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Abstract. Recent analyses show the importance of methane shortwave absorption, which many climate models lack. In particular, Allen et al. (2023) used idealized climate model simulations to show that methane shortwave absorption mutes up to 30 % of the surface warming and 60 % of the precipitation increase associated with its longwave radiative effects. Here, we explicitly quantify the radiative and climate impacts due to shortwave absorption of the present-day methane perturbation. Our results corroborate the hypothesis that present-day methane shortwave absorption mutes the warming effects of longwave absorption. For example, the global mean cooling in response to the present-day methane shortwave absorption is -0.10±0.07 K, which offsets 28 % (7 %–55 %) of the surface warming associated with present-day methane longwave radiative effects. The precipitation increase associated with the longwave radiative effects of the present-day methane perturbation (0.012±0.006 mm d−1) is also muted by shortwave absorption but not significantly so (-0.008±0.009 mm d−1). The unique responses to methane shortwave absorption are related to its negative top-of-the-atmosphere effective radiative forcing but positive atmospheric heating and in part to methane's distinctive vertical atmospheric solar heating profile. We also find that the present-day methane shortwave radiative effects, relative to its longwave radiative effects, are about 5 times larger than those under idealized carbon dioxide perturbations. Additional analyses show consistent but non-significant differences between the longwave versus shortwave radiative effects for both methane and carbon dioxide, including a stronger (negative) climate feedback when shortwave radiative effects are included (particularly for methane). We conclude by reiterating that methane remains a potent greenhouse gas. 

Abstract Although greenhouse gases absorb primarily long-wave radiation, they also absorb short-wave radiation. Recent studies have highlighted the importance of methane short-wave absorption, which enhances its stratospherically adjusted radiative forcing by up to ~ 15%. The corresponding climate impacts, however, have been only indirectly evaluated and thus remain largely unquantified. Here we present a systematic, unambiguous analysis using one model and separate simulations with and without methane short-wave absorption. We find that methane short-wave absorption counteracts ~30% of the surface warming associated with its long-wave radiative effects. An even larger impact occurs for precipitation as methane short-wave absorption offsets ~60% of the precipitation increase relative to its long-wave radiative effects. The methane short-wave-induced cooling is due largely to cloud rapid adjustments, including increased low-level clouds, which enhance the reflection of incoming short-wave radiation, and decreased high-level clouds, which enhance outgoing long-wave radiation. The cloud responses, in turn, are related to the profile of atmospheric solar heating and corresponding changes in temperature and relative humidity. Despite our findings, methane remains a potent contributor to global warming, and efforts to reduce methane emissions are vital for keeping global warming well below 2 °C above preindustrial values. 

Abstract India is largely devoid of high‐quality and reliable on‐the‐ground measurements of fine particulate matter (PM_2.5). Ground‐level PM_2.5concentrations are estimated from publicly available satellite Aerosol Optical Depth (AOD) products combined with other information. Prior research has largely overlooked the possibility of gaining additional accuracy and insights into the sources of PM using satellite retrievals of tropospheric trace gas columns. We evaluate the information content of tropospheric trace gas columns for PM_2.5estimates over India within a modeling testbed using an Automated Machine Learning (AutoML) approach, which selects from a menu of different machine learning tools based on the data set. We then quantify the relative information content of tropospheric trace gas columns, AOD, meteorological fields, and emissions for estimating PM_2.5over four Indian sub‐regions on daily and monthly time scales. Our findings suggest that, regardless of the specific machine learning model assumptions, incorporating trace gas modeled columns improves PM_2.5estimates. We use the ranking scores produced from the AutoML algorithm and Spearman’s rank correlation to infer or link the possible relative importance of primary versus secondary sources of PM_2.5as a first step toward estimating particle composition. Our comparison of AutoML‐derived models to selected baseline machine learning models demonstrates that AutoML is at least as good as user‐chosen models. The idealized pseudo‐observations (chemical‐transport model simulations) used in this work lay the groundwork for applying satellite retrievals of tropospheric trace gases to estimate fine particle concentrations in India and serve to illustrate the promise of AutoML applications in atmospheric and environmental research.