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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.
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.
Projections of changes in Antarctic Ice Sheet (AIS) surface mass balance indicate a negative contribution to sea level because of the expected increase in precipitation due to the higher moisture holding capacity of warmer air(1). Observations over the past decades, however, are unable to constrain the relation between temperature and accumulation changes because both are dominated by strong natural variability(2-5). Here we derive a consistent continental-scale increase in accumulation of approximately 5 +/- 1% K-1, through the assessment of ice-core data (spanning the large temperature change during the last deglaciation, 21,000 to 10,000 years ago), in combination with palaeo-simulations, future projections by 35 general circulation models (GCMs), and one high-resolution future simulation. The ice-core data and modelling results for the last deglaciation agree, showing uniform local sensitivities of similar to 6% K-1. The palaeo-simulation allows for a continental-scale aggregation of accumulation changes reaching 4.3% K-1. Despite the different timescales, these sensitivities agree with the multi-model mean of 6.1 +/- 2.6% K-1 (GCMprojections) and the continental-scale sensitivity of 4.9% K-1 (high-resolution future simulation). Because some of the mass gain of the AIS is offset by dynamical losses induced by accumulation(6,7), we provide a response function allowing projections of sea-level fall in terms of continental-scale accumulation changes that compete with surface melting and dynamical losses induced by other mechanisms(6,8,9).
In the last decade record-breaking rainfall events have occurred in many places around the world causing severe impacts to human society and the environment including agricultural losses and floodings. There is now medium confidence that human-induced greenhouse gases have contributed to changes in heavy precipitation events at the global scale. Here, we present the first analysis of record-breaking daily rainfall events using observational data. We show that over the last three decades the number of record-breaking events has significantly increased in the global mean. Globally, this increase has led to 12 % more record-breaking rainfall events over 1981-2010 compared to those expected in stationary time series. The number of record-breaking rainfall events peaked in 2010 with an estimated 26 % chance that a new rainfall record is due to long-term climate change. This increase in record-breaking rainfall is explained by a statistical model which accounts for the warming of air and associated increasing water holding capacity only. Our results suggest that whilst the number of rainfall record-breaking events can be related to natural multi-decadal variability over the period from 1901 to 1980, observed record-breaking rainfall events significantly increased afterwards consistent with rising temperatures.