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- ItemA multi-model analysis of risk of ecosystem shifts under climate change(Bristol : IOP Publishing, 2013) Warszawski, Lila; Friend, Andrew; Ostberg, Sebastian; Frieler, Katja; Lucht, Wolfgang; Schaphoff, Sibyll; Beerling, David; Cadule, Patricia; Ciais, Philippe; Clark, Douglas B.; Kahana, Ron; Ito, Akihiko; Keribin, Rozenn; Kleidon, Axel; Lomas, Mark; Nishina, Kazuya; Pavlick, Ryan; Rademacher, Tim Tito; Buechner, Matthias; Piontek, Franziska; Schewe, Jacob; Serdeczny, Olivia; Schellnhuber, Hans JoachimClimate change may pose a high risk of change to Earth's ecosystems: shifting climatic boundaries may induce changes in the biogeochemical functioning and structures of ecosystems that render it difficult for endemic plant and animal species to survive in their current habitats. Here we aggregate changes in the biogeochemical ecosystem state as a proxy for the risk of these shifts at different levels of global warming. Estimates are based on simulations from seven global vegetation models (GVMs) driven by future climate scenarios, allowing for a quantification of the related uncertainties. 5–19% of the naturally vegetated land surface is projected to be at risk of severe ecosystem change at 2 ° C of global warming (ΔGMT) above 1980–2010 levels. However, there is limited agreement across the models about which geographical regions face the highest risk of change. The extent of regions at risk of severe ecosystem change is projected to rise with ΔGMT, approximately doubling between ΔGMT = 2 and 3 ° C, and reaching a median value of 35% of the naturally vegetated land surface for ΔGMT = 4 °C. The regions projected to face the highest risk of severe ecosystem changes above ΔGMT = 4 °C or earlier include the tundra and shrublands of the Tibetan Plateau, grasslands of eastern India, the boreal forests of northern Canada and Russia, the savanna region in the Horn of Africa, and the Amazon rainforest.
- ItemAssessing inter-sectoral climate change risks: The role of ISIMIP(Bristol : IOP Publishing, 2017) Rosenzweig, Cynthia; Arnell, Nigel W.; Ebi, Kristie L.; Lotze-Campen, Hermann; Raes, Frank; Rapley, Chris; Smith, Mark Stafford; Cramer, Wolfgang; Frieler, Katja; Reyer, Christopher P.O.; Schewe, Jacob; van Vuuren, Detlef; Warszawski, LilaThe aims of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) are to provide a framework for the intercomparison of global and regional-scale risk models within and across multiple sectors and to enable coordinated multi-sectoral assessments of different risks and their aggregated effects. The overarching goal is to use the knowledge gained to support adaptation and mitigation decisions that require regional or global perspectives within the context of facilitating transformations to enable sustainable development, despite inevitable climate shifts and disruptions. ISIMIP uses community-agreed sets of scenarios with standardized climate variables and socio-economic projections as inputs for projecting future risks and associated uncertainties, within and across sectors. The results are consistent multi-model assessments of sectoral risks and opportunities that enable studies that integrate across sectors, providing support for implementation of the Paris Agreement under the United Nations Framework Convention on Climate Change.
- ItemThe role of storage dynamics in annual wheat prices(Bristol : IOP Publishing, 2017) Schewe, Jacob; Otto, Christian; Frieler, Katja; Bodirsky, Benjamin Leo; Kriegler, Elmar; Lotze-Campen, Hermann; Popp, AlexanderIdentifying the drivers of global crop price fluctuations is essential for estimating the risks of unexpected weather-induced production shortfalls and for designing optimal response measures. Here we show that with a consistent representation of storage dynamics, a simple supply–demand model can explain most of the observed variations in wheat prices over the last 40 yr solely based on time series of annual production and long term demand trends. Even the most recent price peaks in 2007/08 and 2010/11 can be explained by additionally accounting for documented changes in countries' trade policies and storage strategies, without the need for external drivers such as oil prices or speculation across different commodity or stock markets. This underlines the critical sensitivity of global prices to fluctuations in production. The consistent inclusion of storage into a dynamic supply-demand model closes an important gap when it comes to exploring potential responses to future crop yield variability under climate and land-use change.
- ItemThe critical role of the routing scheme in simulating peak river discharge in global hydrological models(Bristol : IOP Publishing, 2017) Zhao, Fang; Veldkamp, Ted I.E.; Frieler, Katja; Schewe, Jacob; Ostberg, Sebastian; Willner, Sven; Schauberger, Bernhard; Gosling, Simon N.; Müller Schmied, Hannes; Portmann, Felix T.; Leng, Guoyong; Huang, Maoyi; Liu, Xingcai; Tang, Qiuhong; Hanasaki, Naota; Biemans, Hester; Gerten, Dieter; Satoh, Yusuke; Pokhrel, Yadu; Stacke, Tobias; Ciais, Philippe; Chang, Jinfeng; Ducharne, Agnes; Guimberteau, Matthieu; Wada, Yoshihide; Kim, Hyungjun; Yamazaki, DaiGlobal hydrological models (GHMs) have been applied to assess global flood hazards, but their capacity to capture the timing and amplitude of peak river discharge—which is crucial in flood simulations—has traditionally not been the focus of examination. Here we evaluate to what degree the choice of river routing scheme affects simulations of peak discharge and may help to provide better agreement with observations. To this end we use runoff and discharge simulations of nine GHMs forced by observational climate data (1971–2010) within the ISIMIP2a project. The runoff simulations were used as input for the global river routing model CaMa-Flood. The simulated daily discharge was compared to the discharge generated by each GHM using its native river routing scheme. For each GHM both versions of simulated discharge were compared to monthly and daily discharge observations from 1701 GRDC stations as a benchmark. CaMa-Flood routing shows a general reduction of peak river discharge and a delay of about two to three weeks in its occurrence, likely induced by the buffering capacity of floodplain reservoirs. For a majority of river basins, discharge produced by CaMa-Flood resulted in a better agreement with observations. In particular, maximum daily discharge was adjusted, with a multi-model averaged reduction in bias over about 2/3 of the analysed basin area. The increase in agreement was obtained in both managed and near-natural basins. Overall, this study demonstrates the importance of routing scheme choice in peak discharge simulation, where CaMa-Flood routing accounts for floodplain storage and backwater effects that are not represented in most GHMs. Our study provides important hints that an explicit parameterisation of these processes may be essential in future impact studies.
- ItemA statistically predictive model for future monsoon failure in India(Bristol : IOP Publishing, 2012) Schewe, Jacob; Levermann, AndersIndian monsoon rainfall is vital for a large share of the world's population. Both reliably projecting India's future precipitation and unraveling abrupt cessations of monsoon rainfall found in paleorecords require improved understanding of its stability properties. While details of monsoon circulations and the associated rainfall are complex, full-season failure is dominated by large-scale positive feedbacks within the region. Here we find that in a comprehensive climate model, monsoon failure is possible but very rare under pre-industrial conditions, while under future warming it becomes much more frequent. We identify the fundamental intraseasonal feedbacks that are responsible for monsoon failure in the climate model, relate these to observational data, and build a statistically predictive model for such failure. This model provides a simple dynamical explanation for future changes in the frequency distribution of seasonal mean all-Indian rainfall. Forced only by global mean temperature and the strength of the Pacific Walker circulation in spring, it reproduces the trend as well as the multidecadal variability in the mean and skewness of the distribution, as found in the climate model. The approach offers an alternative perspective on large-scale monsoon variability as the result of internal instabilities modulated by pre-seasonal ambient climate conditions.