Previous climate model projections of climate change accounted for external forcing from natural and anthropogenic sources but did not attempt to predict internally generated natural variability. We present a new modeling system that predicts both internal variability and externally forced changes and hence forecasts surface temperature with substantially improved skill throughout a decade, both globally and in many regions. Our system predicts that internal variability will partially offset the anthropogenic global warming signal for the next few years. However, climate will continue to warm, with at least half of the years after 2009 predicted to exceed the warmest year currently on record.
[1] Three prominent quasi-global patterns of variability and change are observed using the Met Office's sea surface temperature (SST) analysis and almost independent night marine air temperature analysis. The first is a global warming signal that is very highly correlated with global mean SST. The second is a decadal to multidecadal fluctuation with some geographical similarity to the El Niño-Southern Oscillation (ENSO). It is associated with the Pacific Decadal Oscillation (PDO), and its Pacific-wide manifestation has been termed the Interdecadal Pacific Oscillation (IPO). We present model investigations of the relationship between the IPO and ENSO. The third mode is an interhemispheric variation on multidecadal timescales which, in view of climate model experiments, is likely to be at least partly due to natural variations in the thermohaline circulation. Observed climatic impacts of this mode also appear in model simulations. Smaller-scale, regional atmospheric phenomena also affect climate on decadal to interdecadal timescales. We concentrate on one such mode, the winter North Atlantic Oscillation (NAO). This shows strong decadal to interdecadal variability and a correspondingly strong influence on surface climate variability which is largely additional to the effects of recent regional anthropogenic climate change. The winter NAO is likely influenced by both SST forcing and stratospheric variability. A full understanding of decadal changes in the NAO and European winter climate may require a detailed representation of the stratosphere that is hitherto missing in the major climate models used to study climate change.
An ensemble of twenty four coupled oceanatmosphere models has been compared with respect to their performance in the tropical Paci®c. The coupled models span a large portion of the parameter space and dier in many respects. The intercomparison includes TOGA (Tropical Ocean Global Atmosphere)-type models consisting of high-resolution tropical ocean models and coarse-resolution global atmosphere models, coarse-resolution global coupled models, and a few global coupled models with high resolution in the equatorial region in their ocean components. The performance of the annual mean state, the seasonal cycle and the interannual variability are investigated. The primary quantity analysed is sea surface temperature (SST). Additionally, the evolution of interannual heat content variations in the tropical Paci®c and the relationship between the interannual SST variations in the equatorial Paci®c to¯uctuations in the strength of the Indian summer monsoon are investigated. The results can be summarised as follows: almost all models (even those employing¯ux corrections) still have problems in simulating the SST climatology, although some
Long-range weather forecasting is a notoriously difficult area of environmental science. However, recent improved understanding of atmospheric dynamics and better observations indicate that useful progress, rooted in scientifically sound ideas, may be possible with longrange forecasting in the tropics. We describe recent research into the mechanisms and prediction of rainfall in the sub-Sahara during the main summer rainfall season, concentrating on the Sahel region. We use a complex physical model of the atmosphere (a 'general circulation' model) and two relatively simple statistical models to show that large-scale variations in sea surface temperature (SST) can strongly influence seasonal Sahel rainfall. Persistence of patterns of SST anomalies (deviations from long-term average) is sufficient to allow useful forecasting techniques to be based on fields of SST anomalies observed in the preceding spring. However, persistence of the SST anomalies may not always be sufficient to provide a skilful forecast.
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