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Quantifying Differences in Circulation Patterns Based on Probabilistic Models: IPCC AR4 Multimodel Comparison for the North Atlantic

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  • 1 LSCE-IPSL, CNRS/CEA/UVSQ, Gif-sur-Yvette, France
  • | 2 LOCEAN, Université Pierre et Marie Curie, Paris, France
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Abstract

The comparison of circulation patterns (CPs) obtained from reanalysis data to those from general circulation model (GCM) simulations is a frequent task for model validation, downscaling of GCM simulations, or other climate change–related studies. Here, the authors suggest a set of measures to quantify the differences between CPs. A combination of clustering using Gaussian mixture models with a set of related difference measures allows for taking cluster size and shape information into account and thus provides more information than the Euclidean distances between cluster centroids. The characteristics of the various distance measures are illustrated with a simple simulated example. Subsequently, a five-component Gaussian mixture to define circulation patterns for the North Atlantic region from reanalysis data and GCM simulations is used. CPs are obtained independently for the NCEP–NCAR reanalysis and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), as well as for twentieth-century simulations from 14 GCMs of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) database. After discussing the difference of CPs based on spherical and nonspherical clusters for the reanalysis datasets, the authors give a detailed evaluation of the cluster configuration for two GCMs relative to NCEP–NCAR. Finally, as an illustration, the capability of reproducing the NCEP–NCAR probability density function (pdf) defining the Greenland anticyclone CP is evaluated for all 14 GCMs, considering that the size and shape of the underlying pdfs complement the commonly used Euclidean distance of CPs’ mean values.

Corresponding author address: Henning W. Rust, Laboratoire des Sciences du Climat et de l’Environnement (LSCE-IPSL, CNRS/CEA/UVSQ), Orme des Merisiers, Bât. 701, F-91191 Gif-sur-Yvette, France. Email: hrust@lsce.ipsl.fr

Abstract

The comparison of circulation patterns (CPs) obtained from reanalysis data to those from general circulation model (GCM) simulations is a frequent task for model validation, downscaling of GCM simulations, or other climate change–related studies. Here, the authors suggest a set of measures to quantify the differences between CPs. A combination of clustering using Gaussian mixture models with a set of related difference measures allows for taking cluster size and shape information into account and thus provides more information than the Euclidean distances between cluster centroids. The characteristics of the various distance measures are illustrated with a simple simulated example. Subsequently, a five-component Gaussian mixture to define circulation patterns for the North Atlantic region from reanalysis data and GCM simulations is used. CPs are obtained independently for the NCEP–NCAR reanalysis and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), as well as for twentieth-century simulations from 14 GCMs of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) database. After discussing the difference of CPs based on spherical and nonspherical clusters for the reanalysis datasets, the authors give a detailed evaluation of the cluster configuration for two GCMs relative to NCEP–NCAR. Finally, as an illustration, the capability of reproducing the NCEP–NCAR probability density function (pdf) defining the Greenland anticyclone CP is evaluated for all 14 GCMs, considering that the size and shape of the underlying pdfs complement the commonly used Euclidean distance of CPs’ mean values.

Corresponding author address: Henning W. Rust, Laboratoire des Sciences du Climat et de l’Environnement (LSCE-IPSL, CNRS/CEA/UVSQ), Orme des Merisiers, Bât. 701, F-91191 Gif-sur-Yvette, France. Email: hrust@lsce.ipsl.fr

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