., Zaehle, S., Keeling, R., & Heimann, M. (2018). History of El Niño impacts on the global carbon cycle 1957–2017: A quantification from atmospheric CO₂ data. *Philosophical Transactions of the Royal Society B: Biological Sciences, 373*(1760), 20170303. https://doi.org/10.1098/rstb.2017.0303

Rosan, T. M., Sitch, S., O'Sullivan, M., Basso, L. S., Wilson, C., Silva, C., et al. (2024). Synthesis of the land carbon fluxes of the Amazon region between 2010 and 2020. *Communications Earth & Environment, 5*(1), 1–15. https://doi.org/10.1038/s43247-024-01205-0

Sakschewski, B., von Bloh, W., Boit, A., Poorter, L., Peña-Claros, M., Heinke, J., et al. (2016). Resilience of Amazon forests emerges from plant trait diversity. *Nature Climate Change, 6*(11), 1032–1036. https://doi.org/10.1038/nclimate3109

Sanchez-Martinez, P., Martius, L. R., Bittencourt, P., Silva, M., Binks, O., Coughlin, L., et al. (2025). Amazon rainforest adjusts to long-term experimental drought. *Nature Ecology & Evolution, 9*(6), 1–10. https://doi.org/10.1038/s41559-025-02702-x

Schaaf, C., & Wang, Z. (2021). MODIS/Terra+Aqua BRDF/Albedo nadir BRDF-Adjusted Ref daily L3 global 0.05Deg CMG V061 [Dataset]. NASA EOSDIS Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MCD43C4.061

Schimel, D., Stephens, B. B., & Fisher, J. B. (2015). Effect of increasing CO₂ on the terrestrial carbon cycle. *Proceedings of the National Academy of Sciences of the United States of America, 112*(2), 436–441. https://doi.org/10.1073/pnas.1407302112

Silva Junior, C. H. L., Anderson, L. O., Silva, A. L., Almeida, C. T., Dalagnol, R., Pletsch, M. A. J. S., et al. (2019). Fire responses to the 2010 and 2015/2016 Amazonian droughts. *Frontiers in Earth Science, 7*, 97. https://doi.org/10.3389/feart.2019.00097

Silva Junior, C. H. L., Aragão, L. E. O. C., Anderson, L. O., Fonseca, M. G., Shimabukuro, Y. E., Vancutsem, C., et al. (2020). Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses. *Science Advances, 6*(40), eaaz8360. https://doi.org/10.1126/sciadv.aaz8360

Sitch, S., O'Sullivan, M., Robertson, E., Friedlingstein, P., Albergel, C., Anthoni, P., et al. (2024). Trends and drivers of terrestrial sources and sinks of carbon dioxide: An overview of the TRENDY project. *Global Biogeochemical Cycles, 38*(7), e2024GB008102. https://doi.org/10.1029/2024GB008102

Stocker, B. (2021). `cwd v1.0: R package for cumulative water deficit calculation [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.5359053`

Trenberth, K. E., Caron, J. M., Stepaniak, D. P., & Worley, S. (2002). Evolution of El Niño–Southern Oscillation and global atmospheric surface temperatures. *Journal of Geophysical Research, 107*(D8), AAC5-1–AAC5-17. https://doi.org/10.1029/2000JD000298

van Schaik, E., Killaars, L., Smith, N. E., Koren, G., Van Beek, L. P. H., Peters, W., & van der Laan-Luijkhx, I. T. (2018). Changes in surface hydrology, soil moisture and gross primary production in the Amazon during the 2015/2016 El Niño. *Philosophical Transactions of the Royal Society B: Biological Sciences, 373*(1760), 20180084. https://doi.org/10.1098/rstb.2018.0084

van der Laan-Luijkhx, I. T., van der Velde, I. R., Krol, M. C., Gatti, L. V., Domingues, L. G., Correia, C. S. C., et al. (2015). Response of the Amazon carbon balance to the 2010 drought derived with CarbonTracker South America. *Global Biogeochemical Cycles, 29*(7), 1092–1108. https://doi.org/10.1002/2014GB005082

van der Werf, G. R., Randerson, J. T., Giglio, L., Leeuwen, T. T., Chen, Y., Rogers, B. M., et al. (2017). Global fire emissions estimates during 1997–2016. *Earth System Science Data, 9*(2), 697–720. https://doi.org/10.5194/essd-9-697-2017

Wang, J., Zeng, N., Wang, M., Jiang, F., Chevallier, F., Crowell, S., et al. (2023). Anomalous net biome exchange over amazonian rainforests induced by the 2015/16 El Niño: Soil dryness-shaped spatial pattern but temperature-dominated total flux. *Geophysical Research Letters, 40*(11), e2023GL103379. https://doi.org/10.1029/2023GL103379

Wigneron, J.-P., Fan, L., Ciais, P., Bastos, A., Brandt, M., Chave, J., et al. (2020). Tropical forests did not recover from the strong 2015–2016 El Niño event. *Science Advances, 6*(6), eaay4603. https://doi.org/10.1126/sciadv.aay4603

Yang, H., Ciais, P., Wigneron, J.-P., Chave, J., Cartus, O., Chen, X., et al. (2022). Climatic and biotic factors influencing regional declines and recovery of tropical forest biomass from the 2015/16 El Niño. *Proceedings of the National Academy of Sciences of the United States of America, 119*(26), e2101388119. https://doi.org/10.1073/pnas.2101388119

Yang, J., Tian, H., Pan, S., Chen, G., Zhang, B., & Dangal, S. (2018). Amazon drought and forest response: Largely reduced forest photosynthesis but slightly increased canopy greenness during the extreme drought of 2015/2016. *Global Change Biology, 24*(5), 1919–1934. https://doi.org/10.1111/gcb.14056
Yang, T., Chen, B., Wang, S., Xu, X., Artaxo, P., Li, T., et al. (2025). Soil moisture dominates the severe decline in gross primary productivity during a 2023–2024 compound heatwave-drought event over the Amazon. *Environmental Research Letters*, 20(3), 034024. https://doi.org/10.1088/1748-9326/adb01e1

Zaehle, S., Friend, A. D., Friedlingstein, P., Dentener, F., Peylin, P., & Schulz, M. (2010). Carbon and nitrogen cycle dynamics in the O-CN land surface model: 2. Role of the nitrogen cycle in the historical terrestrial carbon balance. *Global Biogeochemical Cycles*, 24(1), GB1006. https://doi.org/10.1029/2009gb003522

Zhang, Y., Dannenberg, M. P., Hwang, T., & Song, C. (2019). El Niño-Southern Oscillation-induced variability of terrestrial gross primary production during the satellite era. *Journal of Geophysical Research: Biogeosciences*, 124(8), 2419–2431. https://doi.org/10.1029/2019JG005117

## References From the Supporting Information

Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O. L., Asner, G. P., et al. (2016). An integrated pan-tropical biomass map using multiple reference datasets. *Global Change Biology*, 22(4), 1406–1420. https://doi.org/10.1111/gcb.13139

Baccini, A., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M., Sulla-Menashe, D., et al. (2012). Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. *Nature Climate Change*, 2(3), 182–185. Article 3. https://doi.org/10.1038/nclimate1354

Badgley, G., Field, C. B., & Berry, J. A. (2017). Canopy near-infrared reflectance and terrestrial photosynthesis. *Science Advances*, 3(3), e1602244. https://doi.org/10.1126/sciadv.1602244

Bastos, A., Ciais, P., Friedlingstein, P., Sitch, S., Pongratz, J., Fan, L., et al. (2020). Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity. *Science Advances*, 6(24), eab2724. https://doi.org/10.1126/sciadv.aba2724

Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., et al. (2021). The ERA5 global reanalysis: Preliminary extension to 1950. *Quarterly Journal of the Royal Meteorological Society*, 147(741), 4186–4227. https://doi.org/10.1002/qj.4174

Brandt, M., Wigneron, J.-P., Chave, J., Tagesson, T., Penuelas, J., Ciais, P., et al. (2018). Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands. *Nature Ecology & Evolution*, 2(5), 827–835. https://doi.org/10.1038/s41559-018-0530-6

Chau, T.-T.-T., Chevallier, F., & Gehlen, M. (2024). Global analysis of surface ocean CO₂ fugacity and air-sea fluxes with low latency. *Geophysical Research Letters*, 51(8), e2023GL106670. https://doi.org/10.1029/2023GL106670

Chau, T.-T.-T., Gehlen, M., Metzl, N., & Chevallier, F. (2024). CMEMS-LSCE: A global, 0.25°, monthly reconstruction of the surface ocean carbonate system. *Earth System Science Data*, 16(1), 121–160. https://doi.org/10.5194/essd-16-121-2024

Chevallier, F., Zheng, B., Broquet, G., Ciais, P., Liu, Z., Davis, S. J., et al. (2020). Local anomalies in the column-averaged dry air mole fractions of carbon dioxide across the globe during the first months of the coronavirus recession. *Geophysical Research Letters*, 47(22), e2020GL090244. https://doi.org/10.1029/2020GL090244

Chini, L., Hurtt, G., Sahajpal, R., Frolking, S., Klein Goldewijk, K., Sitch, S., et al. (2021). Land-use harmonization datasets for annual global carbon budgets. *Earth System Science Data*, 13(8), 4175–4189. https://doi.org/10.5194/essd-13-4175-2021

Copernicus Climate Change Service. (2022). ERA5-land hourly data from 1950 to present. *Copernicus Climate Change Service (C3S) Climate Data Store (CDS)*. https://doi.org/10.24381/cds.e2161bac

Dubayah, R. O., Armston, J., Healey, S. P., Yang, Z., Patterson, P. L., Saarela, S., et al. (2022). *GEDI LAB gridded aboveground biomass density, version 2*. ORNL DAAC. https://doi.org/10.3334/ORNLDAAC/2017

Earth Science Data Systems, N. (2020). MODIS COLLECTION 6 hotspot/active fire detections MCD14ML distributed from NASA FIRMS [Dataset]. *Earth Science Data Systems, NASA. Fire Information for Resource Management System*. Retrieved from https://firms.modaps.eosdis.nasa.gov/active_fire/

Fan, L., Wigneron, J.-P., Ciais, P., Chave, J., Brandt, M., Fensholt, R., et al. (2019). Satellite-observed pantropical carbon dynamics. *Nature Plants*, 5(9), 944–951. https://doi.org/10.1038/s41477-019-0478-9

Fan, L., Wigneron, J.-P., Ciais, P., Chave, J., Brandt, M., Sitch, S., et al. (2023). Siberian carbon sink reduced by forest disturbances. *Nature Geoscience*, 16(1), 56–62. https://doi.org/10.1038/s41561-022-01087-x

Foken, T., Göockede, M., Mauder, M., Mahrt, L., Amiro, B., & Munger, W. (2005). Post-field data quality control. In X. Lee, W. Massman, & B. Law (Eds.), *Handbook of Micrometeorology: A Guide for Surface Flux Measurement and Analysis* (pp. 181–208). Springer. https://doi.org/10.1007/1-4020-2265-4_9

Frankenberg, C., Fisher, J. B., Worden, J., Badgley, G., Saatchi, S. S., Lee, J.-E., et al. (2011). New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity. *Geophysical Research Letters*, 38(17). https://doi.org/10.1029/2011GL048738

Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Bakker, D. C. E., Hauck, J., et al. (2023). Global carbon budget 2023. *Earth System Science Data*, 15(12), 5301–5369. https://doi.org/10.5194/essd-15-5301-2023

Jones, M. W., Andrew, R. M., Peters, G. P., Janssens-Maenhout, G., De-Gol, A. J., Ciais, P., et al. (2021). Gridded fossil CO₂ emissions and related O₂ combustion consistent with national inventories 1959–2018. *Scientific Data*, 8(1), 2. https://doi.org/10.1038/s41597-020-00779-6

Lan, X., Tans, P., & Thoning, K. W. (2024). Trends in globally-averaged CO₂ determined from NOAA Global Monitoring Laboratory measurements (version Tuesday, 05-Nov-2024 13:17:54 MST) [Dataset]. https://doi.org/10.15138/9N0H-ZH07

Lawrence, P. J., & Chase, T. N. (2007). Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0). *Journal of Geophysical Research*, 112(G1), G01023. https://doi.org/10.1029/2006JG000168

Mauder, M., & Foken, T. (2004). Documentation and instruction manual of the eddy covariance software package TK2.

Qin, Y., Xiao, X., Wigneron, J.-P., Ciais, P., Brandt, M., Fan, L., et al. (2021). Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. *Nature Climate Change*, 11(5), 442–448. https://doi.org/10.1038/s41558-021-01026-5

Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T. A., Salas, W., et al. (2011). Benchmark map of forest carbon stocks in tropical regions across three continents. *Proceedings of the National Academy of Sciences of the United States of America*, 108(24), 9899–9904. https://doi.org/10.1073/pnas.1019576108

Santoro, M., Cartus, O., Carvalhais, N., Rozendaal, D. M. A., Avitabile, V., Araza, A., et al. (2021). The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. *Earth System Science Data*, 13(8), 3927–3950. https://doi.org/10.5194/essd-13-3927-2021

Smith, N. E., Kooijmans, L. M. J., Koren, G., van Schaik, E., van der Woude, A. M., Wanders, N., et al. (2020). Spring enhancement and summer reduction in carbon uptake during the 2018 drought in northwestern Europe. *Philosophical Transactions of the Royal Society B: Biological Sciences*, 375(1810), 20190509. https://doi.org/10.1098/rstb.2019.0509
spawn, S. A., Sullivan, C. C., Lark, T. J., & Gibbs, H. K. (2020). Harmonized global maps of above and belowground biomass carbon density in the year 2010. *Scientific Data*, 7(1), 112. https://doi.org/10.1038/s41597-020-0444-4

Stocker, B. D., Tumber-Dávila, S. J., Konings, A. G., Anderson, M. C., Hain, C., & Jackson, R. B. (2023). Global patterns of water storage in the rooting zones of vegetation. *Nature Geoscience*, 16(3), 250–256. https://doi.org/10.1038/s41561-023-01125-2

van Asperen, H., Jones, S., Lavric, J., Walter, D., Sierra, C., Horna, V., et al. (2024). Long term continuous high-precision greenhouse gas observation at the ATTO fieldsite: An overview. Retrieved from https://proceedings.science/icdc-2024/papers/long-term-continuous-high-pre- cision-greenhouse-gas-observation-at-the-atto-field?lang=en

van der Woude, A. M., Peters, W., Joetzjer, E., Lafont, S., Koren, G., Ciais, P., et al. (2023). Temperature extremes of 2022 reduced carbon uptake by forests in Europe. *Nature Communications*, 14(1), 6218. https://doi.org/10.1038/s41467-023-41851-0

van Geffen, J. H. G. M., Eskes, H. J., Boersma, K. F., & Veefkind, J. P. (2019). TROPOMI ATBD of the total and tropospheric NO₂ data products. Retrieved from https://sentinel.esa.int/documents/247904/2476257/Sentinel-5P-TROPOMI-ATBD-NO2-data-products.pdf

Vickers, D., & Mahrt, L. (1997). Quality control and flux sampling problems for tower and aircraft data. *Journal of Atmospheric and Oceanic Technology*, 14(3), 512–526. https://doi.org/10.1175/1520-0426(1997)014<0512:QCAFP>S2.0.CO;2

Wigneron, J.-P., Ciais, P., Li, X., Brandt, M., Canadell, J. G., Tian, F., et al. (2024). Global carbon balance of the forest: Satellite-based L-VOD results over the last decade. *Frontiers in Remote Sensing*, 5, 1338618. https://doi.org/10.3389/frsen.2024.1338618

Wigneron, J.-P., Li, X., Frappart, F., Fan, L., Al-Yaari, A., De Lannoy, G., et al. (2021). SMOS-IC data record of soil moisture and L-VOD: Historical development, applications and perspectives. *Remote Sensing of Environment*, 254, 112238. https://doi.org/10.1016/j.rse.2020.112238

Winderlich, J., Gerbig, C., Kolle, O., & Heimann, M. (2014). Inferences from CO₂ and CH₄ concentration profiles at the Zotino Tall Tower Observatory (ZOTTO) on regional summertime ecosystem fluxes. *Biogeosciences*, 11(7), 2055–2068. https://doi.org/10.5194/bg-11-2055-2014

Yang, H., Ciais, P., Frappart, F., Li, X., Brandt, M., Fensholt, R., et al. (2023). Global increase in biomass carbon stock dominated by growth of northern young forests over past decade. *Nature Geoscience*, 16(10), 886–892. https://doi.org/10.1038/s41561-023-01274-4