Computer Science – Performance
Scientific paper
Aug 2000
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2000georl..27.2585c&link_type=abstract
Geophysical Research Letters, Volume 27, Issue 16, p. 2585-2588
Computer Science
Performance
30
Meteorology And Atmospheric Dynamics: Numerical Modeling And Data Assimilation, Meteorology And Atmospheric Dynamics: Ocean/Atmosphere Interactions, Oceanography: Physical: El Nino
Scientific paper
A serious problem in the initialization of a climate forecast model is the model-data incompatibility caused by systematic model biases. Here we use the Lamont model to demonstrate that these biases can be effectively reduced with a simple statistical correction, and the bias-corrected model can have a more realistic internal variability as well as an improved forecast performance. The results reported here should be of practical use to other ocean-atmosphere coupled models for climate prediction.
Cane Mark A.
Cañizares Rafael
Chen Dake
Kaplan Alexey
Zebiak Stephen E.
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