A NEW GENERAL-CIRCULATION MODEL - FORMULATION AND PRELIMINARY-RESULTSIN A SINGLEPROCESSOR AND MULTIPROCESSOR ENVIRONMENT

This article describes a new general circulation model (GCM) developed jointly by The University of New South Wales (UNSW) and the Universit y 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 peri ods extending from seasons, through annual and decadal periods, and be yond. The model can be coupled with ocean models that vary in complexi ty from simple ''swamp'' oceans to complex ocean GCMs. The atmospheric GCM also has a number of novel features, particularly in the numerica l integration scheme which is a high-order, mass-conserving, semi-impl icit semi-Lagrangian scheme, thereby removing the stability restrictio n on the time-step and allowing efficient long-term integrations. The emphasis here will be on demonstrating that the new model performs eff ectively on the usual measures of skill (statistics such as mean error s, root-mean-square errors and anomaly correlations) in several standa rd applications upon which new models usually are assessed. These appl ications include medium range weather forecasts out to 10 days on a da ily basis over a one year period; a limited 10-year simulation climato logy, prediction of atmospheric anomalies using SST anomalies in an El Nine 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 l arge variations in grid spacing without changing the time step size. F inally, 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 sig nificant for both medium range NWP and very long-term GCM integrations .