Abstract: Radar altimeters are important tools to monitor the volume of the ice sheets. The penetration of radar waves in the snowpack is a major source of uncertainty to retrieve surface elevation. To correct this effect, a better understanding of the sensitivity of the radar waveforms to snow properties is needed. Here, we present an extension of the Snow Model Radiative Transfer (SMRT) to compute radar waveforms and conduct a series of simulations on the Antarctic ice sheet. SMRT is driven by snow and surface roughness properties measured over a large latitudinal range during two field campaigns on the Antarctic Plateau. These measurements show that the snowpack is rougher, denser, less stratified, warmer, and has smaller snow grains near the coast than on the central Plateau. These simulations are compared to satellite observations in the Ka, Ku, and S bands. SMRT reproduces the observed waveforms well. For all sites and all sensors, the main contribution comes from the surface echo. The echo from snow grains (volume scattering) represents up to 40% of the amplitude of the total waveform power in the Ka band, and less at the lower frequencies. The highest amplitude is observed on the central Plateau due to the combination of higher reflection from the surface, higher scattering by snow grains in the Ka and Ku bands, and higher inter-layer reflections in the S band. In the Ka band, the wave penetrates in the snowpack less deeply on the central Plateau than near the coast because of the strong scattering caused by the larger snow grains. The opposite is observed in the S band, the wave penetrates deeper on the central Plateau because of the lower absorption due to the lower snow temperatures. The elevation bias caused by wave penetration into the snowpack show a constant bias of 10 cm for all sites in the Ka band, and a bias of 11 cm, and 21 cm in the Ku band for sites close to the coast and the central Plateau, respectively. Now that SMRT is performing waveform simulations, further work will address how the snowpack properties affect the parameters retrieved by more advanced retracking algorithms such as ICE-2 for different snow cover surfaces.

Abstract: Benthic life in the Southern Ocean (SO) features unique life history traits and species assemblages, but the origin and evolution of many of these taxonomic groups is still unclear. Sea stars (Asteroidea) are a diversified and abundant component of benthic ecosystems in the SO, in which they can play key ecological roles. Former studies suggest that the diversity of the entire class is still poorly known and underestimated, hampering the assessment of the origin and evolution of the class in the SO. In the present study, we analyse spatial patterns of SO sea star diversity using an occurrence database of ~14,000 entries. The biogeographic analysis is coupled with the exploration of an extensive molecular phylogeny based on over 4,400 specimen sequences to inform, support and/or question the observed diversity patterns. We show that the current taxonomy of SO asteroids needs revision and that their diversity has generally been overlooked and misinterpreted. Molecular results highlight the recent diversification of most studied taxa, at genus and species levels, which supports an evolutionary scenario referring to successive invasion and exchange events between the SO and adjacent regions, and clade diversification during periods of rapid environmental changes driven by the succession of glacial cycles. Our work advocates for employing, and endorsing the use of extensive genetic barcode libraries for biodiversity studies.

Abstract: This Paper Reports On Consolidated Ground-based Validation Results Of The Atmospheric No2 Data Produced Operationally Since April 2018 By The Tropospheric Monitoring Instrument (Tropomi) On Board Of The Esa/eu Copernicus Sentinel-5 Precursor (S5p) Satellite. Tropospheric, Stratospheric, And Total No2 Column Data From S5p Are Compared To Correlative Measurements Collected From, Respectively, 19 Multi-axis Differential Optical Absorption Spectroscopy (Max-doas), 26 Network For The Detection Of Atmospheric Composition Change (Ndacc) Zenith-scattered-light Doas (Zsl-doas), And 25 Pandonia Global Network (Pgn)/pandora Instruments Distributed Globally. The Validation Methodology Gives Special Care To Minimizing Mismatch Errors Due To Imperfect Spatio-temporal Co-location Of The Satellite And Correlative Data, E.g. By Using Tailored Observation Operators To Account For Differences In Smoothing And In Sampling Of Atmospheric Structures And Variability And Photochemical Modelling To Reduce Diurnal Cycle Effects. Compared To The Ground-based Measurements, S5p Data Show, On Average, (I) A Negative Bias For The Tropospheric Column Data, Of Typically ?23 % To ?37 % In Clean To Slightly Polluted Conditions But Reaching Values As High As ?51 % Over Highly Polluted Areas; (Ii) A Slight Negative Median Difference For The Stratospheric Column Data, Of About ?0.2 Pmolec Cm?2, I.e. Approx. ?2 % In Summer To ?15 % In Winter; And (Iii) A Bias Ranging From Zero To ?50 % For The Total Column Data, Found To Depend On The Amplitude Of The Total No2 Column, With Small To Slightly Positive Bias Values For Columns Below 6 Pmolec Cm?2 And Negative Values Above. The Dispersion Between S5p And Correlative Measurements Contains Mostly Random Components, Which Remain Within Mission Requirements For The Stratospheric Column Data (0.5 Pmolec Cm?2) But Exceed Those For The Tropospheric Column Data (0.7 Pmolec Cm?2). While A Part Of The Biases And Dispersion May Be Due To Representativeness Differences Such As Different Area Averaging And Measurement Times, It Is Known That Errors In The S5p Tropospheric Columns Exist Due To Shortcomings In The (Horizontally Coarse) A Priori Profile Representation In The Tm5-mp Chemical Transport Model Used In The S5p Retrieval And, To A Lesser Extent, To The Treatment Of Cloud Effects And Aerosols. Although Considerable Differences (Up To 2 Pmolec Cm?2 And More) Are Observed At Single Ground-pixel Level, The Near-real-time (Nrti) And Offline (Offl) Versions Of The S5p No2 Operational Data Processor Provide Similar No2 Column Values And Validation Results When Globally Averaged, With The Nrti Values Being On Average 0.79 % Larger Than The Offl Values.