La prévision d'ensemble à l'échelle saisonnière avec des modèles de circulation générale a connu un essor certain au cours des vingt dernières années avec la croissance exponentielle des capacités de calcul, l'amélioration de la résolution des modèles, et l'introduction progressive dans ceux-ci des différentes composantes (océan, atmosphère, surfaces continentales et glace de mer) régissant l'évolution du climat à cette échelle. Malgré ces efforts, prévoir la température et les précipitations de la saison à venir reste délicat, non seulement sur les latitudes tempérées mais aussi sur des régions sujettes à des aléas climatiques forts comme l'Afrique de l'ouest pendant la saison de mousson. L'une des clés d'une bonne prévision est la prise en compte des incertitudes liées à la formulation des modèles (résolution, paramétrisations, approximations et erreurs). Une méthode éprouvée est l'approche multi-modèle consistant à regrouper les membres de plusieurs modèles couplés en un seul ensemble de grande taille. Cette approche a été mise en œuvre notamment dans le cadre du projet européen ENSEMBLES, et nous montrons qu'elle permet généralement d'améliorer les rétro-prévisions saisonnières des précipitations sur plusieurs régions d'Afrique par rapport aux modèles pris individuellement. On se propose dans le cadre de cette thèse d'étudier une autre piste de prise en compte des incertitudes du modèle couplé CNRM-CM5, consistant à ajouter des perturbations stochastiques de la dynamique du modèle d'atmosphère ARPEGE-Climat. Cette méthode, baptisée “dynamique stochastique”, consiste à introduire des perturbations additives de température, humidité spécifique et vorticité corrigeant des estimations d'erreur de tendance initiale du modèle. Dans cette thèse, deux méthodes d'estimation des erreurs de tendance initiale ont été étudiées, basées sur la méthode de nudging (guidage) du modèle vers des données de référence. Elles donnent des résultats contrastés en termes de scores des rétro-prévisions selon les régions étudiées. Si on estime les corrections d'erreur de tendance initiale par une méthode de nudging itéré du modèle couplé vers les réanalyses ERA-Interim, on améliore significativement les scores sur l'hémisphère Nord en hiver en perturbant les prévisions saisonnières en tirant aléatoirement parmi ces corrections. Cette amélioration est accompagnée d'une nette réduction des biais de la hauteur de géopotentiel à 500 hPa. Une rétro-prévision en utilisant des perturbations dites“optimales” correspondant aux corrections d'erreurs de tendance initiale du mois en cours de prévision montre l'existence d'une information à l'échelle mensuelle qui pourrait permettre de considérablement améliorer les prévisions. La dernière partie de cette thèse explore l'idée d'un conditionnement des perturbations en fonction de l'état du modèle en cours de prévision, afin de se rapprocher si possible des améliorations obtenues avec ces perturbations optimales / Over the last twenty years, research in ensemble predictions at a seasonal timescale using general circulation models has undergone a considerable development due to the exponential growth rate of computing capacities, the improved model resolution and the introduction of more and more components (ocean, atmosphere, land surface and sea-ice) that have an impact on climate at this time scale. Regardless of these efforts, predicting temperature and precipitation for the upcoming season is a difficult task, not only over mid-latitudes but also over regions subject to high climate risk, like West Africa during the monsoon season. One key to improving predictions is to represent model uncertainties (due to resolution, parametrizations, approximations and model error). The multimodel approach is a well-tried method which consists in pooling members from different individual coupled models into a single superensemble. This approach was undertaken as part of the European Commission funded ENSEMBLES project, and we find that it usually improves seasonal precipitation re-forecasts over several regions of Africa with respect to individual model predictions. The main goal of this thesis is to study another approach to addressing model uncertainty in the global coupled model CNRM-CM5, by adding stochastic perturbations to the dynamics of the atmospheric model ARPEGE-Climat. Our method, called “stochastic dynamics”, consists in adding additive perturbations to the temperature, specific humidity and vorticity fields, thus correcting estimations of model initial tendency errors. In this thesis, two initial tendency error estimation techniques were studied, based on nudging the model towards reference data. They yield different results in terms of re-forecast scores, depending on the regions studied. If the initial tendency error corrections are estimated using an iterative nudging method towards the ERA-Interim reanalysis, seasonal prediction scores over the Northern Hemisphere in winter are significantly improved by drawing random corrections. The 500 hPa geopotential height is also clearly reduced. A re-forecast using “optimal” perturbations drawn within the initial tendency error corrections from the current forecast month shows that useful information at a monthly timescale exists, and could allow significant forecast improvement. The last part of this thesis focuses on the idea of classifying the model perturbations according to its current state during the forecast, in order to take a step closer (if possible) to the improvements noted with these optimal perturbations

Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico / A dinÃmica dos processos relacionados à qualidade da Ãgua em reservatÃrios à funÃÃo da sua morfologia, da aÃÃo das variÃveis meteorolÃgicas e das afluÃncias e defluÃncias, em maior grau. Prever o comportamento hidrodinÃmico de reservatÃrios e o impacto causado por mudanÃas ou variabilidades na forÃante meteorolÃgica à essencial ao gerenciamento da qualidade da Ãgua e foi o objetivo principal desta tese. Para tanto foram utilizados modelos climÃticos, hidrolÃgicos, hidrodinÃmicos e de balanÃo de energia, em cascata. O comportamento da hidrodinÃmica resultante da modelagem mostrou resultados consonantes com reservatÃrios de regiÃes tropicais, representando os padrÃes diÃrios de circulaÃÃo e a formaÃÃo de estratificaÃÃes tÃrmicas no reservatÃrio modelado. As principais variaÃÃes hidrodinÃmicas sazonais puderam ser modeladas, ainda que com um alto Ãndice de incerteza. Foi realizado um monitoramento no reservatÃrio Pereira de Miranda que forneceu meios para dar inÃcio ao ciclo de modelagem e monitoramento integrado. Foi apresentada a tÃcnica de downscaling dinÃmico para a obtenÃÃo das variÃveis meteorolÃgicas de previsÃo regionalizadas, demostrando algumas possibilidades de aplicaÃÃo dos resultados dos modelos climÃticos na modelagem hidrodinÃmica de reservatÃrios, indispensÃvel na modelagem da qualidade da Ãgua. Os resultados mostraram a possibilidade de calibraÃÃo e validaÃÃo do modelo hidrodinÃmico CE-QUAL-W2 com o uso de dados de reanÃlise atmosfÃrica, aplicaÃÃo de tÃcnicas de previsÃo climÃtica na avaliaÃÃo e previsÃo dos padrÃes hidrodinÃmicos de reservatÃrios e a necessidade de um sistema de monitoramento como subsidiÃrio de informaÃÃes relevantes à modelagem, no sentido de melhorar os sistemas existentes e aumentar o nÃvel de conhecimento sobre a dinÃmica de reservatÃrios localizados no semiÃrido. / The dynamics of water quality related processes in reservoirs is a function of its morphology, the action of meteorological variables and defluÃncias inflows and, to a greater extent. Predict the hydrodynamic behavior of reservoirs and the impact of changes or variability in weather forcing is essential to the management of water quality and was the main objective of this thesis. Therefore, we used climate models, hydrological, hydrodynamic and energy balance in cascade. The behavior of the resulting hydrodynamic modeling showed results in line with tropical reservoirs, representing the daily patterns of movement and the formation of thermal stratification in modeled reservoir. The main hydrodynamic seasonal variations could be modeled, albeit with a high level of uncertainty. Monitoring on a Miranda Pereira reservoir that provided a means to begin the modeling and integrated monitoring cycle was performed. The dynamic downscaling technique to obtain the meteorological variables of regionalized forecast was presented, showing some application possibilities of the results of climate models in hydrodynamic modeling of reservoirs, essential in modeling of water quality. The results showed the possibility of calibration and validation of the hydrodynamic model CE-QUAL-W2 using atmospheric reanalysis data, application of climate prediction techniques in assessing and predicting the hydrodynamic patterns of tanks and the need for a monitoring system as Subsidiary information relevant to modeling, to improve existing systems and increase the level of knowledge about the dynamics of reservoirs located in the semiarid.

The South African Weather Service rainfall seasonal forecasts are verified for the period of January-February-March to October-November-December 1998-2004. These forecasts are compiled using different models from different institutions. Probability seasonal forecasts can be evaluated using different skill measures, but in this study the Ranked Probability Skill Score (RPSS), Reliability Diagram (RD) and Relative Operating Characteristics (ROC) are used. The RPSS is presented in the form of maps whereas the RD and ROC are analyses are presented in the form of graphs. The aim of the study is to present skill estimates of operational seasonal forecasts issued at South African Weather Service A limited number of forecasts show positive RPSS value throughout the validation period. From RD and ROC analysis, there is no skill in predicting the normal category as compared to below-normal and above-normal categories. Notwithstanding, the frequency diagrams show that the normal category was often given a large weight in the operational forecasts. The value of verifying seasonal forecast accuracy from the user’s perspective is important. The understanding of seasonal forecast performance helps decision makers to determine when and how to respond to expected climate anomalies. Therefore the frequent update of the seasonal forecast verification is important in order to help Users make better decisions. Copyright / Dissertation (MSc)--University of Pretoria, 2008. / Geography, Geoinformatics and Meteorology / Unrestricted

Uncertainty in rainfall forecasts affects the level of quality and assurance for decisions made to manage water resource-based systems. However, eliminating uncertainty in a complete manner could be difficult, decision-makers thus are challenged to make decisions in the light of uncertainty. This study provides statistical models as an approach to cope with uncertainty, including: a) a statistical method relying on a Gaussian mixture (GM) model to assist in better characterize uncertainty in climate model projections and evaluate their performance in matching observations; b) a stochastic model that incorporates the El Niño–Southern Oscillation (ENSO) cycle to narrow uncertainty in seasonal rainfall forecasts; and c) a statistical approach to determine to what extent drought events forecasted using ENSO information could be utilized in the water resources decision-making process. This study also investigates the relationship between calibration and lead time on the ability to narrow the interannual uncertainty of forecasts and the associated usefulness for decision making. These objectives are demonstrated for the northwest region of Costa Rica as a case study of a developing country in Central America. This region of Costa Rica is under an increasing risk of future water shortages due to climate change, increased demand, and high variability in the bimodal cycle of seasonal rainfall. First, the GM model is shown to be a suitable approach to compare and characterize long-term projections of climate models. The GM representation of seasonal cycles is then employed to construct detailed comparison tests for climate models with respect to observed rainfall data. Three verification metrics demonstrate that an acceptable degree of predictability can be obtained by incorporating ENSO information in reducing error and interannual variability in the forecast of seasonal rainfall. The predictability of multicategory rainfall forecasts in the late portion of the wet season surpasses that in the early portion of the wet season. Later, the value of drought forecast information for coping with uncertainty in making decisions on water management is determined by quantifying the reduction in expected losses relative to a perfect forecast. Both the discrimination ability and the relative economic value of drought-event forecasts are improved by the proposed forecast method, especially after calibration. Positive relative economic value is found only for a range of scenarios of the cost-loss ratio, which indicates that the proposed forecast could be used for specific cases. Otherwise, taking actions (no-actions) is preferred as the cost-loss ratio approaches zero (one). Overall, the approach of incorporating ENSO information into seasonal rainfall forecasts would provide useful value to the decision-making process - in particular at lead times of one year ahead.

Cette thèse analyse (i) la variabilité pluviométrique contemporaine en Nouvelle-Calédonie et ses téléconnexions avec les températures de surface océanique (TSO) du Pacifique tropical et (ii) l'impact des anomalies atmosphériques sur l'activité des incendies estimés par satellites. L'objectif est de construire un modèle permettant de prévoir l'intensité de la saison des feux entre septembre et décembre (SOND). Le croisement de trois bases de données de feux détectés par satellites avec le réseau des stations météorologiques montre de forts déficits pluviométriques jusqu'à trois mois avant les feux. Ces déficits pluviométriques sont partiellement liés aux phases chaudes du phénomène El Niño Southern Oscillation (ENSO) et plus particulièrement à celles durant lesquelles les anomalies thermiques se situent à proximité de la ligne de changement de date équatoriale lors du printemps austral. Ces anomalies renforcent la circulation moyenne de Hadley et la subsidence au niveau des latitudes néo-calédoniennes. La téléconnexion entre les TSO du Pacifique central et les précipitations du Pacifique SW s'affaiblit à partir du mois de décembre au moment où l'ENSO atteint, paradoxalement, son intensité maximale. Cette modulation saisonnière est le produit d'une interaction entre (i) le cycle saisonnier des TSO brutes dans le Pacifique central, (ii) le cycle de vie des anomalies thermiques des épisodes chauds et (iii) l'intensité du gradient zonal des TSO le long de l'équateur. Une analyse en ondelettes montre que les pluies néo-calédoniennes sont également sensibles à des modes de variations plus lents (> 8 ans) du Pacifique central entre septembre et novembre. / This PhD analyses (i) New Caledonian rainfall variability and its relationships with sea surface temperature (SST) in the tropical Pacific ocean and (ii) the impacts of atmospheric variability on fire activity. Our main goal is to build an empirical statistical scheme for predicting the September to December fires. We examined the relationships between fires detected by ATSR and MODIS sensors and local-scale atmospheric conditions. While the signal in maximum temperature is weak and not robust among the fire records, the local-scale anomalies of rainfall are always clearly negative for at least 3 months before the fires. These rainfall anomalies are related to warm El Niño Southern Oscillation (ENSO) events and specially to those exhibiting highest SST anomalies in the central Pacific during the austral spring. The warm central Pacific events strengthen the southern Hadley cell around New Caledonian longitudes, with positive rainfall anomalies in the equatorial Pacific leading to an anomalous release of latent heat in the upper troposphere and an increased subsidence in the SW Pacific. Atmospheric anomalies are strongest in September–November because of a combination of a rather strong zonal SST gradient with the warmest SST in the equatorial Pacific just west of the dateline. Squared wavelet coherence between New Caledonia rainfall and Niño 4 SST index shows that their negative correlations are mostly carried by two distinct timescales : the classical ENSO variability and a quasi-decadal one, mainly during the September-November season.