Climate Variability and Prediction

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What we do
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Climate Variability and Prediction

CLIVAP division builds on CMCC climate models and tools to:

i) improve our understanding of the mechanisms underpinning climate variability, climate predictability and climate change
ii) improve our understanding of the relationship between climate and extreme events
iii) provide and improve CMCC operational climate forecasts from seasonal to decadal and multi-decadal time horizons
iv) investigate long term climate projections
v) collaborate with CMCC institutes to implement a model chain for impact predictions at different time horizons.


The division is structured in three main units: Climate Variability and Change (CLIVAP-CVC) unit and Decadal (CLIVAP-DEC) and  Seasonal (CLIVAP-SEAS) Prediction units.

Within CLIVAP-CVC, the main aim is to improve our knowledge of the climate system (i, ii) also to identify internal relationships useful to  the improvement of our CMCC operational forecasts maintained by the CLIVAP-DEC and CLIVAP-SEAS units.
Within the prediction units, together with the operational forecast production (iii), new sources of predictability and the relative importance of model components and settings are investigated  for the  different time horizons.
The prediction (iii) and projection (iv) products are made available, and tailored, based on the requirements of the impact community (v).

CLIVAP Projects

  • WeatherGenerator

    The project will build the WeatherGenerator – the world’s best generative Foundation…


  • CLIVAP Publications

    Cause of the Recent Tendency of Tropical Cyclones Approaching Coasts as Revealed by HighResMIP-PRIMAVERA Simulations

    Ogawa F., Minobe S., Roberts M.J., Haarsma R., Putrasahan D., Scoccimarro E., Terray L., Vidale P.L.
    2025, Journal of Climate, Volume 38, Issue 5, Pages 1351 - 1364, doi: 10.1175/JCLI-D-24-0173.1


    A statistical learning approach to Mediterranean cyclones

    Roveri L., Fery L., Cavicchia L., Grotto F.
    2025, Chaos, doi: 10.1063/5.0241107

    Division Director

    Enrico Scoccimarro

    Division Manager

    Marco Vannini

    Contacts

    Via C. Berti Pichat 6/2 – 40127 Bologna, Italy
    [email protected]

    Research Units

    Leader
    Enrico Scoccimarro

    Within CLIVAP-CVC, the main aim is to improve our knowledge of the climate system (i, ii) also to identify internal relationships useful to  the improvement of our CMCC operational forecasts maintained by the CLIVAP-DEC and CLIVAP-SEAS units.

    Leader
    Panos Athanasiadis

    The CLIVAP-DEC prediction unit:
    –  conducts research on topics related to climate predictability with a focus on ocean–atmosphere interactions;
    –  produces decadal predictions operationally (WMOLC) and contribute to WCRP international modelling experiments (DCPPEPESC);
    –  analyzes ensemble predictions employing innovative methods to improve their skill assisting the development of climate services.

    Leader
    Leone Cavicchia

    Operations Officer: Antonella Sanna

    The CLIVAP-SEAS prediction unit:
    –  investigates sources of climate predictability at the seasonal scale, with a focus on extreme events;
    –  produces operational seasonal forecasts, which are distributed through Copernicus and the WMO Integrated Processing and Prediction systems;
    –  investigates novel methods aimed at increasing the skill of climate predictions, with a focus on applications based on AI.

    CMCC_sps

    CMCC Seasonal Prediction System

    The Euro-Mediterranean Center on Climate Change operates routinely a Global Seasonal Ensemble Prediction System (CMCC-SPS3.5) based on a Coupled Atmosphere-Ocean-Land-Cryosphere Global Model.

    Models

    CMCC-CESM-NEMO – Climate coupled model

    The CMCC–CESM–NEMO is the physical basis of the new CMCC Earth System Model (CMCC-ESM). It is a global coupled climate model derived from the NCAR coupled model CESM version 1.1.2 (Hurrell et al., 2013), where the ocean component is NEMO (Madec et al., 2012) rather than the NCAR ocean model.

    Data

    Near-term climate prediction for CMIP6

    Decadal prediction experiments consist of 10-member ensembles of 5-year hindcasts, starting every second year from 1960 to the present, using historical radiative forcing conditions (including green-house gases, aerosols and solar irradiance variability), followed by SSP2-4.5 scenario settings for the future.

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