Influence of a stochastic parameterization on the frequency of occurrence of North Pacific weather regimes in the ECMWF model

[1] One common problem of many atmospheric circulation models is the overestimation of the mean westerly winds in the mid-latitude North Pacific. This westerly wind bias is also a prominent feature in a recent version of the ECMWF model. Here we use the ECMWF model to investigate whether the use of stochastic parameterizations help reduce this error, using the concept of weather regimes. The focus is on the winter season when the atmospheric regime structure is most pronounced. It is shown that the operational version of the ECMWF stochastic physics scheme has little impact on the frequency of occurrence of North Pacific weather regimes. A recently developed scheme, however, which is based on combining a cellular automaton with a stochastic backscatter component, leads to substantial improvements in the simulation of the frequency of occurrence of North Pacific weather regimes and therefore a reduction of the westerly wind bias.

[1]  F. Molteni,et al.  Seasonal climate and variability of the ECMWF ERA-40 model , 2004 .

[2]  Roberto Buizza,et al.  Targeting Observations Using Singular Vectors , 1999 .

[3]  T. Palmer,et al.  Stochastic representation of model uncertainties in the ECMWF ensemble prediction system , 2007 .

[4]  Padhraic Smyth,et al.  Multiple Regimes in Northern Hemisphere Height Fields via MixtureModel Clustering* , 1999, Journal of the Atmospheric Sciences.

[5]  Franco Molteni,et al.  Regimes in the wintertime circulation over northern extratropics. II: Consequences for dynamical predictability , 1990 .

[6]  Renate Hagedorn,et al.  Representing model uncertainty in weather and climate prediction , 2005 .

[7]  L. Isaksen,et al.  The ERA-40 Reanalysis , 2004 .

[8]  G. D. Nastrom,et al.  A Climatology of Atmospheric Wavenumber Spectra of Wind and Temperature Observed by Commercial Aircraft , 1985 .

[9]  Thomas Jung,et al.  Systematic errors of the atmospheric circulation in the ECMWF forecasting system , 2005 .

[10]  Michael Ghil,et al.  Multiple Flow Regimes in the Northern Hemisphere Winter. Part II: Sectorial Regimes and Preferred Transitions , 1993 .

[11]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[12]  S. M. Marlais,et al.  An Overview of the Results of the Atmospheric Model Intercomparison Project (AMIP I) , 1999 .

[13]  Thomas Jung,et al.  Some aspects of systematic error in the ECMWF model , 2005 .

[14]  G. Shutts.  A kinetic energy backscatter algorithm for use in ensemble prediction systems , 2005 .

[15]  Maurice K. Wong,et al.  Algorithm AS136: A k-means clustering algorithm. , 1979 .

[16]  T. Palmer.  A nonlinear dynamical perspective on model error: A proposal for non‐local stochastic‐dynamic parametrization in weather and climate prediction models , 2001 .

[17]  Michael Ghil,et al.  Multiple flow regimes in the Northern Hemisphere winter. I - Methodology and hemispheric regimes. II - Sectorial regimes and preferred transitions , 1993 .

[18]  A. Sterl,et al.  The ERA‐40 re‐analysis , 2005 .