A new general circulation model: formulation and preliminary results in a single- and multi-processor environment

Abstract.

This article describes a new general circulation model (GCM) developed jointly by The University of New South Wales (UNSW) and the University of Hamburg. The model is versatile in that it can be run as a medium-range (1 to 15 days) global numerical weather prediction (NWP) model; as an extended range (15 to 30 days) NWP model; and as a GCM for periods extending from seasons, through annual and decadal periods, and beyond. The model can be coupled with ocean models that vary in complexity from simple "swamp" oceans to complex ocean GCMs. The atmospheric GCM also has a number of novel features, particularly in the numerical integration scheme which is a high-order, mass-conserving, semi-implicit semi-Lagrangian scheme, thereby removing the stability restriction on the time-step and allowing efficient long-term integrations. The emphasis here will be on demonstrating that the new model performs effectively on the usual measures of skill (statistics such as mean errors, root-mean-square errors and anomaly correlations) in several standard applications upon which new models usually are assessed. These applications include medium range weather forecasts out to 10 days on a daily basis over a one year period; a limited 10-year simulation climatology, prediction of atmospheric anomalies using SST anomalies in an El Nino year; and an alternative two-way approach to regional modelling (the "down-scaling problem") made possible because the unconditional stability of the semi-implicit, semi-Lagrangian formulation permits large variations in grid spacing without changing the time step size. Finally, the model is run on a variety of parallel computing platforms and it is shown that near-linear speed-up can be attained. This is significant for both medium range NWP and very long-term GCM integrations.