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Framework for analysis of PM_2.5estimates. 

Abstract. In 2020 the International Maritime Organization (IMO) implemented strict new regulations on the emissions of sulphate aerosol from the world's shipping fleet. This can be expected to lead to a reduction in aerosol-driven cooling, unmasking a portion of greenhouse gas warming. The magnitude of the effect is uncertain, however, due to the large remaining uncertainties in the climate response to aerosols. Here, we investigate this question using an 18-member ensemble of fully coupled climate simulations evenly sampling key modes of climate variability with the NCAR CESM2 model. We show that while there is a clear physical response of the climate system to the IMO regulations, including a surface temperature increase, we do not find global mean temperature influence that is significantly different from zero. The 20-year average global mean warming for 2020–2040 is +0.03 °C, with a 5–95 % confidence range of [-0.09, 0.19], reflecting the weakness of the perturbation relative to internal variability. We do, however, find a robust, non-zero regional temperature response in part of the North Atlantic. We also find that the maximum annual-mean ensemble-mean warming occurs around a decade after the perturbation in 2029, which means that the IMO regulations have likely had very limited influence on observed global warming to date. We further discuss our results in light of other, recent publications that have reached different conclusions. Overall, while the IMO regulations may contribute up to at 0.16 °C [-0.17, 0.52] to the global mean surface temperature in individual years during this decade, consistent with some early studies, such a response is unlikely to have been discernible above internal variability by the end of 2023 and is in fact consistent with zero throughout the 2020–2040 period. 

Abstract Air pollution in Africa is a significant public health issue responsible for 1.1 million premature deaths annually. Sub-Saharan Africa has the highest rate of population growth and urbanization of any region in the world, with substantial potential for future emission growth and worsening air quality. Accurate and extensive observations of meteorology and atmospheric composition have underpinned successful air pollution mitigation strategies in the Global North, yet Africa in general and East Africa in particular remain among the most sparsely observed regions in the world. This paper is based on the discussion of these issues during two international workshops, one held virtually in the United States in July 2021 and one in Kigali, Rwanda, in January 2023. The workshops were designed to develop a measurement, capacity building, and collaboration strategy to improve air quality-relevant measurements, modeling, and data availability in East Africa. This paper frames the relevant scientific needs and describes the requirements for training and infrastructure development for an integrated observing and modeling strategy that includes partnerships between East African scientists and organizations and their counterparts in the developed world. 

Maternal PM_2.5exposures in informal settlements in Nairobi exceeded WHO air quality targets, with low-quality cooking fuel use identified as the most important non-ambient source. 

Abstract This study examines the Arctic surface air temperature response to regional aerosol emissions reductions using three fully coupled chemistry–climate models: National Center for Atmospheric Research-Community Earth System Model version 1, Geophysical Fluid Dynamics Laboratory-Coupled Climate Model version 3 (GFDL-CM3) and Goddard Institute for Space Studies-ModelE version 2. Each of these models was used to perform a series of aerosol perturbation experiments, in which emissions of different aerosol types (sulfate, black carbon (BC), and organic carbon) in different northern mid-latitude source regions, and of biomass burning aerosol over South America and Africa, were substantially reduced or eliminated. We find that the Arctic warms in nearly every experiment, the only exceptions being the U.S. and Europe BC experiments in GFDL-CM3 in which there is a weak and insignificant cooling. The Arctic warming is generally larger than the global mean warming (i.e. Arctic amplification occurs), particularly during non-summer months. The models agree that changes in the poleward atmospheric moisture transport are the most important factor explaining the spread in Arctic warming across experiments: the largest warming tends to coincide with the largest increases in moisture transport into the Arctic. In contrast, there is an inconsistent relationship (correlation) across experiments between the local radiative forcing over the Arctic and the simulated Arctic warming, with this relationship being positive in one model (GFDL-CM3) and negative in the other two. Our results thus highlight the prominent role of poleward energy transport in driving Arctic warming and amplification, and suggest that the relative importance of poleward energy transport and local forcing/feedbacks is likely to be model dependent. 

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.