Anomaly Forcing in an Ensemble Regional Climate Model Simulation

In recent years climate prediction initiatives have generated significant interest in the ensemble climate modeling methodology. Many studies have examined the issue of climate predictability using multi-member global climate model (GCM) ensembles. Likewise, climate change research has generated interest in the viability of "downscaling" relatively coarse GCM data to regionally higher resolutions. This study is an examination of the ensemble climate modeling methodology applied to regional climate modeling and explores the issues of climate predictability, simulated climate variability, and data downscaling. The overall purpose of this project is validation of the RegCM2 regional climate model simulation of interannual climate variability.

A six-member ensemble of the NCAR CCM3 GCM at T31 resolution is initiated using different initial conditions but forced by the same 25 year NCEP observed monthly varying SST data set. Each of these CCM3 simulations provides the 12 hourly one-way boundary conditions for 6 RegCM2 simulations centered at 45N, 100W with a 108 km resolution. The simulated period for the CCM3 and RegCM2 ensemble is 1969 to 1993.

As a preview to the analysis of the variability of the 25-year regional model simulations, analyses of forced variance of the 25 year GCM ensemble shows areas of high predictability in the tropics and areas of moderate to low predictability over North America. This result is consistent with other published results.

Results from the first 5 years of the regional ensemble are presented. For example, the December, January, and February averaged 500 mb heights, precipitation, and surface temperature anomalies simulated by the RegCM2 and CCM3 ensembles for the 1972/73 El Niño event are compared. The simulated anomalies are also compared to observed and NCEP analyzed climate records. The implications of the differences between the CCM3 and RegCM2 simulated climate are discussed.

Future plans include a comparison of the simulated variance over North America for both the RegCM2 and CCM3 ensembles. In addition, the structure of model empirical orthogonal functions and singular value decomposition analysis between the models will provide information about the variability of the models relative to the observational record.