Improving Seasonal Climate Predictability in the Colorado River Basin for Enhanced Decision Support

The water resource management community is increasingly seeking skillful seasonal climate forecasts with long lead times. But predicting wet or dry climate with sufficient lead time (3 months) for a season (especially winter) in the Colorado River Basin (CRB) is a challenging problem. The typical approach taken to predicting winter climate is based on using climate indices and climate models to predict precipitation or streamflow in the Colorado River Basin. In addition to this approach; which may have a long lead time, water supply forecasts are also generated based on current observations by the Colorado River Forecast Center. Recently, the effects of coupled atmospheric-ocean phenomena such as ENSO over North America, and atmospheric circulation patterns at the 500 mb pressure level, which make the CRB wet or dry, have been studied separately. In the current work we test whether combining climate indices and circulation patterns improve predictability in the CRB. To accomplish this, the atmospheric circulation data from the Earth System Research Laboratory (ESRL) and climate indices data from the Climate Prediction Center were combined using logical functions. To quantify the skill in prediction, statistics such as the hit ratio and false alarm ratio were computed. The results from using a combination of climate indices and atmospheric circulation patterns suggest that there is an improvement in the prediction skill with hit ratios higher than 0.8, as compared to using either predictor individually (which typically produced a hit ratio of 0.6). Based on this result, there is value in using this hybrid approach when compared to a black box statistical model, as the climate index is an analog to the moisture availability and the right atmospheric circulation pattern helps in transporting that moisture to the Basin.