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ASPECT aims for the setup and demonstration of a seamless climate information (SCI) system with a time horizon up to 30yr, accompanied by underpinning research and utilisation of climate information for sectoral applications. The goal is to improve adaptation through the improvement of existing climate prediction systems.
ASPECT is designed around seven work packages. WPs 1 to 3 provide the climate forecast and projection data across time-scales, including improved understanding, skill assessment, downscaling approaches and identification of extremes. WPs 4 to 7 form a cluster that focuses on engaging with the users, delivering data, information, methods and guidance to drive and inform adaptation, as well as wider communication, dissemination and exploitation activities.
The information package will incorporate baseline forecasts and projections (plus uncertainty), but also new frontiers will be explored (e.g., extremes which are of socioeconomic high-level interest). To be successful the research will encompass: Understanding and attribution of various processes along the timescales (such as exploring signal-to-noise ratio) and their impact on predictability, new ways of initialisation of the prediction systems, merging predictions with projections, provision of regional SCI for Europe by downscaling (statistical methods, AI) and HighRes models (including convection-permitting models) and innovative post-processing method enhancing the skill and robustness of the climate forecasts.
Project information
Lead
Barcelona Supercomputing service, Spain
Partners
Max-Plank Institute fur meteorologie, Denmark
CMCC Foundation, Italy
European Centre for Medium-Range Weather Forecasts, UK
University of Leeds, UK
University of Zagreb, Croatia
Swedish Meteorological and Hydrological Institute (SMHI), Sweden
Republic Hydrometeorological Service of Serbia, Serbia
CODORNIU SA, Spain
Met Office, UK
University of Oxford, UK
University of Leeds, UK
Published in Climate-ADAPT Jul 28, 2023 - Last Modified in Climate-ADAPT Dec 12, 2023
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