scholarly journals On the Development of GFDL’s decadal prediction system: initialization approaches and retrospective forecast assessment

Author(s):  
Xiaosong Yang ◽  
Thomas L. Delworth ◽  
Fanrong Zeng ◽  
Liping Zhang ◽  
William F. Cooke ◽  
...  
2012 ◽  
Vol 39 (22) ◽  
pp. n/a-n/a ◽  
Author(s):  
W. A. Müller ◽  
J. Baehr ◽  
H. Haak ◽  
J. H. Jungclaus ◽  
J. Kröger ◽  
...  

Author(s):  
Daniel Senftleben ◽  
Veronika Eyring ◽  
Axel Lauer ◽  
Mattia Righi

2019 ◽  
Vol 14 (12) ◽  
pp. 124074 ◽  
Author(s):  
Nicole S Lovenduski ◽  
Gordon B Bonan ◽  
Stephen G Yeager ◽  
Keith Lindsay ◽  
Danica L Lombardozzi

2019 ◽  
Vol 71 (1) ◽  
pp. 1618678 ◽  
Author(s):  
Hendrik Feldmann ◽  
Joaquim g. Pinto ◽  
Natalie Laube ◽  
Marianne Uhlig ◽  
Julia Moemken ◽  
...  

2014 ◽  
Vol 27 (20) ◽  
pp. 7550-7567 ◽  
Author(s):  
Jeff R. Knight ◽  
Martin B. Andrews ◽  
Doug M. Smith ◽  
Alberto Arribas ◽  
Andrew W. Colman ◽  
...  

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.


2016 ◽  
Vol 25 (6) ◽  
pp. 695-707
Author(s):  
Thomas Spangehl ◽  
Marc Schröder ◽  
Sophie Stolzenberger ◽  
Rita Glowienka-Hense ◽  
Alex Mazurkiewicz ◽  
...  

2021 ◽  
Author(s):  
Sebastian Brune ◽  
Vimal Koul ◽  
David Marcolino Nielsen ◽  
Laura Hövel ◽  
Holger Pohlmann ◽  
...  

<p>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.</p>


2016 ◽  
Vol 25 (6) ◽  
pp. 657-671 ◽  
Author(s):  
Sophie Stolzenberger ◽  
Rita Glowienka-Hense ◽  
Thomas Spangehl ◽  
Marc Schröder ◽  
Alex Mazurkiewicz ◽  
...  

Author(s):  
Julia Moemken ◽  
Hendrik Feldmann ◽  
Joaquim G. Pinto ◽  
Benjamin Buldmann ◽  
Natalie Laube ◽  
...  

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