Vertical Distributions of Aerosol and Cloud Microphysical Properties and the Aerosol Impact on a Continental Cumulus Cloud Based on Aircraft Measurements From the Loess Plateau of China

Based on aircraft measurements of aerosols and continental cumulus clouds made over the Loess Plateau of China (Xinzhou, Shanxi Province) on 30 July 2020, this study focuses on the vertical profiles of microphysical properties of aerosols and cumulus clouds, and use them to study aerosol-cloud interactions. During the study period, the boundary layer was stable with a height ∼1,500 m above sea level. Aerosols in the boundary layer mainly came from local emissions, while aerosols above this layer were mostly dust aerosols transported over long distances. Vertical profiles of aerosols and cloud condensation nuclei were obtained, and aerosol activation ratios at different supersaturation (SS) levels ranged from 0.16 to 0.32 at 0.2% SS and 0.70 to 0.85 at 0.8% SS. A thick cumulus cloud in the development stage was observed from the bottom to the top with the horizontal dimension of 10 km by 7 km, the cloud-base height of 2,450 m (15.8°C), and the cloud-top height of 5,400 m (−3°C). The maximum updraft velocity near the cloud top was 13.45 m s−1, and the maximum downdraft velocity occuring in the upper-middle part of the cloud was 4.44 ms−1. The temperature inside the cloud was higher than the outside, with their difference being positively correlated with the cloud water content. The temperature lapse rate inside the cloud was about −6.5°C km−1. The liquid water content and droplet effective radius (Re) increased with increasing height. The cloud droplet number concentration (Nc) increased first then decreased, peaking in the middle and lower part of the cloud, the average values of Nc and Re were 767.9 cm−3 and 5.17 μm, respectively. The cloud droplet spectrum had a multi-peak distribution, with the first appearing at ∼4.5 μm. SS in the cloud first increased then decreased with height. The maximum SS is ∼0.7% appearing at ∼3,800 m. The conversion rate of intra-cloud aerosols to cloud droplets was between 0.2 and 0.54, with the ratio increasing gradually with increasing height. The cloud droplet spectral dispersion and Nc were positively correlated. The aerosol indirect effect (AIE) was estimated to be 0.245 and 0.16, based on Nc and Re, respectively. The cloud droplet dispersion mainly attenuated the AIE, up to ∼34.7%.

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

Tropopause folds are the key process underlying stratosphere-to-troposphere transport (STT) of ozone, thus, affecting 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 some 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.

Reactive species from aviation in the multi-scale climate-chemistry model system MECO(n)

<p>Aviation aims to reduce its climate impact by identifying promising mitigation options which are able to reduce the overall climate effects of aviation considering CO<sub>2</sub> and non-CO<sub>2</sub> effects. While aiming to identify fuel optimal trajectories, aviation also aims to reduce the non-CO<sub>2</sub> effects comprising NO<sub>x</sub>-induced changes of atmospheric ozone and methane. Here climate-chemistry models are required which are able to quantify perturbations in atmospheric composition of reactive species (NO<sub>x</sub>, O<sub>3</sub>) and the associated radiative forcings of aviation emissions relying on advanced modelling capabilities, realistic emission inventory data and global-scale observational datasets from research infrastructures like IAGOS and DLR aircraft measurement campaign data.</p> <p>We use the multi-scale climate-chemistry MECO(n) system which is a “MESSy-fied ECHAM and COSMO nested n-times”, relying on the Modular Earth Submodel System (MESSy) framework. For this purpose, both models have been equipped with the MESSy infrastructure, implying that the same process formulations (MESSy submodels) are available for both models. Modelled atmospheric distributions are systematically compared to observational data from aircraft measurements in the upper troposphere and lower stratosphere. Nudging of meteorology to ERA5 interim data, and special diagnostics available within the modular MESSy infrastructure are implemented in the numerical simulations. Online sampling along aircraft trajectories allows to extract model data with a high temporal resolution (MESSy submodel S4D), in order to evaluate model representation and key processes.</p> <p>Beyond systematic evaluation with IAGOS scheduled aircraft measurements, episodes will be evaluated where dedicated measurements from aircraft campaigns are available, comprising Spring 2014 (ML-CIRRUS campaign), early summer 2020 (Blue Sky campaign) and summer 2021 (Cirrus-HL campaign). Our analysis of reactive species, NO<sub>y</sub> and ozone, identifies those weather pattern and synoptic situations where aviation contributes strong signals, resulting in recommendations on measurement strategies to detect aviation signal in the atmosphere. We evaluate model representation of the NO<sub>x</sub>-induces effect on radiatively active species ozone and methane in both model instances, ECHAM5 and COSMO. This is key for advancing the scientific understanding of NO<sub>x</sub>-induced effects from aviation which is required in order to quantify potential compensation and trade-offs when identifying robust mitigation options for sustainable aviation.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875036 (ACACIA, Advancing the Science for Aviation and Climate) and has been supported by the DLR-Projekt Eco2Fly. This work uses measurement data from the European Research Infrastructure IAGOS/CARIBIC. High-Performance Super Computing simulations have been performed by the Deutsches Klima-Rechenzentrum (DKRZ, Hamburg) and the Leibniz-Rechenzentrum (LRZ, München).</p>

Satellite measurements of peroxyacetyl nitrate from the Cross-Track Infrared Sounder: Comparison with ATom aircraft measurements

We present an overview of an optimal estimation algorithm to retrieve peroxyacetyl nitrate (PAN) from single field of view Level 1B radiances measured by the Cross-Track Infrared Sounder (CrIS). CrIS PAN retrievals show peak sensitivity in the mid-troposphere, with degrees of freedom for signal less than or equal to 1.0. We show comparisons with two sets of aircraft measurements from the Atmospheric Tomography Mission (ATom), the PAN and Trace Hydrohalocarbon ExpeRiment (PANTHER) and the Georgia Tech Chemical Ionization Mass Spectrometer (GT-CIMS). We find a systematic difference between the two aircraft datasets, with vertically averaged mid-tropospheric values from the GT-CIMS around 14 % lower than equivalent values from the PANTHER. However, the two sets of aircraft measurements are strongly correlated (R2 value of 0.92) and do provide a consistent view of the large-scale variation of PAN. We demonstrate that the retrievals of PAN from CrIS show skill in measurement of these large-scale PAN distributions in the remote mid-troposphere compared to the retrieval prior. The standard deviation of individual CrIS-aircraft differences is 0.08 ppbv, which we take as an estimate of the uncertainty of the CrIS mid-tropospheric PAN for a single satellite field of view. The standard deviation of the CrIS-aircraft comparisons for averaged CrIS retrievals (median of 20 satellite co-incidences with each aircraft profile) is lower, at 0.05 ppbv. This would suggest that the retrieval error reduces with averaging, although not with the square root of the number of observations. We find a negative bias of order 0.1 ppbv in the CrIS PAN results with respect to the aircraft measurements. This bias does not appear to show a dependence on latitude or season.

New photolytic converter for improving aircraft measurements of NO₂ via chemiluminescence

Nitrogen oxides (NOx ≡ NO + NO2) are centrally involved in the photochemical processes taking place in the earth’s atmosphere. Measurements of NO2, particularly in remote areas where concentrations are of the order of pptv, are still a challenge and subject to extensive research. In this study, we present NO2 measurements via photolysis-chemiluminescence during the research aircraft campaign CAFE Africa (Chemistry of the Atmosphere – Field Experiment in Africa) 2018 around Cabo Verde as well as the results of laboratory experiments to characterize the photolytic converter used. We identify a memory effect within the conventional photolytic converter associated with high NO concentrations and rapidly changing water vapor concentrations, accompanying changes in altitude during aircraft measurements, which is due to the porous structure of the converter material. We test and characterize an alternative photolytic converter made from quartz glass which improves the reliability of NO2 measurements in laboratory and field studies.

The retrieval of snow properties from SLSTR Sentinel-3 – Part 2: Results and validation

To evaluate the performance of the eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm, presented in the Part 1 companion paper to this paper, we apply the XBAER algorithm to the Sea and Land Surface Temperature Radiometer (SLSTR) instrument on board Sentinel-3. Snow properties – snow grain size (SGS), snow particle shape (SPS) and specific surface area (SSA) – are derived under cloud-free conditions. XBAER-derived snow properties are compared to other existing satellite products and validated by ground-based and aircraft measurements. The atmospheric correction is performed on SLSTR for cloud-free scenarios using Modern-Era Retrospective Analysis for Research and Applications (MERRA) aerosol optical thickness (AOT) and the aerosol typing strategy according to the standard XBAER algorithm. The optimal SGS and SPS are estimated iteratively utilizing a look-up-table (LUT) approach, minimizing the difference between SLSTR-observed and SCIATRAN-simulated surface directional reflectances at 0.55 and 1.6 µm. The SSA is derived for a retrieved SGS and SPS pair. XBAER-derived SGS, SPS and SSA have been validated using in situ measurements from the recent campaign SnowEx17 during February 2017. The comparison shows a relative difference between the XBAER-derived SGS and SnowEx17-measured SGS of less than 4 %. The difference between the XBAER-derived SSA and SnowEx17-measured SSA is 2.7 m2/kg. XBAER-derived SPS can be reasonably explained by the SnowEx17-observed snow particle shapes. Intensive validation shows that (1) for SGS and SSA, XBAER-derived results show high correlation with field-based measurements, with correlation coefficients higher than 0.85. The root mean square errors (RMSEs) of SGS and SSA are around 12 µm and 6 m2/kg. (2) For SPS, aggregate SPS retrieved by XBAER algorithm is likely to be matched with rounded grains while single SPS in XBAER is possibly linked to faceted crystals. The comparison with aircraft measurements, during the Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP) campaign held in March 2018, also shows good agreement (with R=0.82 and R=0.81 for SGS and SSA, respectively). XBAER-derived SGS and SSA reveal the variability in the aircraft track of the PAMARCMiP campaign. The comparison between XBAER-derived SGS results and the Moderate Resolution Imaging Spectroradiometer (MODIS) Snow-Covered Area and Grain size (MODSCAG) product over Greenland shows similar spatial distributions. The geographic distribution of XBAER-derived SPS over Greenland and the whole Arctic can be reasonably explained by campaign-based and laboratory investigations, indicating a reasonable retrieval accuracy of the retrieved SPS. The geographic variabilities in XBAER-derived SGS and SSA both over Greenland and Arctic-wide agree with the snow metamorphism process.