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Author/Chair
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  • Pierre Gentine

    Columbia University
Notes
Meeting roles in:
Refined Long-Term Satellite Leaf Area Index Reveals Sustained Global Greening and Accurate Tropical Seasonality
Water Savings Drive Enhancement of Ecosystem Water-Use Efficiency Under Rising CO2: Insights from Eddy Covariance and Machine Learning
Deep learning for flash drought prediction and interpretation
Comparison of Machine Learning-Based Pedotransfer Functions Using High-Resolution Soil Water Retention Data for Improved Ecohydrological Modeling
Advance in understanding of the changes in the carbon cycle and its linkage to the water cycle during the 2023-2024 El Niño in Amazon region
Quantifying Internal Modulation of Convective Organization Using Koopman–VAE
Differentiable Land Model Reveals Global Environmental Controls on Latent Ecological Functions­
Integrating Transformer-Based Land Surface Phenology into the Community Land Model for Climate-Responsive Vegetation Dynamics
Data-driven models of a coefficient in a higher-order closure of atmospheric boundary layer turbulence
Incorporating Multivariate Consistency in ML-Based Weather Forecasting with Latent Constraints: A WC-4DVar Perspective
Catastrophic “Hyperclustering” and Recurrent Losses: Diagnosing U.S. Flood Insurance Insolvency Triggers
Global model data fusion to unravel land carbon sinks and their changes
DifferBESS: Uncovering the Role of Canopy Temperature in Ecosystem Carbon and Water Fluxes Using a Hybrid Modeling Approach
Physically Consistent Global Atmospheric Data Assimilation with Machine Learning in Latent Space
Parsimony versus complexity
ClimateBench2.0: Probabilistic Climate Model Scoring
Understanding the sensitivity of stomatal conductance to heat and drought using Physics-Informed Neural Network
Physically-Constrained Deep Generative Modeling
Do canopy temperatures below air temperature exist?
An Attention-Based Stochastic Simulator for Nonstationary Multisite, Multivariate Extremes to Evaluate Climate-Conditional, Cascading Flood Risk
Topographic Regulation of Vegetation Productivity Uncovered by Causality-Guided Machine Learning
How Interpretable Machine Learning Advances Our Understanding of Climate Extremes and Their Impacts on Ecosystems and Infrastructure

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