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Improvement of Aerosol Representation in a Chemical-Transport Model: Modelling and Data Assimilation

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Improvement of Aerosol Representation in a Chemical-Transport Model: Modelling and Data Assimilation THÈSE En vue de l’obtention du DOCTORAT DE L’UNIVERSITÉ DE TOULOUSE Délivré par : l’Université Toulouse 3 Paul Sabatier (UT3 Paul Sabatier) Présentée et soutenue le 28/11/2014 par : Bojan SIČ Amélioration de la représentation des aérosols dans un modèle de chimie-transport : Modélisation et assimilation de données JURY Jean-Luc ATTIÉ (Université Toulouse III) Président du Jury Matthias BEEKMANN (LISA) Rapporteur Patrick CHAZETTE (CEA/DSM/LSCE) Rapporteur Laurent MENUT (LMD) Rapporteur Angela BENEDETTI (ECMWF, UK) Examinateur Virginie MARÉCAL (CNRM) Directeur de thése Laaziz EL AMRAOUI (CNRM) Codirecteur de thése Alain DABAS (CNRM) Co-encandrant de thése École doctorale et spécialité : SDU2E : Océan, Atmosphère et Surfaces Continentales Unité de Recherche : Centre National de Recherches Météorologiques – Groupe d’études de l’Atmosphère Météorologiques (UMR 3589) Directeur(s) de Thèse : Virginie MARÉCAL, Laaziz EL AMRAOUI et Alain DABAS Rapporteurs : Matthias BEEKMANN , Patrick CHAZETTE et Laurent MENUT Improvement of aerosol representation in a chemical-transport model: Modelling and data assimilation Summary: The improvement of the aerosol representation in a chemical transport model (CTM) permits better understanding of aerosol properties, forecasts, and their widespread effects. The main goal of this thesis is to improve the aerosol representation in the CTM MOCAGE. The work may be divided into two approaches to achieve the main goal: the direct improvement of aerosol physical schemes and parameterizations, and the development of a data assimilation system able to assimilate aerosol optical depth (AOD) and lidar profiles into the model. On the modelling side, the processes that underwent the important im- provements were sea salt, desert dust and volcanic aerosol emissions, wet deposi- tion and sedimentation. The ambition is related to improve the model biases compared to observations, and to implement more physically detailed schemes in the model. We evaluated the impacts of these changes and compared the modelled fields to observations. The implemented updates significantly enhanced the model agreement with the observations and the inter-model comparison data. The results also confirmed that large uncertainties in models can come from the use of different parameterizations. The aerosol data assimilation is implemented to further reduce the model uncertainties. The set of observation operators and their tangent linear and adjoint operators for AOD and lidar profile observations are developed to link the model and the observation space. Aerosol assimilation proved to be very efficient to reduce the differences between the model and the observations. AOD observations assimilated for the periods of the extensive field campaigns over the Mediterranean basin in 2012 and 2013 and the period of volcanic ash plume from the Eyjafjöll eruption in 2010 in Iceland showed that the AOD assimilation is able to significantly improve the model performance in terms of AOD, but also other aerosol parameters such as concentrations. Assimilation of different elastic backscatter lidar profile measurements, namely of the backscatter signal, the extinction coefficient and the backscatter coefficient, also showed an efficient influence on the modelled aerosol vertical profiles. Amélioration de la représentation des aérosols dans un modèle de chimie-transport : Modélisation et assimilation de données Resumé: L’amélioration de la représentation des aérosols dans un modèle de chimie- transport (en anglais, Chemical-Transport Model, CTM) permet une meilleure compréhension des propriétés des aérosols et leurs nombreux effets ainsi que leur prévision. Sur cette base, l’objectif général de cette thèse est d’améliorer la représentation des aérosols dans le CTM MOCAGE. Pour remplir les objectifs de la thèse, dans un premier temps, nous avons modifié directement la représentation des aérosols dans le CTM MOCAGE en réexaminant et améliorant les différents processus déjà présents via la prise en compte de schémas et de paramétrisations plus détaillés. Les processus ayant subi les améliorations les plus importantes sont les émissions des aérosols du type sel marin, poussière désertique et cendre volcanique, le dépôt humide et la sédimentation. Nous avons évalué les impacts de ces changements et comparé les champs modélisés avec des observations. Les modifications implémentées ont permis d’améliorer significativement l’accord entre modèle et observations et celui du modèle avec les données de la comparaison inter-modèle AeroCom. Comme approche complémentaire pour répondre à l’objectif de cette thèse, nous avons également implémenté dans le CTM MOCAGE l’assimilation de données des aérosols. Le système d’assimilation de données est capable d’assimiler les observations de l’épaisseur optique des aérosols (en anglais, Aerosol Optical Depth, AOD), ainsi que les différentes grandeurs obtenues par les mesures lidar. Nous avons réalisé une validation rigoureuse du système d’assimilation de l’AOD en assimilant les données de l’instrument MODIS pour les périodes correspondant aux campagnes de mesure des projets TRAQA (TRAnsport à longue distance et Qualité de l’Air dans le bassin méditerranéen) et ChArMEx (CHemistry and AeRosol MEditerranean EXperiment) durant l’été 2012 et 2013, respectivement, et en comparant les champs directement modélisés et assimilés avec les observations. Lorsque l’on compare ces résultats avec des observations indépendantes d’AOD, les champs assimilés ont des indicateurs statistiques meilleurs que ceux du modèle direct. Les mesures in-situ de TRAQA et ChArMEx ont également été utilisées pour évaluer l’impact de l’assimilation des AOD sur d’autres paramètres des aérosols modélisés. Les résultats ont montré que l’assimilation des AOD était un outil particulièrement efficace pour améliorer les performances du modèle en terme d’AOD, mais également pour les autres paramètres des aérosols tel que la concentration. Les observations lidar fournissent des informations importantes sur la répartition des aérosols sur la verticale. Nous avons implémenté un système d’assimilation des profils lidar mesurés pour des lidars à rétrodiffusion élastique. Le système est capable d’assimiler le signal de rétrodiffusion et les grandeurs inversées: les coefficients de rétrodiffusion et d’extinction. Les premiers tests effectués à partir des données d’un lidar au sol ont montré un impact cohérent sur le modèle en assimilant séparément les 3 différents types de mesures lidars implémentées. Ceci constitue un premier résultat très prometteur. Acknowledgements: Finishing a PhD is a long and demanding journey. Through it, many people helped me and made easier to pass all unfamiliar terrain that I encountered. I am indebted to all of them, and, here, I would like to acknowledge all their support. First of all, I am grateful to my supervisors Virginie Marécal and Laaziz El Amraoui, for being a great support during my whole PhD pursuit. They supported me with great promptness and care, and were always encouraging and patient with me throughout the thesis. They taught how a good research is done, and many insightful suggestions and discussions with them led to a better understanding of the problems and provided a constant advancement. During the thesis, it is Laaziz with whom I worked the most closely. All his scientific advices and knowledge were a priceless help for the accomplished work. He always listened and encouraged my new ideas, guided me in good directions, and his hard work gave me an excellent example. The same appreciation goes to Virginie. Her flexibility in scheduling, gentle encouragement and relaxed behaviour helped me a lot and made me to believe that we were doing a good thing, and especially were important at the end, when all what we did during the thesis had to be transferred to the manuscript. Also, the supervisors taught me that it is not only about science that we learn during a PhD. They gave important insights that helped me to grow as a researcher, colleague and person. Thank you for all that. I extend my gratitude also to Alain Dabas whose remarkable expertise in lidars and all his help were indispensable for our lidar assimilation. Besides supervisors, there very numerous colleagues that I have been privileged to get to know and to work with. From all in CARMA and CAIAC groups I learned a lot about different problems, techniques to solve them and in general, life. I would also like to thank to the board of CNRM/Meteo-France for believing in me and in the project from the very beginning, and for giving us all the necessary support. I would like to express gratitude to Jean-Luc Attié, who multiple times was there for me when it was time to choose my path (before my master internship and before the PhD thesis). For sure, this had a great impact on my achievements, and without this help I would not have arrived where I am today. Special thanks go also to my master thesis supervisor Philippe Ricaud who gave me an excellent basis how to do research well, which permitted me to start well my PhD thesis; and who, by chance, followed me and moved to CNRM soon after I started my thesis, which allowed me to have his help in many numerous ways during the PhD thesis. At the beginning of the second year of my thesis, before we seriously tackled with the aerosol assimilation, Laaziz and I visited ECMWF in Reading. There, we met Angela Benedetti and Jean-Jacques Morcrette who generously explained us and transferred their experience in the aerosol assimilation. This helped us to better and faster identify the the main steps, problems and choices that we have for the development of the aerosols assimilation algorithm. For all the help during the visit and later, I am indebted to Angela and Jean-Jacques. The implementation of the aerosol assimilation in the Valentina assimi- lation system would not have been possible without expertise of Cerfacs team. I am indebted to Andrea Piacentini and Emanuele Emili for their unselfish help. Special gratitude goes to Andrea for all the help in the implementation of the developed observation operators to Valentina.
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