HR: 0830h
AN: A41A-07    [Abstracts]
TI: Large Improvement in CFS Prediction Skill by Regime Dependent Model Bias Corrections
AU: * Chen, W Y
EM: wilbur.chen@noaa.gov
AF: Climate Prediction Center, 5200 Auth Road, Camp Springs, MD 20746 United States
AB: The potential predictability of the recent NCEP coupled Climate Forecast System (CFS) is evaluated first, pin-pointing where on the globe the climate signals are likely to emerge and become potentially predictable. The real-time prediction problems are then looked into focusing on finding a new way to conduct model bias correction in order to raise the practical prediction skill. Large potential predictability can be found in the tropics, as expected. For the northern extra-tropical latitudes, only the Pacific/North American sector has some significant potential predictability. That predictability comes mainly from El Nino warm winters, much less from La Nina cold winters, and literarily none from ENSO neutral winters. The conventional model bias correction for prediction skill improvement deals with only a fixed difference: between model and observed climatology. We argue that the fact should be recognized that the bias of a climate model is sensitively dependent upon the prevailing circulation regime. At least, there are three major circulation regimes for our recent climate: the El Nino, La Nina, and ENSO neutral type of time-mean basic flows. We'll show that the model bias is distinct for a distinctive circulation regime. The regime dependent bias is also sensitive to prediction lead time. If we conduct regime dependent and lead time sensitive bias corrections, the practical prediction skill can be raised by a huge amount. A gain of at least 10 points in anomaly correlation metric for 1-month lead DJF predictions and a close to 20 points gain for 6 months lead predictions can be achieved.
DE: 1600 GLOBAL CHANGE (New category)
DE: 1620 Climate dynamics (3309)
DE: 1630 Impact phenomena
SC: Atmospheric Sciences [A]
MN: 2005 Joint Assembly