PREDICTOR SELECTION AND MODEL EVALUATION FOR FUTURE RAINFALL PROJECTION IN HAWAIʻI

Abstract

Statistical downscaling methods bridge the gap between global-scale climate changes represented in general circulation models and local-scale regional impacts that are most relevant for decision makers. While traditional statistical methods such as multiple linear regression (MLR) are still useful tools for statistical downscaling, finding the best predictive large-scale climate information for the targeted local climate variables remains a challenge. A new method for large-scale atmospheric predictor evaluation is presented for use in statistical downscaling to project rainfall for the Hawaiian Islands. A pool of sixteen commonly used predictor variables (e.g. geopotential height, moisture transport, vertical temperature gradient) were assessed for correlation with rainfall and multicollinearity among other predictors. MLR was used to derive relationships between all possible predictor variable combinations and seasonal rainfall, and models having a delta Akaike Information Criterion (ΔAIC) value of two or lower were retained for further evaluation. This process of model ranking revealed the influence of each variable in the models and showed that variables have varying significance between seasons. Leave-one-out cross validation was performed as an additional method to test model skill. Results showed that all models within ΔAIC of less than or equal to two have similar predictive skill, and taking an additional step of screening predictors leads to increased model skill when compared to the variables selected ad hoc in the previous statistical model for Hawai‛i. Moreover, because selecting a single best model is not justifiable based on number of high-ranking models by ΔAIC value and similar statistical skill between models, results for the suite of competitive models are provided. While statistics imply that predictor selection significantly affects the models, future projections are needed as a next step to quantify the differences in projected rainfall resulting from changes in the selection of predictors.