This project focuses on the processing of high-resolution climate reanalysis data to generate easy-to-use agro-environmental indicators and variables. The core scientific idea is to bridge the temporal
The ClimAVA (https://climate.usu.edu/climava/) dataset provides high-resolution (4km), bias-corrected, downscaled future climate projections derived from 17 CMIP6 General Circulation Models. It includes
ICON is a flexible, scalable, high-performance modelling framework for weather, climate and environmental prediction that provides actionable information for society and advances our understanding of the
This project, running from January 2023 to December 2026, will reveal and quantify the role of ocean mesoscale processes in shaping the climate trajectory over seasonal to centennial time scales. To this
Tropical cyclones, heatwaves and extreme droughts are examples of extreme climate events that are difficult to predict. Climate change has increased the likelihood and severity of such events, and predicting
This project investigates CO2 and CO dispersion, focusing on German metropolitan areas. Its aim is to provide a high-quality, high-resolution, long-term dataset of CO2 and CO concentrations and meteorology
This project investigates how intelligent sponge city technologies can mitigate urban heat islands (UHI) in the Qinghai-Tibet Plateau. It aims to develop scalable models for climate-resilient urban planning
Annual maxima of sub-daily precipitation were extracted from each of 35 members of a EURO-CORDEX ensemble with a spatial resolution of 0.11° (EURO-CORDEX: https://www.euro-cordex.net/). Precipitation durations
The primary goal of DCENT_MLE is to combine instrumental observations with physically realistic statistical models to produce maximum likelihood estimates of surface temperature anomalies and other physical
NEX-GDDP-CMIP6 is intended to support climate impact assessments, risk analysis, and adaptation planning in sectors such as hydrology, agriculture, energy, infrastructure, and public health. By increasing
The forcing datasets (and boundary conditions) needed for CMIP6 experiments are being prepared by a number of different experts. Initially many of these datasets may only be available from those experts,
The University of Texas at Austin (UT Austin) created a Hindcast dataset using the Google DeepMind Graphcast operational model from 1979 to 2024. This project was supported by the United States Department