Spectropolarimetry as a tool for understanding the diversity of planetary atmospheres

The polarization state of starlight reflected by a planetary atmosphere uniquely reveals coverage, particle size, and composition of aerosols as well as changing cloud patterns. It is not possible to obtain a comparable level of detail from flux-only observations. It is therefore a powerful tool to better understand the crucial role played by clouds and aerosols in the chemistry, dynamics, and radiative balance of a planet. Furthermore, polarization observations can probe the atmosphere of planets independently of the orbital geometry (hence it applies to both transiting and non-transiting exoplanets). A high-resolution spectropolarimeter with a broad wavelength coverage, particularly if attached to a large space telescope, would enable simultaneous study of the polarimetric planetary properties of the continuum and to look for and characterize the polarimetric signal due to scattering from single molecules, providing detailed information about the composition and vertical structure of the atmosphere.

C³ONTEXT: A Common Consensus on Convective OrgaNizaTion during the EUREC⁴A eXperimenT

The C3ONTEXT (A Common Consensus on Convective OrgaNizaTion during the EUREC4A eXperimenT) dataset is presented as an overview about the meso-scale cloud patterns identified during the EUREC4A field campaign in early 2020. Based on infrared and visible satellite images, 50 researchers of the EUREC4A science team manually identified the four prevailing meso-scale patterns of shallow convection observed by Stevens et al. (2020). The common consensus on the observed meso-scale cloud patterns emerging from these manual classifications is presented. It builds the basis for future studies and reduces the subjective nature of these visually defined cloud patterns. This consensus makes it possible to contextualize the measurements of the EUREC4A field campaign and interpret them in the meso-scale setting. Commonly used approaches to capture the meso-scale patterns are computed for comparison and show good agreement with the manual classifications. All four patterns as classified by Stevens et al. (2020) were present in January–February 2020 although not all were dominant during the observing period of EUREC4A. The full dataset including postprocessed datasets for easier usage are openly available at the Zenodo archive at https://doi. org/10.5281/zenodo.5724585 (Schulz, 2021b).

Modeling Polarization Signals from Cloudy Brown Dwarfs Luhman 16 A and B in Three Dimensions

The detection of disk-integrated polarization from Luhman 16 A and B in the H band, and subsequent modeling, has been interpreted in the framework of zonal cloud bands on these bodies. Recently, Tan and Showman investigated the 3D atmospheric circulation and cloud structures of brown dwarfs with general circulation models (GCMs), and their simulations yielded complex cloud distributions showing some aspects of zonal jets, but also complex vortices that cannot be captured by a simple model. Here we use these 3D GCMs specific to Luhman 16 A and B, along with the 3D Monte Carlo radiative transfer code ARTES, to calculate their polarization signals. We adopt the 3D temperature–pressure and cloud profiles from the GCMs as our input atmospheric structures. Our polarization calculations at 1.6 μm agree well with the measured degree of linear polarization from both Luhman 16 A and B. Our calculations reproduce the measured polarization for both objects with cloud particle sizes between 0.5 and 1 μm for Luhman 16 A and of 5 μm for Luhman 16 B. We find that the degree of linear polarization can vary on hour-long timescales over the course of a rotation period. We also show that models with azimuthally symmetric band-like cloud geometries, typically used for interpreting polarimetry observations of brown dwarfs, overpredict the polarization signal if the cloud patterns do not include complex vortices within these bands. This exploratory work shows that GCMs are promising for modeling and interpreting polarization signals of brown dwarfs.

Classification and Evaluation of Stable and Unstable Cloud Forecasts

A physics-based cloud identification scheme, originally developed for a machine learning forecast system, was applied to verify cloud location and coverage bias errors from two years of 6-hour forecasts. The routine identifies stable and unstable environments based on the potential for buoyant versus stable cloud formation. The efficacy of the scheme is documented by investigating its ability to identify cloud patterns and systematic forecast errors. Results showed stable cloud forecasts contained widespread, persistent negative cloud cover biases most likely associated with turbulent, radiative and microphysical feedback processes. In contrast, unstable clouds were better predicted despite being poorly resolved. This suggests that scale aliasing, while energetically problematic, results in less severe short-term cloud cover errors.This study also evaluated Geostationary Operational Environmental Satellite (GOES) cloud base retrievals for their effectiveness at identifying regions of lower tropospheric cloud cover. Retrieved cloud base heights were sometimes too high with respect to their actual values in regions of deep-layered clouds, resulting in underestimates of the extent of low cloud cover in these areas. Sensitivity experiments indicate the most accurate cloud base estimates existed in regions with cloud tops at or below 8 km.

Influence of Atmospheric Circulation on Cloudiness and Cloud Types in Petuniabukta and Svalbard-Lufthavn in Summer 2016

The paper analyzes the influence of atmospheric circulation on cloudiness and cloud types during July and August of 2016 in Petuniabukta and Svalbard-Lufthavn. For the meteorological parameters, basic statistical measures were calculated and the average diurnal cloud patterns were analyzed. Taking the data from meteorological reanalysis (NCEP/NCAR-The National Centers for Atmospheric Prediction/The National Center for Atmospheric Research) regarding the mean sea-level pressure (SLP), 500 hPa geopotential height, and air temperature at 850 hPa (T850), composite maps of the synoptic situation for the studied area were constructed. For the observed types of clouds, the frequency of their occurrence in particular types of atmospheric circulation was then determined according to the Niedźwiedź classification. Differences in the amount of cloudiness in the examined measuring points were ascertained. The occurrence of cloud types is associated with both the direction of air mass advection and type of circulation. The results may also indicate the possibility of influence from specific, local environmental features on cloudiness.

Integrated water vapour observations in the Caribbean arc from a network of ground-based GNSS receivers during EUREC⁴A

Ground-based Global Navigation Satellite System (GNSS) measurements from nearly 50 stations distributed over the Caribbean arc have been analysed for the period 1 January–29 February 2020 in the framework of the EUREC4A (Elucidate the Couplings Between Clouds, Convection and Circulation) field campaign. The aim of this effort is to deliver high-quality integrated water vapour (IWV) estimates to investigate the moisture environment of mesoscale cloud patterns in the trade winds and their feedback on the large-scale circulation and energy budget. This paper describes the GNSS data processing procedures and assesses the quality of the GNSS IWV retrievals from four operational streams and one reprocessed research stream which is the main data set used for offline scientific applications. The uncertainties associated with each of the data sets, including the zenith tropospheric delay (ZTD)-to-IWV conversion methods and auxiliary data, are quantified and discussed. The IWV estimates from the reprocessed data set are compared to the Vaisala RS41 radiosonde measurements operated from the Barbados Cloud Observatory (BCO) and to the measurements from the operational radiosonde station at Grantley Adams International Airport (GAIA), Bridgetown, Barbados. A significant dry bias is found in the GAIA humidity observations with respect to the BCO sondes (−2.9 kg m−2) and the GNSS results (−1.2 kg m−2). A systematic bias between the BCO sondes and GNSS is also observed (1.7 kg m−2), where the Vaisala RS41 measurements are moister than the GNSS retrievals. The IWV estimates from a collocated microwave radiometer agree with the BCO soundings after an instrumental update on 27 January, while they exhibit a dry bias compared to the soundings and to GNSS before that date. IWV estimates from the ECMWF fifth-generation reanalysis (ERA5) are overall close to the GAIA observations, probably due to the assimilation of these observations in the reanalysis. However, during several events where strong peaks in IWV occurred, ERA5 is shown to significantly underestimate the GNSS-derived IWV peaks. Two successive peaks are observed on 22 January and 23–24 January which were associated with heavy rain and deep moist layers extending from the surface up to altitudes of 3.5 and 5 km, respectively. ERA5 significantly underestimates the moisture content in the upper part of these layers. The origins of the various moisture biases are currently being investigated. We classified the cloud organization for five representative GNSS stations across the Caribbean arc using visible satellite images. A statistically significant link was found between the cloud patterns and the local IWV observations from the GNSS sites as well as the larger-scale IWV patterns from the ECMWF ERA5 reanalysis. The reprocessed ZTD and IWV data sets from 49 GNSS stations used in this study are available from the French data and service centre for atmosphere (AERIS) (https://doi.org/10.25326/79; Bock, 2020b).

On the imprint of the mesoscale organization of tradewind clouds at cloud base and below

<p><span>Trade-wind clouds </span><span>can </span><span>exhibit </span><span>different</span><span> patterns of mesoscale organization. These patterns were observed during the EUREC</span><sup><span>4</span></sup><span>A </span><span>(Elucidating the role of cloud-circulation coupling in climate) </span><span>field campaign that took place in Jan-Feb 2020 over the western tropical Atlantic near Barbados: </span><span>w</span><span>hile the HALO aircraft </span><span>was observing clouds from</span> <span>above</span><span> and </span><span>was </span><span>characteri</span><span>z</span><span>ing</span> <span>the </span><span>large-scale</span><span> environment</span> <span>with</span><span> dropsondes</span><span>, the ATR-42 research aircraft was flying </span><span>in</span><span> the </span><span>lower troposphere</span><span>,</span> <span>characteriz</span><span>ing</span><span> cloud</span><span>s </span><span>and turbulence </span><span>with horizontal radar-lidar measurements and in-situ </span><span>probes and </span><span>sensors</span><span>. </span><span>By</span><span> analyz</span><span>ing</span> <span>these data </span><span>for different cloud patterns</span><span>, </span><span>we</span> <span>investigate the </span><span>extent to which the </span><span>cloud</span><span> organization </span><span>i</span><span>s imprinted </span><span>in</span><span> cloud-base </span><span>properties </span><span>and</span><span> subcloud-layer </span><span>heterogeneities</span><span>. </span><span>The implications of our findings for understanding the roots of the mesoscale organization </span><span>of tradewind clouds</span><span> will be discussed.</span></p>

Covariability of trade-wind cloudiness and environmental conditions in large-eddy simulations and observations

<p>Shallow convection in the downwind trades occurs in form of different cloud patterns with characteristic cloud arrangements at the meso-scale. The four most dominant patterns were previously named Sugar, Gravel, Flowers and Fish and have been identified to be associated with different net cloud radiative effects.</p><p>By using long-term observations, we reveal that these differences can be mainly attributed to the stratiform cloud component that varies in extent across the patterns as opposed to the cloudiness at the lifting condensation level that is fairly constant independent of the patterns.</p><p>The observations reveal further, that each pattern is associated with a different environmental condition whose characteristics originate not soley from within the trades. Sugar air-masses are characterized by weak winds and of tropical origin, while Fish are driven by convergence lines originating from synoptical disturbances. Gravel and Flowers are most native to the trades, but distinguish themselves with slightly stronger winds and stronger subsidence in the first case and greater stability in the latter.</p><p>How well this covariability of cloudiness and environmental conditions is represented in simulations is important to project the occurrence of the patterns in a warmer climate and evaluated by realistic large-eddy simulations of the recent EUREC4A field campaign.</p>

IWV observations in the Caribbean Arc from a network of ground-based GNSS receivers during EUREC⁴A

Ground-based Global Navigation Satellite System (GNSS) measurements from nearly fifty stations distributed over the Caribbean Arc have been analysed for the period 1 January–29 February 2020 in the framework of the EUREC4A (Elucidate the Couplings Between Clouds, Convection and Circulation) field campaign. The aim of this effort is to deliver high-quality Integrated Water Vapour (IWV) estimates to investigate the moisture environment of mesoscale cloud patterns in the Tradewinds and their feedback on the large-scale circulation and energy budget. This paper describes the GNSS data processing procedures and assesses the quality of the GNSS IWV retrievals from four operational streams and one reprocessed research stream which is the main data set used for offline scientific applications. The uncertainties associated with each of the data sets, including the zenith tropospheric delay (ZTD) to IWV conversion methods and auxiliary data, are quantified and discussed. The IWV estimates from the reprocessed data set are compared to the Vaisala RS41 radiosonde measurements operated from the Barbados Cloud Observatory (BCO) and to the measurements from the operational radiosonde station at Grantley Adams international airport (GAIA). A significant dry bias is found in the GAIA humidity observations with respect to the BCO sondes (−2.9 kg m−2) and the GNSS results (−1.2 kg m−2). A systematic bias between the BCO sondes and GNSS is also observed (1.7 kg m−2) where the Vaisala RS41 measurements are moister than the GNSS retrievals. The IWV estimates from a colocated microwave radiometer agree with the BCO soundings after an instrumental update on 27 January, while they exhibit a dry bias compared to the soundings and to GNSS before that date. IWV estimates from the ECMWF fifth generation reanalysis (ERA5) are overall close to the GAIA observations, probably due to the assimilation of these observations in the reanalysis. However, during several events where strong peaks in IWV occurred, ERA5 is shown to significantly underestimate the GNSS derived IWV peaks. Two successive peaks are observed on 22 January and 23/24 January which were associated with heavy rain and deep moist layers extending from the surface up to altitudes of 3.5 and 5 km, respectively. ERA5 significantly underestimates the moisture content in the upper part of these layers. The origins of the various moisture biases are currently being investigated. We classified the cloud organisation for five representative GNSS stations across the Caribbean Arc and found that the environment of Fish cloud patterns to be moister than that of Flowers cloud patterns which, in turn, is moister than the environment of Gravel cloud patterns. The differences in the IWV means between Fish and Gravel were assessed to be significant. Finally, the Gravel moisture environment was found to be similar to that of clear, cloud-free conditions. The moisture environment associated with the Sugar cloud pattern has not been assessed because it was hardly observed during the first two months of 2020. The reprocessed ZTD and IWV data set from 49 GNSS stations used in this study are available from the AERIS data center (https://doi.org/10.25326/79; Bock (2020b)).