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Process-based agricultural models, applied in novel ways, can reproduce historical crop yield anomalies in the US, with median absolute deviation from observations of 6.7% at national-level and 11% at state-level. In seasons for which drought is the overriding factor, performance is further improved. Historical counterfactual scenarios for the 1988 and 2012 droughts show that changes in agricultural technologies and management have reduced system-level drought sensitivity in US maize production by about 25% in the intervening years. Finally, we estimate the economic costs of the two droughts in terms of insured and uninsured crop losses in each US county (for a total, adjusted for inflation, of $9 billion in 1988 and $21.6 billion in 2012). We compare these with cost estimates from the counterfactual scenarios and with crop indemnity data where available. Model-based measures are capable of accurately reproducing the direct agro-economic losses associated with extreme drought and can be used to characterize and compare events that occurred under very different conditions. This work suggests new approaches to modeling, monitoring, forecasting, and evaluating drought impacts on agriculture, as well as evaluating technological changes to inform adaptation strategies for future climate change and extreme events.

We present a framework for massively parallel climate impact simulations: the parallel System for Integrating Impact Models and Sectors (pSIMS). This framework comprises a) tools for ingesting and converting large amounts of data to a versatile datatype based on a common geospatial grid; b) tools for translating this datatype into custom formats for site-based models; c) a scalable parallel framework for performing large ensemble simulations, using any one of a number of different impacts models, on clusters, supercomputers, distributed grids, or clouds; d) tools and data standards for reformatting outputs to common datatypes for analysis and visualization; and e) methodologies for aggregating these datatypes to arbitrary spatial scales such as administrative and environmental demarcations. By automating many time-consuming and error-prone aspects of large-scale climate impacts studies, pSIMS accelerates computational research, encourages model intercomparison, and enhances reproducibility of simulation results. We present the pSIMS design and use example assessments to demonstrate its multi-model, multi-scale, and multi-sector versatility. Open-source framework for efficient massively parallel climate impact simulations.Enables analysis of dozens of crop and tree species with DSSAT, APSIM, and CenW.Multi-model multi-scale assessment of maize yield in Africa using DSSAT and APSIM.High-resolution climate impact assessment of New Zealand forest productivity.Computational scaling behavior of the framework to assess the efficiency gain attained.

One of the central ways that the costs of global warming are incorporated into U.S. law is in cost-benefit analysis of federal regulations. In 2010, to standardize analyses, an Interagency Working Group (IAWG) established a central estimate of the social cost of carbon (SCC) of $21/tCO2 drawn from three commonly-used models of climate change and the global economy. These models produced a relatively narrow distribution of SCC values, consistent with previous studies. We use one of the IAWG models, DICE, to explore which assumptions produce this apparent robustness. SCC values are constrained by a shared feature of model behavior: though climate damages become large as a fraction of economic output, they do not significantly alter economic trajectories. This persistent growth is inconsistent with the widely held belief that climate change may have strongly detrimental effects to human society. The discrepancy suggests that the models may not capture the full range of possible consequences of climate change. We examine one possibility untested by any previous study, that climate change may directly affect productivity, and find that even a modest impact of this type increases SCC estimates by many orders of magnitude. Our results imply that the SCC is far more uncertain than shown in previous modeling exercises and highly sensitive to assumptions. Understanding the societal impact of climate change requires understanding not only the magnitude of losses at any given time but also how those losses may affect future economic growth.

The 2012 growing season saw one of the worst droughts in a generation in much of the United States and cast a harsh light on the need for better analytic tools and a comprehensive approach to predicting and preparing for the effects of extreme weather on agriculture. We present an example of a simulation-based forecast for the 2012 US maize growing season produced as part of a high-resolution multi-scale predictive mechanistic modeling study designed for decision support, risk management, and counterfactual analysis. We estimate national average yields of 7.507 t/ha for 2012, 24.6% below the expected value based on increasing trend yield alone, with an interval based on resampled forecasts errors stretching from 5.586 to 8.967 t/ha. On average, the median yield simulations deviate from NASS observations by 8.3% from 1979 to 2011.