The Agency has an interest in supporting young scientists in ESA Member States, covering leading edge research activities contributing to the achievement of the CCI (Climate Change Initiative) Programme by maximising the use of ESA data and EO assets. With this Partnership Agreement referred as “the Post-doctoral Scholar”, the CMCC has undertaken to carry out research work regarding FEVERSEA: Framework for marine heat waves (MHWs) EVEnts integrating Remote SEnsing and numericAl simulations.
“Marine Heat Waves (MHWs) induce significant impacts on marine ecosystems. There is a growing need for knowledge about extreme climate events to better inform decision-makers on future climate-related risks. MHWs research is still in its infancy: the extreme temperature anomalies are usually examined individually in terms of their definition, physical and climate drivers and ecological impacts, and the prediction of these extreme events is very challenging. In this context, FEVERSEA aims to provide a global assessment of MHWs under a consistent framework by combining data sets generated within the ESA SST_CCI, SSS_CCI, SeaLevel_CCI and OceanColour_CCI activities, European EO datasets (e.g. GLOBCURRENT, ERS1-2, ASCAT), non-European EO datasets (QUIKSCAT) and model simulations (ocean reanalyses, atmospheric reanalyses and CIMP6 coupled models). The main goals of FEVERSEA project are: 1) to study and to document MHWs extreme events: surface and sub-surface observed characteristics and ecological impacts, 2) to detect the local and large-scale climate precursors and 3) to exploit the potential of novel deep machine learning method in a prediction framework.”
24 months from 29/03/2021 to 29/03/2023
The main goals of FEVERSEA project are: (GOAL 1) to study and to document MHWs extreme events: surface and sub-surface observed characteristics and ecological impacts, (GOAL 2) to detect the local and large-scale climate precursors and (GOAL 3) to exploit the potential of novel deep machine learning method in a prediction framework.
The project will be carried out by a researcher employed by the CMCC: Dr. Giulia Bonino.
WP1: Identification and classification of MHWs and their impact on primary production in CCI observations and models
WP2: Local and large-scale climate forcing of MHWs
WP3: MHWs Detection and Prediction using deep machine learning technique
The outputs corresponds to the deliverables of each WP, which tackles the corresponding goal.
Deliverables WP1: Python scripts to identify and characterize (metrics and impacts) MHWs in SST_CCI, C-GLORS Reanalysis and in CMIP6-ESM models, a peer-review scientific paper on the global assessment of MHWs from SST_CCI, C- GLORS Ocean Reanalysis and CMIP6-ESM models, their identification, classification and impacts over 1982-2019 period.
Deliverables WP2: Python scripts to identify and characterize MHWs local and large-scale climate forcing, and a peer-review scientific paper on global assessment of local and large-scale climate drivers of MHWs from SST_CCI, C-GLORS Reanalysis and CMIP6-ESM models.
Deliverables WP3: Python scripts with CNN implementation for MHWs detection and prediction, and a peer-review scientific paper on MHWs detection and prediction using deep machine learning technique.