Open Access

Katja Frieler, Anders Levermann, Joshua Elliot, Jens Heinke, Almut Arneth, Marc F. P. Bierkens, Philippe Ciais, Douglas B. Clark, Delphine Deryng, Petra Döll, Pete Falloon, Balázs M. Fekete, Christian Folberth, Andrew D. Friend, Catrin Gellhorn, Simon N. Gosling, Ingjerd Haddeland, Nikolay Khabarov, Marc R. Lomas, Yusuke Masaki, Kazuya Nishina, Kathleen Neumann, Taikan Oki, Ryan Pavlick, Alex C. Ruane, Erwin Schmid, Christoph Schmitz, Tobias Stacke, Elke Stehfest, Qiuhong Tang, Dominik Wisser, Veronika Huber, Franziska Piontek, Lila Warszawski, Jacob Schewe, Hermann Lotze-Campen, Hans Joachim Schellnhuber

• 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.