@article{FrielerSchaubergerArnethetal.2017,
  author    = {Frieler, Katja and Schauberger, Bernhard and Arneth, Almut and Balkovic, Juraj and Chryssanthacopoulos, James and Deryng, Delphine and Elliott, Joshua and Folberth, Christian and Khabarov, Nikolay and M{\"u}ller, Christoph and Olin, Stefan and Pugh, Thomas A. M. and Schaphoff, Sibyll and Schewe, Jacob and Schmid, Erwin and Warszawski, Lila and Levermann, Anders},
  title     = {Understanding the weather signal in national crop-yield variability},
  series = {Earths future},
  volume    = {5},
  journal   = {Earths future},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {2328-4277},
  doi       = {10.1002/2016EF000525},
  pages     = {605 -- 616},
  year      = {2017},
  abstract  = {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.},
  language  = {en}
}
@article{WarszawskiKrieglerLentonetal.2021,
  author    = {Warszawski, Lila and Kriegler, Elmar and Lenton, Timothy M. and Gaffney, Owen and Jacob, Daniela and Klingenfeld, Daniel and Koide, Ryu and Costa, Mar{\´i}a M{\´a}{\~n}ez and Messner, Dirk and Nakicenovic, Nebojsa and Schellnhuber, Hans Joachim and Schlosser, Peter and Takeuchi, Kazuhiko and van der Leeuw, Sander and Whiteman, Gail and Rockstr{\"o}m, Johan},
  title     = {All options, not silver bullets, needed to limit global warming to 1.5 °C},
  series = {Environmental research letters},
  volume    = {16},
  journal   = {Environmental research letters},
  number    = {6},
  publisher = {IOP Publishing},
  address   = {Bristol},
  issn      = {1748-9326},
  doi       = {10.1088/1748-9326/abfeec},
  pages     = {15},
  year      = {2021},
  abstract  = {Climate science provides strong evidence of the necessity of limiting global warming to 1.5 °C, in line with the Paris Climate Agreement. The IPCC 1.5 °C special report (SR1.5) presents 414 emissions scenarios modelled for the report, of which around 50 are classified as '1.5 °C scenarios', with no or low temperature overshoot. These emission scenarios differ in their reliance on individual mitigation levers, including reduction of global energy demand, decarbonisation of energy production, development of land-management systems, and the pace and scale of deploying carbon dioxide removal (CDR) technologies. The reliance of 1.5 °C scenarios on these levers needs to be critically assessed in light of the potentials of the relevant technologies and roll-out plans. We use a set of five parameters to bundle and characterise the mitigation levers employed in the SR1.5 1.5 °C scenarios. For each of these levers, we draw on the literature to define 'medium' and 'high' upper bounds that delineate between their 'reasonable', 'challenging' and 'speculative' use by mid century. We do not find any 1.5 °C scenarios that stay within all medium upper bounds on the five mitigation levers. Scenarios most frequently 'over use' CDR with geological storage as a mitigation lever, whilst reductions of energy demand and carbon intensity of energy production are 'over used' less frequently. If we allow mitigation levers to be employed up to our high upper bounds, we are left with 22 of the SR1.5 1.5 °C scenarios with no or low overshoot. The scenarios that fulfil these criteria are characterised by greater coverage of the available mitigation levers than those scenarios that exceed at least one of the high upper bounds. When excluding the two scenarios that exceed the SR1.5 carbon budget for limiting global warming to 1.5 °C, this subset of 1.5 °C scenarios shows a range of 15-22 Gt CO2 (16-22 Gt CO2 interquartile range) for emissions in 2030. For the year of reaching net zero CO2 emissions the range is 2039-2061 (2049-2057 interquartile range).},
  language  = {en}
}
@article{FrielerLevermannElliottetal.2015,
  author    = {Frieler, Katja and Levermann, Anders and Elliott, J. and Heinke, Jens and Arneth, A. and Bierkens, M. F. P. and Ciais, Philippe and Clark, D. B. and Deryng, D. and Doell, P. and Falloon, P. and Fekete, B. and Folberth, Christian and Friend, A. D. and Gellhorn, C. and Gosling, S. N. and Haddeland, I. and Khabarov, N. and Lomas, M. and Masaki, Y. and Nishina, K. and Neumann, K. and Oki, T. and Pavlick, R. and Ruane, A. C. and Schmid, E. and Schmitz, C. and Stacke, T. and Stehfest, E. and Tang, Q. and Wisser, D. and Huber, Veronika and Piontek, Franziska and Warszawski, Lila and Schewe, Jacob and Lotze-Campen, Hermann and Schellnhuber, Hans Joachim},
  title     = {A framework for the cross-sectoral integration of multi-model impact projections},
  series = {Earth system dynamics},
  volume    = {6},
  journal   = {Earth system dynamics},
  number    = {2},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {2190-4979},
  doi       = {10.5194/esd-6-447-2015},
  pages     = {447 -- 460},
  year      = {2015},
  abstract  = {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.},
  language  = {en}
}