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The Joint Effort for Data assimilation Integration (JEDI) is an international collaboration aimed at developing an open software ecosystem for model agnostic data assimilation. This paper considers implementation of the model‐agnostic family of the local volume solvers in the JEDI framework. The implemented solvers include the Local Ensemble Transform Kalman Filter (LETKF), the Gain form of the Ensemble Transform Kalman Filter (GETKF), and the optimal interpolation variant of the LETKF (LETKF‐OI). This paper documents the implementation strategy for the family of the local volume solvers within the JEDI framework. We also document an expansive set of localization approaches that includes generic distance‐based localization, localization based on modulated ensemble products, and localizations specific to ocean (based on the Rossby radius of deformation), and land (based on the terrain difference between observation and model grid point). Finally, we apply the developed solvers in a limited set of experiments, including single‐observation experiments in atmosphere and ocean, and cycling experiments for the atmosphere, ocean, land, and aerosol assimilation. We also illustrate how JEDI Ensemble Kalman Filter solvers can be used in a strongly coupled framework using the interface solver approximation, which provides increments to the ocean based on observations from the ocean and atmosphere. Plain Language Summary: The Joint Effort for Data assimilation Integration (JEDI) is an international collaboration aimed at reducing the time it takes to transition research on initialization of the Earth system models to operation. The JEDI framework is designed to be agnostic of the specific numerical model and, hence, can facilitate collaboration between research institutions and operational centers. This paper documents implementation of the Ensemble Kalman filtering framework within JEDI. The implementation strategy supports a variety of algorithmic approaches to the Ensemble Kalman Filtering and is appropriate for multiple Earth system applications. Specifically, we demonstrate applications for atmosphere, atmospheric composition, ocean, and land data assimilation. Key Points: Local volume solvers are developed in the Joint Effort for Data assimilation Integration (JEDI) softwareLocalization operators for various components of the Earth system are developedCase studies are presented with various components of the Earth system [ABSTRACT FROM AUTHOR]

The paper presents dynamic cores v.4 and v.5 of the System for Integrated modeLling of Atmospheric coMposition SILAM v.5.5 based on the advection algorithm of Michael Galperin. This advection routine, so far weakly presented in international literature, is non-diffusive, positively defined, stable with regard to Courant number significantly above one, and very efficient computationally. For the first time, we present a rigorous description of its original version, along with several updates that improve its monotonicity and allow applications to long-living species in conditions of complex atmospheric flows. The other extension allows the scheme application to dynamics of aerosol spectra. The scheme is accompanied with the previously developed vertical diffusion algorithm, which encapsulates the dry deposition process as a boundary condition. Connection to chemical transformation modules is outlined, accounting for the specifics of transport scheme. Quality of the advection routine is evaluated using a large set of tests. The original approach has been previously compared with several classic algorithms widely used in operational models. The basic tests were repeated for the updated scheme, along with demanding global 2-D tests recently suggested in literature, which allowed positioning the scheme with regard to sophisticated state-of-the-art approaches. The model performance appeared close to the top of the list with very modest computational costs. [ABSTRACT FROM AUTHOR]

A current obstacle to the Observation System Simulation Experiments (OSSEs) used to quantify the potential performance of future atmospheric composition remote sensing systems is a computationally efficient method to define the scene-dependent vertical sensitivity of measurements as expressed by the retrieval averaging kernels (AKs). We present a method for the efficient prediction of AKs for multispectral retrievals of carbon monoxide (CO) and ozone (O3) based on actual retrievals from MOPITT on EOS-Terra and TES and OMI on EOS-Aura, respectively. This employs a multiple regression approach for deriving scene-dependent AKs using predictors based on state parameters such as the thermal contrast between the surface and lower atmospheric layers, trace gas volume mixing ratios (VMR), solar zenith angle, water vapor amount, etc. We first compute the singular vector decomposition (SVD) for individual cloud-free AKs and retain the 1st three ranked singular vectors in order to fit the most significant, orthogonal components of the AK in the subsequent multiple regression on a training set of retrieval cases. The resulting fit coefficients are applied to the predictors from a different test set of retrievals cased to reconstruct predicted AKs, which can then be evaluated against the true test set retrieval AKs. By comparing the VMR profile adjustment resulting from the use of the predicted vs. true AKs, we quantify the CO and O3 VMR profile errors associated with the use of the predicted AKs compared to the true AKs that might be obtained from a computationally expensive full retrieval calculation as part of an OSSE. Similarly, we estimate the errors in CO and O3 VMRs from using a single regional average AK to represent all retrievals, which has been a common approximation in chemical OSSEs performed to-date. For both CO and O3 in the lower troposphere, we find a significant reduction in error when using the predicted AKs as compared to a single average AK. This study examined data from the continental United States (CONUS) for 2006, but the approach could be applied to other regions and times. [ABSTRACT FROM AUTHOR]

The three GOME-2 instruments will provide unique and long data sets for atmospheric research and applications. The complete time period will be 2007-2022, including the period of ozone depletion as well as the beginning of ozone layer recovery. Besides ozone chemistry, the GOME-2 products are important e.g. for air quality studies, climate modeling, policy monitoring and hazard warnings. The heritage for GOME-2 is in the ERS/GOME and Envisat/SCIAMACHY instruments. The current Level 2 (L2) data cover a wide range of products such as trace gas columns (NO2, BrO, H2CO, H2O, SO2), tropospheric columns of NO2, total ozone columns and vertical ozone profiles in high and low spatial resolution, absorbing aerosol indices from the main science channels as well as from the polarization channels (AAI, AAI-PMD), Lambertian-equivalent reflectivity database, clear-sky and cloud-corrected UV indices and surface UV fields with different weightings and photolysis rates. The Ozone Monitoring and Atmospheric Composition Satellite Application Facility (O3M SAF) processing and data dissemination is operational and running 24/7. Data quality is quarantined by the detailed review processes for the algorithms, validation of the products as well as by a continuous quality monitoring of the products and processing. This is an overview paper providing the O3M SAF project background, current status and future plans to utilization of the GOME-2 data. An important focus is the provision of summaries of the GOME-2 products including product principles and validation examples together with the product sample images. Furthermore, this paper collects the references to the detailed product algorithm and validation papers. [ABSTRACT FROM AUTHOR]

This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. The paper gives an overall picture of its status at the end of MACC-II (summer 2014). This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O3, NO2, SO2, CO, PM10, PM2.5, NO, NH3, total NMVOCs and PAN+PAN precursors) over 8 vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations. The performances of the system are assessed daily, weekly and 3 monthly (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the median ensemble to forecast regional ozone pollution events. The time period of this case study is also used to illustrate that the median ensemble generally outperforms each of the individual models and that it is still robust even if two of the seven models are missing. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO2 and PM10 and show an overall improvement over time. The change of the skills of the ensemble over the past two summers for ozone and the past two winters for PM10 are discussed in the paper. While the evolution of the ozone scores is not significant, there are improvements of PM10 over the past two winters that can be at least partly attributed to new developments on aerosols in the seven individual models. Nevertheless, the year to year changes in the models and ensemble skills are also linked to the variability of the meteorological conditions and of the set of observations used to calculate the statistical indicators. In parallel, a scientific analysis of the results of the seven models and of the ensemble is also done over the Mediterranean area because of the specificity of its meteorology and emissions. The system is robust in terms of the production availability. Major efforts have been done in MACC-II towards the operationalisation of all its components. Foreseen developments and research for improving its performances are discussed in the conclusion. [ABSTRACT FROM AUTHOR]

A new algorithm to retrieve nitrogen dioxide (NO2) column densities using MKIV Brewer spectrophotometers is described. The method includes several improvements, such as a more recent spectroscopic dataset, the reduction of the measurement noise and interferences by other atmospheric species and instrumental settings, and a better determination of the air mass enhancement factors. The technique was tested during an ad-hoc calibration campaign at the high-altitude site of Izaña (Tenerife, Spain) and provided results compatible to those obtained from a spectrometer associated to the Network for the Detection of Atmospheric Composition Change (NDACC), with deviations of less than 0.02DU. To determine the extraterrestrial constant, an easily implementable generalisation of the standard Langley technique was developed which takes into account the daytime linear drift of nitrogen dioxide due to the photochemistry. Estimates obtained from different observation geometries, by collecting the light from either the sun or the zenith sky, were found to be comparable within the measurement uncertainty. The latter was thoroughly determined by using a Monte Carlo technique. Finally, a method to retrieve additional products such as the degree of linear polarisation of the zenith sky and the oxygen dimer optical depth is presented. The new algorithm is backward-compatible, thus allowing for the reprocessing of historical datasets. [ABSTRACT FROM AUTHOR]

The Canadian Terrestrial Ecosystem Model (CTEM) is the interactive vegetation component in the Earth system model of the Canadian Centre for Climate Modelling and Analysis. CTEM models land-atmosphere exchange of CO2 through the response of carbon in living vegetation, and dead litter and soil pools, to changes in weather and climate at timescales of days to centuries. Version 1.0 of CTEM uses prescribed fractional coverage of plant functional types (PFTs) although, in reality, vegetation cover continually adapts to changes in climate, atmospheric composition, and anthropogenic forcing. Changes in the spatial distribution of vegetation occur on timescales of years to centuries as vegetation distributions inherently have inertia. Here, we present version 2.0 of CTEM which includes a representation of competition between PFTs based on a modified version of the Lotka-Volterra (L-V) predator-prey equations. Our approach is used to dynamically simulate the fractional coverage of CTEM's seven natural, non-crop PFTs which are then compared with available observation-based estimates. Results from CTEM v. 2.0 show the model is able to represent the broad spatial distributions of its seven PFTs at the global scale. However, differences remain between modelled and observation-based fractional coverages of PFTs since representing the multitude of plant species globally, with just seven non-crop PFTs, only captures the large scale climatic controls on PFT distributions. As expected, PFTs that exist in climate niches are difficult to represent either due to the coarse spatial resolution of the model, and the corresponding driving climate, or the limited number of PFTs used. We also simulate the fractional coverages of PFTs using unmodified L-V equations to illustrate its limitations. The geographic and zonal distributions of primary terrestrial carbon pools and fluxes from the versions of CTEM that use prescribed and dynamically simulated fractional coverage of PFTs compare reasonably well with each other and observation-based estimates. The parametrization of competition between PFTs in CTEM v. 2.0 based on the modified L-V equations behaves in a reasonably realistic manner and yields a tool with which to investigate the changes in spatial distribution of vegetation in response to future changes in climate. [ABSTRACT FROM AUTHOR]

The HelioClim-3 database (HC3v3) provides records of surface solar irradiation every 15 min estimated by processing images from the geostationary meteorological Meteosat satellites using climatological data sets of atmospheric Linke turbidity factor. This technical note proposes a method to improve a posteriori HC3v3 by combining it with data records of the irradiation under clear sky from the new clear-sky model McClear whose inputs are the advanced global aerosol properties forecasts and physically consistent total column content in water vapour and ozone produced by the MACC projects. The method is validated by comparison with a series of ground measurements for 15 min and 1 h for 6 stations and for daily irradiation for 23 stations. The correlation coefficient is large, greater than respectively 0.92, 0.94, and 0.97, for 15 min, 1 h and daily irradiation. The bias ranges between -4 and 4% of the mean observed irradiation for most sites. The relative root mean square difference (RMSD) varies between 14 and 38% for 15 min, 12 and 33% for 1 h irradiation, and 6 and 20% for daily irradiation. As a rule of thumb, the farther from the nadir of the Meteosat satellite located at latitude 0° and longitude 0°, and the greater the occurrence of fragmented cloud cover, the greater the relative RMSD. The method improves HC3v3 in most cases and no degradation in the others. A systematic correction of HC3v3 with McClear is recommended. [ABSTRACT FROM AUTHOR]

​​​​​​​As a crucial constituent of fine particulate matter (PM2.5), secondary organic aerosols (SOAs) influence public health, regional air quality, and global climate patterns. This paper highlights the use of hydrophilic interaction liquid chromatography (HILIC) which effectively retains strongly polar analytes that might exhibit incomplete or no retention in reverse chromatography, resulting in superior separation efficiency. A HILIC column was used to analyze six standards, environmental standards (1648a and 1649b), and samples collected in urban environments in Guangzhou in the Pearl River Delta region, which serve as valuable reference points for evaluating the organic composition of the atmospheric environment. The results indicate a high degree of accuracy in the analytical method. Sodium octyl- d17 sulfate serves as the internal standard, with a linear correlation coefficient of the six standards, boasting a linear correlation coefficient r ranging from 0.993–0.9991 and a slope, k , of the linear equation from 0.966–1.882. The instrument detection limits (IDLs) are established at 0.03–0.20 µgmL-1 , while the method detection limits (MDLs) fall within the range of 0.30–1.75 ngm-3 , demonstrating the method's exceptional sensitivity. Since isoprene-derived organosulfates (iOSs) are highly polar due to containing a hydrophilic bond to the hydroxyl group and a hydrophobic bond to the sulfate, and as such showed strong retention using this method, this technique employs sodium ethyl sulfate and sodium octyl sulfate standards for semi-quantitative compound analysis of iOSs. The error in sample analysis (EA) ranged from 12.25 %–95.26 %, and the two standards maintained a consistent recovery rate between 116 %–131 % and 86.4 %–127 %. These findings indicate a high level of precision when semi-quantifying compounds with similar structural characteristics, affirming the analysis method's minimal relative error and underscoring its repeatability, process stability, and the reliability of its results for iOSs. To enhance the method's reliability assessment, the study analyzed polar organic components of standard particulate matter samples (1648a and 1649b), providing precise determinations of several iOSs using this method. Methyltetrol sulfate (m/z 215, C5H11SO7-) is the highest concentration in the ambient samples, up to 67.3 ngm-3 in the daytime. These results serve as valuable reference points for assessing the organic composition of the atmospheric environment. [ABSTRACT FROM AUTHOR]

Biosphere-atmosphere interactions play a critical role in governing atmospheric composition, mediating the concentration of key species such as ozone and aerosol, thereby influencing air quality and climate. The exchange of reactive trace gases and their oxidation products (both gas and particle phase) is of particular importance in this process. The FORCAsT (FORest Canopy AtmoSphere Transfer) one-dimensional model is developed to study the emission, deposition, chemistry and transport of volatile organic compounds (VOCs) and their oxidation products in the atmosphere within and above the forest canopy. We include an equilibrium partitioning scheme, making FORCAsT one of the few canopy models currently capable of simulating the formation of secondary organic aerosols (SOA) from VOC oxidation in a forest environment. We evaluate the capability of FORCAsT to reproduce observed concentrations of key gas-phase species and report modeled SOA concentrations within and above a mixed forest at the University of Michigan Biological Station (UMBS) during the Community Atmosphere-Biosphere Interactions Experiment (CABINEX) field campaign in summer 2009. We examine the impact of two different gas-phase chemical mechanisms on modelled concentrations of short-lived primary emissions, such as isoprene and monoterpenes, and their oxidation products. While the two chemistry schemes perform similarly under high-NOx conditions, they diverge at the low levels of NOx at UMBS. We identify peroxy radical and alkyl nitrate chemistry as the key causes of the differences, highlighting the importance of this chemistry in understanding the fate of biogenic VOCs (bVOCs) for both the modelling and measurement communities. [ABSTRACT FROM AUTHOR]

Atmospheric methane column-mean mole fractions measured with ground-based Fourier-transform spectroscopy near Saint-Petersburg, Russia (59.9° N, 29.8° E) are compared with similar data obtained with the Japanese GOSAT satellite in years 2009– 2012. Average CH4 mole fractions for the GOSAT data version V01.xx are by −13 ppb less than the corresponding values obtained from ground-based measurements on the same date (with standard deviation ~26ppb). For the GOSAT data version V02.xx the average difference is ~4ppb and standard deviation ~15ppb. This shows that FTIR spectroscopic observations near Saint-Petersburg could agree with GOSAT satellite data. [ABSTRACT FROM AUTHOR]

The New Baseline Surface Radiation (NBSR) system was established at the Shangdianzi (SDZ) regional Global Atmosphere Watch (GAW) station in 2013 to observe nine broadband radiation components, i.e. the global, direct, diffuse, and upwelling shortwave irradiance (GSWI, DSWI, DifSWI, and UpSWI); the photosynthetically active radiation (PAR); the ultraviolet irradiance (UVAI and UVBI); and the down- and upwelling longwave irradiance (DnLWI and UpLWI). To test the 1 min raw radiometric data, a Hybrid Algorithm for Radiation Data Quality Control (HARDQC) is presented in this study based on well-established methods, together with the solar irradiance dataset and the spectral features of the instrument bands. Subsequently, a NBSR dataset, which consists of radiation data at multiple timescales (i.e. 1 min, hourly, daily, monthly, monthly average hourly, and monthly average daily) over 2013–2022, is established and evaluated. Results show that more than 98.7 % of all radiation components passed the physical possibility test. The percentages of those that passed the extremely rare test are greater than 98.6 % for all radiation components except for the DnLWI (97.1 %). The percentages of those that passed the comparison test are greater than 83.3 % (GSWI), 78.3 % (DSWI), 81.7 % (DifSWI), 93.1 % (UpSWI), 88.9 % (PAR), 95.6 % (UVAI), 96.3 % (UVBI), 99.8 % (DnLWI), and 99.7 % (UpLWI), respectively. Due to data logger faults, removal of the instruments for calibration, and lightning strikes, some apparent data gaps in the upwelling radiation components (January 2015–August 2017) and all radiation components (December 2018; July to September 2021) were detected. Despite the existence of a few imperfections in the NBSR dataset, it is still reliable to apply it in many fields such as the validation of satellite products and numerical models, the investigation of relationships between radiation and atmospheric composition, and the detection of changes in the surface fluxes. The dataset described in this paper is available at 10.1594/PANGAEA.963330 (Quan et al., 2023b). [ABSTRACT FROM AUTHOR]

Satellite-based hyper-spectral infrared (IR) sensors such as the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric Sounding Interferometer (IASI) cover many methane (CH4) spectral features, including the ν1 vibrational band near 1300 cm−1 (7.7 μm); therefore, they can be used to monitor CH4 concentrations in the atmosphere. However, retrieving CH4 remains a challenge due to the limited spectral information provided by IR sounder measurements. The information required to resolve the weak absorption lines of CH4 is often obscured by interferences from signals originating from other trace gases, clouds, and surface emissions within the overlapping spectral region. Consequently, currently available CH4 data product derived from IR sounder measurements still have large errors and uncertainties that limit their application scope for high-accuracy climate and environment monitoring applications. In this paper, we describe the retrieval of atmospheric CH4 profiles using a novel spectral fingerprinting methodology and our evaluation of performance using measurements from the CrIS sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. The spectral fingerprinting methodology uses optimized CrIS radiances to enhance the CH4 signal and a machine learning classifier to constrain the physical inversion scheme. We validated our results using the atmospheric composition reanalysis results and data from airborne in situ measurements. An inter-comparison study revealed that the spectral fingerprinting results can capture the vertical variation characteristics of CH4 profiles that operational sounder products may not provide. The latitudinal variations in CH4 concentration in these results appear more realistic than those shown in existing sounder products. The methodology presented herein could enhance the utilization of satellite data to comprehend methane's role as a greenhouse gas and facilitate the tracking of methane sources and sinks with increased reliability. [ABSTRACT FROM AUTHOR]

Surface solar irradiance varies on scales down to seconds, and detailed long-term observational datasets of this variable are rare but in high demand. Here, we present an observational dataset of global, direct, and diffuse solar irradiance sampled at 1 Hz as well as fully resolved variability until at least 0.1 Hz over a period of 10 years from the Baseline Surface Radiation Network (BSRN) station at Cabauw, the Netherlands. The dataset is complemented with irradiance variability classifications, clear-sky irradiance and aerosol reanalysis, information about the solar position, observations of clouds and sky type, and wind measurements up to 200 m above ground level. Statistics of variability derived from all time series include approximately 185 000 detected events of both cloud enhancement and cloud shadows. Additional observations from the Cabauw measurement site are freely available from the open-data platform of the Royal Netherlands Meteorological Institute. This paper describes the observational site, quality control, classification algorithm with validation, and the processing method of complementary products. Additionally, we discuss and showcase (potential) applications, including limitations due to sensor response time. These observations and derived statistics provide detailed information to aid research into how clouds and atmospheric composition influence solar irradiance variability as well as information to help validate models that are starting to resolve variability at higher fidelity. The main datasets are available at 10.5281/zenodo.7093164 and 10.5281/zenodo.7462362 ; the reader is referred to the "Code and data availability" section of this paper for the complete list. [ABSTRACT FROM AUTHOR]

Since November 1994, the GROund-based Millimeter-wave Ozone Spectrometer (GROMOS) measures stratospheric and lower mesospheric ozone in Bern, Switzerland (47.95° N, 7.44° E). GROMOS is part of the Network for the Detection of Atmospheric Composition Change (NDACC). In July 2009, a Fast-Fourier-Transform spectrometer (FFTS) has been added as backend to GROMOS. The new FFTS and the original filter bench (FB) measured parallel for over two years. In October 2011, the FB has been turned off and the FFTS is now used to continue the ozone time series. For a consolidated ozone time series in the frame of NDACC, the quality of the stratospheric ozone profiles obtained with the FFTS has to be assessed. The FFTS results from July 2009 to December 2011 are compared to ozone profiles retrieved by the FB. FFTS and FB of the GROMOS microwave radiometer agree within 5% above 20 hPa. A later harmonization of both time series will be realized by taking the FFTS as benchmark for the FB. Ozone profiles from the FFTS are also compared to coinciding lidar measurements from the Observatoire Haute Provence (OHP), France. For the time period studied a maximum mean difference (lidar – GROMOS FFTS) of +3.8% at 3.1 hPa and a minimum mean difference of +1.4% at 8 hPa is found. Further, intercomparisons with ozone profiles from other independent instruments are performed: satellite measurements include MIPAS onboard ENVISAT, SABER onboard TIMED, MLS onboard EOS Aura and ACE-FTS onboard SCISAT-1. Additionally, ozonesondes launched from Payerne, Switzerland, are used in the lower stratosphere. Mean relative differences of GROMOS FFTS and these independent instruments are less than 10% between 50 and 0.1 hPa. [ABSTRACT FROM AUTHOR]

The paper describes the development and performance of the Double Extended Stratospheric–Tropospheric (DEST vn1.0) chemistry scheme, which forms a part of the Met Office's Unified Model coupled to the United Kingdom Chemistry and Aerosol (UM–UKCA) chemistry–climate model, which is the atmospheric composition model of the United Kingdom Earth System Model (UKESM). The scheme extends the standard Stratospheric–Tropospheric chemistry scheme (StratTrop) by including a range of important updates to the halogen chemistry. These allow process-oriented studies of stratospheric ozone depletion and recovery, including the impacts from both controlled long-lived ozone-depleting substances (ODSs) and emerging issues around uncontrolled very short-lived substances (VSLS). The main updates in DEST are (i) an explicit treatment of 14 of the most important long-lived ODSs; (ii) an inclusion of brominated VSLS (Br-VSLS) emissions and chemistry; and (iii) an inclusion of chlorinated VSLS (Cl-VSLS) emissions/LBCs (lower boundary conditions) and chemistry. We evaluate the scheme's performance by comparing DEST simulations against analogous runs made with the standard StratTrop scheme and against observational and reanalysis datasets. Overall, our scheme addresses some significant shortcomings in the representation of atmospheric halogens in the standard StratTrop scheme and will thus be particularly relevant for studies of ozone layer recovery and processes affecting it, in support of future World Meteorological Organization (WMO) Ozone Assessment Reports. [ABSTRACT FROM AUTHOR]

Observations of the atmospheric sources and sinks of carbon dioxide (CO2) and methane (CH4) in the pan-Arctic domain are extremely scarce, limiting our knowledge of carbon turnover in this climatically sensitive environment and the fate of the enormous carbon reservoirs conserved in the permafrost. Especially critical are the gaps in the high latitudes of Siberia, covered by the vast permafrost underlain tundra, where only several atmospheric monitoring sites are operational. This paper presents the first two years (September 2018–January 2021) of accurate continuous observations of atmospheric CO2 and CH4 dry mole fractions at the recently deployed tower-based measurement station "DIAMIS" (73.5068° N, 80.5198° E) located on the southwestern coast of the Taimyr Peninsula, Siberia, at the Gulf of the Yenisei River that opens to the Kara Sea (Arctic Ocean). In this paper, we summarized the scientific rationale of the site, examined the seasonal footprint of the station with an analysis of terrestrial vegetation and maritime sector contributing to the captured atmospheric signal, and illustrated temporal patterns of CO2 and CH4 for the daytime mixed atmospheric layer over the continent–sea interface. Along with the temporal variations reflecting a signal caused pan-Arctic and not very much influenced by the local processes, we analyzed the spatiotemporal distribution of the synoptic anomalies representing the atmospheric signatures of regional sources and sinks of CO2 and CH4 for the studied high-arctic Siberian domain of ~625 thousand km2, with nearly equal capturing the land surface (54%) and the ocean (46%) throughout the year. Both for CO2 and CH4, we have observed a sea–continent declining trend, presuming a larger depletion of trace gases in the maritime air masses compared to the continental domain. So far, over the Kara Sea, we have not detected any prominent signals of CH4 that might have indicated processes of subsea permafrost degradation and occurrence of cold seeps–still mainly observed in the eastern Arctic Seas—The Laptev Sea and the East-Siberian Sea. [ABSTRACT FROM AUTHOR]

Nineteen years of atmospheric composition measurements made at the University of Toronto Atmospheric Observatory (TAO; 43.66 ∘ N, 79.40 ∘ W; 174 m a.s.l.) are presented. These are retrieved from Fourier transform infrared (FTIR) solar absorption spectra recorded with an ABB Bomem DA8 spectrometer from May 2002 to December 2020. The retrievals have been performed for 14 species: O3 , HCl, HF, HNO3 , CH4 , C2H6 , CO, HCN, N2O , C2H2 , H2CO , CH3OH , HCOOH, and NH3 using the SFIT4 algorithm. The measurements have been archived in the Network for the Detection of Atmospheric Composition Change (NDACC) data repository in Hierarchical Data Format version 4 (HDF4) files following the Generic Earth Observation Metadata Standard (GEOMS) and are also publicly available on Borealis, the Canadian Dataverse Repository (10.5683/SP2/VC8JMC, Yamanouchi et al., 2022). In this paper, we describe the instrumentation, the retrieval strategy, the vertical sensitivity of the retrievals, the quality assurance process, and the error analysis of the TAO FTIR measurements and present the current version of the time series. [ABSTRACT FROM AUTHOR]

The near-infrared transmission spectrum of the warm sub-Neptune exoplanet GJ 1214 b has been observed to be flat and featureless, implying a high metallicity atmosphere with abundant aerosols. Recent JWST MIRI Low Resolution Spectrometer observations of a phase curve of GJ 1214 b showed that its transmission spectrum is flat out into the mid-infrared. In this paper, we use the combined near- and mid-infrared transmission spectrum of GJ 1214 b to constrain its atmospheric composition and aerosol properties. We generate a grid of photochemical haze models using an aerosol microphysics code for a number of background atmospheres spanning metallicities from 100 to 1000× solar, as well as a steam atmosphere scenario. The flatness of the combined data set largely rules out atmospheric metallicities ≤300× solar due to their large corresponding molecular feature amplitudes, preferring values ≥1000× solar and column haze production rates ≥10−10 g cm−2 s−1. The steam atmosphere scenario with similarly high haze production rates also exhibits sufficiently small molecular features to be consistent with the transmission spectrum. These compositions imply that atmospheric mean molecular weights ≥15 g mol−1 are needed to fit the data. Our results suggest that haze production is highly efficient on GJ 1214 b and could involve non-hydrocarbon, non-nitrogen haze precursors. Further characterization of GJ 1214 b's atmosphere would likely require multiple transits and eclipses using JWST across the near- and mid-infrared, potentially complemented by ground-based high-resolution transmission spectroscopy. [ABSTRACT FROM AUTHOR]

This paper presents a solution for efficiently and accurately solving separable least squares problems with multiple datasets. These problems involve determining linear parameters that are specific to each dataset while ensuring that the nonlinear parameters remain consistent across all datasets. A well-established approach for solving such problems is the variable projection algorithm introduced by Golub and LeVeque, which effectively reduces a separable problem to its nonlinear component. However, this algorithm assumes that the datasets have equal sizes and identical auxiliary model parameters. This article is motivated by a real-world remote sensing application where these assumptions do not apply. Consequently, we propose a generalized algorithm that extends the original theory to overcome these limitations. The new algorithm has been implemented and tested using both synthetic and real satellite data for atmospheric carbon dioxide retrievals. It has also been compared to conventional state-of-the-art solvers, and its advantages are thoroughly discussed. The experimental results demonstrate that the proposed algorithm significantly outperforms all other methods in terms of computation time, while maintaining comparable accuracy and stability. Hence, this novel method can have a positive impact on future applications in remote sensing and could be valuable for other scientific fitting problems with similar properties. [ABSTRACT FROM AUTHOR]

The tropical western Pacific (TWP) plays an important role in global stratosphere–troposphere exchange and is an active region of the interhemispheric transport (IHT). Common indicators for transport between the hemispheres like the tropical rain belt are too broad or lack precision in the TWP. In this paper, we provide a method to determine the atmospheric chemical equator (CE), which is a boundary for air mass transport between the two hemispheres in the tropics. This method used the model output from an artificial passive tracer simulated by the chemical transport model GEOS-Chem in the troposphere. We investigated the movement of the CE in the tropics, which indicates the migration of atmospheric circulation systems and air mass origins. Our results show the CE on different timescales, suggesting that the different features of the IHT in different regions are highly related to the variation in the circulation systems. We compared the CE with the tropical wind fields, indicating that the region of IHT does not coincide with the convergence of the 10 m wind fields in the tropical land sectors and the TWP region. We compared the CE with the atmospheric composition such as satellite data of CH4 and model simulation of sulfur hexafluoride (SF 6). The results show that the CE and north–south gradient of CH4 in the Indian Ocean in January are well consistent with each other, which indicates the CE has good potential to estimate the IHT inferred by observations. We discussed the vertical extent and the meridional extent of the IHT. We find that the vertical structure above 2 km has a slight northern tilt in the Northern Hemisphere (NH) winter season and a southern tilt in the NH summer, meaning the seasonality of the migration of the CE at the lower altitude is larger than that at the higher altitude. The meridional extent of the CE indicates a narrow transition zone where IHT happens throughout the year. We find that the meridional extent above South America is larger compared to other regions. The distribution of the land–sea contrast plays an important role in the meridional extent of IHT. We focus on the TWP region and further compared the tropical rain belt with the CE. There is a broad region of high precipitation occurring in the TWP region, and it is difficult to determine the IHT by the rain belt. In the NH winter, the CE is not consistent with the tropical rain belt in the TWP but is confined to the southern branch of the peak of the rain belt. For the other seasons, both indicators of IHT agree. [ABSTRACT FROM AUTHOR]

The observed radiation data from the second-generation Hyperspectral Infrared Atmospheric Sounder (HIRAS-II) on the Fengyun-3E (FY-3E) satellite contain useful vertical atmosphere information which can distinguish and retrieve vertical profiles of atmospheric gas components including ozone (O3), carbon monoxide (CO), and methane (CH4). This paper utilizes FY-3E/HIRAS-II observational data to optimize each gas channel using the improved Optimal Sensitivity Profile method (OSP) channel algorithm and establishes a typical convolutional neural network model (CNN) and a representative U-shaped network model (UNET) with deep features and shallow feature links to perform atmospheric profile retrieval calculations of O3, CO, and CH4. We chose the clear sky data of the Indian and its southern seas in December 2021 and January 2022, with reanalysis data from European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) and European Center for Medium-Range Weather Forecasts Atmospheric Composition Reanalysis v4 (EAC4) serving as the reference values. The retrieval outcomes were then compared against advanced numerical forecast models including the Whole Atmosphere Community Climate Model (WACCM), Global Forecast System (GFS), and satellite products from an Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI). Experimental results show that the generalization ability and retrieval accuracy of CNN are slightly higher compared with UNET. For O3 profile retrieval, the mean percentage error (MPE) of the whole layers for CNN and UNET data in relation to ERA5 data was less than 8%, while the root-mean-square error (RMSE) was below 1.5 × 10−7 kg/kg; for CH4 profile retrieval, the MPE of the whole layers for CNN and UNET data in relation to EAC4 data was less than 0.7%, while the RMSE was below 1.5 × 10−8 kg/kg. The retrieval of O3 and CH4 are resulted in a significant improvement compared to the forecast data and satellite products in most pressure levels; for CO profile retrieval, the MPE of the whole layers for CNN and UNET data in relation to EAC4 data was less than 11%, while the RMSE was below 4 × 10−8 kg/kg. The error of the CO retrieval results was higher than that of the forecast data at the pressure level of 200~500 hPa and lower than that of similar satellite products with most pressure levels. The experiments indicated that the neural network method effectively determines the atmospheric gas profiles using infrared hyperspectral data, exhibiting a positive performance in accuracy and retrieval speed. [ABSTRACT FROM AUTHOR]

We continue our comprehensive theoretical investigation of the spectral evolution of white dwarfs based on sophisticated simulations of element transport. In this paper, we focus on the transformation of PG 1159 stars into DO/DB white dwarfs due to the gravitational settling of heavy elements and then into DQ white dwarfs through the convective dredge-up of carbon. We study the impact of several physical parameters on the evolution of the surface carbon abundance over a wide range of effective temperatures. In the hot PG 1159 and DO phases, our calculations confirm that the temperature of the PG 1159-to-DO transition depends sensitively on the stellar mass and the wind mass-loss rate. We show that measured carbon abundances of DOZ white dwarfs are mostly accounted for by our models, with the notable exception of the coolest DOZ stars. In the cooler DB and DQ phases, the predicted atmospheric composition is strongly influenced by the stellar mass, the thickness of the envelope, the initial carbon content, the efficiency of convective overshoot, and the presence of residual hydrogen. We demonstrate that, under reasonable assumptions, our simulations reproduce very well the observed carbon abundance pattern of DQ stars, which thus allows us to constrain the extent of the overshoot region in cool helium-rich white dwarfs. We also argue that our calculations naturally explain a number of recent empirical results, such as the relative excess of low-mass DQ stars and the presence of trace hydrogen and/or carbon at the surface of most DC and DZ stars. [ABSTRACT FROM AUTHOR]

GHOST: Globally Harmonised Observations in Space and Time, represents one of the biggest collection of harmonised measurements of atmospheric composition at the surface. In total, 7,275,148,646 measurements from 1970–2023, of 227 different components, from 38 reporting networks, are compiled, parsed, and standardised. Components processed include gaseous species, total and speciated particulate matter, and aerosol optical properties. The main goal of GHOST is to provide a dataset that can serve as a basis for the reproducibility of model evaluation efforts across the community. Exhaustive efforts have been made towards standardising almost every facet of provided information from the major public reporting networks, saved in 21 data variables, and 163 metadata variables. Extensive effort in particular is put towards the standardisation of measurement process information, and station classifications. Extra complementary information is also associated with measurements, such as metadata from various popular gridded datasets (e.g. land use), and temporal classifications per measurement (e.g. day / night). A range of standardised network quality assurance flags are associated with each individual measurement. GHOST own quality assurance is also performed and associated with measurements. Measurements prefiltered by some default GHOST quality assurance are also provided. In this paper, we outline all steps undertaken to create the GHOST dataset, and give insights and recommendations for data providers based on experiences gleaned through our efforts. The GHOST dataset is made freely available via the following repository: https://doi.org/10.5281/zenodo.10637449 (Bowdalo, 2024). [ABSTRACT FROM AUTHOR]

The aerosol optical depth (AOD) is an indicator of particles suspended in the atmosphere, which measures the attenuation of transmitted spectral radiant energy through aerosols. It has been proven to be an essential factor in the estimation of air pollutants concentration and surface solar radiation. However, due to the sparse distribution of ground-based measurements and the high missing rate of satellite retrieval products, the reanalysis data with high spatial–temporal continuity and large spatial range is an excellent supplementary data source. However, compared with satellite monitoring and ground-based measurements, the reliability of the reanalysis data has been questioned. Therefore, in this paper, we evaluate the accuracy of the Monitoring Atmospheric Composition and Climate (MACC) AOD values at the wavelength of 550 nm (AOD550) using the ground-based observations data from 19 AERONET stations in the Chinese mainland for the period 2003–2007. In addition, a data fusion correction model based on the random forest regression is constructed and analyzed. The correction results demonstrate that the correction model, which captures the non-linear relationship, has a satisfactory correction effect overall, with R from 0.771 to 0.978, MAE from 0.225 to 0.047, and RMSE from 0.400 to 0.120. Furthermore, with the aid of model-agnostic machine learning explanation algorithms, we weigh the relative importance of the independent variables and visualize the complicated relationship between the model errors and the important input features. In general, the availability of MACC AOD was improved, and the correction model also provided a reference for the correction of other reanalysis data. [ABSTRACT FROM AUTHOR]

The three Infrared Atmospheric Sounding Interferometer (IASI) instruments on board the Metop family of satellites have been sounding the atmospheric composition since 2006. More than 30 atmospheric gases can be measured from the IASI radiance spectra, allowing the improvement of weather forecasting and the monitoring of atmospheric chemistry and climate variables. The early detection of extreme events such as fires, pollution episodes, volcanic eruptions, or industrial releases is key to take safety measures to protect the inhabitants and the environment in the impacted areas. With its near-real-time observations and good horizontal coverage, IASI can contribute to the series of monitoring systems for the systematic and continuous detection of exceptional atmospheric events in order to support operational decisions. In this paper, we describe a new approach to the near-real-time detection and characterization of unexpected events, which relies on the principal component analysis (PCA) of IASI radiance spectra. By analyzing both the IASI raw and compressed spectra, we applied a PCA-granule-based method on various past, well-documented extreme events such as volcanic eruptions, fires, anthropogenic pollution, and industrial accidents. We demonstrate that the method is well suited to the detection of spectral signatures for reactive and weakly absorbing gases, even for sporadic events. Consistent long-term records are also generated for fire and volcanic events from the available IASI/Metop-B data record. The method is running continuously, delivering email alerts on a routine basis, using the near-real-time IASI L1C radiance data. It is planned to be used as an online tool for the early and automatic detection of extreme events, which was not done before. [ABSTRACT FROM AUTHOR]

A major motivation of spectroscopic observations of giant exoplanets is to unveil planet formation processes from atmospheric compositions. Several recent studies suggested that atmospheric nitrogen, like carbon and oxygen, can provide important constraints on planetary formation environments. Since nitrogen chemistry can be far from thermochemical equilibrium in warm atmospheres, we extensively investigate under what conditions, and with what assumptions, the observable NH3 abundances can diagnose an atmosphere's bulk nitrogen abundance. In the first paper of this series, we investigate atmospheric T–P profiles across equilibrium temperature, surface gravity, intrinsic temperature, atmospheric metallicity, and C/O ratio using a 1D radiative–convective equilibrium model. Models with the same intrinsic temperature and surface gravity coincide with a shared "universal" adiabat in the deep atmosphere, across a wide equilibrium temperature range (250–1200 K), which is not seen in hotter or cooler models. We explain this behavior in terms of the classic "radiative zero solution" and then establish a semianalytical T–P profile of the deep atmospheres of warm exoplanets. This profile is then used to predict vertically quenched NH3 abundances. At solar metallicity, our results show that the quenched NH3 abundance only coincides with the bulk nitrogen abundance (within 10%) at low intrinsic temperature, corresponding to a planet with a sub-Jupiter mass (≲1 M J) and old age (≳1 Gyr). If a planet has a high-metallicity (≳10× solar) atmosphere, the quenched NH3 abundance significantly underestimates the bulk nitrogen abundance at almost all planetary masses and ages. We suggest modeling and observational strategies to improve the assessment of bulk nitrogen from NH3. [ABSTRACT FROM AUTHOR]

Retrieving high-precision concentrations of atmospheric trace gases from FTIR (Fourier transform infrared) spectrometry requires a precise knowledge of the instrumental performance. In this context, this paper examines the impact on the ozone (O 3) retrievals of several approaches used to characterize the instrumental line shape (ILS) function of ground-based FTIR spectrometers within NDACC (Network for the Detection of Atmospheric Composition Change). The analysis has been carried out at the subtropical Izaña Observatory (IZO, Spain) by using the 20-year time series of the high-resolution FTIR solar absorption spectra acquired between 1999 and 2018. The theoretical quality assessment and the comparison to independent O 3 observations available at IZO (Brewer O 3 total columns and electrochemical concentration cell, ECC, sondes) reveal consistent findings. The inclusion of a simultaneous retrieval of the ILS parameters in the O 3 retrieval strategy allows, on the one hand, a rough instrumental characterization to be obtained and, on the other hand, the precision of the FTIR O 3 products to be slightly improved. The improvement is of special relevance above the lower stratosphere, where the cross-interference between the O 3 vertical distribution and the instrumental performance is more significant. However, it has been found that the simultaneous ILS retrieval leads to a misinterpretation of the O 3 variations on daily and seasonal scales. Therefore, in order to ensure the independence of the O 3 retrievals and the instrumental response, the optimal approach to deal with the FTIR instrumental characterization is found to be the continuous monitoring of the ILS function by means of independent observations, such as gas cell measurements. [ABSTRACT FROM AUTHOR]

In this paper, we report on the first implementation of atmospheric chemistry and aerosol as part of the European Centre for Medium-Range Weather Forecasts (ECMWF) OpenIFS model. OpenIFS is a portable version of ECMWF's global numerical weather prediction model. Modules and input data for model cycle CY43R3, which have been developed as part of the Copernicus Atmosphere Monitoring Service (CAMS), have been ported to OpenIFS with the modified CB05 tropospheric chemistry scheme, the bulk bin tropospheric aerosol module, and the option to use Belgian Assimilation System for Chemical ObsErvations (BASCOE)-based stratospheric ozone chemistry. We give an overview of the model, and describe the datasets used for emissions and dry deposition, which are similar to those used in the model configuration applied to create the CAMS reanalysis. We evaluate two reference model configurations with and without the stratospheric chemistry extension against standard observational datasets for tropospheric ozone, surface carbon monoxide (CO), tropospheric nitrogen dioxide (NO 2), and aerosol optical depth. The results give basic confidence in the model implementation and configuration. This OpenIFS version with atmospheric composition components is open to the scientific user community under a standard OpenIFS license. [ABSTRACT FROM AUTHOR]

A hyperspectral infrared (IR) sounder from geostationary orbit provides nearly continuous measurements of atmospheric thermodynamic and dynamic information within a weather cube, specifically the atmospheric temperature, moisture, and wind information at different pressure levels that are critical for improving high-impact weather (HIW) nowcasting and numerical weather prediction (NWP). Geostationary hyperspectral IR sounders (GeoHIS) have been on board China's Fengyun-4 series since 2016 and will be on board Europe's Meteosat Third Generation (MTG) series in the 2024 time frame; the United States and other countries are also planning to include GeoHIS instruments on their next generation of geostationary weather satellites. Although availability of on-orbit GeoHIS data are limited currently, studies have been conducted and progress has been made on developing the applications of high-temporal-resolution GeoHIS observations. These include but are not limited to deriving three-dimensional wind fields for nowcasting and NWP applications, trending atmospheric instability for warning in preconvective environments, conducting impact studies with data from the experimental Geostationary Interferometric Infrared Sounder (GIIRS) on board Fengyun-4A, preparing observing system simulation experiments (OSSEs), and monitoring diurnal variation of atmospheric composition. This paper provides an overview of the current applications of GeoHIS, discusses the data processing challenges, and provides perspectives on future development. The purpose is to provide direction on utilization of the current and assist preparation for the upcoming GeoHIS observations for nowcasting, NWP and other applications. [ABSTRACT FROM AUTHOR]

The Copernicus Atmosphere Monitoring Service (CAMS), part of the European Union's Earth observation program Copernicus, entered operations in July 2015. Implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) as a truly European effort with over 23,500 direct data users and well over 200 million end users worldwide as of March 2022, CAMS delivers numerous global and regional information products about air quality, inventory-based emissions and observation-based surface fluxes of greenhouse gases and from biomass burning, solar energy, ozone and UV radiation, and climate forcings. Access to CAMS products is open and free of charge via the Atmosphere Data Store. The CAMS global atmospheric composition analyses, forecasts, and reanalyses build on ECMWF's Integrated Forecasting System (IFS) and exploit over 90 different satellite data streams. The global products are complemented by coherent higher-resolution regional air quality products over Europe derived from multisystem analyses and forecasts. CAMS information products also include policy support such as quantitative impact assessment of short- and long-term pollutant-emission mitigation scenarios, source apportionment information, and annual European air quality assessment reports. Relevant CAMS products are cited and used for instance in IPCC Assessment Reports. Providing dedicated support for users operating smartphone applications, websites, or TV bulletins in Europe and worldwide is also integral to the service. This paper presents key achievements of the CAMS initial phase (2014–21) and outlines some of its new components for the second phase (2021–28), e.g., the new Copernicus anthropogenic CO2 emissions Monitoring and Verification Support capacity that will monitor global anthropogenic emissions of key greenhouse gases. [ABSTRACT FROM AUTHOR]

In recent years, major progress has been made in measuring weakly absorbing atmospheric trace gases from high spectral resolution space observations. In this paper, we apply the so‐called whitening transformation on spectra of the Infrared Atmospheric Sounding Interferometer, and show that it allows removing most of the climatological background from spectra, leaving a residual that contains those spectral signatures that depart from normality. These can subsequently be attributed to changes in the abundance of trace species. This is illustrated for two diverging cases: (1) a biomass burning plume from the 2019/2020 Australian bushfires, leading to the unambiguous identification of nine reactive trace gases, including a first observation of glycolaldehyde; (2) spectra observed a decade apart, from which changes in eight long‐lived halogenated substances are identified; three of them never observed before by a nadir sounder. Plain Language Summary: In recent years, several techniques have been developed for the detection of gases present in very small quantities in the atmosphere, which has significantly improved our knowledge on atmospheric composition and chemistry. In this paper, we describe a powerful complementary technique that transforms atmospheric spectra to highlight and attribute the spectral signatures of different species. We apply it on spectra measured by the spaceborne Infrared Atmospheric Sounding Interferometer for two different cases: (1) a plume from the 2019/2020 Australian bushfires leading to the clear identification of nine rare gases including a first observation from space of glycolaldehyde; (2) spectra observed a decade apart, of which eight long‐lived halogenated substances were identified; three of them never observed before by a nadir sounder. The detection of these reactive and long‐lived trace gases is very promising for the monitoring of their temporal evolution and the attribution of their sources. Key Points: The whitening transformation allows analyzing anomalies in hyperspectral infrared spectraIn a biomass burning plume we identify nine compounds including a first observation of glycolaldehydeOn spectra observed a decade apart we identify atmospheric changes in eight long‐lived halocarbons [ABSTRACT FROM AUTHOR]

As a crucial constituent of fine particulate matter (PM2.5), secondary organic aerosols (SOA) influence public health, regional air quality, and global climate patterns. This paper highlights the use of Hydrophilic interaction liquid chromatography (HILIC) which effectively retains strongly polar analytes that might exhibit incomplete or no retention in reverse chromatography, resulting in superior separation efficiency. A HILIC column was used to analyze 7 standards, environmental standards (1648a and 1649b), and samples collected in urban environments in the Pearl River Delta region of Guangzhou. That serve as valuable reference points for evaluating the organic composition of the atmospheric environment. The results indicate a high degree of accuracy in the analytical method, sodium octyl-d17 sulfate serves as the internal standard, with a linear correlation coefficient of the 7 standards, boasting a linear correlation coefficient R ranging from 0.987-0.999 and a slope, k, of the linear equation from 0.9662-2.2927. The instrument detection limit (IDLs) is established at 0.0026-0.0300 µg mL-1, while the method detection limit (MDLs) falls within the range of 0.0077-0.2300 ng m-3, demonstrating the method's exceptional sensitivity. Since isoprene sulphates are highly polar due to containing a hydrophilic bond to the hydroxyl group and a hydrophobic bond to the sulphate, and as such showed strong retention using this method. This technique employs Sodium ethyl sulfate and Sodium octyl sulfate standards for semi-quantitative compound analysis isoprene-derived OSs, the error in sample analysis (EA) ranged from 12.25-95.26% and the two standards maintaining a consistent recovery rate between 116%-131% and 86%-127%. These findings indicate a high level of precision when semi-quantifying compounds with similar structural characteristics, affirming the analysis method's minimal relative error and underscoring its repeatability, process stability, and the reliability of its results for isoprene OSs. To enhance the method's reliability assessment, the study analyzed polar organic components of standard particulate matter samples (1648a and 1649b), providing precise determinations of several isoprene OSs using this method. Methyltetrol sulfate (m/z 215) is the highest concentration in the ambient samples, up to 67.33 ng m-3 at daytime. These results serve as valuable reference points for assessing the organic composition of the atmospheric environment. [ABSTRACT FROM AUTHOR]

The Tropospheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel 5 Precursor (S5P) satellite, launched in October 2017, provides a wealth of atmospheric composition data, including total columns of carbon monoxide (TCCO) at high horizontal resolution (5.5 km × 7 km). Near-real-time TROPOMI TCCO data have been monitored in the global data assimilation system of the Copernicus Atmosphere Monitoring Service (CAMS) since November 2018 to assess the quality of the data. The CAMS system already routinely assimilates TCCO data from the Measurement of Pollution in the Troposphere (MOPITT) instrument and the Infrared Atmospheric Sounding Interferometer (IASI) outside the polar regions. The assimilation of TROPOMI TCCO data in the CAMS system was tested for the period 6 July to 31 December 2021, i.e. after the TROPOMI algorithm update to version 02.02.00 in July 2021. By assimilating TROPOMI TCCO observations, the CAMS CO columns increase by on average 8 %, resulting in an improved fit to independent observations (IAGOS aircraft profiles and NDACC Fourier transform infrared (FTIR) tropospheric and total-column CO data) compared to a version of the CAMS system where only TCCO from MOPITT and IASI is assimilated. The largest absolute and relative changes from the assimilation of TROPOMI CO are found in the lower and middle troposphere, i.e. that part of the atmosphere that is not already well constrained by the assimilated TIR MOPITT and IASI data. The largest impact near the surface comes from clear-sky TROPOMI data over land, and additional vertical information comes from the retrievals of measurements in cloudy conditions. July and August 2021 saw record numbers of boreal wildfires over North America and Russia, leading to large amounts of CO being released into the atmosphere. The paper assesses the impact of TROPOMI CO assimilation on selected CO plumes more closely. While the CO column can be well constrained by the assimilation of TROPOMI CO data, and the fit to individual IAGOS CO profiles in the lower and middle troposphere is considerably improved, the TROPOMI CO columns do not provide further constraints on individual plumes that are transported across continents and oceans at altitudes above 500 hPa. [ABSTRACT FROM AUTHOR]

Drought is a hydroclimatic extreme that causes perturbations to the terrestrial biosphere and acts as a stressor on vegetation, affecting emissions patterns. During severe drought, isoprene emissions are reduced. In this paper, we focus on capturing this reduction signal by implementing a new percentile isoprene drought stress (yd) algorithm in NASA GISS ModelE based on the MEGAN3 (Model of Emissions of Gases and Aerosols from Nature Version 3) approach as a function of a photosynthetic parameter (Vc,max) and water stress (β). Four global transient simulations from 2003–2013 are used to demonstrate the effect without yd (Default_ModelE) and with online yd (DroughtStress_ModelE). DroughtStress_ModelE is evaluated against the observed isoprene measurements at the Missouri Ozarks AmeriFlux (MOFLUX) site during the 2012 severe drought where improvements in the correlation coefficient indicate it is a suitable drought stress parameterization to capture the reduction signal during severe drought. The application of yd globally leads to a decadal average reduction of ∼2.7 %, which is equivalent to ∼14.6 Tg yr -1 of isoprene. The changes have larger impacts in regions such as the southeastern US. DroughtStress_ModelE is validated using the satellite Ω HCHO column from the Ozone Monitoring Instrument (OMI) and surface O 3 observations across regions of the US to examine the effect of drought on atmospheric composition. It was found that the inclusion of isoprene drought stress reduced the overestimation of Ω HCHO in Default_ModelE during the 2007 and 2011 southeastern US droughts and led to improvements in simulated O 3 during drought periods. We conclude that isoprene drought stress should be tuned on a model-by-model basis because the variables used in the parameterization responses are relative to the land surface model hydrology scheme (LSM) and the effects of yd application could be larger than seen here due to ModelE not having large biases of isoprene during severe drought. [ABSTRACT FROM AUTHOR]

Atmospheric particulate matter (PM) has a significant impact on both the natural environment and human health. Iron is one of the most abundant elements in the earth's crust, playing an important role in geochemical processes, and is also an important trace component in atmospheric PM. In recent years, with the rapid development of non-traditional (metal) stable isotope technologies, new solutions and methods for the source apportionments of heavy metal elements have been put forward. Stable iron isotope analysis has become an effective tool to trace iron in atmospheric particles. This review paper briefly summarizes the recent progress of atmospheric iron isotope geochemistry. We show that some of the major natural and anthropogenic PM sources have different iron isotopic compositions. A Bayesian isotopic mixing model MixSIAR was used to quantitatively re-evaluate the contributions of different sources to iron in both urban and marine aerosols based on iron isotopic data in the literature. The results highlight the value of stable iron isotope analyses as an effective tool in the source apportionment of atmospheric aerosols. [ABSTRACT FROM AUTHOR]

Accurate observations of atmospheric ozone (O 3) are essential to monitor in detail its key role in atmospheric chemistry. The present paper examines the performance of different O 3 retrieval strategies from FTIR (Fourier transform infrared) spectrometry by using the 20-year time series of the high-resolution solar spectra acquired from 1999 to 2018 at the subtropical Izaña Observatory (IZO, Spain) within NDACC (Network for the Detection of Atmospheric Composition Change). In particular, the effects of two of the most influential factors have been investigated: the inclusion of a simultaneous atmospheric temperature profile fit and the spectral O 3 absorption lines used for the retrievals (the broad spectral region of 1000–1005 cm -1 and single micro-windows between 991 and 1014 cm -1). Additionally, the water vapour (H 2 O) interference in O 3 retrievals has been evaluated, with the aim of providing an improved O 3 strategy that minimises its impact and, therefore, could be applied at any NDACC FTIR station under different humidity conditions. The theoretical and experimental quality assessments of the different FTIR O 3 products (total column (TC) amounts and volume mixing ratio (VMR) profiles) provide consistent results. Combining a simultaneous temperature retrieval with the optimal selection of single O 3 micro-windows results in superior FTIR O 3 products, with a precision of better than 0.6 %–0.7 % for O 3 TCs as compared to coincident NDACC Brewer observations taken as a reference. However, this improvement can only be achieved provided the FTIR spectrometer is properly characterised and stable over time. For unstable instruments, the temperature fit is found to exhibit a strong negative influence on O 3 retrievals due to the increase in the cross-interference between the temperature retrieval and instrumental performance (given by the instrumental line shape function and measurement noise), which leads to a worsening of the precision of FTIR O 3 TCs of up to 2 %. This cross-interference becomes especially noticeable beyond the upper troposphere/lower stratosphere, as documented theoretically as well as experimentally by comparing FTIR O 3 profiles to those measured using electrochemical concentration cell (ECC) sondes within NDACC. Consequently, it should be taken into account for the reliable monitoring of the O 3 vertical distribution, especially over long-term timescales. [ABSTRACT FROM AUTHOR]