HR: 1340h
AN: GC43A-0939    [Abstracts]
TI: Seasonal Forecast Datasets: a Resource for Calibrating Regional Climate Change Projections
AU: * Doblas-Reyes, F
EM: f.doblas-reyes@ecmwf.int
AF: ECMWF, Shinfield Park, Reading, RG2 9AX, United Kingdom
AU: Palmer, T N
EM: t.palmer@ecmwf.int
AF: ECMWF, Shinfield Park, Reading, RG2 9AX, United Kingdom
AU: Weisheimer, A
EM: a.weisheimer@ecmwf.int
AF: ECMWF, Shinfield Park, Reading, RG2 9AX, United Kingdom
AU: Rodwell, M J
EM: m.rodwell@ecmwf.int
AF: ECMWF, Shinfield Park, Reading, RG2 9AX, United Kingdom
AB: Probabilistic projections of regional climate change are currently being used for long-term planning. These projections are of value provided the associated probabilities are trustworthy. However, by the nonlinear nature of climate, finite computational models of climate are inherently deficient in their ability to simulate regional climatic variability with enough accuracy. Therefore, in the light of such generic deficiencies, the hypothesis of whether regional climate-change projections are untrustworthy should be tested. A calibration method is proposed whose basis lies in the notion of seamless prediction: if essentially the same ensemble forecasting system can be validated probabilistically on timescales where validation data exist, ie on daily, seasonal and decadal timescales, then climate-change probabilities obtained with the same systems could be objectively modified or calibrated using probabilistic forecasts on shorter timescales. Specifically, calibrated probabilities of regional climate change are derived from analyses of the statistical reliability of seasonal probabilistic predictions obtained from multi-model ensembles. The method is demonstrated by calibrating probabilistic projections from the multi-model ensemble of the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) using reliability analyses from the seasonal-forecast DEMETER multi-model dataset. The focus is on climate-change projections of regional precipitation, though the methodology is more general. The examples provide some justification for the development of seamless prediction systems across weather and climate timescales.
DE: 1616 Climate variability (1635, 3305, 3309, 4215, 4513)
DE: 1626 Global climate models (3337, 4928)
DE: 1637 Regional climate change
DE: 3245 Probabilistic forecasting (3238)
DE: 3354 Precipitation (1854)
SC: Global Environmental Change [GC]
MN: 2007 Fall Meeting