Advancing the estimation of future climate impacts within the United States.

Evidence of the physical and economic impacts of climate change is a critical input to policy development and decision-making. In addition to the magnitude of potential impacts, detailed estimates of where, when, and to whom those damages may occur; the types of impacts that will be most damaging; uncertainties in these damages; and the ability of adaptation to reduce potential risks are all interconnected and important considerations. This study utilizes the reduced-complexity model, the Framework for Evaluating Damages and Impacts (FrEDI), to rapidly project economic and physical impacts of climate change across 10 000 future scenarios for multiple impact sectors, regions, and populations within the contiguous United States (US). Results from FrEDI show that net national damages increase overtime, with mean climate-driven damages estimated to reach USD 2.9 trillion (95 % confidence interval (CI): USD 510 billion to USD 12 trillion) annually by 2090. Detailed FrEDI results show that for the analyzed sectors the majority of annual long-term (e.g., 2090) damages are associated with climate change impacts to human health, including mortality attributable to climate-driven changes in temperature and air pollution (O3 and PM2.5) exposure. Regional results also show that annual long-term climate-driven damages vary geographically. The Southeast (all regions are as defined in Fig. 5) is projected to experience the largest annual damages per capita (mean: USD 9300 per person annually; 95 % CI: USD 1800-USD 37 000 per person annually), whereas the smallest damages per capita are expected in the Southwest (mean: USD 6300 per person annually; 95 % CI: USD 840-USD 27 000 per person annually). Climate change impacts may also broaden existing societal inequalities, with, for example, Black or African Americans being disproportionately affected by additional premature mortality from changes in air quality. Lastly, FrEDI projections are extended through 2300 to estimate the net present climate-driven damages within US borders from marginal changes in greenhouse gas emissions. Combined, this analysis provides the most detailed illustration to date of the distribution of climate change impacts within US borders.

Improving reduced complexity model assessment and usability.

Reduced complexity climate models are useful tools with practical policy applications, yet evaluation of their performance and application is nascent. We call for stakeholder-driven development and assessment to address user needs, including provision of open-source code and guidance to inform model selection and application.

A temperature binning approach for multi-sector climate impact analysis.

Characterizing the future risks of climate change is a key goal of climate impacts analysis. Temperature binning provides a framework for analyzing sector-specific impacts by degree of warming as an alternative or complement to traditional scenario-based approaches in order to improve communication of results, comparability between studies, and flexibility to facilitate scenario analysis. In this study, we estimate damages for nine climate impact sectors within the contiguous United States (US) using downscaled climate projections from six global climate models, at integer degrees of US national warming. Each sector is analyzed based on socioeconomic conditions for both the beginning and the end of the century. The potential for adaptive measures to decrease damages is also demonstrated for select sectors; differences in damages across adaptation response scenarios within some sectors can be as much as an order of magnitude. Estimated national damages from these sectors based on a reactive adaptation assumption and 2010 socioeconomic conditions range from $600 million annually per degree of national warming for winter recreation to $8 billion annually per degree of national warming for labor impacts. Results are also estimated per degree of global temperature change and for 2090 socioeconomic conditions.

Joint emulation of Earth System Model temperature-precipitation realizations with internal variability and space-time and cross-variable correlation: fldgen v2.0 software description.

Earth System Models (ESMs) are excellent tools for quantifying many aspects of future climate dynamics but are too computationally expensive to produce large collections of scenarios for downstream users of ESM data. In particular, many researchers focused on the impacts of climate change require large collections of ESM runs to rigorously study the impacts to both human and natural systems of low-frequency high-importance events, such as multi-year droughts. Climate model emulators provide an effective mechanism for filling this gap, reproducing many aspects of ESMs rapidly but with lower precision. The fldgen v1.0 R package quickly generates thousands of realizations of gridded temperature fields by randomizing the residuals of pattern scaling temperature output from any single ESM, retaining the spatial and temporal variance and covariance structures of the input data at a low computational cost. The fldgen v2.0 R package described here extends this capability to produce joint realizations of multiple variables, with a focus on temperature and precipitation in an open source software package available for community use (https://github.com/jgcri/fldgen). This substantially improves the fldgen package by removing the requirement that the ESM variables be normally distributed, and will enable researchers to quickly generate covarying temperature and precipitation data that are synthetic but faithful to the characteristics of the original ESM.