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UBC Theses and Dissertations

Comparison of linearly and nonlinearly statistically downscaled atmospheric variables in terms of future climate indices and daily variability Gaitan Ospina, Carlos Felipe


Statistical downscaling (SD) of global climate model output assumes that the SD skills in present climate are retained in future climate (i.e. time-invariant). To check this assumption, I used regional climate model output as pseudo-observations to verify the downscaled models’ performance in terms of both daily variability and climate indices for historical (1971-2000) and future (2041-2070) periods. The variables of interest are daily maximum and minimum temperatures, daily precipitation occurrences and amounts, and surface wind speed. In particular, a variety of nonlinear statistical/machine learning models (e.g. Bayesian neural network (BNN), adaptive regression sufficiently smooth polynomials, and classification and regression trees (CART)) and multiple linear regression models were used to downscale the Canadian Global Climate Model 3.1 output using the Canadian Regional Climate Model 4.2 output as pseudo-observations. The regions of interest are southern Ontario and Quebec, Canada, for temperature and precipitation, and Haida Guaii, British Columbia, Canada, for surface wind speed. The results indicate that choosing the best model based on the historical period performance could result in having one of the worst models for the future period. In particular, when downscaling temperatures, using SD models with greater ability to model complicated relations, by having either nonlinear capability or additional non-temperature predictors, seemed to alleviate the drop in performance found in future climate conditions. When downscaling precipitation occurrences, nonlinear methods outperformed their linear counterparts in terms of the Peirce skill score and the skill did not diminish for future climate. On the other hand, when downscaling precipitation amounts, the model performances deteriorated in future climate, and a BNN model had the best future performance in terms of daily variability, even though the model’s performance varied widely among individual climate indices. Finally, the Wind INDices for the evaluation of EXtremes (WINDEX) were introduced, and it was shown that a BNN model and a probabilistic model were the best in simulating pseudo-observed surface wind speed daily variability and the WINDEX climate indices, respectively.

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