Skill of Canonical Correlation Analysis Forecasts of 3-Month Mean Surface Climate in Hawaii and Alaska

Abstract

Statistical short-term climate predictive skills and their sources for 3-month mean local surface climate (temperature and precipitation) in Hawaii and Alaska have been explored at lead times of up to one year using a canonical correlation analysis (CCA). Four consecutive 3-month predictor periods are followed by a variable lead time and then a single 3-month predictand period. Predictor fields are quasi-global sea surface temperature, Northern Hemisphere 700-mb height, and prior values of the predictand field itself Forecast skill is estimated using cross-validation. Short-term global climate fluctuations such as the El Niño?Southern Oscillation (ENSO) phenomenon are found to play an important role in the climate variability in Hawaii and the southern half of Alaska. During the late winter and spring of mature warm (cold) ENSO events, Hawaii tends to be anomalously warm and dry (wet and cool), while southern Alaska tends to be warm (cold). Hawaii's responses occur more strongly the year after a mature ENSO event rather than the year of the event, even if the opposite phase of ENSO has already begun. Persistence is the best seasonal temperature prediction for Hawaii at short leads. Winter and spring temperature (precipitation) can be predicted up to one year (a few months) in advance with modest but usable skill for Hawaii, where temperature forecasts are generally more skillful. Southern Alaska has temperature prediction possibilities up to 7?10 months in advance. While Alaskan seasonal precipitation prediction is poor on the large spatial scale, forecasts on terrain-dependent local scales may he more fruitful using methods other than CCA.