Assessment of the APCC/CliPAS 14-Model Ensemble Retrospective Seasonal Prediction
Dr. June-Yi Lee
International Pacific Research Center, University of Hawaii, USA
28 March, 2008
Dr. Tianjun Zhou introduces Dr. June-yi Lee
Dr. June-yi Lee is giving the presentation
Assessment of the APCC/CliPAS 14-Model Ensemble
Retrospective Seasonal Prediction
June-Yi Lee and Bin Wang
International Pacific Research Center, University of Hawaii, USA jylee@soest.hawaii.edu
This study assesses the skill of the multi-model ensemble (MME) seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate models (7 one-tier and 7 two-tier models) that participate in the Climate Prediction and its Application to Society (CliPAS) project, which is sponsored by the Asian-Pacific Economic Cooperation (APEC) Climate Center (APCC).
Equatorial sea surface temperature (SST) anomalies are the primary source of climate predictability worldwide. The MME one-month lead hindcast can predict, with high fidelity, the spatial-temporal structures of the first two leading EOF modes for both the JJA and DJF seasons, which accounts for about 80% to 90% of the total variance. The major bias is a westward shift of the SST anomaly between the dateline and 120°E, which results in a significant error in the western Pacific SST and potentially degrades the teleconnection associated with the western Pacific SST. The temporal correlation coefficient (TCC) skill of the MME forecast of the Niño 3.4 index at a six-month lead reaches 0.81 and 0.85 for the boreal summer and winter seasons, respectively. The TCC for SST predictions over the equatorial eastern Indian Ocean (EIO) reaches about 0.68 with a six-month lead forecast. However, the TCC skill for the Indian Ocean Dipole (IOD) index drops below 0.40 for the three-month lead forecast of both the May and November initiations. IOD prediction barriers exist across January and July.
The seasonal dependence of the 14-model MME one-month lead seasonal prediction skill differs between air temperature and precipitation. For 2m air temperature prediction, the time mean PCC score over the tropics (30°S–30°N) for the JJA season (0.52) is better than that for the DJF season (0.47). In contrast, the PCC score for precipitation in DJF (0.53) is significantly higher than that in JJA (0.44) over the global tropics. Winter monsoon precipitation predictions in both hemispheres have considerable skill due to teleconnection associated with ENSO (El Niño-Southern Oscillation). Precipitation predictions over land and during local summer have little skill.
The MME prediction skill highly depends on the strength and phases of ENSO. The PCC skill is positively well correlated with the amplitude of Niño 3.4 SST variation. The performance for temperature forecasts in El Niño years is better than in La Niña years. The precipitation and circulation are predicted better in the ENSO-decaying JJA than in the ENSO-developing JJA. There is virtually no skill in the ENSO-neutral years.
