The influence of anthropogenic emissions on aerosol distributions and the hydrological cycle are examined with a focus on monsoon precipitation over the Indian subcontinent, during January 2001 to December 2005, using the European Centre for Medium-Range Weather Forecasts-Hamburg (ECHAM5.5) general circulation model extended by the Hamburg Aerosol Module (HAM). The seasonal variability of aerosol optical depth (AOD) retrieved from the MODerate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellite is broadly well simulated (R 0.6–0.85) by the model. The spatial distribution and seasonal cycle of the precipitation observed over the Indian region are reasonably well simulated (R 0.5 to 0.8) by the model, while in terms of absolute magnitude, the model underestimates precipitation, in particular in the south-west (SW) monsoon season. The model simulates significant anthropogenic aerosol-induced changes in clear-sky net surface solar radiation (dimming greater than -7 W m-2), which agrees well with the observed trends over the Indian region. A statistically significant decreasing precipitation trend is simulated only for the SWmonsoon season over the central-north Indian region, which is consistent with the observed seasonal trend over the Indian region. In the model, this decrease results from a reduction in convective precipitation, where there is an increase in stratiform cloud droplet number concentration (CDNC) and solar dimming that resulted from increased stability and reduced evaporation. Similarities in spatial patterns suggest that surface cooling, mainly by the aerosol indirect effect, is responsible for this reduction in convective activity. When changes in large-scale dynamics are allowed by slightly disturbing the initial state of the atmosphere, aerosol absorption in addition leads to a further stabilization of the lower troposphere, further reducing convective precipitation.

To date, no observation-based proxy for climate change has been successful in quantifying the feedbacks between clouds and climate. The most promising, yet demanding, avenue to gain confi dence in cloud–climate feedback estimates is to utilize observations and large-eddy simulations (LES) or cloud-resolving modeling (CRM) to improve cloud process parameterizations in large-scale models. Sustained and improved satellite observations are essential to evaluate large-scale models. A reanalysis of numerical prediction models with assimilation of cloud, aerosol, and precipitation observations would provide a valuable dataset for examining cloud interactions. The link between climate modeling and numerical weather prediction (NWP) may be exploited by evaluating how accurate cloud characteristics are represented by the parameterization schemes in NWP models. A systematic simplifi cation of large-scale models is an important avenue to isolate key processes linked to cloud–climate feedbacks and would guide the formulation of testable hypotheses for fi eld studies. Analyses of observation-derived correlations between cloud and aerosol properties in combination with modeling studies may allow aerosol–cloud interactions to be detected and quantifi ed. Reliable representations of cloud dynamic and physical processes in large-scale models are a prerequisite to assess aerosol indirect effects on a large scale with confi dence. To include aerosol indirect effects in a consistent manner, we recommend that a “radiative fl ux perturbation” approach be considered as a complement to radiative forcing.

Aktivitäten und Publikationen des Institut für Meteorologie der Universität Leipzig für 2005

Jahresbericht und Forschungsprojekte des Instituts für Meteorologie der Universität Leipzig für das Jahr 2014

Forschungsberichte aus dem Institut für Meteorologie der Universität Leipzig

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Aktivitäten und Publikationen des Institut für Meteorologie der Universität Leipzig für 2008.

Aktivitäten und Publikationen des Institut für Meteorologie der Universität Leipzig für 2002

Forschungsaktivitäten des Instituts für Meteorologie der Universität Leipzig

Hochwasser stellt ein großes Risiko für Wohngebäude in Europa dar, und es wird erwartet, dass das Risiko in der Zukunft aufgrund klimatischer und sozioökonomischer Veränderungen zunehmen wird. Aktuelle Hochwasserrisikomodelle basieren meist auf einfachen Wasserstands-Schadenskurven. Diese Ansätze vereinfachen die Hochwasserschadensprozesse stark, können ungenau sein und bergen große Unsicherheiten, die oft nicht quantifiziert. Die Doktorarbeit stellt die Integration neuer Daten in probabilistische, multivariable Schadensmodelle zur Verbesserung ihrer Übertragbarkeit vor. Diese neuen Datenquellen und Modellierungsansätze werden verwendet, um zukünftige Veränderung des Hochwasserrisikos für Wohngebäude in Europa abzuschätzen und Risikokomponenten zu analysieren. Die Arbeit zeigt, OpenStreetMap (OSM) Daten liefern nützliche Informationen für die Modellierung von Hochwasserschäden und ermöglichen Modelltransfers. Die Integration von aus OSM abgeleiteten Gebäudeeigenschaften und Hochwassererfahrung aus Ereignisdatenbanken in das Bayes’sche Netzwerk basierte Hochwasserschadensmodelle für den privaten Sektor (BN-FLEMOps) ermöglichte die Implementierung auf der Mesoskala. Durch Vergleiche von Schadensschätzungen mit beobachteten Schäden in mehreren Fallstudien in Europa wurde das Modell validiert und detailliert mit einem Ensemble aus 20 Schadensmodellen verglichen. In einer abschließenden Studie werden die zukünftigen Veränderungen des Risikos für Wohngebäude in Europa modelliert. Die erwarteten jährlichen Schäden bis zum Ende des 21. Jahrhunderts werden um das 10-fache ansteigen. Die Britischen Inseln und der größte Teil von Zentral-Europa müssen mit einer starken Risikozunahme rechnen. Teile Skandinaviens und des Mittelmeerraums werden dagegen ein stagnierendes oder abnehmendes Hochwasserrisiko verzeichnen. Eine Verbesserung der privaten Vorsorgemaßnahmen könnte das Hochwasserrisiko im Mittel um 15 % und in einigen europäischen Regionen um bis zu 20 % verringern. / Flooding poses great risks for residential buildings in Europe and is expected to increase in the future, driven by climatic and socio-economic change. Current flood risk models rely mostly on simple stage-damage curves for flood loss estimation. This approach oversimplifies flood damage processes, can be inaccurate and harbour large uncertainties that often are not quantified and transparently communicated. This thesis presents research that integrates new data sources into probabilistic, multi-variable loss models to improve their transferability. These new data sources and approaches are used to estimate future fluvial flood risk change for residential buildings in Europe. Contributions of the three risk components, hazard, exposure, and vulnerability are analysed and compared independently and in combination. OpenStreetMap (OSM) data are identified as a valuable source of information for flood loss modelling and enables model transfers while retaining high predictive performance. Integrating OSM derived building characteristics and flood experience information from flood event databases into the Bayesian Network Flood Loss Estimation MOdel for the private sector (BN-FLEMOps) enables the spatio-temporal and scale transformation of the model. The model is validated with reported losses in multiple case studies in Europe and compared in detail with a model ensemble of 20 internationally published flood loss models. In a final study, the future flood risk changes for residential buildings in Europe are modelled. The expected annual damage will increase up to 10-fold until the end of the 21st century. Most of Central Europe and the British Isles have to expect strong risk increases. Parts of Scandinavia and the Mediterranean on the other hand will see stagnating or decreasing fluvial flood risk. Improving private precaution could reduce flood risk by 15 % on average and up to 20 % in some European regions.