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Cloud-scale Uncertainties - B7: Identification of robust cloud patterns via inverse methods

Das Projekt "Cloud-scale Uncertainties - B7: Identification of robust cloud patterns via inverse methods" wird vom Umweltbundesamt gefördert und von Johannes Gutenberg-Universität Mainz, Institut für Physik der Atmosphäre durchgeführt. Cloud patterns and structures in clouds depend crucially on the atmospheric flow field as well as thermodynamic conditions at cloud formation. However, it is not clear how robust these structures are in terms of variations in environmental conditions (e.g., humidity, temperature, etc.) as well as parameters in cloud parameterizations. Since cloud patterns on the order of few tens of kilometers can in turn influence the atmospheric flow via organized latent heat release or radiation feedbacks, the robustness of cloud structures is an important feature. In this project we will investigate variations in cloud variables and cloud structures due to different sources of uncertainties. First, variations in cloud variables are driven by parameters in cloud parameterizations (i.e. in the representation of cloud processes in the cloud models). Second, variations in environmental conditions might lead to different pathways of cloud formation and evolution. In order to determine the variations due to different sources of uncertainties, we will apply inverse methods. We will setup a simple but realistic analytical cloud model, consisting of a set of ordinary differential equations, which will subsequently be coupled to hyperbolic conservation laws associated to sedimentation processes. This model will be coupled to simple dynamics in the sense of kinematic frameworks. We will use a Bayesian approach to obtain confidence intervals for the unknown model parameters, in combination with sparsity enhancing priors. This analysis will also point out potentials for further reduction of the model complexity. In order to assess the variations of initial cloud conditions, we will use two different but complementary methods. As first method, we will use the analytical cloud model coupled to simple dynamics for time-reversal calculations, integrating the model backward in time and evaluating its variation due to perturbed 'initial' conditions. The method will lead to a full spread in variations, but might break down at bifurcations in the system. Complementary to the first approach, we will develop an adjoint model for the analytical cloud model, to be employed for an iterative solution of the inverse problem. This sophisticated approach will provide possible initial cloud configurations under the assumption of convergence, but will not address possible pathways and not detect different initial states that give similar 'observations at weather stations'. Finally, we will collect results from the different but complementary methods in order to determine in a synthesis the variability of cloud variables and cloud patterns due to variations in model parameter as well as cloud environmental conditions.

Towards robust PROjections of European FOrests UNDer climate change (PROFOUND)

Das Projekt "Towards robust PROjections of European FOrests UNDer climate change (PROFOUND)" wird vom Umweltbundesamt gefördert und von Potsdam-Institut für Klimafolgenforschung e.V. durchgeführt. Changes in climate, environment and management are altering the worlds ecosystems. Forests are of particular importance in this context due to the significant economic, ecological and cultural services they provide. Projecting changes of these services for the next decades is crucial for a concerted European response to environmental change. There are a number of challenges to meet. Mechanistic forest models that can be extrapolated to new environmental conditions are still associated with considerable predictive uncertainty. The reasons are the lack of harmonized datasets at larger scales and the difficulty to obtain robust methods for parameterisation, evaluation and model comparison that make optimal use of the range of available data types and sources. PROFOUND addresses these problems by: 1) suggesting ways to harmonize and integrate European forest data for model projections 2) comparing forest modelling approaches in terms of scale, processes captured, data requirements and predictive uncertainty 3) developing standards for (Bayesian) model parameterisation, calibration, evaluation and comparison, and 4) facilitating probabilistic multi-model projections under climatic change. As a result, PROFOUND will strengthen the integration of forest modellers and data-providing experts across Europe and provide more reliable information about the uncertainty of model predictions to decision makers.

Sub project: Impact of land-use and functional diversity on diversity and stability of grassland communities in fragmented landscapes

Das Projekt "Sub project: Impact of land-use and functional diversity on diversity and stability of grassland communities in fragmented landscapes" wird vom Umweltbundesamt gefördert und von Universität Göttingen, Büsgen-Institut, Abteilung Ökosystemmodellierung durchgeführt. With a dramatic decline in species richness, it becomes increasingly important to predict the long-term impact of global changes (climate change and land-use intensification on grassland plant community composition and services. However, to date, our ability for such predictions is very limited. To improve our predictive ability, we have to learn more about the interplay between components of community structure and ecosystem functioning. Motivated by a recent call to rebuild community ecology from functional traits, we propose a new approach to link the relationship between the response trait diversity of plant communities, i.e. diversity of life-history traits, and the long-term persistence of species diversity and ecosystem productivity in fragmented landscapes. Our approach consists of the combination of three complementary tools: an equation-based metacommunity simulation model, species distribution models, and Bayesian belief networks. Using this diverse toolbox, we seek to combine information concerning the life-history, dispersal, and geographic range of species with information about the distribution of suitable grassland habitats in space. This approach will be applied to grassland metacommunities in the Biodiversity Exploratories. Mechanistic models are an essential component of this project because of their ability to incorporate knowledge from disparate domains and different spatial scales. Specifically, using the framework proposed here, models link traits and performance measures such as persistence of species diversity and ecosystem productivity and this would not be possible without modelling. Ultimately, we seek to integrate both conceptual and empirical knowledge. Thus, we give an outlook towards the development of causal models scheduled for a follow-up phase. Causal models are easy to communicate and can be continually updated when new data or conceptual knowledge becomes available. However, the steps planned for the current project phase will already noticeably improve our predictive ability on metacommunity responses to global change.

Establishment of Teak plantations for high-value timber production in Ghana

Das Projekt "Establishment of Teak plantations for high-value timber production in Ghana" wird vom Umweltbundesamt gefördert und von Universität Hamburg, Arbeitsbereich für Weltforstwirtschaft und Institut für Weltforstwirtschaft des Friedrich-Löffler-Institut, Bundesforschungsinstitut für Tiergesundheit durchgeführt. Background and Objectives: The project area is located in the Ashanti Region of Ghana / West Africa in the transition zone of the moist semideciduous forest and tropical savannah zone. Main land use in this region is subsistence agriculture with large fallow areas. As an alternative land-use, forest plantations are under development by the Ghanaian wood processing company DuPaul Wood Treatment Ltd. Labourers from the surrounding villages are employed as permanent or casual plantation workers. Within three forest plantation projects of approximately 6,000 ha, DuPaul offers an area of 164 ha (referred to as Papasi Plantation) - which is mainly planted with Teak (Tectona grandis) - for research purposes. In return, the company expects consultations to improve the management for sustainable timber and pole production with exotic and native tree species. Results: In a first research approach, the Papasi Plantation was assessed in terms of vegetation classification, timber resources (in qualitative and quantitative terms) and soil and site conditions. A permanent sampling plot system was established to enable long-term monitoring of stand dynamics including observation of stand response to silvicultural treatments. Site conditions are ideally suited for Teak and some stands show exceptionally good growth performances. However, poor weed management and a lack of fire control and silvicultural management led to high mortality and poor growth performance of some stands, resulting in relative low overall growth averages. In a second step, a social baseline study was carried out in the surrounding villages and identified landowner conflicts between some villagers and DuPaul, which could be one reason for the fire damages. However, the study also revealed a general interest for collaboration in agroforestry on DuPaul land on both sides. Thirdly, a silvicultural management concept was elaborated and an improved integration of the rural population into DuPaul's forest plantation projects is already initiated. If landowner conflicts can be solved, the development of forest plantations can contribute significantly to the economic income of rural households while environmental benefits provide long-term opportunities for sustainable development of the region. Funding: GTZ supported PPP-Measure, Foundation

BSCALE: Downscaling von Niederschlag: Entwicklung, Kalibrierung und Validierung eines Bayes'schen probabilisitischen Ansatzes.

Das Projekt "BSCALE: Downscaling von Niederschlag: Entwicklung, Kalibrierung und Validierung eines Bayes'schen probabilisitischen Ansatzes." wird vom Umweltbundesamt gefördert und von Universität Siegen, Forschungsinstitut Wasser und Umwelt, Lehrstuhl Wasserwirtschaft durchgeführt. Downscaling von Atmosphärenmodellausgaben, insbesondere von Niederschlagsdaten, ist erforderlich um Variablen von der niedrigaufgelösten Skala des Modells zur Punktskala des Standortes hin zu transformieren, auf der die entsprechenden Variablen für praktische Anwendungen genutzt werden. Dazu gehören unter anderem, das Füllen von Datenlücken, hydrologische oder glaziologische Anwendungen, Klimaprognosen, Anwendungen in der Bewässerung oder Vorhersagen für Energieversorger. Statistisches Downscaling besteht darin, stochastische Beziehungen zwischen Beobachtungen oder Modellausgaben auf großer Skala, die als Prädiktoren dienen, und die an einem Standort zu schätzende Größe, dem Prädiktand, herzustellen. Die dazu angewandten Beziehungen sind häufig lineare Regressionen, es kommen aber auch nicht-lineare Transformationen, wie nicht-lineare Regressionen oder das Quantile-Matching zur Anwendung. In besagten Fällen wird ein stationärer, homoskedastischer Zusammenhang zwischen stochastischen Variablen angenommen, die zwar den bedingten Erwartungswert, aber nicht die Ränder der Verteilung, welche die meteorologischen Extreme abbilden, adäquat transformieren. Im vorliegendem Antrag wird ein probabilistischer Prozessor für das Downscaling von Niederschlagsdaten als Ansatz vorgeschlagen, der als bedingter Bayesscher Prozessor implementiert wird und die nicht-lineare Umformungen zwischen Prädiktoren von der Meso-Skala hin zur Skala eines Standortes unterstützt. In diesem Zusammenhang werden stochastische Zusammenhänge zwischen Prädiktoren und Prädiktanden im Gaußschen Raum modelliert. Die Methode ermöglicht es, mehrere Indikatoren innerhalb eines räumlichen Fensters von Modellzellen gleichzeitig zu verwenden, und kann auf die Anwendung von Prädiktoren, die von mehreren unterschiedlichen Vorhersagemodellen stammen, ausgeweitet werden. Durch die Anwendung multivariater abgeschnittener Normalverteilungen können auch heteroskedastische Beziehungen von stochastischen Variablen abgebildet, analytisch nach den Prädiktoren marginalisiert und anschließend in den Herkunftsraum zurücktransformiert werden. Das Downscaling der Schätzung des Prädikanten von der Skala der Modellzelle auf den Standort erfolgt anschließend mit Hilfe eines nicht-Markovschen, nicht-stationären stochastischen Wettergenerators. Sowohl der Bayessche Prozessor als auch der stochastische Wettergenerator müssen über ein ausreichend weites Zeitfenster anhand von Beobachtungsreihen und Simulationsergebnissen geeicht und validiert werden.

FOR1695: Agrarlandschaften unter dem Einfluss des globalen Klimawandels - Prozessverständnis und Wechselwirkungen auf der regionalen Skala (Regionaler Klimawandel) - P7: Mikroökonomische Analyse des Landnutzungs-Managements unter dem Einfluss des Klimawandels mit besonderer Berücksichtigung des Lern- und Risikoverhaltens

Das Projekt "FOR1695: Agrarlandschaften unter dem Einfluss des globalen Klimawandels - Prozessverständnis und Wechselwirkungen auf der regionalen Skala (Regionaler Klimawandel) - P7: Mikroökonomische Analyse des Landnutzungs-Managements unter dem Einfluss des Klimawandels mit besonderer Berücksichtigung des Lern- und Risikoverhaltens" wird vom Umweltbundesamt gefördert und von Universität Gießen, Institut für Betriebslehre der Agrar- und Ernährungswirtschaft durchgeführt. Neuere Forschungsarbeiten zeigen, dass das Wissen darüber fehlt, wie Lernvorgänge über komplexe Vorgänge wie Klimawandel auf landwirtschaftlichen Betrieben tatsächlich ablaufen. Es ist unklar, wie schnell die Anpassung an diese Vorgänge abläuft. Deshalb wird dieses Teilprojekt das Wissen über Lernstrategien und Verhaltensweisen von landwirtschaftlichen Betrieben erweitern. Es ist geplant, verschiedene Lern-Algorithmen im Modell zu implementieren (rationale Erwartungen, adaptive Erwartungen, Bayessches Lernen) und ihren Effekt auf das modellierte Verhalten zu vergleichen. Jedoch ist die empirische Basis für diese Methoden noch schwach. Dieses Defizit soll durch empirische Felduntersuchungen geschlossen werden, unter anderem mit Studierenden, die mit landwirtschaftlicher Betriebslehre vertraut sind. Diese Experimente werden statistisch ausgewertet und zur Parametrisierung verschiedener Verhaltensweisen des entsprechenden Betriebsmodells verwendet. In ähnlicher Weise werden genauere Informationen über Risikoeinschätzung und -bewältigung in das Modell einbezogen, so dass Analysen des Verhaltens der Landwirte bei einem sich verändernden Klima möglich sind.

Wirksamkeit der Überwachungs-, Vorbeugungs- und Bekämpfungs-Strategien der Vogelgrippe in der Schweiz

Das Projekt "Wirksamkeit der Überwachungs-, Vorbeugungs- und Bekämpfungs-Strategien der Vogelgrippe in der Schweiz" wird vom Umweltbundesamt gefördert und von Bundesamt für Lebensmittelsicherheit und Veterinärwesen BLV durchgeführt. The spread of highly pathogenic avian influenza (HPAI) is a global threat to all countries with a poultry industry, semi-commercial production and backyard poultry and has already caused enormous economic losses. Since 1997, H5N1 viruses which have infected humans have included Haemagglutinins from several clades and variable genotypes. Therefore, all HPAI H5N1 viruses must be considered a potential threat to public health. This increases the scope of viruses with pandemic potential and the importance of continued surveillance of H5N1 avian influenza outbreaks. WHO and the OIE are urging countries worldwide to initiate surveillance programmes tailored to an early detection of cases of HPAI. There is an international demand to reduce random sampling and redirect the scarce resources to a targeted sampling, which focuses on the high-risk population, which is even more true for developing countries e.g. in Africa, which are almost devoid of surveillance capacity. In these cases, risk-based surveillance, and aiming at the most probable source of disease to save scarce resources are even more justified. This project aims: 1) To develop a statistical risk based framework for the combined analysis of surveillance data on avian influenza virus originating from various sources. 2) To develop a model for the assessment and optimisation of the effectiveness of different surveillance strategies for avian influenza. 3) To develop models to assess the effectiveness of different control strategies to prevent infection and spread of HPAI in commercial poultry. The approach is based on the Swiss Tropical Institute's competence in Bayesian spatial risk analyses, transmission modelling of vector borne and zoonotic diseases and its international network in Africa and Asia. This project will focus on Switzerland but within the global context of transport, trade and wild bird migration. It will collaborate with all involved institutions in Switzerland dealing with domestic poultry and wild birds. Expected results and innovations are: 1. Risk maps and contributions to risk maps for LPAI and HPAI on wild and domestic birds in Switzerland. 2. Decision tree for AI risk based surveillance in Switzerland applicable also to low income countries. 3. Risk based surveillance map and sampling plan for AI in Switzerland. 4. Performance indicators of surveillance sensitivity and cost-effectiveness of surveillance of AI in Switzerland and 5. A transmission model of HPAI adapted to Switzerland capable to simulate different intervention strategies.

Health effects of indoor pollutants: integrating microbial, toxicological and epidemiological approaches (HITEA)

Das Projekt "Health effects of indoor pollutants: integrating microbial, toxicological and epidemiological approaches (HITEA)" wird vom Umweltbundesamt gefördert und von Terveyden ka Hyvinvoinnin Laitos durchgeführt. Objective: Healthy housing and good indoor air quality are important goals of public health. However, biological indoor pollution due to dampness, moisture and mold is an emerging environmental health issue, as recognized in EU indoor air policy documents. Prevalence of dampness is remarkable, and may still increase due to demands of energy savings and extreme weather periods and floods associated with climate change. The exposure may lead to long-term impacts such as asthma. The documentation is strong on association between building mold and health, but the causative agents and disease mechanisms are largely unknown, which impedes recognition of a mold-affected patient in health care. Efficient control and regulation are hampered by the insufficient understanding of these causalities. Understanding of the links between building practices and health is lacking. There is an urgent need for European-wide knowledge to form a basis for establishing building-associated criteria for healthy indoor environments. The aim of this proposal is to clarify the health impacts of indoor exposures on children and adults by providing comprehensive exposure data on biological and chemical factors in European indoor environments.

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