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Peitao Peng, Arun Kumar (2005)
A Large Ensemble Analysis of the Influence of Tropical SSTs on Seasonal Atmospheric VariabilityJournal of Climate, 18
G. Marshall (2003)
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D. Straus, J. Shukla, D. Paolino, S. Schubert, M. Suárez, P. Pegion, Arun Kumar (2003)
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Peitao Peng, Arun Kumar, A. Barnston, L. Goddard (2000)
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Analysis of model-calculated soil moisture over the United States (1931-1993) and applications to long-range temperature forecastsJournal of Climate, 9
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K. Trenberth (1984)
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Wanqiu Wang, Arun Kumar (1998)
A GCM assessment of atmospheric seasonal predictability associated with soil moisture anomalies over North AmericaJournal of Geophysical Research, 103
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Dynamical Seasonal Forecasts during the 1997/98 ENSO Using Persisted SST AnomaliesJournal of Climate, 14
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Causes of exceptional atmospheric circulation changes in the Southern HemisphereGeophysical Research Letters, 31
K. Trenberth, G. Branstator, D. Karoly, Arun Kumar, N. Lau, C. Ropelewski (1998)
Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperaturesJournal of Geophysical Research, 103
Arun Kumar, M. Hoerling (1995)
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Arun Kumar, A. Barnston, Peitao Peng, M. Hoerling, L. Goddard (2000)
Changes in the Spread of the Variability of the Seasonal Mean Atmospheric States Associated with ENSOJournal of Climate, 13
J. Derome, G. Brunet, A. Plante, N. Gagnon, G. Boer, F. Zwiers, S. Lambert, J. Sheng, H. Ritchie (2001)
Seasonal predictions based on two dynamical modelsAtmosphere-Ocean, 39
P. Sardeshmukh, G. Compo, C. Penland (2000)
Changes of Probability Associated with El NiñoJournal of Climate, 13
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Feasibility of Seasonal Forecasts Inferred from Multiple GCM SimulationsJournal of Climate, 8
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Time Scales of Layered Soil Moisture Memory in the Context ofLand–Atmosphere InteractionJournal of Climate, 17
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Sensitivity of Climate to Changes in NDVIJournal of Climate, 13
M. Hoerling, Arun Kumar (2002)
Atmospheric Response Patterns Associated with Tropical ForcingJournal of Climate, 15
M. Phelps, Arun Kumar, J. O'Brien (2004)
Potential Predictability in the NCEP CPC Dynamical Seasonal Forecast SystemJournal of Climate, 17
M. Kanamitsu, Cheng-Hsuan Lu, J. Schemm, W. Ebisuzaki (2003)
The Predictability of Soil Moisture and Near-Surface Temperature in Hindcasts of the NCEP Seasonal Forecast ModelJournal of Climate, 16
J. Shukla, Jeffrey Anderson, D. Baumhefner, Č. Branković, Yehui Chang, E. Kalnay, L. Marx, T. Palmer, D. Paolino, J. Ploshay, S. Schubert, D. Straus, M. Suárez, J. Tribbia (2000)
Dynamical Seasonal PredictionBulletin of the American Meteorological Society, 81
Arun Kumar, Fanglin Yang (2003)
Comparative Influence of Snow and SST Variability on Extratropical Climate in Northern WinterJournal of Climate, 16
F. Zwiers, Xiaolan Wang, J. Sheng (2000)
Effects of specifying bottom boundary conditions in an ensemble of atmospheric GCM simulationsJournal of Geophysical Research, 105
Arun Kumar, M. Hoerling (2000)
Analysis of a Conceptual Model of Seasonal Climate Variability and Implications for Seasonal PredictionBulletin of the American Meteorological Society, 81
Arun Kumar, S. Schubert, M. Suárez (2003)
Variability and predictability of 200‐mb seasonal mean heights during summer and winterJournal of Geophysical Research, 108
D. Rowell (1998)
Assessing Potential Seasonal Predictability with an Ensemble of Multidecadal GCM SimulationsJournal of Climate, 11
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.
Journal of Climate – American Meteorological Society
Published: Jan 19, 2006
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