Presentation Transcript

Realizing Additional Climate Predictability through Spatial and Temporal Downscaling http://ccnmtl.columbia.edu/projects/climate/ Seasonal Climate Prediction for Regional Scales by Neil Ward 3-month Number of Dry Days 3-month Rainfall Amount Time series for observed and forecasted number of dry days (left), and rainfall amount (right), with correlations of 0.433 and 0.585, respectively. Tropical Pacific and Indian Ocean SST for Aug-Sep-Oct was the �predictor� for concurrent (Aug-Sep-Oct) precipitation at a station in Singapore. cor=0.58 cor=0.43 Mar- Apr May ? Aug- Sep- Oct ? Skill Comparison: Amount (left) vs. Freq of dry Day (right) Amt Amt Freq Freq Eastern Equatorial Africa, Oct-Nov-Dec. Correlation Skill = 0.74 Central Europe, Jan-Feb-Mar. Correlation Skill = 0.06 training training independent forecasting independent forecasting Multiple regression forecasts for Eastern Africa, for OND. Predictors are two rotated EOFs of SST. After Mutai et al, 1998, Int. J. of Climatol. How downscaling can help make local forecasts better Effects of spatial resolution on topographic detail grid grid Temporal Downscaling Dynamical downscaling can produce more realistic daily rainfall sequences Here we see model output for Jul-Aug-Sep for Senegal, West Africa, for a dry year (1983) and a wet year (1975). Predictions of probability distribution of streamflow into Oros reservoir in north- eastern Brazil using two SST indices as predictors: (1) east-central tropical Pac- ific and (2) tropical Atlantic. Observations and marginal (climatological) distribu- tions are shown. Predictions are for calendar year totals, from previous July. De Souza Filho and Lall (2002)