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Coupled modelling and data assimilation of dynamics and chemistry of the atmosphere

As the societal impacts of hazardous weather and other environmental pressures grow, the need for integrated predictions which can represent the numerous feedbacks and linkages between physical and chemical atmospheric processes is greater than ever. This has led to development of a new generation of high resolution multi-scale coupled prediction tools to represent the two-way interactions between aerosols, chemical composition, meteorological processes such as radiation and cloud microphysics.
Contributions are invited on different aspects of integrated model and data assimilation development, evaluation and understanding. A number of application areas of new integrated modelling developments are expected to be considered, including:
i) improved numerical weather prediction and chemical weather forecasting with feedbacks between aerosols, chemistry and meteorology,
ii) two-way interactions between atmospheric composition and climate variability.
This session aims to share experience and best practice in integrated prediction, including:
a) strategy and framework for online integrated meteorology-chemistry modelling;
b) progress on design and development of seamless coupled prediction systems;
c) improved parameterisation of weather-composition feedbacks;
d) data assimilation developments;
e) evaluation, validation, and applications of integrated systems.
This Section is organised in cooperation with the Copernicus Atmosphere Monitoring Service (CAMS) and the WMO Global Atmosphere Watch (GAW) Programme.
This year session is dedicated to the Global Air Quality Forecasting and Information Systems (GAFIS) - an initiative of WMO and several international organizations - to enable and provide science-based air quality forecasting and information services in a globally harmonized and standardized way tailored to the needs of society.

Co-sponsored by WMO and CAMS
Convener: Alexander Baklanov | Co-conveners: Johannes Flemming, Georg Grell, Lu RenECSECS
| Thu, 26 May, 15:55–18:24 (CEST)
Room 0.11/12

Thu, 26 May, 15:10–16:40

Chairpersons: Alexander Baklanov, Lu Ren

Introduction to the session


NOAA is developing a next-generation air quality prediction system using a new limited-area, high-resolution online-coupled numerical weather and atmospheric composition model. This system integrates high-resolution meteorology provided by the Rapid Refresh Forecast System (RRFS) with atmospheric column chemistry from the EPA’s Community Multiscale Air Quality (CMAQ) modeling system within the Unified Forecast System (UFS) framework (https://ufscommunity.org/). RRFS-CMAQ uses anthropogenic emissions based on the U.S. EPA’s National Emissions Inventory and natural emissions estimated from process-based emission models such as FENGSHA and Biogenic Emission Inventory System.


In addition to the desire to unify modeling system codes for various applications and to introduce coupling of Earth system modeling components, such as those for weather and chemistry, a strong motivation for the development of RRFS-CMAQ is to provide a better representation of wildfire impacts on air quality. Wildfire emissions cause extremely high concentrations of air pollutants near fire locations. Depending on meteorological conditions, wildfire impacts can be carried downwind and affect air quality far away, even across the continent, like in recent summers when smoke from the U.S. west coast and Canadian fires impacted the eastern U.S. coast. Due to uncertainties in wildfire emission strength, evolution, composition and rise of smoke plumes, the impacts of wildfires on air quality are difficult to predict. Wildfire emissions in RRFS-CMAQ are specified by the NESDIS Blended Global Biomass Burning Emissions Product (GBBEPx). An evaluation system based on FIREX-AQ field data has been developed and used to evaluate current operational air quality predictions to establish a baseline that will be used to evaluate the prototype RRFS-CMAQ system as development continues. Planned refinements of RRFS-CMAQ include improvements in resolution, lateral boundary conditions, and the representation of wildfire emissions, such as smoke plume rise and diurnal variations of smoke emissions. Data assimilation is used to constrain distributions of atmospheric pollutants using observations of fine particulate matter (PM2.5) from AirNow, Aerosol Optical Depth (AOD) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) and NO2 retrievals from the TROPOspheric Monitoring Instrument (TROPOMI). To improve computational efficiency, machine learning emulators are also being developed for prediction of chemical transformations and tracer transport. To improve prediction accuracy, a bias correction post-processing procedure is planned to be introduced.

How to cite: Stajner, I. and the NOAA's FY19 Disaster Supplemental Wildfire 1 Project Team: Development of Next-Generation Air Quality Predictions for the United States, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10491, https://doi.org/10.5194/egusphere-egu22-10491, 2022.

Hong Wang and Xiaoye Zhang

The updated Chinese Unified Atmospheric Chemistry Environment (CUACE) model is driven by the operational mesoscale version of Global/Regional Assimilation and PrEdiction System (GRAPES_Meso 5.1) developed by China Meteorological Administration (CMA) to build the single way online atmosphere chemical model GRAPES-Meso v5.1/CUACE. Based on this, the aerosol-cloud-radiation interaction and its’ feedback on numerical weather prediction (NWP) is achieved leading to chemistry-weather interaction in GRAPES-Meso v5.1/CUACE and the first version t(v 1.) of double way model GRAPES-Meso v5.1/CUACE CW v1 is established in this study, which is used in the in Better Air Quality Forecasting in China

 The cloud condensation nuclei (CCN) number concentration activated from aerosols is crucial for understanding aerosol indirect effects and characterizing these effects in models. Morrison two moment cloud microphysical scheme combine with a size-resolved CCN parameterization scheme based on situ measurements of aerosol activation properties in China in implemented in meso-scale atmospheric chemical model GRAPES_CUACE to study the aerosols effects on cloud macro, micro features and radiation process under heavy polluted condition in East China.

How to cite: Wang, H. and Zhang, X.: Development of Chemistry-Weather Fully Interacted Model System GRAPES_Meso5.1/CUACE CW v1.0 and Its Application in Better Air Quality Forecasting in China, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13207, https://doi.org/10.5194/egusphere-egu22-13207, 2022.

Virtual presentation
Johannes Flemming et al.

The Copernicus Atmosphere Monitoring Service (CAMS) produces operationally global 5-day forecast of atmospheric composition and the weather using ECMWF’s Integrated Forecasting System (IFS) since 2015.Beginning with a system upgrade in June 2018 (45r1), the ozone and aerosol fields have been used in the radiation scheme to account for their radiative impact in the global CAMS forecasts. This approach replaced an aerosol and ozone climatology, which had been used before and which is still used in ECMWF's operational high-resolution medium-range NWP forecasts. The CAMS forecast system, which runs at a resolution of about 40 km, is applied here as a test-bed to explore the importance of aerosol direct feedback in an operational configuration, which can guide developments on composition-weather feedbacks for ECMWF's medium-range, monthly and seasonal forecasts. The CAMS prognostic aerosol simulations and the assimilation of different AOD retrievals (MODIS, VIIRS, S5P) have been substantially further modified in several upgrades of the CAMS operational system since 2018.

We will discuss the changes and improvements of temperature forecast errors focusing on the impact of changing the aerosol simulation and AOD assimilation in the recent cycles. These changes were introduced to improve the realism of the aerosol forecasts, which is a key CAMS forecast product, and not developed specifically considering their impact on NWP.

We will show NWP scores, evaluation with synop-observations and satellite radiation products to demonstrate the impact of the prognostic aerosols. We will also compare the results of the NWP forecast of the CAMS suite, with the NWP scores of ECMWF high resolution forecast run ay 9 km spatial resolution globally. We will further demonstrate that the consistent updates of both the climatological and prognostic aerosol fields are an important prerequisite for a sound assessment of the importance of prognostic aerosol in NWP applications.

How to cite: Flemming, J., Remy, S., Hogan, R., Huijnen, V., Haiden, T., Kipling, Z., Parrington, M., Inness, A., and Garrigues, S.: The impact of CAMS prognostic aerosols on temperature forecast with the ECMWF weather forecast model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12175, https://doi.org/10.5194/egusphere-egu22-12175, 2022.

Virtual presentation
Raffaele Montuoro et al.

Recent NOAA collaborative efforts supported through the Unified Forecast System Research-to-Operations (UFS-R2O) Project have led to the development of advanced coupled systems to improve aerosol predictions on a global scale. These systems, integrated within the UFS framework, include online-coupled prognostic model components for atmosphere, ocean, sea ice, and waves, and rely upon state-of-the-science interoperable atmospheric physics schemes accessible via the Common Community Physics Package (CCPP) framework. Interoperability has been a key design element for those systems from the start, and thus it has also driven the incorporation of predictive aerosol processes. The approach to aerosol development within the UFS focuses on two primary outcomes: to build the next-generation upgrade to the currently operational Global Ensemble Forecast System (GEFS), and to create a research-oriented platform that allows developing and assessing the latest physical and chemical processes updates. In collaboration with NASA/GMAO, a novel aerosol component (UFS-Aerosols) was developed to succeed GEFS-Aerosols. This UFS component implements NASA’s 2nd generation GOCART model and brings the MAPL infrastructure layer into the UFS framework, enabling tighter collaborations with NASA and ensuring model interoperability across U.S. modeling centers thanks to its NUOPCcompliant interface. It also includes an updated FENGSHA dust scheme along with refinements to surface emissions. Furthermore, a CCPP-compliant implementation of aerosol processes based on GEFS-Aerosols was developed within the UFS framework to support and advance atmospheric chemistry research. This presentation will provide an overview of the architecture of each system as well as results from preliminary evaluations.

How to cite: Montuoro, R., Clune, T. L., Silva, A. M. D., Baker, B., Zhang, L., Pan, L., Bhattacharjee, P. S., Wang, S., He, J., Heinzeller, D., Ahmadov, R., Chawla, A., Stajner, I., Frost, G. J., Grell, G. A., McQueen, J., and Kondragunta, S.: Advancing the U.S. global chemical weather forecasting capabilities with next-generation,UFS-based fully coupled prediction systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13536, https://doi.org/10.5194/egusphere-egu22-13536, 2022.

Virtual presentation
Gaurav Govardhan et al.

Delhi, the national capital of India, has been experiencing a polluted environment, especially during the post-monsoon and winter season in the recent decade. The post-harvest burning of paddy-crop residue in the north-western states of India and the ever-rising local anthropogenic emissions of pollutants are known to be primary factors responsible for the poor air quality in the capital city. Responding to air quality degradation, the Indian Institute of Tropical Meteorology, in collaboration with the India Meteorological Department under the guidance of the Ministry of Earth Sciences, India, developed an 'Air Quality Early Warning System' (AQEWS) to inform the citizens and the policymakers about potential severe air-pollution episodes, about one week in advance. However, the policymakers seek more information, mainly on the sources possibly responsible for the degradation of air quality during a forecast air-pollution episode. This information would assist them in making policy-level decisions to manage the air quality. Understanding this requirement, we have now developed a 'Decision Support System' (DSS) for air-quality management in Delhi. The DSS, an extension of AQEWS, assimilates surface observations of PM2.5 mass concentrations (for more than 150 stations across northern India) and satellite retrievals of aerosol optical depth and active fire counts. It provides quantification of the contribution of a). the emissions from Delhi and the surrounding 19 districts to the air pollution load in Delhi b). the emissions from 8 different emission sectors in Delhi to the air quality of Delhi c). the stubble-burning activities in the neighboring states to the air quality in Delhi. Such information highlights the most significant emission sources for the degraded air quality in Delhi. Additionally, DSS also quantifies the effects of possible source-level interventions on the forecast air-quality event in Delhi. All this information assists the authorities in managing the air quality on time. The DSS has been operational since the post-monsoon season of 2021. This study depicts the underlying methodology employed in DSS, which makes it useful for decision-making purposes. We discuss the performance of DSS in simulating PM2.5 mass concentration in Delhi for the post-monsoon and winter seasons of 2021-22. We also show the source apportionment for PM2.5 in Delhi during the study period. We further discuss the various scenarios of emission reductions and their effects on the ambient PM2.5 in Delhi. With a plethora of quantitative information, the DSS has become a critical tool for the policymakers for air-quality management in Delhi and the surrounding region.

How to cite: Govardhan, G., Ghude, S., Kumar, R., Sharma, S., and Nanjundiah, R.: Decision Support System for Air-quality management in Delhi, India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11715, https://doi.org/10.5194/egusphere-egu22-11715, 2022.

Thu, 26 May, 17:00–18:30

Chairpersons: Georg Grell, Johannes Flemming

Ravan Ahmadov et al.

NOAA has been transitioning its numerical weather predictions (NWP) models to the new models, which are based on Unified Forecasting System [https://ufscommunity.org/]. NOAA Global Systems Laboratory (GSL) in collaboration with other teams has been developing a new storm-scale NWP model – Rapid Refresh Forecasting System (RRFS) based on UFS. Currently the RRFS model is running in real time to provide experimental weather forecasting products [https://rapidrefresh.noaa.gov/RRFS/]. In the future the RRFS model will replace NOAA’s current operational High-Resolution Rapid Refresh (HRRR) NWP system.

Following on the successful HRRR-Smoke implementation in 2020, we started transitioning the smoke emission, plume rise, dry and wet removal simulation capabilities into the RRFS based using the Common Community Physics Package (CCPP) framework. The CCPP framework also ensures consistency between the physics and smoke/aerosol parameterizations. There are a number of new capabilities implemented in RRFS-Smoke. The high spatial resolution VIIRS I-band and high-frequency GOES-16/17 fire radiative power (FRP) data are ingested into the model to estimate both biomass burning (BB) emissions and fire heat fluxes every hour. Inline turbulent mixing of smoke within the boundary layer scheme, hourly wildfire potential to predict the evolution of the BB emissions, smoke interactions with the double-moment microphysics scheme and other new capabilities are implemented into the new RRFS-Smoke model.

The RRFS-Smoke model is simulated for August 2019 over the US by focusing on the FIREX-AQ field campaign [https://csl.noaa.gov/projects/firex-aq/]. The wide range of in-situ and remote sensing observations obtained onboard the DC-8 aircraft during FIREX-AQ provide valuable datasets to evaluate and improve the capabilities of the RRFS-Smoke model to accurately simulate BB emissions, smoke transport and mixing, and fire plume rise. Here, we present the simulations and evaluations of the RRFS-Smoke model for fire weather and smoke for some of the FIREX-AQ cases.

How to cite: Ahmadov, R., James, E., Li, H., Romero-Alvarez, J., Trahan, S., Grell, G., Kliewer, A., Olson, J., Wang, S., Zhang, X., Li, F., and Kondragunta, S.: Simulating smoke dispersion and fire weather using NOAA’s next-generation numerical weather prediction model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11242, https://doi.org/10.5194/egusphere-egu22-11242, 2022.

Virtual presentation
Haiqin Li et al.

Aerosols play a significant role in the atmospheric precipitation physics of microphysics and convection. A physics suite, which includes the aerosol-aware double momentum Thompson microphysics scheme (Thompson MP), and the scale-aware and aerosol-aware Grell-Freitas (GF) convection scheme, was developed at NOAA Global System Laboratory (GSL).  In the Thompson MP, the hygroscopic aerosol is referred as a “water friendly” aerosol (WFA), and the non-hygroscopic ice-nucleating aerosol is referred as “ice friendly” aerosol (IFA). For usual Thompson applications, WFA and IFA are derived using climatologies from NASA’s Goddard Chemistry Aerosol Radiation and Transport (GOCART) model. The Common Community Physics Package (CCPP), which is designed to facilitate a host-model agnostic implementation of physics parameterizations, is a community development and is used by many model developers. All physics parameterizations in the NOAA Unified Forecast System (UFS) Weather Model must be CCPP-compliant. Here we embedded sea-salt, dust emission, and biomass burning and plumerise emission modules as well as anthropogenic aerosol emissions into the UFS by using CCPP. These aerosol modules are directly called within the physics package. The prognostic emission of sea-salt, sulfate, and organic carbon are combined to represent the WFA emission, while the prognostic emission of dust is used to represent IFA emission. Wet-scavenging is included in both, resolved and non-resolved precipitation physics. Dry deposition is also parameterized. Subgrid scale transport is included in PBL and convection. There are no additional tracer variables introduced in this simple approach. In the global forecast with C768 (~13km) horizontal resolution and 128 vertical levels, the initial results are promising.

How to cite: Li, H., Barnes, H., Grell, G., Zhang, L., Ahmadov, R., Sun, S., and Schnell, J.: A simple and realistic aerosol emission approach for use in a double moment aerosol-aware microphysics scheme in the NOAA UFS Weather Model , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6558, https://doi.org/10.5194/egusphere-egu22-6558, 2022.

Shan Sun et al.

The interactions between aerosols, radiation and clouds are one of the key climate  uncertainties despite recent improvements in observational systems and model complexity. Here we investigate the impacts of aerosol direct and semi-direct effects on subseasonal predictions using NOAA’s coupled Unified Forecast System (UFS) – specifically, the coupled atmosphere (FV3), ocean (MOM6), and sea ice (CICE6) model combined with the GOCART aerosol component based on the GEFS-Aerosols model. We perform experiments with 32-day long integrations initialized in May and September from 2003 to 2019. Two more parallel sets of experiments are carried out as well, using either modeled climatological aerosol concentrations or zero aerosol concentration in lieu of GOCART. We found in the multi-year simulations, the estimated aerosol optical depth from the UFS-GOCART model is in good agreement with the satellite observations. The radiative forcing of the total aerosol direct effect shows negative, while the impact on precipitation is not obvious. In addition, the UFS using the modeled climatological aerosol concentrations is able to capture most of the radiative forcing seen in the UFS-GOCART experiments. This suggests a possible alternative of replacing the costly chemistry module with the modeled aerosol concentration climatology in the subseasonal applications. 

How to cite: Sun, S., Grell, G., Zhang, L., Henderson, J., Yang, F., and Huang, A.: Evaluating Direct and Semi Direct Effect of Aerosol on Subseasonal Prediction Using a Coupled UFS Model with and without Prognostic Aerosols, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6777, https://doi.org/10.5194/egusphere-egu22-6777, 2022.

Virtual presentation
Yasmine Bennouna et al.

IAGOS (In-service Aircraft for a Global Observing System) is a European Research Infrastructure for global observations of atmospheric composition using commercial aircraft. Commercial aircraft are ideal platforms for providing long-term in-situ measurements with high vertical and temporal resolution, particularly at cruise altitude (between 9 and 13 km) in the climate-sensitive region of the atmosphere known as the upper troposphere-lower stratosphere (UTLS) and throughout the depth of the troposphere during the landing and take off phases of the flights.  Fully automated instruments are permanently installed on Airbus A330 aircraft operated by different airlines. Data are collected on about 500 flights per aircraft per year. The aircraft measure the abundances of many essential climate variables and the data are transmitted in near real time to weather services and are freely available for the scientific community, national air quality prediction centres and the Copernicus Atmosphere Monitoring Service (CAMS).  The data are routinely used to validate the operational ECMWF chemical forecasting model through the CAMS-84 project.  Up to now this effort has focused on reactive gases and water vapour. New to IAGOS are the measurements of the greenhouse gases (GHG),  CO2 and CH4.  We present the first comparison of the CAMS GHG system  (Global analyses and high resolution forecasts of greenhouse gases) with the new IAGOS CO2 and CH4 measurements and  show how this  will  be part of  the future validation of the CAMS operational model.


How to cite: Bennouna, Y., Gerbig, C., Clark, H., and Agusti-Panareda, A.: First validation of the CAMS greenhouse gas system with  IAGOS aircraft measurements of CO2 and CH4, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7373, https://doi.org/10.5194/egusphere-egu22-7373, 2022.

On-site presentation
Anne Caroline Lange et al.

Emission data as main input to air quality forecast models introduce large uncertainties. These data originate from emission inventories that provide estimates of spatially distributed emissions in general as an annual total amount. The temporal variations of the emission data follow prefixed time profiles in models. Yet, the impact of variable societal behaviour and meteorological implications are rarely considered. Furthermore, environmental agencies that raise the data input for emission inventories depend on rough emission estimates from bottom-up and top-down strategies.

To evaluate the annual emission totals of European and in particular German inventories, we perform a full year reanalysis of European air quality in 2016 applying chemistry four-dimensional variational (4D-var) data assimilation with the European Air pollution Dispersion – Inverse Model (EURAD-IM). Assimilating ground-based, airborne, as well as satellite observation data within 24 hours assimilation windows, we successively assess initial value optimisations and emission correction factors for anthropogenic emissions of nitrogen oxides (NOx), carbon monoxide (CO), particulate matter (PM), sulphur oxides (SOx), ammonia (NH3), and non-methane volatile organic compounds (NMVOCs), achieving consistency with observations. The analysis is performed on different model grids of 15 km horizontal resolution for Europe, 5 km for Central Europe, and 1 km for three selected regions in Germany.

Analysing the inferred temporal evolution of emission correction factors reveals that the total NOx emissions are underestimated in Germany, while NH3 emissions are found too high leading to an overestimation of modelled NH3 concentrations using standard emission data. Other emission species show clear seasonal dependence in the correction factors. Comparing the emission correction factors of the different European countries, we find a significant discrepancy of correction strengths between north-western and south and eastern European countries. Analysis results for different model nests vary not only due to finer structures but also in the strength or sometimes even the direction of corrections. Spatially, the distribution of correction factors is driven by areas characterized by high emissions while care must be taken that the deployment of assimilated observation stations still matters. In this context, we also discuss the limits of our analysis technique regarding the observation network configuration and the statistical method of the assimilation technique.

How to cite: Lange, A. C., Franke, P., and Elbern, H.: A full year reanalysis of European air quality in 2016 focusing on the evaluation of anthropogenic emissions by applying advanced spatio-temporal inversion, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8023, https://doi.org/10.5194/egusphere-egu22-8023, 2022.

Virtual presentation
Vincent Huijnen et al.

The Copernicus Atmosphere Monitoring Service (CAMS) provides global analyses and forecasts of atmospheric composition, relying on the Integrated Forecasting System (IFS) of ECMWF.  The CAMS global model consists of the aerosol model of the IFS, IFS-AER, which is a sectional-bulk scheme, while the chemistry scheme is based on a CB05-based carbon-bond mechanism, with the option to couple this to BASCOE-based stratospheric chemistry.

The composition model is updated regularly, aligned with updates of ECMWF’s operational meteorological model. Here we report on updates planned for the next operational version, referred to as CY48R1. This concern revisions on a large range of topics, as developed over the recent years, and therefore impacting many aspects of chemistry and aerosol composition in troposphere and stratosphere. Main aspects concern:

  • Isoprene oxidation has been redefined, resulting in increased OH recycling and including a first model description of glyoxal as well as basic aromatics chemistry. 
  • NOy chemistry has been updated to include HONO and the longer-lived species CH3O2NO2.
  • A coupling with secondary organic aerosol formation has been established, and the coupling of secondary inorganic aerosol has been revisited.
  • the option to use the BASCOE-based stratospheric chemistry for operational application is actively considered.
  • Dry and wet deposition parameterizations have been revisited by including a GEOS-Chem based deposition parameterization using tile fraction specific deposition velocities. 
  • Aerosol dust emissions, and their optical properties have been revised.
  • The emissions handling has been updated, along with updates of the CAMS global emission inventories themselves.

The updates of the composition model and its emissions are tested in combination with updates to the tracer transport and data assimilation aspects, and the optimal configuration will be selected for operational application.

In this contribution we provide an overview of expected changes with emphasis on changes in composition modeling aspects. We will present the subsequent impacts on key atmospheric composition aspects, including air quality performance for major pollution regions across the world, aerosol optical depth, dust, and stratospheric composition products.  

How to cite: Huijnen, V., Remy, S., Williams, J. E., Chabrillat, S., Guevara, M., Kipling, Z., and Flemming, J.: Changes to the IFS atmospheric composition model in support to the CAMS update for CY48R1, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9435, https://doi.org/10.5194/egusphere-egu22-9435, 2022.

Olga Klochikhina

Atmospheric air is the vital for life on Earth. The quality of air is the main issue that humankind faces every day. The quality of air in towns and cities is an essential aspect of health of the population. According to WHO report, 91% of all population lives in places where level of air pollution exceeds established standards. According to Rosgidromet report, the level of air pollution is estimated as ‘very high’ or ‘the highest’ in 40 Russian cities including Perm.

The objectives of this research are to determine what pollutants are the most significant and what sources of pollution make the greatest contribution in air pollution in Perm.

Data for this study comes from national net of measurements of air pollution of Rosgridromet and researches of air quality in different areas of Perm were conducted on demand of local government. In this study there has been little quantitative analysis of results of measurements that was hold close to valleys of minor rivers Daniliha and Egoshiha since 2016 to 2020. Air quality was estimated by comparing measurements of concentration of chemical substances with established hygienic standards. The spatial model of air quality in Perm was created using Unified Program of air pollution estimation ‘Ecologist’.

The first set of questions aimed to finding out the most significant pollutants in the air of Perm. The analyzed data show that the main pollutants in air of Perm are Formaldehyde, Nitrogen Dioxide, Phenol and Ethylbenzene. Exceedances of maximum one-time concentrations of all significant pollutants were identified. The most surprising aspect of the analysis of data is in the observing of exceedances of not only maximum one-time concentrations but of mean daily concentrations of formaldehyde that happen during all the period of study. A possible explanation for presence of nitrogen dioxide, formaldehyde and ethylbenzene in the air of Perm might be that burning of fossil fuels by combustion engine of transport and by fuel power industry make the greatest contribution in air pollution. These results match those observed in earlier studies. Exceedances of maximum one-time concentrations of phenol may be explained by impact of mechanical engineering plants.

The results of this study indicate that the national net of measurements of air pollution of Rosgridromet is non-effective and doesn’t give enough information for estimation of air quality in Perm. More monitoring sites and more measured pollutants, especially carcinogenic, are required to determine the air quality and the impact of transport or industry or other sources of air pollution on human health.

How to cite: Klochikhina, O.: Estimate of air quality in valleys of Yegoshikha river and Danilikha river , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1349, https://doi.org/10.5194/egusphere-egu22-1349, 2022.

Virtual presentation
Buhalqem Mamtimin et al.

We conducted CH4 simulations for Europe by using the ICON (ICOsahedral Non-hydrostatic)-ART (Aerosols and Reactive Trace gases) model and emissions from EDGAR.

With respect to the forecast of CH4 in Limited Area Mode (Europe, 6.5 x 6.5 km), the model requires as accurate as possible initial and boundary atmospheric conditions. While the intial data denote here the state of the atmosphere at the start of the model run, the boundary conditions shall denote the data in the lateral boundary zone where the model is forced by the meteorological and CH4 concentration data out side the domain.

The meteorological conditions can be obtained, for example,  from both the DWD's operational numerical weather prediction output or from Copernicus. The Copernicus Atmosphere Monitoring Service (CAMS) provides the necessary boundary CH4 data for the ICON-ART run in Limited Area Mode. The  CH4 initial concentrations can be obtained from Copernicus or from a previous ICON-ART simulation run (e.g., the 24 h CH4 forecast from the previous day).

This way, ICON-ART in Limited Area Mode (LAM) allows for a flexible choice of boundary data and respective sensitivity testing. 

To combine the meteorological data of the ICON with the CH4 concentration data of CAMS as forcing data at the boundary, the CAMS data has to be provided on the same horizontal grid and the same vertical model levels as the ICON data. Since CAMS uses a vertical coordinate of a hybrid sigma-pressure system, the data has, in addition to the horizontal interpolation, to be interpolated vertically to the height based SLEVE coordinate system of ICON.

Also, the EDGAR emission datasets are interpolated to the target ICON grid. Both interpolations are characterized with respect introducing uncertainties.

Thirdly, variation in meteorological conditions is simulated by running ensembles in the ICON-ART LAM.

In this work, the ICON-ART CH4 simulation setup forced by ICON meteorology and CAMS CH4 boundary data is shown to be a useful method to simulate the CH4 atmospheric concentrations at the regional scale and for the purposes of regional atmospheric inversions.

This work has been supported by the project Prototype system for a Copernicus CO2 service (COCO2).


How to cite: Mamtimin, B., Roth, F., Sunkisala, A., Förstner, J., Reinert, D., and Kaiser-Weiss, A.: Sensitivities of simulated atmospheric CH4 concentrations in the ICON-ART Limited Area Mode, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5097, https://doi.org/10.5194/egusphere-egu22-5097, 2022.

Virtual presentation
Krzysztof Wargan et al.

The past decade has witnessed a growing interest in the quickly developing field of chemical composition reanalyses, that is, multiyear records of assimilated observations of atmospheric constituent gases. Composition reanalyses typically assimilate observations of atmospheric constituents using full chemistry and transport models driven by assimilated meteorology. Most, although not all, of these reanalyses to date focus on tropospheric composition. This presentation introduces a new chemical reanalysis of stratospheric constituents developed and produced at NASA’s Global Modeling and Assimilation Office (GMAO). Named Global Earth Observing System (GEOS) Stratospheric Composition Reanalysis with Aura MLS (GEOS-SCREAM), this product consists of assimilated global three-dimensional fields of stratospheric ozone, water vapor, hydrogen chloride (HCl), nitric acid (HNO3), and nitrous oxide (N2O) mixing ratios and covers the period since the beginning of MLS observations in September 2004 to 2021. The assimilated instantaneous fields are produced at a three-hourly frequency. GEOS-SCREAM assimilates version 4.2 MLS profiles of the five constituents alongside total ozone column from the Aura Ozone Monitoring Instrument with the recently developed Constituent Data Assimilation System. It is also constrained by tropospheric water vapor from several satellite sensors and in situ measurements with the existing MERRA-2 meteorological data assimilation system. GEOS-SCREAM provides an accurate and dynamically consistent high-resolution data record of the five constituents, all of which are of primary importance to stratospheric chemistry and transport studies. We will present a description of GEOS-SCREAM and selected results of a process-based evaluation of this product using independent data. We will also discuss potential scientific applications of GEOS-SCREAM and outline plans for an upcoming comprehensive composition reanalysis that is being developed at NASA GMAO.

How to cite: Wargan, K., Weir, B., Manney, G. L., Cohn, S. E., Knowland, K. E., Wales, P. A., and Livesey, N. J.: GEOS-SCREAM: A Stratospheric Composition Reanalysis with Aura MLS , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10669, https://doi.org/10.5194/egusphere-egu22-10669, 2022.

Swen Metzger et al.

The Integrated Forecasting System (IFS) of ECMWF is used within the Copernicus Atmosphere Monitoring Service (CAMS) to provide global analyses and forecasts of atmospheric composition, including aerosols as well as reactive trace gases and greenhouse gases. Here we provide a first description and assessment of cloud and aerosol pH as computed in EQSAM4CLIM when implemented in the IFS. The more flexible pH computation is furthermore coupled to the aqueous phase chemistry governing the SO2 in-cloud oxidation, as well as to the wet deposition routine for gases. 

Currently the IFS describes acidity via the scavenging strong inorganic acids (HNO3, H2SO4) and the contribution from SO2 oxidation and buffering via NH3 using a simplistic description, which results in a pH ranging between 3-5. To provide a more diverse range in pH we have coupled the CB05 chemistry scheme and AER-aerosol components of IFS exploiting EQSAM4Clim-based [H+] calculations by introducing the resulting pH into the aqueous phase chemistry and wet deposition processes. The subsequent representation of aerosol acidity in the solution (aerosol/cloud/rain water) has been evaluated against observations and previous modelling studies using the GEOS-Chem global CTM. The use of aerosol induced acidity in the computation of aqueous phase chemical reaction rates was found to be most important for inorganic soluble gas and aerosol phase species, e.g. SO2, and their subsequent oxidative products, e.g. HNO3. The impact on simulated SO2 concentrations at the surface is significant, through changes to the aqueous phase chemistry and subsequent wet deposition. The impact on PM2.5 is generally small but regionally positive, over Europe, US and China. There is a positive impact on surface O3 with a reduction in the annual mean bias at the surface for Europe, the US and China when compared against observational networks for 2019. Surface SO42-concentrations are generally closer to observations, especially during wintertime over Europe and U.S., while surface NH3 shows only moderate changes. NH3 shows no significant improvement in observed biases against observations, but the impact on simulated surface concentration of NH4+ aerosol is generally positive, particularly in winter. Further improvements here can be expected by improving the coupling with mineral cations (Na+, K+, Ca2+, Mg2+), and by including major organic acids in the aerosol neutralisation reactions. For EQSAM4Clim, the neutralisation reactions and the total liquid water content (of aerosols, fog/cloud, rain) are key for the pH computation and the associated gas/aerosol partitioning, and most critical for NH3. Ammonia forms here the only volatile cation (NH4+), those presence in the aerosol phase critically depends on water and mineral cations.

In summary, the production efficacy of sulphate and ammonium aerosol is critically dependent on an accurate representation of acidity in aqueous droplets and aerosol species at global scale. This development is not only expected to bring a much improved constraint on the modelling of surface concentrations of sulfur and nitrogen, together with its deposition. Also the cloud and rain water pH itself are within reach as new products of the CAMS global service.

How to cite: Metzger, S., Remy, S., Williams, J. E., Huijnen, V., Meziane, M., Kipling, Z., Flemming, J., and Engelen, R.: Representing acidity in the IFS using a coupled IFS-EQSAM4Clim approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11122, https://doi.org/10.5194/egusphere-egu22-11122, 2022.

Beatriz Monge-Sanz et al.

Our study shows the relevance of the interactions between atmospheric chemistry and physics to achieve better weather predictions. We provide evidence of the role that stratospheric ozone plays to improve weather forecasts on several timescales, and highlight the need for seamless models to include realistic prognostic ozone interactive with radiation.

Representing realistic feedbacks between ozone, radiation, temperature and dynamics is essential to correctly simulate the behaviour of the stratosphere and its links with tropospheric weather and climate. We have implemented an alternative stratospheric ozone model (Monge-Sanz et al., 2011) in the ECMWF system interactively with radiation, and we have assessed its performance and feedbacks with meteorological fields for different timescales, from medium-range to seasonal.  Here we will discuss results from the experiments and analyses conducted in our study (Monge-Sanz et al., 2021), showing the feasibility of this ozone model for a seamless numerical weather prediction approach.

We will show that the stratospheric ozone distribution provided by this new prognostic ozone model compares very well with observations even for unusual meteorological conditions. On assessing impacts on meteorological variables, the new ozone model improves the representation of the stratosphere, clearly reducing temperature biases in this region. We will also show the benefits it brings to tropospheric meteorological fields, highlighting the potential of this new ozone description to exploit stratospheric sources of predictability and improve weather predictions over Europe on a range of time scales.

Therefore, our results show the value of this prognostic stratospheric ozone model for seamless Earth System Models, as well as for global systems where atmospheric composition is coupled to weather forecasts, like the systems being run within the Copernicus Atmosphere Monitoring Service (CAMS). We will also discuss challenges and possible strategies for the inclusion of chemistry-dynamics feedbacks in seamless Earth System Models.



Monge-Sanz BM, Chipperfield MP, Cariolle D, Feng W. Results from a new linear O3 scheme with embedded heterogeneous chemistry compared with the parent full-chemistry 3-D CTM. Atmos. Chem. Phys. 11, 1227-1242, 2011.

Monge-Sanz, B. M., Bozzo, A., Byrne, N., Chipperfield, M. P., Diamantakis, M., Flemming, J., Gray, L. J., Hogan, R. J., Jones, L., Magnusson, L., Polichtchouk, I., Shepherd, T. G., Wedi, N., and Weisheimer, A.: A stratospheric prognostic ozone for seamless Earth System Models: performance, impacts and future, Atmos. Chem. Phys., accepted, https://doi.org/10.5194/acp-2020-1261, 2021.

How to cite: Monge-Sanz, B., Bozzo, A., Byrne, N., Chipperfield, M., Diamantakis, M., Flemming, J., Gray, L., Hogan, R., Jones, L., Magnusson, L., Polichtchouk, I., Shepherd, T., Wedi, N., and Weisheimer, A.: Stratospheric prognostic ozone for seamless Earth System Models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12148, https://doi.org/10.5194/egusphere-egu22-12148, 2022.

Meng Gao

Urbanization took place rapidly over recent decades and is expected to continue in the future, producing a series of environmental issues, including heat stress. Cool roof and green roof strategies have been adopted in a number of megacities to mitigate urban heat and carbon emissions, yet China is lagging behind developed countries in the implementation. One reason is the lack of careful and thoughtful assessment of potential effects of roof strategies, including their influences on winter PM2.5. With numerical simulations in this study, we assess how cool and green roof strategies affect winter PM2.5 pollution in North China, and we find that adoptions of cool roofs tend to aggravate PM2.5 pollution in lightly polluted regions. When PM2.5 pollution worsens, the negative effects of cool roofs are likely to be diminished. Green roofs cause less enhancements of PM2.5 pollution as a result of inhibited evapotranspiration in winter. We demonstrate that the effects of roof strategies are regulated by pollution severity and conclude that green roofs with suppressed evapotranspiration and thus weaker penalty on winter PM2.5 pollution seem to be better choices given the current pollution severity level in China, especially for regions suitable for growth of broadleaf plants.

How to cite: Gao, M.: Pollution severity regulates the effects of roof strategies on China’s winter PM2.5, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12749, https://doi.org/10.5194/egusphere-egu22-12749, 2022.