Thursday, 17 January 2002: 10:45 AM
Predictable Skill and Its Associated Sea-Surface Temperature Variability in an Ensemble Climate Simulation
The performance of an ensemble climate-simulation at simulating near-surface air temperature (Ta) and precipitation over North America is assessed. The ensemble climate-simulation was constructed with the Center for Ocean Land Atmosphere (COLA) atmospheric general circulation model (AGCM) in conjunction with the Atmospheric Model Intercomparison Project II (AMIP II). To diagnose the ensemble simulation, a measure of "predictable skill" is formalized. This diagnostic is based upon the statistical significance of spatial correlation for a given region (i.e. North America) between the ensemble mean and observed anomalies, and the inter-member scatter of the spatial correlation. Using this measure, epochs (within the AMIP II simulation period) of predictable skill in simulated North American Ta and precipitation are identified. Through a point-wise correlation technique, spatial patterns of contemporaneous sea-surface temperature (SST) variability are also constructed.
Spatially coherent patterns of SST variability are found to be associated with predictable skill. These patterns are, not surprisingly, primarily related to the El Nino Southern Oscillation (ENSO). However, the strongest ENSO associations to predictable skill (for the COLA AGCM) are found in sub-tropical regions of the western and central Pacific. Moreover, skill in North American precipitation is seen to be associated with inter El Nino variability. Other associations of precipitation predictable skill are seen with East Indian Ocean variability. In addition, a strong contemporaneous association of Ta predictable skill with tropical Atlantic SST variability is found. Complementary simulations are performed with the COLA AGCM to verify and assess these associations between predictable skill and SST variability. The results of which will also be presented.