Decadal prediction uses climate models forced by changing greenhouse gases, as in the International Panel for Climate Change, but unlike longer range predictions they also require initialization with observations of the current climate. In particular, the upperocean heat content and circulation have a critical influence. Decadal prediction is still in its infancy and there is an urgent need to understand the important processes that determine predictability on these timescales. We have taken the first Hadley Centre Decadal Prediction System (DePreSys) and implemented it on several NERC institute compute clusters in order to study a wider range of initial condition impacts on decadal forecasting, eventually including the state of the land and cryosphere. The eScience methods are used to manage submission and output from the many ensemble model runs required to assess predictive skill. Early results suggest initial condition skill may extend for several years, even over land areas, but this depends sensitively on the definition used to measure skill, and alternatives are presented. The Grid for Coupled Ensemble Prediction (GCEP) system will allow the UK academic community to contribute to international experiments being planned to explore decadal climate predictability.Keywords: climate forecasting; decadal forecasting; coupled models; climate predictability Beyond scenario based climate predictionThe International Panel for Climate Change (IPCC) monitors and assesses the evidence for climate change and the probable future scenarios of that change. The modelling that is used to assess these scenarios defines, as external drivers of the climate, concentrations of CO 2 and other greenhouse gases, atmospheric aerosols and the ongoing changes in the solar cycle. The climate models themselves consist of representations of the atmosphere, oceans, land surface and cryosphere systems, although not all feedbacks between these systems are normally represented. Multiple runs of these models are made in order to average over the internally generated variability of the system, in order to give an estimate of the most probable climate evolution, rather than just one possible outcome. Furthermore, multi-model or multi-parameter results from the same
The ability of climate models to reproduce and predict land surface anomalies is an important but little-studied topic. In this study, an atmosphere and ocean assimilation scheme is used to determine whether HadCM3 can reproduce and predict snow water equivalent and soil moisture during the 1997-1998 El Niño Southern Oscillation event. Soil moisture is reproduced more successfully, though both snow and soil moisture show some predictability at 1-and 4-month lead times. This result suggests that land surface anomalies may be reasonably well initialized for climate model predictions and hydrological applications using atmospheric assimilation methods over a period of time.
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