Stefano Materia

Comment on egusphere-2026-3206

Monalisa Sahoo, Stefano Materia, Markus G. Donat

Abstract. Land–atmosphere coupling has long been recognized to modulate the surface fluxes partitioning in transitional evaporative regimes, where soil moisture anomalies control evapotranspiration. However, globally available in-situ observations for these variables remain limited. This study provides a comprehensive assessment of the similarities, …

es, it (code pays fourni par la source)

The rise of AI in weather and climate information and its impact on global inequality

Amirpasha Mozaffari, Amanda Duarte, Lina Teckentrup, Stefano Materia et autres

AI development’s current trajectory risks automating and amplifying the North-South divide in the global climate information system. Frontier models are built almost exclusively in the Global North, and this inequality continues through inputs, processes, and outputs, from biased training data to unrepresentative …

es, it, gb (code pays fourni par la source)

Summer Land-Atmosphere Coupling over Europe: A Comparative Evaluation of Observation-based Datasets

Monalisa Sahoo, Stefano Materia, Markus G. Donat

Abstract. Land–atmosphere coupling has long been recognized to modulate the surface fluxes partitioning in transitional evaporative regimes, where soil moisture anomalies control evapotranspiration. However, globally available in-situ observations for these variables remain limited. This study provides a comprehensive assessment of the similarities, …

es, it (code pays fourni par la source)

Scalable Deep Learning Framework for Global High-Resolution Land Use Reconstruction

Amirpasha Mozaffari, Marina Castaño, Stefano Materia, Étienne Tourigny et autres

Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly driven by the uncertainties affecting the land surface representation and variability in Earth system models. To address this limitation, we present a data-driven framework AI4Land, for generating high-resolution …

Scalable Deep Learning Framework for Global High-Resolution Land Use Reconstruction

Amirpasha Mozaffari, Marina Castaño, Stefano Materia, Étienne Tourigny et autres

Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly driven by the uncertainties affecting the land surface representation and variability in Earth system models. To address this limitation, we present a data-driven framework AI4Land, for generating high-resolution …

es (code pays fourni par la source)

AI4Land: A Scalable Deep Learning Framework on MareNostrum5 for Global High-Resolution Land Use Reconstruction

Amirpasha Mozaffari, Marina Castaño, Stefano Materia, Étienne Tourigny et autres

Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly driven by the uncertainties affecting the land surface representation and variability in Earth system models. To address this limitation, we present a data-driven framework —AI4Land— for generating high-resolution …

es (code pays fourni par la source)

Peer Review Report For: Common errors in Generative AI systems used for knowledge extraction in the climate action domain [version 1; peer review: 1 approved, 1 approved with reservations]

Amanda Duarte, Stefano Materia

Review of Xue et al. "Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction Project, Phase I (LS4P-I): Organization and Experimental design"

Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaïla Diallo et autres

Abstract. Subseasonal-to-seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging, but also has substantial societal impacts. Motivated by preliminary studies, the Global Energy and Water Exchanges (GEWEX)/Global Atmospheric System Study (GASS) …

us, cn, fr, jp, de, kr, it, in, gb, au, br, ca (code pays fourni par la source)