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2375-2548
American Association for the Advancement of Science

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10.1126/sciadv.adk5861
Research Article
Earth, Environmental, Ecological, and Space Sciences
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Environmental Studies
Water deficit and storm disturbances co-regulate Amazon rainforest seasonality
Drivers of Amazon rainforest seasonality
https://orcid.org/0000-0002-1428-3529
Lian Xu Conceptualization Formal analysis Investigation Methodology Project administration Software Validation Visualization Writing - original draft Writing - review & editing 1 *
https://orcid.org/0000-0002-6121-2483
Morfopoulos Catherine Conceptualization Methodology 2
https://orcid.org/0000-0002-0845-8345
Gentine Pierre Conceptualization Formal analysis Funding acquisition Methodology Project administration Resources Supervision Validation Visualization Writing - original draft Writing - review & editing 1 3 4
1 Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA.
2 Department of Life Sciences, Imperial College London, Silwood Park, London, UK.
3 Center for Learning the Earth with Artificial intelligence and Physics (LEAP), Columbia University, New York, NY, USA.
4 Climate School, Columbia University, New York, NY, USA.
* Corresponding author. Email: xl3179@columbia.edu
06 9 2024
06 9 2024
10 36 eadk586130 8 2023
30 7 2024
Copyright © 2024 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).
2024
The Authors
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

Canopy leaf abundance of Amazon rainforests increases in the dry season but decreases in the wet season, contrary to earlier expectations of water stress adversely affecting plant functions. Drivers of this seasonality, particularly the role of water availability, remain debated. We introduce satellite-based ecophysiological indicators to demonstrate that Amazon rainforests are constrained by water during dry seasons despite light-driven canopy greening. Evidence includes a shifted partitioning of photosynthetically active radiation toward more isoprene emissions and synchronized declines in leaf and xylem water potentials. In addition, we find that convective storms attenuate light-driven ecosystem greening in the late dry season and then reverse to net leaf loss in the wet season, improving rainforest leaf area predictability by 24 to 31%. These findings highlight the susceptibility of Amazon rainforests to increasing risks of drought and windthrow disturbances under warming.

Satellite data reveal that Amazon rainforests are susceptive to water deficit in dry seasons and storm damages in wet seasons.

http://dx.doi.org/10.13039/100010661 Horizon 2020 Framework Programme 787203 REALM VESRI LEMONTREE VESRI LEMONTREE License OptionCC BY-NC
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pmcINTRODUCTION

A large proportion of Amazon rainforests undergo distinct dry-to-wet season transitions each year, driving pronounced seasonal changes in leaf foliage, leaf photosynthesis, carbon allocation, water transport, storage, and use for canopy transpiration (1–4). Observational evidence suggests that some intact Amazon rainforests are close to being carbon neutral at the annual timescale (5, 6). This carbon balance points to the seasonal oscillation in the role of rainforests between a carbon source and carbon sink at the ecosystem scale within the year, as supported by tower-based carbon flux measurements (7, 8). This sink-to-source shift is often associated with episodic disturbances such as droughts (9, 10), fires (9, 11), and windthrows (12), each occurring predominantly during a particular season. Therefore, a refined mechanistic understanding of the seasonal variations in Amazon rainforests is imperative for accurately predicting the responses of rainforests in Amazon and other tropical regions to ongoing and projected future increase of stresses with climate change.

A prevailing perspective suggests that the seasonality of Amazon rainforests is governed by local hydroclimatic conditions, particularly seasonal variations of light/water availability (13–15). During the light-abundant dry season, satellite and ground observations consistently show increased canopy greenness and leaf area because more sunlight favors the emergence and development of new leaves (2, 16, 17). Despite the rich light availability and greener leaves, the photosynthetic rates of leaves are instead reduced during the early- to mid-dry season because of the relatively low photosynthetic capacity of newly emerged leaves (2, 3). The co-occurrence of enhanced greenness but suppressed photosynthesis during the light-rich dry season points to inconsistent behaviors of leaf phenology and leaf physiology. Current mechanistic understanding of this inconsistency mostly pertains to the effects of light availability, and ongoing debate still exists concerning the degree to which rainforest functioning is constrained by water during dry spells (18–21). In a warmer climate, a major concern on the Amazon’s future is the potential for enormous carbon emissions due to increasing drought frequency and severity (22) if the forests were on the edge of water stress. This emphasizes the need to ascertain the extent of water stress sensed by Amazon rainforests during dry seasons.

Previous interpretations of water constraints on forest seasonality relied mostly on the empirical plant-moisture relationship. One such study suggested that the photosynthetic capacity of rainforests is non-water–stressed in dry seasons over regions with annual rainfall exceeding 2000 mm because moisture surplus in wet seasons is sufficient to meet the high water demand in subsequent dry seasons (14). Conversely, rainforests falling below this threshold are considered water-stressed in dry seasons as the seasonal compensation cannot meet dry-season needs (14). However, the reliance on plant-moisture relationship as an indicator to infer rainforest water stress are inherently biased because (i) plant physiological regulation of water demand (e.g., stomatal control) and supply (e.g., stem water storage as an internal source for transpiration) is not well captured by leaf area or productivity anomalies (23); (ii) plant, water, and light conditions strongly interact in the Amazon (24–26); thereby, light-driven phenological shifts and vegetation feedbacks on hydrology are important confounders of the inferred ecosystem-moisture dependence. Better understanding the water-stress regulation of rainforests necessitates the use of water stress–sensitive ecophysiological metrics such as canopy water content, leaf/xylem water potentials (27), and volatile isoprenoids (28). Although such ecophysiological metrics have been growingly implemented to examine forest responses to severe droughts, e.g., the 2005, 2010, and 2015–2016 events (10, 28), these indicators have not been used specifically to inform the susceptibility of rainforests to dry periods within normal years. To date, it remains an open question whether rainforests are constrained by water during dry seasons of normal years.

Climatic controls of rainforest dynamics during the wet season are even less understood than in the dry season, although wet periods contribute greatly to the annual carbon uptake of rainforests (7). In the wet season, observational evidence shows a reduction of rainforest leaf foliage indicative of net leaf abscission (2, 4, 29) and physiological constraints of light on forest productivity due to the limited solar irradiance in rainy/cloudy days (4) and a decreased CO2 diffusion of moistened leaves (30). During this water-abundant period, in situ observations however show that moisture surplus during intensely rainy periods limits productivity and growth of tropical forests (31). The warm and moist low troposphere in the Amazon favors the development of deep convection, known as mesoscale convective systems (MCS), which organize storms with intense convective rainfall, strong surface winds, and lightning activities (32). In addition to constraints on physiological processes, rainstorms could cause important physical damages ranging from massive defoliation to snapping and wind-throwing of trees (12, 33–35). The potential effects of the defoliation and windthrow damages by storms are superimposed on the Amazon rainforests’ light- and water-driven phenological cycle, yet whether this signal is distinguishable from physiologically mediated phenology during wet seasons at broad scales remains unknown. This knowledge is essential to predicting future changes of wet-season carbon storage and ecosystem resilience in a warmer climate that favors more occurrences of convective storms (36, 37).

In this study, we aim to provide a mechanistic understanding of the climatological seasonal cycle of Amazon rainforests under present climatic conditions. We pay particular attention to the role of physiological water stress during the dry season and convective storms occurring predominantly during the wet season. While the adverse effects of severe droughts or storms on rainforest carbon uptake and growth has been well established in Amazon (10, 12, 38, 39), a key knowledge gap pertains to the climatological seasonal cycle of the rainforest in terms of both function and structure. Understanding whether tropical rainforests are water-stressed during normal years would shed light on their hydraulic safety margins under climate change. Understanding the impact of disturbances across the season and their dependence on the environment will help better grasp future changes.

RESULTS

Seasonality of photosynthesis, leaf area, and water dynamics

We examine the climatological seasonal cycle of various ecosystem attributes over the rainforest-dominated Amazon using multiple streams of satellite observations (Fig. 1 and fig. S1, A and D). These ecosystem attributes include solar-induced chlorophyll fluorescence [SIF, from both TROPOspheric Monitoring Instrument (TROPOMI) and moderate resolution imaging spectroradiometer (MODIS) reflectance-reconstructed contiguous SIF (CSIF)] as a proxy for photosynthesis, leaf area index (LAI) as a proxy for leaf canopy coverage, X-band vegetation optical depth (X-VOD) as a proxy for leaf water content and phenology, and L-band VOD (L-VOD) for stem water content and total biomass (Materials and Methods and table S1). In view of the dependence of forest seasonality on the moisture gradients and dry-season duration (14, 40), we also look at the seasonality separately for the wettest (northwest) and seasonally dry (southeast) parts of the basin (fig. S1A), as delineated using a rainfall seasonality index (fig. S1C and Materials and Methods).

Fig. 1. Seasonal trajectories of Amazon rainforest attributes.

Left, shows the seasonal variations of SIF, SIF/APAR, isoprene30°C/APAR (curves), surface incident radiation, and its diffuse and direct components (bars) aggregated for all (A), wet (C), and seasonally dry (E) regions of Amazonia, respectively. Right, shows the seasonal variations of LAI, L-VOD, X-VOD (curves), and accumulated monthly rainfall (bars) aggregated for all (B), wet (D), and seasonally dry (F) regions of Amazonia, respectively. LAI and VOD are expressed as precent changes relative to their annual means. “A” and “D” indicate the values retrieved at ascending and descending overpass time, respectively. All curves are monthly mean values averaged over all available years, with shaded areas indicating 1 SD of interannual variability. Light gray shadings represent the dry season duration. The plots show the co-occurrence of light-driven leaf flushing (high LAI) and water stress on leaf physiology (low SIF/APAR, high isoprene30°C/APAR) during dry seasons of Amazonia.

Climatologically, TROPOMI SIF and CSIF show consistent seasonality of decreasing photosynthesis from wet to dry seasons (Fig. 1A). As expected, the photosynthesis decrease toward the dry season is much more pronounced in seasonally dry regions than wet regions (Fig. 1, C and E). In wet regions, the reduction of photosynthesis is also robustly detected for the wettest months of March to June (Fig. 1, C and D). The declining photosynthesis from wet to dry seasons including those water-abundant periods is corroborated by eddy covariance–based estimates of gross primary productivity and maximum carboxylation rate (VCmax25) at most of the available Amazon sites (fig. S2 and Materials and Methods). By conducting partial correlation analysis, we show that photosynthetically active radiation (PAR) and its components (diffuse and direct radiation) are the dominant drivers of SIF seasonality even in seasonally dry regions, while water availability plays a secondary role (figs. S3 to S5). Nonetheless, by removing variations of light availability and canopy structure through normalizing SIF by absorbed PAR (SIF/APAR), the remaining physiological component of SIF exhibits a delayed minimum by 1 to 2 months, closely aligning with the timing of rainfall minimum (Fig. 1). Hence, the lowest physiological photosynthetic capacity does occur in the driest months of the year.

Satellite data unambiguously reveal a decoupling between LAI and SIF during the early dry season (June and July), with rapidly increased LAI co-occurring with decreased SIF (Fig. 1). This well-documented phenomenon is linked to light-driven flushing of new leaves with relatively low photosynthetic capacity (18, 41). Starting in the late dry season (September), LAI sharply declines until February, after which LAI partially recovers before resuming the downward trend after March (Fig. 1, B, D, and F). The declining LAI during the late wet season is also captured by phenology cameras (2) and is in parallel to increased litterfall production as documented by field-based studies (42) (fig. S6). Leaf phenology (LAI) and leaf water content (X-VOD) vary asynchronously across seasons, with the latter following seasonal soil moisture (SM) dynamics (Fig. 1, B, D, and F, and fig. S6, B, D, and F). X-VOD shows a continuous reduction throughout the dry season, suggesting decreasing leaf water content during this green-up period (Fig. 1, B, D, and F). Furthermore, while SM and X-VOD both show more pronounced seasonality toward drier regions, the seasonal range of LAI instead decreases from wet (15%, expressed in percentage) to seasonally dry (11%) regions (Fig. 1, D and F, and fig. S6, D and F). These findings indicate that leaf phenology is somewhat decoupled from leaf water dynamics and soil water availability.

Plant susceptibility to water stress during dry seasons

The divergence between structural (LAI) and physiological (SIF/APAR) changes during dry spells raises questions as to whether rainforests are subject to water stress. The correlations between LAI (or SIF) and SM with controlling for other covariates are generally negative in the dry season (figs. S4 and S5), implying that this vegetation-moisture relationship is primarily demand-driven (i.e., photosynthesis causes SM deficit) rather than supply-driven (i.e., growth constrained by SM deficit). In this sense, this correlation-based analysis does not ascertain whether forests are water-stressed. We also identify a surge in biogenic isoprene emissions based on top-down–based emission estimates, following the decrease of photosynthesis during dry spells. This signal has factored out emission of biomass burning resulting from fire disturbances as well as potential confounding effects of light and temperature variations (Materials and Methods) (Fig. 1A). The rapidly increase of isoprene emission during the dry season is robustly supported by field measurements of isoprene flux or isoprene mixing ratio at available Amazon sites (fig. S8C). Under mild to moderate droughts, as an analog to normal dry seasons, the photosynthetic apparatus is not damaged, yet carbon assimilation is reduced by the partial stomatal closure and thus reduced intercellular CO2 concentration (Ci) (43). The resulting excess of reducing power is often dissipated as extra isoprene emissions (28, 44). Hence, the divergent responses of decreased carbon uptake but increased isoprene emission are a physiologically relevant indicator of ecosystem water stress during dry seasons. In particular, this photosynthesis-isoprene decoupling is growingly prominent moving from the wet to seasonally dry regions (Fig. 1, C and E), indicative of higher susceptibility to water stress for drier biomes.

We further obtain evidence of dry-season water stress using the day-night ratio of X-VOD (DNRXVOD) and L-VOD (DNRLVOD) as indicators of plant water potential during the day relative to its nighttime level (Materials and Methods) (45). The theoretical basis for these indices is that, diurnally, the xylem supplies water for leaf transpiration during daytime (demand) and is replenished by soil water reservoir during nighttime (supply) (46, 47); thus, DNRXVOD and DNRLVOD measure the demand-supply balance (i.e., water stress) of plant water use (Materials and Methods). The normalization by nighttime values factors out the confounding effects of leaf area or biomass that are invariant diurnally, leading to contrasting seasonal behaviors compared to the original VOD values (Figs. 1, B, D, and F, and 2A). Taking the Amazon rainforests as a whole, we detect synchronized decreases in DNRXVOD and DNRLVOD following a growing rainfall deficit from May to September (Fig. 2A). The lowering water potentials provide compelling evidence of the increasing water stress in both stems and leaves toward the driest period (Fig. 2A) and is responsible for the simultaneous decrease of canopy photosynthetic capacity (Fig. 1, A, C, and E).

Fig. 2. Seasonality of estimated diurnal variations in leaf and stem water potentials.

(A) The seasonal variations of the DNRXVOD, the DNRLVOD (curves), and accumulated monthly rainfall (bars) aggregated for all, wet, and seasonally dry regions of Amazonia, respectively. Light shadings represent the dry season duration. (B) Scatterplot of the seasonal range of DNRXVOD against the log-transformed seasonal dryness contrast (SDC) index across rainforest-dominated grids in Amazonia. The inset upper-left map shows the distribution of the seasonal range of DNRXVOD. (C) Scatterplot of the seasonal range of DNRLVOD against the log-transformed SDC index across rainforest-dominated grids in Amazonia. The inset upper-left map shows the distribution of the seasonal range of DNRLVOD. In (B) and (C), the black solid lines and blue dashed lines denote the best-fitted line and 95% confidence intervals, respectively, of the regressions. The plots show synchronized decreases in leaf and stem water potentials (DNRXVOD and DNRLVOD), indicative of growing water stress, during dry seasons.

Spatially, we find a clear dependence of the seasonal range of DNRXVOD and DNRLVOD on the ecosystem dryness level, both correlated positively with the seasonal range of rainfall (DNRXVOD: r = 0.43, P < 0.01; DNRLVOD: r = 0.27, P < 0.01) (Fig. 2, B and C). This spatial gradient points to a greater seasonal fluctuation of leaf/stem water potentials toward drier regions (Fig. 2A). By looking separately at wet and seasonally dry regions, we find similar seasonality of stem water potential although with a noticeably lower minimum in drier regions (Fig. 2A). However, leaf water potential responds differently. In wet regions, DNRXVOD is relatively low during dry seasons, implying a certain degree of water stress, but is preceded by relatively high values before dry seasons as rainforests can still access abundant water from soil or stem reservoirs (Fig. 2A and fig. S7D). In seasonally dry regions, DNRXVOD decreases more substantially and persistently following a growing rainfall deficit and also recovers rapidly with rainfall replenishment (Fig. 2A). This rapid drop and recovery of DNRLVOD both lag behind that of DNRXVOD (Fig. 2A), suggesting a slower response of xylem than leaf water reservoirs during drought periods.

Storm-related defoliation during wet seasons

Light-driven leaf abscission as litterfall has been highlighted as the main process describing the LAI decrease during wet seasons (48). During the late dry season when new leaf production ceases, the persistent, albeit slowing-down, litterfall production contributes to the rapid decrease of LAI (Fig. 1, B, D, and F, and fig. S6B). We hypothesize that the widespread wet-season LAI reduction over the Amazon (Fig. 1B) is associated with defoliation damages by convective storms. To test this hypothesis, we use convective available potential energy (CAPE) as a metric for the frequency/intensity of convective storms (12). In the Amazon, CAPE shows two detectable seasonal peaks with the higher one occurring in the dry-to-wet season transitional period (August–November) and the lower one in the mid-late wet season (March and April) (Fig. 3A). The seasonality of convective storms skillfully captures that of visually identified windthrow events occurred in the Amazon (Fig. 3A).

Fig. 3. Impacts of convective storms on leaf phenology.

(A) Curves show monthly mean CAPE averaged for all, wet, and seasonally dry regions of Amazonia, respectively. Bars show the monthly number of reported satellite-identified windthrow events (12). Red boxes represent the two storm-prone periods including the mid-late wet season (WS) and the dry-to-wet transitional period. Light shadings represent the dry season duration. (B) Partial dependence plots of the net LAI change (dLAI) against CAPE for the two storm-prone periods. The thick curves represent the running mean of the values. The subplot in the upper right represents the fraction of dLAI variance (based on coefficient of determination, r2) explained by all predictors versus that explained by all predictors excluding CAPE. (C to H) Spatial patterns of CAPE, dLAI, and its changing rate (d2LAI) averaged for months exceeding a CAPE threshold of 898 J kg−1, shown for mid-late WS (left) and dry-to-wet transitional period (right), respectively. The plots show potential defoliation damages (LAI reduction) by convective storms during mid-late WS and the dry-to-wet transitional period.

By overlapping patterns of storm intensity (i.e., CAPE) and net LAI changes (dLAI; Materials and Methods), we observe a clear consistency between CAPE and dLAI across space during the mid-late wet season. Strong negative dLAI (<−0.15) is identified in the northwestern and southeastern parts of Amazon that experience the most intense storms (Fig. 3, C and E), suggesting a critical role of storms in facilitating leaf loss during this period. This storm-prone period is also synchronized with leaf senescence and thus heightened probability of leaf loss, as evidenced by tower-based camera observations and litterfall measurements (fig. S6, B and C) (2), which suggests that the incidence of storms accelerates the natural shedding of aged leaves. However, during the dry-to-wet transitional period, while the southern Amazon shows a LAI decrease following storms, the northern part instead shows an increase (Fig. 3, D and F). In the wettest northern Amazon where convective storms occur since the onset of the dry season (Fig. 3A), the phenology-driven leaf flushing dominates this LAI increase (2, 16). In a further test, we calculate the changing rate of dLAI (denoted as d2LAI) to partly remove the slowly changing light-driven signal while retaining the rapidly varying storm-driven signal (Materials and Methods). The d2LAI shows consistent seasonal timing of minimum d2LAI values and peak CAPE values (fig. S9A), confirming the effect of storm-mediated defoliation. Moreover, d2LAI shows widespread negative values across the Amazon including its northern part (Fig. 3H). This pattern indicates that although convective storms do not reverse entirely the light-driven greening trend during the dry season in the northern Amazon (Fig. 3F), they attenuate some of the light-driven increase.

To evaluate the relative contribution of convective storms to leaf phenology, we use a machine learning model (XGboost) to predict dLAI (or d2LAI) as a function of a group of hydroclimatic factors (Materials and Methods). These factors, taken together, explain more than 90% of the spatiotemporal variations of dLAI and d2LAI (Fig. 3B). By conducting a pair of simulations that differ only in the inclusion or exclusion of CAPE as a predictor, we find that convective storms account for 24 to 31% of the explained variance of dLAI during the wet season across the basin (Fig. 3B). Using d2LAI as the target variable that partially filters out light-driven signals, convective storms can explain up to 44 and 70% of the variability during the dry-to-wet transition and mid-late wet season, respectively (fig. S9B). Therefore, CAPE can greatly improve the predictability of LAI dynamics during these leaf abscission periods. A partial dependence plot shows a continuous decrease of dLAI (and d2LAI) with increasing CAPE for both storm-prone periods (Fig. 3B), demonstrating that more intense convective storms are linked to a more substantial loss of leaves. This mechanism also underlies the partial recovery of LAI in the mid-wet season (Fig. 1, B, D, and F) with less frequent occurrence of convective storms (Fig. 3A). This storm-related defoliation also aligns with a meta-based analysis showing a significant positive correlation between litterfall and rainfall seasonality (48).

In addition to leaf abundance, whole-plant biomass and water content, as indicated by L-VOD, also decline starting at the onset of the wet season and persisting to the end of it (Fig. 1, B, D, and F). We further explore whether this biomass loss is attributable to carbon loss from disturbances (windthrows) and/or decreased carbon gain through photosynthesis. We fail to detect a robust association between monthly changes of abovegroundwood biomass (dAGB, inferred from L-VOD, Methods) and CAPE at broad spatial scales (fig. S10), implying that storms do not appear to induce large biomass and carbon loss along with foliar defoliation. By further linking dAGB to SIF while controlling for simultaneous variations of water storage (Materials and Methods), we find positive, and often statistically significant, correlations from the mid-dry season to the early wet season (July to December) (Fig. 4). Therefore, these months feature an effective translation of carbon assimilation into carbon storage, making them the most pivotal ones to the carbon storage potential of rainforests but also the ones most sensitive to climate change. Wet regions have substantially longer periods of effective forest growth (June to December) than seasonally dry regions (September to November) (Fig. 4), with the former coinciding with the emergence of new foliage. The months with positive dAGB-SIF correlation are generally storm-prone ones (Figs. 3A and 4), suggesting that any storm-induced damage could easily result in a loss of rainforest carbon stock with an increase in storm intensity or frequency. While the VOD-based biomass dynamics provide valuable insights, the results should be interpreted with caution since water- and carbon-driven dynamics are strongly intertwined at seasonal scales. Empirical approaches are inherently deficient in separating these signals from the integrated satellite data, thereby undermining the reliability of validation against in situ biomass measurements.

Fig. 4. Linkages between carbon stock and photosynthesis.

Maximum Pearson correlation coefficients between dAGB and SIF over the past 0 to 3 months, after factoring out covarying effects of DNRLVOD. The correlations are shown for all, wet, and seasonally dry regions of Amazonia, respectively. The lead month (0 to 3) having the largest correlation coefficient is labeled under (or above) the bars. ***P < 0.01; **P < 0.05; *P < 0.1; ×P > 0.1. Light shadings represent the dry season duration. The plots show that carbon storage of rainforests is widely limited by carbon source during the dry-to-wet transitional period.

DISCUSSION

In this study, we provide improved understanding of the observed seasonal patterns in Amazon rainforests, complementing the traditional paradigm of light-water trade-offs. On the basis of our results, the dry-season green-up of rainforests, mainly resulting from increased light availability, does not mean that rainforests are non–water-stressed. During this period, rainforests develop new leaves yet at the cost of reduced photosynthesis (Fig. 5), which implies that plant-accessible water is used preferentially for leaf production rather than carbon uptake. We illustrate that trees take this conservative water use strategy, i.e., a compromise between water needed for photosynthesis or for leaf production because the available SM is insufficient to meet both growth and photosynthetic needs. The primary climatic constraints on tropical rainforests vary between leaf phenology and leaf physiology: The leaf phenology/production is more sensitive to light availability, with the peak levels of leaf abundance occurring in the most light-rich period (Fig. 1B) (2, 16, 17). However, leaf physiology is more sensitive to water availability, with the lowest photosynthetic capacity observed near the most water-limited period (Fig. 1A). This physiological water stress is also evidenced by an observed increase in leaf isoprene emissions, which occurs in situations of partial stomata closure under stress (Fig. 1A). Furthermore, microwave-based satellite observations show parallel declines in leaf and xylem water potentials during the driest months (Fig. 2A). This physiological water stress, in conjunction with the low photosynthetic capacity of new leaves (18, 41), together, contribute to the observed low photosynthetic rates during dry seasons (Fig. 5).

Fig. 5. Schematic of Amazon rainforest seasonality and its drivers.

Curves represent the climatological seasonal cycles of LAI (green), AGB (megenta), and photosynthesis (indicated by SIF, blue) of Amazon rainforests. The three attributes vary consistently (coupled) during the mid-late wet season but vary inconsistently (decoupled) during the dry season and early wet season. Labeled arrows indicate the driving mechanisms of rainforest changes at the corresponding seasonal stages based on existing theories in the literature and our results.

Another underexplored mechanism explaining the wet season behavior is the massive defoliation and localized windthrow-induced mortality due to frequent convective storms. This mechanism can explain about one-fourth of the variability in leaf phenology during storm-prone periods. During the dry-to-wet season transition, convective storms are a prominent factor that first decelerates the light-driven LAI increase and then reverses this to a net LAI decrease (Fig. 5). The physical damage causes a decoupling between LAI and photosynthesis during this period (Fig. 5): While decreased LAI propagates to lowered photosynthetic rates, photosynthesis overall increases because of the relaxed physiological constraints including reduced water stress and enhanced photosynthetic capacity of maturing leaves (Fig. 5). Moving toward the late wet season, LAI is first partially recovered from preceding loss during a period of reduced storm damage (January to February) but then again suffers from convective storms afterward (March to April) (Fig. 5). During this period, a strong coupling is detected between LAI and photosynthesis, where the storm-driven leaf abscission exacerbates the decreased photosynthesis due to light limitations (Fig. 5).

We emphasize that our unraveled mechanisms of the Amazon rainforest seasonality should be accounted for in predicting the carbon storage potential of Amazon rainforests. The long-term lengthening of dry seasons (49, 50) and increasing frequency of severe droughts occurring predominantly in dry seasons (51) may pose a critical threat to the sustainable functioning of Amazon rainforests in the future. Our finding that rainforests experience water stress during dry seasons underscores their precarious situation. Although plant physiological regulations (i.e., stomatal control) can buffer abiotic water stress (52), any further increase in dryness—especially during the already dry periods—might push the system progressively toward a lethal threshold, beyond which rainforests might fail to maintain effective metabolism and risk hydraulic failure and even mortality (39, 53). Long-term drought experiments conducted in Amazon rainforests also demonstrate that drought-induced loss of hydraulic conductivity can trigger tree die-off, supporting the high vulnerability of rainforests to drought stress (39). The role of physical damages caused by convective storms during wet seasons has also been underappreciated. Global warming is believed to favor the development of severe storms in the tropics (36, 37), pointing to a possible amplified wet-season carbon loss from storms in a warmer climate. This mechanism is particularly important for the early wet season as this is the period when the rainforest carbon uptake can effectively translate carbon uptake into carbon storage (Fig. 2). We, however, note potential uncertainties surrounding the use of satellite data for characterizing the complex ecophysiological processes of tropical rainforests. For example, satellite-derived LAI inferred from surface reflectance retrievals captures mainly canopy greenness changes determined by both leaf quantity and leaf quality such that observed LAI decline results from both defoliation and lowered leaf quality. The degraded leaf quality is related to the natural leaf aging (2) and the increased abundance of fungal pathogens as favored by hot-wet environments (54). Furthermore, the uncertainty of L-VOD–based AGB changes sources from internal uncertainty from transferring the L-VOD to AGB and external uncertainty from reference biomass maps, which is on the order of 20 to 30% over tropics (55). At the seasonal scale, removing confounding effects of canopy/stem water content on the scaling between forest biomass and L-VOD remains challenging.

Our discovered mechanisms are critical for improving model predictions of rainforest dynamics under global warming. The water constraints on rainforest physiology and physical damages by rainstorms are currently underrepresented or even missing in the latest generations of Earth system models, underlying their poor performance in reproducing the mean phenological cycle and rainforest vulnerability and resilience to hydroclimatic anomalies in Amazonia (56, 57). Explicit representations of these processes not only enhance our understanding of the seasonality in rainforests better but also reduce the uncertainties in the projected future rainforest changes and the biochemical and biophysical feedback to Earth’s climate.

MATERIALS AND METHODS

Satellite-derived vegetation attributes

We examined the seasonal cycle of a range of key ecosystem attributes of the Amazon rainforests including leaf abundance (LAI), photosynthesis (SIF), canopy water content (X-VOD), above-ground biomass (L-VOD), and isoprene emissions (table S1). LAI was obtained from MODIS Collection 6 product (MOD15A2H) available as 8-day composites with 500-m spatial resolution for 2000 to present. We excluded low-quality observations contaminated by clouds, aerosols, shadows, snow, and/or ice based on the quality flags. The MODIS LAI data have been validated comprehensively against ground measurements despite a lower accuracy in tropical rainforests because of the frequent data gaps due to cloud contamination (58). This limitation, however, has minor impacts on our analysis of the mean seasonal cycle. The satellite-observed seasonal cycle of LAI was also corroborated by in situ measurements of leaf abscission proxies including litterfall production (16 sites) (42) and camera-observed fraction of leafless crown (2 sites) (2, 16). Seasonal LAI changes are jointly determined by leaf loss (litterfall) and new leaf production (2). During the mid-to-late wet season, site-observed litterfall production increases with the aging of leaves (2), corresponding to low LAI levels (fig. S6, A and B). The increasing litterfall production continues to the mid-dry season, paralleled by a high fraction of leafless crown (fig. S6, B and C), whereas LAI increases because of the prevalence of new leaf production during this period (2). During the dry-to-wet transitional period, LAI decreases with leaf production ceasing (2) and leaf shedding continuing yet slowing (fig. S6, A and B).

SIF was derived from two data sources including TROPOMI (3, 59) and a MODIS reconstruction, the globally CSIF based on Orbiting Carbon Observatory-2 (OCO-2) (60). TROPOMI enables spatiotemporal monitoring of SIF with a small footprint size (7 km by 3.5 km at nadir) and almost daily spatially continuous coverage (59). This finely resolved SIF product is available only from 2018 May onward. We used the daily average of the 743- to 758-nm retrievals scaled from instantaneous SIF values using the geometry of incoming solar radiation (61). CSIF was generated using neural networks to map from MODIS surface reflectance bands to the clear-sky, nadir mode OCO-2 SIF soundings (60). This reconstructed SIF product is available as 4-day composites and 0.05° spatial resolution for a longer period than TROPOMI SIF, over 2000–2020, thus providing more replicates of the seasonal cycle. All the above data were aggregated to monthly values by averaging all available values within the corresponding month.

Passive microwave-based VOD monitors vegetation dynamics of biomass and water storage (62). VOD is available at different frequencies with varying extinction effects and penetration ability through the vegetation cover. The high-frequency VOD (i.e., Ku-, C, or X-bands) is sensitive to processes occurring at the top of canopy (63, 64), whereas the low-frequency VOD (L-band, 1.4 GHz) is sensitive to all aboveground vegetation components (stems and leaves) (55). This study used the diurnal variations of both the Advanced Microwave Scanning Radiometer 2 (AMSR-2) X-VOD and the Soil Moisture and Ocean Salinity INRA-CESBIO (SMOS-IC) L-VOD (version 1.6). The AMSR-2 X-VOD available since July 2012 was retrieved by the sun-synchronous Aqua satellite with ascending/descending orbit crossing time at 1330 and 0130, respectively. The L-VOD available since January 2010 was retrieved by a microwave radiometer on board the SMOS satellite (65). The satellite orbits the Earth at a local overpass time of 0600 for ascending and 1800 for descending. We filtered out daily observations with high root mean square error of brightness temperature (>6 K) or VOD (>0.05) (10) and those affected by snow cover (based on quality flags) or canopy interception of rainfall [Tropical Rainfall Mapping Mission (TRMM) rainfall >0 mm] (66). We derived monthly L-VOD by averaging available daily values for ascending and descending data, respectively.

Isoprene emissions, a key component of biologic volatile organic compounds, were obtained from two top-down source inverse estimates based on space-borne column observations of formaldehyde (a high-yield product in the oxidation of isoprene) retrieved from TROPOMI and the ozone monitoring instrument (OMI) spectrometer (67, 68), respectively. The top-down fluxes of volatile organic compounds are estimated by the MAGRITTE chemistry-transport model (IMAGESv2) (69), starting from a priori inventory of fire emissions (from the Global Fire Emission Database), and biogenic emissions estimated by the MEGAN-MOHYCAN emission model (70). The isolated estimation of pyrogenic and biogenic emissions helps factor out confounding effects of wildfires. The TROPOMI-based isoprene flux is available over South America (32°W ~ 85°W, 34°S ~ 15°N) at a 0.5° × 0.5° spatial resolution for 2018 (68). The OMI-based isoprene flux is available over South America on a monthly basis with a 0.5° × 0.5° resolution for 2005–2017 (67). The top-down estimates have been evaluated against various bottom-up inventories such as ALBERI (71), CAMS-GLOB-BIO (72), MEGAN-MACC, and GUESS-ES (67). In particular, the MEGAN model used as an a priori for inversion-based satellite observations has been validated against ground-based measurements (73, 74), showing high capacity of capturing the seasonal cycle of isoprene emissions at an Amazonian field site (74). To further test the reliability of the top-down estimates, we compared the mean seasonal cycle of top-down estimates with available field measurements of isoprene flux or isoprene mixing ratio in Amazonia (fig. S8) collected from six independent site studies (75–79).

Hydroclimatic data

We included a set of hydroclimatic variables to analyze the drivers of rainforest seasonality, including precipitation (P), vapor pressure deficit (VPD), PAR, incoming surface shortwave solar radiation (RAD), direct surface solar radiation (RADdir), CAPE, and total column SM. Among these, VPD, RAD, RADdir, and CAPE were obtained from the ERA5 climate reanalysis datasets (80) with a spatial resolution of 0.25° and hourly intervals, all covering the 2000–2021 period. The fraction of diffuse solar radiation to RAD (fdiff) was calculated indirectly from RADdir (fdiff = 1 − RADdir/RAD). CAPE quantifies the amount of buoyant energy available for a parcel of air to ascend vertically through the atmosphere, which serves as a key index in meteorology for assessing the potential for convective storm development (81). As convective storms occur commonly between afternoon and evening, we only used afternoon values of CAPE averaged for the local time 1300 to 1900 (UTC 1700 to 2300).

PAR with 5-km resolution, 4-day interval maps for 2000 to 2019 is generated by the Breathing Earth System Simulator (82). We also calculated the APAR as the product of PAR and fPAR, where fPAR is the fraction of total PAR absorbed by plant canopies derived from the MODIS C6 product (MOD15A2H). Daily rainfall at 0.25° spatial resolution for 1998 to 2019 was derived from the TRMM 3B42 v7 product. This rainfall dataset is generated by merging observations acquired at microwave and infrared wavelengths. Total SM with ~9-km grid spacing for 1979 to 2021 was derived from the ERA5-land surface reanalysis dataset, aggregated for all four soil depths (0 to 7 cm, 7 to 28 cm, 28 to 100 cm, and 100 to 289 cm). Caution is however needed in the inferior quality of SM estimates over densely vegetated Amazon due to the absence of deep-layer satellite SM data for assimilation and the scarcity of in situ measurements for validation.

Eddy-covariance data

Eddy-covariance measurements at nine flux towers in Brazil are available to test the robustness of satellite-based results. Eight of the sites (BAN, FNS, RJA, K34, CAX, K64, K77, and K83) were obtained from the Large-Scale Biosphere-Atmosphere Experiment (LBA-ECO) flux tower network, and the rest one (GF-Guy) was from the FLUXNET2015 database. The dominant vegetation type is evergreen broadleaf forest for all these sites. The distribution of the nine flux sites is shown in fig. S1. We used monthly gross primary productivity (GPP) estimates based on a daytime partitioning approach and meteorological variables including precipitation and PAR. We also calculated the maximum carboxylation rate at a common temperature of 25°C (VCmax25) using half-hourly data and following a recent study (83).

Analysis of Amazon rainforest seasonality

This study focused on the mean seasonal cycle of Amazon rainforests, so we aggregated different ecosystem attributes over the rainforest-dominated Amazonia for all available years within the 2000–2021 period. The dry and wet seasons were defined as months with climatological rainfall amount less and more than 180 mm, respectively. We excluded regions dominated by other vegetation types (savanna, shrubland, grassland, and cropland) based on the MODIS land cover maps (MOD12C1), for which vegetation varies proportionally and strongly with SM across space and time. Rainforest seasonality varies considerably across moisture gradients, so we defined an index of seasonal dryness contrast (SDC), with which the Amazonia was divided, almost evenly in area, into wet (SDC ≤ 3.5), transitional (3.5 < SDC < 7), and seasonally dry (SDC ≥ 7) regions. The SDC index was defined for each grid as the ratio of cumulative rainfall of the three wettest months to that of the three driest months (fig. S1A). For better intercomparison of seasonality, all stock-related variables (LAI, L-VOD, and X-VOD) were transformed to percent anomalies relative to their annual mean values (Fig. 1).

We first regridded all observation-based data into a common resolution of 0.5° × 0.5°. To test how rainforest seasonality is controlled by light-water trade-offs, we performed a partial correlation analysis of monthly SIF (or LAI) against PAR, P, VPD, SM, and fdiff using detrended anomalies of all available years. This analysis was conducted both on a monthly basis for values aggregated over all, wet, and seasonally dry regions (fig. S4), and also on a grid basis for the dry season (the three consecutive months having the least rainfall), early wet season (the 3 months after the dry season), and late wet season (the 3 months before the dry season) (fig. S5). The dominant driving factor was identified as the one showing the strongest correlation with SIF (or LAI).

In addition, we analyzed the connections between monthly changes of aboveground wood biomass (dAGB) and carbon uptake (SIF) to understand whether the rainforest seasonal cycle is regulated by carbon source (photosynthesis) or carbon sink (the allocation of assimilated carbon to plant tissue expansion) activities (84, 85). The AGB was approximated from microwave-retrieved L-VOD data. For each month, we first selected the 25th percentile of daily L-VOD values within it to filter out potential contribution of canopy intercepted water and the climate-driven day-to-day variability of canopy/xylem water storage. Next, we estimated the AGB (Mg ha−1) by establishing a spatial calibration function between a benchmark AGB map from GlobBiomass (86) and annual mean L-VOD (87) (fig. S11)AGB=a∙arctan b∙(VOD−c)−arctan−b∙carctanb∙Inf−c−arctan−b∙c+d(1)

where a, b, c, and d are the fitted coefficients. The fitting of calibration parameters was conducted for 0.25°C grids within tropics of South America (30°S–30°N) for the year 2011. The GlobBiomass product was developed by integrating spaceborne synthetic aperture radar, light detection and ranging, and optical observations with forest inventory databases (86). The quality of this dataset was ensured through an intercomparison with independent AGB products and validation against in situ measurements (86). Using the seasonally resolved AGB maps inferred from L-VOD, we calculated dAGB as the difference between AGB values of the next month and the current month. Partial correlation coefficients were calculated between monthly dAGB and SIF while controlling for simultaneous variations of DNRLVOD. Here, seasonal changes of AGB derived empirically from L-VOD are determined by dynamics of both carbon and water storage. The DNRLVOD, which has removed carbon-driven dynamics on a daily basis, is an indicator of water storage dynamics across the seasonal cycle (see details in “Detection of ecosystem water stress during dry seasons”). The correlation analysis was conducted using a 3-month running window for all years from 2010 to 2019 to ensure enough samples. Considering potential lagged effect of antecedent carbon assimilation, we computed the correlations between dAGB and SIF averaged over the past 0 to 3 months and selected the largest correlation for the focused month.

Detection of ecosystem water stress during dry seasons

Our rationale for monitoring ecosystem water stress stems from the understanding that plant physiology exhibits two pivotal alterations under stressed conditions: (i) An increasing fraction of PAR is used to produce isoprene compromised by a reduction of carbon uptake, related to a partial closure of leaf stomata and weakened dark reactions (28, 88). Some studies also suggest that plants emit isoprene to supress the accumulation of reactive oxygen species under stress that is harmful to plant metabolism (89). (ii) A reduction of plant water potential triggered by partial stomal closure to avoid excessive water loss (27). With this conceptual basis, we leveraged multi-source satellite data to explore whether Amazon rainforests are water-stressed during dry seasons.

To understand the first process, we compared the seasonal cycles of photosynthesis (SIF) and isoprene flux of Amazon rainforests (Fig. 1, A, C, and E). With an emphasis on the leaf-level physiological responses, the canopy-level SIF and isoprene flux were both divided by APAR. This normalization controls for synchronized changes of incident solar radiation (indicated by PAR) and canopy structure/phenology (indicated by fPAR). Moreover, the seasonality of isoprene production is affected by temperature variations owing to a strong dependence of isoprene synthase activity on temperature. The temperature response of enzymatic activity can be described by the Niinemets model (90)STk=ec−Ha/RTk1+eSTk−Hd/RTk(2)

where STk the strength of isoprene synthase activity (μmol isoprene g−1 synthase s−1) at the leaf temperatureTk (K), c (= 35.478) is a scaling constant, Ha (= 83.129 J mol−1) is an activation energy, Hd (= 284.60 J mol−1) is a deactivation energy, S (= 0.8875 J mol−1 K−1) is an entropy term, and R (= 8314 J mo l−1 K−1) is the gas constant (90). Tk (K) is leaf temperature, which was derived from MODIS-observed land surface temperature (MYD11A2). On the basis of this temperature response model, we adjusted the leaf-level isoprene flux to a level given a common temperature of 30°C (Eq. 3). This normalization of temperature variation helps remove the effects of thermal stress while retaining potential water stressIsoprene30°C=IsopreneTk∙STk/S30°C(3)

To characterize the seasonal variations of plant water potential, we used microwave-based X-VOD and L-VOD as proxies for canopy and xylem water potentials, respectively, as supported by both theoretical and experimental evidences (91, 92). Note that although L-VOD captures whole-plant water dynamics, its variations come primarily from stems that contribute to most of the whole-plant water storage (93). Our derivation of the DNRXVOD and DNRLVOD can be expressed as DNRXVOD=X-VODA/X-VODD;DNRLVOD=L-VODD/L-VODA(4)

where the subscripts “A” and “D” represent values retrieved at ascending and descending overpass time, respectively. Here, the nighttime measurements use the descending time of X-VOD and ascending time of L-VOD, respectively. The derivation of the day-night ratio brings two notable benefits: (i) It factors out confounding effects of LAI/carbon stock seasonality that is almost unchanged diurnally; (ii) this ratio describes a balance between water need (daytime transpiration) and supply (the refilling of canopy/xylem water storage by available SM) (46, 47) on a daily basis, with its seasonal time series characterizing variations of canopy/stem water content and experienced water stress.

Assessment of convective storm impacts during wet seasons

We used afternoon CAPE as a metric for the severity/frequency of damaging convective storms in Amazonia, following a recent study that found a strong spatial correlation between windthrow density and mean CAPE (12). Feng et al. (12) indicated that the number of windthrow events identified with satellite data increases proportionally with CAPE when exceeding a CAPE threshold of 1014 (95% confidence interval: 898 to 1200) J kg−1. Satellite-based disturbance intensity metrics were also verified to capture field-measured tree mortality within windthrows (94, 95). Compared with those localized windthrow events, defoliation by convective storms is more widespread and may be detected from satellite imagery at a lower CAPE threshold. We, therefore, used the lower confidence bound of 898 J kg−1 as the CAPE threshold for our research purpose. On a monthly basis, we first identified two periods with mean CAPE greater than this threshold during the mid-late and early wet seasons, respectively. We next quantified the potential defoliation effect using the net LAI changes (dLAI, calculated as the central difference between neighboring months, Eq. 5)dLAIt=(LAIt+1−LAIt−1)/2(5)

Note that, even without physical damages by storms, leaf flushing and senescence of rainforests would also occur in response to seasonal variations of PAR. This light-driven leaf phenology evolves slowly, in contrast to the rapid canopy changes triggered by damaging convective storms. In view of this, we also calculated the second order of central difference for LAI (d2LAI, Eq. 6), which represents the changing rate of dLAI. This metric helps remove the slowly changing signal while keeping the rapidly changing onesd2LAIt=LAIt+1−LAIt−LAIt−LAIt−1=LAIt+1+LAIt−1−2LAIt(6)

To assess the relative importance of convective storms, we implemented an extreme gradient boosting (XGboost) model to predict dLAI (or d2LAI variations) as a function of six environmental factors including annual mean LAI, CAPE, PAR, VPD, SM, and fdiff. The climatological mean LAI, as the averaged value from 2000 to 2021, represents the background structural and composition difference across space. The XGboost algorithm creates a sequential ensemble of decision trees that collaborate to capture complicated patterns within data. Two XGboost models were ran for the early and mid-late wet seasons separately, forced by those grids/months exceeding the CAPE threshold within the periods. We performed a pair of factorial simulations, one prescribed with observed values of all predictors, and the other with observations of all but CAPE, which is held constant at the mean value of all samples. The contribution of CAPE to the predictability of dLAI (or d2LAI) was determined by the difference between the two simulations. We also implemented the SHapley Additive exPlanations (SHAP) values (96) to assist explanation of the output from the XGboost models. SHAP is a game theoretic approach that connects optimal credit allocation with local explanations using the classic Shapley values from game theory (96). This framework estimates the change in the expected model prediction when conditioning on a specific predictor, as used to derive the partial dependence of dLAI (or d2LAI) on CAPE (Fig. 3B). To examine potential carbon stock losses by storms, we reproduced the abovementioned analyses yet using dAGB as the target variable.

Acknowledgments

Funding: X.L. and P.G. would like to acknowledge support from the LEMONTREE (Land Ecosystem Models based on New Theory, obseRvations and ExperimEnts) project, funded through the generosity of E. and W. Schmidt by recommendation of the Schmidt Futures programme. C.M. acknowledge support from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no: 787203 REALM).

Author contributions: Conceptualization: X.L. and P.G. Methodology: X.L. and P.G. Investigation: X.L. Visualization: X.L. Supervision: P.G. Writing—original draft: X.L. Writing—review and editing: X.L., C.M., and P.G.

Competing interests: The authors declare that they have no competing interests.

Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. In detail, the MODIS LAI/fPAR data are available at https://e4ftl01.cr.usgs.gov/MOLT/MOD15A2H.061/. The ERA5 reanalysis product is available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form. The TRMM precipitation data are available at https://climatedataguide.ucar.edu/climate-data/trmm-tropical-rainfall-measuring-mission. The TROPOMI SIF data are available at: ftp://ftp.sron.nl/ /open-access-data-2/TROPOMI/tropomi/sif/v2.1/l2b. The CSIF data are available at: http://doi.org/10.11888/Ecolo.tpdc.271751. The TROPOMI-based isoprene emission data are available at: https://emissions.aeronomie.be/index.php/tropomi-based/isoprene-sa. The OMI-based isoprene emission data are available at: https://emissions.aeronomie.be/index.php/omi-based/isoprene-sa. The LBA-ECO EC data are available at: https://daac.ornl.gov/LBA/guides/CD32_Fluxes_Brazil.html. The FLUXNET2015 EC data are available at https://fluxnet.fluxdata.org/2015/12/31/fluxnet2015-dataset-release/. The AMSR2 X-VOD product is available at: https://ib.remote-sensing.inrae.fr/index.php/amsr2-ib-x-vod/.

Supplementary Materials

This PDF file includes:

Table S1

Figs. S1 to S11
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REFERENCES AND NOTES

1 R. Albright, A. Corbett, X. Jiang, E. Creecy, S. Newman, K. F. Li, M. C. Liang, Y. L. Yung, Seasonal variations of solar-induced fluorescence, precipitation, and carbon dioxide over the Amazon. Earth Space Sci. 9 , e2021EA002078 (2022).
2 J. Wu, L. P. Albert, A. P. Lopes, N. Restrepo-Coupe, M. Hayek, K. T. Wiedemann, K. Guan, S. C. Stark, B. Christoffersen, N. Prohaska, J. V. Tavares, S. Marostica, H. Kobayashi, M. L. Ferreira, K. S. Campos, R. da Silva, P. M. Brando, D. G. Dye, T. E. Huxman, A. R. Huete, B. W. Nelson, S. R. Saleska, Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests. Science 351 , 972–976 (2016).26917771
3 R. Doughty, P. Kohler, C. Frankenberg, T. S. Magney, X. Xiao, Y. Qin, X. Wu, B. Moore III, TROPOMI reveals dry-season increase of solar-induced chlorophyll fluorescence in the Amazon forest. Proc. Natl. Acad. Sci. U.S.A. 116 , 22393–22398 (2019).31611384
4 R. B. Myneni, W. Yang, R. R. Nemani, A. R. Huete, R. E. Dickinson, Y. Knyazikhin, K. Didan, R. Fu, R. I. Negron Juarez, S. S. Saatchi, H. Hashimoto, K. Ichii, N. V. Shabanov, B. Tan, P. Ratana, J. L. Privette, J. T. Morisette, E. F. Vermote, D. P. Roy, R. E. Wolfe, M. A. Friedl, S. W. Running, P. Votava, N. El-Saleous, S. Devadiga, Y. Su, V. V. Salomonson, Large seasonal swings in leaf area of Amazon rainforests. Proc. Natl. Acad. Sci. U.S.A. 104 , 4820–4823 (2007).17360360
5 L. V. Gatti, L. S. Basso, J. B. Miller, M. Gloor, L. Gatti Domingues, H. L. G. Cassol, G. Tejada, L. E. O. C. Aragão, C. Nobre, W. Peters, L. Marani, E. Arai, A. H. Sanches, S. M. Corrêa, L. Anderson, C. Von Randow, C. S. C. Correia, S. P. Crispim, R. A. L. Neves, Amazonia as a carbon source linked to deforestation and climate change. Nature 595 , 388–393 (2021).34262208
6 L. S. Basso, C. Wilson, M. P. Chipperfield, G. Tejada, H. L. G. Cassol, E. Arai, M. Williams, T. L. Smallman, W. Peters, S. Naus, J. B. Miller, M. Gloor, Atmospheric CO2 inversion reveals the Amazon as a minor carbon source caused by fire emissions, with forest uptake offsetting about half of these emissions. Atmos. Chem. Phys. 23 , 9685–9723 (2023).
7 S. R. Saleska, S. D. Miller, D. M. Matross, M. L. Goulden, S. C. Wofsy, H. R. da Rocha, P. B. de Camargo, P. Crill, B. C. Daube, H. C. de Freitas, L. Hutyra, M. Keller, V. Kirchhoff, M. Menton, J. W. Munger, E. H. Pyle, A. H. Rice, H. Silva, Carbon in Amazon forests: Unexpected seasonal fluxes and disturbance-induced losses. Science 302 , 1554–1557 (2003).14645845
8 M. N. Hayek, M. Longo, J. Wu, M. N. Smith, N. Restrepo-Coupe, R. Tapajós, R. da Silva, D. R. Fitzjarrald, P. B. Camargo, L. R. Hutyra, L. F. Alves, B. Daube, J. W. Munger, K. T. Wiedemann, S. R. Saleska, S. C. Wofsy, Carbon exchange in an Amazon forest: From hours to years. Biogeosciences 15 , 4833–4848 (2018).
9 C. H. L. Silva Junior, L. O. Anderson, A. L. Silva, C. T. Almeida, R. Dalagnol, M. A. J. S. Pletsch, T. V. Penha, R. A. Paloschi, L. E. O. C. Aragão, Fire responses to the 2010 and 2015/2016 Amazonian droughts. Front. Earth Sci. 7 , 97 (2019).
10 J.-P. Wigneron, L. Fan, P. Ciais, A. Bastos, M. Brandt, J. Chave, S. Saatchi, A. Baccini, R. Fensholt, Tropical forests did not recover from the strong 2015–2016 El Niño event. Sci. Adv. 6 , eaay4603 (2020).32076648
11 D. I. Kelley, C. Burton, C. Huntingford, M. A. J. Brown, R. Whitley, N. Dong, Technical note: Low meteorological influence found in 2019 Amazonia fires. Biogeosciences 18 , 787–804 (2021).
12 Y. Feng, R. I. Negrón-Juárez, D. M. Romps, J. Q. Chambers, Amazon windthrow disturbances are likely to increase with storm frequency under global warming. Nat. Commun. 14 , 101 (2023).36609508
13 J. K. Green, J. Berry, P. Ciais, Y. Zhang, P. Gentine, Amazon rainforest photosynthesis increases in response to atmospheric dryness. Sci. Adv. 6 , eabb7232 (2020).33219023
14 K. Guan, M. Pan, H. Li, A. Wolf, J. Wu, D. Medvigy, K. K. Caylor, J. Sheffield, E. F. Wood, Y. Malhi, M. Liang, J. S. Kimball, S. R. Saleska, J. Berry, J. Joiner, A. I. Lyapustin, Photosynthetic seasonality of global tropical forests constrained by hydroclimate. Nat. Geosci. 8 , 284–289 (2015).
15 M. O. Jones, J. S. Kimball, R. R. Nemani, Asynchronous Amazon forest canopy phenology indicates adaptation to both water and light availability. Environ. Res. Lett. 9 , 124021 (2014).
16 A. P. Lopes, B. W. Nelson, J. Wu, P. M. L. de Alencastro Graça, J. V. Tavares, N. Prohaska, G. A. Martins, S. R. Saleska, Leaf flush drives dry season green-up of the Central Amazon. Remote Sens. Environ. 182 , 90–98 (2016).
17 M. Detto, S. J. Wright, O. Calderón, H. C. Muller-Landau, Resource acquisition and reproductive strategies of tropical forest in response to the El Niño–Southern Oscillation. Nat. Commun. 9 , 913 (2018).29500347
18 S. R. Saleska, J. Wu, K. Guan, A. C. Araujo, A. Huete, A. D. Nobre, N. Restrepo-Coupe, Dry-season greening of Amazon forests. Nature 531 , E4–E5 (2016).26983544
19 D. C. Morton, J. Nagol, C. C. Carabajal, J. Rosette, M. Palace, B. D. Cook, E. F. Vermote, D. J. Harding, P. R. North, Amazon forests maintain consistent canopy structure and greenness during the dry season. Nature 506 , 221–224 (2014).24499816
20 S. R. Saleska, K. Didan, A. R. Huete, H. R. da Rocha, Amazon forests green-up during 2005 drought. Science 318 , 612 (2007).17885095
21 O. L. Phillips, L. E. Aragao, S. L. Lewis, J. B. Fisher, J. Lloyd, G. Lopez-Gonzalez, Y. Malhi, A. Monteagudo, J. Peacock, C. A. Quesada, G. van der Heijden, S. Almeida, I. Amaral, L. Arroyo, G. Aymard, T. R. Baker, O. Banki, L. Blanc, D. Bonal, P. Brando, J. Chave, A. C. de Oliveira, N. D. Cardozo, C. I. Czimczik, T. R. Feldpausch, M. A. Freitas, E. Gloor, N. Higuchi, E. Jimenez, G. Lloyd, P. Meir, C. Mendoza, A. Morel, D. A. Neill, D. Nepstad, S. Patino, M. C. Penuela, A. Prieto, F. Ramirez, M. Schwarz, J. Silva, M. Silveira, A. S. Thomas, H. T. Steege, J. Stropp, R. Vasquez, P. Zelazowski, E. A. Davila, S. Andelman, A. Andrade, K. J. Chao, T. Erwin, A. Di Fiore, C. E. Honorio, H. Keeling, T. J. Killeen, W. F. Laurance, A. P. Cruz, N. C. Pitman, P. N. Vargas, H. Ramirez-Angulo, A. Rudas, R. Salamao, N. Silva, J. Terborgh, A. Torres-Lezama, Drought sensitivity of the Amazon rainforest. Science 323 , 1344–1347 (2009).19265020
22 P. B. Duffy, P. Brando, G. P. Asner, C. B. Field, Projections of future meteorological drought and wet periods in the Amazon. Proc. Natl. Acad. Sci. U.S.A. 112 , 13172–13177 (2015).26460046
23 G. Manoli, V. Y. Ivanov, S. Fatichi, Dry-season greening and water stress in Amazonia: The role of modeling leaf phenology. J. Geophys. Res. Biogeo. 123 , 1909–1926 (2018).
24 J. S. Wright, R. Fu, J. R. Worden, S. Chakraborty, N. E. Clinton, C. Risi, Y. Sun, L. Yin, Rainforest-initiated wet season onset over the southern Amazon. Proc. Natl. Acad. Sci. U.S.A. 114 , 8481–8486 (2017).28729375
25 A. Staal, O. A. Tuinenburg, J. H. C. Bosmans, M. Holmgren, E. H. van Nes, M. Scheffer, D. C. Zemp, S. C. Dekker, Forest-rainfall cascades buffer against drought across the Amazon. Nat. Clim. Chang. 8 , 539–543 (2018).
26 U. Anber, P. Gentine, S. Wang, A. H. Sobel, Fog and rain in the Amazon. Proc. Natl. Acad. Sci. U.S.A. 112 , 11473–11477 (2015).26324902
27 A. G. Konings, S. S. Saatchi, C. Frankenberg, M. Keller, V. Leshyk, W. R. L. Anderegg, V. Humphrey, A. M. Matheny, A. Trugman, L. Sack, E. Agee, M. L. Barnes, O. Binks, K. Cawse-Nicholson, B. O. Christoffersen, D. Entekhabi, P. Gentine, N. M. Holtzman, G. G. Katul, Y. Liu, M. Longo, J. Martinez-Vilalta, N. McDowell, P. Meir, M. Mencuccini, A. Mrad, K. A. Novick, R. S. Oliveira, P. Siqueira, S. C. Steele-Dunne, D. R. Thompson, Y. Wang, R. Wehr, J. D. Wood, X. Xu, P. A. Zuidema, Detecting forest response to droughts with global observations of vegetation water content. Glob. Chang. Biol. 27 , 6005–6024 (2021).34478589
28 C. Morfopoulos, J.-F. Müller, T. Stavrakou, M. Bauwens, I. De Smedt, P. Friedlingstein, I. C. Prentice, P. Regnier, Vegetation responses to climate extremes recorded by remotely sensed atmospheric formaldehyde. Glob. Chang. Biol. 28 , 1809–1822 (2022).34510653
29 H. Tang, R. Dubayah, Light-driven growth in Amazon evergreen forests explained by seasonal variations of vertical canopy structure. Proc. Natl. Acad. Sci. U.S.A. 114 , 2640–2644 (2017).28223505
30 M. G. Letts, M. Mulligan, The impact of light quality and leaf wetness on photosynthesis in north-west Andean tropical montane cloud forest. J. Trop. Ecol. 21 , 549–557 (2005).
31 J. A. Giraldo, J. I. del Valle, S. González-Caro, D. A. David, T. Taylor, C. Tobón, C. A. Sierra, Tree growth periodicity in the ever-wet tropical forest of the Americas. J. Ecol. 111 , 889–902 (2023).
32 A. Rehbein, T. Ambrizzi, C. R. Mechoso, Mesoscale convective systems over the Amazon basin. Part I: Climatological aspects. Int. J. Climatol. 38 , 215–229 (2018).
33 R. Negron-Juarez, D. Magnabosco-Marra, Y. Feng, J. D. Urquiza-Muñoz, W. J. Riley, J. Q. Chambers, Windthrow characteristics and their regional association with rainfall, soil, and surface elevation in the Amazon. Environ. Res. Lett. 18 , 014030 (2023).
34 A. E. Lugo, Visible and invisible effects of hurricanes on forest ecosystems: An international review. Austral Ecol. 33 , 368–398 (2008).
35 C. G. Fontes, J. Q. Chambers, N. Higuchi, Revealing the causes and temporal distribution of tree mortality in central Amazonia. Forest Ecol. Manag. 424 , 177–183 (2018).
36 C. Lepore, R. Abernathey, N. Henderson, J. T. Allen, M. K. Tippett, Future global convective environments in CMIP6 models. Earth's Future 9 , e2021EF002277 (2021).
37 A. H. Sobel, S. J. Camargo, Projected future seasonal changes in tropical summer climate. J. Climate 24 , 473–487 (2011).
38 A. Esquivel-Muelbert, O. L. Phillips, R. J. W. Brienen, S. Fauset, M. J. P. Sullivan, T. R. Baker, K.-J. Chao, T. R. Feldpausch, E. Gloor, N. Higuchi, J. Houwing-Duistermaat, J. Lloyd, H. Liu, Y. Malhi, B. Marimon, B. H. Marimon Junior, A. Monteagudo-Mendoza, L. Poorter, M. Silveira, E. V. Torre, E. A. Dávila, J. del Aguila Pasquel, E. Almeida, P. A. Loayza, A. Andrade, L. E. O. C. Aragão, A. Araujo-Murakami, E. Arets, L. Arroyo, G. A. Aymard C, M. Baisie, C. Baraloto, P. B. Camargo, J. Barroso, L. Blanc, D. Bonal, F. Bongers, R. Boot, F. Brown, B. Burban, J. L. Camargo, W. Castro, V. C. Moscoso, J. Chave, J. Comiskey, F. C. Valverde, A. L. da Costa, N. D. Cardozo, A. Di Fiore, A. Dourdain, T. Erwin, G. F. Llampazo, I. C. G. Vieira, R. Herrera, E. H. Coronado, I. Huamantupa-Chuquimaco, E. Jimenez-Rojas, T. Killeen, S. Laurance, W. Laurance, A. Levesley, S. L. Lewis, K. L. L. M. Ladvocat, G. Lopez-Gonzalez, T. Lovejoy, P. Meir, C. Mendoza, P. Morandi, D. Neill, A. J. N. Lima, P. N. Vargas, E. A. de Oliveira, N. P. Camacho, G. Pardo, J. Peacock, M. Peña-Claros, M. C. Peñuela-Mora, G. Pickavance, J. Pipoly, N. Pitman, A. Prieto, T. A. M. Pugh, C. Quesada, H. Ramirez-Angulo, S. M. de Almeida Reis, M. Rejou-Machain, Z. R. Correa, L. R. Bayona, A. Rudas, R. Salomão, J. Serrano, J. S. Espejo, N. Silva, J. Singh, C. Stahl, J. Stropp, V. Swamy, J. Talbot, H. ter Steege, J. Terborgh, R. Thomas, M. Toledo, A. Torres-Lezama, L. V. Gamarra, G. van der Heijden, P. van der Meer, P. van der Hout, R. V. Martinez, S. A. Vieira, J. V. Cayo, V. Vos, R. Zagt, P. Zuidema, D. Galbraith, Tree mode of death and mortality risk factors across Amazon forests. Nat. Commun. 11 , 5515 (2020).33168823
39 L. Rowland, A. C. da Costa, D. R. Galbraith, R. S. Oliveira, O. J. Binks, A. A. Oliveira, A. M. Pullen, C. E. Doughty, D. B. Metcalfe, S. S. Vasconcelos, L. V. Ferreira, Y. Malhi, J. Grace, M. Mencuccini, P. Meir, Death from drought in tropical forests is triggered by hydraulics not carbon starvation. Nature 528 , 119–122 (2015).26595275
40 H. Wang, J.-P. Wigneron, P. Ciais, Y. Yao, L. Fan, X. Liu, X. Li, J. K. Green, F. Tian, S. Tao, W. Li, F. Frappart, C. Albergel, M. Wang, S. Li, Seasonal variations in vegetation water content retrieved from microwave remote sensing over Amazon intact forests. Remote Sens. Environ. 285 , 113409 (2023).
41 J. Wu, H. Kobayashi, S. C. Stark, R. Meng, K. Guan, N. N. Tran, S. Gao, W. Yang, N. Restrepo-Coupe, T. Miura, R. C. Oliviera, A. Rogers, D. G. Dye, B. W. Nelson, S. P. Serbin, A. R. Huete, S. R. Saleska, Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest. New Phytol. 217 , 1507–1520 (2018).29274288
42 F. H. Wagner, B. Hérault, V. Rossi, T. Hilker, E. E. Maeda, A. Sanchez, A. I. Lyapustin, L. S. Galvão, Y. Wang, L. E. O. C. Aragão, Climate drivers of the Amazon forest greening. PLOS ONE 12 , e0180932 (2017).28708897
43 G. Cornic, J. M. Briantais, Partitioning of photosynthetic electron flow between CO2 and O2 reduction in a C3 leaf (Phaseolus vulgaris L.) at different CO2 concentrations and during drought stress. Planta 183 , 178–184 (1991).24193618
44 U. Niinemets, Mild versus severe stress and BVOCs: Thresholds, priming and consequences. Trends Plant Sci. 15 , 145–153 (2010).20006534
45 Y. Zhang, S. Zhou, P. Gentine, X. Xiao, Can vegetation optical depth reflect changes in leaf water potential during soil moisture dry-down events? Remote Sens. Environ. 234 , 111451 (2019).
46 G. Goldstein, J. L. Andrade, F. C. Meinzer, N. M. Holbrook, J. Cavelier, P. Jackson, A. Celis, Stem water storage and diurnal patterns of water use in tropical forest canopy trees. Plant Cell Environ. 21 , 397–406 (1998).
47 A. G. Konings, P. Gentine, Global variations in ecosystem-scale isohydricity. Glob. Change Biol. 23 , 891–905 (2017).
48 J. Chave, D. Navarrete, S. Almeida, E. Álvarez, L. E. O. C. Aragão, D. Bonal, P. Châtelet, J. E. Silva-Espejo, J. Y. Goret, P. von Hildebrand, E. Jiménez, S. Patiño, M. C. Peñuela, O. L. Phillips, P. Stevenson, Y. Malhi, Regional and seasonal patterns of litterfall in tropical South America. Biogeosciences 7 , 43–55 (2010).
49 R. Fu, L. Yin, W. Li, P. A. Arias, R. E. Dickinson, L. Huang, S. Chakraborty, K. Fernandes, B. Liebmann, R. Fisher, R. B. Myneni, Increased dry-season length over southern Amazonia in recent decades and its implication for future climate projection. Proc. Natl. Acad. Sci. U.S.A. 110 , 18110–18115 (2013).24145443
50 H. Xu, X. Lian, I. J. Slette, H. Yang, Y. Zhang, A. Chen, S. Piao, Rising ecosystem water demand exacerbates the lengthening of tropical dry seasons. Nat. Commun. 13 , 4093 (2022).35835788
51 N. Wunderling, A. Staal, B. Sakschewski, M. Hirota, O. A. Tuinenburg, J. F. Donges, H. M. J. Barbosa, R. Winkelmann, Recurrent droughts increase risk of cascading tipping events by outpacing adaptive capacities in the Amazon rainforest. Proc. Natl. Acad. Sci. U.S.A. 119 , e2120777119 (2022).35917341
52 W. Peters, I. R. van der Velde, E. van Schaik, J. B. Miller, P. Ciais, H. F. Duarte, I. T. van der Laan-Luijkx, M. K. van der Molen, M. Scholze, K. Schaefer, P. L. Vidale, A. Verhoef, D. Warlind, D. Zhu, P. P. Tans, B. Vaughn, J. W. C. White, Increased water-use efficiency and reduced CO2 uptake by plants during droughts at a continental-scale. Nat. Geosci. 11 , 744–748 (2018).30319710
53 B. Choat, T. J. Brodribb, C. R. Brodersen, R. A. Duursma, R. Lopez, B. E. Medlyn, Triggers of tree mortality under drought. Nature 558 , 531–539 (2018).29950621
54 P. Baldrian, R. López-Mondéjar, P. Kohout, Forest microbiome and global change. Nat. Rev. Microbiol. 21 , 487–501 (2023).36941408
55 L. Fan, J.-P. Wigneron, P. Ciais, J. Chave, M. Brandt, R. Fensholt, S. S. Saatchi, A. Bastos, A. Al-Yaari, K. Hufkens, Y. Qin, X. Xiao, C. Chen, R. B. Myneni, R. Fernandez-Moran, A. Mialon, N. J. Rodriguez-Fernandez, Y. Kerr, F. Tian, J. Peñuelas, Satellite-observed pantropical carbon dynamics. Nat. Plants 5 , 944–951 (2019).31358958
56 N. Restrepo-Coupe, N. M. Levine, B. O. Christoffersen, L. P. Albert, J. Wu, M. H. Costa, D. Galbraith, H. Imbuzeiro, G. Martins, A. C. da Araujo, Y. S. Malhi, X. Zeng, P. Moorcroft, S. R. Saleska, Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison. Glob. Chang. Biol. 23 , 191–208 (2017).27436068
57 X. Chen, F. Maignan, N. Viovy, A. Bastos, D. Goll, J. Wu, L. Liu, C. Yue, S. Peng, W. Yuan, A. C. da Conceição, M. O’Sullivan, P. Ciais, Novel representation of leaf phenology improves simulation of Amazonian evergreen forest photosynthesis in a land surface model. J. Adv. Model. Earth Syst. 12 , e2018MS001565 (2020).
58 K. Yan, T. Park, G. Yan, Z. Liu, B. Yang, C. Chen, R. R. Nemani, Y. Knyazikhin, R. B. Myneni, Evaluation of MODIS LAI/FPAR product collection 6. Part 2: Validation and intercomparison. Remote Sens. 8 , 460 (2016).
59 P. Köhler, C. Frankenberg, T. S. Magney, L. Guanter, J. Joiner, J. Landgraf, Global retrievals of solar-induced chlorophyll fluorescence with TROPOMI: First results and intersensor comparison to OCO-2. Geophys. Res. Lett. 45 , 10,456–10,463 (2018).
60 Y. Zhang, J. Joiner, S. H. Alemohammad, S. Zhou, P. Gentine, A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences 15 , 5779–5800 (2018).
61 C. Frankenberg, A. Butz, G. C. Toon, Disentangling chlorophyll fluorescence from atmospheric scattering effects in O2 A-band spectra of reflected sun-light. Geophys. Res. Lett. 38 , L03801 (2011).
62 M. Brandt, J. P. Wigneron, J. Chave, T. Tagesson, J. Penuelas, P. Ciais, K. Rasmussen, F. Tian, C. Mbow, A. Al-Yaari, N. Rodriguez-Fernandez, G. Schurgers, W. Zhang, J. Chang, Y. Kerr, A. Verger, C. Tucker, A. Mialon, L. V. Rasmussen, L. Fan, R. Fensholt, Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands. Nat. Ecol. Evol. 2 , 827–835 (2018).29632351
63 F. Tian, J. P. Wigneron, P. Ciais, J. Chave, J. Ogee, J. Penuelas, A. Raebild, J. C. Domec, X. Tong, M. Brandt, A. Mialon, N. Rodriguez-Fernandez, T. Tagesson, A. Al-Yaari, Y. Kerr, C. Chen, R. B. Myneni, W. Zhang, J. Ardo, R. Fensholt, Coupling of ecosystem-scale plant water storage and leaf phenology observed by satellite. Nat. Ecol. Evol. 2 , 1428–1435 (2018).30104750
64 L. Moesinger, W. Dorigo, R. de Jeu, R. van der Schalie, T. Scanlon, I. Teubner, M. Forkel, The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA). Earth Syst. Sci. Data 12 , 177–196 (2020).
65 J.-P. Wigneron, X. Li, F. Frappart, L. Fan, A. Al-Yaari, G. De Lannoy, X. Liu, M. Wang, E. Le Masson, C. Moisy, SMOS-IC data record of soil moisture and L-VOD: Historical development, applications and perspectives. Remote Sens. Environ. 254 , 112238 (2021).
66 X. Lian, W. Zhao, P. Gentine, Recent global decline in rainfall interception loss due to altered rainfall regimes. Nat. Commun. 13 , 7642 (2022).36496496
67 M. Bauwens, T. Stavrakou, J. F. Müller, I. De Smedt, M. Van Roozendael, G. R. van der Werf, C. Wiedinmyer, J. W. Kaiser, K. Sindelarova, A. Guenther, Nine years of global hydrocarbon emissions based on source inversion of OMI formaldehyde observations. Atmos. Chem. Phys. 16 , 10133–10158 (2016).
68 I. De Smedt, N. Theys, H. Yu, T. Danckaert, C. Lerot, S. Compernolle, M. Van Roozendael, A. Richter, A. Hilboll, E. Peters, M. Pedergnana, D. Loyola, S. Beirle, T. Wagner, H. Eskes, J. van Geffen, K. F. Boersma, P. Veefkind, Algorithm theoretical baseline for formaldehyde retrievals from S5P TROPOMI and from the QA4ECV project. Atmos. Meas. Tech. 11 , 2395–2426 (2018).
69 J. F. Müller, T. Stavrakou, J. Peeters, Chemistry and deposition in the Model of Atmospheric composition at Global and Regional scales using Inversion Techniques for Trace gas Emissions (MAGRITTE v1.1)–Part 1: Chemical mechanism. Geosci. Model Dev. 12 , 2307–2356 (2019).
70 T. Stavrakou, J.-F. Müller, M. Bauwens, I. De Smedt, M. Van Roozendael, A. Guenther, Impact of short-term climate variability on volatile organic compounds emissions assessed using OMI satellite formaldehyde observations. Geophys. Res. Lett. 45 , 8681–8689 (2018).
71 B. Opacka, J. F. Müller, T. Stavrakou, M. Bauwens, K. Sindelarova, J. Markova, A. B. Guenther, Global and regional impacts of land cover changes on isoprene emissions derived from spaceborne data and the MEGAN model. Atmos. Chem. Phys. 21 , 8413–8436 (2021).
72 K. Sindelarova, J. Markova, D. Simpson, P. Huszar, J. Karlicky, S. Darras, C. Granier, High resolution biogenic global emission inventory for the time period 2000–2019 for air quality modelling. Earth Syst. Sci. Data 14 , 251–270 (2022).
73 C. A. DiMaria, D. B. A. Jones, H. Worden, A. A. Bloom, K. Bowman, T. Stavrakou, K. Miyazaki, J. Worden, A. Guenther, C. Sarkar, R. Seco, J.-H. Park, J. Tota, E. G. Alves, V. Ferracci, Optimizing the isoprene emission model MEGAN with satellite and ground-based observational constraints. J. Geophys. Res. Atmos. 128 , e2022JD037822 (2023).
74 K. Sindelarova, C. Granier, I. Bouarar, A. Guenther, S. Tilmes, T. Stavrakou, J. F. Müller, U. Kuhn, P. Stefani, W. Knorr, Global data set of biogenic VOC emissions calculated by the MEGAN model over the last 30 years. Atmos. Chem. Phys. 14 , 9317–9341 (2014).
75 D. Wei, J. D. Fuentes, T. Gerken, M. Chamecki, A. M. Trowbridge, P. C. Stoy, G. G. Katul, G. Fisch, O. Acevedo, A. Manzi, C. von Randow, R. M. N. dos Santos, Environmental and biological controls on seasonal patterns of isoprene above a rain forest in central Amazonia. Agric. For. Meteorol. 256-257 , 391–406 (2018).
76 E. G. Alves, K. Jardine, J. Tota, A. Jardine, A. M. Yãnez-Serrano, T. Karl, J. Tavares, B. Nelson, D. Gu, T. Stavrakou, S. Martin, P. Artaxo, A. Manzi, A. Guenther, Seasonality of isoprenoid emissions from a primary rainforest in central Amazonia. Atmos. Chem. Phys. 16 , 3903–3925 (2016).
77 B. Langford, E. House, A. Valach, C. Hewitt, P. Artaxo, M. P. Barkley, J. Brito, E. Carnell, B. Davison, A. R. MacKenzie, E. A. Marais, M. J. Newland, A. R. Rickard, M. D. Shaw, A. M. Yáñez-Serrano, E. Nemitz, Seasonality of isoprene emissions and oxidation products above the remote Amazon. Environ. Sci. Atmos. 2 , 230–240 (2022).
78 C. Trostdorf, L. Gatti, A. Yamazaki, M. Potosnak, A. Guenther, W. Martins, J. Munger, Seasonal cycles of isoprene concentrations in the Amazonian rainforest. Atmos. Chem. Phys. Discuss. 4 , 1291–1310 (2004).
79 E. Gomes Alves, R. Aquino Santana, C. Quaresma Dias-Júnior, S. Botía, T. Taylor, A. M. Yáñez-Serrano, J. Kesselmeier, E. Bourtsoukidis, J. Williams, P. I. L. S. de Assis, G. Martins, R. de Souza, S. D. Júnior, A. Guenther, D. Gu, A. Tsokankunku, M. Sörgel, B. Nelson, D. Pinto, S. Komiya, D. M. Rosa, B. Weber, C. Barbosa, M. Robin, K. J. Feeley, A. Duque, V. L. Lemos, M. P. Contreras, A. Idarraga, N. López, C. Husby, B. Jestrow, I. M. C. Toro, Intra- and interannual changes in isoprene emission from central Amazonia. Atmos. Chem. Phys. 23 , 8149–8168 (2023).
80 H. Hersbach, B. Bell, P. Berrisford, S. Hirahara, A. Horányi, J. Muñoz-Sabater, J. Nicolas, C. Peubey, R. Radu, D. Schepers, A. Simmons, C. Soci, S. Abdalla, X. Abellan, G. Balsamo, P. Bechtold, G. Biavati, J. Bidlot, M. Bonavita, G. De Chiara, P. Dahlgren, D. Dee, M. Diamantakis, R. Dragani, J. Flemming, R. Forbes, M. Fuentes, A. Geer, L. Haimberger, S. Healy, R. J. Hogan, E. Hólm, M. Janisková, S. Keeley, P. Laloyaux, P. Lopez, C. Lupu, G. Radnoti, P. de Rosnay, I. Rozum, F. Vamborg, S. Villaume, J.-N. Thépaut, The ERA5 global reanalysis. Q. J. Roy. Meteor. Soc. 146 , 1999–2049 (2020).
81 F. D’Andrea, P. Gentine, A. K. Betts, B. R. Lintner, Triggering deep convection with a probabilistic plume model. J. Atmos. Sci. 71 , 3881–3901 (2014).
82 Y. Ryu, C. Jiang, H. Kobayashi, M. Detto, MODIS-derived global land products of shortwave radiation and diffuse and total photosynthetically active radiation at 5km resolution from 2000. Remote Sens. Environ. 204 , 812–825 (2018).
83 Z. Fu, P. Ciais, I. C. Prentice, P. Gentine, D. Makowski, A. Bastos, X. Luo, J. K. Green, P. C. Stoy, H. Yang, T. Hajima, Atmospheric dryness reduces photosynthesis along a large range of soil water deficits. Nat. Commun. 13 , 989 (2022).35190562
84 S. Fatichi, S. Leuzinger, C. Körner, Moving beyond photosynthesis: From carbon source to sink-driven vegetation modeling. New Phytol. 201 , 1086–1095 (2014).24261587
85 A. Cabon, S. A. Kannenberg, A. Arain, F. Babst, D. Baldocchi, S. Belmecheri, N. Delpierre, R. Guerrieri, J. T. Maxwell, S. McKenzie, F. C. Meinzer, D. J. P. Moore, C. Pappas, A. V. Rocha, P. Szejner, M. Ueyama, D. Ulrich, C. Vincke, S. L. Voelker, J. Wei, D. Woodruff, W. R. L. Anderegg, Cross-biome synthesis of source versus sink limits to tree growth. Science 376 , 758–761 (2022).35549405
86 M. Santoro, O. Cartus, S. Mermoz, A. Bouvet, T. Le Toan, N. Carvalhais, D. Rozendaal, M. Herold, V. Avitabile, S. Quegan, in EGU General Assembly Conference Abstracts. (2018), pp. 18932.
87 S. S. Saatchi, N. L. Harris, S. Brown, M. Lefsky, E. T. A. Mitchard, W. Salas, B. R. Zutta, W. Buermann, S. L. Lewis, S. Hagen, S. Petrova, L. White, M. Silman, A. Morel, Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl. Acad. Sci. U.S.A. 108 , 9899–9904 (2011).21628575
88 T. B. Rodrigues, C. R. Baker, A. P. Walker, N. McDowell, A. Rogers, N. Higuchi, J. Q. Chambers, K. J. Jardine, Stimulation of isoprene emissions and electron transport rates as key mechanisms of thermal tolerance in the tropical species Vismia guianensis. Glob. Chang. Biol. 26 , 5928–5941 (2020).32525272
89 K. J. Jardine, R. F. Zorzanelli, B. O. Gimenez, L. R. O. Piva, A. Teixeira, C. G. Fontes, E. Robles, N. Higuchi, J. Q. Chambers, S. T. Martin, Leaf isoprene and monoterpene emission distribution across hyperdominant tree genera in the Amazon basin. Phytochemistry 175 , 112366 (2020).32278887
90 Ü. Niinemets, J. D. Tenhunen, P. C. Harley, R. Steinbrecher, A model of isoprene emission based on energetic requirements for isoprene synthesis and leaf photosynthetic properties for Liquidambar and Quercus. Plant Cell Environ. 22 , 1319–1335 (1999).
91 N. M. Holtzman, L. D. L. Anderegg, S. Kraatz, A. Mavrovic, O. Sonnentag, C. Pappas, M. H. Cosh, A. Langlois, T. Lakhankar, D. Tesser, N. Steiner, A. Colliander, A. Roy, A. G. Konings, L-band vegetation optical depth as an indicator of plant water potential in a temperate deciduous forest stand. Biogeosciences 18 , 739–753 (2021).
92 M. Momen, J. D. Wood, K. A. Novick, R. Pangle, W. T. Pockman, N. G. McDowell, A. G. Konings, Interacting effects of leaf water potential and biomass on vegetation optical depth. J. Geophys. Res. Biogeo. 122 , 3031–3046 (2017).
93 P. Köcher, V. Horna, C. Leuschner, Stem water storage in five coexisting temperate broad-leaved tree species: Significance, temporal dynamics and dependence on tree functional traits. Tree Physiol. 33 , 817–832 (2013).23999137
94 R. I. Negrón-Juárez, J. Q. Chambers, G. Guimaraes, H. Zeng, C. F. M. Raupp, D. M. Marra, G. H. P. M. Ribeiro, S. S. Saatchi, B. W. Nelson, N. Higuchi, Widespread Amazon forest tree mortality from a single cross-basin squall line event. Geophys. Res. Lett. 37 , 10.1029/2010GL043733 (2010).
95 R. I. Negrón-Juárez, J. A. Holm, D. M. Marra, S. W. Rifai, W. J. Riley, J. Q. Chambers, C. D. Koven, R. G. Knox, M. E. McGroddy, A. V. Di Vittorio, J. Urquiza-Muñoz, R. Tello-Espinoza, W. A. Muñoz, G. H. P. M. Ribeiro, N. Higuchi, Vulnerability of Amazon forests to storm-driven tree mortality. Environ. Res. Lett. 13 , 054021 (2018).
96 S. M. Lundberg, S.-I. Lee, A unified approach to interpreting model predictions. Adv. Neural Inf. Process. 30 , 4768– 4777 (2017).
