Climate Dynamics

, Volume 52, Issue 3–4, pp 1343–1357 | Cite as

An effective drift correction for dynamical downscaling of decadal global climate predictions

  • Heiko PaethEmail author
  • Jingmin Li
  • Felix Pollinger
  • Wolfgang A. Müller
  • Holger Pohlmann
  • Hendrik Feldmann
  • Hans-Jürgen Panitz


Initialized decadal climate predictions with coupled climate models are often marked by substantial climate drifts that emanate from a mismatch between the climatology of the coupled model system and the data set used for initialization. While such drifts may be easily removed from the prediction system when analyzing individual variables, a major problem prevails for multivariate issues and, especially, when the output of the global prediction system shall be used for dynamical downscaling. In this study, we present a statistical approach to remove climate drifts in a multivariate context and demonstrate the effect of this drift correction on regional climate model simulations over the Euro-Atlantic sector. The statistical approach is based on an empirical orthogonal function (EOF) analysis adapted to a very large data matrix. The climate drift emerges as a dramatic cooling trend in North Atlantic sea surface temperatures (SSTs) and is captured by the leading EOF of the multivariate output from the global prediction system, accounting for 7.7% of total variability. The SST cooling pattern also imposes drifts in various atmospheric variables and levels. The removal of the first EOF effectuates the drift correction while retaining other components of intra-annual, inter-annual and decadal variability. In the regional climate model, the multivariate drift correction of the input data removes the cooling trends in most western European land regions and systematically reduces the discrepancy between the output of the regional climate model and observational data. In contrast, removing the drift only in the SST field from the global model has hardly any positive effect on the regional climate model.


Drift correction Decadal prediction Dynamical downscaling 



We thank the Max-Planck Institute for Meteorology for providing the MPI-ESM decadal predictions and the EU-FP6 project ENSEMBLES and the ECA&D project for making the E-OBS 14.0 data set available. The NOAA-20C reanalysis was kindly provided by the National Oceanic and Atmospheric Administration (NOAA) and the Cooperative Institute for Research in Environmental Sciences (CIRES). The HadISST data set has been made available by the Met Office Hadley Centre. This work was carried out in the framework of the German MiKlip project and supported by the German Minister of Education and Research (BMBF) under Grants no. 01LP1129A-F and 01LP1519A.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Geography and GeologyUniversity of WürzburgWürzburgGermany
  2. 2.Deutscher Wetterdienst, Seewetteramt HamburgHamburgGermany
  3. 3.Max Planck Institute for MeteorologyHamburgGermany
  4. 4.Institute of Meteorology and Climate ResearchKarlsruhe Institute of TechnologyKarlsruheGermany

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