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Developing future climate projections begins with choosing future emissions scenarios. While scenarios are often based on storylines, here instead we produce a probabilistic multi-million-member ensemble of radiative forcing trajectories to assess the relevance of future forcing thresholds. We coupled a probabilistic database of future greenhouse gas emission scenarios with a probabilistically calibrated reduced complexity climate model. In 2100, we project median forcings of 5.1 watt per square meters (5th to 95th percentiles of 3.3 to 7.1), with roughly 0.5% probability of exceeding 8.5 watt per square meters, and a 1% probability of being lower than 2.6 watt per square meters. Although the probability of 8.5 watt per square meters scenarios is low, our results support their continued utility for calibrating damage functions, characterizing climate in the 22 nd century (the probability of exceeding 8.5 watt per square meters increases to about 7% by 2150), and assessing low-probability/high-impact futures. The probability of exceeding various climate thresholds has been calculated. While the warmest future scenario has less than a 1 percent chance of being exceeded this century, the paper discusses why such warm scenarios are still relevant.

Methane is a greenhouse gas that oxidizes to form ground-level ozone, itself a greenhouse gas and a health-harmful air pollutant. Reducing methane emissions will both slow anthropogenic climate change and reduce ozone-related mortality. We estimate the benefits of reducing methane emissions anywhere in the world for ozone-related premature mortality globally and for eight geographic regions. Our methods are consistent with those used by the US Government to estimate the social cost of carbon (SCC). We find that the global short- and long-term premature mortality benefits due to reduced ozone production from methane mitigation are (2011) $790 and $1775 per tonne methane, respectively. These correspond to approximately 70 and 150 % of the valuation of methane’s global climate impacts using the SCC after extrapolating from carbon dioxide to methane using global warming potential estimates. Results for monetized benefits are sensitive to a number of factors, particularly the choice of elasticity to income growth used when calculating the value of a statistical life. The benefits increase for emission years further in the future. Regionally, most of the global mortality benefits accrue in Asia, but 10 % accrue in the United States. This methodology can be used to assess the benefits of methane emission reductions anywhere in the world, including those achieved by national and multinational policies.

Wildfire activity in the western United States (US) has been increasing, a trend that has been correlated with changing patterns of temperature and precipitation associated with climate change. Health effects associated with exposure to wildfire smoke and fine particulate matter (PM 2.5 ) include short- and long-term premature mortality, hospital admissions, emergency department visits, and other respiratory and cardiovascular incidents. We estimate PM 2.5 exposure and health impacts for the entire continental US from current and future western US wildfire activity projected for a range of future climate scenarios through the 21st century. We use a simulation approach to estimate wildfire activity, area burned, fine particulate emissions, air quality concentrations, health effects, and economic valuation of health effects, using established and novel methodologies. We find that climatic factors increase wildfire pollutant emissions by an average of 0.40% per year over the 2006–2100 period under Representative Concentration Pathway (RCP) 4.5 (lower emissions scenarios) and 0.71% per year for RCP8.5. As a consequence, spatially weighted wildfire PM 2.5 concentrations more than double for some climate model projections by the end of the 21st century. PM 2.5 exposure changes, combined with population projections, result in a wildfire PM2.5-related premature mortality excess burden in the 2090 RCP8.5 scenario that is roughly 3.5 times larger than in the baseline period. The combined effect of increased wildfire activity, population growth, and increase in the valuation of avoided risk of premature mortality over time results in a large increase in total economic impact of wildfire-related PM 2.5 mortality and morbidity in the continental US, from roughly$7 billion per year in the baseline period to roughly $ 36 billion per year in 2090 for RCP4.5, and $43 billion per year in RCP8.5. The climate effect alone accounts for a roughly 60% increase in wildfire PM2.5-related premature mortality in the RCP8.5 scenario, relative to baseline conditions.

Workers in climate exposed industries such as agriculture, construction, and manufacturing face increased health risks of working on high temperature days and may make decisions to reduce work on high-heat days to mitigate this risk. Utilizing the American Time Use Survey (ATUS) for the period 2003 through 2018 and historical weather data, we model the relationship between daily temperature and time allocation, focusing on hours worked by high-risk laborers. The results indicate that labor allocation decisions are context specific and likely driven by supply-side factors. We do not find a significant relationship between temperature and hours worked during the Great Recession (2008–2014), perhaps due to high competition for employment, however during periods of economic growth (2003–2007, 2015–2018) we find a significant reduction in hours worked on high-heat days. During periods of economic growth, for every degree above 90 on a particular day, the average high-risk worker reduces their time devoted to work by about 2.6 minutes relative to a 90-degree day. This effect is expected to intensify in the future as temperatures rise. Applying the modeled relationships to climate projections through the end of century, we find that annual lost wages resulting from decreased time spent working on days over 90 degrees across the United States range from$36.7 to $ 80.0 billion in 2090 under intermediate and high emission futures, respectively.

Future changes in climate are likely to adversely affect human health by affecting concentrations of particulate matter sized less than 2.5 μm (PM2.5) and ozone (O3) in many areas. However, the degree to which these outcomes may be mitigated by reducing air pollutant emissions is not well understood. To model the associations between future changes in climate, air quality, and human health for 2 climate models and under 2 air pollutant emission scenarios. This modeling study simulated meteorological conditions over the coterminous continental US during a 1995 to 2005 baseline and over the 21st century (2025-2100) by dynamically downscaling representations of a high warming scenario from the Community Earth System Model (CESM) and the Coupled Model version 3 (CM3) global climate models. Using a chemical transport model, PM2.5 and O3 concentrations were simulated under a 2011 air pollutant emission data set and a 2040 projection. The changes in PM2.5 and O3-attributable deaths associated with climate change among the US census-projected population were estimated for 2030, 2050, 2075, and 2095 for each of 2 emission inventories and climate models. Data were analyzed from June 2018 to June 2020. The main outcomes were simulated change in summer season means of the maximum daily 8-hour mean O3, annual mean PM2.5, population-weighted exposure, and the number of avoided or incurred deaths associated with these pollutants. Results are reported for 2030, 2050, 2075, and 2095, compared with 2000, for 2 climate models and 2 air pollutant emissions data sets. The projected increased maximum daily temperatures through 2095 were up to 7.6 °C for the CESM model and 11.8 °C for the CM3 model. Under each climate model scenario by 2095, compared with 2000, an estimated additional 21 000 (95% CI, 14 000-28 000) PM2.5-attributable deaths and 4100 (95% CI, 2200-6000) O3-attributable deaths were projected to occur. These projections decreased to an estimated 15 000 (95% CI, 10 000-20 000) PM2.5-attributable deaths and 640 (95% CI, 340-940) O3-attributable deaths when simulated using a future emission inventory that accounted for reduced anthropogenic emissions. These findings suggest that reducing future air pollutant emissions could also reduce the climate-driven increase in deaths associated with air pollution by hundreds to thousands.

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.

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.

Coccidioidomycosis, or valley fever, is an infectious fungal disease currently endemic to the southwestern United States. Symptoms of valley fever range in severity from flu-like illness to severe morbidity and mortality. Warming temperatures and changes in precipitation patterns may cause the area of endemicity to expand northward throughout the western United States, putting more people at risk for contracting valley fever. This may increase the health and economic burdens from this disease. We developed an approach to describe the relationship between climate conditions and valley fever incidence using historical data and generated projections of future incidence in response to both climate change and population trends using the Climate Change Impacts and Risk Analysis (CIRA) framework developed by the U.S. Environmental Protection Agency. We also developed a method to estimate economic impacts of valley fever that is based on case counts. For our 2000–15 baseline time period, we estimated annual medical costs, lost income, and economic welfare losses for valley fever in the United States were $400,000 per case, and the annual average total cost was $3.9 billion per year. For a high greenhouse gas emission scenario and accounting for population growth, we found that total annual costs for valley fever may increase up to 164% by year 2050 and up to 380% by 2090. By the end of the twenty-first century, valley fever may cost $620,000 per case and the annual average total cost may reach $18.5 billion per year. This work contributes to the broader effort to monetize climate change–attributable damages in the United States.

Background: A warming climate will affect future temperature-attributable premature deaths. This analysis is the first to project these deaths at a near national scale for the United States using city and month-specific temperature-mortality relationships. Methods: We used Poisson regressions to model temperature-attributable premature mortality as a function of daily average temperature in 209 U.S. cities by month. We used climate data to group cities into clusters and applied an Empirical Bayes adjustment to improve model stability and calculate cluster-based month-specific temperature-mortality functions. Using data from two climate models, we calculated future daily average temperatures in each city under Representative Concentration Pathway 6.0. Holding population constant at 2010 levels, we combined the temperature data and cluster-based temperature-mortality functions to project city-specific temperature-attributable premature deaths for multiple future years which correspond to a single reporting year. Results within the reporting periods are then averaged to account for potential climate variability and reported as a change from a 1990 baseline in the future reporting years of 2030, 2050 and 2100. Results: We found temperature-mortality relationships that vary by location and time of year. In general, the largest mortality response during hotter months (April - September) was in July in cities with cooler average conditions. The largest mortality response during colder months (October-March) was at the beginning (October) and end (March) of the period. Using data from two global climate models, we projected a net increase in premature deaths, aggregated across all 209 cities, in all future periods compared to 1990. However, the magnitude and sign of the change varied by cluster and city. Conclusions: We found increasing future premature deaths across the 209 modeled U.S. cities using two climate model projections, based on constant temperature-mortality relationships from 1997 to 2006 without any future adaptation. However, results varied by location, with some locations showing net reductions in premature temperature-attributable deaths with climate change.

The 100-year global warming potential (GWP) is the primary metric used to compare the climate impacts of emissions of different greenhouse gases (GHGs). The GWP relies on radiative forcing rather than damages, assumes constant future concentrations, and integrates over a timescale of 100 years without discounting; these choices lead to a metric that is transparent and simple to calculate, but have also been criticized. In this paper, we take a quantitative approach to evaluating the choice of time horizon, accounting for many of these complicating factors. By calculating an equivalent GWP timescale based on discounted damages resulting from CH4 and CO2 pulses, we show that a 100-year timescale is consistent with a discount rate of 3.3 % (interquartile range of 2.7 % to 4.1 % in a sensitivity analysis). This range of discount rates is consistent with those often considered for climate impact analyses. With increasing discount rates, equivalent timescales decrease. We recognize the limitations of evaluating metrics by relying only on climate impact equivalencies without consideration of the economic and political implications of metric implementation.