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- ItemSimilarity estimators for irregular and age-uncertain time series(München : European Geopyhsical Union, 2014) Rehfeld, K.; Kurths, J.Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many data sets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age-uncertain time series. We compare the Gaussian-kernel-based cross-correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case, coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60–55% (in the linear case) to 53–42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity contributes less, particularly for the adapted Gaussian-kernel-based estimators and the event synchronization function. The introduced link strength concept summarizes the hypothesis test results and balances the individual strengths of the estimators: while gXCF is particularly suitable for short and irregular time series, gMI and the ESF can identify nonlinear dependencies. ESF could, in particular, be suitable to study extreme event dynamics in paleoclimate records. Programs to analyze paleoclimatic time series for significant dependencies are included in a freely available software toolbox.
- ItemClimate of the last millennium: Ensemble consistency of simulations and reconstructions(Göttingen : Copernicus, 2013) Bothe, O.; Jungclaus, J.H.; Zanchettin, D.; Zorita, E.Are simulations and reconstructions of past climate and its variability consistent with each other? We assess the consistency of simulations and reconstructions for the climate of the last millennium under the paradigm of a statistically indistinguishable ensemble. In this type of analysis, the null hypothesis is that reconstructions and simulations are statistically indistinguishable and, therefore, are exchangeable with each other. Ensemble consistency is assessed for Northern Hemisphere mean temperature, Central European mean temperature and for global temperature fields. Reconstructions available for these regions serve as verification data for a set of simulations of the climate of the last millennium performed at the Max Planck Institute for Meteorology. Consistency is generally limited to some sub-domains and some sub-periods. Only the ensemble simulated and reconstructed annual Central European mean temperatures for the second half of the last millennium demonstrates unambiguous consistency. Furthermore, we cannot exclude consistency of an ensemble of reconstructions of Northern Hemisphere temperature with the simulation ensemble mean. If we treat simulations and reconstructions as equitable hypotheses about past climate variability, the found general lack of their consistency weakens our confidence in inferences about past climate evolutions on the considered spatial and temporal scales. That is, our available estimates of past climate evolutions are on an equal footing but, as shown here, inconsistent with each other.
- ItemThe effect of univariate bias adjustment on multivariate hazard estimates(Göttingen : Copernicus Publ., 2019) Zscheischler, Jakob; Fischer, Erich M.; Lange, StefanBias 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.
- ItemThe 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.
- ItemHistorical 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, RayAtmospheric 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).
- ItemCompiling 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.
- ItemA 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.
- ItemGreenland ice sheet model parameters constrained using simulations of the Eemian Interglacial(München : European Geopyhsical Union, 2011) Robinson, A.; Calov, R.; Ganopolski, A.Using a new approach to force an ice sheet model, we performed an ensemble of simulations of the Greenland Ice Sheet evolution during the last two glacial cycles, with emphasis on the Eemian Interglacial. This ensemble was generated by perturbing four key parameters in the coupled regional climate-ice sheet model and by introducing additional uncertainty in the prescribed "background" climate change. The sensitivity of the surface melt model to climate change was determined to be the dominant driver of ice sheet instability, as reflected by simulated ice sheet loss during the Eemian Interglacial period. To eliminate unrealistic parameter combinations, constraints from present-day and paleo information were applied. The constraints include (i) the diagnosed present-day surface mass balance partition between surface melting and ice discharge at the margin, (ii) the modeled present-day elevation at GRIP; and (iii) the modeled elevation reduction at GRIP during the Eemian. Using these three constraints, a total of 360 simulations with 90 different model realizations were filtered down to 46 simulations and 20 model realizations considered valid. The paleo constraint eliminated more sensitive melt parameter values, in agreement with the surface mass balance partition assumption. The constrained simulations resulted in a range of Eemian ice loss of 0.4–4.4 m sea level equivalent, with a more likely range of about 3.7–4.4 m sea level if the GRIP δ18O isotope record can be considered an accurate proxy for the precipitation-weighted annual mean temperatures.
- ItemSimulation of the last glacial cycle with a coupled climate ice-sheet model of intermediate complexity(München : European Geopyhsical Union, 2010) Ganopolski, A.; Calov, R.; Claussen, M.A new version of the Earth system model of intermediate complexity, CLIMBER-2, which includes the three-dimensional polythermal ice-sheet model SICOPOLIS, is used to simulate the last glacial cycle forced by variations of the Earth's orbital parameters and atmospheric concentration of major greenhouse gases. The climate and ice-sheet components of the model are coupled bi-directionally through a physically-based surface energy and mass balance interface. The model accounts for the time-dependent effect of aeolian dust on planetary and snow albedo. The model successfully simulates the temporal and spatial dynamics of the major Northern Hemisphere (NH) ice sheets, including rapid glacial inception and strong asymmetry between the ice-sheet growth phase and glacial termination. Spatial extent and elevation of the ice sheets during the last glacial maximum agree reasonably well with palaeoclimate reconstructions. A suite of sensitivity experiments demonstrates that simulated ice-sheet evolution during the last glacial cycle is very sensitive to some parameters of the surface energy and mass-balance interface and dust module. The possibility of a considerable acceleration of the climate ice-sheet model is discussed.
- ItemGlacial CO 2 cycle as a succession of key physical and biogeochemical processes(München : European Geopyhsical Union, 2012) Brovkin, V.; Ganopolski, A.; Archer, D.; Munhoven, G.During glacial-interglacial cycles, atmospheric CO2 concentration varied by about 100 ppmv in amplitude. While testing mechanisms that have led to the low glacial CO2 level could be done in equilibrium model experiments, an ultimate goal is to explain CO2 changes in transient simulations through the complete glacial-interglacial cycle. The computationally efficient Earth System model of intermediate complexity CLIMBER-2 is used to simulate global biogeochemistry over the last glacial cycle (126 kyr). The physical core of the model (atmosphere, ocean, land and ice sheets) is driven by orbital changes and reconstructed radiative forcing from greenhouses gases, ice, and aeolian dust. The carbon cycle model is able to reproduce the main features of the CO2 changes: a 50 ppmv CO2 drop during glacial inception, a minimum concentration at the last glacial maximum 80 ppmv lower than the Holocene value, and an abrupt 60 ppmv CO2 rise during the deglaciation. The model deep ocean δ13C also resembles reconstructions from deep-sea cores. The main drivers of atmospheric CO2 evolve in time: changes in sea surface temperatures and in the volume of bottom water of southern origin control atmospheric CO2 during the glacial inception and deglaciation; changes in carbonate chemistry and marine biology are dominant during the first and second parts of the glacial cycle, respectively. These feedback mechanisms could also significantly impact the ultimate climate response to the anthropogenic perturbation.