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End date: 2019 × Start date: 2010 × Type: Text × Subject: climate effect × Subject: climate variation ×

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Now showing 1 - 5 of 5
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    The effect of univariate bias adjustment on multivariate hazard estimates
    (Göttingen : Copernicus Publ., 2019) Zscheischler, Jakob; Fischer, Erich M.; Lange, Stefan
    Bias adjustment is often a necessity in estimating climate impacts because impact models usually rely on unbiased climate information, a requirement that climate model outputs rarely fulfil. Most currently used statistical bias-adjustment methods adjust each climate variable separately, even though impacts usually depend on multiple potentially dependent variables. Human heat stress, for instance, depends on temperature and relative humidity, two variables that are often strongly correlated. Whether univariate bias-adjustment methods effectively improve estimates of impacts that depend on multiple drivers is largely unknown, and the lack of long-term impact data prevents a direct comparison between model outputs and observations for many climate-related impacts. Here we use two hazard indicators, heat stress and a simple fire risk indicator, as proxies for more sophisticated impact models. We show that univariate bias-adjustment methods such as univariate quantile mapping often cannot effectively reduce biases in multivariate hazard estimates. In some cases, it even increases biases. These cases typically occur (i) when hazards depend equally strongly on more than one climatic driver, (ii) when models exhibit biases in the dependence structure of drivers and (iii) when univariate biases are relatively small. Using a perfect model approach, we further quantify the uncertainty in bias-adjusted hazard indicators due to internal variability and show how imperfect bias adjustment can amplify this uncertainty. Both issues can be addressed successfully with a statistical bias adjustment that corrects the multivariate dependence structure in addition to the marginal distributions of the climate drivers. Our results suggest that currently many modeled climate impacts are associated with uncertainties related to the choice of bias adjustment. We conclude that in cases where impacts depend on multiple dependent climate variables these uncertainties can be reduced using statistical bias-adjustment approaches that correct the variables' multivariate dependence structure. © 2019 Copernicus GmbH. All rights reserved.
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    The impact of climate change and variability on the generation of electrical power
    (Stuttgart : Gebrueder Borntraeger Verlagsbuchhandlung, 2015) Koch, H.; Vögele, S.; Hattermann, F.F.; Huang, S.
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    Historical greenhouse gas concentrations for climate modelling (CMIP6)
    (München : European Geopyhsical Union, 2017) Meinshausen, Malte; Vogel, Elisabeth; Nauels, Alexander; Lorbacher, Katja; Meinshausen, Nicolai; Etheridge, David M.; Fraser, Paul J.; Montzka, Stephen A.; Rayner, Peter J.; Trudinger, Cathy M.; Krummel, Paul B.; Beyerle, Urs; Canadell, Josep G.; Daniel, John S.; Enting, Ian G.; Law, Rachel M. Law; Lunder, Chris R.; O'Doherty, Simon; Prinn, Ron G.; Reimann, Stefan; Rubino, Mauro; Velders, Guus J.M.; Vollmer, Martin K.; Wang, Ray H.J.; Weiss, Ray
    Atmospheric greenhouse gas (GHG) concentrations are at unprecedented, record-high levels compared to the last 800000 years. Those elevated GHG concentrations warm the planet and – partially offset by net cooling effects by aerosols – are largely responsible for the observed warming over the past 150 years. An accurate representation of GHG concentrations is hence important to understand and model recent climate change. So far, community efforts to create composite datasets of GHG concentrations with seasonal and latitudinal information have focused on marine boundary layer conditions and recent trends since the 1980s. Here, we provide consolidated datasets of historical atmospheric concentrations (mole fractions) of 43 GHGs to be used in the Climate Model Intercomparison Project – Phase 6 (CMIP6) experiments. The presented datasets are based on AGAGE and NOAA networks, firn and ice core data, and archived air data, and a large set of published studies. In contrast to previous intercomparisons, the new datasets are latitudinally resolved and include seasonality. We focus on the period 1850–2014 for historical CMIP6 runs, but data are also provided for the last 2000 years. We provide consolidated datasets in various spatiotemporal resolutions for carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), as well as 40 other GHGs, namely 17 ozone-depleting substances, 11 hydrofluorocarbons (HFCs), 9 perfluorocarbons (PFCs), sulfur hexafluoride (SF6), nitrogen trifluoride (NF3) and sulfuryl fluoride (SO2F2). In addition, we provide three equivalence species that aggregate concentrations of GHGs other than CO2, CH4 and N2O, weighted by their radiative forcing efficiencies. For the year 1850, which is used for pre-industrial control runs, we estimate annual global-mean surface concentrations of CO2 at 284.3ppm, CH4 at 808.2ppb and N2O at 273.0ppb. The data are available at https://esgf-node.llnl.gov/search/input4mips/ and http://www.climatecollege.unimelb.edu.au/cmip6. While the minimum CMIP6 recommendation is to use the global- and annual-mean time series, modelling groups can also choose our monthly and latitudinally resolved concentrations, which imply a stronger radiative forcing in the Northern Hemisphere winter (due to the latitudinal gradient and seasonality).
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    Compiling geophysical and geological information into a 3-D model of the glacially-affected island of Föhr
    (Munich : EGU, 2012) Burschil, T.; Scheer, W.; Kirsch, R.; Wiederhold, H.
    Within the scope of climatic change and associated sea level rise, coastal aquifers are endangered and are becoming more a focus of research to ensure the future water supply in coastal areas. For groundwater modelling a good understanding of the geological/hydrogeological situation and the aquifer behavior is necessary. In preparation of groundwater modelling and assessment of climate change impacts on coastal water resources, we setup a geological/hydrogeological model for the North Sea Island of Föhr. Data from different geophysical methods applied from the air, the surface and in boreholes contribute to the 3-D model, e.g. airborne electromagnetics (SkyTEM) for spatial mapping the resistivity of the entire island, seismic reflections for detailed cross-sections in the groundwater catchment area, and geophysical borehole logging for calibration of these measurements. An iterative and integrated evaluation of the results from the different geophysical methods contributes to reliable data as input for the 3-D model covering the whole island and not just the well fields. The complex subsurface structure of the island is revealed. The local waterworks use a freshwater body embedded in saline groundwater. Several glaciations reordered the youngest Tertiary and Quaternary sediments by glaciotectonic thrust faulting, as well as incision and refill of glacial valleys. Both subsurface structures have a strong impact on the distribution of freshwater-bearing aquifers. A digital geological 3-D model reproduces the hydrogeological structure of the island as a base for a groundwater model. In the course of the data interpretation, we deliver a basis for rock identification. We demonstrate that geophysical investigation provide petrophysical parameters and improve the understanding of the subsurface and the groundwater system. The main benefit of our work is that the successful combination of electromagnetic, seismic and borehole data reveals the complex geology of a glacially-affected island. A sound understanding of the subsurface structure and the compilation of a 3-D model is imperative and the basis for a groundwater flow model to predict climate change effects on future water resources.
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    A vital link: Water and vegetation in the anthropocene
    (Chichester : John Wiley and Sons Ltd, 2013) Gerten, D.
    This paper argues that the interplay of water, carbon and vegetation dynamics fundamentally links some global trends in the current and conceivable future Anthropocene, such as cropland expansion, freshwater use, and climate change and its impacts. Based on a review of recent literature including geographically explicit simulation studies with the process-based LPJmL global biosphere model, it demonstrates that the connectivity of water and vegetation dynamics is vital for water security, food security and (terrestrial) ecosystem dynamics alike. The water limitation of net primary production of both natural and agricultural plants - already pronounced in many regions - is shown to increase in many places under projected climate change, though this development is partially offset by water-saving direct CO2 effects. Natural vegetation can to some degree adapt dynamically to higher water limitation, but agricultural crops usually require some form of active management to overcome it - among them irrigation, soil conservation and eventually shifts of cropland to areas that are less water-limited due to more favourable climatic conditions. While crucial to secure food production for a growing world population, such human interventions in water-vegetation systems have, as also shown, repercussions on the water cycle. Indeed, land use changes are shown to be the second-most important influence on the terrestrial water balance in recent times. Furthermore, climate change (warming and precipitation changes) will in many regions increase irrigation demand and decrease water availability, impeding rainfed and irrigated food production (if not CO2 effects counterbalance this impact - which is unlikely at least in poorly managed systems). Drawing from these exemplary investigations, some research perspectives on how to further improve our knowledge of human-water-vegetation interactions in the Anthropocene are outlined.