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- 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 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 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, KenichiThis 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).
- ItemSubsampling 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.