Open AccessPredictions of Climate Several Years Ahead Using an Improved Decadal Prediction System
Author(s) -
Jeff Knight,
Martin B. Andrews,
Doug Smith,
Alberto Arribas,
Andrew Colman,
Nick Dunstone,
Rosie Eade,
Leon Hermanson,
Craig MacLachlan,
K. Andrew Peterson,
Adam A. Scaife,
Andrew H Williams
Publication year - 2014
Publication title -
journal of climate
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.315
H-Index - 287
eISSN - 1520-0442
pISSN - 0894-8755
DOI - 10.1175/jcli-d-14-00069.1
Subject(s) - climatology , initialization , environmental science , climate model , climate system , computer science , climate change , meteorology , geography , geology , oceanography , programming language
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.