Assessing a GCM`s suitability for making seasonal predictions
- NOAA, Camp Springs, MD (United States); and others
This study investigates the predictability of seasonal mean circulation anomalies associated purely with the influence of anomalous sea surface temperatures (SSTs). Within this framework, seasonal mean atmospheric anomalies on a case by case basis are understood to consist of a potentially predictable boundary-forced component and an unpredictable naturally varying component. The predictive capability of an atmospheric general circulation model (AGCM) for seasonal timescales should therefore be assessed in terms of the average skill over many cases, since it is only then that the boundary-forced predictable signal in observations can be identified. To illustrate, experiments for 1982-1993 using two versions of an AGCM are presented. The models, referred to here as MRF8 and MRF9, differ in the parameterization of a single process. Each model is run nine times for the 12 years using different initial conditions but identical observed global SSTs. The nine-member ensemble mean anomalies for each season in 1982-1993 are compared with observed anomalies over the Pacific-North American (PNA) region. Several different measures of the impact of SST boundary forcing on the extratropical flow suggest that MRF9 is a better model for seasonal prediction purposes. The two AGCMs have substantially different zonal-mean climatologies in the Tropics and subtropics, with MRF9 significantly better. It is argued that the improved mean flow in MRF9 enhances its midlatitude sensitivity to tropical forcing. The results highlight the importance of continued GCM development and give reason to hope that an even better model would lead to further improved forecasts of seasonal anomalies over the PNA sector. 27 refs., 9 figs.
- OSTI ID:
- 241234
- Journal Information:
- Journal of Climate, Journal Name: Journal of Climate Journal Issue: 1 Vol. 9; ISSN JLCLEL; ISSN 0894-8755
- Country of Publication:
- United States
- Language:
- English
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