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Realizing Additional Climate Predictability through Spatial and Temporal Downscaling. http://ccnmtl.columbia.edu/projects/climate/ Seasonal Climate Prediction for Regional Scales by Neil Ward. cor=0.43. cor=0.58.
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Realizing Additional Climate Predictability through Spatial and Temporal Downscaling http://ccnmtl.columbia.edu/projects/climate/ Seasonal Climate Prediction for Regional Scales by Neil Ward
cor=0.43 cor=0.58 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 0.433and 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.
Mar- Apr May ↔ Freq Amt Skill Comparison: Amount (left) vs. Freq of dry Day (right) Aug- Sep- Oct ↔ Amt Freq
Eastern Equatorial Africa, Oct-Nov-Dec. Correlation Skill = 0.74 Central Europe, Jan-Feb-Mar. Correlation Skill = 0.06
independent forecasting training Multiple regression forecasts for Eastern Africa, for OND. Predictors are two rotated EOFs of SST. After Mutai et al, 1998, Int. J. of Climatol. independent forecasting training
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).
De Souza Filho and Lall (2002) 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.