Abstract: Biophysical interactions are influential in determining the scale of key ecological processes within marine ecosystems. For oceanic predators, this means foraging behaviour is influenced by processes shaping the distribution of prey. However, oceanic prey is difficult to observe and its abundance and distribution is regionally generalised. We use a spatiotemporally resolved simulation model to describe mid-trophic prey distribution within the Southern Ocean and demonstrate insights that this modelled prey field provides into the foraging behaviour of a widely distributed marine predator, the southern elephant seal. From a five-year simulation of prey biomass, we computed climatologies of mean prey biomass (average prey conditions) and prey biomass variability (meso-scale variability). We also compiled spatially gridded metrics of seal density and diving behaviour from 13 yr of tracking data. We statistically modelled these metrics as non-linear functions of prey biomass (both mean and variability) and used these to predict seal distribution and behaviour. Our predictions were consistent with observations (R2adj = 0.23), indicating that seals aggregate in regions of high mesoscale activity where eddies concentrate prey. Here, seals dived deeper (R2marg = 0.12, R2cond = 0.51) and spent less time hunting (R2marg = 0.05, R2cond = 0.56), likely targeting deep but profitable prey patches. Seals generally avoided areas of low eddy activity where prey was likely dispersed. Most seals foraged south of the Subantarctic Front, despite north of the front exhibiting consistently high simulated prey biomasses. This likely reflects seal prey or habitat preferences, but also emphasises the importance of mesoscale prey biomass variability relative to regionally high mean biomass. This work demonstrates the value of coupling mechanistic representations of prey biomass with predator observations to provide insight into how biophysical processes combine to shape species distributions. This will be increasingly important for the robust prediction of species’ responses to rapid system change.

Abstract: The Minamata Convention On Mercury (Hg) Entered Into Force In 2017, Committing Its 116 Parties (As Of January 2019) To Curb Anthropogenic Emissions. Monitoring Of Atmospheric Concentrations And Trends Is An Important Part Of The Effectiveness Evaluation Of The Convention. A Few Years Ago (In 2017) We Reported An Increasing Trend In Atmospheric Hg Concentrations At The Cape Point Global Atmosphere Watch (Gaw) Station In South Africa (34.3535∘ S, 18.4897∘ E) For The 2007–2015 Period. With 2 More Years Of Measurements At Cape Point And The 2012–2017 Data From Amsterdam Island (37.7983∘ S, 77.5378∘ E) In The Remote Southern Indian Ocean, A More Complex Picture Emerges: At Cape Point The Upward Trend For The 2007–2017 Period Is Still Significant, But No Trend Or A Slightly Downward Trend Was Detected For The Period 2012–2017 At Both Cape Point And Amsterdam Island. The Upward Trend At Cape Point Is Driven Mainly By The Hg Concentration Minimum In 2009 And Maxima In 2014 And 2012. Using Ancillary Data On 222rn, Co, O3, Co2, And Ch4 From Cape Point And Amsterdam Island, The Possible Reasons For The Trend And Its Change Are Investigated. In A Companion Paper This Analysis Is Extended For The Cape Point Station By Calculations Of Source And Sink Regions Using Backward-trajectory Analysis.

Abstract: L’étude sismologique de la glace permet de mieux comprendre la déformation des systèmes glaciaires et leur stabilité́ dans un contexte de réchauffement climatique global. La cryosphère est très active sismiquement. De nombreux évènements comme des icequakes, des trémors glaciaires ou des évènements à l’interface glace/terre solide émanent de la base des glaciers, des marges de cisaillement, des vêlages au front des glaciers ou encore des chenaux d’eau sous- glaciaire. Nous avons observé les signaux d’une dizaine de stations sismologiques sur les côtes de l’antarctique et parmi tous les évènements visibles, un signal de quelques heures présente une forte répétabilité́ dans le temps et dans l’espace. Pour l’étudier nous avons extrait des séries temporelles d’énergie sismique sur trois stations : drv, thur, vna1 et nous avons montré sa modulation avec les forçages de marée d’abord sur l’année 2018 puis sur 8 ans à thur, 10 ans à vna1 et 20 ans à drv. Le signal d’énergie sismique présente toutes les périodicités des forçages de marée avec cependant quelques décalages temporels, indicateurs de délais de réponse du système glaciaire et d’un comportement rhéologique viscoélastique de la glace.

Abstract: Penguins are the only extant family of flightless diving birds. They currently comprise at least 18 species, distributed from polar to tropical environments in the Southern Hemisphere. The history of their diversification and adaptation to these diverse environments remains controversial. We used 22 new genomes from 18 penguin species to reconstruct the order, timing, and location of their diversification, to track changes in their thermal niches through time, and to test for associated adaptation across the genome. Our results indicate that the penguin crown-group originated during the Miocene in New Zealand and Australia, not in Antarctica as previously thought, and that Aptenodytes is the sister group to all other extant penguin species. We show that lineage diversification in penguins was largely driven by changing climatic conditions and by the opening of the Drake Passage and associated intensification of the Antarctic Circumpolar Current (ACC). Penguin species have introgressed throughout much of their evolutionary history, following the direction of the ACC, which might have promoted dispersal and admixture. Changes in thermal niches were accompanied by adaptations in genes that govern thermoregulation and oxygen metabolism. Estimates of ancestral effective population sizes (Ne) confirm that penguins are sensitive to climate shifts, as represented by three different demographic trajectories in deeper time, the most common (in 11 of 18 penguin species) being an increased Ne between 40 and 70 kya, followed by a precipitous decline during the Last Glacial Maximum. The latter effect is most likely a consequence of the overall decline in marine productivity following the last glaciation.

Abstract: This Paper Reports On Consolidated Ground-based Validation Results Of The Atmospheric No2 Data Produced Operationally Since April 2018 By The Tropomi Instrument 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 Doas (Max-doas), 26 Ndacc Zenith-scattered-light Doas (Zsl-doas), And 25 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 An 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 Bias For The Stratospheric Column Data, Of About −0.2 Pmolec/cm2, 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/cm2 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/cm2), But Exceed Those For The Tropospheric Column Data (0.7 Pmolec/cm2). While A Part Of The Biases And Dispersion May Be Due To Representativeness Differences, 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 Chemistry Transport Model Used In The S5p Retrieval, And To A Lesser Extent, To The Treatment Of Cloud Effects. Although Considerable Differences (Up To 2 Pmolec/cm2 And More) Are Observed At Single Ground-pixel Level, The Near-real-time (Nrti) And Off-line (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.