Measuring the potential utility of seasonal climate predictions

[1] Variation of sea surface temperature (SST) on seasonal-to-interannual time-scales leads to changes in seasonal weather statistics and seasonal climate anomalies. Relative entropy, an information theory measure of utility, is used to quantify the impact of SST variations on seasonal precipitation compared to natural variability. An ensemble of general circulation model (GCM) simulations is used to estimate this quantity in three regions where tropical SST has a large impact on precipitation: South Florida, the Nordeste of Brazil and Kenya. We find the yearly variation of relative entropy is strongly correlated with shifts in ensemble mean precipitation and weakly correlated with ensemble variance. Relative entropy is also found to be related to measures of the ability of the GCM to reproduce observations.

[1]  Richard D. Deveaux,et al.  Applied Smoothing Techniques for Data Analysis , 1999, Technometrics.

[2]  Andrew J. Majda,et al.  A mathematical framework for quantifying predictability through relative entropy , 2002 .

[3]  M. Claussen,et al.  The atmospheric general circulation model ECHAM-4: Model description and simulation of present-day climate , 1996 .

[4]  N. Graham,et al.  Importance of the Indian Ocean for simulating rainfall anomalies over eastern and southern Africa , 1999 .

[5]  Antonio D. Moura,et al.  International research institute for climate prediction : a proposal , 1992 .

[6]  Andrew J. Majda,et al.  Predictability in a Model of Geophysical Turbulence , 2005 .

[7]  Prashant D. Sardeshmukh,et al.  Changes of Probability Associated with El Niño , 2000 .

[8]  C. Ropelewski,et al.  Global and Regional Scale Precipitation Patterns Associated with the El Niño/Southern Oscillation , 1987 .

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

[10]  A. Moore,et al.  Reliability of ENSO Dynamical Predictions , 2005 .

[11]  Jeffrey L. Anderson,et al.  Evaluating the Potential Predictive Utility of Ensemble Forecasts , 1996 .

[12]  Andrew F. Loughe,et al.  The Relationship between Ensemble Spread and Ensemble Mean Skill , 1998 .

[13]  Andrew M. Moore,et al.  A New Method for Determining the Reliability of Dynamical ENSO Predictions , 1999 .

[14]  S. Griffies,et al.  A Conceptual Framework for Predictability Studies , 1999 .

[15]  Timothy DelSole,et al.  Predictability and Information Theory. Part I: Measures of Predictability , 2004 .

[16]  A. Barnston,et al.  Changes in the Spread of the Variability of the Seasonal Mean Atmospheric States Associated with ENSO , 2000 .

[17]  S. Mason,et al.  Probabilistic Precipitation Anomalies Associated with ENSO , 2001 .

[18]  Francis W. Zwiers,et al.  Improved Seasonal Probability Forecasts , 2003 .

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

[20]  P. Jones,et al.  Representing Twentieth-Century Space-Time Climate Variability. Part II: Development of 1901-96 Monthly Grids of Terrestrial Surface Climate , 2000 .

[21]  R. Kleeman.  Measuring Dynamical Prediction Utility Using Relative Entropy , 2002 .