Interannual variation in moisture sources for the first rainy season in South China estimated by the FLEXPART model

The first rainy season (April-May-June) of South China includes the phases before and after the onset of South China Sea Summer Monsoon (hereafter SCSSM). Abundant moisture supply is the key dynamic process for precipitation formation. Thus, we employ the FLEXPART model to explore the corresponding moisture sources for the two phases. Before the onset of SCSSM, land regions contribute more moisture to the precipitation over South China than the ocean sources. The main source regions are Southeastern Asia (27.01%), South China Sea (25.96%), South China (11.12), and southern part of northwestern Pacific (10.23%). Land sources (66.87%) play a more important role than ocean sources (33.13%) in the interannual variations, with the contributions mainly from Southeastern Asia (47.56%) and South China Sea (28.79%). After the onset of SCSSM, the climatological contribution of ocean sources is larger than that of land regions, and the main source regions are South China Sea (20.78%), Southeastern Asia (17.51%), Bay of Bengal (13.76%), and South China (11.21%). For the interannual variations, the contributions of land sources and ocean regions are comparable, and mainly from Southeastern Asia (33.53%) and the Bay of Bengal (32.26%). The moisture transports for the interannual variations in FRS precipitation over South China before and after the onset of SCSSM are significantly correlated with the east-west contrast of sea surface temperature anomalies over northern part of North Pacific and the uniform warming over Indian Ocean, respectively. This study provides important guidance in improving the regional precipitation predictions and understanding the water resources changes.

Prediction and Source Contribution Analysis of PM2.5 Using a Combined FLEXPART Model and Bayesian Method over the Beijing-Tianjin-Hebei Region in China

Fine particulate matter (PM2.5) has a serious impact on human health. Forecasting PM2.5 levels and analyzing the pollution sources of PM2.5 are of great significance. In this study, the Lagrangian particle dispersion (LPD) model was developed by combining the FLEXPART model and the Bayesian inventory optimization method. The LPD model has the capacity for real-time forecasting and determination of pollution sources of PM2.5, which refers to the contribution ratio and spatial distribution of each type of pollution (industry, power, residential, and transportation). In this study, we applied the LPD model to the Beijing-Tianjin-Hebei (BTH) region to optimize the a priori PM2.5 emission inventory estimates during 15–20 March 2018. The results show that (1) the a priori estimates have a certain degree of overestimation compared with the a posteriori flux of PM2.5 for most areas of BTH; (2) after optimization, the correlation coefficient (R) between the forecasted and observed PM2.5 concentration increased by an average of approximately 10%, the root mean square error (RMSE) decreased by 30%, and the IOA (index of agreement) index increased by 16% at four observation sites (Aotizhongxin_Beijing, Beichenkejiyuanqu_Tianjin, Dahuoquan_Xintai, and Renmingongyuan_Zhangjiakou); and (3) the main sources of pollution at the four sites mainly originated from industrial and residential emissions, while power factory and transportation pollution accounted for only a small proportion. The concentration of PM2.5 forecasts and pollution sources in each type of analysis can be used as corresponding reference information for environmental governance and protection of public health.

Ammonia and PM2.5 Air Pollution in Paris during the 2020 COVID Lockdown

During the COVID-19 pandemic, the lockdown reduced anthropogenic emissions of NO2 in Paris. NO2 concentrations recorded in 2020 were the lowest they have been in the past 5 years. Despite these low-NO2 levels, Paris experienced PM2.5 pollution episodes, which were investigated here based on multi-species and multi-platform measurements. Ammonia (NH3) measurements over Paris, derived from a mini-DOAS (differential optical absorption spectroscopy) instrument and the Infrared Atmospheric Sounding Interferometer (IASI) satellite, revealed simultaneous enhancements during the spring PM2.5 pollution episodes. Using the IASI maps and the FLEXPART model, we show that long-range transport had a statistically significant influence on the degradation of air quality in Paris. In addition, concentrations of ammonium (NH4+) and PM2.5 were strongly correlated for all episodes observed in springtime 2020, suggesting that transport of NH3 drove a large component of the PM2.5 pollution over Paris. We found that NH3 was not the limiting factor for the formation of ammonium nitrate (NH4NO3), and we suggest that the conversion of ammonia to ammonium may have been the essential driver.

TRAJECTORY STUDIES OF THE MIDDLE ATMOSPHERE

A short description and some applications of the trajectory and dispersion models developed in Central Aerological Observatory (CAO) for studying the stratospheric and tropospheric transport of pollutants are presented. The TRACAO trajectory model is applied to investigate the processes related to the ozone depletion in the winter polar stratosphere, in order to study the mid-latitude stratosphere-troposphere exchange, as well as to analyze balloon and aircraft (M55 “Geophysics,” Yak-42D “Roshydromer”) observations. Then based on the TRACAO, the GLADIM dispersion model that simulates trajectories of the set of particles with the eddy diffusion parameterization and determines the pollutant concentration at the regular grid points, was developed. The dispersion model was applied to simulate volcanic ash dispersion and carbon dioxide profile reconstruction. The model validation was done by comparisons with the results of the widely used FLEXPART model. Nowadays these models are used at the “Middle Atmosphere” Regional Information and Analytic Center established in CAO.

Continuous monitoring of surface water vapour isotopic compositions at Neumayer Station III, East Antarctica

In this study, the first fully-continuous monitoring of water vapour isotopic composition at Neumayer Station III, Antarctica, during the two-year period from February 2017 to January 2019 is presented. Seasonal and synoptic-scale variations of both stable water isotopes H218O and HDO are reported, and their link to variations of key meteorological variables are analysed. Changes in local temperature and humidity are the main drivers for the variability of δO18 and δD in vapour at Neumayer Station III, both on seasonal and shorter time scales. In contrast to the measured δO18 and δD variations, no seasonal cycle in the Deuterium excess signal d–excess in vapour is detected. However, a rather high uncertainty of measured d–excess values especially in austral winter limits the confidence of this finding. Overall, the d–excess signal shows a stronger inverse correlation with humidity than with temperature, and this inverse correlation between d–excess and humidity is stronger for the cloudy-sky conditions than for clear-sky conditions during summertime. Back trajectory simulations performed with the FLEXPART model show that seasonal and synoptic variations of δO18 and δD in vapour coincide with changes in the main sources of water vapour transported to Neumayer Station. In general, moisture transport pathways from the east lead to higher temperatures and more enriched δO18 values in vapour, while weather situations with southerly winds lead to lower temperatures and more depleted δO18 values. However, for several occasions, δO18 variations linked to wind direction changes were observed, which were not accompanied by a corresponding temperature change. Comparing isotopic compositions of water vapour at Neumayer Station III and snow samples taken in the vicinity of the station reveals almost identical slopes, both for the δO18–δD relation and for the temperature–δO18 relation.

Comparison study for different approaches in applying High-Resolution Atmospheric Transport Modelling based on validation with Xe-133 observations in Europe

<p>The International Data Centre (IDC) of the Comprehensive Nuclear-Test-Ban Treaty Organisation (CTBTO) investigates the best method to add the utilisation of High-Resolution Atmospheric Transport Modelling (HRATM) in their operational and automatised pipeline. Supporting the decision process, the IDC accomplished a comparison study with different approaches for applying HRATM. An initial validation study with the HRATM Flexpart-WRF, which is a Lagrangian particle dispersion model (LPDM), showed a performance which is dependent on the scenario and delivered results comparable to the conventional Flexpart model. The approach uses the Weather Research and Forecasting model (WRF) to generate high-resolution meteorological input data for Flexpart-WRF and WRF was driven by the National Centers for Environmental Prediction (NCEP) data having a horizontal resolution of 0.5 degrees and time resolution of 1h. Based on this initial study, an extended study was conducted to compare the results to FLEXPART-WRF using input data from the European Centre for Medium-Range Weather Forecasts  (ECMWF) for WRF and to results from the conventional Flexpart model using high-resolution ECMWF input data. Furthermore, a sensitivity study was performed to optimize the physical and computational parameters of WRF to test possible meteorological improvements prior to the comparison study.</p><p>The performance of the different approaches is evaluated by using observational data and includes statistical metrics which were established during the first ATM challenge in 2016. Observational data of seven episodes of elevated Xe-133 concentrations were selected from the IMS (International Monitoring System) noble gas system DEX33 located in Germany. Each episode consists of 6 to 11 subsequent samples with each sample being taken over 24 hours. Both Flexpart models were using the source terms from a medical isotope production facility in Belgium to simulate the resulting concentration time series at the DEX33 station for different output resolutions. Backward simulations for each sample were conducted, and in the case of Flexpart-WRF nested input of increased resolution around the source and receptor was used.</p><p>The simulated concentrations, as well as the measurements, are also compared to the simulated results produced by the conventional Flexpart model to guide the decision-making process.</p>

Aerosol monitoring in Siberia using an 808 nm automatic compact lidar

Our study provides new information on aerosol-type seasonal variability and sources in Siberia using observations (ground-based lidar and sun photometer combined with satellite measurements). A micropulse lidar emitting at 808 nm provided almost continuous aerosol backscatter measurements for 18 months (April 2015 to September 2016) in Siberia, near the city of Tomsk (56∘ N, 85∘ E). A total of 540 vertical profiles (300 daytime and 240 night-time) of backscatter ratio and aerosol extinction have been retrieved over periods of 30 min, after a careful calibration factor analysis. Lidar ratio and extinction profiles are constrained with sun-photometer aerosol optical depth at 808 nm (AOD808) for 70 % of the daytime lidar measurements, while 26 % of the night-time lidar ratio and AOD808 greater than 0.04 are constrained by direct lidar measurements at an altitude greater than 7.5 km and where a low aerosol concentration is found. An aerosol source apportionment using the Lagrangian FLEXPART model is used in order to determine the lidar ratio of the remaining 48 % of the lidar database. Backscatter ratio vertical profile, aerosol type and AOD808 derived from micropulse lidar data are compared with sun-photometer AOD808 and satellite observations (CALIOP space-borne lidar backscatter and extinction profiles, Moderate Resolution Imaging Spectroradiometer (MODIS) AOD550 and Infrared Atmospheric Sounding Interferometer (IASI) CO column) for three case studies corresponding to the main aerosol sources with AOD808>0.2 in Siberia. Aerosol typing using the FLEXPART model is consistent with the detailed analysis of the three case studies. According to the analysis of aerosol sources, the occurrence of layers linked to natural emissions (vegetation, forest fires and dust) is high (56 %), but anthropogenic emissions still contribute to 44 % of the detected layers (one-third from flaring and two-thirds from urban emissions). The frequency of dust events is very low (5 %). When only looking at AOD808>0.1, contributions from taiga emissions, forest fires and urban pollution become equivalent (25 %), while those from flaring and dust are lower (10 %–13 %). The lidar data can also be used to assess the contribution of different altitude ranges to the large AOD. For example, aerosols related to the urban and flaring emissions remain confined below 2.5 km, while aerosols from dust events are mainly observed above 2.5 km. Aerosols from forest fire emissions are observed to be the opposite, both within and above the planetary boundary layer (PBL).

Aerosol monitoring in Siberia using an 808 nm automatic compact lidar

Our study is providing new information on aerosol type climatology and sources in Siberia using observations (ground-based lidar and sun-photometer combined with satellite measurements). A micropulse lidar emitting at 808 nm provided almost continuous aerosol backscatter measurements for 18 months (April 2015 to September 2016) in Siberia, near the city of Tomsk (56° N, 85° E). A total of 540 vertical profiles (300 daytime and 240 nighttime) of backscatter ratio and aerosol extinction have been retrieved over periods of 30 min, after a careful calibration factor analysis. Lidar ratio and extinction profiles are constrained with sun-photometer Aerosol Optical Depth (AOD) for 70 % of the daytime lidar measurements, while 26 % of the nighttime lidar ratio and AOD greater than 0.04 are constrained by direct lidar measurements at an altitude greater than 7.5 km and where a low aerosol concentration is found. It was complemented by an aerosol source apportionment using the Lagrangian FLEXPART model in order to determine the lidar ratio of the remaining 48 % of the lidar data. Comparisons of micropulse lidar data with satellite observations (CALIOP spaceborne lidar aerosol extinction profiles, Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and Infrared Atmospheric Sounding Interferometer (IASI) CO column) are discussed for three case studies corresponding to different aerosol types and season. Aerosol typing using the FLEXPART model is consistent with the detailed analysis of the three case studies. According to the analysis of aerosol sources, the occurrence of layers linked to natural emissions (vegetation, forest fires and dust) is high (56 %), but anthropogenic emissions still contribute to 44 % of the detected layers (1/3 from flaring and 2/3 from urban emissions). The frequency of dust events is very low (5 %). When only looking at AOD > 1, contributions from Taiga emissions, forest fires and urban pollution become equivalent (20–25 %), while those from flaring and dust are lower (15 %). The lidar data can also be used to assess the contribution of different altitude ranges to the large AOD. For example, aerosols related to the urban and flaring emissions remain confined below 2.5 km, while aerosols from dust events are mainly observed above 2.5 km. Aerosols from forest fire emissions are on the opposite observed both within and above the Planetary Boundary Layer (PBL).