Systematic errors of the atmospheric circulation in the ECMWF forecasting system

The aim of this study is to document some of the key systematic errors of the atmospheric circulation as simulated by the ECMWF model during the winter season (December–March). Two major topics are addressed. First, key systematic circulation errors are described and it is shown how they grow from the short range all the way to the extended range. Second, it is shown how systematic circulation errors changed during the last two decades. Results are presented for 500 hPa geopotential height fields, eddy kinetic energy, synoptic variability, and atmospheric blocking. A concise summary is somewhat hampered by the fact that systematic error characteristics significantly depend on the parameter, region, and the forecast range being considered. In general, though, it has been found that systematic circulation errors have been reduced considerably in recent years, particularly from the 1980s to the early 1990s. Model improvements are primarily manifest through a reduction of the rate at which systematic errors grow; their spatial structure, however, remains more or less unchanged for most of the parameters. On the other hand, and somewhat surprisingly, it has been found that for some parameters and regions systematic circulation errors have slightly deteriorated since the mid-1990s. Moreover, it turns out that during the first few days of the integration the spatial structure of systematic circulation errors undergoes considerable changes; in the medium range and beyond their spatial structure remains virtually unchanged. The most prominent systematic circulation errors identified in this study are: the development of a large anticyclonic bias in the central North Pacific in the medium-range and extended-range, the underestimation of kinetic energy of the transient eddies, a pronounced underestimation of synoptic activity in high latitudes, and the underestimation of atmospheric blocking in the extended range. In the medium range over Europe, however, it is shown that recent cycles of the ECMWF model are able to produce realistic blocking frequencies. Copyright © 2005 Royal Meteorological Society

[1]  A. Hollingsworth,et al.  Some aspects of the improvement in skill of numerical weather prediction , 2002 .

[2]  Heini Wernli,et al.  A Lagrangian‐based analysis of extratropical cyclones. I: The method and some applications , 1997 .

[3]  Min Zhong,et al.  El Niño, La Niña, and the Nonlinearity of Their Teleconnections , 1997 .

[4]  Brian J. Hoskins,et al.  A new perspective on blocking , 2003 .

[5]  B. Hoskins,et al.  New perspectives on the Northern Hemisphere winter storm tracks , 2002 .

[6]  Frederic Vitart,et al.  Monthly Forecasting at ECMWF , 2004 .

[7]  Renate Hagedorn,et al.  Comparison of the ECMWF seasonal forecast System 1 and 2, including the relative performance for the 1997/98 El Nino , 2002 .

[8]  Tim N. Palmer,et al.  Forcing singular vectors and other sensitive model structures , 2003 .

[9]  M. Rodwell,et al.  Systematic Errors in the ECMWF Forecasting System , 2003 .

[10]  E. Klinker Investigation of systematic errors by relaxation experiments , 1990 .

[11]  E. Roeckner,et al.  Climatology of Northern Hemisphere blocking in the ECHAM model , 1997 .

[12]  B. Hoskins,et al.  The Generation of Global Rotational Flow by Steady Idealized Tropical Divergence , 1988 .

[13]  F. Molteni,et al.  The ECMWF Ensemble Prediction System: Methodology and validation , 1996 .

[14]  Heini Wernli,et al.  Dynamical aspects of the life cycle of the winter storm ‘Lothar’ (24–26 December 1999) , 2002 .

[15]  F. Molteni,et al.  Diagnosis of Extratropical Variability in Seasonal Integrations of the ECMWF Model , 1994 .

[16]  L. Ferranti,et al.  Northern Hemisphere atmospheric blocking as simulated by 15 atmospheric general circulation models in the period 1979–1988 , 1998 .

[17]  J. Peixoto,et al.  Physics of climate , 1992 .

[18]  Prashant D. Sardeshmukh,et al.  The Diagnosis of Mechanical Dissipation in the Atmosphere from Large-Scale Balance Requirements , 1992 .

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

[20]  Franco Molteni,et al.  On the operational predictability of blocking , 1990 .

[21]  Č. Branković,et al.  Impact of horizontal resolution on seasonal integrations , 2001 .

[22]  Tim N. Palmer,et al.  Signature of recent climate change in frequencies of natural atmospheric circulation regimes , 1999, Nature.

[23]  Tim N. Palmer,et al.  Tropical-Extratropical Interaction Associated with the 30–60 Day Oscillation and Its Impact on Medium and Extended Range Prediction , 1990 .

[24]  Tim N. Palmer,et al.  Extended range predictions with ECMWF models : influence of horizontal resolution on systematic error and forecast skill , 1990 .

[25]  Tim N. Palmer,et al.  Extended-range predictions with ecmwf models: Interannual variability in operational model integrations , 1990 .

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

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

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

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

[30]  Robert Vautard,et al.  Reducing systematic errors by empirically correcting model errors , 2000 .

[31]  F. Molteni,et al.  Sensitivity of the ECMWF model northern winter climate to model formulation , 1996 .

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