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Start date: 2020 × End date: 2019 × Start date: 2010 × Subject: Climate models ×

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Now showing 1 - 10 of 30
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    Enhanced stratosphere/troposphere coupling during extreme warm stratospheric events with strong polar-night jet oscillation
    (Basel : MDPI AG, 2018) Peters, D.H.W.; Schneidereit, A.; Karpechko, A.Y.
    Extreme warm stratospheric events during polar winters from ERA-Interim reanalysis and CMIP5-ESM-LR runs were separated by duration and strength of the polar-night jet oscillation (PJO) using a high statistical confidence level of three standard deviations (strong-PJO events). With a composite analysis, we demonstrate that strong-PJO events show a significantly stronger downward propagating signal in both, northern annular mode (NAM) and zonal mean zonal wind anomaly in the stratosphere in comparison with non-PJO events. The lower stratospheric EP-flux-divergence difference in ERA-Interim was stronger in comparison to long-term CMIP5-ESM-LR runs (by a factor of four). This suggests that stratosphere-troposphere coupling is stronger in ERA-Interim than in CMIP5-ESM-LR. During the 60 days following the central date (CD), the Arctic oscillation signal was more intense during strong-PJO events than during non-PJO events in ERA-Interim data in comparison to CMIP5-ESM-LR runs. During the 15-day phase after CD, strong PJO events had a significant increase in stratospheric ozone, upper tropospheric zonally asymmetric impact, and a regional surface impact in ERA-Interim. Finally, we conclude that the applied high statistical threshold gives a clearer separation of extreme warm stratospheric events into strong-PJO events and non-PJO events including their different downward propagating NAM signal and tropospheric impacts. © 2018 by the authors.
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    Natural streamflow simulation for two largest river basins in Poland: A baseline for identification of flow alterations
    (Göttingen : Copernicus, 2016) Piniewski, Mikołaj; Cudennec, Christophe
    The objective of this study was to apply a previously developed large-scale and high-resolution SWAT model of the Vistula and the Odra basins, calibrated with the focus of natural flow simulation, in order to assess the impact of three different dam reservoirs on streamflow using the Indicators of Hydrologic Alteration (IHA). A tailored spatial calibration approach was designed, in which calibration was focused on a large set of relatively small non-nested sub-catchments with semi-natural flow regime. These were classified into calibration clusters based on the flow statistics similarity. After performing calibration and validation that gave overall positive results, the calibrated parameter values were transferred to the remaining part of the basins using an approach based on hydrological similarity of donor and target catchments. The calibrated model was applied in three case studies with the purpose of assessing the effect of dam reservoirs (Włocławek, Siemianówka and Czorsztyn Reservoirs) on streamflow alteration. Both the assessment based on gauged streamflow (Before-After design) and the one based on simulated natural streamflow showed large alterations in selected flow statistics related to magnitude, duration, high and low flow pulses and rate of change. Some benefits of using a large-scale and high-resolution hydrological model for the assessment of streamflow alteration include: (1) providing an alternative or complementary approach to the classical Before-After designs, (2) isolating the climate variability effect from the dam (or any other source of alteration) effect, (3) providing a practical tool that can be applied at a range of spatial scales over large area such as a country, in a uniform way. Thus, presented approach can be applied for designing more natural flow regimes, which is crucial for river and floodplain ecosystem restoration in the context of the European Union's policy on environmental flows.
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    The economically optimal warming limit of the planet
    (Göttingen : Copernicus Publ., 2019) Ueckerd, Falko; Frieler, Katja; Lange, Stefan; Wenz, Leonie; Luderer, Gunnar; Levermann, Anders
    Both 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.
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    The challenge to detect and attribute effects of climate change on human and natural systems
    (Dordrecht [u.a.] : Springer, 2013) Stone, D.; Auffhammer, M.; Carey, M.; Hansen, G.; Huggel, C.; Cramer, W.; Lobell, D.; Molau, U.; Solow, A.; Tibig, L.; Yohe, G.
    Anthropogenic climate change has triggered impacts on natural and human systems world-wide, yet the formal scientific method of detection and attribution has been only insufficiently described. Detection and attribution of impacts of climate change is a fundamentally cross-disciplinary issue, involving concepts, terms, and standards spanning the varied requirements of the various disciplines. Key problems for current assessments include the limited availability of long-term observations, the limited knowledge on processes and mechanisms involved in changing environmental systems, and the widely different concepts applied in the scientific literature. In order to facilitate current and future assessments, this paper describes the current conceptual framework of the field and outlines a number of conceptual challenges. Based on this, it proposes workable cross-disciplinary definitions, concepts, and standards. The paper is specifically intended to serve as a baseline for continued development of a consistent cross-disciplinary framework that will facilitate integrated assessment of the detection and attribution of climate change impacts.
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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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    Modelling mineral dust in the Central Asian region
    (Les Ulis : EDP Sciences, 2019) Heinold, Bernd; Tegen, Ina
    In Central Asia, climate and air quality are largely affected by local and long-travelled mineral dust. For the last century, the area has experienced severe land-use changes and water exploitation producing new dust sources. Today global warming causes rapid shrinking of mountain glaciers with yet unknow consequences for dust and its climate effects. Despite the importance for a growing population, only little is known about sources, transport pathways and properties of Central Asian dust. A transport study with a global aerosol-climate model is undertaken to investigate the life cycle of mineral dust in Central Asia for the period of a remote-sensing campaign in Tajikistan in 2015-2016. An initial evaluation with sun photometer measurements shows reasonable agreement for the average amount of dust, but a significant weakness of the model in reproducing the seasonality of local dust with maximum activity in summer. Source apportionment reveals a major contribution from Arabia throughout the year in accordance with observations. In the model, local sources mainly contribute in spring and autumn while summer-time dust production is underestimated. The results underline the importance of considering long-range transport and, locally, a detailed representation of atmospheric dynamics and surface characteristics for modelling dust in Central Asia. © 2019 The Authors, published by EDP Sciences.
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    The role of methane in future climate strategies: mitigation potentials and climate impacts
    (Dordrecht [u.a.] : Springer Science + Business Media B.V, 2019) Harmsen, Mathijs; Mathijs, Detlef P.; Bodirsky, Benjamin Leon; Chateau, Jean; Durand-Lasserve, Olivier; Drouet, Laurent; Fricko, Oliver; Fujimori, Shinichiro; Gernaat, David E.H.J.; Hanaoka, Tatsuya; Hilaire, Jérôme; Keramidas, Kimon; Luderer, Gunnar; Moura, Maria Cecilia P.; Sano, Fuminori; Smith, Steven J.; Wada, Kenichi
    This study examines model-specific assumptions and projections of methane (CH4) emissions in deep mitigation scenarios generated by integrated assessment models (IAMs). For this, scenarios of nine models are compared in terms of sectoral and regional CH4 emission reduction strategies, as well as resulting climate impacts. The models’ projected reduction potentials are compared to sector and technology-specific reduction potentials found in literature. Significant cost-effective and non-climate policy related reductions are projected in the reference case (10–36% compared to a “frozen emission factor” scenario in 2100). Still, compared to 2010, CH4 emissions are expected to rise steadily by 9–72% (up to 412 to 654 Mt CH4/year). Ambitious CO2 reduction measures could by themselves lead to a reduction of CH4 emissions due to a reduction of fossil fuels (22–48% compared to the reference case in 2100). However, direct CH4 mitigation is crucial and more effective in bringing down CH4 (50–74% compared to the reference case). Given the limited reduction potential, agriculture CH4 emissions are projected to constitute an increasingly larger share of total anthropogenic CH4 emissions in mitigation scenarios. Enteric fermentation in ruminants is in that respect by far the largest mitigation bottleneck later in the century with a projected 40–78% of total remaining CH4 emissions in 2100 in a strong (2 °C) climate policy case. © 2019, The Author(s).
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    Subsampling impact on the climate change signal over poland based on simulations from statistical and dynamical downscaling
    (Boston, Mass. : AMS, 2019) Mezghani, Abdelkader; Dobler, Andreas; Benestad, Rasmus; Haugen, Jan Erik; Parding, Kajsa M.; Piniewski, Mikolaj; Kundzewicz, Zbigniew W.
    Most impact studies using downscaled climate data as input assume that the selection of few global climate models (GCMs) representing the largest spread covers the likely range of future changes. This study shows that including more GCMs can result in a very different behavior. We tested the influence of selecting various subsets of GCMs on the climate change signal over Poland from simulations based on dynamical and empirical-statistical downscaling methods. When the climate variable is well simulated by the GCM, such as temperature, results showed that both downscaling methods agree on a warming over Poland by up to 2° or 5°C assuming intermediate or high emission scenarios, respectively, by 2071-2100. As a less robust simulated signal through GCMs, precipitation is expected to increase by up to 10% by 2071-2100 assuming the intermediate emission scenario. However, these changes are uncertain when the high emission scenario and the end of the twenty-first century are of interest. Further, an additional bootstrap test revealed an underestimation in the warming rate varying from 0.5° to more than 4°C over Poland that was found to be largely influenced by the selection of few driving GCMs instead of considering the full range of possible climate model outlooks. Furthermore, we found that differences between various combinations of small subsets from the GCM ensemble of opportunities can be as large as the climate change signal. © 2019 American Meteorological Society.
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    Assessing the influence of the Merzbacher Lake outburst floods on discharge using the hydrological model SWIM in the Aksu headwaters, Kyrgyzstan/NW China
    (Chichester : John Wiley and Sons Ltd, 2013) Wortmann, M.; Krysanova, V.; Kundzewicz, Z.W.; Su, B.; Li, X.
    Glacial lake outburst floods (GLOF) often have a significant impact on downstream users. Including their effects in hydrological models, identifying past occurrences and assessing their potential impacts are challenges for hydrologists working in mountainous catchments. The regularly outbursting Merzbacher Lake is located in the headwaters of the Aksu River, the most important source of water discharge to the Tarim River, northwest China. Modelling its water resources and the evaluation of potential climate change impacts on river discharge are indispensable for projecting future water availability for the intensively cultivated river oases downstream of the Merzbacher Lake and along the Tarim River. The semi-distributed hydrological model SWIM was calibrated to the outlet station Xiehela on the Kumarik River, by discharge the largest tributary to the Aksu River. The glacial lake outburst floods add to the difficulties of modelling this high-mountain, heavily glaciated catchment with poor data coverage and quality. The aims of the study are to investigate the glacier lake outburst floods using a modelling tool. Results include a two-step model calibration of the Kumarik catchment, an approach for the identification of the outburst floods using the measured gauge data and the modelling results and estimations of the outburst flood volumes. Results show that a catchment model can inform GLOF investigations by providing 'normal' (i.e. without the outburst floods) catchment discharge. The comparison of the simulated and observed discharge proves the occurrence of GLOFs and highlights the influences of the GLOFs on the downstream water balance.
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    SPITFIRE within the MPI Earth system model: Model development and evaluation
    (Hoboken, NJ : Blackwell Publishing Ltd, 2014) Lasslop, G.; Thonicke, K.; Kloster, S.
    Quantification of the role of fire within the Earth system requires an adequate representation of fire as a climate-controlled process within an Earth system model. To be able to address questions on the interaction between fire and the Earth system, we implemented the mechanistic fire model SPITFIRE, in JSBACH, the land surface model of the MPI Earth system model. Here, we document the model implementation as well as model modifications. We evaluate our model results by comparing the simulation to the GFED version 3 satellite-based data set. In addition, we assess the sensitivity of the model to the meteorological forcing and to the spatial variability of a number of fire relevant model parameters. A first comparison of model results with burned area observations showed a strong correlation of the residuals with wind speed. Further analysis revealed that the response of the fire spread to wind speed was too strong for the application on global scale. Therefore, we developed an improved parametrization to account for this effect. The evaluation of the improved model shows that the model is able to capture the global gradients and the seasonality of burned area. Some areas of model-data mismatch can be explained by differences in vegetation cover compared to observations. We achieve benchmarking scores comparable to other state-of-the-art fire models. The global total burned area is sensitive to the meteorological forcing. Adjustment of parameters leads to similar model results for both forcing data sets with respect to spatial and seasonal patterns. Key Points The SPITFIRE fire model was evaluated within the JSBACH land surface model A modified wind speed response improved the spatial pattern of burned area Regional gradients in burned area are driven by vegetation and fuel properties.