Reply on RC3

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> In response to the need for securing a spatiotemporally more up-to-date emissions inventory and the impending release of new geostationary platform-derived observational data generated by the Geostationary Environment Monitoring Spectrometer (GEMS) and its sister instruments, this study, using a series of GEMS data fusion product and its proxy data and CTM-based inverse modeling techniques, aims to establish a top-down approach for adjusting aerosol precursor emissions over East Asia. We begin by sequentially adjusting bottom-up estimates of nitrogen oxides (NO_x) and primary particulate matter (PM) emissions, both of which significantly contribute to aerosol loadings over East Asia, to reduce model biases in aerosol optical depth (AOD) simulations during the year 2019. While the model initially underestimates AOD by 50.73 % on average, the sequential emissions adjustments that led to overall increases in the amounts of NO_x emissions by 122.79 % and of primary PM emissions by 76.68 % and 114.63 % (single- and multiple-instrument-derived emissions adjustments, respectively), reduce the extent of AOD underestimation to 33.84 % and 19.60 %, respectively. We consider the outperformance of the model using the emissions constrained by the data fusion product the result of the improvement in the quantity of available data. Taking advantage of the data fusion product, we perform sequential emissions adjustments during the spring of 2022, the period during which the substantial reductions in anthropogenic emissions took place accompanied by the COVID-19 pandemic lockdowns over highly industrialized and urbanized regions in China. While the model initially overestimates surface PM_2.5 concentrations by 47.58 % and 20.60 % in the North China Plain (NCP) region and Korea, the sequential emissions adjustments that led to overall decreases in NO_x and primary PM emissions by 7.84 % and 9.03 %, respectively, substantially reduce the extent of PM_2.5 underestimation to 19.58 % and 6.81 %, respectively. These findings indicate that the series of emissions adjustments performed in this study are generally effective at reducing model biases in simulations of aerosol loading over East Asia; in particular, the model performance tends to improve to a greater extent on the condition that spatiotemporally more continuous and frequent observational references are used to capture variations in bottom-up estimates of emissions. In addition to reconfirming the close association between aerosol precursor emissions and AOD as well as surface PM_2.5 concentrations, the findings of this study could provide a useful basis for how to most effectively exploit multi-source top-down information for capturing highly varying anthropogenic emissions.