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Abstract. The dynamics of atmospheric CO2 has received considerable attention in the literature, yet significant uncertainties remain within the estimates of contribution from the terrestrial flux and the influence of atmospheric mixing. In this study we apply the WRF-Chem model configured with the Vegetation Photosynthesis and Respiration Model (VPRM) option for biomass fluxes in China to characterize the dynamics of CO2 in the atmosphere. The online coupled WRF-Chem model is able to simulate biosphere processes (photosynthetic uptake and ecosystem respiration) and meteorology in one coordinate system. We apply WRF-Chem for a multi-year simulation (2016–2018) with integrated data from a satellite product, flask samplings, and tower measurements to diagnose the spatio-temporal variations of CO2 fluxes and concentrations in China. We find that the spatial distribution of CO2 was dominated by anthropogenic emissions, while its seasonality (with maxima in April 15 ppmv higher than minima in August) was dominated by the terrestrial flux and background CO2. Observations and simulations revealed a consistent increasing trend in column-averaged CO2 (XCO2) of 2.46 ppmv (0.6 % yr−1) resulting from anthropogenic emission growth and biosphere uptake. WRF-Chem successfully reproduced ground-based measurements of surface CO2 concentration with a mean bias of −0.79 ppmv and satellite-derived XCO2 with a mean bias of 0.76 ppmv. The model-simulated seasonality was also consistent with observations, with correlation coefficients of 0.90 and 0.89 for ground-based measurements and satellite data, respectively. Tower observations from a background site at Lin'an (30.30∘ N, 119.75∘ E) revealed a strong correlation (−0.98) between vertical CO2 and temperature gradients, suggesting a significant influence of boundary layer thermal structure on the accumulation and depletion of atmospheric CO2.

The uncertainty of the climatic effect of Black carbon (BC) remains large. One critical uncertainty source that needs to be captured is BC aging. Here we use the Community Atmosphere Model version 6 (CAM6) configured with the four‐mode version of the Modal Aerosol Module (MAM4) to evaluate the modeled BC aging process with recent laboratory and in‐situ measurements over China. As revealed by the comparison of BC aging timescale and number fraction of aged BC against recent measurements, the modeled condensation aging timescale is estimated to be about 0.8 hr (17%) faster than the chamber measurement, and the diurnal variations of modeled BC aging degree are typically higher than observations mainly due to the fast increase in modeled BC aging degree during daytime. Further analysis shows that the condensation aging dominates (>70%) BC aging across China. More specifically, the condensation of secondary organic aerosol (SOA) vapor contributes most to BC aging over China. Slowing down BC aging increases the modeled surface BC concentration over remote Western China and BC burden, but hardly changes surface BC concentration over Eastern China. Our results suggest that BC aging representation in the MAM4 needs to be further improved toward slowing down the BC aging rate, especially the condensation aging by SOA, to improve the BC simulation over remote areas and its impact on BC transport in MAM4.

Observed surface organic aerosols (OA) concentrations slightly increased in the western US (WUS) but significantly decreased in the eastern US (EUS) in summer, and continuously decreased in winter over the US region. To understand the driving factors for the long-term surface OA trend, we apply a revised version of the Community Atmosphere Model version 6 with comprehensive tropospheric and stratospheric chemistry representation, considering the heterogeneous formation of isoprene-epoxydiol-derived secondary organic aerosols (SOAIE) and fast photolysis rate of monoterpene-derived secondary organic aerosols (MTSOA) to diagnose the OA evolution in 1988-2019. Compared to older versions, the revised model better reproduces the climatology, seasonal cycle, and long-term trend of surface OA as evaluated against the Interagency Monitoring of Protected Visual Environments measurements. We find the decrease in EUS summertime OA is likely attributed to the interplay between SOAIE and MTSOA. With anthropogenic emissions reduction, primary organic aerosols (POA) declined, SOAIE decreased along with sulfate, while MTSOA increased along with biogenic emissions driven by a warming climate. POA from wildfires with a significant trend of 2.9% yr −1 and considerable interannual variation of 62.8% drive the statistically insignificant but increasing WUS summertime OA, while anthropogenic POA dominates the decreasing wintertime OA in the US. Through sensitivity experiments, we find MTSOA show linear responses to the increasing monoterpenes emissions and negligible responses to NO x emissions reduction due to the mutual offsets between MTSOA components from different oxidation pathways. This study reveals the increasingly important role of MTSOA in summertime OA under a warming climate. Plain Language SummaryAs the major components of fine particles, organic aerosols (OA) increased in the western United States and decreased in the eastern United States in the summer, and kept decreasing in the winter in the past decades. The driving factors for the long-term trend of OA and their components remain unclear and are investigated by conducting a series of long-term simulations. We find the isoprene-epoxydiol-derived secondary organic aerosols decrease with sulfate emission controls, which is partly offset by the increasing monoterpene-derived secondary organic aerosols (MTSOA) under global warming and the statistically insignificant increase of primary organic aerosols driven by wildfires in summer. In winter, anthropogenic emissions dominate the declining surface OA. We also find MTSOA are more sensitive to increasing biogenic emissions than anthropogenic emissions reduction. Our results reveal the important role of MTSOA in total summertime OA under a warming climate.LIU ET AL.

In the context of the rapid development of the times, digitization, digital informatization, and digital transformation bring us closer to the humanist vision of a sustainable society in the digital age, and it also provides more possibilities for the work of safeguarding intangible cultural heritage. To keep them alive, they must be protected but also continuously transmitted and recreated from one generation to another. Based on an in-depth study of the Qing Dynasty horse-face skirt, this paper proposes to combine artificial intelligence, digital economy, big data, and other high-tech approaches to introduce the Qing Dynasty’s horse-face skirt into the virtual world, to activate digital cultural heritage. The research expects to explore the digital protection and inheritance of the Qing Dynasty horse-face skirt, achieve the digitalization of culture, and bring the historical study of Chinese civilization to a deeper level.