TY - GEN A1 - Frieler, Katja A1 - Levermann, Anders A1 - Elliott, J. A1 - Heinke, J. A1 - Arneth, A. A1 - Bierkens, M. F. P. A1 - Ciais, Philippe A1 - Clark, D. B. A1 - Deryng, D. A1 - Doell, P. A1 - Falloon, P. A1 - Fekete, B. A1 - Folberth, Christian A1 - Friend, A. D. A1 - Gellhorn, C. A1 - Gosling, S. N. A1 - Haddeland, I. A1 - Khabarov, N. A1 - Lomas, M. A1 - Masaki, Y. A1 - Nishina, K. A1 - Neumann, K. A1 - Oki, T. A1 - Pavlick, R. A1 - Ruane, A. C. A1 - Schmid, E. A1 - Schmitz, C. A1 - Stacke, T. A1 - Stehfest, E. A1 - Tang, Q. A1 - Wisser, D. A1 - Huber, V. A1 - Piontek, Franziska A1 - Warszawski, L. A1 - Schewe, Jacob A1 - Lotze-Campen, Hermann A1 - Schellnhuber, Hans Joachim T1 - A framework for the cross-sectoral integration of multi-model impact projections BT - land use decisions under climate impacts uncertainties T2 - Earth system dynamics N2 - Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making. Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop-and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 457 KW - global food demand KW - water availability KW - elevated CO2 KW - future KW - carbon KW - system KW - productivity KW - agriculture KW - emissions KW - scarcity Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-407968 ER - TY - JOUR A1 - Frieler, Katja A1 - Levermann, Anders A1 - Elliott, J. A1 - Heinke, Jens A1 - Arneth, A. A1 - Bierkens, M. F. P. A1 - Ciais, Philippe A1 - Clark, D. B. A1 - Deryng, D. A1 - Doell, P. A1 - Falloon, P. A1 - Fekete, B. A1 - Folberth, Christian A1 - Friend, A. D. A1 - Gellhorn, C. A1 - Gosling, S. N. A1 - Haddeland, I. A1 - Khabarov, N. A1 - Lomas, M. A1 - Masaki, Y. A1 - Nishina, K. A1 - Neumann, K. A1 - Oki, T. A1 - Pavlick, R. A1 - Ruane, A. C. A1 - Schmid, E. A1 - Schmitz, C. A1 - Stacke, T. A1 - Stehfest, E. A1 - Tang, Q. A1 - Wisser, D. A1 - Huber, Veronika A1 - Piontek, Franziska A1 - Warszawski, Lila A1 - Schewe, Jacob A1 - Lotze-Campen, Hermann A1 - Schellnhuber, Hans Joachim T1 - A framework for the cross-sectoral integration of multi-model impact projections BT - land use decisions under climate impacts uncertainties JF - Earth system dynamics N2 - Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making. Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop-and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making. Y1 - 2015 U6 - https://doi.org/10.5194/esd-6-447-2015 SN - 2190-4979 SN - 2190-4987 VL - 6 IS - 2 SP - 447 EP - 460 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Frieler, Katja A1 - Schauberger, Bernhard A1 - Arneth, Almut A1 - Balkovic, Juraj A1 - Chryssanthacopoulos, James A1 - Deryng, Delphine A1 - Elliott, Joshua A1 - Folberth, Christian A1 - Khabarov, Nikolay A1 - Müller, Christoph A1 - Olin, Stefan A1 - Pugh, Thomas A. M. A1 - Schaphoff, Sibyll A1 - Schewe, Jacob A1 - Schmid, Erwin A1 - Warszawski, Lila A1 - Levermann, Anders T1 - Understanding the weather signal in national crop-yield variability JF - Earths future N2 - Year-to-year variations in crop yields can have major impacts on the livelihoods of subsistence farmers and may trigger significant global price fluctuations, with severe consequences for people in developing countries. Fluctuations can be induced by weather conditions, management decisions, weeds, diseases, and pests. Although an explicit quantification and deeper understanding of weather-induced crop-yield variability is essential for adaptation strategies, so far it has only been addressed by empirical models. Here, we provide conservative estimates of the fraction of reported national yield variabilities that can be attributed to weather by state-of-the-art, process-based crop model simulations. We find that observed weather variations can explain more than 50% of the variability in wheat yields in Australia, Canada, Spain, Hungary, and Romania. For maize, weather sensitivities exceed 50% in seven countries, including the United States. The explained variance exceeds 50% for rice in Japan and South Korea and for soy in Argentina. Avoiding water stress by simulating yields assuming full irrigation shows that water limitation is a major driver of the observed variations in most of these countries. Identifying the mechanisms leading to crop-yield fluctuations is not only fundamental for dampening fluctuations, but is also important in the context of the debate on the attribution of loss and damage to climate change. Since process-based crop models not only account for weather influences on crop yields, but also provide options to represent human-management measures, they could become essential tools for differentiating these drivers, and for exploring options to reduce future yield fluctuations. Y1 - 2017 U6 - https://doi.org/10.1002/2016EF000525 SN - 2328-4277 VL - 5 SP - 605 EP - 616 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Herzschuh, Ulrike A1 - Borkowski, Janett A1 - Schewe, Jacob A1 - Mischke, Steffen A1 - Tian, Fang T1 - Moisture-advection feedback supports strong early-to-mid Holocene monsoon climate on the eastern Tibetan Plateau as inferred from a pollen-based reconstruction JF - Palaeogeography, palaeoclimatology, palaeoecology : an international journal for the geo-sciences N2 - (Paleo-)climatologists are challenged to identify mechanisms that cause the observed abrupt Holocene monsoon events despite the fact that monsoonal circulation is assumed to be driven by gradual insolation changes. Here we provide proxy and model evidence to show that moisture-advection feedback can lead to a non-linear relationship between sea-surface and continental temperatures and monsoonal precipitation. A pollen record from Lake Ximencuo (Nianbaoyeze Mountains) indicates that vegetation from the eastern margin of the Tibetan Plateau was characterized by alpine deserts and glacial flora after the Last Glacial Maximum (LGM) (21-15.5 cal kyr BP), by alpine meadows during the Late Glacial (15.5-10.4 cal kyr BP) and second half of the Holocene (5.0 cal kyr BP to present) and by mixed forests during the first half of the Holocene (10.4-5.0 cal kyr BP). The application of pollen-based transfer functions yields an abrupt temperature increase at 10.4 cal kyr BP and a decrease at 5.0 cal kyr BP of about 3 degrees C. By applying endmember modeling to grain-size data from the same sediment core we infer that frequent fluvial events (probably originating from high-magnitude precipitation events) were more common in the early and mid Holocene. We assign the inferred exceptional strong monsoonal circulation to the initiation of moisture-advection feedback, a result supported by a simple model that reproduces this feedback pattern over the same time period. (C) 2014 Published by Elsevier B.V. KW - Moisture-advection feedback KW - Monsoon KW - Tibetan Plateau KW - Holocene KW - Last Glacial Maximum KW - Pollen-climate calibration Y1 - 2014 U6 - https://doi.org/10.1016/j.palaeo.2014.02.022 SN - 0031-0182 SN - 1872-616X VL - 402 SP - 44 EP - 54 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Kluge, Lucas A1 - Schewe, Jacob T1 - Evaluation and extension of the radiation model for internal migration JF - Physical review : E, Statistical, nonlinear and soft matter physics N2 - Human migration is often studied using gravity models. These models, however, have known limitations, including analytic inconsistencies and a dependence on empirical data to calibrate multiple parameters for the region of interest. Overcoming these limitations, the radiation model has been proposed as an alternative, universal approach to predicting different forms of human mobility, but has not been adopted for studying migration. Here we show, using data on within-country migration from the USA and Mexico, that the radiation model systematically underpredicts long-range moves, while the traditional gravity model performs well for large distances. The universal opportunity model, an extension of the radiation model, shows an improved fit of long-range moves compared to the original radiation model, but at the cost of introducing two additional parameters. We propose a more parsimonious extension of the radiation model that introduces a single parameter. We demonstrate that it fits the data over the full distance spectrum and also-unlike the universal opportunity model-preserves the analytical property of the original radiation model of being equivalent to a gravity model in the limit of a uniform population distribution. Y1 - 2021 U6 - https://doi.org/10.1103/PhysRevE.104.054311 SN - 2470-0045 SN - 2470-0053 SN - 2470-0061 VL - 104 IS - 5 PB - American Physical Society CY - College Park ER - TY - GEN A1 - Levermann, Anders A1 - Petoukhov, Vladimir A1 - Schewe, Jacob A1 - Schellnhuber, Hans Joachim T1 - Abrupt monsoon transitions as seen in paleorecords can be explained by moisture-advection feedback T2 - Proceedings of the National Academy of Sciences of the United States of America Y1 - 2016 U6 - https://doi.org/10.1073/pnas.1603130113 SN - 0027-8424 VL - 113 SP - E2348 EP - E2349 PB - National Acad. of Sciences CY - Washington ER - TY - JOUR A1 - Lissner, Tabea Katharina A1 - Reusser, Dominik Edwin A1 - Schewe, Jacob A1 - Lakes, T. A1 - Kropp, Jürgen T1 - Climate impacts on human livelihoods: where uncertainty matters in projections of water availability JF - Earth system dynamics N2 - Climate change will have adverse impacts on many different sectors of society, with manifold consequences for human livelihoods and well-being. However, a systematic method to quantify human well-being and livelihoods across sectors is so far unavailable, making it difficult to determine the extent of such impacts. Climate impact analyses are often limited to individual sectors (e.g. food or water) and employ sector-specific target measures, while systematic linkages to general livelihood conditions remain unexplored. Further, recent multi-model assessments have shown that uncertainties in projections of climate impacts deriving from climate and impact models, as well as greenhouse gas scenarios, are substantial, posing an additional challenge in linking climate impacts with livelihood conditions. This article first presents a methodology to consistently measure what is referred to here as AHEAD (Adequate Human livelihood conditions for wEll-being And Development). Based on a trans-disciplinary sample of concepts addressing human well-being and livelihoods, the approach measures the adequacy of conditions of 16 elements. We implement the method at global scale, using results from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) to show how changes in water availability affect the fulfilment of AHEAD at national resolution. In addition, AHEAD allows for the uncertainty of climate and impact model projections to be identified and differentiated. We show how the approach can help to put the substantial inter-model spread into the context of country-specific livelihood conditions by differentiating where the uncertainty about water scarcity is relevant with regard to livelihood conditions - and where it is not. The results indicate that livelihood conditions are compromised by water scarcity in 34 countries. However, more often, AHEAD fulfilment is limited through other elements. The analysis shows that the water-specific uncertainty ranges of the model output are outside relevant thresholds for AHEAD for 65 out of 111 countries, and therefore do not contribute to the overall uncertainty about climate change impacts on livelihoods. In 46 of the countries in the analysis, water-specific uncertainty is relevant to AHEAD. The AHEAD method presented here, together with first results, forms an important step towards making scientific results more applicable for policy decisions. Y1 - 2014 U6 - https://doi.org/10.5194/esd-5-355-2014 SN - 2190-4979 SN - 2190-4987 VL - 5 IS - 2 SP - 355 EP - 373 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Menon, Arathy A1 - Levermann, Anders A1 - Schewe, Jacob T1 - Enhanced future variability during India's rainy season JF - Geophysical research letters N2 - The Indian summer monsoon shapes the livelihood of a large share of the world's population. About 80% of annual precipitation over India occurs during the monsoon season from June through September. Next to its seasonal mean rainfall, the day-to-day variability is crucial for the risk of flooding, national water supply, and agricultural productivity. Here we show that the latest ensemble of climate model simulations, prepared for the AR-5 of the Intergovernmental Panel on Climate Change, consistently projects significant increases in day-to-day rainfall variability under unmitigated climate change. The relative increase by the period 2071-2100 with respect to the control period 1871-1900 ranges from 13% to 50% under the strongest scenario (Representative Concentration Pathways, RCP-8.5), in the 10 models with the most realistic monsoon climatology; and 13% to 85% when all the 20 models are considered. The spread across models reduces when variability increase per degree of global warming is considered, which is independent of the scenario in most models, and is 8% +/- 4%/K on average. This consistent projection across 20 comprehensive climate models provides confidence in the results and suggests the necessity of profound adaptation measures in the case of unmitigated climate change. KW - monsoon KW - variability KW - CMIP-5 Y1 - 2013 U6 - https://doi.org/10.1002/grl.50583 SN - 0094-8276 VL - 40 IS - 12 SP - 3242 EP - 3247 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Menon, Arathy A1 - Levermann, Anders A1 - Schewe, Jacob A1 - Lehmann, J. A1 - Frieler, Katja T1 - Consistent increase in Indian monsoon rainfall and its variability across CMIP-5 models JF - Earth system dynamics N2 - The possibility of an impact of global warming on the Indian monsoon is of critical importance for the large population of this region. Future projections within the Coupled Model Intercomparison Project Phase 3 (CMIP-3) showed a wide range of trends with varying magnitude and sign across models. Here the Indian summer monsoon rainfall is evaluated in 20 CMIP-5 models for the period 1850 to 2100. In the new generation of climate models, a consistent increase in seasonal mean rainfall during the summer monsoon periods arises. All models simulate stronger seasonal mean rainfall in the future compared to the historic period under the strongest warming scenario RCP-8.5. Increase in seasonal mean rainfall is the largest for the RCP-8.5 scenario compared to other RCPs. Most of the models show a northward shift in monsoon circulation by the end of the 21st century compared to the historic period under the RCP-8.5 scenario. The interannual variability of the Indian monsoon rainfall also shows a consistent positive trend under unabated global warming. Since both the long-term increase in monsoon rainfall as well as the increase in interannual variability in the future is robust across a wide range of models, some confidence can be attributed to these projected trends. Y1 - 2013 U6 - https://doi.org/10.5194/esd-4-287-2013 SN - 2190-4979 SN - 2190-4987 VL - 4 IS - 2 SP - 287 EP - 300 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Mester, Benedikt A1 - Willner, Sven N. A1 - Frieler, Katja A1 - Schewe, Jacob T1 - Evaluation of river flood extent simulated with multiple global hydrological models and climate forcings JF - Environmental research letters : ERL / Institute of Physics N2 - Global flood models (GFMs) are increasingly being used to estimate global-scale societal and economic risks of river flooding. Recent validation studies have highlighted substantial differences in performance between GFMs and between validation sites. However, it has not been systematically quantified to what extent the choice of the underlying climate forcing and global hydrological model (GHM) influence flood model performance. Here, we investigate this sensitivity by comparing simulated flood extent to satellite imagery of past flood events, for an ensemble of three climate reanalyses and 11 GHMs. We study eight historical flood events spread over four continents and various climate zones. For most regions, the simulated inundation extent is relatively insensitive to the choice of GHM. For some events, however, individual GHMs lead to much lower agreement with observations than the others, mostly resulting from an overestimation of inundated areas. Two of the climate forcings show very similar results, while with the third, differences between GHMs become more pronounced. We further show that when flood protection standards are accounted for, many models underestimate flood extent, pointing to deficiencies in their flood frequency distribution. Our study guides future applications of these models, and highlights regions and models where targeted improvements might yield the largest performance gains. KW - global flood model KW - validation KW - model intercomparison KW - flood risk KW - global hydrological model Y1 - 2021 U6 - https://doi.org/10.1088/1748-9326/ac188d SN - 1748-9326 VL - 16 IS - 9 PB - IOP Publ. Ltd. CY - Bristol ER -