Climate Simulations and Predictions

The CSP Division contributes to the development of the CMCC climate and earth system models, and uses them to explore and improve our understanding of the mechanisms underpinning climate variability, climate predictability and climate change, by means of numerical simulations. In collaboration with the ODA Division, CSP produces climate change scenarios, contributing to the World Climate Research Programme (WCRP)’s Coupled Model Intercomparison Project (CMIP) project, to inform the Intergovernmental Panel on Climate Change (IPCC) assessments and in support of emerging climate service activities. Furthermore, CSP produces operational climate forecasts from seasonal to multi-annual time scales.

CSP Projects

  • SOCLIMPACT - DownScaling CLImate imPACTs and decarbonisation pathways in EU islands, and enhancing socioeconomic and non-market evaluation of Climate Change for Europe, for 2050 and beyond

    The SOCLIMPACT consortium involves 24 partners, many of them coming from 12 European islands and archipelagos: European Small States Islands: Cyprus and Malta; European islands: Baltic...


  • ADRIADAPT - A Resilience information platform for Adriatic cities and towns

    ADRIADAPT aims at promoting local and regional resilience by developing the knowledge base required to identify suitable climate adaptation and planning options, thereby enabling local authorities to...

    Cities & Coasts.


  • WINDSURFER - WIND and wave Scenarios, Uncertainty and climate Risk assessments for Forestry, Energy and Reinsurance

    Extreme winds pose major risks to life, property and forestry, while extreme ocean waves can impact on offshore infrastructures and coastal communities WINDSURFER is a 3-year project that will bring...

    Cities & Coasts, Energy, Future Earth, Oceans, Water, Food and Land Use.

  • CSP Publications

    Global Mean Climate and Main Patterns of Variability in the CMCC-CM2 Coupled Model

    Cherchi A., Fogli P. G. , Lovato T., Peano D., Iovino D., Gualdi S., Masina S., Scoccimarro E., Materia S., Bellucci A., Navarra A.
    2019, Journal of Advances in Modeling Earth Systems, DOI: https://doi.org/ 10.1029/2018MS001369, web page


    The effects of meteorological conditions and daylight on nature-based recreational physical activity in England

    Lewis R. E., White M.P., Sarran C., Grellier J., Garrett J.K., Scoccimarro E., Smalley A.J., Fleming L.E.
    2019, Urban Forestry & Urban Greening, DOI: https://doi.org/10.1016/j.ufug.2019.05.005, web page


    Sea-ice algal phenology in a warmer Arctic

    Tedesco L., Vichi M., Scoccimarro E.
    2019, Science Advances, DOI: DOI: 10.1126/sciadv.aav4830, web page

    Division Director

    Silvio Gualdi

    Division Manager

    Loredana Amato

    Contacts

    via C. Berti Pichat 6/2 – 40127 Bologna, Italy

    loredana.amato@cmcc.it

    Research Units

    Research Unit Leader
    Alessio Bellucci

    Description
    This unit is devoted to the understanding of the mechanisms underpinning climate predictability at seasonal-to-multi annual time scales. It produces operational seasonal forecasts that are provided to other CMCC divisions and in support of emerging climate services.

    Research Unit Leader
    Silvio Gualdi

    Description
    This unit contributes to the development of the CMCC climate and Earth system models, and uses them to explore and improve our understanding of the mechanisms of climate variability and climate change by means of numerical simulations. Furthermore, in collaboration with the ODA Division, it produces the CMCC climate change scenario contributions to the World Climate Research Programme (WCRP)’s Coupled Model Intercomparison Project (CMIP) project, to inform the Intergovernmental Panel on Climate Change (IPCC) assessments and in support of emerging climate services.

    CMCC_sps

    CMCC Seasonal Prediction System

    The Euro-Mediterranean Center on Climate Change operates routinely a Global Seasonal Ensemble Prediction System (CMCC-SPS3) based on a Coupled Atmosphere-Ocean-Land-Cryosphere Global Model.

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