Climate model

Neural networks leverage climate model data to predict how extreme rainfall, hail, and winds will shift geographically as climate changes, accounting for terrain effects.

Cropland expansion across tropical Africa produces nighttime cooling but hydroclimatically-dependent daytime effects, driven by turbulent heat flux changes tied to vegetation differences.

High-resolution climate projections for Pampanga and Pasig-Marikina-Laguna-Lake basins reveal intensifying rainfall extremes, elevated design rainfall, and increased seasonal variability.

Study projects atmospheric river frequency to double over South Pacific by mid-century, with robust trends emerging within 10-20 years first affecting southern New Zealand and Tasmania.

Climate models show fire weather conditions driven by human warming already detectable in 39% of burnable areas, with dangerous extremes emerging at 2–3°C warming in multiple regions.

Study shows extreme droughts, floods, and wildfires could occur at 2°C warming, exceeding impacts projected for 3-4°C. New sector-focused assessment reveals risks hidden by standard climate models.

Theoretical analysis explains why climate foundation models succeed at field prediction but fail at extreme event detection through in-context learning complexity.

EMAC earth system model accurately simulates global atmospheric hydrogen dynamics, achieving correlation coefficients exceeding 0.9 at remote polar stations against observational data.

Study examines how aerosol and greenhouse gas reductions under carbon neutrality create regionally divergent wildfire impacts in China, with competing mechanisms driving risk changes.

A statistical method corrects biases in Greenland ice sheet snow accumulation estimates from climate models, reducing uncertainties in sea-level rise projections.

CMIP6 climate models project intensified precipitation extremes in the Kosi Basin, with 47-79% increases in rainfall by 2100, critical for flood risk management and water resource planning.

Multidimensional weighting framework for emissions scenario ensembles accounting for relevance, quality, and diversity, with application to IPCC climate scenarios.

Machine learning reveals how seasonal climate and synoptic conditions differently control storm activity, with climate trends more strongly affecting storm heat anomalies than intensity.