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The present work describes the implementation of the state of the art Cloud- J v7.3 photolysis rate calculation code in the EMEP MSC-W chemistry-transport model. Cloud- J calculates photolysis rates and accounts for cloud and aerosol optical properties at model run time, replacing the old system based on tabulated values. The performance of Cloud- J is evaluated against aerial photolysis rate observations made over the Pacific Ocean and against surface observations from three measurement sites in Europe. Numerical experiments are performed to investigate the sensitivity of the calculated photolysis rates to the spatial and temporal model resolution, input meteorology model, simulated ozone column, and cloud effect parameterization. These experiments indicate that the calculated photolysis rates are most sensitive to the choice of input meteorology model and cloud effect parameterization while also showing that surface ozone photolysis rates can vary by up to 20 % due to daily variations in total ozone column. Further analysis investigates the impact of Cloud- J on the oxidizing capacity of the troposphere, aerosol–photolysis interactions, and surface air quality predictions. Results find that the annual mean mass-weighted tropospheric hydroxyl concentration is increased by 26 %, while the photolytic impact of aerosols is mostly limited to large tropical biomass-burning regions. Overall, Cloud- J represents a major improvement over the tabulated system, leading to improved model performance for predicting carbon monoxide and daily maximum ozone surface concentrations. [ABSTRACT FROM AUTHOR]

Ground-based high resolution observations of downward longwave irradiance (DLI), surface air temperature, water vapour surface partial pressure and column amount, zenith sky infrared (IR) radiance in the atmospheric window, and all-sky camera images are regularly obtained at the Thule High Arctic Atmospheric Observatory (THAAO, 76.5°N, 68.8°W), North-West Greenland. The datasets for the years 2017 and 2018 have been used to assess the performance of different empirical formulas to infer clear sky DLI. An algorithm to identify clear sky observations has been developed, based on value, variability, and persistence of zenith sky IR radiance. Seventeen different formulas to estimate DLI have been tested against the THAAO dataset, using the originally determined coefficients. The formulas which combine information on total column water vapour and surface air temperature appear to perform better than others, with a mean bias with respect to the measured DLI smaller than 1 W/m² and a root mean squared error (RMSE) around 6 W/m². Some formulas, specifically developed for the Arctic, are found to produce poor statistical results; this is attributed partly to limitations in the originally used dataset, which does not cover a whole year, or is relative to very specific conditions (i.e., the ice sheet). The bias displays a significant improvement when the coefficients of the different formulas are calculated using the THAAO dataset. The presence of two full years of data allows the investigation of the inter-annual variability, and the use of different years for the determination of the coefficients and the evaluation of results. The smallest values of the bias and RMSE reach 0.1 W/m² and 5 W/m², respectively. Overall, best results are found for formulas which use both surface parameters and total water vapour column, and have been developed from global datasets. Conversely, formulas which express the atmospheric emissivity as a linear function of the logarithm of the column integrated water vapour appear to poorly reproduce the observations at THAAO. [ABSTRACT FROM AUTHOR]

The Arctic climate is influenced by the interaction of shortwave (SW) and longwave (LW) radiation with the atmosphere and the surface. The comprehensive evolution of the Surface Radiative Fluxes (SRF) on different time scales is of paramount importance to understanding the complex mechanisms governing the Arctic climate. However, only a few sites located in the Arctic region provide long-term time series of SRF allowing for capturing the seasonality of atmospheric and surface parameters and carrying out validation of satellite products and/or reanalyses. This paper presents the daily and monthly SRF record collected at the Thule High Arctic Atmospheric Observatory (THAAO, 76.5° N, 68.8° W), in North-Western Greenland. The downwelling components of the SW (DSI) and the LW (DLI) irradiances have been measured at THAAO since 2009, while the collection of the upwelling SW (USI) and LW (ULI) irradiance was started in 2016, together with additional measurements, such as e.g., meteorological parameters and column water vapour. The datasets of DSI (Meloni et al., 2022a; https://doi.org/10.13127/thaao/dsi), USI (Meloni et al., 2022b; https://doi.org/10.13127/thaao/usi), DLI (Meloni et al., 2022c; https://doi.org/10.13127/thaao/dli), ULI (Meloni et al., 2022d; https://doi.org/10.13127/thaao/uli), and near surface air temperature (Muscari et al., 2018; https://doi.org/10.13127/thaao/met), can be accessed through the THAAO web site (https://www.thuleatmos-it.it/data). DSI is absent (solar zenith angle=90°) from 29 October to 13 February, assuming maxima in June (monthly mean of 277.0 Wm-2), when it is about half of the total incoming irradiance. The USI maximum occurs in May (132.4 Wm-2) due to the combination of moderately high DSI values and high albedo. The shortwave surface albedo (A) assumes an average of 0.16 during summer, when the surface is free of snow. Differently, during months of snow-covered surface, when solar radiation allows estimating A, its values are greater than 0.6. A large interannual variability is observed in May and September, months characterized by rapidly changing surface conditions, which appear to be linked with air temperature anomalies. DLI and ULI maxima occur in July and August, and minima in February and March. ULI is always larger than DLI and shows a wider annual cycle. ULI is well described by a fourth-order polynomial fit to the air temperature (R²>0.99 for monthly data and R²>0.97 for daily data). The surface radiation budget (SRB) is positive from April to August, when absorption of solar radiation exceeds the infrared net cooling, with a maximum value of 153.2 Wm-2 in June. From November to February, during the polar night, the LW net flux varies between -34.5 and -43.0 Wm-2. In March and September, the negative LW net flux overcomes the positive SW contribution, producing a negative SRB. THAAO measurements show clear evidence of the influence of several regional weather/climate events, which appear strongly linked with SRF anomalies. These anomalies are found for example during summer 2012, when a large ice melting event took place over Greenland, and during winter 2019-2020, extraordinarily cold in the Arctic region. [ABSTRACT FROM AUTHOR]

This paper presents the results on simultaneous high time resolution measurements of biogenic aerosol (methane sulfonic acid-MSA, non-sea salt sulfate nssSO42-) with its gaseous precursor dimethylsulfide (DMS) performed at the Italian coastal base Mario Zucchelli Station (MZS) in Terra Nova Bay (MZS) during two summer campaigns (2018-2019 and 2019-2020). The study provides information on marine biological activity in the nearby polynya in the Ross Sea and on the influence of biogenic and atmospheric processes on biogenic aerosol formation. Data on atmospheric DMS concentration are scarce especially in Antarctica. The DMS-maximum at MZS occurs in December, one month earlier than at other Antarctic stations. The maximum of DMS concentration is connected with the phytoplanktonic senescent phase following the bloom of Phaeocystis antarctica that occurs in the polynya when sea ice opens up. The second plankton bloom occurs in January and, despite the high Dimethylsulfopropionate (DMSP) concentration in sea water, atmospheric DMS remains low probably due to its fast biological turnover in sea water in this period. The intensity and timing of the DMS evolution during the two years suggests that only the portion of the polynya close to the sampling site produces a discernible effect on the measured DMS. The closeness to the DMS source area, and the occurrence of air masses containing DMS and freshly formed oxidation products allow to study the kinetic of biogenic aerosol formation and the reliable derivation of the branch ratio between MSA and nssSO42- from DMS oxidation that is estimated to be 0.84 ±0.06. Conversely, for aged airmasses with low DMS content, an enrichment of nssSO42- with respect to MSA, due to the presence of background concentration of nssSO42-from volcanic origin (Erebus) or from long range transport, takes place. Therefore, the aged air mass presents an MSA/ nssSO42- ratio lower than in newly formed biogenic aerosol. By considering the sum of MSA and biogenic nssSO42-, we estimate that the mean contribution of biogenic particulate matter to PM10 is 17%, with a maximum of 56%. The high contribution of biogenic aerosol to the total PM10 mass in summer in this area highlights the dominant role of the polynya on biogenic aerosol formation. Finally, due to the regional and year-to year variability of DMS and related biogenic aerosol formation, we stress the need of long-term measurements of atmospheric DMS and biogenic aerosol along the Antarctic coast and in the Southern Ocean. [ABSTRACT FROM AUTHOR]

Measurements of aerosol composition and size distributions were taken during the summer of 2013 at the remote island of Lampedusa in the southern central Mediterranean Sea. These measurements were part of the ChArMEx/ADRIMED (Chemistry and Aerosol Mediterranean Experiment/Aerosol Direct Radiative Forcing on the Mediterranean Climate) framework and took place during the Special Observation Period 1a (SOP-1a) from 11 June until 5 July 2013. From compact time-of-flight aerosol mass spectrometer (cToF-AMS) measurements in the size range below 1 μm in aerodynamic diameter (PM1), particles were predominately comprised of ammonium and sulphate. On average, ammonium sulphate contributed 63 % to the non-refractory PM1 mass, followed by organics (33 %). The organic aerosol was generally very highly oxidised (f44 values were typically between 0.25 and 0.26). The contribution of ammonium sulphate was generally higher than organic aerosol in comparison to measurements taken in the western Mediterranean but is consistent with studies undertaken in the eastern basin. Source apportionment of organics using a statistical (positive matrix factorisation) model revealed four factors; a hydrocarbon-like organic aerosol (HOA), a methanesulfonic acid related oxygenated organic aerosol (MSA-OOA), a more oxidised oxygenated organic aerosol (MO-OOA) and a less oxidised oxygenated organic aerosol we label (LO-OOA). The MO-OOA was the dominant factor for most of the campaign (53 % of the PM1 OA mass). It was well correlated with SO42−, highly oxidised, and generally more dominant during easterly air masses originating from the eastern Mediterranean and central Europe. The LO-OOA factor had a very similar composition to the MO-OOA factor, but was more prevalent during westerly winds with air masses originating from the Atlantic Ocean, the western Mediterranean, and in high altitudes over France and Spain from mistral winds. The MSA-OOA factor contributed an average 12 % to the PM1 OA and was more dominant during the mistral winds. The HOA, representing observed primary organic aerosol only contributed 8 % of the average PM1 OA during the campaign. Even though Lampedusa is one of the most remote sites in the Mediterranean, PM1 concentrations (10 ± 5 μg m−3) were comparable to those observed in coastal cities and sites closer to continental Europe. Cleaner conditions corresponded to higher wind speeds. Nucleation and growth of new aerosol particles was observed during periods of northwesterly winds. From a climatology analysis from 1999 until 2012, these periods were much more prevalent during the measurement campaign than during the preceding 13 years. These results support previous findings that highlight the importance of different large-scale synoptic conditions in determining the regional and local aerosol composition and oxidation and also suggest that a non-polluted surface atmosphere over the Mediterranean is rare. [ABSTRACT FROM AUTHOR]

Air backward trajectory calculations are commonly used in a variety of atmospheric analyses, in particular for source attribution evaluation. The accuracy of backward trajectory analysis is mainly determined by the quality and the spatial and temporal resolution of the underlying meteorological data set, especially in the cases of complex terrain. This work describes a new tool for the calculation and the statistical elaboration of backward trajectories. To take advantage of the high-resolution meteorological database of the Italian national air quality model MINNI, a dedicated set of procedures was implemented under the name of M-TraCE (MINNI module for Trajectories Calculation and statistical Elaboration) to calculate and process the backward trajectories of air masses reaching a site of interest. Some outcomes from the application of the developed methodology to the Italian Network of Special Purpose Monitoring Stations are shown to assess its strengths for the meteorological characterization of air quality monitoring stations. M-TraCE has demonstrated its capabilities to provide a detailed statistical assessment of transport patterns and region of influence of the site under investigation, which is fundamental for correctly interpreting pollutants measurements and ascertaining the official classification of the monitoring site based on meta-data information. Moreover, M-TraCE has shown its usefulness in supporting other assessments, i.e., spatial representativeness of a monitoring site, focussing specifically on the analysis of the effects due to meteorological variables. [ABSTRACT FROM AUTHOR]