Articles | Volume 16, issue 1
https://doi.org/10.5194/gmd-16-179-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-179-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
05 Jan 2023
Model experiment description paper |
| 05 Jan 2023
| 05 Jan 2023
The Euro-Mediterranean Center on Climate Change (CMCC) decadal prediction system
Climate Simulation and Prediction (CSP) division, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna 40127, Italy
Alessio Bellucci
Climate Simulation and Prediction (CSP) division, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna 40127, Italy
now at: Consiglio Nazionale delle Ricerche, Istituto di Scienze
dell'Atmosfera e del Clima (CNR-ISAC), Bologna 40129, Italy
Paolo Ruggieri
Climate Simulation and Prediction (CSP) division, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna 40127, Italy
now at: Department of Physics and Astronomy, University of Bologna,
Bologna 40126, Italy
Panos J. Athanasiadis
Climate Simulation and Prediction (CSP) division, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna 40127, Italy
Stefano Materia
Climate Simulation and Prediction (CSP) division, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna 40127, Italy
Daniele Peano
Climate Simulation and Prediction (CSP) division, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna 40127, Italy
Giusy Fedele
Climate Simulation and Prediction (CSP) division, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna 40127, Italy
Riccardo Hénin
Climate Simulation and Prediction (CSP) division, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna 40127, Italy
Silvio Gualdi
Climate Simulation and Prediction (CSP) division, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna 40127, Italy
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Cited
14 citations as recorded by crossref.
- Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran M. Akbarian et al. 10.1016/j.jhydrol.2023.129480
- Decadal prediction skill for Eurasian surface air temperature in CMIP6 models Y. Huang et al. 10.1016/j.aosl.2023.100377
- Identifying a Pattern of Predictable Decadal North Pacific SST Variability in Historical Observations E. Gordon & N. Diffenbaugh 10.1029/2024GL112729
- Ocean-atmosphere feedbacks key to NAO decadal predictability C. Patrizio et al. 10.1038/s41612-025-01027-7
- Initialization shock in the ocean circulation reduces skill in decadal predictions of the North Atlantic subpolar gyre I. Polkova et al. 10.3389/fclim.2023.1273770
- Estimating the decadal-scale climate predictability limit using nonlinear local Lyapunov exponent with optimal local dynamic analogues R. Li et al. 10.1007/s00382-024-07573-9
- Seasonal forecasting of East African short rains A. Tefera et al. 10.1038/s41598-025-86564-0
- Deep Learning‐Based Seasonal Forecast of Sea Ice Considering Atmospheric Conditions Y. Zhu et al. 10.1029/2023JD039521
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- Gross primary productivity and the predictability of CO2: more uncertainty in what we predict than how well we predict it I. Dunkl et al. 10.5194/bg-20-3523-2023
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- Decadal predictions outperform climate projections in forecasting Mediterranean wintertime precipitation D. Nicolì et al. 10.1088/1748-9326/adb59e
- Decadal prediction of Northeast Asian winter precipitation with CMIP6 models X. Xin et al. 10.1007/s00382-023-07063-4
- Predicting precipitation on the decadal timescale: A prototype climate service for the hydropower sector E. Tsartsali et al. 10.1016/j.cliser.2023.100422
13 citations as recorded by crossref.
- Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran M. Akbarian et al. 10.1016/j.jhydrol.2023.129480
- Decadal prediction skill for Eurasian surface air temperature in CMIP6 models Y. Huang et al. 10.1016/j.aosl.2023.100377
- Identifying a Pattern of Predictable Decadal North Pacific SST Variability in Historical Observations E. Gordon & N. Diffenbaugh 10.1029/2024GL112729
- Ocean-atmosphere feedbacks key to NAO decadal predictability C. Patrizio et al. 10.1038/s41612-025-01027-7
- Initialization shock in the ocean circulation reduces skill in decadal predictions of the North Atlantic subpolar gyre I. Polkova et al. 10.3389/fclim.2023.1273770
- Estimating the decadal-scale climate predictability limit using nonlinear local Lyapunov exponent with optimal local dynamic analogues R. Li et al. 10.1007/s00382-024-07573-9
- Seasonal forecasting of East African short rains A. Tefera et al. 10.1038/s41598-025-86564-0
- Deep Learning‐Based Seasonal Forecast of Sea Ice Considering Atmospheric Conditions Y. Zhu et al. 10.1029/2023JD039521
- On the skill of Indo-Pacific decadal sea level predictions and its connection with skilful AMO and PDO predictions J. Deepa & C. Gnanaseelan 10.1007/s00382-024-07456-z
- Gross primary productivity and the predictability of CO2: more uncertainty in what we predict than how well we predict it I. Dunkl et al. 10.5194/bg-20-3523-2023
- Impact of volcanic eruptions on CMIP6 decadal predictions: a multi-model analysis R. Bilbao et al. 10.5194/esd-15-501-2024
- Decadal predictions outperform climate projections in forecasting Mediterranean wintertime precipitation D. Nicolì et al. 10.1088/1748-9326/adb59e
- Decadal prediction of Northeast Asian winter precipitation with CMIP6 models X. Xin et al. 10.1007/s00382-023-07063-4
Latest update: 23 Apr 2025
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 are useful for informing decision-makers on future climate-related risks. The predictive skill for key variables is assessed from the operational decadal prediction system compared with non-initialized historical simulations so as to quantify the added value of initialization.
Decadal climate predictions, obtained by constraining the initial condition of a dynamical model...
