Abstract:By applying wavelet-based empirical orthogonal function (WEOF) analysis to gridded precipitation (P) and empirical orthogonal function (EOF) analysis to gridded air temperature (T), potential evapotranspiration (PET), net precipitation (P-PET) and runoff (Q), this paper examines the spatial, temporal and frequency patterns of Alberta's climate variability. It was found that only WEOF-based precipitation patterns, possibly modulated by El Nino Southern Oscillation (ENSO) and Pacific Decadal Oscillation(PDO), delineated Alberta into four major regions which geographically represent northern Alberta Boreal forests, southern Alberta grasslands and Aspen Parklands and the Rocky Mountains and Foothills. The leading mode of wavelet-based precipitation variability WPC1 showed that between 1900 and 2000, a wet climate dominated northern Alberta with significant 4-8, 11 and 25-year periodic cycles, while the second mode WPC2 showed that between 1960 and 2000, southern Alberta grasslands were characterized by decreasing precipitation, dominated by 11-year cycles, and the last two modes WPC3 and WPC4 were characterized by 4-7 and 25-year cycles and both delineated regions where moisture from the Pacific Ocean penetrated the Rocky Mountains, accounted for much of the sub-alpine climate. These results show that WEOF is superior to EOF in delineating Alberta precipitation variability to sub-regions that more closely agree with its eco-climate regions. Further, it was found that while WPC2 could not explain runoff variations in southern Alberta, WPC1, WPC3 and WPC4 accounted for runoff variability in their respective sub-regions.

By applying wavelet analysis and wavelet principal component analysis (WPCA) to individual wavelet-scale power and scale-averaged wavelet power, homogeneous zones of rainfall variability and predictability were objectively identified for September–November (SON) rainfall in East Africa (EA). Teleconnections between the SON rainfall and the Indian Ocean and South Atlantic Ocean sea surface temperatures (SST) were also established for the period 1950–97. Excluding the Great Rift Valley, located along the western boundaries of Tanzania and Uganda, and Mount Kilimanjaro in northeastern Tanzania, EA was found to exhibit a single leading mode of spatial and temporal variability. WPCA revealed that EA suffered a consistent decrease in the SON rainfall from 1962 to 1997, resulting in 12 droughts between 1965 and 1997. Using SST predictors identified in the April–June season from the Indian and South Atlantic Oceans, the prediction skill achieved for the SON (one-season lead time) season by the nonlinear model known as artificial neural network calibrated by a genetic algorithm (ANN-GA) was high [Pearson correlation ρ ranged between 0.65 and 0.9, Hansen–Kuipers (HK) scores ranged between 0.2 and 0.8, and root-mean-square errors (rmse) ranged between 0.4 and 0.75 of the standardized precipitation], but that achieved by the linear canonical correlation analysis model was relatively modest (ρ between 0.25 and 0.55, HK score between −0.05 and 0.3, and rmse between 0.4 and 1.2 of the standardized precipitation).

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Using wavelet analysis and wavelet-based empirical orthogonal function analysis on scale-averaged-wavelet power and individual scale power, we identified the non-stationary sea-surface temperature (SST) fields of the South Atlantic and Indian Oceans that are associated with coherent regions of rainfall variability in central southern Africa (CSA). The dominant mode of CSA rainfall is out of phase between the coastal areas and the centre of CSA and has been decreasing consistently since 1970. The frequencies associated with this mode are between 2-2.4 and 5.6-8 years. The Benguela ocean current SSTs form the dominant spatial pattern of the South Atlantic Ocean, and the Brazil and Guinea ocean current SSTs form the second leading mode. The Benguela spatial patterns were found to migrate seasonally between Africa's west coast and South America's east coast. The northern Indian Ocean SST forms the leading mode of variability, followed by the south Indian Ocean SST. Using predictor fields identified from both oceans, we achieved encouraging results of predicted CSA rainfall using a non-linear statistical teleconnection artificial neural network-genetic algorithm model. At 3 month lead time, correlations of between 0.8 and 0.9, root-mean-square errors of between 0.4 and 0.9 and Hansen Kuipers skill scores of between 0.4 and 0.8 were obtained between observed and predicted CSA rainfall.