Credible projections of future local climate change are in demand. One way to accomplish this is to statistically downscale General Circulation Models (GCM’s). A new method for statistical downscaling is proposed in which the seasonal cycle is first removed, a physically based predictor selection process is employed and principal component regression is then used to train the regression. A regression model between daily maximum and minimum temperature at Shearwater, NS, and NCEP principal components in the 1961-2000 period is developed and validated and output from the CGCM3 is then used to make future projections. Projections suggest Shearwater’s mean temperature will be five degrees warmer by 2100.

The statistical prediction of local sea surface winds at a number of locations over the global ocean (Northeast Pacific, Northwest Atlantic and Pacific, tropical Pacific and Atlantic) is investigated using a surface wind statistical downscaling model based on multiple linear regression. The predictands (mean and standard deviation of both vector wind components and wind speed) calculated from ocean buoy observations on daily, weekly and monthly temporal scales are regressed on upper level predictor fields (derived from zonal wind, meridional wind, wind speed, and air temperature) from reanalysis products. The predictor fields are subject to a combined Empirical Orthogonal Function (EOF) analysis before entering the regression model. It is found that in general the mean vector wind components are more predictable than mean wind speed in the North Pacific and Atlantic, while in the tropical Pacific and Atlantic the difference in predictive skill between mean vector wind components and wind speed is not substantial. The predictability of wind speed relative to vector wind components is interpreted by an idealized Gaussian model of wind speed probability density function, which indicates that the wind speed is more sensitive to the standard deviations (which generally are not well predicted) than to the means of vector wind component in the midlatitude region and vice versa in the tropical region. This sensitivity of wind speed statistics to those of vector wind components can be characterized by a simple scalar quantity theta=arctan(mu/sigma) (in which mu is the magnitude of average vector wind and sigma is the isotropic standard deviation of the vector winds). The quantity theta is found to be dependent on season, geographic location and averaging timescale of wind statistics. While the idealized probability model does a good job of characterizing month-to-month variations in the mean wind speed based on those of the vector wind statistics, month-to-month variations in the standard deviation of speed are not well modelled. A series of Monte Carlo experiments demonstrates that the inconsistency in the characterization of wind speed standard deviation is the result of differences of sampling variability between the vector wind and wind speed statistics. / Graduate

Climate change studies are crucial to assist decision-makers in understanding future risks and planning adequate adaptation measures. In general, Global/Regional Climate Models (GCMs/RCMs) achieve coarse resolutions, and are thus unable to provide sufficient information to conduct local climate assessments. Downscaling, defined as a method that derives local to regional-scale (10 to 100 km) information from larger-scale models or data analyses, is used to address this deficiency. In this thesis, a particular downscaling technique, known as the Quantile-Quantile transformation, was used to adjust the statistical distribution of RCM variables to match the statistical distribution of the observed variables generated by two RCMs: the Canadian Regional Climate Model version 3.7.1 and the ARPEGE model, on the historical period (1961-2001). The analyses presented in this study were applied to daily precipitation and maximum and minimum temperatures in the South Nation watershed in Eastern Ontario, Canada. The two-sample Kolmogorov–Smirnov test indicated that the Quantile-Quantile transformation improved the shape of the PDF of RCM-simulated climate variables. The results suggest that, under the A1B scenario, temperatures in the watershed would rise significantly and there would be an increment in precipitation occurrence and intensity. Trend analysis was performed on the 1961 to 2001 and 2041 to 2081 timeframes, using the Mann-Kendall test and the Sen's slope estimator. Discernible, often significant, increases of maximum and minimum temperatures were found for the 1961 to 2001 period, and stronger ascending slopes for the 2041 to 2081 period. However, there was marginal evidence of changes in the time series of maximum and accumulated annual precipitation for both periods. The study also outlined how the frequency and intensity of some extreme weather events will evolve in the 2041-2081 period in response to the rise in atmospheric GHG concentrations. Projected impacts were investigated by tracking future changes in four extreme temperature indices and three precipitation indices. It was predicted that heavy precipitation events and warm spells will occur more frequently and intensely, while extreme cold events will be weaker, and some will be hardly observed.

There is a strong interest in the climate community to improve the ability to accurately predict future trends of climate variables. Recently, machine learning methods have proven their ability to contribute to more accurate predictions of historical data on a variety of climate variables. There is also a strong interest in using statistical downscaling to predict local station data from the output of multi-model ensembles. This project looks at using the machine learning algorithm XGBoost and evaluating its ability to accurately predict historical monthly precipitation, with a focus of applying this method to simulate future precipitation trends. / Thesis / Master of Science (MSc)

A statistical downscaling approach based on multiple linear-regression is used to investigate the predictability of land surface winds over the Canadian prairies and Ontario. This study's model downscales mid-tropospheric predictors (wind components and speed, temperature, and geopotential height) from reanalysis products to predict historical wind observations at thirty-one airport-based weather surface stations in Canada. The model's performance is assessed as a function of: season; geographic location; averaging timescale of the wind statistics; and wind regime, as defined by how variable the vector wind is relative to its mean amplitude. Despite large differences in predictability characteristics between sites, several systematic results are observed. Consistent with recent studies, a strong anisotropy of predictability for vector quantities is observed, while some components are generally well predicted, others have no predictability. The predictability of mean quantities is greater on shorter averaging timescales. In general, the predictability of the surface wind speeds over the Canadian prairies and Ontario is poor; as is the predictability of sub-averaging timescale variability. These results and the relative predictability of vector and scalar wind quantities are interpreted with theoretically- and empirically-derived wind speed sensitivities to the resolved and unresolved variability in the vector winds. At most sites, and on longer averaging timescales, the scalar wind quantities are found to be highly sensitive to unresolved variability in the vector winds. These results demonstrate limitations to the statistical downscaling of wind speed and suggest that deterministic models which resolve the short-timescale variability may be necessary for successful predictions. / Graduate

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No description available.

No description available.

Dans un contexte de changement climatique, l'intégration des énergies renouvelables aux systèmes électriques est un enjeu majeur des décennies à venir. Les énergies liées au climat (photovoltaïque, éolien et hydro-électricité) peuvent contribuer à une réduction des émissions de gaz à effet de serre. Cependant, elles sont fortement intermittentes et la production électrique associée peine à répondre à la demande.Cette étude vise à évaluer la faisabilité météorologique du développement d'un système de production électrique basé sur les sources d'énergie liées au climat (CRE - Climate-Related Energy). Nous considérons uniquement leurs variations spatiotemporelles et supposons un équilibre entre production et demande moyennes. Nous avons développé CRE-Mix, une chaîne de modèles permettant de convertir les variables météorologiques en chroniques énergétiques. Cet outil permet l'estimation des fluctuations spatiotemporelles de production et de demande énergétiques résultant de la variabilité hydro-climatique. Pour une sélection de régions en Europe, nous évaluons la facilité d'intégration des CRE en fonctions de leur cohérence temporelle avec la demande. Pour chaque source d'énergie et de multiples mix énergétiques nous estimons successivement (i) le taux de pénétration moyen (PE), qui quantifie la proportion de demande satisfaite sur une longue période et (ii) les caractéristiques des périodes de faible pénétration pour lesquelles le taux journalier de demande satisfaite reste bas pendant plusieurs jours consécutifs. Les résultats montrent que les systèmes basés sur une seule source ont du mal à répondre à la demande et souffrent de longues périodes de faible PE, en raison de leur variabilité temporelle. Cependant, une combinaison d'énergies, l'utilisation de systèmes de stockage ou l'échange d'énergie entre régions, permettent d'augmenter fortement la fiabilité des CRE (PE proche de 100% et rares/courtes périodes de faible pénétration). Cette étude, basée sur 30 ans, a été étendue à l'ensemble de XXème siècle afin d'évaluer les fluctuations basse fréquence des CRE résultant de la variabilité interne du climat. De longues chroniques régionales de production et de demande ont été générées grâce au développement d'une méthode de descente d'échelle statistique basée sur les analogues atmosphériques (SCAMP). Cet outil génère des scénarios météorologiques multivariés physiquement cohérents. Les résultats montrent que les variations basse fréquence des CRE sont influencées par les grandes oscillations océano-climatiques. De plus, on montre que les variations multi-décennales de l'hydro-électricité sont particulièrement importantes avec notamment une différence en PE supérieure à 15% d'une décade à l'autre et des périodes de faible pénétration aux caractéristiques très irrégulières.Enfin, nous évaluons la pertinence de systèmes électriques basés sur les CRE en climat futur. SCAMP permet de produire des scénarios régionaux de variables météorologiques à partir des modèles climatiques issus des simulations CMPI5. Pour les précipitations, les tendances simulées par SCAMP sont en désaccord avec de nombreuses études. L'application de SCAMP en "modèle parfait" semble indiquer que le lien entre les situations atmosphériques de grande échelle et les précipitations totales, mais également convectives et stratiformes, change en climat futur. / In the context of climate change, the integration of renewables in electric power systems is one of the main challenges of the coming decades. Climate-Related-Energy sources (CRE - solar, wind and hydro power) can contribute to reduce the greenhouse gas emissions. However, they exhibit large spatio-temporal fluctuations and the associated intermittent electricity generation often leads to an incomplete supply-demand balance. This study aims to evaluate the meteorological feasibility of developing an electric power system that would only rely on CRE sources. We focus on the multi-scale spatio-temporal fluctuations of these renewables by assuming a balance between mean electricity production and mean energy load. We develop and use CRE-mix, a suite of models able to convert meteorological conditions into CRE time series. It gives an assessment the spatio-temporal fluctuations of power production and energy demand, resulting from the multi-scale hydro-climatic variability. For a set of European regions, we assess the ease of integration of CRE sources, regarding their temporal consistency with energy demand. For each CRE source and multiple CRE mixes, we consider in turn (i) the mean penetration rate (PE), which quantifies the proportion of satisfied demand over a long period and (ii) the characteristics of low penetration periods, defined as sequences of days for which the penetration rate is lower than a given threshold. This study proves that single CRE sources have difficulty to meet the energy demand and suffer from long low penetration periods, due to their multi-scale temporal variations. However, using some integrating factors (multi-sources, storage systems, inter-regions electric power transmission), efficiently improves the reliability of CRE-based power systems with PE rates close to 100% and rare low penetration periods.These analyses, based on a 30-yr period, are extended to the entire 20th century in order to assess the low frequency fluctuations of CRE sources resulting from the internal variability of climate. Long regional series of production and demand, were generated thanks to the development of a statistical downscaling method based on atmospheric analogues (SCAMP). It simulates physically-consistent multivariate series of meteorological parameters. The results demonstrate that these fluctuations are related to some large scale oceano-climatic oscillations. Moreover, the multi-decennial variations of hydro power are particularly large: changes in PE rates exceeding 15% from one decade to the other and uneven energy droughts characteristics.Finally, we evaluate the relevance of the CRE sources under future climate conditions. SCAMP is used to produce downscaled projections of meteorological drivers of CRE sources for the 21st century from a selection of CMIP5 climate models. The resulting scenarios for precipitation are not consistent with other studies focusing of the future modifications of this variable in Europe. The application of SCAMP in a perfect-model approach seems to indicate that the large-scale-meteorology/local-precipitation relationship is changing in the course of the 21st century, for all total, convective and stratiform precipitation.

Les scénarios hydrologiques requis pour les études d'impacts hydrologiques nécessitent de disposer de scénarios météorologiques non biaisés et qui soient de surcroît adaptés aux échelles spatiales et temporelles des hydro-systèmes considérés. Les scénarios météorologiques obtenus en sortie brute des modèles de climat et/ou des modèles de prévision numérique du temps sont de ce fait non appropriées. Les sorties de ces modèles sont par suite souvent adaptées à l'aide de Méthodes de Descente d'Echelle Statistique (MDES). Depuis les années 2000, les MDES ont beaucoup été utilisées pour la génération de scénarios météorologiques en un site. En revanche, la génération de scénarios spatiaux couvrant de larges territoires est une tâche plus difficile, en particulier lorsque l'on souhaite respecter la cohérence spatiale des précipitations à prédire. Parmi les MDES usuelles, les approches basées sur la recherche de situations analogues passées permettent de satisfaire cette contrainte. Dans cette thèse, nous évaluons la capacité d'un Modèle Analog (MA) – où l'analogie porte sur les géopotentiels 1 000 et 500 hPa – pour la génération de scénarios de précipitation spatialement cohérents pour le territoire Français métropolitain. Dans un premier temps, la transposition spatiale du modèle MA est évaluée : le modèle s'avère utilisable pour la génération de scénarios spatiaux cohérents sur des territoires couvrant plusieurs dizaines de milliers de kilomètres carrés dès lors qu'aucune barrière climatique n'est rencontrée. Dans un second temps, nous évaluons la sensibilité des performances de prédiction à l'agrégation spatiale de la variable à prédire. L'augmentation de performance avec l'agrégation s'explique alors par la diminution de la variabilité du prédictand, pour autant que les variables de grande échelle considérées soient de bons prédicteurs pour la région considérée. Dans une dernière étude, nous explorons la possibilité d'améliorer la performance locale du modèle analogue par l'ajout de prédicteurs locaux. Le modèle combiné qui en résulte permet d'accroître sensiblement les performances de prédiction par l'adaptation du lien d'échelle sur la base d'un jeu de prédicteurs additionnels. Il apparaît de plus que la pertinence de ces prédicteurs dépend de la situation de grande échelle rencontrée ainsi que de la région considérée. / Hydrological scenarios required for the impact studies need to have unbiased meteorological scenarios adapted to the space and time scales of the considered hydro-systems. Hence, meteorological scenarios obtained from global climate models and/or numerical weather prediction models are not really appropriated. Outputs of these models have to be post-processed, which is often carried out thanks to Statistical Downscaling Methods (SDMs). Since the 2000's, SDMs are widely used for the generation of scenarios at a single site. The generation of relevant precipitation fields over large regions or hydro-systems is conversely not straightforward, in particular when the spatial consistency has to be satisfied. One strategy to fulfill this constraint is to use a SDM based on the search of past analog situations. In this PhD, we evaluate the ability of an Analog Model (AM) – where the analogy is applied to the geopotential heights 1000 and 500 hPa – for the generation of spatially coherent precipitation scenarios over the French metropolitan territory. In a first part, the spatial transferability of an AM is evaluated: the model appears to be usable for the generation of spatial coherent scenarios over territories covering several tens of thousands squared kilometers if no climatological barrier is met in between. In a second part, we evaluate the sensitivity of the prediction performance to the spatial aggregation of the predictand. The performance increases with the aggregation level as long as the large scale variables are good predictors of precipitation for the region under consideration. This performance increase has to be related to the decrease of the predictand variability. We finally explore the possibility of improving the local performance of the AM using additional local scale predictors. For each prediction day, the prediction is obtained from a parametric regression model, for which predictors and parameters are estimated from the analog dates. The resulting combined model noticeably allows increasing the prediction performance by adapting the downscaling link for each prediction day. The selected predictors for a given prediction depend on the large scale situation and on the considered region.