Assessing Agricultural Risks of Climate Change in the 21st Century in a Global Gridded Crop Model Intercomparison

Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies

Climate Impacts on Economic Growth as Drivers of Uncertainty in the Social Cost of Carbon

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

Climate Impacts on Economic Growth as Drivers of Uncertainty in the Social Cost of Carbon

We reexamine estimates of the social cost of carbon (SCC) used by agencies as the price of carbon emissions in cost-benefit analysis, focusing on those by the federal Interagency Working Group on SCC (IWG). We show that the models used by the IWG assume continued economic growth in the face of substantial temperature increases, which suggests that they may not capture the full range of possible consequences of climate change. Using the DICE integrated assessment model, we examine the possibility that climate change may directly affect productivity and find that even a modest impact of this type increases SCC estimates substantially. The SCC appears to be highly uncertain and sensitive to modeling assumptions. Understanding the impact of climate change therefore requires understanding how climate-related harms may affect productivity and economic growth. Furthermore, we suggest that misunderstandings about growth assumptions in the model may underlie the debate surrounding the proper discount rate

Evaluating the Sensitivity of Agricultural Model Performance to Different Climate Inputs: Supplemental Material

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled and observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources reanalysis, reanalysis that is bias corrected with observed climate, and a control dataset and compared with observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by non-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. Some issues persist for all choices of climate inputs: crop yields appear to be oversensitive to precipitation fluctuations but under sensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves

Characterizing Agricultural Impacts of Recent Large-Scale US Droughts and Changing Technology and Management

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