Evaluating the ability of gridded climate datasets to capture temperature and precipitation trends and extremes.

Data informs policy and drives decisions. With an increasing number of regulations requiring entities to use local climate data in their planning, it's more important than ever to understand the strengths and limitations of data we use. While they have been shown to capture long-term statistics on global or regional levels, the ability of gridded climate datasets to capture trends and extreme events is not common knowledge.

Comprehensive evidence implies a higher social cost of CO.

The social cost of carbon dioxide (SC-CO) measures the monetized value of the damages to society caused by an incremental metric tonne of CO emissions and is a key metric informing climate policy. Used by governments and other decision-makers in benefit-cost analysis for over a decade, SC-CO estimates draw on climate science, economics, demography and other disciplines. However, a 2017 report by the US National Academies of Sciences, Engineering, and Medicine (NASEM) highlighted that current SC-CO estimates no longer reflect the latest research.

New science of climate change impacts on agriculture implies higher social cost of carbon.

Despite substantial advances in climate change impact research in recent years, the scientific basis for damage functions in economic models used to calculate the social cost of carbon (SCC) is either undocumented, difficult to trace, or based on a small number of dated studies. Here we present new damage functions based on the current scientific literature and introduce these into an integrated assessment model (IAM) in order to estimate a new SCC. We focus on the agricultural sector, use two methods for determining the yield impacts of warming, and the GTAP CGE model to calculate the economic consequences of yield shocks.