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Summary Amazonian droughts are predicted to become increasingly frequent and intense, and the vulnerability of Amazonian trees has become increasingly documented. However, little is known about the physiological mechanisms and the diversity of drought tolerance of tropical trees due to the lack of quantitative measurements. Leaf water potential at wilting or turgor loss point (πtlp) is a determinant of the tolerance of leaves to drought stress and contributes to plant‐level physiological drought tolerance. Recently, it has been demonstrated that leaf osmotic water potential at full hydration (πo) is tightly correlated with πtlp. Estimating πtlp from osmometer measurements of πo is much faster than the standard pressure–volume curve approach of πtlp determination. We used this technique to estimate πtlp for 165 trees of 71 species, at three sites within forests in French Guiana. Our data set represents a significant increase in available data for this trait for tropical tree species. Tropical trees showed a wider range of drought tolerance than previously found in the literature, πtlp ranging from −1·4 to −3·2 MPa. This range likely corresponds in part to adaptation and acclimation to occasionally extreme droughts during the dry season. Leaf‐level drought tolerance varied across species, in agreement with the available published observations of species variation in drought‐induced mortality. On average, species with a more negative πtlp (i.e. with greater leaf‐level drought tolerance) occurred less frequently across the region than drought‐sensitive species. Across individuals, πtlp correlated positively but weakly with leaf toughness (R2 = 0·22, P = 0·04) and leaf thickness (R2 = 0·03, P = 0·03). No correlation was detected with other functional traits (leaf mass per area, leaf area, nitrogen or carbon concentrations, carbon isotope ratio, sapwood density or bark thickness). The variability in πtlp among species indicates a potential for highly diverse species responses to drought within given forest communities. Given the weak correlations between πtlp and traditionally measured plant functional traits, vegetation models seeking to predict forest response to drought should integrate improved quantification of comparative drought tolerance among tree species. Lay Summary

The year 2022 saw record breaking temperatures in Europe during both summer and fall. Similar to the recent 2018 drought, close to 30% (3.0 million km 2 ) of the European continent was under severe summer drought. In 2022, the drought was located in central and southeastern Europe, contrasting the Northern-centered 2018 drought. We show, using multiple sets of observations, a reduction of net biospheric carbon uptake in summer (56-62 TgC) over the drought area. Specific sites in France even showed a widespread summertime carbon release by forests, additional to wildfires. Partial compensation (32%) for the decreased carbon uptake due to drought was offered by a warm autumn with prolonged biospheric carbon uptake. The severity of this second drought event in 5 years suggests drought-induced reduced carbon uptake to no longer be exceptional, and important to factor into Europe’s developing plans for net-zero greenhouse gas emissions that rely on carbon uptake by forests. Heat and moisture stress can reduce carbon uptake by forests. Here, the authors quantify this effect for the extreme 2022 European summer drought. The widespread reduction of photosynthesis exceeded the large local carbon release by intense fires.

This study introduces CNRM‐ESM2‐1, the Earth system (ES) model of second generation developed by CNRM‐CERFACS for the sixth phase of the Coupled Model Intercomparison Project (CMIP6). CNRM‐ESM2‐1 offers a higher model complexity than the Atmosphere‐Ocean General Circulation Model CNRM‐CM6‐1 by adding interactive ES components such as carbon cycle, aerosols, and atmospheric chemistry. As both models share the same code, physical parameterizations, and grid resolution, they offer a fully traceable framework to investigate how far the represented ES processes impact the model performance over present‐day, response to external forcing and future climate projections. Using a large variety of CMIP6 experiments, we show that represented ES processes impact more prominently the model response to external forcing than the model performance over present‐day. Both models display comparable performance at replicating modern observations although the mean climate of CNRM‐ESM2‐1 is slightly warmer than that of CNRM‐CM6‐1. This difference arises from land cover‐aerosol interactions where the use of different soil vegetation distributions between both models impacts the rate of dust emissions. This interaction results in a smaller aerosol burden in CNRM‐ESM2‐1 than in CNRM‐CM6‐1, leading to a different surface radiative budget and climate. Greater differences are found when comparing the model response to external forcing and future climate projections. Represented ES processes damp future warming by up to 10% in CNRM‐ESM2‐1 with respect to CNRM‐CM6‐1. The representation of land vegetation and the CO2‐water‐stomatal feedback between both models explain about 60% of this difference. The remainder is driven by other ES feedbacks such as the natural aerosol feedback. Key Points This study introduces CNRM‐ESM2‐1 and describes its set‐up for CMIP6 Represented Earth system processes further impact the model response to external forcing than the model performance over present‐day Represented Earth system processes damp future warming by up to 10%

The 2015/16 El Niño brought severe drought and record-breaking temperatures in the tropics. Here, using satellite-based L-band microwave vegetation optical depth, we mapped changes of above-ground biomass (AGB) during the drought and in subsequent years up to 2019. Over more than 60% of drought-affected intact forests, AGB reduced during the drought, except in the wettest part of the central Amazon, where it declined 1 y later. By the end of 2019, only 40% of AGB reduced intact forests had fully recovered to the predrought level. Using random-forest models, we found that the magnitude of AGB losses during the drought was mainly associated with regionally distinct patterns of soil water deficits and soil clay content. For the AGB recovery, we found strong influences of AGB losses during the drought and of γ. γ is a parameter related to canopy structure and is defined as the ratio of two relative height (RH) metrics of Geoscience Laser Altimeter System (GLAS) waveform data—RH25 (25% energy return height) and RH100 (100% energy return height; i.e., top canopy height). A high γ may reflect forests with a tall understory, thick and closed canopy, and/or without degradation. Such forests with a high γ (γ ≥ 0.3) appear to have a stronger capacity to recover than low-γ ones. Our results highlight the importance of forest structure when predicting the consequences of future drought stress in the tropics.

The high-latitude regions of the Northern Hemisphere are a nexus for the interaction between land surface physical properties and their exchange of carbon and energy with the atmosphere. At these latitudes, two carbon pools of planetary significance – those of the permanently frozen soils (permafrost), and of the great expanse of boreal forest – are vulnerable to destabilization in the face of currently observed climatic warming, the speed and intensity of which are expected to increase with time. Improved projections of future Arctic and boreal ecosystem transformation require improved land surface models that integrate processes specific to these cold biomes. To this end, this study lays out relevant new parameterizations in the ORCHIDEE-MICT land surface model. These describe the interactions between soil carbon, soil temperature and hydrology, and their resulting feedbacks on water and CO2 fluxes, in addition to a recently developed fire module. Outputs from ORCHIDEE-MICT, when forced by two climate input datasets, are extensively evaluated against (i) temperature gradients between the atmosphere and deep soils, (ii) the hydrological components comprising the water balance of the largest high-latitude basins, and (iii) CO2 flux and carbon stock observations. The model performance is good with respect to empirical data, despite a simulated excessive plant water stress and a positive land surface temperature bias. In addition, acute model sensitivity to the choice of input forcing data suggests that the calibration of model parameters is strongly forcing-dependent. Overall, we suggest that this new model design is at the forefront of current efforts to reliably estimate future perturbations to the high-latitude terrestrial environment.

We present the latest version of the ISBA‐CTRIP land surface system, focusing on the representation of the land carbon cycle. We review the main improvements since the year 2012, mainly added modules for wild fires, carbon leaching through soil and transport of dissolved organic carbon to the ocean, and land cover changes but also improved representation of photosynthesis, respiration, and plant functional types. This version of ISBA‐CTRIP is fully described in terms of land carbon pools, fluxes, and their interactions. Results are compared with the previous version in an off‐line mode forced by observed climate during the historical time period. The two simulations are presented to demonstrate the model performance compared to an ensemble of observed and observation‐derived data sets for gross and net primary productivity, heterotrophic and autotrophic respiration, above and below ground biomass, litter, and soil carbon pools. New developments specific to the new version such as burned area, fire emissions, carbon leaching, and land cover are also validated against observations. The results show clearly that the latest version of ISBA‐CTRIP outperforms the former version and reproduces generally well the observed mean spatial patterns in carbon pools and fluxes, as well as the seasonal cycle of leaf area index. The trends of the global fluxes over the last 50 years agree with other global models and with available estimates. This comparison gives us confidence that the model represents the main processes involved in the terrestrial carbon cycle and can be used to explore future global change projections. Plain Language Summary The land surface exchanges energy, water, and carbon with the atmosphere and partly controls the atmospheric CO2 concentration. It is therefore crucial to represent correctly the carbon cycle on land in models designed to be used in Earth System Models. We present here the improvements made to the representation of the land carbon cycle by the land surface system ISBA‐CTRIP. We improved the representation of several processes using published data, and we added processes that were not represented. The new version of the model performs better than the previous one at representing the carbon fluxes and pools, when compared to a series of observation data sets. This evaluation suggests that we can use ISBA‐CTRIP to explore the changing climate and carbon cycle. Key Points This paper documents the updates to the biogeochemical module of the ISBA‐CTRIP land surface system for use in the CNRM‐ESM 2‐1 Earth system model The newly represented processes are the leaching of carbon and transport of dissolved organic carbon to the ocean, fire with area burned and carbon emissions, and land cover changes The largest improvements in the representation of net primary productivity are due to improved autotrophic respiration

The Global Carbon Project estimates that the terrestrial biosphere has absorbed about one‐third of anthropogenic CO2 emissions during the 1959–2019 period. This sink‐estimate is produced by an ensemble of terrestrial biosphere models and is consistent with the land uptake inferred from the residual of emissions and ocean uptake. The purpose of our study is to understand how well terrestrial biosphere models reproduce the processes that drive the terrestrial carbon sink. One challenge is to decide what level of agreement between model output and observation‐based reference data is adequate considering that reference data are prone to uncertainties. To define such a level of agreement, we compute benchmark scores that quantify the similarity between independently derived reference data sets using multiple statistical metrics. Models are considered to perform well if their model scores reach benchmark scores. Our results show that reference data can differ considerably, causing benchmark scores to be low. Model scores are often of similar magnitude as benchmark scores, implying that model performance is reasonable given how different reference data are. While model performance is encouraging, ample potential for improvements remains, including a reduction in a positive leaf area index bias, improved representations of processes that govern soil organic carbon in high latitudes, and an assessment of causes that drive the inter‐model spread of gross primary productivity in boreal regions and humid tropics. The success of future model development will increasingly depend on our capacity to reduce and account for observational uncertainties. Plain Language Summary Earth's natural vegetation absorbs about one‐third of CO2 emissions caused by human activities. This value is produced by a group of models rather than through direct observations. Our study assesses how well models reproduce the processes that drive the CO2 exchange between land and atmosphere using a wide range of data sets that are mainly derived from field measurements and satellite images. These reference data sets are prone to errors that are not quantified in a consistent manner. To account for such errors, we first compare different reference data sets against each other. We then compare model output against reference data and assess whether the differences are comparable to the differences among the reference data sets. We conclude that the performance of models is encouraging given how uncertain reference data are, but that ample potential for improvements remains. Key Points Differences between model and observations are often similar compared to differences between independently derived observation‐based data We quantify differences between independently derived observations to disentangle model deficiencies from observational uncertainties Future work should address biases in soil organic carbon, leaf area index, and the large spread of gross primary productivity among models

Climate change impacts forest functioning and dynamics, but large uncertainties remain regarding the interactions between species composition, demographic processes and environmental drivers. While the effects of changing climates on individual plant processes are well studied, few tools dynamically integrate them, which precludes accurate projections and recommendations for long-term sustainable forest management. Forest gap models present a balance between complexity and generality and are widely used in predictive forest ecology, but their lack of explicit representation of some of the processes most sensitive to climate changes, like plant phenology and water use, puts into question the relevance of their predictions. Therefore, integrating trait- and process-based representations of climate-sensitive processes is key to improving predictions of forest dynamics under climate change. In this study, we describe the PHOREAU model, a new semi-empirical forest dynamic model resulting from the coupling of a gap model (FORCEEPS), with two process-based models: a phenology-based species distribution model (PHENOFIT) and a plant hydraulics model (SurEAU), each parametrized for the main European species. The performance of the resulting PHOREAU model was then evaluated over many processes, metrics and time-scales, from the ecophysiology of individuals to the biogeography of species. PHOREAU reliably predicted fine hydraulic processes at both the forest and stand scale for a variety of species and forest types. This, alongside an improved capacity to predict stand leaf areas from inventories, resulted in better annual growth compared to ForCEEPS, and a strong ability to predict potential community compositions. By integrating recent advancements in plant hydraulic, phenology, and competition for light and water into a dynamic, individual-based framework, the PHOREAU model, developed on the Capsis platform, can be used to understand complex emergent properties and trade-offs linked to diversity-effects effects under extreme climatic events, with implications for sustainable forest management strategies.

The future evolution of the Amazon rainforest remains uncertain not only due to uncertain climate projections, but also owing to the intricate balance between tree growth and mortality. Many Earth System Models inadequately represent forest demography processes, especially drought‐induced tree mortality. In this study, we used ORCHIDEE‐CAN‐NHA, a land surface model featuring a mechanistic hydraulic architecture, a tree mortality sub‐model linked to a critical loss of stem conductance and a forest demography module for simulating regrowth. The model was forced by bias‐corrected climate forcing data from the ISIMIP‐2 program, considering two scenarios and four different climate models to project biomass changes in the Amazon rainforest until 2100. These climate models display diverse patterns of climate change across the Amazon region. The simulation conducted with the HadGEM climate model reveals the most significant drying trend, suggesting that the Guiana Shield and East‐central Amazon are approaching a tipping point. These two regions are projected to transition from carbon sinks to carbon sources by the mid‐21st century, with the Brazilian Shield following suit around 2060. This transition is attributed to heightened drought‐induced carbon loss in the future. This study sheds light on uncertainties in the future carbon sink in the Amazon forests, through a well‐calibrated model that incorporates tree mortality triggered by hydraulic damage and the subsequent recovery of drought‐affected forests through demographic processes. Plain Language Summary Whether the Amazon rainforest will remain as net carbon sink or not has long been of great concern as the drought events are predicted to become more frequent and more intense in the future and such extreme events highly threaten the forest net carbon uptake capacity. Here we use a process‐based model embedding drought‐induced tree mortality scheme that can perform well regarding past drought events over Amazon basin, to predict the future drought‐induced tree mortality risk and the evolution of net biomass carbon sink. The climate models present consistent warming but different wetting/drying patterns, although most of them consistently predict a drier trend in northeastern Amazon. Simulations forced by one climate model showed a carbon sink turning to a carbon source in more than half of Amazon rainforest since the middle of the 21st century. This work can inform the forest area with high tree mortality risk in the future, which calls for more concerns on mitigation policies. Key Points We used a land surface model forced by ISIMIP2 climate data to simulate the future drought‐induced tree mortality risk in Amazon rainforest While climate models differ in projections of wetting/drying patterns, many of them suggest a drying trend in the northeastern Amazon Simulations forced by HadGEM model indicate the Guiana Shield and East‐central Amazon will transition from carbon sink to source from 2050s