Browsing by Author "Lange, Stefan"
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- ItemAssessing the impacts of 1.5 °C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b)(München : European Geopyhsical Union, 2017) Frieler, Katja; Lange, Stefan; Piontek, Franziska; Reyer, Christopher P.O.; Schewe, Jacob; Warszawski, Lila; Zhao, Fang; Chini, Louise; Denvil, Sebastien; Emanuel, Kerry; Geiger, Tobias; Halladay, Kate; Hurtt, George; Mengel, Matthias; Murakami, Daisuke; Ostberg, Sebastian; Popp, Alexander; Riva, Riccardo; Stevanovic, Miodrag; Suzuki, Tatsuo; Volkholz, Jan; Burke, Eleanor; Ciais, Philippe; Ebi, Kristie; Eddy, Tyler D.; Elliott, Joshua; Galbraith, Eric; Gosling, Simon N.; Hattermann, Fred; Hickler, Thomas; Hinkel, Jochen; Hof, Christian; Huber, Veronika; Jägermeyr, Jonas; Krysanova, Valentina; Marcé, Rafael; Müller Schmied, Hannes; Mouratiadou, Ioanna; Pierson, Don; Tittensor, Derek P.; Vautard, Robert; van Vliet, Michelle; Biber, Matthias F.; Betts, Richard A.; Bodirsky, Benjamin Leon; Deryng, Delphine; Frolking, Steve; Jones, Chris D.; Lotze, Heike K.; Lotze-Campen, Hermann; Sahajpal, Ritvik; Thonicke, Kirsten; Tian, Hanqin; Yamagata, YoshikiIn Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) invited the Intergovernmental Panel on Climate Change (IPCC) to provide a "special report in 2018 on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways". In Nairobi, Kenya, April 2016, the IPCC panel accepted the invitation. Here we describe the response devised within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to provide tailored, cross-sectorally consistent impact projections to broaden the scientific basis for the report. The simulation protocol is designed to allow for (1) separation of the impacts of historical warming starting from pre-industrial conditions from impacts of other drivers such as historical land-use changes (based on pre-industrial and historical impact model simulations); (2) quantification of the impacts of additional warming up to 1.5°C, including a potential overshoot and long-term impacts up to 2299, and comparison to higher levels of global mean temperature change (based on the low-emissions Representative Concentration Pathway RCP2.6 and a no-mitigation pathway RCP6.0) with socio-economic conditions fixed at 2005 levels; and (3) assessment of the climate effects based on the same climate scenarios while accounting for simultaneous changes in socio-economic conditions following the middle-of-the-road Shared Socioeconomic Pathway (SSP2, Fricko et al., 2016) and in particular differential bioenergy requirements associated with the transformation of the energy system to comply with RCP2.6 compared to RCP6.0. With the aim of providing the scientific basis for an aggregation of impacts across sectors and analysis of cross-sectoral interactions that may dampen or amplify sectoral impacts, the protocol is designed to facilitate consistent impact projections from a range of impact models across different sectors (global and regional hydrology, lakes, global crops, global vegetation, regional forests, global and regional marine ecosystems and fisheries, global and regional coastal infrastructure, energy supply and demand, temperature-related mortality, and global terrestrial biodiversity).
- ItemATTRICI v1.1 – counterfactual climate for impact attribution(Katlenburg-Lindau : Copernicus, 2021) Mengel, Matthias; Treu, Simon; Lange, Stefan; Frieler, KatjaAttribution in its general definition aims to quantify drivers of change in a system. According to IPCC Working Group II (WGII) a change in a natural, human or managed system is attributed to climate change by quantifying the difference between the observed state of the system and a counterfactual baseline that characterizes the system's behavior in the absence of climate change, where “climate change refers to any long-term trend in climate, irrespective of its cause” (IPCC, 2014). Impact attribution following this definition remains a challenge because the counterfactual baseline, which characterizes the system behavior in the hypothetical absence of climate change, cannot be observed. Process-based and empirical impact models can fill this gap as they allow us to simulate the counterfactual climate impact baseline. In those simulations, the models are forced by observed direct (human) drivers such as land use changes, changes in water or agricultural management but a counterfactual climate without long-term changes. We here present ATTRICI (ATTRIbuting Climate Impacts), an approach to construct the required counterfactual stationary climate data from observational (factual) climate data. Our method identifies the long-term shifts in the considered daily climate variables that are correlated to global mean temperature change assuming a smooth annual cycle of the associated scaling coefficients for each day of the year. The produced counterfactual climate datasets are used as forcing data within the impact attribution setup of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a). Our method preserves the internal variability of the observed data in the sense that factual and counterfactual data for a given day have the same rank in their respective statistical distributions. The associated impact model simulations allow for quantifying the contribution of climate change to observed long-term changes in impact indicators and for quantifying the contribution of the observed trend in climate to the magnitude of individual impact events. Attribution of climate impacts to anthropogenic forcing would need an additional step separating anthropogenic climate forcing from other sources of climate trends, which is not covered by our method.
- ItemBias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset(München : European Geopyhsical Union, 2018) Lange, StefanMany meteorological forcing datasets include bias-corrected surface downwelling longwave and shortwave radiation (rlds and rsds). Methods used for such bias corrections range from multi-year monthly mean value scaling to quantile mapping at the daily timescale. An additional downscaling is necessary if the data to be corrected have a higher spatial resolution than the observational data used to determine the biases. This was the case when EartH2Observe (E2OBS; Calton et al., 2016) rlds and rsds were bias-corrected using more coarsely resolved Surface Radiation Budget (SRB; Stackhouse Jr. et al., 2011) data for the production of the meteorological forcing dataset EWEMBI (Lange, 2016). This article systematically compares various parametric quantile mapping methods designed specifically for this purpose, including those used for the production of EWEMBI rlds and rsds. The methods vary in the timescale at which they operate, in their way of accounting for physical upper radiation limits, and in their approach to bridging the spatial resolution gap between E2OBS and SRB. It is shown how temporal and spatial variability deflation related to bilinear interpolation and other deterministic downscaling approaches can be overcome by downscaling the target statistics of quantile mapping from the SRB to the E2OBS grid such that the sub-SRB-grid-scale spatial variability present in the original E2OBS data is retained. Cross validations at the daily and monthly timescales reveal that it is worthwhile to take empirical estimates of physical upper limits into account when adjusting either radiation component and that, overall, bias correction at the daily timescale is more effective than bias correction at the monthly timescale if sampling errors are taken into account.
- ItemClimate impact emergence and flood peak synchronization projections in the Ganges, Brahmaputra and Meghna basins under CMIP5 and CMIP6 scenarios(Bristol : IOP Publ., 2022) Gädeke, Anne; Wortmann, Michel; Menz, Christoph; Islam, AKM Saiful; Masood, Muhammad; Krysanova, Valentina; Lange, Stefan; Hattermann, Fred FokkoThe densely populated delta of the three river systems of the Ganges, Brahmaputra and Meghna is highly prone to floods. Potential climate change-related increases in flood intensity are therefore of major societal concern as more than 40 million people live in flood-prone areas in downstream Bangladesh. Here we report on new flood projections using a hydrological model forced by bias-adjusted ensembles of the latest-generation global climate models of CMIP6 (SSP5-8.5/SSP1-2.6) in comparison to CMIP5 (RCP8.5/RCP2.6). Results suggest increases in peak flow magnitude of 36% (16%) on average under SSP5-8.5 (SSP1-2.6), compared to 60% (17%) under RCP8.5 (RCP2.6) by 2070-2099 relative to 1971-2000. Under RCP8.5/SSP5-8.5 (2070-2099), the largest increase in flood risk is projected for the Ganges watershed, where higher flood peaks become the ‘new norm’ as early as mid-2030 implying a relatively short time window for adaptation. In the Brahmaputra and Meghna rivers, the climate impact signal on peak flow emerges after 2070 (CMIP5 and CMIP6 projections). Flood peak synchronization, when annual peak flow occurs simultaneously at (at least) two rivers leading to large flooding events within Bangladesh, show a consistent increase under both projections. While the variability across the ensemble remains high, the increases in flood magnitude are robust in the study basins. Our findings emphasize the need of stringent climate mitigation policies to reduce the climate change impact on peak flows (as presented using SSP1-2.6/RCP2.6) and to subsequently minimize adverse socioeconomic impacts and adaptation costs. Considering Bangladesh’s high overall vulnerability to climate change and its downstream location, synergies between climate change adaptation and mitigation and transboundary cooperation will need to be strengthened to improve overall climate resilience and achieve sustainable development.
- ItemComplex networks for climate model evaluation with application to statistical versus dynamical modeling of South American climate(Heidelberg : Springer, 2014) Feldhoff, Jan H.; Lange, Stefan; Volkholz, Jan; Donges, Jonathan F.; Kurths, Jürgen; Gerstengarbe, Friedrich-WilhelmIn this study we introduce two new node-weighted difference measures on complex networks as a tool for climate model evaluation. The approach facilitates the quantification of a model’s ability to reproduce the spatial covariability structure of climatological time series. We apply our methodology to compare the performance of a statistical and a dynamical regional climate model simulating the South American climate, as represented by the variables 2 m temperature, precipitation, sea level pressure, and geopotential height field at 500 hPa. For each variable, networks are constructed from the model outputs and evaluated against a reference network, derived from the ERA-Interim reanalysis, which also drives the models. We compare two network characteristics, the (linear) adjacency structure and the (nonlinear) clustering structure, and relate our findings to conventional methods of model evaluation. To set a benchmark, we construct different types of random networks and compare them alongside the climate model networks. Our main findings are: (1) The linear network structure is better reproduced by the statistical model statistical analogue resampling scheme (STARS) in summer and winter for all variables except the geopotential height field, where the dynamical model CCLM prevails. (2) For the nonlinear comparison, the seasonal differences are more pronounced and CCLM performs almost as well as STARS in summer (except for sea level pressure), while STARS performs better in winter for all variables.
- ItemThe economically optimal warming limit of the planet(Göttingen : Copernicus Publ., 2019) Ueckerd, Falko; Frieler, Katja; Lange, Stefan; Wenz, Leonie; Luderer, Gunnar; Levermann, AndersBoth climate-change damages and climate-change mitigation will incur economic costs. While the risk of severe damages increases with the level of global warming (Dell et al., 2014; IPCC, 2014b, 2018; Lenton et al., 2008), mitigating costs increase steeply with more stringent warming limits (IPCC, 2014a; Luderer et al., 2013; Rogelj et al., 2015). Here, we show that the global warming limit that minimizes this century's total economic costs of climate change lies between 1.9 and 2°C, if temperature changes continue to impact national economic growth rates as observed in the past and if instantaneous growth effects are neither compensated nor amplified by additional growth effects in the following years. The result is robust across a wide range of normative assumptions on the valuation of future welfare and inequality aversion. We combine estimates of climate-change impacts on economic growth for 186 countries (applying an empirical damage function from Burke et al., 2015) with mitigation costs derived from a state-of-the-art energy-economy-climate model with a wide range of highly resolved mitigation options (Kriegler et al., 2017; Luderer et al., 2013, 2015). Our purely economic assessment, even though it omits non-market damages, provides support for the international Paris Agreement on climate change. The political goal of limiting global warming to "well below 2 degrees" is thus also an economically optimal goal given above assumptions on adaptation and damage persistence. © 2019 Copernicus GmbH. All rights reserved.
- ItemThe effect of overshooting 1.5 °C global warming on the mass loss of the Greenland ice sheet(Göttingen : Copernicus Publ., 2018) Rückamp, Martin; Falk, Ulrike; Frieler, Katja; Lange, Stefan; Humbert, AngelikaSea-level rise associated with changing climate is expected to pose a major challenge for societies. Based on the efforts of COP21 to limit global warming to 2.0 ∘C or even 1.5 ∘C by the end of the 21st century (Paris Agreement), we simulate the future contribution of the Greenland ice sheet (GrIS) to sea-level change under the low emission Representative Concentration Pathway (RCP) 2.6 scenario. The Ice Sheet System Model (ISSM) with higher-order approximation is used and initialized with a hybrid approach of spin-up and data assimilation. For three general circulation models (GCMs: HadGEM2-ES, IPSL-CM5A-LR, MIROC5) the projections are conducted up to 2300 with forcing fields for surface mass balance (SMB) and ice surface temperature (Ts) computed by the surface energy balance model of intermediate complexity (SEMIC). The projected sea-level rise ranges between 21–38 mm by 2100 and 36–85 mm by 2300. According to the three GCMs used, global warming will exceed 1.5 ∘C early in the 21st century. The RCP2.6 peak and decline scenario is therefore manually adjusted in another set of experiments to suppress the 1.5 ∘C overshooting effect. These scenarios show a sea-level contribution that is on average about 38 % and 31 % less by 2100 and 2300, respectively. For some experiments, the rate of mass loss in the 23rd century does not exclude a stable ice sheet in the future. This is due to a spatially integrated SMB that remains positive and reaches values similar to the present day in the latter half of the simulation period. Although the mean SMB is reduced in the warmer climate, a future steady-state ice sheet with lower surface elevation and hence volume might be possible. Our results indicate that uncertainties in the projections stem from the underlying GCM climate data used to calculate the surface mass balance. However, the RCP2.6 scenario will lead to significant changes in the GrIS, including elevation changes of up to 100 m. The sea-level contribution estimated in this study may serve as a lower bound for the RCP2.6 scenario, as the currently observed sea-level rise is not reached in any of the experiments; this is attributed to processes (e.g. ocean forcing) not yet represented by the model, but proven to play a major role in GrIS mass loss.
- 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 PROFOUND Database for evaluating vegetation models and simulating climate impacts on European forests(Katlenburg-Lindau : Copernics Publications, 2020) Reyer, Christopher P.O.; Silveyra Gonzalez, Ramiro; Dolos, Klara; Hartig, Florian; Hauf, Ylva; Noack, Matthias; Lasch-Born, Petra; Rötzer, Thomas; Pretzsch, Hans; Meesenburg, Henning; Fleck, Stefan; Wagner, Markus; Bolte, Andreas; Sanders, Tanja G.M.; Kolari, Pasi; Mäkelä, Annikki; Vesala, Timo; Mammarella, Ivan; Pumpanen, Jukka; Collalti, Alessio; Trotta, Carlo; Matteucci, Giorgio; D'Andrea, Ettore; Foltýnová, Lenka; Krejza, Jan; Ibrom, Andreas; Pilegaard, Kim; Loustau, Denis; Bonnefond, Jean-Marc; Berbigier, Paul; Picart, Delphine; Lafont, Sébastien; Dietze, Michael; Cameron, David; Vieno, Massimo; Tian, Hanqin; Palacios-Orueta, Alicia; Cicuendez, Victor; Recuero, Laura; Wiese, Klaus; Büchner, Matthias; Lange, Stefan; Volkholz, Jan; Kim, Hyungjun; Horemans, Joanna A.; Bohn, Friedrich; Steinkamp, Jörg; Chikalanov, Alexander; Weedon, Graham P.; Sheffield, Justin; Babst, Flurin; Vega del Valle, Iliusi; Suckow, Felicitas; Martel, Simon; Mahnken, Mats; Gutsch, Martin; Frieler, KatjaProcess-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data on European forests to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO2, nitrogen deposition, tree and forest stand level, and remote sensing data for nine contrasting forest stands distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a “SQLite” relational database or “ASCII” flat file version (at https://doi.org/10.5880/PIK.2020.006/; Reyer et al., 2020). The data policies of the individual contributing datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the ProfoundData R package (https://CRAN.R-project.org/package=ProfoundData; Silveyra Gonzalez et al., 2020), which provides basic functions to explore, plot and extract the data for model set-up, calibration and evaluation.
- ItemProjecting Exposure to Extreme Climate Impact Events Across Six Event Categories and Three Spatial Scales(Hoboken, NJ : Wiley-Blackwell, 2020) Lange, Stefan; Volkholz, Jan; Geiger, Tobias; Zhao, Fang; Vega, Iliusi; Veldkamp, Ted; Reyer, Christopher P.O.; Warszawski, Lila; Huber, Veronika; Jägermeyr, Jonas; Schewe, Jacob; Bresch, David N.; Büchner, Matthias; Chang, Jinfeng; Ciais, Philippe; Dury, Marie; Emanuel, Kerry; Folberth, Christian; Gerten, Dieter; Gosling, Simon N.; Grillakis, Manolis; Hanasaki, Naota; Henrot, Alexandra-Jane; Hickler, Thomas; Honda, Yasushi; Ito, Akihiko; Khabarov, Nikolay; Koutroulis, Aristeidis; Liu, Wenfeng; Müller, Christoph; Nishina, Kazuya; Ostberg, Sebastian; Müller Schmied, Hannes; Seneviratne, Sonia I.; Stacke, Tobias; Steinkamp, Jörg; Thiery, Wim; Wada, Yoshihide; Willner, Sven; Yang, Hong; Yoshikawa, Minoru; Yue, Chao; Frieler, KatjaThe extent and impact of climate-related extreme events depend on the underlying meteorological, hydrological, or climatological drivers as well as on human factors such as land use or population density. Here we quantify the pure effect of historical and future climate change on the exposure of land and population to extreme climate impact events using an unprecedentedly large ensemble of harmonized climate impact simulations from the Inter-Sectoral Impact Model Intercomparison Project phase 2b. Our results indicate that global warming has already more than doubled both the global land area and the global population annually exposed to all six categories of extreme events considered: river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves. Global warming of 2°C relative to preindustrial conditions is projected to lead to a more than fivefold increase in cross-category aggregate exposure globally. Changes in exposure are unevenly distributed, with tropical and subtropical regions facing larger increases than higher latitudes. The largest increases in overall exposure are projected for the population of South Asia. ©2020. The Authors.
- ItemRandom-matrix theory for the Lindblad master equation(Woodbury, NY : American Institute of Physics, 2021) Lange, Stefan; Timm, CarstenOpen quantum systems with Markovian dynamics can be described by the Lindblad equation. The quantity governing the dynamics is the Lindblad superoperator. We apply random-matrix theory to this superoperator to elucidate its spectral properties. The distribution of eigenvalues and the correlations of neighboring eigenvalues are obtained for the cases of purely unitary dynamics, pure dissipation, and the physically realistic combination of unitary and dissipative dynamics. © 2021 Author(s).
- ItemSensoren für eine kooperative Netzwerküberwachung - ESCI : Schlussbericht ; Förderprogramm Unternehmen Region ForMaT2 ; Projektlaufzeit: 01.04.2011 - 31.03.2013(Hannover : Technische Informationsbibliothek (TIB), 2013) Stecklina, Oliver; Krimmling, Jana; Paul, Andreas; Schuster, Franka; Maye, Oliver; Lange, Stefan; Langendörfer, PeterDer Schlussbericht des Projektes „Sensoren für eine kooperative Netzwerküberwachung“ beschreibt die angestrebten und erreichten Ziele des Projektes. Das Projekt hatte sich zum Ziel gesetzt eine verteilte, reaktive Sicherheitsplattform für eine ganzheitliche Sicherheit für die Systeme der industriellen Informationstechnik (IIT) zu erstellen. Die Plattform nimmt hierbei den Soll-Zustand einer Anlage aus den Planungsunterlagen oder mittels maschinellen Lernens auf und vergleicht diesen Zustand anschließend mit dem Ist-Zustand der Anlage. Der Ist-Zustand wird hierbei mittels Sensoren, die in die reale Anlage eingebracht werden, erfasst. Durch die Heterogenität der Systeme der IIT konnten klassische Ansätze der IT-Sicherheit aus der Informationstechnik nicht ohne Modifikationen übertragen werden. So gelten in der IIT insbesondere andere Anforderungen bzgl. der Verfügbarkeit, der Reaktionsfähigkeit und der Flexibilität der Systeme sowie bei den Protokollen. Darüber hinaus ist die IIT geprägt durch eine Vielzahl an verschiedenen Protokollen und Systemumgebungen, die teilweise Hersteller-spezifisch sind. Die VRS-Plattform setzt hierzu auf einen modularen Ansatz und verwendet die Java Systemumgebungen. Damit sind eine unkomplizierte Portierung und ein flexible Erweiterung der Plattform möglich. Hierbei wurde insbesondere die beschränkten Ressourcen der Geräte berücksichtigt und eine Java Virtual Machine gewählt, die auf allen Geräteklassen lauffähig ist. Darüber hinaus wurden Verfahren für eine sichere und vertrauenswürdige Kommunikation zwischen den Komponenten der VRS-Plattform entwickelt. Hierbei lag der Fokus auf Systeme mit sehr geringen Ressourcen, da insbesondere für diese Klasse and Geräte keine adäquaten Lösungen zur Verfügung stehen.
- ItemTemperature-related excess mortality in German cities at 2 °C and higher degrees of global warming(San Diego, Calif. : Elsevier, 2020) Huber, Veronika; Krummenauer, Linda; Peña-Ortiz, Cristina; Lange, Stefan; Gasparrini, Antonio; Vicedo-Cabrera, Ana M.; Garcia-Herrera, Ricardo; Frieler, KatjaBackground: Investigating future changes in temperature-related mortality as a function of global mean temperature (GMT) rise allows for the evaluation of policy-relevant climate change targets. So far, only few studies have taken this approach, and, in particular, no such assessments exist for Germany, the most populated country of Europe. Methods: We assess temperature-related mortality in 12 major German cities based on daily time-series of all-cause mortality and daily mean temperatures in the period 1993–2015, using distributed-lag non-linear models in a two-stage design. Resulting risk functions are applied to estimate excess mortality in terms of GMT rise relative to pre-industrial levels, assuming no change in demographics or population vulnerability. Results: In the observational period, cold contributes stronger to temperature-related mortality than heat, with overall attributable fractions of 5.49% (95%CI: 3.82–7.19) and 0.81% (95%CI: 0.72–0.89), respectively. Future projections indicate that this pattern could be reversed under progressing global warming, with heat-related mortality starting to exceed cold-related mortality at 3 °C or higher GMT rise. Across cities, projected net increases in total temperature-related mortality were 0.45% (95%CI: −0.02–1.06) at 3 °C, 1.53% (95%CI: 0.96–2.06) at 4 °C, and 2.88% (95%CI: 1.60–4.10) at 5 °C, compared to today's warming level of 1 °C. By contrast, no significant difference was found between projected total temperature-related mortality at 2 °C versus 1 °C of GMT rise. Conclusions: Our results can inform current adaptation policies aimed at buffering the health risks from increased heat exposure under climate change. They also allow for the evaluation of global mitigation efforts in terms of local health benefits in some of Germany's most populated cities. © 2020 The Authors
- ItemTesting bias adjustment methods for regional climate change applications under observational uncertainty and resolution mismatch(Hoboken, NJ : Wiley, 2020) Casanueva, Ana; Herrera, Sixto; Iturbide, Maialen; Lange, Stefan; Jury, Martin; Dosio, Alessandro; Maraun, Douglas; Gutiérrez, José M.Systematic biases in climate models hamper their direct use in impact studies and, as a consequence, many statistical bias adjustment methods have been developed to calibrate model outputs against observations. The application of these methods in a climate change context is problematic since there is no clear understanding on how these methods may affect key magnitudes, for example, the climate change signal or trend, under different sources of uncertainty. Two relevant sources of uncertainty, often overlooked, are the sensitivity to the observational reference used to calibrate the method and the effect of the resolution mismatch between model and observations (downscaling effect). In the present work, we assess the impact of these factors on the climate change signal of temperature and precipitation considering marginal, temporal and extreme aspects. We use eight standard and state-of-the-art bias adjustment methods (spanning a variety of methods regarding their nature—empirical or parametric—, fitted parameters and trend-preservation) for a case study in the Iberian Peninsula. The quantile trend-preserving methods (namely quantile delta mapping (QDM), scaled distribution mapping (SDM) and the method from the third phase of ISIMIP-ISIMIP3) preserve better the raw signals for the different indices and variables considered (not all preserved by construction). However, they rely largely on the reference dataset used for calibration, thus presenting a larger sensitivity to the observations, especially for precipitation intensity, spells and extreme indices. Thus, high-quality observational datasets are essential for comprehensive analyses in larger (continental) domains. Similar conclusions hold for experiments carried out at high (approximately 20 km) and low (approximately 120 km) spatial resolutions. © 2020 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.
- ItemWFDE5: Bias-adjusted ERA5 reanalysis data for impact studies(Katlenburg-Lindau : Copernics Publications, 2020) Cucchi, Marco; Weedon, Graham P.; Amici, Alessandro; Bellouin, Nicolas; Lange, Stefan; Müller Schmied, Hannes; Hersbach, Hans; Buontempo, CarloThe WFDE5 dataset has been generated using the WATCH Forcing Data (WFD) methodology applied to surface meteorological variables from the ERA5 reanalysis. The WFDEI dataset had previously been generated by applying the WFD methodology to ERA-Interim. The WFDE5 is provided at 0.5 spatial resolution but has higher temporal resolution (hourly) compared to WFDEI (3-hourly). It also has higher spatial variability since it was generated by aggregation of the higher-resolution ERA5 rather than by interpolation of the lower-resolution ERA-Interim data. Evaluation against meteorological observations at 13 globally distributed FLUXNET2015 sites shows that, on average, WFDE5 has lower mean absolute error and higher correlation than WFDEI for all variables. Bias-adjusted monthly precipitation totals of WFDE5 result in more plausible global hydrological water balance components when analysed in an uncalibrated hydrological model (WaterGAP) than with the use of raw ERA5 data for model forcing. The dataset, which can be downloaded from https://doi.org/10.24381/cds.20d54e34 (C3S, 2020b), is distributed by the Copernicus Climate Change Service (C3S) through its Climate Data Store (CDS, C3S, 2020a) and currently spans from the start of January 1979 to the end of 2018. The dataset has been produced using a number of CDS Toolbox applications, whose source code is available with the data - allowing users to regenerate part of the dataset or apply the same approach to other data. Future updates are expected spanning from 1950 to the most recent year. A sample of the complete dataset, which covers the whole of the year 2016, is accessible without registration to the CDS at https://doi.org/10.21957/935p-cj60 (Cucchi et al., 2020). © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License.