Intercomparison of interannual variability and potential predictability: an AMIP diagnostic subproject

Abstract. The inter-annual variability and potential predictability of 850 hPa temperature (T850), 500 hPa geopotential (φ500) and 300 hPa stream function (ψ300) simulated by the models participating in the Atmospheric Model Intercomparison Project (AMIP) are examined. The total inter-annual variability is partitioned into a potentially predictable component which arises from the forcing implied by the prescribed SST and sea-ice evolution, or from sources internal to the simulated climate, and an unpredictable low frequency component induced by “weather noise”. There is wide variation in the ability to simulate observed inter-annual variability, both total and weather-noise induced. A majority of models under simulate seasonal mean φ500 variability in DJF and JJA and over simulate ψ300 variability in JJA. All but one model simulates less T850 total inter-annual variability than in the analysed data. There is little apparent connection between gross model characteristics and the corresponding ability to simulate observed variability, with the possible exceptions of resolution.

[1]  E. Trenberth,et al.  Global Analyses From ECMWF and Atlas of 1000 to 10 Mb Circulation Statistics , 1992 .

[2]  F. Zwiers,et al.  Interannual variability and predictability in an ensemble of AMIP climate simulations conducted with the CCC GCM2 , 1996 .

[3]  J. Blanchet,et al.  The Canadian Climate Centre Second-Generation General Circulation Model and Its Equilibrium Climate , 1992 .

[4]  A. E. Gill Some simple solutions for heat‐induced tropical circulation , 1980 .

[5]  David P. Rowell,et al.  Assessing Potential Seasonal Predictability with an Ensemble of Multidecadal GCM Simulations , 1998 .

[6]  Peter J. Webster,et al.  Mechanisms Determining the Atmospheric Response to Sea Surface Temperature Anomalies , 1981 .

[7]  H. O. Hartley,et al.  Biometrika Tables for Statisticians. Volume 2. , 1955 .

[8]  W. Gates AMIP: The Atmospheric Model Intercomparison Project. , 1992 .

[9]  T. Phillips,et al.  A summary documentation of the AMIP models , 1994 .

[10]  F. Zwiers,et al.  A Potential Predictability Study Conducted with an Atmospheric General Circulation Model , 1987 .

[11]  M. Hoerling,et al.  Prospects and Limitations of Seasonal Atmospheric GCM Predictions , 1995 .

[12]  B. Hunt,et al.  Chaotic influences and the problem of deterministic seasonal predictions , 1995 .

[13]  Ming Ji,et al.  Assessing a GCM's Suitability for Making Seasonal Predictions , 1996 .

[14]  R. A. Madden,et al.  Estimates of the Natural Variability of Time-Averaged Sea-Level Pressure , 1976 .

[15]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[16]  K. Miyakoda,et al.  Feasibility of Seasonal Forecasts Inferred from Multiple GCM Simulations , 1995 .