Forecasting Meteorological Drought Over California Using the North Pacific High January Anomaly and a Statistical/Dynamical Method

Abstract:

We show that meteorological drought over California can be forecast statistically with high success rate, using as the predictor the dynamically forecast January sea level pressure anomaly at the North Pacific High winter climatological location. We show that the sea-level pressure winter-time anomaly in the North Pacific High provides a superior predictor of seasonal precipitation totals and extremes over most of California, compared to traditional ENSO indices such as SOI, MEI, NINO3.4, and others. The NPH anomaly more closely reflects the effects of ENSO over this region. We show the effectiveness of the NPH winter anomaly when used in conjunction with atmospheric humidity (HUS at 850 hPa level) in a statistical model that predicts seasonal precipitation total, and a statistical model that predicts the likelihood of daily precipitation extremes. We show how our models can use NPH winter anomaly for precipitation forecasting and, in an additional application, how it can also be used to derive precipitation projections based on GCM-projections of NPH and HUS. Large-scale climatic variables have been used as predictors of precipitation totals and extremes in many studies and are used operationally in weather forecasts, to circumvent the difficulty in obtaining robust dynamical simulations of precipitation, which is among the most complex of all climate variables in its mathematical representation in dynamical models. The NPH-based statistical models presented here may find useful applications in drought forecasting, water resources planning, and flood protection planning in California.