Wildfires, while disastrous for ecosystems, also contribute significantly to pollution impacting human health. They also affect meteorological conditions at both local and regional scales by emitting aerosols into the atmosphere, which have both direct and indirect effects. In Ukraine, forest fires are common in the spring, coinciding with the season of agricultural open burning.

The wildfire episode in April 2020 was among the most severe and especially difficult to extinguish because of its origin and spreading in the abandoned Chornobyl Exclusion Zone (CEZ). Applying the seamless online-integrated Enviro-HIRLAM modeling system, we aimed to study aerosol contamination and how its elevated levels affected regional near-surface meteorology. To achieve this, the model was run in 4 modes: reference (REF) run and runs to simulate direct (DAE), indirect (IDAE) and combined (COMB) aerosol effects. These runs were performed at 15 km horizontal resolution (covering large European territory to consider atmospheric circulation) with downscaling to 5 (focusing on Ukraine) and 2 km (focusing on the CEZ).

Elevated black and organic carbon content accounted for 80% of all aerosol species with the prevailing mass concentration in the accumulation mode in the CEZ. The observed meteorology, driven by aerosol effects, was more intensified in these synoptic conditions, and especially at the edges of the fronts. When aerosol effects were included, the wind speed changed up to ±4 m/s and caused spatial shifts in patterns of cloudiness and precipitation. Moreover, verification showed better results for modelling with IDAE, whereas DAE effects can overestimate changes in near-surface meteorology. This aerosol composition led to noticeable cooling and drying effects. The 2-m air temperature decreased by 3℃ and a specific humidity dropped by 1 g/kg at a local scale. However, these effects varied with atmospheric conditions. In particular, when fronts, especially cold fronts, passed through the CEZ, stronger changes in meteorological parameters were observed as expected. As presence of aerosols influences a humidity regime in the boundary layer (including formation and development of cloudiness and precipitation), the spatial positioning of modeled fronts may be shifted leading to changes of opposite signs.

This study was supported by the grant of HPC-Europa3 Transnational Access Programme for projects “Integrated modelling for assessment of potential pollution regional atmospheric transport as result of accidental wildfires” (IMA-WFires, HPC17TRLGW) & “Research and development for integrated meteorology – atmospheric composition multi-scales and – processes modelling” (Enviro-PEEX(Plus) on ECMWF; SPFIMAHU-2021). The CSC – IT Center for Science Computing (Finland) is acknowledged for computational resources.

How to cite: Savenets, M., Mahura, A., Nuterman, R., and Petäjä, T.: Near-surface meteorology changes driven by aerosol effects during the April 2020 wildfires in the Chornobyl Exclusion Zone, Ukraine, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-303, https://doi.org/10.5194/egusphere-egu24-303, 2024.

Surface air pollution is a major hazard for society because of its negative impact on human health, crops yield and on other aspects of the economy. Monitoring the air quality with in-situ instruments is routinely carried out in many countries. These air quality networks have limited coverage. They  are often very sparse or are not even present in many parts of the world, that suffer from the worst air quality.  Satellite observations of atmospheric composition provide the unique prospect to contribute to a more spatially complete monitoring of surface air pollution. However, among several other limitations, only vertically integrated measures of the pollution concentration can usually be retrieved from satellites, and not the surface concentration.  The relation between total column information and surface concentrations depends on the shape of the vertical profile. The correlation may be especially poor in the case of lofted air pollution plumes originating from long range transport.   

Data driven methods (machine learning, statistic etc.) are used to infer surface concentrations from satellite observations based on in-situ surface observations. An alternative approach is data assimilation of satellite retrievals in an atmospheric model, a method which has been developed for numerical weather forecasting.  The global forecasting system of atmospheric composition of the Copernicus Atmosphere Monitoring Service (CAMS) applies 4D-VAR data assimilation to satellite retrievals of Aerosol optical depth (AOD), Ozone, Carbon Monoxide and Nitrogen Dioxide (NO2) to correct the model initial conditions and consequently also the PM2.5 and PM10 surface concentrations.  In our presentation, we will give an overview of the usefulness of atmospheric composition (AC) satellite data assimilation for monitoring of surface air quality with the global CAMS system. We will specifically show to what extent AOD assimilation improves the analysis and forecast of surface PM2.5 for different regions and at different temporal and spatial scales. In particular we will discuss the correlation between AOD and PM2.5 in the observations and in the model. We will also discuss the influence of the model performance and aspects of the data assimilation procedure on the impact of AC satellite data assimilation on surface concentrations.

How to cite: Flemming, J., Ades, M., Di Tomaso, E., Inness, A., Huijnen, V., Jones, L., Paschalidi, Z., Razinger, M., and Remy, S.: The impact of AOD assimilation on surface PM analysis and forecast with the global CAMS system. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11437, https://doi.org/10.5194/egusphere-egu24-11437, 2024.

This study examines the impact of aerosol-radiation interation on subseasonal prediction using the Unified Forecast System (UFS) coupled to an ocean and an aerosol component. The aerosol component is from the current NOAA operational GEFS-Aerosols model, which includes the GOCART aerosol modules, simulating sulfate, dust, black carbon, organic carbon, and sea-salt aerosols. The modeled aerosol optical depth (AOD) is compared to reanalysis from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) and observations from Moderate Resolution Imaging Spectro-radiometer (MODIS) satellite. Despite AOD bias primarily in dust and sea salt, good AOD agreement is achieved. The simulated radiative forcing (RF) from the total aerosol at the top of the atmosphere is approximately -2.5 W/m2 or -16 W/m2 per unit AOD globally. This is consistent with previous studies.

In parallel simulations, the dynamic prognostic aerosols are replaced with modeled climatological aerosol concentrations in the UFS. While regional differences in RF are noticeable in some special events between these twin experiments, the resulting RF, surface temperature, precipitation and geopotential height at 500 hPa, show similarity over multi-years in subseasonal applications. This suggests that replacing the costly chemistry module with the modeled aerosol concentration climatology is a possible alternative in the subseasonal applications.

How to cite: Sun, S., Grell, G., Zhang, L., Henderson, J., and Li, H.: Simulating aerosol-radiation effect on subseasonal prediction in a coupled Unified Forecast System and CCPP-Chem: prescribed aerosol climatology versus dynamic aerosol model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4896, https://doi.org/10.5194/egusphere-egu24-4896, 2024.

The Integrated Forecasting System (IFS-COMPO) at ECMWF is the global model used by the Copernicus Atmosphere Monitoring Service (CAMS) to provide forecasts and analyses of atmospheric composition, including greenhouse gases, aerosols and reactive gases. Within CAMS, the default configuration of IFS-COMPO relies on the CB05 chemistry scheme for the troposphere and the Belgian Assimilation System for Chemical ObsErvations (BASCOE) module for the stratospheric chemistry.

Currently, the photodissociation rates (Js) in IFS-COMPO can be computed with two methods: Joffline (computationally efficient but inaccurate) and Jonline (more accurate but computationally very expensive). In this work, we implement and evaluate an optimized method for the calculations of the Js in the stratosphere that is as accurate as Jonline and as computationally efficient as Joffline. This method (Jvint) computes the Js using Jonline but on a coarser stratospheric vertical grid (L23) compared to the native vertical grid and will be available in the future cycle 49R1.

We compare three IFS-COMPO simulations using the Jvint, Jonline and Joffline methods with the BASCOE Reanalysis of Aura MLS version 3 (BRAM3) for ozone and with observations from the TROPOspheric Monitoring Instrument (TROPOMI) for nitrous dioxide (NO2). For ozone, the Jvint method significantly reduces the bias with respect to BRAM3 compared to the Joffline method in the upper stratosphere. In the mid-lower stratosphere, however, the Jvint configuration delivers slightly larger bias with respect to BRAM3 when compared to the Joffline method, especially over the Arctic. This bias is consistent with the results from the Jonline method and with the bias reduction in the upper stratosphere. For NO2, the Jvint method also reduces the bias with respect to TROPOMI stratospheric columns compared to the Joffline method at all latitudes.

Concerning the computational cost, it increases in the Jvint method compared to the Joffline method by 5% in December, 16% in July and 12% in November, while the Jonline method is two to three times as computationally expensive compared to Joffline. In addition, the Jvint method improves the seasonal variations of the ozone and NO2 columns compared to the Joffline method, and the differences between the Jvint and Jonline methods are insignificant for most of the stratospheric regions.

We demonstrate the capability of the Jvint method to deliver improved stratospheric columns for ozone and NO2 compared to the Joffline method and significantly reduce the computational cost compared to the Jonline method. The Jvint configuration also includes the time-dependence of the solar flux, ensuring a physically consistent representation of the stratospheric photochemistry.

How to cite: Minganti, D., Chabrillat, S., Bingen, C., Huijnen, V., Remy, S., Metzger, S., Williams, J., and Flemming, J.: Optimization of the calculation of the photodissociation rates in the stratosphere in the BASCOE module of the IFS-COMPO, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11188, https://doi.org/10.5194/egusphere-egu24-11188, 2024.

The atmospheric composition forecasting system used to produce the CAMS forecasts of global aerosol and trace gases distributions, IFS-COMPO, undergoes periodic upgrades. In this presentation we describe the development of the future operational cycle 49R1, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12 for describing gas-aerosol partitioning processes for nitrate and ammonium and for providing diagnostic aerosol, cloud and precipitation pH values at global scale. This information on aerosol acidity influences tropospheric chemistry processes associated with aqueous phase chemistry and wet deposition. The other updates to cycle 49R1 include modifications to the description of Desert Dust, Sea-salt aerosols, Carbonaceous aerosols and the size description for the calculation of aerosol optics.

The implementation of EQSAM4Clim significantly improves the partitioning of reactive nitrogen compounds decreasing surface concentrations of both nitrate and ammonium, which reduces PM2.5 biases for Europe, U.S. and China, especially during summertime. For aerosol optical depth there is generally a decrease in the simulated biases for wintertime, and for some regions an increase in the bias for summertime. Improvements in the simulated Ångström exponent is noted for almost all regions, resulting in generally a good agreement with observations.

 The diagnostic aerosol and precipitation pH calculated by EQSAM4Clim have been compared against results from previous simulations (for aerosol pH) and against ground observations (for precipitation pH), with the temporal distribution in the annual mean values showing good agreement against the regional observational datasets. The use of aerosol acidity only has a relatively smaller impact on the aqueous-phase production of sulphate when compared to the changes in gas-to-particle partitioning brought by the use of EQSAM4Clim.

Metzger, S., Rémy, S., Williams, J. E., Huijnen, V., and Flemming, J.: A revised parameterization for aerosol, cloud and precipitation pH for use in chemical forecasting systems (EQSAM4Clim-v12), EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2930, 2023.

How to cite: Remy, S., Metzger, S., Huijnen, V., Williams, J., and Flemming, J.: Improved representation of aerosol acidity in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Clim., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12250, https://doi.org/10.5194/egusphere-egu24-12250, 2024.

NOAA’s Global Systems Laboratory (GSL), in collaboration with other laboratories, is developing and testing a new high-resolution weather model known as the Rapid-Refresh Forecasting System (RRFS). This model, which uses the Finite Volume Cubed-Sphere Dynamical Core, features a grid covering all of North and Central America at 3 km horizontal resolution, with 65 vertical layers. 

The RRFS is initialized every hour through assimilation of the latest weather observations. It incorporates primary aerosol emissions from wildland fires and dust sources. The coupled RRFS-Smoke-Dust (RRFS-SD) model simulates 3D concentrations of smoke, fine and coarse dust aerosol species concurrently with the meteorology, and includes the aerosol radiative feedback. Hourly fire radiative power data from the Regional ABI and VIIRS fire Emissions (RAVE) product is ingested into RRFS to estimate biomass burning emissions and fire heat fluxes. Windblown dust emissions are parameterized by using the FENGSHA scheme. An experimental version of the RRFS-SD model is being tested by NOAA Environmental Modeling Center (EMC) in real time:  https://rapidrefresh.noaa.gov/RRFS-SD/

We will present an evaluation of the RRFS-SD model  for several fire and dust case studies. Ground and aircraft-based in-situ and remote sensing data are extensively utilized to evaluate the model simulations of meteorology, smoke and dust fields. Additionally, we will present the radiative feedback of smoke and dust on the meteorological simulations in RRFS. The challenges and uncertainties affecting the smoke and dust forecasting will be discussed as well. 

How to cite: Ahmadov, R., Li, H., Romero-Alvarez, J., Schnell, J., Bhimireddy, S., James, E., Wong, K. Y., Hu, M., Carley, J., Bhattacharjee, P., Baker, B., Grell, G., Xu, C., Kondragunta, S., Li, F., Trahan, S., and Marvin, M.: Forecasting smoke and dust in NOAA’s next-generation high-resolution coupled numerical weather prediction model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12702, https://doi.org/10.5194/egusphere-egu24-12702, 2024.

Aerosol effects play a significant role in Earth’s radiative balance, cloud formation and thus redistribution and change of meteorological characteristics. This study focuses on the case of heat wave event during July-August 2010 accompanied with wildfires, modeled using the seamless online-integrated Enviro-HIRLAM modeling system. We used reference (REF) run and running modes to simulate direct (DAE), indirect (IDAE) and combined (DAE+IDAE) aerosol effects. These runs fulfilled for domain at 15 km horizontal resolution (includes European territory), with downscaling to 5 km (includes the territory of Ukraine).

During the heat wave event, aerosol effects caused the overall 2-m air temperature nighttime cooling and daytime warming being stronger for DAE. Locally, there were observed reversed dependencies reaching up to 5℃ differences compared to REF runs. Specific humidity changes were consistent with air temperature fluctuations showing a decrease at night and increase during midday hours without heterogeneous spatial distribution. IDAE effects caused homogeneously distributed slight decrease in 2-m air temperature, with no sharp changes in specific humidity. At midday, homogeneity disappeared for 2-m air temperature, whereas aerosol effects had no significant impact on specific humidity. DAE+IDAE caused mostly warming effect during night time, with local increase and decrease for specific humidity. For all running modes, there were no significant changes in grid-scale precipitation driven by aerosol effects.

Occasionally, the heat wave event was accompanied with weather fronts. In comparison to anticyclonic synoptic conditions, the role of aerosol effects significantly increased during these weather fronts passage. In the case of a cold front, DAE showed a decrease in 2-m air temperature by -1,2℃ following the cold front areas, and warming on some distance up to +1,4℃ at midnight.

IDAE effects resulted in significant warming before front and cooling after it. Specific humidity falls after cold front up to -4 g/m³. DAE+IDAE affected 2-m air temperature rise before cold front and cooling after during midday. Specific humidity changes unevenly and increases after cold front. Grid-scale precipitation amounts decreased during the cold front passage due to the impact of aerosol effects for all running modes.

This study was supported by the grant of HPC-Europa3 Transnational Access Programme for projects “Integrated Modelling and Analysis of Influence of Land Cover Changes on Regional Weather Conditions/ Patterns” (MALAWE, HPC17ENAVF) & “Research and development for integrated meteorology – atmospheric composition multi-scales and – processes modelling” (Enviro-PEEX(Plus) on ECMWF; SPFIMAHU-2021). The CSC – IT Center for Science Computing (Finland) is acknowledged for computational resources.

How to cite: Pysarenko, L., Mahura, A., Nuterman, R., and Krakovska, S.: A case study of aerosol effects impact on key meteorological characteristics in Ukraine during heat wave event in July-August 2010, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17895, https://doi.org/10.5194/egusphere-egu24-17895, 2024.

We report on an extensive analysis of CAMS (Copernicus Atmospheric Monitoring Service) formaldehyde (HCHO) simulations in different tropospheric regions, seasons, altitudes and air masses using a comprehensive data set of airborne measured HCHO vertical column densities and mixing ratios. The observations are derived from measurements of the HALO mini-DOAS instrument operated from aboard the German research aircraft DLR HALO during six international research missions in the years 2017 to 2019. In addition, measurements over the South American tropical rain forest in 2014 are included, as this region is of particular interest in the analysis of global biogenic emissions. The analysis of airborne measured and CAMS CY48R1 simulated HCHO vertical profiles shows an overestimation of planetary boundary layer HCHO over the Amazon rain forest by the model on average by 70%. Restricting the comparison to measurements outside of identified anthropogenic emission events (e.g. biomass burning plumes) increases this overestimation of boundary layer HCHO to a factor of five. This finding is further investigated by additionally also including vertical column density measurements of the mini-DOAS instrument to the analysis. This allows the comparison of simulated and airborne derived HCHO with respective TROPOMI satellite observations (for all airborne measurements from May 2018 onwards), hence enabling a comprehensive assessment of tropospheric HCHO. The above findings are included in ongoing research of work package 2.3 of the Horizon Europe CAMEO (CAMS EvOlution) project, which aims to develop an inversion of biogenic emissions within ECMWF’s Integrated Forecasting System (IFS). As a first step towards a successful implementation of HCHO assimilation and inversion capability within the IFS, a simplified HCHO chemistry scheme has been developed and is currently analyzed with a particular focus on its impact on other atmospheric reactive trace gases, such as isoprene and ozone, and on aerosols.

How to cite: Kluge, F., Flemming, J., Huijnen, V., Inness, A., Kelly, C., Müller, J.-F., Oomen, G.-M., Pfeilsticker, K., Stavrakou, T., Ribas, R., Rotermund, M., Weyland, B., and Van der Worp, M.: Initial steps towards an inversion system for biogenic isoprene emissions in CAMS: Verification using HALO mini-DOAS and TROPOMI observations and simplified chemistry, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18181, https://doi.org/10.5194/egusphere-egu24-18181, 2024.

In addition to dynamic and thermodynamic processes, aerosols' chemical and aerodynamic characteristics play a significant role in cloud microphysics and convective development. The region surrounding Manaus, located in the Northern region of Brazil, constitutes a unique environment worldwide for studying the impact of anthropogenic and biogenic emissions on aerosol concentration and, consequently, cloud microphysics. This study investigates chemical-aerosol-cloud interactions during the wet season, employing the Weather Research and Forecasting model with coupled chemistry (WRF-Chem) version 3.9.1.1 and observed data from the GoAmazon2014/5 experiment. The model was configured with a 3 km grid, utilizing the Regional Atmospheric Chemistry Mechanism (RACM) for gas-phase chemistry, and adopting the Modal Aerosol Dynamics Model for Europe (MADE) with parameterization for Secondary Organic Aerosol (SOA) production based on the Volatility Basis Set (VBS) approach for aerosol treatment. The Abdul-Razzak and Ghan option was employed to relate aerosol properties to the 2-moment Morrison cloud microphysics parameterization. The model is initialized with ERA5 and Copernicus Atmosphere Monitoring Service (CAMS) data, incorporating anthropogenic emissions from a regional inventory and biogenic emissions using the Model of Emissions of Gases and Aerosols from Nature (MEGAN). Results indicate that the model effectively represents meteorology and chemistry in the Manaus region. The fully coupled model successfully reproduces plume dispersion and aging. The aerosol concentration peak in the accumulation mode is observed approximately 100 km from Manaus, returning to background concentrations beyond 300 km. The increase in aerosol concentrations is associated with the formation of biogenic and anthropogenic Secondary Organic Aerosol (SOA) and sulfate-derived aerosols with mass peaks at 4, 100, and 1.4 times the background concentration. This aerosol concentration increase significantly correlates with Cloud Condensation Nuclei (CCN) concentration at 0.5% supersaturation. Despite the elevated aerosol concentration in the plume and higher CCN concentration, the CCN/aerosol ratio decreases to 0.02, in contrast to 0.3 in the background region. The distinct chemical and aerodynamic characteristics of aerosols in the background and urban plume regions modulate the Droplet Number Concentration (DNC), Liquid Water Content (LWC), and Effective Radius (Re). Clouds in the plume exhibit higher DNC and LWC, and lower Re. Approximately 40% of clouds in the plume have LWC above 2.5 g/m³, compared to only 10% in background regions. The average Re is 8 and 13 μm in the plume and background regions, respectively. Sensitivity simulations also show that both anthropogenic and biogenic emissions influence cloud processes in the Amazon region. Our results suggest that a more accurate representation of aerosols, often simplified in numerical models, is necessary for enhanced weather and climate modeling.

How to cite: Herdies, D., Reis, A., Nascimento, J., and Vara-Vela, A.: Impact of the Manaus Plume on the Amazon Green Ocean Atmosphere: Aerosol-Cloud Interaction during the Wet Season with fully coupled online chemistry in the WRF model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19415, https://doi.org/10.5194/egusphere-egu24-19415, 2024.

We present a methodology and first results for analytical propagation of emissions uncertainties
through the EMEP MSC-W chemical transport model (CTM) and an application of these uncertainty
estimates to policy products provided by the Copernicus Atmosphere Monitoring Service
(CAMS) for European cities. CTMs are widely employed in atmospheric modeling to simulate
the transport and transformation of pollutants, but uncertainties in emission estimates can
significantly impact the accuracy of air quality predictions. Our study systematically analyzes
the propagation of uncertainties arising from emissions. The emissions’ uncertainties are consistent
with the CAMS regional emissions product and are calculated using detailed, countryspecific
uncertainty estimates in activity data and generic emission factor uncertainties. The
uncertainties are calculated per source sector and country. The Local Fractions/Sensibilities [1]
methodology available in the EMEP MSC-W model is a tool that allows computation of sourcereceptor
relationships more efficiently. In conjunction with analytical methods for uncertainty
propagation, we deliver air quality predictions with uncertainty estimates at a fraction of the
computational cost and with increased traceability compared to modern surrogate modeling
techniques. In our study we focus on PM2.5 and PM10, and first results will be presented for the
impact of emission uncertainties on forecasted PM concentrations in European cities, as well as
uncertainties in contributions from different source sectors and countries. By integrating emission
uncertainty propagation, our study aims to provide decision-makers with a more accurate
assessment of the reliability of CAMS policy products under various atmospheric conditions
and in the future provide these estimates as part of their operational delivery.

References

[1] P. Wind, B. Rolstad Denby, and M. Gauss, “Local fractions – a method for the calculation
of local source contributions to air pollution, illustrated by examples using the emep
msc-w model (rv4 33),” Geoscientific Model Development, vol. 13, no. 3, pp. 1623–1634,
2020. [Online]. Available: https://gmd.copernicus.org/articles/13/1623/2020/

How to cite: Blake, L., Wind, P., Fagerli, H., Valdebenito, A., Super, I., and Kuenen, J.: Analytical Propagation of Emission Uncertainties into CAMS Policy Products, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21289, https://doi.org/10.5194/egusphere-egu24-21289, 2024.

Recognizing the uncertainties associated with fire emission, a crucial factor influencing the fire aerosol prediction, we have initiated studies to improve fire emission for subseasonal to seasonal (S2S) forecasts. Two global aerosol/chemistry forecast models are currently under development and have been fully coupled with the Unified Forecast System (UFS), encompassing ocean, sea ice, wave and land surface components for S2S forecasts at NOAA. One is UFS-Aerosols: the second-generation UFS coupled aerosol system, which embeds NASA’s 2nd-generation GOCART model in a National Unified Operational Prediction Capability (NUOPC) infrastructure, has been collaboratively developed by NOAA and NASA since 2021. It is planned to be implemented into the Global Ensemble Forecast System (GEFS) v13.0 for ensemble prototype 5 (EP5) experiments early this year. The other one is UFS-Chem: an innovative community model of chemistry online coupled with UFS, developed collaboratively between NOAA Oceanic and Atmospheric Research (OAR) laboratories and NCAR. The aerosol component implemented into UFS-Chem is based on the current operational GEFS-Aerosols v12.3 and utilizes the Common Community Physics Package (CCPP) infrastructure with updates to wet deposition, dust and fire emission, etc. Both these two global aerosols forecast models include the direct and semi-direct radiative feedback from online aerosols prediction. Various global fire emission data, as well as their ensemble product, are employed to quantify the uncertainties associated with fire aerosol prediction. The capabilities of UFS-Aerosols and UFS-Chem in medium-range and S2S predictions of fire aerosol are assessed and compared using observations from reanalysis data, ground-based measurements, and satellite data. Additionally, preliminary blending and machine learning methods have been developed to predict fire emission and improve the S2S prediction. 

How to cite: Zhang, L., Grell, G., Bhattacharjee, P., Sun, S., Jensen, A., Schnell, J., Li, H., Li, Y., Baker, B., Henderson, J., Ahmadov, R., Bernardet, L., Tong, D., Sun, Z., Pan, L., Fu, B., Montuoro, R., He, J., Schwantes, R., and Wang, S. and the NOAA team: Predicting Fire Aerosols and their Impact on Subseasonal to Seasonal Weather Forecasts in NOAA’s Global Aerosol Forecast Systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6827, https://doi.org/10.5194/egusphere-egu24-6827, 2024.

The Copernicus Atmosphere Monitoring Service (CAMS) is providing daily analyses and forecasts of the composition of the atmosphere, including the reactive gases such as ozone, CO, NO2, HCHO, SO2, aerosol species and greenhouse gases. The global CAMS analysis system (IFS-COMPO) is based on the ECMWF Integrated Forecast System (IFS) for numerical weather prediction (NWP), and assimilates a large number of composition satellite products on top of the meteorological observations ingested in IFS. 

The global CAMS system is regularly upgraded, and the upgrades are simultaneous with the upgrades of NWP-IFS. The upgrade to Cy48R1, operational since 27 June 2023, introduced a large number of code changes and improvements, both for COMPO and for NWP. The COMPO innovations include the introduction of full stratospheric chemistry, a major update of the emissions, of the aerosol model, including the representation of secondary organic aerosol, several updates of the dust life cycle and optics, and inorganic chemistry in the troposphere. Concering data assimilation, the assimilation of VIIRS AOD and TROPOMI CO were implemented in 2023. 

The CAMS Cy48R1 upgrade was evaluated using a large number of independent measurement datasets, including surface in situ, surface remote sensing, routine aircraft and balloon and satellite observations. In our contribution we present the validation results for Cy48R1. The new cycle is compared with the previous operational system (Cy47R3), with the independent observations as reference. Results are provided for the period October 2022 to June 2023 for which daily forecasts from both cycles are available. 

Major improvements in skill are found for the ozone profile in the lower-middle stratosphere and for stratospheric NO2 due to the inclusion of full stratospheric chemistry. Stratospheric trace gases compare well with ACE-FTS observations between 10-200 hPa, with larger deviations between 1-10 hPa. The impact of the updated emissions is especially visible over East Asia and is beneficial for the trace gases O3, NO2, and SO2. The CO column assimilation is now anchored by IASI instead of MOPITT which is beneficial for most of the CO comparisons, and the assimilation of TROPOMI CO data improves the model CO field in the troposphere. In general the aerosol optical depth has improved globally, but the dust evaluation shows more mixed results. 

In summary, 83% of the evaluation datasets show a neutral or improved performance of Cy48R1 compared to the previous operational CAMS system, while 17% indicate a (slight) degradation, which shows the overall success of this upgrade. 

This presentation summarises the results achieved by a large consortium of scientists working on CAMS, including the ECMWF staff, the developers of the global CAMS modelling system, the data assimilation team at ECMWF, and the CAMS validation teams. We acknowledge their contributions to this work.

How to cite: Eskes, H. and Tsikerdekis, T. and the CAMS global developers and validation team: Evaluation of the Copernicus Atmosphere Monitoring Service Cy48R1 upgrade of June 2023, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8447, https://doi.org/10.5194/egusphere-egu24-8447, 2024.

NOAA is collaborating with the US weather and climate science community to develop the fully coupled Unified Forecast System (UFS) for both research and operations across different temporal and spatial scales.  Active development and evaluation are underway to improve the representation of aerosols and its interaction with radiation and clouds in UFS applications.  Aerosols, acting as cloud condensation nuclei and ice nuclei,  affect cloud droplet number concentration and size, cloud lifetime and consequently cloud radiative properties.   Up to now, the impact of aerosols on clouds has not been included in UFS global applications for operation.  In this study we activate the interactions of aerosols with clouds in the Thompson double moment microphysics scheme used by the UFS-based  Global Forecast System (GFS) application.  MERRA2 aerosol climatologies instead of prognostic aerosols are employed for this study to reduce the complexity and uncertainty in aerosol prediction itself.  GFS free forecasts at the 13-km horizontal resolution for the summer of 2020 were conducted to investigate the uncertainty of the representation of aerosol-cloud interaction and the associated impact on GFS medium-range weather forecasts.   The experiment with aerosol-cloud interaction produced an overall larger number concentration of cloud droplets and cloud liquid mixing ratio, and larger number concentration of cloud ice and ice mixing ratio in the low-middle troposphere, but less above the upper troposphere.  The relationships between aerosol optical depth and cloud droplet number concentration were analyzed and compared with MODIS retrievals. In addition, the relationships between aerosol loading and optical properties with liquid water path, shortwave and long wave radiation were examined.  CCPP single column model was also used to help understand the uncertainty of aerosol-cloud interaction algorithms employed by the UFS.  

How to cite: Yang, F. and Cheng, A.: Uncertainty and Impact of Aerosol-Cloud Interaction in the Unified Forecast System, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12973, https://doi.org/10.5194/egusphere-egu24-12973, 2024.