Simulation and analysis of CO2 concentration in China from 2009 to 2021 based on the WRF-Chem model

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.

To assess photosynthesis and yield components' response of field-grown wheat to increasing ozone (03) concentration (based on diurnal pattern of ambient O-3) in China, winter wheat (Triticum aestivum L.) cv. Jia 403 was planted in open top chambers and exposed to three different O-3 concentrations: O-3-free air (CF), ambient air (NF), and O-3-free air with additional O-3 (CF+O-3). Diurnal changes of gas exchange and net photosynthetic rate (P-N) in response to photosynthetic photon flux density (PPFD) of flag leaves were measured at the filling grain stage, and yield components were investigated at harvest. High O-3 concentration altered diurnal course of gas exchange [PN, stomatal conductance (g,), and intercellular CO2 concentration (C-i)] and decreased significantly their values except for C-i. Apparent quantum yield (AQY), compensation irradiance (CI), and saturation irradiance (SI) were significantly decreased, suggesting photosynthetic capacity was also altered, characterized as reduced photon-saturated photosynthetic rate (P-Nmax). The limit of photosynthetic activity was probably dominated by non-stomatal factors in combination with stomatal closure. The significant reduction in yield was observed in CF+O-3 treatment as a result of a marked decrease in the ear length and the number of grains per ear, and a significant increase in the number of infertile florets per ear. Even though similar responses were also observed in plants exposed to ambient O-3 concentration, no statistical difference was observed at current ambient O-3 concentration in China.

Over the past few decades, concentrations of carbon dioxide (CO2), a key greenhouse gas, have risen at a global rate of approximately 2 ppm/a. China is the largest CO2 emitter and is the principle contributor to the increase in global CO2 levels. Based on a satellite-retrieved atmospheric carbon dioxide column average dry air mixing ratio (XCO2) dataset, derived from the greenhouse gas observation satellite (GOSAT), this paper evaluates the spatial and temporal variations of XCO2 characteristics in China during 2009–2016. Moreover, the factors influencing changes in XCO2 were investigated. Results showed XCO2 concentrations in China increased at an average rate of 2.28 ppm/a, with significant annual seasonal variations of 6.78 ppm. The rate of change of XCO2 was greater in south China compared to other regions across China, with clear differences in seasonality. Seasonal variations in XCO2 concentrations across China were generally controlled by vegetation dynamics, characterized by the Normalized Difference Vegetation Index (NDVI). However, driving factors exhibited spatial variations. In particular, a distinct belt (northeast–southwest) with a significant negative correlation (r < −0.75) between XCO2 and NDVI was observed. Furthermore, in north China, human emissions were identified as the dominant influencing factor of total XCO2 variations (r > 0.65), with forest fires taking first place in southwest China (r > 0.47). Our results in this study can provide us with a potential way to better understand the spatiotemporal changes of CO2 concentration in China with NDVI, human activity and biomass burning, and could have an enlightening effect on slowing the growth of CO2 concentration in China.

Dust emitted from arid and semi-arid areas of China is a main contributor to the global atmospheric aerosols. However, the long-term spatial and temporal variations in dust concentrations in China is still unknown. Here, we simulated the spatial and temporal variations in spring dust concentrations in China from 2000 to 2020 using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The results showed that the configured WRF-Chem model in this study reproduced the spatial patterns and temporal variations of dust aerosols. The annual mean spring dust concentration at the country level was 26.95 g kg−1-dry air and showed a slightly increasing trend in China during 2000–2020. There were clear spatial differences and inter-annual variations in dust concentrations. The dust concentration generally decreased from the dust source regions of the northwest to the southeast regions of China. Obvious increasing and decreasing trends in spring dust concentrations were identified in the regions of northern Xinjiang and Gansu and in the regions of southern Xinjiang and western Inner Mongolia, respectively. In May, the dust concentration showed an increasing trend in most regions of northwestern China. This provided the basic information for insight into the long-term spatial and temporal variations in spring dust concentrations in China.

Full-coverage mapping high-resolution atmospheric CO2 concentrations in China from 2015 to 2020: Spatiotemporal variations and coupled trends with particulate pollution

Monitoring China’s carbon dioxide (CO2) concentration is essential for formulating effective carbon cycle policies to achieve carbon peaking and neutrality. Despite insufficient satellite observation coverage, this study utilizes high-resolution spatiotemporal data from the Orbiting Carbon Observatory 2 (OCO-2), supplemented with various auxiliary datasets, to estimate full-coverage, monthly, column-averaged carbon dioxide (XCO2) values across China from 2015 to 2022 at a spatial resolution of 0.05° via the deep forest model. The 10-fold cross-validation results indicate a correlation coefficient (R) of 0.95 and a determination coefficient (R²) of 0.90. Validation against ground-based station data yielded R values of 0.93, and R² values reached 0.81. Further validation from the Greenhouse Gases Observing Satellite (GOSAT) and the Copernicus Atmosphere Monitoring Service Reanalysis dataset (CAMS) produced R² values of 0.87 and 0.80, respectively. During the study period, CO2 concentrations in China were higher in spring and winter than in summer and autumn, indicating a clear annual increase. The estimates generated by this study could potentially support CO2 monitoring in China.

Significant concurrent decrease in PM2.5 and NO2 concentrations in China during COVID-19 epidemic

The increase in the carbon dioxide (CO2) concentration is a major driver of global warming, presenting significant challenges to ecosystems and human societies. Satellite remote sensing technology can monitor the continuous spatial variation of the atmospheric CO2 column concentration (XCO2), but its global application is limited by the narrow observational swath. To address this, this study effectively integrates XCO2 data retrieved from the GOSAT and OCO-2 satellites using atmospheric profile adjustment and spatial grid integration techniques. Based on this, a multi-machine learning ensemble algorithm (MLE) was developed, which successfully estimated the spatially continuous XCO2 concentration in China from 2010 to 2022 (ChinaXCO2-MLE). The results indicate that, compared to individual satellite observations, the integration of multi-source satellite XCO2 data significantly improves the spatiotemporal coverage. The overall R2 of the MLE model was 0.97, with an RMSE of 0.87 ppmv, outperforming single machine learning models. The ChinaXCO2-MLE shows good consistency with the observational records from two background stations in China, with R2 values of 0.93 and 0.78, and corresponding RMSEs of 1.00 ppmv and 1.32 ppmv. This study also reveals the seasonal and regional variations in China’s XCO2 concentration: the highest concentration occurs in spring, the lowest concentration occurs in northern regions during summer, and the lowest concentration occurs in southern regions during autumn. From 2010 to 2022, the XCO2 concentration continued to rise, but the growth rate has slowed due to the implementation of air pollution prevention and energy conservation policies. The spatially continuous XCO2 data provide a more comprehensive understanding of carbon variation and offer a valuable reference for achieving China’s carbon neutrality goals.

Seamless reconstruction and spatiotemporal analysis of satellite-based XCO2 incorporating temporal characteristics: A case study in China during 2015–2020

Volatilization of ammonia (NH3) from fertilizer application and livestock wastes is an overwhelmingly important pathway of nitrogen losses in agricultural ecosystems and constitutes the largest source of atmospheric NH3. The volatilization of NH3 highly depends on environmental and meteorological conditions, however, this phenomenon is poorly described in current emission inventory and atmospheric models. Here, we develop a dynamic NH3 emission model capable of calculating NH3 emission rate interactively with time- and spatial-varying meteorological and soil conditions. The NH3 flux parameterization relies on several meteorological factors and anthropogenic activity including fertilizer application, livestock waste, traffic, residential and industrial sectors. The model is then embedded into a regional WRF-Chem model and is evaluated against field measurements of NH3 concentrations and emission flux, and satellite retrievals of column loading. The evaluation shows a substantial improvement in the model performance of NH3 flux and ambient concentration in China. The model well represents the spatial and temporal variations of ambient NH3 concentration, indicating the highest emission in the North China Plain (NCP) and Sichuan Basin, especially during summertime. Compared with normal simulations using fixed emission inventory input, this model features superior capability in simulating NH3 emission flux and concentration during drastic weather changes like frontal activities and precipitation. Such advances in emission quantification also improve the model performance of secondary inorganic aerosol on synoptic scales. While more laboratory and field measurements are still needed for better parameterization of NH3 volatilization, the seamless coupling of soil emission with meteorology provides a better understanding of NH3 emission evolution and its contribution to atmospheric chemistry.

Volatilization of ammonia (NH3) from fertilizer application and livestock wastes is an overwhelmingly important pathway of nitrogen losses in agricultural ecosystems and constitutes the largest source of atmospheric NH3. The volatilization of NH3 highly depends on environmental and meteorological conditions, however, this phenomenon is poorly described in current emission inventory and atmospheric models. Here, we develop a dynamic NH3 emission model capable of calculating NH3 emission rate interactively with time- and spatial-varying meteorological and soil conditions. The NH3 flux parameterization relies on several meteorological factors and anthropogenic activity including fertilizer application, livestock waste, traffic, residential and industrial sectors. The model is then embedded into a regional WRF-Chem model and is evaluated against field measurements of NH3 concentrations and emission flux, and satellite retrievals of column loading. The evaluation shows a substantial improvement in the model performance of NH3 flux and ambient concentration in China. The model well represents the spatial and temporal variations of ambient NH3 concentration, indicating the highest emission in the North China Plain (NCP) and Sichuan Basin, especially during summertime. Compared with normal simulations using fixed emission inventory input, this model features superior capability in simulating NH3 emission flux and concentration during drastic weather changes like frontal activities and precipitation. Such advances in emission quantification also improve the model performance of secondary inorganic aerosol on synoptic scales. While more laboratory and field measurements are still needed for better parameterization of NH3 volatilization, the seamless coupling of soil emission with meteorology provides a better understanding of NH3 emission evolution and its contribution to atmospheric chemistry.

Volatilization of ammonia (NH3) from fertilizer application and livestock wastes is an overwhelmingly important pathway of nitrogen losses in agricultural ecosystems and constitutes the largest source of atmospheric NH3. The volatilization of NH3 highly depends on environmental and meteorological conditions, however, this phenomenon is poorly described in current emission inventory and atmospheric models. Here, we develop a dynamic NH3 emission model capable of calculating NH3 emission rate interactively with time- and spatial-varying meteorological and soil conditions. The NH3 flux parameterization relies on several meteorological factors and anthropogenic activity including fertilizer application, livestock waste, traffic, residential and industrial sectors. The model is then embedded into a regional WRF-Chem model and is evaluated against field measurements of NH3 concentrations and emission flux, and satellite retrievals of column loading. The evaluation shows a substantial improvement in the model performance of NH3 flux and ambient concentration in China. The model well represents the spatial and temporal variations of ambient NH3 concentration, indicating the highest emission in the North China Plain (NCP) and Sichuan Basin, especially during summertime. Compared with normal simulations using fixed emission inventory input, this model features superior capability in simulating NH3 emission flux and concentration during drastic weather changes like frontal activities and precipitation. Such advances in emission quantification also improve the model performance of secondary inorganic aerosol on synoptic scales. While more laboratory and field measurements are still needed for better parameterization of NH3 volatilization, the seamless coupling of soil emission with meteorology provides a better understanding of NH3 emission evolution and its contribution to atmospheric chemistry.

Rebuilding high-quality near-surface ozone data based on the combination of WRF-Chem model with a machine learning method to better estimate its impact on crop yields in the Beijing-Tianjin-Hebei region from 2014 to 2019

Volatilization of reactive nitrogen (Nr) gases like HONO, NH3 and NOx from fertilizer application and soil is an important pathway of nitrogen losses in agricultural ecosystems and deteriorate air pollution by contributing to ozone and PM2.5. The volatilization of Nr gases highly depends on environmental and meteorological conditions, however, this phenomenon is poorly described in current emission inventory and atmospheric models. Here, we develop a dynamic soil nitrogen emission model capable of calculating NH3 and HONO emission rate interactively with time- and spatial-varying meteorological and soil conditions. The NH3 flux parameterization relies on several meteorological factors and anthropogenic activity including fertilizer application, livestock waste, traffic, residential and industrial sectors.  HONO emission scheme considers soil temperature and moisture as well as the type of underlying surface. The model is then embedded into a regional WRF-Chem model and is evaluated against field measurements of Nr emission flux and ambient concentration. The evaluation shows a substantial improvement in the model performance of NH3 flux and ambient HONO concentration in China.  Compared with normal simulations using fixed emission inventory input, this model features superior capability in simulating NH3 emission flux and concentration during planting seasons and drastic weather changes like frontal activities and precipitation. Such advances in emission quantification also improve the model performance of secondary inorganic aerosol on synoptic scales. While more laboratory and field measurements are still needed for better parameterization of soil nitrogen volatilization, the seamless coupling of soil emission with meteorology provides a better understanding of NH3 and HONO emission evolution and its contribution to atmospheric chemistry. 

Carbon dioxide (CO2) is one of the major gases that contribute to the global warming. Therefore, studying the distribution of CO2 can help people understand the carbon cycle. Based on the GOSAT retrieved CO2 products, the temporal and spatial distribution and seasonal variation of CO2 concentration were analyzed from 2011 to 2015. CO2 concentration has obvious seasonal variation. It was low in summer, and was high in spring, and the annual increase was about 2 ppm. Nevertheless, the annual growth rate of CO2 concentration in summer was higher than that in spring, it was 0.5425% in summer and was 0.46% in spring. CO2 concentration was low in the northwest and was high in the southeast. The growth rate of CO2 was 2.8 ppm in the northwest and was 3.42 ppm in the southeast. More human’s activities made CO2 concentration higher in the southeast than that in other regions.