Effective assessment of potential climate impacts requires the ability to rapidly predict the time-varying response of climate variables. This prediction must be able to consider different combinations of forcing agents at high resolution. Full-scale ESMs are too computationally intensive to run large scenario ensembles due to their long lead times and high costs. Faster approaches such as intermediate complexity modeling and pattern scaling are limited by low resolution and invariant response patterns, respectively. We propose a generalizable framework for emulating climate variables to overcome these issues, representing the climate system through spatially resolved impulse response functions. We derive impulse response functions by directly deconvolving effective radiative forcing (ERF) and near-surface air temperature profiles. This enables rapid emulation of new scenarios through convolution and derivation of other impulse response functions from any forcing to its response. We present results from an application to near-surface air temperature based on CMIP6 data. We evaluate emulator performance across 5 CMIP6 experiments including the SSPs, demonstrating accurate emulation of global mean and spatially resolved temperature change with respect to CMIP6 ensemble outputs. Global mean relative error in emulated temperature averages 1.49% in mid-century and 1.25% by end-of-century. These errors are likely driven by state-dependent climate feedbacks, such as the non-linear effects of Arctic sea ice melt. We additionally show an illustrative example of our emulator for policy evaluation and impact analysis, emulating spatially resolved temperature change for a 1000 member scenario ensemble in less than a second.

The ability to rapidly simulate the climate implications of a large number of CO2 emissions trajectories is helpful for implementing mitigation and adaptation policies. A key variable of interest is near-surface air temperature, which is approximately proportional to cumulative CO2 emissions. We take advantage of this relationship, diagnosing Green’s Functions for the spatial temperature response to CO2 emissions based on CMIP6 experiment data, creating an emulator that can be used across emissions scenarios to estimate local temperature responses. As compared to CMIP6 experiments, this approach captures the spatial temperature response with some limited accuracy in polar regions. It incorporates emissions path dependency and is useful for evaluating large ensembles of policy scenarios that are otherwise prohibitively expensive to simulate using earth system models. We apply this emulator to show differing local temperature responses when a global mean of 2ºC is reached and to varying trajectories with the same cumulative emissions.

Evaluating the influence of anthropogenic emissions changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emissions changes. However, the ability of these widely-used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using two scenarios simulated by a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic emissions changes in the US (2011 to 2017) and China (2013 to 2017) on PM2.5 and O3, we show that widely-used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30%-42% using a random forest model that incorporates both local and regional scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant emission input and quantify the degree to which emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the effectiveness of emissions reduction policies using statistical approaches.

Nuclear and coal power use in the United States are projected to decline over the coming decades. Here, we explore how simultaneous phase-outs of these energy sources could affect air pollution and distributional health risk with existing grid infrastructure. We develop an energy grid dispatch model to estimate the emissions of CO2, NOx and SO2 from each U.S. electricity generating unit. We couple the emissions from this model with a chemical transport model to calculate impacts on ground-level ozone and fine particulate matter (PM2.5). Our yearlong scenario removing nuclear power results in compensation by coal, gas and oil, leading to increased emissions that impact climate and air quality nationwide. We estimate that changes in PM2.5 and ozone lead to an additional 9,200 yearly mortalities, and that changes in CO2 emissions over that period lead to an order of magnitude higher mortalities throughout the 21st century. Together, air quality and climate impacts incur between \$80.7-\$126.1 billion of annual costs. In a scenario where nuclear and coal power are shut down simultaneously, air quality impacts due to PM2.5 are larger and those due to ozone are smaller, because of more reliance on high emitting gas and oil, and climate impacts are substantially smaller than that of nuclear power shutdowns. With current reliance on non-coal fossil fuels, closures of nuclear and coal plants shift the distribution of health risks, exemplifying the importance of multi-system analysis and unit-level regulations when making energy decisions.

Scientists and regulators commonly use benzo[a]pyrene concentrations to assess cancer risk from complex mixtures of atmospheric polycyclic aromatic hydrocarbons (PAHs). Here, we show that benzo[a]pyrene is a poor indicator of PAH risk distribution and management: nearly 90% of cancer risk worldwide results from other PAHs, including unregulated degradation products of emitted PAHs. We develop and apply a global-scale atmospheric model and conduct health impact analyses to estimate human cancer risk from 16 PAHs and their N-PAH degradation products. We find that benzo[a]pyrene is a minor contributor to the total cancer risks of PAHs (11%); the remaining risk comes from other directly-emitted PAHs (73%) and N-PAHs (15%). We show that assessment and policy-making that relies solely on benzo[a]pyrene exposure provides misleading estimates of risk distribution, the importance of chemical processes, and the prospects for risk mitigation. We conclude that researchers and decision-makers should consider additional PAHs as well as degradation products.

Interventions to mitigate air pollution have impacts on multiple facets of human and environmental well-being. We apply a systems framework for analyzing the overall sustainability impacts of interventions to a case of the rice-wheat cropping system of Punjab (India), where agricultural practices lead to air pollution-related health impacts, over-exploitation of groundwater, over-use of fertilizers and reduced local crop diversity. We use this case to characterize varying degrees of change in interventions and quantify sustainability impacts using an inclusive wealth-based approach. We show that both small and large changes, in this case either improving the existing cropping system or fundamental changes to the cropping system, can lead to substantial and wide-ranging sustainability benefits. We also show that interventions that improve human health show the largest quantitative benefit due to the assumed high marginal value of human life. Accurate localized estimates of marginal values of stocks are needed for estimating overall sustainability impacts.