Abstract
This study addresses seasonal climate forecasts using coupled atmosphere—ocean multimodels. Using as many as 67 different seasonal-forecast runs per season from a variety of coupled (atmosphere—ocean) models consensus seasonal forecasts have been prepared from about 4500 experiments. These include the European Center’s DEMETER (Development of a European Multi-Model Ensemble System for Seasonal to Inter-Annual Prediction) database and a suite of Florida State University (FSU) models (based on different combinations of physical parametrizations). This is one of the largest databases on coupled models. The monsoon region was selected to examine the predictability issue. The methodology involves construction of seasonal anomalies of all model forecasts for a number of variables including precipitation, 850 hPa winds, 2-m/surface temperatures, and sea surface temperatures. This study explores the skills of the ensemble mean and the FSU multimodel superensemble. The metrics for forecast evaluation include computation of hindcast and verification anomalies from model/observed climatology, time-series of specific climate indices, and standard deterministic ensemble mean scores such as anomaly correlation coefficient and root mean square error. The results were deliberately prepared to match the metrics used by European DEMETER models. Invariably in all modes of evaluation, the results from the FSU multimodel superensemble demonstrate greater skill for most of the variables tested here than those obtained in earlier studies. The specific inquiry of this study was on this question: is it going to be wetter or drier, warmer or colder than the long-term recent climatology of the monsoon; and where and when during the next season? These results are most encouraging, and they suggest that this vast database and the superensemble methodology are able to provide some useful answers to the seasonal monsoon forecast issue compared to the use of single climate models or from the conventional ensemble averaging.
Highlights
In recent years, a number of papers have addressed seasonal forecasts of the Asian monsoon
The biggest contribution of this study was in the drastic reduction of RMS error and a slight improvement for the anomaly correlation for seasonal forecasts of the monsoon
The synthetic superensemble used in this study is based on an equal number of proxy seasonal forecasts where observed structures based on principal component time series and empirical orthogonal functions were projected on to the forecast components for each model run
Summary
A number of papers have addressed seasonal forecasts of the Asian monsoon These studies have examined impacts from a range of parameters such as the role of land surface processes, soil moisture and sea surface temperatures (SSTs) anomalies on interannual variability. Some of our earlier research applications on seasonal climate forecasts involved the AMIP1 and AMIP2 global multimodel climate data sets covering long integration over a period of decade each using atmospheric general circulation models (Gates et al, 1999) These models share the same fields of SST and sea ice. The models are quite diverse in their structure, resolution, and physical parametrizations. FSUGSM T63/14 Levs ECMWF with Phy. Init SAS Radiation New (Band Model) HOPEGlobal Global 5◦ × 0.5◦–5◦ 17 Levs Coupled Assimilation Relax Obs SST the multimodel forecasts are better than the simple ensemble mean. Two sets of data from coupled models are used in the present study and are described below
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