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A New Methodology for Estimating the Unpredictable Component of Seasonal Atmospheric Variability

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  • 1 Climate Prediction Center, NCEP/NOAA, Camp Springs, Maryland
  • | 2 RSIS, Climate Prediction Center, NCEP/NOAA, Camp Springs, Maryland
  • | 3 Biospheric Sciences Branch, Goddard Space Flight Center, Greenbelt, Maryland
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Abstract

Predictability limits for seasonal atmospheric climate variability depend on the fraction of variability that is due to factors external to the atmosphere (e.g., boundary conditions) and the fraction that is internal. From the analysis of observed data alone, however, separation of the total seasonal atmospheric variance into its external and internal components remains a difficult and controversial issue. In this paper a simple procedure for estimating atmospheric internal variability is outlined. This procedure is based on the expected value of the mean square error between the observed and the general circulation model simulated (or predicted) seasonal mean anomaly. The end result is a spatial map for the estimate of the observed seasonal atmospheric internal (or unpredictable) variability. As improved general circulation models become available, mean square error estimated from the new generation of general circulation models can be easily included in the procedure proposed herein, bringing the estimate for the internal variability closer to its true estimate.

Corresponding author address: Dr. Arun Kumar, Climate Prediction Center, 5200 Auth Rd., Rm. 800, Camp Springs, MD 20746. Email: arun.kumar@noaa.gov

Abstract

Predictability limits for seasonal atmospheric climate variability depend on the fraction of variability that is due to factors external to the atmosphere (e.g., boundary conditions) and the fraction that is internal. From the analysis of observed data alone, however, separation of the total seasonal atmospheric variance into its external and internal components remains a difficult and controversial issue. In this paper a simple procedure for estimating atmospheric internal variability is outlined. This procedure is based on the expected value of the mean square error between the observed and the general circulation model simulated (or predicted) seasonal mean anomaly. The end result is a spatial map for the estimate of the observed seasonal atmospheric internal (or unpredictable) variability. As improved general circulation models become available, mean square error estimated from the new generation of general circulation models can be easily included in the procedure proposed herein, bringing the estimate for the internal variability closer to its true estimate.

Corresponding author address: Dr. Arun Kumar, Climate Prediction Center, 5200 Auth Rd., Rm. 800, Camp Springs, MD 20746. Email: arun.kumar@noaa.gov

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