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We present a model study that investigates to what extent it is possible to introduce ENSO variability to an Earth system Model of Intermediate Complexity (EMIC). The Zebiak-Cane ENSO model is dynamically coupled to the EMIC CLIMBER-3 alpha, which by itself exhibits no interannual or multidecadal variability. ENSO variability is introduced to CLIMBER-3 alpha by adding ENSO-related sea surface temperature anomalies to the upper layers of the model ocean. For the other coupling direction, changes in the mean CLIMBER-3 alpha climate on decadal time scales are used to change the background state of the ENSO model, achieving a two-way coupling. We compare typical ENSO-related patterns of a fully coupled pre-industrial model run to reanalysis data and point out the possibilities and limitations of this model configuration. Although introduced ENSO-related SST anomalies and other related variables like the Southern Oscillation Index are well reproduced by the EMIC in the forcing domain, teleconnections to other regions are damped, especially in meridional direction. The reason for this limitation is the atmospheric model, which does not sufficiently resolve the necessary transport mechanisms. Despite this limitation the presented coupling method may still be a useful tool in combination with higher resolution atmospheric models as being in development for the successor model CLIMBER-3 and possibly other EMICs.
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.
Tropical cyclones range among the costliest disasters on Earth. Their economic repercussions along the supply and trade network also affect remote economies that are not directly affected. We here simulate possible global repercussions on consumption for the example case of Hurricane Sandy in the US (2012) using the shock-propagation model Acclimate. The modeled shock yields a global three-phase ripple: an initial production demand reduction and associated consumption price decrease, followed by a supply shortage with increasing prices, and finally a recovery phase. Regions with strong trade relations to the US experience strong magnitudes of the ripple. A dominating demand reduction or supply shortage leads to overall consumption gains or losses of a region, respectively. While finding these repercussions in historic data is challenging due to strong volatility of economic interactions, numerical models like ours can help to identify them by approaching the problem from an exploratory angle, isolating the effect of interest. For this, our model simulates the economic interactions of over 7000 regional economic sectors, interlinked through about 1.8 million trade relations. Under global warming, the wave-like structures of the economic response to major hurricanes like the one simulated here are likely to intensify and potentially overlap with other weather extremes.