Hanliang Gui

Understanding the relative roles of phenotypic plasticity and genetic differentiation in shaping plant phenological responses is essential for predicting forest tree responses to climate warming. Although spring and autumn phenophases respond sensitively to rising temperatures, the underlying regulatory mechanisms may differ substantially, particularly across environmental gradients. We investigated intra-specific phenological variation in Quercus petraea across the French Pyrenees using a multi-environment experimental framework. This approach integrated 15 years of in situ phenological monitoring with common garden (CG) and reciprocal transplant experiments spanning an elevational gradient. We quantified key phenological traits—leaf unfolding, leaf senescence, and growing season length—and applied generalized linear mixed models to evaluate the relative effects of temperature, provenance-level differentiation, and their interactions. Leaf unfolding showed strong temperature-associated plastic responses and comparatively limited provenance-level differentiation. In contrast, leaf senescence showed weaker and less consistent temperature responses. Growing season length increased under warmer conditions, largely reflecting the combined effects of earlier leaf unfolding and variable senescence timing. Our findings reveal phase-specific differences in phenological responses, with stronger temperature-associated plasticity in spring than in autumn phenology. These results highlight the need for long-term, multi-environmental data for understanding tree phenology under a changing climate and provide insights for developing adaptive forest management strategies that maintain adaptive capacity.

Ensuring food security is crucial in the context of climate change and increased extreme weather events. Crop production depends not only on yield but also on the harvestable fraction (HF), the ratio of harvested areas to planted areas. While the impacts of climate fluctuations on crop yields are well‐document, the role of HF‐a critical yet underexplored factor‐remains poorly understood. This study introduces HF as a key metric for assessing how temperature, drought, and precipitation affect the production of major staple crops (winter wheat, spring wheat, and maize) in the United States at the county level. Our findings indicate that Killing Degree Days (KDD) and Drought Days (DD) are key drivers of production declines, reducing both yield and HF. From 1982 to 2020, changes in KDD and DD led to more significant reductions in crop production in the Midwest compared to other regions. Projections for 2021–2100 under different Shared Socioeconomic Pathways (SSPs) indicate even steeper declines in yield, HF and production, especially under the high‐emission scenario (SSP5‐8.5), with anticipated net production decreases of 6.08%, 8.2%, and 7.57% for the three crops. Although HF may increase in colder regions, this does not offset losses in warmer areas, leading to net HF decreases of 0.36%, 2.09%, and 5.82% for the three crops. This study finds that drought and extreme heat considerably reduce food production by simultaneously lowering yields and HF and underscores the need for adaptive strategies that address both yield and HF to enhance food security in a changing climate.

Ecosystem temporal stability (TS) determines its ability to maintain structure, function and services under external disturbances, playing a critical role in the global carbon cycle and climate regulation. However, the capability of numerical models to simulate the TS of ecosystem carbon uptake remains insufficiently assessed. This study evaluated the performance of nine terrestrial ecosystem models in simulating gross primary productivity (GPP) and its TS and employed Random Forest (RF) models with Shapley Additive Explanations (SHAP) to identify key factors contributing to model biases. Site-scale analysis based on flux tower observations indicated that most models underestimated GPP while overestimating its TS, with the most pronounced biases occurring at the interannual scale. These discrepancies primarily stemmed from errors in simulating vegetation phenology, specifically the carbon uptake period and physiological traits, particularly peak GPP within a year. At the global scale, regions with higher carbon uptake tended to exhibit greater TS, yet significant discrepancies existed among models. Notably, RF and SHAP analyses indicated that leaf area index was more important than climate and geographical factors in explaining model divergence for simulating GPP and its TS. The study revealed systematic biases in the current models’ representation of TS, highlighting the potential vulnerability of ecosystems. These uncertainties among models may lead to an overestimation of ecosystem resilience, introducing uncertainties in global carbon budget estimates and potentially misguiding scientific assessments and policy decisions regarding future climate change responses. Therefore, improving carbon cycle simulation mechanisms is essential for enhancing model predictive capabilities.

Since agricultural drought plays a leading role in restricting agricultural productivity, accurate forecasting is crucial for agricultural management. Although soil moisture (SM) is the primary variable for identifying and forecasting agricultural drought, accurately predicting SM is challenged by its strong interaction with external meteorological forcings that change rapidly across space and time. To provide a predictive method that reduces uncertainty in SM modeling and compensates for the latency in satellite-based SM products, we propose a meteorologically driven Transformer framework (MDTF). The framework can predict global surface (0-5 cm) and root-zone (0-100 cm) SM utilizing meteorological forecasts and soil physical properties with a prediction latency of 35 days. When validated against satellite-based SM data and in-situ measurements, our proposed model more accurately predicts global SM spatial patterns and seasonal dynamics compared with the physically-based Common Land Model, demonstrating consistent performance across different land covers. MDTF outperforms popular machine learning models, achieving unbiased Root Mean Square Error values of 0.297 m³/m³ and 0.211 m³/m³ for surface and root-zone SM. The predicted seamless global daily SM information could be effectively utilized for agricultural drought forecasting. Our research demonstrates that the MDTF model has unique advantages in modeling key hydrological variables of the earth system, providing a reference for predicting time series of global SM and agricultural drought dynamics.