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Bassler C.,National Park Bayerischer Wald | Muller J.,National Park Bayerischer Wald | Hothorn T.,Ludwig Maximilians University of Munich | Kneib T.,Ludwig Maximilians University of Munich | And 2 more authors.
Ecological Indicators | Year: 2010

The evidence for climate change is increasing, and global warming could lead to the extinction of some species. Here we estimated the extinction risk of six high-montane species of different taxonomic groups (fern, vascular plant, wood-inhabiting fungus, mollusk, saproxylic beetle, and bird) by modeling their occurrence under two global warming scenarios. We also assessed the cross-taxon indicator suitability of the selected species for monitoring climate change in low-mountain-range forests in southeastern Germany (Bavarian Forest National Park). We tested the influence of temperature and other habitat variables by applying semi-parametric spatial generalized linear models with binomial error. The probability of occurrence for each species under the present conditions and under two conditions of global warming was calculated. To assess the cross-taxon suitability, we tested the predictability of the final generalized linear models for each species using the measured occurrence of the other selected species and a discrimination technique. We identified temperature as the main driver for all selected high-montane species. Our statistical models predict a considerable risk of extinction of these species within the Bavarian Forest National Park as a result of global warming. Our discrimination model indicates that these species have essentially similar relationships with the environment and that five of the six species are suitable as indicators of early signs of global warming. The choice of which indicators to use should involve a consideration of the type of monitoring systems already in place. © 2009 Elsevier Ltd. All rights reserved.

Avila G.M.R.,Humboldt University of Berlin | Avila G.M.R.,Potsdam Institute For Klimafolgenforschung | Avila G.M.R.,Higher University of San Andrés | Gapelyuk A.,Humboldt University of Berlin | And 7 more authors.
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences | Year: 2013

We analyse cardiovascular time series with the aim of performing early prediction of preeclampsia (PE), a pregnancy-specific disorder causing maternal and foetal morbidity and mortality. The analysis is made using a novel approach, namely the ε- recurrence networks applied to a phase space constructed by means of the time series of the variabilities of the heart rate and the blood pressure (systolic and diastolic). All the possible coupling structures among these variables are considered for the analysis. Network measures such as average path length, mean coreness, global clustering coefficient and scale-local transitivity dimension are computed and constitute the parameters for the subsequent quadratic discriminant analysis. This allows us to predict PE with a sensitivity of 91.7 per cent and a specificity of 68.1 per cent, thus validating the use of this method for classifying healthy and preeclamptic patients.

Ilgenfritz E.-M.,Bielefeld University | Ilgenfritz E.-M.,Humboldt University of Berlin | Menz C.,Humboldt University of Berlin | Menz C.,Potsdam Institute For Klimafolgenforschung | And 3 more authors.
Physical Review D - Particles, Fields, Gravitation and Cosmology | Year: 2011

We study the Landau gauge gluon and ghost propagators of SU(3) gauge theory, employing the logarithmic definition for the lattice gluon fields and implementing the corresponding form of the Faddeev-Popov matrix. This is necessary in order to consistently compare lattice data for the bare propagators with that of higher-loop numerical stochastic perturbation theory. In this paper we provide such a comparison, and introduce what is needed for an efficient lattice study. When comparing our data for the logarithmic definition to that of the standard lattice Landau gauge we clearly see the propagators to be multiplicatively related. The data of the associated ghost-gluon coupling matches up almost completely. For the explored lattice spacings and sizes discretization artifacts, finite size, and Gribov-copy effects are small. At weak coupling and large momentum, the bare propagators and the ghost-gluon coupling are seen to be approached by those of higher-order numerical stochastic perturbation theory. © 2011 American Physical Society.

Muller A.,Humboldt University of Berlin | Riedl M.,Humboldt University of Berlin | Penzel T.,Charité - Medical University of Berlin | Kurths J.,Humboldt University of Berlin | And 3 more authors.
Somnologie | Year: 2014

The analysis of events such as apneas, hypopneas, and various types of arousals during sleep plays a central role when diagnosing and trying to understand sleep-related disorders and possible sequelae. Often, only the occurrence of these events is regarded instead of putting them into context with other cardiovascular variables and characterizing their mutual interactions. In this article, we present two new methods that allow for such an analysis: the coordigram and the ensemble symbolic-coupling traces. Through a case study of a subject with frequent arousals, the potential of the new tools for quantifying the autonomic response to sleep disturbances is shown. Furthermore, in a reanalysis of patients suffering from obstructive sleep apnea, the diagnostic relevance of cardiorespiratory coordination for risk stratification of an emerging hypertension is demonstrated. © 2014, Springer-Verlag Berlin Heidelberg.

Schmid E.,Potsdam Institute For Klimafolgenforschung | Knopf B.,Potsdam Institute For Klimafolgenforschung | Knopf B.,Mercator Research Institute on Global Commons and Climate Change
Energy Policy | Year: 2015

This paper aims to quantify the long-term economic benefits that arise from an increasing integration of the pan-European electricity system by means of comparing model-based decarbonization scenarios developed with the model LIMES-EU+. It explicitly accounts for the interplay between transmission infrastructure and renewable generation capacity expansion. We confirm earlier findings that, on aggregate, pan-European transmission capacity expansion constitutes a no-regret option for integrating increasing shares of variable renewables. It leads to positive social returns on investment in all mitigation scenarios under analysis. However, the reduction in total discounted system costs stemming from transmission capacity expansion is modest in magnitude. Over the period 2010-2050 it reaches a maximum of 3.5% for a case with massive expansion compared to one in which the status quo remains. In technical terms this means that the optimum is rather flat and taking into account regional and local benefits and distributional aspects could alter the evaluation of the economic benefits. A crucial finding is that the configuration of pan-European transmission infrastructure and the importance of specific country-connections, i.e. a "Southern" versus a "Northern" solution, hinges on the relative development of specific investment costs for solar and wind technologies over the next decades. © 2015 Elsevier Ltd.

Van Der Mheen M.,University Utrecht | Dijkstra H.A.,University Utrecht | Gozolchiani A.,Bar - Ilan University | Den Toom M.,University Utrecht | And 3 more authors.
Geophysical Research Letters | Year: 2013

Early warning indicators of the collapse of the Atlantic Meridional Overturning Circulation (MOC) have up to now mostly been based on temporal correlations in single time series. Here, we propose new indicators based on spatial correlations in the time series of the Atlantic temperature field. To demonstrate the performance of these indicators, we use a meridional-depth model of the MOC for which the critical conditions for collapse can be explicitly computed. An interaction network approach is used to monitor changes in spatial correlations in the model temperature time series as the critical transition is approached. The new early warning indicators are based on changes in topological properties of the network, in particular changes in the distribution functions of the degree and the clustering coefficient. © 2013 American Geophysical Union. All Rights Reserved.

Koch H.,TU Brandenburg | Wechsung F.,Potsdam Institute For Klimafolgenforschung | Grunewald U.,TU Brandenburg
Hydrologie und Wasserbewirtschaftung | Year: 2010

Dry periods with low flows are a particular challenge for water-resources managers. The quantities of water required by users at the moment must be delivered with a certain reliability. However, water supply must be continued also with sufficient reliability in future when the dry period possibly persists. The management of reservoirs must consider additional objectives that impose restrictions on reservoir operation such as flood control (vs. full impoundment) and touristic uses (vs. minimum operation level). In the planning of water-resources management the available natural yield is a key factor. The quantification of the natural yield is subject to large uncertainties. These uncertainties come from variable climate elements, such as precipitation and potential évapotranspiration. Further uncertainty is added by water management actions that are reflected in the measured discharges. Beside climate variability, climatechange effects have gained more attention in the planning of water-resources management recently. One frequently asked question is whether climate-change effects can be detected in measured river-discharge series. This paper describes the elimination of watermanagement effects from measured river-discharges ("naturalization") in the Czech part of the Elbe river basin. These naturalized discharge data are analyzed by statistical means. Moreover, such naturalized discharge data can be used in the calibration of rainfallrunoff-models.

Trusilova K.,German Weather Service | Schubert S.,Potsdam Institute For Klimafolgenforschung | Wouters H.,Catholic University of Leuven | Wouters H.,Flemish Institute for Technological Research | And 4 more authors.
Meteorologische Zeitschrift | Year: 2016

The regional non-hydrostatic climate model COSMO-CLM is increasingly being used on fine spatial scales of 1-5 km. Such applications require a detailed differentiation between the parameterization for natural and urban land uses. Since 2010, three parameterizations for urban land use have been incorporated into COSMOCLM. These parameterizations vary in their complexity, required city parameters and their computational cost. We perform model simulations with the COSMO-CLM coupled to these three parameterizations for urban land in the same model domain of Berlin on a 1-km grid and compare results with available temperature observations. While all models capture the urban heat island, they differ in spatial detail, magnitude and the diurnal variation. © 2015 The authors.

Siegismund F.,University of Hamburg | Romanova V.,University of Hamburg | Romanova V.,Potsdam Institute For Klimafolgenforschung | Kohl A.,University of Hamburg | Stammer D.,University of Hamburg
Journal of Geophysical Research: Oceans | Year: 2011

Ocean bottom pressure variability is analyzed from three monthly products available from (1) the Gravity Recovery and Climate Experiment (GRACE), (2) sterically corrected altimetry, and (3) from a forward run of the German part of the Estimating the Circulation and Climate of the Ocean (GECCO-2) model. Results lead to an approximate error estimate for each of the ocean bottom pressure (OBP) maps under the assumption of noncorrelated errors among the three products. The estimated error maps are consistent with the misfits of individual fields against OBP sensor data, with the caveat that a general underestimation of the signal strength, as a common, correlated error in all products, cannot be recovered by the method. The signal-to-noise ratio (SNR) increases in all products, when a 3 month running mean filter is applied. Using this filter, we estimate globally averaged errors of 8.6, 11.1, and 5.7 mm of equivalent water height for GRACE, nonsteric altimetry, and GECCO2, respectively. Based on resulting uncertainties, a new OBP product is being produced by merging all three data sets. When validated with bottom pressure observations this new OBP product has a 20% increased SNR compared to the best individual product (GECCO2-ref). Estimated total ocean mass variations explain a considerable part of OBP variability with a SNR above 1 in most of the ocean. In some regions the nonuniform part is weaker than the estimated error. However, most dynamic ocean models are designed to reproduce only the nonuniform, dynamic, OBP variability, but do not accurately describe total mass variability. Copyright 2011 by the American Geophysical Union.

Weather-related yield volatility is an important production risk for agriculture. Especially, negative yield anomalies could increase through climate change. We develop and investigate statistical crop yield models which can be used to predict crop yield impacts of weather and climate projections. The models are applied to winter wheat and silage maize, which are the most important annual crops as winter and spring crops, respectively, in Germany. The yields of both crops were modelled on county level, but evaluated on federal state, river basin or national level. We use three regression methods: separate time series model, panel data model, and random coefficient model. Within the Cobb-Douglas production function, relative changes (of yield and factor anomalies) are related to each other. To include the conditions of vegetative and generative plant development, we use climate variables summed to quarter- and half-year values. Furthermore, our models are controlled with proxy variables for economic impacts to estimate unbiased climatic parameters. Our study shows that the simple separate time series models explain (measured by the Nash-Sutcliffe model efficiency coefficient) yield anomalies best. They perform generally better (0.81) than the panel data models (0.72) due to a more accurate reproduction of exceptional yield changes at the county level. The random coefficient models performed between the separate time series models and panel data models (0.78). The aggregation of county yields to federal state and river basin yields improves the model accuracy by + 0.14. The aggregation effect is at highest for the panel data model on river basin scale (+0.26). The models for both crops achieve a similar goodness of fit. The spatial distribution of model parameters reflects the prevailing soil and climate characteristics within Germany relevant for the different plant development periods. Our statistical models capture collinear factors within yield formation. These are, for example, pests and diseases, or the adaptation behaviour of farmers on changing climatic or economic conditions. Due to the normalization, the yield changes are independent of technological levels and can be combined with weather and climate projection without any bias correction. The coarse temporal subdivision of the climatic variables supports robust assessments of climate change projections. To conclude, our models are suitable for the combination of yield assessments with weather and climate projections, because they reproduce yields from out-of-sample years robustly. In general, the separate time series models reproduce best the measured yield changes. © 2015, Verlag Eugen Ulmer. All rights reserved

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