Preprints
https://doi.org/10.5194/gmd-2022-181
https://doi.org/10.5194/gmd-2022-181
Submitted as: model experiment description paper
21 Jul 2022
Submitted as: model experiment description paper | 21 Jul 2022
Status: this preprint is currently under review for the journal GMD.

The CMCC Decadal Prediction System

Dario Nicolì1, Alessio Bellucci1,a, Paolo Ruggieri1,b, Panos Athanasiadis1, Stefano Materia1, Daniele Peano1, Giusy Fedele1, and Silvio Gualdi1 Dario Nicolì et al.
  • 1Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna, 40127, Italy
  • anow at: Consiglio Nazionale delle Ricerche, Istituto di Scienze dell’Atmosfera e del Clima (CNR-ISAC), Bologna, 40129, Italy
  • bnow at: Department of Physics and Astronomy, University of Bologna, Bologna, 40126, Italy

Abstract. Decadal climate predictions, obtained by constraining the initial condition of a dynamical model through a truthful estimate of the observed climate state, provide an accurate assessment of climate change in the near-term range and a useful tool to inform decision-makers on future climate-related risks.

Here we present results from the CMIP6 DCPP-A decadal hindcasts produced with the operational CMCC decadal prediction system (CMCC DPS), based on the fully-coupled CMCC-CM2-SR5 dynamical model. A 15-member suite of 10-year retrospective forecasts, initialized every year from 1960 to 2020, is performed using a full-field initialization strategy. 

The predictive skill for key variables is assessed and compared with the skill of an ensemble of non-initialized historical simulations so as to quantify the added value of initialization. In particular, the CMCC DPS is able to skilfully reproduce past-climate surface temperature fluctuations over large parts of the globe. The North Atlantic Ocean is the region that benefits the most from initialization, with the largest skill enhancement occurring over the subpolar region compared to historical simulations. On the other hand, the predictive skill over the Pacific Ocean rapidly decays with forecast time, especially over the North Pacific. In terms of precipitation, the CMCC DPS skill is significantly higher than that of the historical simulations over a few specific regions, including Sahel, Northern Eurasia and over the Western and Central Europe.

The Atlantic Multidecadal Variability is also skilfully predicted, and this likely contributes to the skill found over remote areas through downstream influence, circulation changes and teleconnections. Considering the relatively small ensemble size, a remarkable prediction skill is also found for the North Atlantic Oscillation, with maximum correlations obtained in the 1–9 lead-year range.

Model systematic errors also affect the forecast quality of the CMCC DPS, featuring a prominent cold bias over the Northern Hemisphere, which is not found in the historical runs. This lack of agreement suggests that in some areas the adopted full-value initialization strategy likely perturbs the equilibrium state of the model climate quite significantly.

Dario Nicolì et al.

Status: open (until 15 Sep 2022)

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Dario Nicolì et al.

Dario Nicolì et al.

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Short summary
Decadal climate predictions, obtained by constraining the initial condition of a dynamical model through a truthful estimate of the observed climate state, provide an accurate assessment of the near-term climate and a useful tool to inform decision-makers on future climate-related risks. The predictive skill for key variables is assessed from the operational CMCC decadal prediction system compared with non-initialized historical simulations so as to quantify the added value of initialization.