Das Projekt "Future role of Methane Emissions in the climate System (FuMES)" wird vom Umweltbundesamt gefördert und von Eidgenössische Technische Hochschule Zürich, Institut für Atmosphäre und Klima durchgeführt. Future role of Methane Emissions in the climate System (FuMES) In its most recent assessment report the IPCC stated that the observed warming of the climate system is most likely caused by increased anthropogenic greenhouse gas emissions. Methane (CH4) emissions caused by human activities have led to a doubling of atmospheric CH4 concentrations since pre-industrial times, making CH4 to be the second most important long-lived greenhouse gas after CO2 (in terms of radiative forcing). With a 25-times larger global warming potential than CO2 (on a 100-yr time horizon), methane would become even more important as driver of climate change if its atmospheric concentrations continued to rise. In light of this development several mitigation options to reduce anthropogenic CH4 emissions have been proposed. However, CH4 emissions from natural wetlands might increase in a warmer climate and offset future efforts to reduce anthropogenic emissions. Substantial uncertainties in the understanding of the complex interactions between atmospheric methane and other atmospheric quantities like OH, CO, O3, tropospheric UV fluxes, aerosols and clouds make reliable projections difficult. To contribute to a clarification of these uncertainties we propose here to run an ensemble of short- and long-term model simulations using the state-of-the-art atmosphere-chemistry-climate model SOCOL coupled to a dynamic global vegetation model. The following questions will be addressed in detail: - What is the relative importance of individual source and sink processes for the short- and long-term variability of the atmospheric methane abundance? Which processes determine the strength of individual methane sources and sinks? - How will methane emissions from natural wetlands change in a warmer climate? - How will the oxidation capacity of the troposphere and, therefore, the main atmospheric methane sink change in future? What are the implications for air quality? - How do proposed methane mitigation measures compare with a climate-change related aggravation of natural methane emissions? The envisaged model system will be able to simulate the atmospheric methane cycle in a self-consistent manner, including atmospheric methane sinks, CH4 uptake in soils as well as methane emissions from wetlands and other sectors. Furthermore, the model will capture the relevant feedback effects between methane emissions and the coupled chemistry-climate system of troposphere and stratosphere, including land-surface processes. The application of this novel model is expected to allow for a more reliable estimate of the future role of atmospheric methane in the climate system.
Das Projekt "Public health impacts in URban environments of Greenhouse gas Emissions reduction strategies (PURGE)" wird vom Umweltbundesamt gefördert und von London School of Hygiene and Tropical Medicine durchgeführt. Objective: The project will examine the health impacts of greenhouse gas (GHG) reduction policies in urban settings in Europe, China and India, using case studies of 3-4 large urban centres and three smaller urban centres. Sets of realistic interventions will be proposed, tailored to local needs, to meet published abatement goals for GHG Emissions for 2020, 2030 and 2050. Mitigation actions will be defined in four main sectors: power generation/industry, household energy, transport and food and agriculture. The chief pathways by which such measures influence health will be described, and models developed to quantify changes in health-related 'exposures' and health behaviours. Models will include ones relating to outdoor air pollution, indoor air quality and temperature, physical activity, dietary intake, road injury risks and selected other exposures. Integrated quantitative models of health impacts will be based on life table methods encompassing both mortality and morbidity outcomes modelled over 20 year time horizons. Where possible, exposure-response relationships will be based on review evidence published by the Comparative Risk Assessment initiative or systematic reviews. Uncertainties in model estimates will be characterized using a mathematical framework to quantify the influence of uncertainties in both model structure and parameter estimates. Particular attention will be given to economic assessments, both in terms of behavioural choices/uptake of various forms of mitigation measure (with new surveys to address evidence gaps), and in terms of health benefits and costs calculated from societal, health service and household perspectives. A decision analysis framework will be developed to compare different mitigation options. Experts and user groups will be consulted to define the mitigation questions to be examined, and the results will be discussed in consultative workshops scheduled for the final months of the project.
Das Projekt "Analysing climate change mitigation and adaptation strategies for sustainable rural land use and landscape developments in Austria (CC-ILA)" wird vom Umweltbundesamt gefördert und von Universität für Bodenkultur Wien, Institut für Landschaftsentwicklung, Erholungs- und Naturschutzplanung (ILEN) durchgeführt. Changes in European agricultural landscapes have gained on intensification in the second half of the last century. Among others, they are driven by global change phenomena such as climate change, demographic change and migration, increasing global bio-energy demands and changing human diets as well as by trade liberalisation, technological progress, and leakage effects of land use policy interventions. Farmers usually respond to such changes by adapting production and land use systems to efficiently utilize and manage their farm resource endowments. However, this process often leads to adverse impacts on the diversity of agricultural landscapes and environmental qualities. EU policies have been formulated as a reaction to singular or sectoral problems (e.g. the Common Agricultural Policy, the Water Framework Directive, the Nitrates Directive, NATURA2000), which are usually differently implemented among member states by using a variety of legislative or incentive based instruments. Consequently, more coordination among policies is required to minimize the trade-offs between different land use policy targets (i.e. land conservation versus boosting biomass production), and between private (adaptive) and societal (mitigative) land use benefits. Mitigation and adaptation are often separately analysed due to the nature of the problem i.e. mitigation is often considered as public good versus adaptation as private or club good. However, it is necessary to consider both in assessing the mutual benefits of cost-effective land uses and farm mitigation and adaptation measures, which mainly depend on spatial heterogeneity of natural and farming conditions. Consequently, it is important to consider bio-physical, ecological, and economic relationships in assessing the mitigative (public) and adaptive (private) potentials and trade-offs of alternative land uses and farm management measures.In this project we implement a data-model-policy fusion concept, which shall guarantee cost-effective mitigation and adaptation of farms and sustainable landscape and biodiversity developments in the context of climate, market, and policy instrument changes. The concept is applied to two case-study landscapes in the Mostviertel region in Austria and contains an integrated spatially explicit modelling framework to simulate the land use changes at field, farm, and landscape level as well as cost-effective farm mitigation and adaptation portfolios. The land use changes are assessed with farm economic, biodiversity, abiotic, and landscape indicators including GIS-modelling and field observations. Biodiversity effects are central in the integrated assessment acknowledging the roles of landscape structure and land use intensity. Geo-referenced land uses and land use attributes are a major interface in the data-model-policy fusion concept. The results will help farmers and regional stakeholders to identify best management practices for climate change mitigation and adaptation i
Das Projekt "Analysing climate change mitigation and adaptation strategies for sustainable rural land use and landscape developments in Austria (CC-ILA) - Analysing climate change mitigation and adaptation strategies for sustainable rural land use and landscape developments in Austria - Teilprojekt TS" wird vom Umweltbundesamt gefördert und von Universität für Bodenkultur Wien, Institut für Landschaftsentwicklung, Erholungs- und Naturschutzplanung (ILEN) durchgeführt. Changes in European agricultural landscapes have gained on intensification in the second half of the last century. Among others, they are driven by global change phenomena such as climate change, demographic change and migration, increasing global bio-energy demands and changing human diets as well as by trade liberalisation, technological progress, and leakage effects of land use policy interventions. Farmers usually respond to such changes by adapting production and land use systems to efficiently utilize and manage their farm resource endowments. However, this process often leads to adverse impacts on the diversity of agricultural landscapes and environmental qualities. EU policies have been formulated as a reaction to singular or sectoral problems (e.g. the Common Agricultural Policy, the Water Framework Directive, the Nitrates Directive, NATURA2000), which are usually differently implemented among member states by using a variety of legislative or incentive based instruments. Consequently, more coordination among policies is required to minimize the trade-offs between different land use policy targets (i.e. land conservation versus boosting biomass production), and between private (adaptive) and societal (mitigative) land use benefits. Mitigation and adaptation are often separately analysed due to the nature of the problem i.e. mitigation is often considered as public good versus adaptation as private or club good. However, it is necessary to consider both in assessing the mutual benefits of cost-effective land uses and farm mitigation and adaptation measures, which mainly depend on spatial heterogeneity of natural and farming conditions. Consequently, it is important to consider bio-physical, ecological, and economic relationships in assessing the mitigative (public) and adaptive (private) potentials and trade-offs of alternative land uses and farm management measures.In this project we implement a data-model-policy fusion concept, which shall guarantee cost-effective mitigation and adaptation of farms and sustainable landscape and biodiversity developments in the context of climate, market, and policy instrument changes. The concept is applied to two case-study landscapes in the Mostviertel region in Austria and contains an integrated spatially explicit modelling framework to simulate the land use changes at field, farm, and landscape level as well as cost-effective farm mitigation and adaptation portfolios. The land use changes are assessed with farm economic, biodiversity, abiotic, and landscape indicators including GIS-modelling and field observations. Biodiversity effects are central in the integrated assessment acknowledging the roles of landscape structure and land use intensity. Geo-referenced land uses and land use attributes are a major interface in the data-model-policy fusion concept. The results will help farmers and regional stakeholders to identify best management practices for climate change mitigation and adaptation i
Das Projekt "Vorbereitung, Durchführung und Abwicklung einer internationalen Fachveranstaltung 'Risk-mitigation measures for biocidal products - efficacy and practicability" wird vom Umweltbundesamt gefördert und von Hydrotox Labor für Ökotoxikologie und Gewässerschutz GmbH durchgeführt.
Das Projekt "Quantifying climate change uncertainty from the CMIP3 ensemble of global coupled climate models" wird vom Umweltbundesamt gefördert und von Eidgenössische Technische Hochschule Zürich, Institut für Atmosphäre und Klima durchgeführt. The Earth's climate is changing as a response to anthropogenic emissions of fossil fuels, and is very likely to do so over the next decades to centuries. The projected changes from multiple numerical climate models differ significantly, because some feedbacks and processes are poorly understood or cannot be resolved in the models. Yet a quantitative picture of the uncertainty associated with the expected changes on regional and global scales is crucial, in order to quantify impacts, and decide on adaptation and mitigation measures. The objective of this project is to assess the uncertainty in large-scale regional to global climate change projections based on the CMIP3 dataset, a collection of simulations of about twenty global climate models coordinated for the recent IPCC report. While the amount of data produced by climate models and the desire for ever more detailed projections and probabilistic information is growing rapidly, there is a lack of understanding on how to define the skill of a climate model for a forecast that in practice cannot be verified. So far, models are often averaged regardless of their ability to satisfy even basic requirements, and the community is reluctant to define a set of metrics that would seem important for a model to be credible. Also, a consensus of uncertainty in climate change projections on regional to global scales is missing. We propose to develop a framework to evaluate climate models for their suitability in predicting changes by a set of metrics that are important to be simulated correctly from a physical point of view, rather than comparing present day simulations to observations. In addition, by treating one model M as observations, statistical methods can be used to find optimal predictors and to define model weight, such that a weighted mean or a probabilistic method using the rest of the models can best predict M. The issue of common model error and model dependence will be addressed. We also propose novel methods to aggregate results in regional averages using clustering techniques, and a more flexible approach to communicating results using climate change indices. As milestones of the project, we will attempt to answer the following questions: How large are the uncertainties of model projections on different spatial and temporal scales? On what spatial scale can the current models provide robust information? How can we quantify model performance for projections, and use that to define model weight? Is a small set of 'good' models more useful than a large number of 'good' and 'bad' models? How do model dependence, common biases, and overconfidence affect projection uncertainties? How can we attach confidence to climate change indices? The proposed work combines efforts in climate research and statistics. The results will be used in improving a Bayesian hierarchical model to generate probabilistic climate change information for the spatial scales on which .
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