Cel Climate prediction is the next frontier in climate research. Prediction of climate on timescales from a season to a decade has shown progress, but beyond the ocean skill remains low. And while the historical evolution of climate at global scales can be reasonably simulated, agreement at a regional level is limited and large uncertainties exist in future climate change. These large uncertainties pose a major challenge to those providing climate services and to informing policy makers.This proposal aims to investigate the potential of an innovative technique to reduce model systematic error, and hence to improve climate prediction skill and reduce uncertainties in future climate projections. The current practice to account for model systematic error, as for example adopted by the Intergovernmental Panel on Climate Change, is to perform simulations with ensembles of different models. This leads to more reliable predictions, and to a better representation of climate. Instead of running models independently, we propose to connect the different models in manner that they synchronise and errors compensate, thus leading to a model superior to any of the individual models – a super model. The concept stems from theoretical non-dynamics and relies on advanced machine learning algorithms. Its application to climate modelling has been rudimentary. Nevertheless, our initial results show it holds great promise for improving climate prediction. To achieve even greater gains, we will extend the approach to allow greater connectivity among multiple complex climate models to create a true super climate model. We will assess the approach’s potential to enhance seasonal-to-decadal prediction, focusing on the Tropical Pacific and North Atlantic, and to reduce uncertainties in climate projections. Importantly, this work will improve our understanding of climate, as well as how systematic model errors impact prediction skill and contribute to climate change uncertainties. Dziedzina nauki natural sciencesearth and related environmental sciencesatmospheric sciencesclimatologyclimatic changesnatural sciencescomputer and information sciencesartificial intelligencemachine learningnatural sciencescomputer and information sciencessoftwaresoftware applicationssimulation software Program(-y) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Temat(-y) ERC-CoG-2014 - ERC Consolidator Grant Zaproszenie do składania wniosków ERC-2014-CoG Zobacz inne projekty w ramach tego zaproszenia System finansowania ERC-COG - Consolidator Grant Instytucja przyjmująca UNIVERSITETET I BERGEN Wkład UE netto € 1 999 388,75 Adres MUSEPLASSEN 1 5020 Bergen Norwegia Zobacz na mapie Region Norge Vestlandet Vestland Rodzaj działalności Higher or Secondary Education Establishments Linki Kontakt z organizacją Opens in new window Strona internetowa Opens in new window Uczestnictwo w unijnych programach w zakresie badań i innowacji Opens in new window sieć współpracy HORIZON Opens in new window Koszt całkowity € 1 999 388,75 Beneficjenci (1) Sortuj alfabetycznie Sortuj według wkładu UE netto Rozwiń wszystko Zwiń wszystko UNIVERSITETET I BERGEN Norwegia Wkład UE netto € 1 999 388,75 Adres MUSEPLASSEN 1 5020 Bergen Zobacz na mapie Region Norge Vestlandet Vestland Rodzaj działalności Higher or Secondary Education Establishments Linki Kontakt z organizacją Opens in new window Strona internetowa Opens in new window Uczestnictwo w unijnych programach w zakresie badań i innowacji Opens in new window sieć współpracy HORIZON Opens in new window Koszt całkowity € 1 999 388,75