Représentation du cycle de vie des systèmes convectifs dans le modèle LMDZ pendant la campagne AMMA 2006

Cette thèse a pour objectif d'étudier et d'améliorer la représentation du cycle de vie des systèmes convectifs de l'Afrique de l'Ouest (orages tropicaux, lignes de grains, ...) dans les modèles de circulation atmosphérique. Ces modèles de circulation sont devenus un des outils de base pour à la fois les recherches en physique du climat et pour la prévision opérationnelle du temps. La gamme d'échelles spatiales impliquée rend particulièrement complexe la modélisation de la mousson Ouest Africaine. Cependant, beaucoup de progrès ont été réalisés ces dernières années au Laboratoire de Météorologie Dynamique dans le développement des paramétrisations de convection nuageuse (Rio and Hourdin, 2008; Grandpeix et al., 2010) et le modèle LMDZ est capable de représenter de façon correcte les caractéristiques moyennes du système de mousson Ouest Africaine. Notre travail de thèse repose en grande partie sur l'exploitation des données recueillies au cours de l'été 2006 pendant la période d'observation intensive (mesures sol, avions, ballons) de la campagne d'Analyse Multidisciplinaire de la Mousson Africaine (AMMA). L'amélioration de la représentation de la convection dans ces régions est un enjeu de première importance, à la fois pour l'amélioration des prévisions du temps et pour essayer de prévoir les possibles variations du climat sur la région dans le cadre du réchauffement global du climat. La discrimination des systèmes locaux et propagatifs a permis de faire la comparaison des systèmes locaux avec le modèle qui ne représente pas pour le moment la propagation des systèmes convectifs. Ainsi le cycle diurne des systèmes locaux est comparable au cycle diurne du modèle. Un des résultats marquant obtenu durant cette thèse concerne la représentation du cycle diurne des précipitations convectives. Nous avons en particulier, montré que la nouvelle "paramétrisation" de la convection orageuse, prenant en compte explicitement les "poches froides" créées sous les nuages par réévaporation des pluies convectives, développée par Grandpeix et al., (2010), permet de décaler dans la soirée les pluies convectives (qui se produisent en générale beaucoup trop tot dans les modèles, vers 12h, en phase avec l'insolation), en bien meilleur accord avec les observations. Nous avons aussi mis au point une approche d'initialisation physique de la convection par des températures de brillance du satellite Météosat Seconde Génération (MSG). Cette approche d'initialisation détectant les zones convectives donne une meilleure représentation spatiale de la convection de manière cohérente avec les observationsPARIS-BIUSJ-Biologie recherche (751052107) / SudocSudocFranceF

Évolution récente de la pluviométrie en Afrique de l’ouest à travers deux régions : la Sénégambie et le bassin du Niger moyen

La mousson ouest-africaine rythme le calendrier agricole de toute l’Afrique de l’Ouest; celui-ci est de plus en plus court au fur et à mesure que l’on se déplace vers le Nord, comme la durée et l’abondance de la mousson diminuent. Après une période de sécheresse de 1968 à 1995, l’Afrique de l’Ouest connaît plutôt depuis la fin du dernier millénaire un retour à des conditions pluviométriques plus humides; celles-ci, aux latitudes soudano-sahéliennes, sont similaires, en termes de moyenne et de variabilité interannuelle, à celles qui ont été observées de 1900 à 1950. L’objectif est de montrer en quoi l’évolution pluviométrique récente explique la dynamique hydrologique et agronomique de la région ouest-africaine, en particulier l’occurrence accrue des inondations et le faible regain des rendements agricoles en dépit du retour à une pluviométrie plus favorable. Des méthodes statistiques simples sont utilisées dans deux sous-régions, la Sénégambie et le bassin du Niger Moyen, pour mettre en évidence l’évolution, sur la période 1950-2013, des caractéristiques de la mousson qui ont un intérêt hydrologique et agronomique (cumuls annuels, pluies extrêmes, date de début et de fin et durée de la saison des pluies). On observe que les périodes 1900-1950 et 1995-2015 peuvent être considérées comme des périodes de pluviométrie moyenne, les périodes 1951-1967 et 1968-1995 étant des périodes respectivement humides et sèches. Par ailleurs, on observe une augmentation des jours de pluie de fort cumul bien plus rapide que celle de la pluie elle-même. Enfin, si la saison des pluies est à présent sensiblement plus longue que durant la phase sèche, on observe pourtant ces dernières années dans le Sahel central un retour des « mauvais » hivernages au sens agronomique du terme

Intensity-duration-frequency (IDF) rainfall curves in Senegal

International audienceUrbanization resulting from sharply increasing demographic pressure and infrastructure development has made the populations of many tropical areas more vulnerable to extreme rainfall hazards. Characterizing extreme rainfall distribution in a coherent way in space and time is thus becoming an overarching need that requires using appropriate models of intensity-duration-frequency (IDF) curves. Using a 14 series of 5 min rainfall records collected in Senegal, a comparison of two generalized extreme value (GEV) and scaling models is carried out, resulting in the selection of the more parsimonious one (four parameters), as the recommended model for use. A bootstrap approach is proposed to compute the uncertainty associated with the estimation of these four parameters and of the related rainfall return levels for durations ranging from 1 to 24 h. This study confirms previous works showing that simple scaling holds for characterizing the temporal scaling of extreme rainfall in tropical regions such as sub-Saharan Africa. It further provides confidence intervals for the parameter estimates and shows that the uncertainty linked to the estimation of the GEV parameters is 3 to 4 times larger than the uncertainty linked to the inference of the scaling parameter. From this model, maps of IDF parameters over Senegal are produced, providing a spatial vision of their organization over the country, with a north to south gradient for the location and scale parameters of the GEV. An influence of the distance from the ocean was found for the scaling parameter. It is acknowledged in conclusion that climate change renders the inference of IDF curves sensitive to increasing non-stationarity effects, which requires warning end-users that such tools should be used with care and discernment

Development of an Updated Global Land In Situ‐Based Data Set of Temperature and Precipitation Extremes: HadEX3

We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org.Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101) and thanks Nick Rayner and Lizzie Good for helpful comments on the manuscript. Lisa Alexander is supported by the Australian Research Council (ARC) Grants DP160103439 and CE170100023. Markus Donat acknowledges funding by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 Grant Reference RYC‐2017‐22964. Mohd Noor'Arifin Bin Hj Yussof and Muhammad Khairul Izzat Bin Ibrahim thank the Brunei Darussalam Meteorological Department (BDMD). Ying Sun was supported by China funding agencies 2018YFA0605604 and 2018YFC1507702. Fatemeh Rahimzadeh and Mahbobeh Khoshkam thank I.R. of Iranian Meteorological Organization (IRIMO) and the Atmospheric Science and Meteorological Organization Research Center (ASMERC) for Data and also sharing their experiences, especially Abbas Rangbar. Jose Marengo was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014‐1, FAPESP Grants 2014/50848‐9 and 2015/03804‐9, and the National Coordination for High Level Education and Training (CAPES) Grant 88887.136402‐00INCT. The team that worked on the data in West Africa received funding from the UK's National Environment Research Council (NERC)/Department for International Development DFID) Future Climate For Africa programme, under the AMMA‐2050 project (Grants NE/M020428/1 and NE/M019969/1). Data from Southeast Asia (excl. Indonesia) was supported by work on using ClimPACT2 during the Second Workshop on ASEAN Regional Climate Data, Analysis and Projections (ARCDAP‐2), 25–29 March 2019, Singapore, jointly funded by Meteorological Service Singapore and WMO through the Canada‐Climate Risk and Early Warning Systems (CREWS) initiative. This research was supported by Thai Meteorological Department (TMD) and Thailand Science Research and Innovation (TSRI) under Grant RDG6030003. Daily data for Mexico were provided by the Servicio Meteorológico Nacional (SMN) of Comisión Nacional del Agua (CONAGUA). We acknowledge the data providers in the ECA&D project (https://www.ecad.eu), the SACA&D project (https://saca-bmkg.knmi.nl), and the LACA&D project (https://ciifen.knmi.nl). We thank the three anonymous reviewers for their detailed comments which improved the manuscript.Peer ReviewedPostprint (published version

Development of an updated global land in situ‐based data set of temperature and precipitation extremes: HadEX3

We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org.Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101) and thanks Nick Rayner and Lizzie Good for helpful comments on the manuscript. Lisa Alexander is supported by the Australian Research Council (ARC) Grants DP160103439 and CE170100023. Markus Donat acknowledges funding by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 Grant Reference RYC‐2017‐22964. Mohd Noor'Arifin Bin Hj Yussof and Muhammad Khairul Izzat Bin Ibrahim thank the Brunei Darussalam Meteorological Department (BDMD). Ying Sun was supported by China funding agencies 2018YFA0605604 and 2018YFC1507702. Fatemeh Rahimzadeh and Mahbobeh Khoshkam thank I.R. of Iranian Meteorological Organization (IRIMO) and the Atmospheric Science and Meteorological Organization Research Center (ASMERC) for Data and also sharing their experiences, especially Abbas Rangbar. Jose Marengo was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014‐1, FAPESP Grants 2014/50848‐9 and 2015/03804‐9, and the National Coordination for High Level Education and Training (CAPES) Grant 88887.136402‐00INCT. The team that worked on the data in West Africa received funding from the UK's National Environment Research Council (NERC)/Department for International Development DFID) Future Climate For Africa programme, under the AMMA‐2050 project (Grants NE/M020428/1 and NE/M019969/1). Data from Southeast Asia (excl. Indonesia) was supported by work on using ClimPACT2 during the Second Workshop on ASEAN Regional Climate Data, Analysis and Projections (ARCDAP‐2), 25–29 March 2019, Singapore, jointly funded by Meteorological Service Singapore and WMO through the Canada‐Climate Risk and Early Warning Systems (CREWS) initiative. This research was supported by Thai Meteorological Department (TMD) and Thailand Science Research and Innovation (TSRI) under Grant RDG6030003. Daily data for Mexico were provided by the Servicio Meteorológico Nacional (SMN) of Comisión Nacional del Agua (CONAGUA). We acknowledge the data providers in the ECA&D project (https://www.ecad.eu), the SACA&D project (https://saca-bmkg.knmi.nl), and the LACA&D project (https://ciifen.knmi.nl). We thank the three anonymous reviewers for their detailed comments which improved the manuscript.Peer ReviewedPostprint (published version