The regional MiKlip decadal prediction system for Europe: Hindcast skill for extremes and user‐oriented variables

Abstract Decadal climate predictions are now established as a source of information on future climate alongside longer-term climate projections. This information has the potential to provide key evidence for decisions on climate change adaptation, especially at regional scales. Its importance implies that following the creation of an initial generation of decadal prediction systems, a process of continual development is needed to produce successive versions with better predictive skill. Here, a new version of the Met Office Hadley Centre Decadal Prediction System (DePreSys 2) is introduced, which builds upon the success of the original DePreSys. DePreSys 2 benefits from inclusion of a newer and more realistic climate model, the Hadley Centre Global Environmental Model version 3 (HadGEM3), but shares a very similar approach to initialization with its predecessor. By performing a large suite of reforecasts, it is shown that DePreSys 2 offers improved skill in predicting climate several years ahead. Differences in skill between the two systems are likely due to a multitude of differences between the underlying climate models, but it is demonstrated herein that improved simulation of tropical Pacific variability is a key source of the improved skill in DePreSys 2. While DePreSys 2 is clearly more skilful than DePreSys in a global sense, it is shown that decreases in skill in some high-latitude regions are related to errors in representing long-term trends. Detrending the results focuses on the prediction of decadal time-scale variability, and shows that the improvement in skill in DePreSys 2 is even more marked.

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Evaluation of the MiKlip decadal prediction system using satellite based cloud products

Meteorologische Zeitschrift ◽

10.1127/metz/2015/0602 ◽

2016 ◽

Vol 25 (6) ◽

pp. 695-707

Author(s):

Thomas Spangehl ◽

Marc Schröder ◽

Sophie Stolzenberger ◽

Rita Glowienka-Hense ◽

Alex Mazurkiewicz ◽

...

Keyword(s):

Prediction System ◽

Decadal Prediction

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A large ensemble decadal prediction system with MPI-ESM

10.5194/egusphere-egu21-9263 ◽

2021 ◽

Author(s):

Sebastian Brune ◽

Vimal Koul ◽

David Marcolino Nielsen ◽

Laura Hövel ◽

Holger Pohlmann ◽

...

Keyword(s):

North Atlantic ◽

Prediction Skill ◽

Ensemble Prediction ◽

Prediction System ◽

Decadal Prediction ◽

Ensemble Size ◽

Large Ensemble ◽

Ensemble Prediction System ◽

Atlantic Sector ◽

Ensemble Mean

Current state-of-the-art decadal ensemble prediction systems are run with an ensemble size of 10 to 40 members, their retrospective forecasts of the past are used to assess the system's prediction skill. Here, we present an attempt for a large ensemble decadal prediction system for the time period 1960-today, with an ensemble size of 80 members, based on the low resolution version of the Max Planck Institute Earth system model (MPI-ESM-LR). The ensemble is forced with CMIP6 conditions and initialized every year in November through a weakly coupled assimilation using atmospheric reanalyses via nudging and observed oceanic temperature and salinity profiles via a 16-member ensemble Kalman filter. To generate ensemble members beyond 16, we use additional physical perturbations at stratospheric height. The analysis of our large ensemble prediction system presented here aims for answering two questions: (1) How does the ensemble mean deterministic prediction skill for global and North Atlantic key climate indices change with ensemble size? (2) How well may the 80-member ensemble serve as a basis for a robust statistical analysis of probabilities of extremes in the North Atlantic sector? Preliminary results for global and regional air surface temperature show that in terms of ensemble mean ACC and full ensemble CPRSS with reference data, the 80-member ensemble leads to similar prediction skill as the 16-member ensemble. This indicates that the additional ensemble members may lead to a better sampling of the distribution of model trajectories, paving the way for a more robust statistical probabilistic analysis.

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Review of "Development and prospects of the regional MiKlip decadal prediction system over Europe: Predictive skill, added value of regionalization and ensemble size dependency"

10.5194/esd-2017-70-rc2 ◽

2017 ◽

Author(s):

Anonymous

Keyword(s):

Added Value ◽

Prediction System ◽

Decadal Prediction ◽

Ensemble Size ◽

Size Dependency ◽

Predictive Skill

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Revealing skill of the MiKlip decadal prediction system by three-dimensional probabilistic evaluation

Meteorologische Zeitschrift ◽

10.1127/metz/2015/0606 ◽

2016 ◽

Vol 25 (6) ◽

pp. 657-671 ◽

Cited By ~ 4

Author(s):

Sophie Stolzenberger ◽

Rita Glowienka-Hense ◽

Thomas Spangehl ◽

Marc Schröder ◽

Alex Mazurkiewicz ◽

...

Keyword(s):

Three Dimensional ◽

Prediction System ◽

Decadal Prediction ◽

Probabilistic Evaluation

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Predicting Climate Change over the multi-annual range: a perspective from CMCC Decadal Prediction System

10.5194/ems2021-150 ◽

2021 ◽

Author(s):

Dario Nicolì ◽

Alessio Bellucci ◽

Paolo Ruggieri ◽

Panos Athanasiadis ◽

Giusy Fedele ◽

...

Keyword(s):

Climate Change ◽

North Atlantic ◽

Decadal Variability ◽

Climate Prediction ◽

Added Value ◽

Prediction System ◽

Decadal Prediction ◽

Predictive Skill ◽

Annual Range ◽

Decadal Climate

After the early pioneering studies during the 2000s, and the first coordinated multi-model effort within the framework of the 5th Coupled Model Inter-comparison Project (CMIP5) in early 2010s, decadal climate predictions are now entering a more mature phase of their historical development. Near-term climate prediction activities have been recently endorsed by the World Climate Research Programme (WCRP) as one of the Grand Challenges in climate science research, and the Lead Centre for Annual-to-Decadal Climate Prediction, collecting hindcasts and forecasts from several contributing centres worldwide has been established by the WMO.Here we present results from the CMIP6 DCPP-A decadal hindcasts produced with the CMCC decadal prediction system (CMCC DPS), based on the fully-coupled CMCC-CM2-SR5 dynamical model. A 10-member suite of 10-year retrospective forecasts, initialized every year from 1960 to 2019, is performed using a full-field initialization strategy.The predictive skill for key quantities is assessed and compared with a non-initialized historical simulation, so as to verify the added value of initialization. In particular, the CMCC DPS is capable to skilfully reproduce past-climate surface temperature over the North Atlantic ocean, the Indian ocean and the Western Pacific ocean, as well as over most part of the continents. Beyond the contribution of the climate change, predictive skill emerges, among other regions, for the subpolar North Atlantic sea-surface temperatures, resembling the imprint of the extra-tropical part of the Atlantic Multidecadal Variability.In terms of precipitation, CMCC DPS is able to capture most of the decadal variability over the Northern part of the Eurasian continent. Indeed, a set of regional diagnostics is aimed to investigate the process at stake behind this high predictive skill.

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