Open AccessInitialization and Ensemble Generation for Decadal Climate Predictions: A Comparison of Different Methods
Author(s) -
Polkova Iuliia,
Brune Sebastian,
Kadow Christopher,
Romanova Vanya,
Gollan Gereon,
Baehr Johanna,
GlowienkaHense Rita,
Greatbatch Richard J.,
Hense Andreas,
Illing Sebastian,
Köhl Armin,
Kröger Jürgen,
Müller Wolfgang A.,
Pankatz Klaus,
Stammer Detlef
Publication year - 2019
Publication title -
journal of advances in modeling earth systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.03
H-Index - 58
ISSN - 1942-2466
DOI - 10.1029/2018ms001439
Subject(s) - initialization , anomaly (physics) , ensemble kalman filter , climatology , filter (signal processing) , computer science , kalman filter , environmental science , climate model , data assimilation , ensemble forecasting , meteorology , geology , climate change , extended kalman filter , artificial intelligence , physics , oceanography , computer vision , programming language , condensed matter physics
Five initialization and ensemble generation methods are investigated with respect to their impact on the prediction skill of the German decadal prediction system “Mittelfristige Klimaprognose” (MiKlip). Among the tested methods, three tackle aspects of model‐consistent initialization using the ensemble Kalman filter, the filtered anomaly initialization, and the initialization method by partially coupled spin‐up (MODINI). The remaining two methods alter the ensemble generation: the ensemble dispersion filter corrects each ensemble member with the ensemble mean during model integration. And the bred vectors perturb the climate state using the fastest growing modes. The new methods are compared against the latest MiKlip system in the low‐resolution configuration (Preop‐LR), which uses lagging the climate state by a few days for ensemble generation and nudging toward ocean and atmosphere reanalyses for initialization. Results show that the tested methods provide an added value for the prediction skill as compared to Preop‐LR in that they improve prediction skill over the eastern and central Pacific and different regions in the North Atlantic Ocean. In this respect, the ensemble Kalman filter and filtered anomaly initialization show the most distinct improvements over Preop‐LR for surface temperatures and upper ocean heat content, followed by the bred vectors, the ensemble dispersion filter, and MODINI. However, no single method exists that is superior to the others with respect to all metrics considered. In particular, all methods affect the Atlantic Meridional Overturning Circulation in different ways, both with respect to the basin‐wide long‐term mean and variability and with respect to the temporal evolution at the 26° N latitude.