Marcus C. Sarofim

Probabilistic Forecast for Twenty-First-Century Climate Based on Uncertainties in Emissions (Without Policy) and Climate Parameters

Abstract The Massachusetts Institute of Technology (MIT) Integrated Global System Model is used to make probabilistic projections of climate change from 1861 to 2100. Since the model’s first projections were published in 2003, substantial improvements have been made to the model, and improved estimates of the probability distributions of uncertain input parameters have become available. The new projections are considerably warmer than the 2003 projections; for example, the median surface warming in 2091–2100 is 5.1°C compared to 2.4°C in the earlier study. Many changes contribute to the stronger warming; among the more important ones are taking into account the cooling in the second half of the twentieth century due to volcanic eruptions for input parameter estimation and a more sophisticated method for projecting gross domestic product (GDP) growth, which eliminated many low-emission scenarios. However, if recently published data, suggesting stronger twentieth-century ocean warming, are used to determine...

Uncertainty in emissions projections for climate models

Future global climate projections are subject to large uncertainties. Major sources of this uncertainty are projections of anthropogenic emissions. We evaluate the uncertainty in future anthropogenic emissions using a computable general equilibrium model of the world economy. Results are simulated through 2100 for carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulfur hexafluoride (SF6), sulfur dioxide (SO2), black carbon (BC) and organic carbon (OC), nitrogen oxides (NOx), carbon monoxide (CO), ammonia (NH3) and non-methane volatile organic compounds (NMVOCs). We construct mean and upper and lower 95% emissions scenarios (available from the authors at 11 � 11 latitude–longitude grid). Using the MIT Integrated Global System Model (IGSM), we find a temperature change range in 2100 of 0.9 to 4.01C, compared with the Intergovernmental Panel on Climate Change emissions scenarios that result in a range of 1.3 to 3.61C when simulated through MIT IGSM. r 2002 Elsevier Science Ltd. All rights reserved.

Analysis of climate policy targets under uncertainty

Although policymaking in response to the climate change threat is essentially a challenge of risk management, most studies of the relation of emissions targets to desired climate outcomes are either deterministic or subject to a limited representation of the underlying uncertainties. Monte Carlo simulation, applied to the MIT Integrated Global System Model (an integrated economic and earth system model of intermediate complexity), is used to analyze the uncertain outcomes that flow from a set of century-scale emissions paths developed originally for a study by the U.S. Climate Change Science Program. The resulting uncertainty in temperature change and other impacts under these targets is used to illustrate three insights not obtainable from deterministic analyses: that the reduction of extreme temperature changes under emissions constraints is greater than the reduction in the median reduction; that the incremental gain from tighter constraints is not linear and depends on the target to be avoided; and that comparing median results across models can greatly understate the uncertainty in any single model.

Projections of temperature-attributable premature deaths in 209 U.S. cities using a cluster-based Poisson approach

BackgroundA 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.MethodsWe 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.ResultsWe 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.ConclusionsWe 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.

Climate change impacts on extreme temperature mortality in select metropolitan areas in the United States

This paper applies city-specific mortality relationships for extremely hot and cold temperatures for 33 Metropolitan Statistical Areas in the United States to develop mortality projections for historical and potential future climates. These projections, which cover roughly 100 million of 310 million U.S. residents in 2010, highlight a potential change in health risks from uncontrolled climate change and the potential benefits of a greenhouse gas (GHG) mitigation policy. Our analysis reveals that projected mortality from extremely hot and cold days combined increases significantly over the 21st century because of the overwhelming increase in extremely hot days. We also find that the evaluated GHG mitigation policy could substantially reduce this risk. These results become more pronounced when accounting for projected population changes. These results challenge arguments that there could be a mortality benefit attributable to changes in extreme temperatures from future warming. This finding of a net increase in mortality also holds in an analog city sensitivity analysis that incorporates a strong adaptation assumption. While our results do not address all sources of uncertainty, their scale and scope highlight one component of the potential health risks of unmitigated climate change impacts on extreme temperatures and draw attention to the need to continue to refine analytical tools and methods for this type of analysis.

The GTP of Methane: Modeling Analysis of Temperature Impacts of Methane and Carbon Dioxide Reductions

The Global Temperature Potential (GTP) has recently been proposed as an alternative to the Global Warming Potential (GWP). Using two different Earth Models of Intermediate Complexity, we show that the solution to the 100-year sustained GTP for methane is significantly larger than the equivalent GWP due to the inclusion of future changes in greenhouse gas concentrations in the reference scenario and different atmospheric chemistry assumptions. This result suggests that methane reductions may be undervalued when using GWPs, but the policy implications depend on how the objectives of greenhouse gas policy are defined.

Marginal abatement cost curves for US black carbon emissions

There has been increasing recognition of the important role that aerosols such as black carbon (BC) play in influencing net climate forcing, in particular the high rates of warming in the Arctic, and there is significant interest in reducing BC emissions. In order to assess mitigation options, there is a need for a better understanding of how current air quality policies designed for particulate matter reductions affect BC emissions and for the development of improved BC specific marginal abatement cost (MAC) curves. Using data from the United States (US), we assess the effects of existing air quality regulations on projected BC emissions (diesel fuel regulations in particular are already having significant effects). We also identify key US-specific abatement strategies and present MAC curves for further reducing BC emissions from two key US sources. This analysis may serve to inform similar research on BC mitigation in other regions and sectors.

Future Arctic temperature change resulting from a range of aerosol emissions scenarios

Abstract The Arctic temperature response to emissions of aerosols—specifically black carbon (BC), organic carbon (OC), and sulfate—depends on both the sector and the region where these emissions originate. Thus, the net Arctic temperature response to global aerosol emissions reductions will depend strongly on the blend of emissions sources being targeted. We use recently published equilibrium Arctic temperature response factors for BC, OC, and sulfate to estimate the range of present‐day and future Arctic temperature changes from seven different aerosol emissions scenarios. Globally, Arctic temperature changes calculated from all of these emissions scenarios indicate that present‐day emissions from the domestic and transportation sectors generate the majority of present‐day Arctic warming from BC. However, in all of these scenarios, this warming is more than offset by cooling resulting from SO2 emissions from the energy sector. Thus, long‐term climate mitigation strategies that are focused on reducing carbon dioxide (CO2) emissions from the energy sector could generate short‐term, aerosol‐induced Arctic warming. A properly phased approach that targets BC‐rich emissions from the transportation sector as well as the domestic sectors in key regions—while simultaneously working toward longer‐term goals of CO2 mitigation—could potentially avoid some amount of short‐term Arctic warming.

Climate Benefits of U.S. EPA Programs and Policies That Reduced Methane Emissions 1993–2013

The United States (U.S.) Environmental Protection Agency (EPA) has established voluntary programs to reduce methane (CH4) emissions, and regulations that either directly reduce CH4 or provide co-benefits of reducing CH4 emissions while controlling for other air pollutants. These programs and regulations address four sectors that are among the largest domestic CH4 emissions sources: municipal solid waste landfills, oil and natural gas, coal mining, and agricultural manure management. Over the 1993-2013 time period, 127.9 Tg of CH4 emissions reductions were attributed to these programs, equal to about 18% of the counterfactual (or potential) domestic emissions over that time, with almost 70% of the abatement due to landfill sector regulations. Reductions attributed to the voluntary programs increased nearly continuously during the study period. We quantified how these reductions influenced atmospheric CH4 concentration and global temperature, finding a decrease in concentration of 28 ppb and an avoided temperature rise of 0.006 °C by 2013. Further, we monetized the climate and ozone-health impacts of the CH4 reductions, yielding an estimated benefit of

Emergency Department Visits and Ambient Temperature: Evaluating the Connection and Projecting Future Outcomes

255 billion. These results indicate that EPA programs and policies have made a strong contribution to CH4 abatement, with climate and air quality benefits.