Sensitivity of Seasonal Climate Forecasts to Persisted SST Anomalies
- Sensitivity of Seasonal Climate Forecasts to Persisted SST Anomalies
- Goddard, Lisa M.
Mason, Simon J.
- Technical reports
- International Research Institute for Climate and Society
Earth and Environmental Sciences
- Persistent URL:
- IRI Technical Report
- Part Number:
- International Research Institute for Climate Prediction
- Publisher Location:
- Palisades, N.Y.
- Most estimates of the skill of atmospheric general circulation models (AGCMs) for forecasting seasonal climate anomalies have been based on simulations with actual observed sea surface temperatures (SSTs) as lower boundary forcing. Similarly estimates of the climatological response characteristics of AGCMs used for seasonal-to-interannual climate prediction frequently rest on historical simulations using "perfect" SST forecasts. This paper examines the errors and biases introduced into the seasonal climate response of an AGCM forced with persisted SST anomalies, which are generally considered to constitute a good prediction of SST in the first 3-month season. However, the added uncertainty introduced by the predicted SST anomalies weakens, and in some cases nullifies, the skill of atmospheric predictions that is possible given perfect SST forcing. The use of persisted SST anomalies also leads to changes in local signal-to-noise characteristics. Thus, it is argued that seasonal-to-interannual forecasts using AGCMs should be interpreted relative to historical runs that were subject to the same strategy of boundary forcing used in the current forecast in order to properly account for errors and biases introduced by the particular SST prediction strategy. Two case studies are examined to illustrate how the sensitivity of the climate response to predicted SSTs may be used as a diagnostic to suggest improvements to the predicted SSTs.
- Environmental science
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- Suggested Citation:
- Lisa M. Goddard, Simon J. Mason, 2001, Sensitivity of Seasonal Climate Forecasts to Persisted SST Anomalies, Columbia University Academic Commons, http://hdl.handle.net/10022/AC:P:8801.