J7.6
Long-lead wintertime potential predictability:an assessment from NCEP's climate model
Wilbur Y. Chen, NOAA/NWS/NCEP/CPC, Camp Springs, MD
The response of the Climate Prediction Center's new climate model to a prescribed sea surface temperatures (SST) for the last 21 years is investigated. In addition to the time-mean response, particular attention is paid to spread among members of several ensemble hindcasts. An ensemble of 10 integrations each out to 7 months is produced every month with each member initiated at 12 hours apart on the first five days of each month. For the target wintertime JFM season, the hindcasts available for examination are the ensembles initiated in December, November, October, and September, with increasing lead time to JFMseason for each ensemble. The model climatology and standard deviation simulate well when compared to real atmospheric data, despite existence of some systematic model biases. The time-mean response of the model to various SST anomalies also compares well, with approximately equal magnitude of climate signal over the eastern North Pacific for the model while stronger climate signals during El Nino winters for the real atmosphere. However, the spread among ensemble members, which is an indication of the magnitude of model's natural variability or climate noise, show profound difference between the El Nino and La Nina winters over the regions where climate signals are generally developed. The model climate noise is significantly smaller during El Nino JFMs. The implication is, for the El Nino winters, the model's potential predictability is significantly larger over the regions where climate signal and noise are most prominent. This result is consist with Chen and Van den Dool's finding in 1997 with the real atmospheric data.
Joint Session 7, Joint session with the 13th Symposium on Global Change and Climate Variations and the Symposium on Observations, Data Assimilation, and Probabilistic Prediction
Wednesday, 16 January 2002, 1:30 PM-3:30 PM
Previous paper Next paper