Abstract: Electronic tracking technologies revolutionized wildlife ecology, notably for studying the movements of elusive species such as seabirds. Those advances are key to seabird conservation, for example in guiding the design of marine protected areas for this highly threatened group. Tracking data are also boosting scientific understanding of marine ecosystem dynamics in the context of global change. To optimize future tracking efforts, we performed a global assessment of seabird tracking data. We identified and mined 689 seabird tracking studies, reporting on > 28,000 individuals of 216 species from 17 families over the last four decades. We found substantial knowledge gaps, reflecting a historical neglect of tropical seabird ecology, with biases toward species that are heavier, oceanic, and from high-latitude regions. Conservation status had little influence on seabird tracking propensity. We identified 54 threatened species for which we did not find published tracking records, and 19 with very little data. Additionally, much of the existing tracking data are not yet available to other researchers and decision-makers in online databases. We highlight priority species and regions for future tracking efforts. More broadly, we provide guidance toward an ethical, rational, and efficient global tracking program for seabirds, as a contribution to their conservation.

Abstract: The accurate prediction of tides is vital for the operation of many industries, early warning of coastal flooding and scientific understanding of ocean processes. In this paper, we describe the creation method of a global dataset of tidal harmonics using NEMO (Nucleus for European Modelling of the Ocean) for the first time and an offline objective analysis scheme. Data are assimilated as part of a post-processing step, reducing the computational resources required. A reduced ensemble of tidal harmonics is generated, where each member is run for a shorter period of time than a central background state. This ensemble is used to estimate a single background covariance state, which is used for analysis. Output is validated using an ensemble of objective analyses. For each ensemble member, random selections of observations are omitted and validation is performed at these locations. Improvements in both Mean Absolute Error (MAE) and correlation coefficients (R2) are seen across all 6 of the largest diurnal and semi-diurnal constituents. MAEs in amplitude and phase are reduced by up to 78% and 89%, respectively, and correlations by as much as 0.14. In addition, the majority of locations (between 70 and 80%) see significant improvement.

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.

Abstract: In this paper, echo occurrence rates for the Dome C East (DCE) and the new Dome C North (DCN) radars are studied. We report the ionospheric and ground scatter echo occurrence rates for selected periods around equinoxes and solstices in the final part of the solar cycle XXIV. The occurrence maps built in Altitude Adjusted Corrected Geomagnetic latitude and Magnetic Local Time coordinates show peculiar patterns highly variable with season. The comparisons of the radar observations with the International Reference Ionosphere model electron density and with ray tracing simulations allow us to explain the major features of observed patterns in terms of electron density variations. The study shows the great potential of the DCE and DCN radar combination to the Super Dual Auroral Radar Network (SuperDARN) convection mapping in terms of monitoring key regions of the high-latitude ionosphere critical for understanding of the magnetospheric dynamics.