We present a new method for the statistical downscaling of coarse-resolution General Circulation Model (GCM) fields to predict local climate change. Most atmospheric variables have strong seasonal cycles. We show that the prediction of the non-seasonal variability of maximum and minimum daily surface temperature is improved if the seasonal cycle is removed prior to the statistical analysis. The new method consists of three major steps. First, the average seasonal cycles of both predictands and predictors are removed. Second, a principal component-based multiple linear regression model between the deseasonalized predictands and predictors is developed and validated. Finally, the regression is used to make projections of future changes in maximum and minimum daily surface temperature at Shearwater, Nova Scotia. This projection is made using the local gridscale variables of the Canadian General Circulation Model Version 3 (CGCM3) climate model as predictors. Our statistical downscaling method indicates significant skill in predicting the observed distribution of temperature using GCM predictors. Projections suggest minimum and maximum temperatures at Shearwater will be up to about five degrees warmer by 2100 under the current "business-as-usual" scenario.RÉSUMÉ [Traduit par la rédaction] Nous présentons une nouvelle méthode pour la réduction d'échelle statistique des champs des modèles de circulation générale (MCG) à faible résolution pour prévoir les changements du climat local. La plupart des variables atmosphériques ont des cycles saisonniers bien marqués. Nous démontrons que la prédiction de la variabilité non saisonnière de la température de surface quotidienne minimum et maximum est meilleure si on retranche le cycle saisonnier avant de procéder à l'analyse statistique. Voici les trois grandes étapes de cette nouvelle méthode. D'abord, nous retirons les cycles saisonniers moyens des prédictants et des prédicteurs. Ensuite, nous concevons et validons un modèle de régression linéaire multiple sur composantes principales entre les prédictants et les prédicteurs désaisonnalisés. Enfin, nous nous servons de la régression afin d'établir des projections pour les changements à venir dans la température de surface quotidienne minimum et maximum à Shearwater en Nouvelle-Écosse. Cette projection est établie au moyen des variables locales à l'échelle du maillage de la troisième version du modèle canadien de circulation générale (MCCG3). Notre méthode de réduction d'échelle statistique se révèle très efficace pour prédire la répartition observée de la température au moyen des prédicteurs du MCG. D'après les projections, les températures minimum et maximum à Shearwater connaîtront une augmentation d'environ cinq degrés d'ici 2100 dans le scénario actuel de type « statu quo ».
Tropical cyclones can cause major loss of life and have devastating impacts on infrastructure and transportation. The North Atlantic exhibits the highest variability of tropical cyclone (TC) activity of any ocean basin (Gray & Klotzbach, 2014) and the associated damages exhibit significant variability on interannual and longer timescales (Landsea, 2015; Pielke & Landsea, 1998). The recent upward trend in TC activity in the North Atlantic basin (Emanuel, 2005; Goldenberg et al., 2001; Kunkel et al., 2013) increases the need for accurate seasonal forecasts. While such forecasts currently show skill with regard to overall activity, they are presently of limited practical use due to lack of regionalization (Caron et al., 2020). Various methods have been used to seasonally forecast TC activity. Statistical forecasts have the longest history and have been issued in real-time by Colorado State University, for example, since 1984 (Gray, 1984, 1984b; Klotzbach & Gray, 2009). These statistical forecasts are still used today and have exhibited modest improvement over time (Klotzbach et al., 2017). As computing power increased, dynamical seasonal TC forecasting began in Europe (Vitart et al., 2007) and was quickly followed by the United States (Camargo & Barnston, 2009). Hybrid statistical-dynamical models have also been developed (Vecchi et al., 2011). Canada is affected by about one-third of the TCs that form in the North Atlantic basin. These TCs can either remain tropical or undergo extratropical transition by the time they impact Canada. Storms that have caused major impacts in Canada include Hazel (1954), Juan (2003), Igor (2010), and Dorian (2019). In this study, we focus on reconstructing historical TC variability as a step toward building a Canadian TC seasonal forecasting capability. Xie et al. (2005) identified several spatio-temporal modes of hurricane activity in the North Atlantic (20°-50°N, 50°-86°W) based on empirical orthogonal function analysis. The two dominant modes accounted for over 40% of the total variance. The modes represent (1) the overall level of hurricane track density in the study region and (2) zonal shifts in hurricane tracks. Overall, hurricane activity was found to be statistically
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