Chonggang Xu

Drought is a major driver of crop production loss, threatening global food security as the population rises toward 9 billion by 2050. Using a process-based crop model within an Earth system model, we assess drought-related impacts on maize, soybean, rice, and wheat production at global and country levels. We then develop a food insecurity index combining drought impacts with socio-economic factors. Our results indicate that by 2050, globally averaged drought losses for combined maize, soybean, rice, and wheat production are <2%, though soybean losses reach 3.6%. Despite these small average changes at the global scale, 62 countries experience maximum production losses over 10%, and 24 countries over 20%. Our index identifies regions at greatest risk, including large parts of South America, Africa, Eastern Europe, and Southeast Asia. These results illustrate the need for drought adaptation to mitigate future drought impacts on crop production.

Tropical forests represent the warmest and wettest of Earth’s biomes, but with continued anthropogenic warming, they will be pushed to climate states with no current analogue^1,2. Droughts in the tropics are already becoming more intense as they occur at successively higher temperatures^3,4,5. Here we synthesize multiple datasets to assess the effects of hot droughts on a central Amazon forest. First, a more than 30-year record of annually resolved forest demographic data from a selective logging experiment showed higher tree mortality during intense droughts, particularly among fast-growing pioneer species with low wood density. Second, analysis of ecophysiological field measurements from the 2015 and 2023 El Niño droughts identified a soil moisture threshold beyond which transpiration rates rapidly declined. As rainless days beyond this threshold continued, drought conditions intensified, increasing the potential for tree mortality from hydraulic failure and carbon starvation. Third, analyses from the Coupled Model Intercomparison Project Phase 6 demonstrated that under high-emission scenarios, a large area of tropical forest will shift to a hotter ‘hypertropical’ climate by 2100. Last, under a hypertropical climate, temperature and moisture conditions during typical dry season months will more frequently exceed identified drought mortality thresholds, elevating the risk of forest dieback. Present-day hot droughts are harbingers of this emerging climate, offering a window for studying tropical forests under expected extreme future conditions^6,7,8.

Summary: Projections of terrestrial climate feedbacks from Earth System Models (ESMs) depend on simulated carbon and water dynamics of vegetation, and their credibility depends on realistic representation of water stress responses. Here, we discuss how models simulate plant hydraulic processes and identify opportunities to improve confidence in model outputs. We focus on the vegetation sub‐models that represent plant physiology and hydrodynamics, and also on groundwater flow models, which could influence water availability. Recently implemented plant hydrodynamic frameworks allow ESMs to represent water transport through plants but depend on crudely estimated fixed trait values. Few data are available for parameterizing, assessing, and validating these models across landscapes and at large scales. Furthermore, global models do not account for fine‐scale soil water and groundwater flow patterns that influence growth and mortality. We encourage the development of model‐compatible databases that include vetted data on plant hydraulic traits, states, and responses to drought. We also encourage efficient model representation of sub‐grid variation in slopes, soil depths, and groundwater accessibility. Finally, we recommend using physically informed artificial intelligence approaches to integrate knowledge from process‐based models and data from field observations, experiments, and remote‐sensing products toward better prediction of ecosystem dynamics under water stress at large scales.

Net photosynthesis (A_N) is a key component of the global carbon cycle influencing climate feedback over decadal scales. Although plant acclimation to environmental changes can modify A_N, traditional vegetation models in Earth system models (ESMs) often rely on plant functional type (PFT)‐specific parameterizations or simplified acclimation assumptions limiting generalizability across time, space, and PFTs. In this study, we developed a differentiable photosynthesis model to learn the environmental dependencies ofV_c,max25 (maximum carboxylation rate at 25°C, representing photosynthetic capacity), as this genre of hybrid physics‐informed machine learning can seamlessly train neural networks and process‐based equations together. Compared to PFT‐specific parameterization of V_c,max25, learning the environment dependencies of key photosynthetic parameters improved model spatiotemporal generalizability. Applying environmental acclimation to V_c,max25 led to substantial variations in global mean A_N indicating the need to address acclimation in ESMs. The model effectively captured multivariate observations (V_c,max25, A_N, and stomatal conductance (g_s)) simultaneously with multivariate constraints, improving generalization across space and PFTs. It also learned sensible acclimation relationships of V_c,max25 to different environmental conditions. The model explained more than 54%, 57%, and 62% of the variance of A_N, g_s, and V_c,max25, respectively, presenting a first global‐scale spatial test benchmark of A_N and g_s. These results highlight the potential for differentiable modeling to enhance process‐based modules in ESMs and effectively leverage information from large, multivariate data sets.

Summary: Grass‐dominated ecosystems cover wide areas of the land surface yet have received far less attention from the Earth System Model (ESM) community. This limits model projections of ecosystem dynamics in response to global change and coupled vegetation–climate dynamics. We used the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a dynamic vegetation demography model, to determine ecosystem sensitivity to alternate, observed grass allometries and biophysical traits, and evaluated model performance in capturing California C₃ annual grasslands structure and fire regimes. Grass allometry, leaf physiology, plant phenology, and plant mortality all drove the seasonal variation in matter and energy exchange and fire dynamics in California annual grasslands. Allometry influenced grassland structure and function mainly through canopy architecture‐mediated space and light competition instead of through carbon partitioning strategy. Regional variation in grassland annual burned area was driven by variation in ecosystem productivity. Our study advances the modeling of grassy ecosystems in ESMs by establishing the importance of grass allometry and plant phenology and mortality in driving C₃ annual grassland seasonal dynamics and fire regime. The calibrated annual grass allometry and biophysical traits presented can be applied in future studies to project climate–vegetation–fire feedbacks in annual grass‐dominant ecosystems under global change.

The feedback of topsoil moisture (SM) content on convective clouds and precipitation is not well understood and represented in the current generation of weather and climate models. Here, we use functional decomposition of satellite‐derived SM and cloud vertical profiles (CVP) to quantify the relationship between SM and the vertical distribution of cloud water in the central US. High‐dimensional model representation is used to disentangle the contributions of SM and other land‐surface and atmospheric variables to the CVP. Results show that the sign and strength of the SM‐cloud‐precipitation feedback varies with cloud height and time lag and displays a large spatial variability. Positive anomalies in antecedent 7‐hr SM and land‐surface temperature enhance cloud reflectivity up to 4 dBZ in the lower atmosphere about 1–3 km above the surface. Our approach presents new insights into the SM‐cloud‐precipitation feedback and aids in the diagnosis of land‐atmosphere interactions simulated by weather and climate models.

We investigated global food security in response to drought using a process-based crop model within an Earth system model. Globally, future drought losses for annual maize, soybean, rice, and wheat production are projected to be 10% and 24 countries >20%. Vulnerability is highest in countries with lower GDP and higher projected population growth.

Summary: Future climate presents conflicting implications for forest biomass. We evaluate how plant hydraulic traits, elevated CO₂ levels, warming, and changes in precipitation affect forest primary productivity, evapotranspiration, and the risk of hydraulic failure. We used a dynamic vegetation model with plant hydrodynamics (FATES‐HYDRO) to simulate the stand‐level responses to future climate changes in a wet tropical forest in Barro Colorado Island, Panama. We calibrated the model by selecting plant trait assemblages that performed well against observations. These assemblages were run with temperature and precipitation changes for two greenhouse gas emission scenarios (2086–2100: SSP2‐45, SSP5‐85) and two CO₂ levels (contemporary, anticipated). The risk of hydraulic failure is projected to increase from a contemporary rate of 5.7% to 10.1–11.3% under future climate scenarios, and, crucially, elevated CO₂ provided only slight amelioration. By contrast, elevated CO₂ mitigated GPP reductions. We attribute a greater variation in hydraulic failure risk to trait assemblages than to either CO₂ or climate. Our results project forests with both faster growth (through productivity increases) and higher mortality rates (through increasing rates of hydraulic failure) in the neo‐tropics accompanied by certain trait plant assemblages becoming nonviable.

Vegetation plays a key role in the global carbon cycle and thus is an important component within Earth system models (ESMs) that project future climate. Many ESMs are adopting methods to resolve plant size and ecosystem disturbance history, using vegetation demographic models. These models make it feasible to conduct more realistic simulation of processes that control vegetation dynamics. Meanwhile, increasing understanding of the processes governing plant water use, and ecosystem responses to drought in particular, has led to the adoption of dynamic plant water transport (i.e., hydrodynamic) schemes within ESMs. However, the extent to which variations in plant hydraulic traits affect both plant water stress and the risk of mortality in trait-diverse tropical forests is understudied. In this study, we report on a sensitivity analysis of an existing hydrodynamic scheme (HYDRO) model that is updated and incorporated into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) (FATES–HYDRO V1.0). The size- and canopy-structured representation within FATES is able to simulate how plant size and hydraulic traits affect vegetation dynamics and carbon–water fluxes. To better understand this new model system, and its functionality in tropical forest systems in particular, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We assembled 942 observations of plant hydraulic traits on 306 tropical plant species for stomata, leaves, stems, and roots and determined the best-fit statistical distribution for each trait, which was used in model parameter sampling to assess the parametric sensitivity. We showed that, for simulated leaf water potential and loss of hydraulic conductivity across different plant organs, the four most important traits were associated with xylem conduit taper (buffers increasing hydraulic resistance with tree height), stomatal sensitivity to leaf water potential, maximum stem hydraulic conductivity, and the partitioning of total hydraulic resistance above vs. belowground. Our analysis of individual ensemble members revealed that trees at a high risk of hydraulic failure and potential tree mortality generally have a lower conduit taper, lower maximum xylem conductivity, lower stomatal sensitivity to leaf water potential, and lower resistance to xylem embolism for stem and transporting roots. We expect that our results will provide guidance on future modeling studies using plant hydrodynamic models to predict the forest responses to droughts and future field campaigns that aim to better parameterize plant hydrodynamic models.