Copernicus Atmosphere Monitoring Service Reanalysis Research Articles

Comparison of CAMS and CMAQ analyses of surface-level PM2.5 and O3 over the conterminous United States (CONUS)

To reduce economic and health impacts from poor air quality (AQ) in the U.S., the National Air Quality Forecasting Capability (NAQFC) at the National Oceanic and Atmospheric Administration (NOAA) produces forecasts of surface-level ozone (O3), fine particulate matter (PM2.5), and other pollutants so that advance notice and warning can be issued to help individuals and communities limit their exposure. The NAQFC uses the U.S. Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model for operational forecasts. This study is a first step in proposing a potential upgrade to the current operational NAQFC bias-correction system, by examining potential candidates for a gridded analysis (“truth”) dataset.In this paper, we compare the performance of the “analysis” time series over the period of August 2020–December 2021 at EPA AirNow stations for both PM2.5 and O3 from raw Copernicus Atmosphere Monitoring Service (CAMS) reanalyses, raw CAMS near real-time forecasts, raw near real-time CMAQ forecasts, bias-corrected CAMS forecasts, and bias-corrected CMAQ forecasts (CMAQ FC BC). This 17-month period spans two wildfire seasons, to assess model “analysis” performance in high-end AQ events. In addition to determining the best-performing gridded product, this process allows us to benchmark the performance of CMAQ forecasts against other global datasets (CAMS reanalysis and forecasts). For both PM2.5 and O3, the bias correction algorithm employed here greatly improved upon the raw model time series, and CMAQ FC BC was the best-performing model “analysis” time series, having the lowest RMSE, smallest bias error, and largest critical success index at multiple thresholds.

Predicting Particulate Matter (PM 10) Levels in Morocco: A 5-Day Forecast Using the Analog Ensemble Method.

Accurate prediction of Particulate Matter (PM 10) levels, an indicator of natural pollutants such as those resulting from dust storms, is crucial for public health and environmental planning. This study aims to provide accurate forecasts of PM 10 over Morocco for five days. The Analog Ensemble (AnEn) and the Bias Correction (AnEnBc) techniques were employed to post-process PM 10 forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS) global atmospheric composition forecasts, using CAMS reanalysis data as a reference. The results show substantial prediction improvements: the Root Mean Square Error (RMSE) decreased from 63.83 μg/m 3 in the original forecasts to 44.73 μg/m 3 with AnEn and AnEnBc, while the Mean Absolute Error (MAE) reduced from 36.70 μg/m 3 to 24.30 μg/m 3. Additionally, the coefficient of determination (R 2) increased more than twofold from 29.11% to 65.18%, and the Pearson correlation coefficient increased from 0.61 to 0.82. This is the first use of this approach for Morocco and the Middle East and North Africa and has the potential for translation into early and more accurate warnings of PM 10 pollution events. The application of such approaches in environmental policies and public health decision making can minimize air pollution health impacts.

Aircraft engine dust ingestion at global airports

Abstract. Atmospheric mineral dust aerosol constitutes a threat to aircraft engines from deterioration of internal components. Here we fulfil an overdue need to quantify engine dust ingestion at airports worldwide. The vertical distribution of dust is of key importance since ascent/descent rates and engine power both vary with altitude and affect dust ingestion. We use representative jet engine power profile information combined with vertically and seasonally varying dust concentrations to calculate the “dust dose” ingested by an engine over a single ascent or descent. Using the Copernicus Atmosphere Monitoring Service (CAMS) model reanalysis, we calculate climatological and seasonal dust dose at 10 airports for 2003–2019. Dust doses are mostly largest in Northern Hemisphere summer for descent, with the largest at Delhi in June–August (JJA; 6.6 g) followed by Niamey in March–May (MAM; 4.7 g) and Dubai in JJA (4.3 g). Holding patterns at altitudes coincident with peak dust concentrations can lead to substantial quantities of dust ingestion, resulting in a larger dose than the take-off, climb, and taxi phases. We compare dust dose calculated from CAMS to spaceborne lidar observations from two dust datasets derived from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). In general, seasonal and spatial patterns are similar between CAMS and CALIOP, though large variations in dose magnitude are found, with CAMS producing lower doses by a factor of 1.9 to 2.8, particularly when peak dust concentration is very close to the surface. We show that mitigating action to reduce engine dust damage could be achieved, firstly by moving arrivals and departures to after sunset and secondly by altering the altitude of the holding pattern away from that of the local dust peak altitude, reducing dust dose by up to 44 % and 41 % respectively. We suggest that a likely low bias of dust concentration in the CAMS reanalysis should be considered by aviation stakeholders when estimating dust-induced engine wear.

Assessment of surface ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran

Abstract. Tropospheric ozone time series consist of the effects of various scales of motion, from meso-scales to large timescales, which are often challenging for global models to capture. This study uses two global datasets, namely the reanalysis and the daily forecast of the Copernicus Atmosphere Monitoring Service (CAMS), to assess the capability of these products in presenting ozone's features on regional scales. We obtained 16 relevant meteorological and several pollutant species, such as O3, CO, NOx, etc., from CAMS. Furthermore, we employed a comprehensive set of in situ measurements of ozone at 27 urban stations in Iran for the year 2020. We decomposed the time series into three spectral components, i.e., short (S), medium (M), and long (L) terms. To cope with the scaling issue between the measured data and the CAMS' products, we developed a downscaling approach based on a long short-term memory (LSTM) neural network method which, apart from modeled ozone, also assimilated meteorological quantities as well as lagged O3 observations. Results show the benefit of applying the LSTM method instead of using the original CAMS products for providing O3 over Iran. It is found that lagged O3 observation has a larger contribution than other predictors in improving the LSTM. Compared with the S, the M component shows more associations with observations, e.g., correlation coefficients larger than 0.7 for the S and about 0.95 for the M in both models. The performance of the models varies across cities; for example, the highest error is for areas with high emissions of O3 precursors. The robustness of the results is confirmed by performing an additional downscaling method. This study demonstrates that coarse-scale global model data, such as CAMS, need to be downscaled for regulatory purposes or policy applications at local scales. Our method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.

Interpreting hourly mass concentrations of PM2.5 chemical components with an optimal deep-learning model

PM2.5 constitutes a complex and diverse mixture that significantly impacts the environment, human health, and climate change. However, existing observation and numerical simulation techniques have limitations, such as a lack of data, high acquisition costs, and multiple uncertainties. These limitations hinder the acquisition of comprehensive information on PM2.5 chemical composition and effectively implement refined air pollution protection and control strategies. In this study, we developed an optimal deep learning model to acquire hourly mass concentrations of key PM2.5 chemical components without complex chemical analysis. The model was trained using a randomly partitioned multivariate dataset arranged in chronological order, including atmospheric state indicators, which previous studies did not consider. Our results showed that the correlation coefficients of key chemical components were no less than 0.96, and the root mean square errors ranged from 0.20 to 2.11 µg/m3 for the entire process (training and testing combined). The model accurately captured the temporal characteristics of key chemical components, outperforming typical machine-learning models, previous studies, and global reanalysis datasets (such as Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service ReAnalysis (CAMSRA)). We also quantified the feature importance using the random forest model, which showed that PM2.5, PM1, visibility, and temperature were the most influential variables for key chemical components. In conclusion, this study presents a practical approach to accurately obtain chemical composition information that can contribute to filling missing data, improved air pollution monitoring and source identification. This approach has the potential to enhance air pollution control strategies and promote public health and environmental sustainability.

Comparative analysis of CAMS aerosol optical depth data and AERONET observations in the Eastern Mediterranean over 19 years

Aerosol optical depth (AOD) is an essential metric for evaluating the atmospheric aerosol load and its impacts on climate, air quality, and public health. In this study, the AOD data from the Copernicus Atmosphere Monitoring Service (CAMS) were validated against ground-based measurements from the Aerosol Robotic Network (AERONET) throughout the Eastern Mediterranean, a region characterized by diverse aerosol types and sources. A comparative analysis was performed on 3-hourly CAMS AOD values at 550 nm against observations from 20 AERONET stations across Cyprus, Greece, Israel, Egypt, and Turkey from 2003 to 2021. The CAMS AOD data exhibited a good overall agreement with AERONET AOD data, demonstrated by a Pearson correlation coefficient of 0.77, a mean absolute error (MAE) of 0.08, and a root mean square error (RMSE) of 0.11. Nonetheless, spatial and temporal variations were observed in the CAMS AOD data performance, with site-specific correlation coefficients ranging from 0.57 to 0.85, the lowest correlations occurring in Egypt and the highest in Greece. An underestimation of CAMS AOD was noted at inland sites with high AOD levels, while a better agreement was observed at coastal sites with lower AOD levels. The diurnal variation analysis indicated improved CAMS reanalysis performance during the afternoon and evening hours. Seasonally, CAMS reanalysis showed better agreement with AERONET AODs in spring and autumn, with lower correlation coefficients noted in summer and winter. This study marks the first comprehensive validation of CAMS AOD performance in the Eastern Mediterranean, offering significant enhancements for regional air quality and climate modeling, and underscores the essential role of consistent validation in refining aerosol estimations within this complex and dynamic geographic setting.

Variability of nocturnal aerosol optical properties in China and correlations with meteorological variables during 2003–2022

Aerosol optical properties in daytime have been widely explored based on the ground-based and satellite-based passive remote sensing. However, few studies have investigated long-term variability of nocturnal aerosol optical properties and its impact factors mainly due to scarce observations. Nocturnal aerosol optical depth (NAOD) and Ångström exponent (NAE) derived from Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA) from 2003 to 2022 are analyzed to investigate the spatiotemporal variability of nocturnal aerosol optical properties and their meteorological impact factors in China. The high NAOD ranging from 0.8 to 1.4 was mainly found over inland of southeastern China, especially in the Sichuan basin and North and Central China plain, corresponding with heavily populated, economically developed and low-lying regions, but observably decreased since 2018. The NAE was less than 0.5 in arid and semiarid regions of the northwest China, while more than 0.7 in monsoon-influenced eastern China. The NAOD differs seasonally, and is generally higher in spring and summer. Reversely, the seasonal mean NAE increased from spring to winter. The monthly mean NAOD of China, which was high in February to October of 2006–2016, ranged from 0.9 to 1.1 related to the rapid urbanization and industrialization. The low NAE ranging from 0.7 to 0.8 mainly occurred in April, May and June due to the dust transportation from northwestern China in spring. The correlation between NAOD and potential evapotranspiration (PE), surface pressure (SP) and relative humidity (RH) was negative in northern China, while positive in southern China. The NAOD in most China was correlated positively to total precipitation (TP), but negatively to 10m u-component of wind (U10). The correlation between NAOD and 10m v-component of wind (V10) was positive in northeastern China, while negative in northwestern China. NAE in most China was correlated positively to PE and SP, while negatively to TP and T. The correlation between NAE and RH/V10 was positive in northwestern China, while negative in eastern China. The correlation between NAE and U10 was positive in northeastern and northwestern China, while negative in North and Central China. The findings in the study enable more comprehensive understanding of spatiotemporal variability of nocturnal aerosols optical properties and its impact factors in China.

Characterization of the major aerosol species over Egypt based on 10 years of CAMS reanalysis data

This study investigates the spatiotemporal distribution of the major aerosol species (sulfate, organic matter, black carbon, dust, and sea salt) and their contributions to the total aerosol optical depth (AOD) over Egypt from 2010 to 2019 by using the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis data. Dust was the major aerosol species, with an average contribution of 48% to the total AOD. The highest concentrations of dust were found in spring because of the significant impact of the Khamsin wind in carrying sand aerosols from deserts. The dispersion of dust aerosols is greatly influenced by topography. While lowlands, such as the Qattara Depression, accumulate sand aerosols from the surroundings, the mountainous highlands disrupt horizontal diffusion. El-Farafra and Siwa Oasis, which are close to the Qattara depression, had significant dust contributions to the total AOD. Because the Nile Delta, Suez Canal, and Nile River regions are densely populated and industrialized, organic matter and sulfate showed considerable contributions of 24% and 23%, respectively, to the total AOD. The highest concentrations of organic matter and sulfate were found in summer because of the high temperatures that accelerate photochemical reactions. While fossil fuels and biomass burning increase carbon emissions in cities such as Cairo and Tanta, shipping in the Suez Canal and natural gas flares in the eastern Mediterranean increase sulfate aerosols in cities such as Port Said, Suez, and Arish. Black carbon and sea salt were the minor aerosol species, with average contributions of 3% and 2%, respectively, to the total AOD.

The prototype NOAA Aerosol Reanalysis version 1.0: description of the modeling system and its evaluation

Abstract. In this study, we describe the first prototype version of global aerosol reanalysis at the National Oceanic and Atmospheric Administration (NOAA), the prototype NOAA Aerosol Reanalysis version 1.0 (pNARA v1.0) that was produced for the year 2016. In pNARA v1.0, the forecast model is an early version of the operational Global Ensemble Forecast System-Aerosols (GEFS-Aerosols) model. The three-dimensional ensemble-variational (3D-EnVar) data assimilation (DA) system configuration is built using elements of the Joint Effort for Data Assimilation Integration (JEDI) framework being developed at the Joint Center for Satellite Data Assimilation (JCSDA). The Neural Network Retrievals (NNR) of aerosol optical depth (AOD) at 550 nm from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments are assimilated to provide reanalysis of aerosol mass mixing ratios. We evaluate pNARA v1.0 against a wide variety of Aerosol Robotic Network (AERONET) observations, against the National Aeronautics and Space Administration's Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA-2; Gelaro et al., 2017; Randles et al., 2017; Buchard et al., 2017) and the European Centre for Medium-Range Weather Forecasts' Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA; Inness et al., 2019), and against measurements of surface concentrations of particulate matter 2.5 (PM2.5) and aerosol species. Overall, the 3D-EnVar DA system significantly improves AOD simulations compared with observations, but the assimilation has limited impact on chemical composition and size distributions of aerosols. We also identify deficiencies in the model's representations of aerosol chemistry and their optical properties elucidated from evaluation of pNARA v1.0 against AERONET observations. A comparison of seasonal profiles of aerosol species from pNARA v1.0 with the other two reanalyses exposes significant differences among datasets. These differences reflect uncertainties in simulating aerosols in general.

Cloud condensation nuclei concentrations derived from the CAMS reanalysis

Abstract. Determining number concentrations of cloud condensation nuclei (CCN) is one of the first steps in the chain in analysis of cloud droplet formation, the direct microphysical link between aerosols and cloud droplets, and a process key for aerosol–cloud interactions (ACI). However, due to sparse coverage of in situ measurements and difficulties associated with retrievals from satellites, a global exploration of their magnitude, source as well as temporal and spatial distribution cannot be easily obtained. Thus, a better representation of CCN numbers is one of the goals for quantifying ACI processes and achieving uncertainty-reduced estimates of their associated radiative forcing. Here, we introduce a new CCN dataset which is derived based on aerosol mass mixing ratios from the latest Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) in a diagnostic model that uses CAMSRA aerosol properties and a simplified kappa-Köhler framework suitable for global models. The emitted aerosols in CAMSRA are not only based on input from emission inventories using aerosol observations, they also have a strong tie to satellite-retrieved aerosol optical depth (AOD) as this is assimilated as a constraining factor in the reanalysis. Furthermore, the reanalysis interpolates for cases of poor or missing retrievals and thus allows for a full spatiotemporal quantification of CCN numbers. The derived CCN dataset captures the general trend and spatial and temporal distribution of total CCN number concentrations and CCN from different aerosol species. A brief evaluation with ground-based in situ measurements demonstrates the improvement of the modelled CCN over the sole use of AOD as a proxy for CCN as the overall correlation coefficient improved from 0.37 to 0.71. However, we find the modelled CCN from CAMSRA to be generally high biased and find a particular erroneous overestimation at one heavily polluted site which emphasises the need for further validation. The CCN dataset (https://doi.org/10.26050/WDCC/QUAERERE_CCNCAMS_v1, Block, 2023), which is now freely available to users, features 3-D CCN number concentrations of global coverage for various supersaturations and aerosol species covering the years 2003–2021 with daily frequency. This dataset is one of its kind as it offers lots of opportunities to be used for evaluation in models and in ACI studies.

Evaluation of MERRA-2 and CAMS reanalysis for black carbon aerosol in China

Black carbon (BC) constitutes a pivotal component of atmospheric aerosols, significantly impacting regional and global radiation balance, climate, and human health. In this study, we evaluated BC data in two prominent atmospheric composition reanalysis datasets: the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and the Copernicus Atmosphere Monitoring Service (CAMS), and analyzed the causes of their deviations. This assessment is based on observational data collected from 34 monitoring stations across China from 2006 to 2022. Our research reveals a significant and consistent decline in BC concentrations within China, amounting to a reduction exceeding 67.33%. However, both MERRA-2 and CAMS reanalysis data fail to capture this declining trend. The average annual decrease of BC in MERRA-2 from 2006 to 2022 is only 0.06 μg/m3 per year, while the BC concentration in CAMS even increased with an average annual value of 0.014 μg/m3 per year. In 2022, MERRA-2 had overestimated BC concentration by 20% compared to observational data, while CAMS had overestimated it by approximately 66%. In the regional BC concentration analysis, the data quality of the reanalysis data is better in the South China (RM = 0.59, RC = 0.53), followed by the North China (RM = 0.50, RC = 0.42). Reanalysis BC data in Northwest China and the Tibetan Plateau are difficult to use for practical analysis due to their big difference with observation. In a comparison of the anthropogenic BC emissions inventory used in the two atmospheric composition reanalysis datasets with the Multi-resolution Emission Inventory model for Climate and air pollution research (MEIC) emissions inventory, we found that: Despite the significant decline in China's BC emissions, MERRA-2 still relies on the 2006 emissions inventory, while CAMS utilizes emission inventories that even show an increasing trend. These factors will undoubtedly lead to greater deviations between reanalysis and observational data.

Global reanalysis products cannot reproduce seasonal and diurnal cycles of tropospheric ozone in the Congo Basin

Tropospheric ozone (O3) is a secondary pollutant and a greenhouse gas with a positive radiative forcing. Many studies have documented its negative impacts on plant growth and human health. Historically, studies have focused on determining levels of exposure in mid- and high-latitude regions. In the tropics, high O3 concentrations are expected due to large concurrent and future precursor emissions. In Africa, seasonal biomass burning (from both natural and anthropogenic fires) during the dry season plays a crucial role in O3 precursor production. However, O3 observational studies in tropical Africa are currently missing. To fill this major knowledge gap, we established in November 2019 a continuous monitoring of near-surface O3 in the Congo Basin at the Yangambi research centre in the Democratic Republic of the Congo. Using this unique dataset in the heart of the second-largest tropical forest in the world, we assessed the ability of current remote sensing products to capture the magnitude and temporal dynamics of in situ tropospheric O3 concentrations, especially O3 concentration variation between dry and wet seasons until March of 2022. We compared near-surface atmospheric O3 measurements collected in Yangambi and four different reanalysis products: European Centre for Medium-Range Weather Forecasts Reanalysis (ECMWF) v5 (ERA5), Copernicus Atmospheric Monitoring Service reanalysis (CAMSRA), Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) and Japanese Reanalysis (JRA-55). The results show that reanalysis products overestimated the magnitude of near-surface O3 across the region with a mean bias of 27.3 ppbv, 19.9 ppbv, 10.8 ppbv and 1.0 ppbv for ERA5, CAMSRA, MERRA-2 and JRA-55, respectively. ERA5 and CAMSRA reanalysis were the only products able to capture, to some extent, the observed annual variation, showing higher O3 concentrations during dry season months, despite the inability to reproduce the daily cycle of near-surface O3.

Localizing SDG 11.6.2 via Earth Observation, Modelling Applications, and Harmonised City Definitions: Policy Implications on Addressing Air Pollution

While Earth observation (EO) increasingly provides a multitude of solutions to address environmental issues and sustainability from the city to global scale, their operational integration into the Sustainable Development Goals (SDG) framework is still falling behind. Within this framework, SDG Indicator 11.6.2 asks countries to report the “annual mean levels of fine particulate matter (PM2.5) in cities (population-weighted)”. The official United Nations (UN) methodology entails aggregation into a single, national level value derived from regulatory air quality monitoring networks, which are non-existent or sparse in many countries. EO, including, but not limited to remote sensing, brings forth novel monitoring methods to estimate SDG Indicator 11.6.2 alongside more traditional ones, and allows for comparability and scalability in the face of varying city definitions and monitoring capacities which impact the validity and usefulness of such an indicator. Pursuing a more harmonised global approach, the H2020 SMURBS/ERA-PLANET project provides two EO-driven approaches to deliver the indicator on a more granular level across Europe. The first approach provides both city and national values for SDG Indicator 11.6.2 through exploiting the Copernicus Atmospheric Monitoring Service reanalysis data (0.1° resolution and incorporating in situ and remote sensing data) for PM2.5 values. The SDG Indicator 11.6.2 values are calculated using two objective city definitions—“functional urban area” and “urban centre”—that follow the UN sanctioned Degree of Urbanization concept, and then compared with official indicator values. In the second approach, a high-resolution city-scale chemical transport model ingests satellite-derived data and calculates SDG Indicator 11.6.2 at intra-urban scales. Both novel approaches to calculating SDG Indicator 11.6.2 using EO enable exploration of air pollution hotspots that drive the indicator as well as actual population exposure within cities, which can influence funding allocation and intervention implementation. The approaches are introduced, and their results frame a discussion around interesting policy implications, all with the aim to help move the dial beyond solely reporting on SDGs to designing the pathways to achieve the overarching targets.

Estimation of Stratospheric Intrusions During Indian Cyclones

AbstractDeep convection associated with tropical cyclones leads to stratosphere‐troposphere exchange (STE), which affects the upper‐tropospheric ozone concentrations in the vicinity of the cyclones. This study estimates the ozone enhancements over India due to the North Indian Ocean (NIO) cyclones‐driven STE. Indicators such as stratospheric fraction and potential vorticity calculated using the reanalysis data sets suggest that roughly 70% of the cyclones show anomalously high stratospheric intrusions. Aircraft observations over different locations across India also show elevated ozone concentrations in the mid‐to‐upper troposphere on cyclone days. Further, ozone and stratospheric ozone tracer concentrations from Goddard Earth Observing System‐Chemistry simulations and the Copernicus Atmosphere Monitoring Service reanalysis data sets show up to 40 ppb of excess upper tropospheric ozone over India, of which stratospheric ozone accounts for roughly 60%. Stratospheric intrusion due to the Bay of Bengal and the Arabian Sea cyclones affected the upper tropospheric ozone amounts over North and South India, respectively. The stratospheric ozone was observed to propagate downwards into the troposphere, often reaching ∼600 hPa and, in some cases, even the surface.

Estimation of OH in urban plumes using TROPOMI-inferred NO2 ∕ CO

Abstract. A new method is presented for estimating urban hydroxyl radical (OH) concentrations using the downwind decay of the ratio of nitrogen dioxide over carbon monoxide column-mixing ratios (XNO2/XCO) retrieved from the Tropospheric Monitoring Instrument (TROPOMI). The method makes use of plumes simulated by the Weather Research and Forecast model (WRF-Chem) using passive-tracer transport, instead of the encoded chemistry, in combination with auxiliary input variables such as Copernicus Atmospheric Monitoring Service (CAMS) OH, Emission Database for Global Atmospheric Research v4.3.2 (EDGAR) NOx and CO emissions, and National Center for Environmental Protection (NCEP)-based meteorological data. NO2 and CO mixing ratios from the CAMS reanalysis are used as initial and lateral boundary conditions. WRF overestimates NO2 plumes close to the center of the city by 15 % to 30 % in summer and 40 % to 50 % in winter compared to TROPOMI observations over Riyadh. WRF-simulated CO plumes differ by 10 % with TROPOMI in both seasons. The differences between WRF and TROPOMI are used to optimize the OH concentration, NOx, CO emissions and their backgrounds using an iterative least-squares method. To estimate OH, WRF is optimized using (a) TROPOMI XNO2/XCO and (b) TROPOMI-derived XNO2 only. For summer, both the NO2/CO ratio optimization and the XNO2 optimization increase the prior OH from CAMS by 32 ± 5.3 % and 28.3 ± 3.9 %, respectively. EDGAR NOx and CO emissions over Riyadh are increased by 42.1 ± 8.4 % and 101 ± 21 %, respectively, in summer. In winter, the optimization method doubles the CO emissions while increasing OH by ∼ 52 ± 14 % and reducing NOx emissions by 15.5 ± 4.1 %. TROPOMI-derived OH concentrations and the pre-existing exponentially modified Gaussian function fit (EMG) method differ by 10 % in summer and winter, confirming that urban OH concentrations can be reliably estimated using the TROPOMI-observed NO2/CO ratio. Additionally, our method can be applied to a single TROPOMI overpass, allowing one to analyze day-to-day variability in OH, NOx and CO emission.

Spatio-temporal changes of spring-summer dust AOD over the Eastern Mediterranean and the Middle East: Reversal of dust trends and associated meteorological effects

High dust concentrations in the Eastern Mediterranean - Middle East (EMME) region have serious effects on air quality, human health and climate. This study used long-term aerosol datasets during the main dusty season (April–July: AMJJ) over the EMME from 2000 to 2020, based on Moderate-Resolution Imaging Spectroradiometer (MODIS)/Terra-C6.1, Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), and Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA) retrievals and analyzed the spatio-temporal variations and trends of dust, as well as the influencing factors. The dust aerosol optical depth (DAOD) experienced a significant upward trend during 2000–2010, followed by a significant decrease during 2010–2017. After 2017 and till 2020, the DAOD presented rather a stable trend. Aerosol Robotic Network (AERONET) data in the EMME region display trends compatible to those of both MERRA-2 and CAMSRA DAOD. The DAOD trends were linked to changes in regional meteorological parameters in the EMME. A significant downward trend in AMJJ sea-level pressure (SLP) during the early period (2000−2010) induced hot and dry winds from desert regions towards the EMME, which reduced relative humidity (RH) and raised temperature, thus favored soil drying and dust outbreaks through enhancing evaporation. In contrast, a significant increase in winter SLP during the late period (2010–2017), accompanying an increase in North Atlantic Oscillation index, induced cold, wet winds from northwest regions, which increased RH and lowered temperature, thus reducing dust loading in EMME. Positive anomalies in winter soil moisture persisted in the following AMJJ, and consequently suppressed dust activity. DAOD variability over the dust-prone regions was linked to various meteorological parameters via a multiple linear regression (MLR) model. The results show that climatic variability strongly affects the dust trends and contribute to better understanding of meteorological – dust dynamics in the EMME region.

Evaluation of the CAMS reanalysis for atmospheric black carbon and carbon monoxide over the north China plain

Black carbon (BC) and carbon monoxide (CO) at different model levels from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis were comprehensively evaluated against observations performed simultaneously on both surface and mountain sites in winter and summer in the North China Plain for the first time. CAMS could capture the seasonal difference in BC and CO emission on both sites but showed significant and persistent biases. Biases were high on the surface site and low on the mountain site for both seasons, implying the uncertainties in emission inventories used in the CAMS reanalysis which may have more influence near source. Biases were reduced and the correlation coefficient of CAMS BC with observed BC increased when two datasets were compared on a daily basis, which suggests daily or longer time averaged CAMS BC could be more suitable for trend analysis. Although CAMS could generally reproduce the distinct diurnal variation of BC and CO on both sites, the inaccurate representation of the daily evolution of planetary boundary layer (PBL) in model may bring more uncertainties to the concentration biases on surface from midnight to early morning. BC hydrophilic ratio from CAMS displayed large biases compared to observations with no seasonal difference on both sites, which was probably resulted from the initial emission state of BC hygroscopicity for all source types in model. Uncertainties in the removal processes and the simplified aging processes in model could further induce uncertainty in modelling BC hydrophilic ratio in the CAMS. These results could not only be referenced for the improvement on CAMS reanalysis but also facilitate model or trend analysis of BC and CO pollution by utilizing the CAMS reanalysis product from both short- and long-term perspectives, which will be beneficial to both the mitigation and policy-making on primary emissions in China.

Arctic spring and summertime aerosol optical depth baseline from long-term observations and model reanalyses – Part 1: Climatology and trend

Abstract. We present an Arctic aerosol optical depth (AOD) climatology and trend analysis for 2003–2019 spring and summertime periods derived from a combination of multi-agency aerosol reanalyses, remote-sensing retrievals, and ground observations. This includes the U.S. Navy Aerosol Analysis and Prediction System ReAnalysis version 1 (NAAPS-RA v1), the NASA Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and the Copernicus Atmosphere Monitoring Service ReAnalysis (CAMSRA). Spaceborne remote-sensing retrievals of AOD are considered from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging SpectroRadiometer (MISR), and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Ground-based data include sun photometer data from AErosol RObotic NETwork (AERONET) sites and oceanic Maritime Aerosol Network (MAN) measurements. Aerosol reanalysis AODs and spaceborne retrievals show consistent climatological spatial patterns and trends for both spring and summer seasons over the lower Arctic (60–70∘ N). Consistent AOD trends are also found for the high Arctic (north of 70∘ N) from reanalyses. The aerosol reanalyses yield more consistent AOD results than climate models, can be verified well with AERONET, and corroborate complementary climatological and trend analysis. Speciated AODs are more variable than total AOD among the three reanalyses and a little more so for March–May (MAM) than for June–August (JJA). Black carbon (BC) AOD in the Arctic comes predominantly from biomass burning (BB) sources in both MAM and JJA, and BB overwhelms anthropogenic sources in JJA for the study period. AOD exhibits a multi-year negative MAM trend and a positive JJA trend in the Arctic during 2003–2019, due to an overall decrease in sulfate/anthropogenic pollution and a significant JJA increase in BB smoke. Interannual Arctic AOD variability is significantly large, driven by fine-mode and, specifically, BB smoke, with both smoke contribution and interannual variation larger in JJA than in MAM. It is recommended that climate models should account for BB emissions and BB interannual variabilities and trends in Arctic climate change studies.

Regional evaluation of the performance of the global CAMS chemical modeling system over the United States (IFS cycle 47r1)

Abstract. The Copernicus Atmosphere Monitoring Service (CAMS) provides routine analyses and forecasts of trace gases and aerosols on a global scale. The core is the European Centre for Medium Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), where modules for atmospheric chemistry and aerosols have been introduced and which allows for data assimilation of satellite retrievals of composition. We have updated both the homogeneous and heterogeneous NOx chemistry applied in the three independent tropospheric–stratospheric chemistry modules maintained within CAMS, referred to as IFS(CB05BASCOE), IFS(MOCAGE) and IFS(MOZART). Here we focus on the evaluation of main trace gas products from these modules that are of interest as markers of air quality, namely lower-tropospheric O3, NO2 and CO, with a regional focus over the contiguous United States. Evaluation against lower-tropospheric composition reveals overall good performance, with chemically induced biases within 10 ppb across species for regions within the US with respect to a range of observations. The versions show overall equal or better performance than the CAMS reanalysis, which includes data assimilation. Evaluation of surface air quality aspects shows that annual cycles are captured well, albeit with variable seasonal biases. During wintertime conditions there is a large model spread between chemistry schemes in lower-tropospheric O3 (∼ 10 %–35 %) and, in turn, oxidative capacity related to NOx lifetime differences. Analysis of differences in the HNO3 and PAN formation, which act as reservoirs for reactive nitrogen, revealed a general underestimate in PAN formation over polluted regions, likely due to too low organic precursors. Particularly during wintertime, the fraction of NO2 sequestered into PAN has a variability of 100 % across chemistry modules, indicating the need for further constraints. Notably, a considerable uncertainty in HNO3 formation associated with wintertime N2O5 conversion on wet particle surfaces remains. In summary, this study has indicated that the chemically induced differences in the quality of CAMS forecast products over the United States depends on season, trace gas, altitude and region. While analysis of the three chemistry modules in CAMS provide a strong handle on uncertainties associated with chemistry modeling, the further improvement of operational products additionally requires coordinated development involving emissions handling, chemistry and aerosol modeling, complemented with data-assimilation efforts.

A process-oriented evaluation of CAMS reanalysis ozone during tropopause folds over Europe for the period 2003–2018

Abstract. Tropopause folds are the key process underlying stratosphere-to-troposphere transport (STT) of ozone, and thus they affect tropospheric ozone levels and variability. In the present study we perform a process-oriented evaluation of Copernicus Atmosphere Monitoring Service (CAMS) reanalysis (CAMSRA) O3 during folding events over Europe and for the time period from 2003 to 2018. A 3-D labeling algorithm is applied to detect tropopause folds in CAMSRA, while ozonesonde data from WOUDC (World Ozone and Ultraviolet Radiation Data Centre) and aircraft measurements from IAGOS (In-service Aircraft for a Global Observing System) are used for CAMSRA O3 evaluation. The profiles of observed and CAMSRA O3 concentrations indicate that CAMSRA reproduces the observed O3 increases in the troposphere during the examined folding events. Nevertheless, at most of the examined sites, CAMSRA overestimates the observed O3 concentrations, mostly at the upper portion of the observed increases, with a median fractional gross error (FGE) among the examined sites >0.2 above 400 hPa. The use of a control run without data assimilation reveals that the aforementioned overestimation of CAMSRA O3 arises from the data assimilation implementation. Overall, although data assimilation assists CAMSRA O3 to follow the observed O3 enhancements in the troposphere during the STT events, it introduces biases in the upper troposphere resulting in no clear quantitative improvement compared to the control run without data assimilation. Less biased assimilated O3 products, with finer vertical resolution in the troposphere, in addition to higher IFS (Integrated Forecasting System) vertical resolution, are expected to provide a better representation of O3 variability during tropopause folds.