Abstract: Because the amount of available ground‐motion data has increased over the last decades, the need for automated processing algorithms has also increased. One difficulty with automated processing is to screen clipped records. Clipping occurs when the ground‐motion amplitude exceeds the dynamic range of the linear response of the instrument. Clipped records in which the amplitude exceeds the dynamic range are relatively easy to identify visually yet challenging for automated algorithms. In this article, we seek to identify a reliable and fully automated clipping detection algorithm tailored to near‐real‐time earthquake response needs. We consider multiple alternative algorithms, including (1) an algorithm based on the percentage difference in adjacent data points, (2) the standard deviation of the data within a moving window, (3) the shape of the histogram of the recorded amplitudes, (4) the second derivative of the data, and (5) the amplitude of the data. To quantitatively compare these algorithms, we construct development and holdout datasets from earthquakes across a range of geographic regions, tectonic environments, and instrument types. We manually classify each record for the presence of clipping and use the classified records. We then develop an artificial neural network model that combines all the individual algorithms. Testing on the holdout dataset, the standard deviation and histogram approaches are the most accurate individual algorithms, with an overall accuracy of about 93%. The combined artificial neural network method yields an overall accuracy of 95%, and the choice of classification threshold can balance precision and recall.

Abstract: Integrative and Comprehensive Understanding on Polar Environments (iCUPE) project developed 24 novel datasets utilizing in-situ observational capacities within the Arctic or remote sensing observations from ground or from space. The datasets covered atmospheric, cryospheric, marine, and terrestrial domains. This paper connects the iCUPE datasets to United Nations’ Sustainable Development Goals and showcases the use of selected datasets as knowledge provision services for policy- and decision-making actions. Inclusion of indigenous and societal knowledge into the data processing pipelines enables a feedback mechanism that facilitates data driven public services.

Abstract: Marine animals equipped with biological and physical electronic sensors have produced long-term data streams on key marine environmental variables, hydrography, animal behavior and ecology. These data are an essential component of the Global Ocean Observing System (GOOS). The Animal Borne Ocean Sensors (AniBOS) network aims to coordinate the long-term collection and delivery of marine data streams, providing a complementary capability to other GOOS networks that monitor Essential Ocean Variables (EOVs), essential climate variables (ECVs) and essential biodiversity variables (EBVs). AniBOS augments observations of temperature and salinity within the upper ocean, in areas that are under-sampled, providing information that is urgently needed for an improved understanding of climate and ocean variability and for forecasting. Additionally, measurements of chlorophyll fluorescence and dissolved oxygen concentrations are emerging. The observations AniBOS provides are used widely across the research, modeling and operational oceanographic communities. High latitude, shallow coastal shelves and tropical seas have historically been sampled poorly with traditional observing platforms for many reasons including sea ice presence, limited satellite coverage and logistical costs. Animal-borne sensors are helping to fill that gap by collecting and transmitting in near real time an average of 500 temperature-salinity-depth profiles per animal annually and, when instruments are recovered (∼30% of instruments deployed annually, n = 103 ± 34), up to 1,000 profiles per month in these regions. Increased observations from under-sampled regions greatly improve the accuracy and confidence in estimates of ocean state and improve studies of climate variability by delivering data that refine climate prediction estimates at regional and global scales. The GOOS Observations Coordination Group (OCG) reviews, advises on and coordinates activities across the global ocean observing networks to strengthen the effective implementation of the system. AniBOS was formally recognized in 2020 as a GOOS network. This improves our ability to observe the ocean’s structure and animals that live in them more comprehensively, concomitantly improving our understanding of global ocean and climate processes for societal benefit consistent with the UN Sustainability Goals 13 and 14: Climate and Life below Water. Working within the GOOS OCG framework ensures that AniBOS is an essential component of an integrated Global Ocean Observing System.

Abstract: Interspecific feeding refers to behavior where an adult of one species feeds the young of another species, with the exclusion of brood parasitism. In birds, most of observed cases concern passerines and this behavior has so far never been described among seabirds. We report on interspecific feeding provided by an adult gentoo penguin Pygoscelis papua to an Adélie penguin P. adeliae chick on the Antarctic Peninsula.

Abstract: We examine upper mantle anisotropy across the Antarctic continent using 102 new shear wave splitting measurements obtained from teleseismic SKS, SKKS, and PKS phases combined with 107 previously published results. For the new measurements, an eigenvalue technique is used to estimate the fast polarization direction and delay time for each phase arrival, and high-quality measurements are stacked to determine the best-fit splitting parameters at each seismic station. The ensemble of splitting measurements shows largely NE-SW-oriented fast polarization directions across Antarctica, with a broadly clockwise rotation in polarization directions evident moving from west to east across the continent. Although the first-order pattern of NE-SW-oriented polarization directions is suggestive of a single plate-wide source of anisotropy, we argue the observed pattern of anisotropy more likely arises from regionally variable contributions of both lithospheric and sub-lithospheric mantle sources. Anisotropy observed in the interior of East Antarctica, a region underlain by thick lithosphere, can be attributed to relict fabrics associated with Precambrian tectonism. In contrast, anisotropy observed in coastal East Antarctica, the Transantarctic Mountains (TAM), and across much of West Antarctica likely reflects both lithospheric and sub-lithospheric mantle fabrics. While sub-lithospheric mantle fabrics are best associated with either plate motion-induced asthenospheric flow or small-scale convection, lithospheric mantle fabrics in coastal East Antarctica, the TAM, and West Antarctica generally reflect Jurassic—Cenozoic tectonic activity.