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{"references": ["Hassell, D., Gregory, J., Blower, J., Lawrence, B. N., and Taylor, K. E.: A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1), Geosci. Model Dev., 10, 4619\u20134646, https://doi.org/10.5194/gmd-10-4619-2017, 2017.", "Hassell et al., (2020). cfdm: A Python reference implementation of the CF data model. Journal of Open Source Software, 5(54), 2717, https://doi.org/10.21105/joss.02717"]} A CF-compliant Earth Science data analysis library Version 3.14.0 is the first to use Dask.

A Python reference implementation of the CF data model.

Tropospheric ozone (O3) concentrations depend on a combination of hemispheric, regional, and local-scale processes. Estimates of how much O3 is produced locally vs. transported from further afield are essential in air quality management and regulatory policies. Here, a tagged-ozone mechanism within the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) is used to quantify the contributions to surface O3 in the UK from anthropogenic nitrogen oxide (NOx) emissions from inside and outside the UK during May–August 2015. The contribution of the different source regions to three regulatory O3 metrics is also examined. It is shown that model simulations predict the concentration and spatial distribution of surface O3 with a domain-wide mean bias of −3.7 ppbv. Anthropogenic NOx emissions from the UK and Europe account for 13 % and 16 %, respectively, of the monthly mean surface O3 in the UK, as the majority (71 %) of O3 originates from the hemispheric background. Hemispheric O3 contributes the most to concentrations in the north and the west of the UK with peaks in May, whereas European and UK contributions are most significant in the east, south-east, and London, i.e. the UK's most populated areas, intensifying towards June and July. Moreover, O3 from European sources is generally transported to the UK rather than produced in situ. It is demonstrated that more stringent emission controls over continental Europe, particularly in western Europe, would be necessary to improve the health-related metric MDA8 O3 above 50 and 60 ppbv. Emission controls over larger areas, such as the Northern Hemisphere, are instead required to lessen the impacts on ecosystems as quantified by the AOT40 metric.

This research was supported by the National Decommissioning Research Initiative (NDRI Australia). We acknowledge the time contribution of all co-authors and additionally via research undertaken through the UKRI INSITE Programme including projects ANChor, CHASANS (NE/T010886/1), EcoConnect, EcoSTAR (NE/T010614/1), FuECoMMS (NE/T010800/1), MAPS, NSERC. DMP was supported through The Marine Alliance for Science and Technology for Scotland (MASTS) funded by the Scottish Funding Council and contributing institutions. SNRB and KH (Cefas) were funded by Cefas and the UK INSITE North Sea programme. Offshore platforms, subsea pipelines, wells and related fixed structures supporting the oil and gas (O&G) industry are prevalent in oceans across the globe, with many approaching the end of their operational life and requiring decommissioning. Although structures can possess high ecological diversity and productivity, information on how they interact with broader ecological processes remains unclear. Here, we review the current state of knowledge on the role of O&G infrastructure in maintaining, altering or enhancing ecological connectivity with natural marine habitats. There is a paucity of studies on the subject with only 33 papers specifically targeting connectivity and O&G structures, although other studies provide important related information. Evidence for O&G structures facilitating vertical and horizontal seascape connectivity exists for larvae and mobile adult invertebrates, fish and megafauna; including threatened and commercially important species. The degree to which these structures represent a beneficial or detrimental net impact remains unclear, is complex and ultimately needs more research to determine the extent to which natural connectivity networks are conserved, enhanced or disrupted. We discuss the potential impacts of different decommissioning approaches on seascape connectivity and identify, through expert elicitation, critical knowledge gaps that, if addressed, may further inform decision making for the life cycle of O&G infrastructure, with relevance for other industries (e.g. renewables). The most highly ranked critical knowledge gap was a need to understand how O&G structures modify and influence the movement patterns of mobile species and dispersal stages of sessile marine species. Understanding how different decommissioning options affect species survival and movement was also highly ranked, as was understanding the extent to which O&G structures contribute to extending species distributions by providing rest stops, foraging habitat, and stepping stones. These questions could be addressed with further dedicated studies of animal movement in relation to structures using telemetry, molecular techniques and movement models. Our review and these priority questions provide a roadmap for advancing research needed to support evidence-based decision making for decommissioning O&G infrastructure. Publisher PDF Peer reviewed

The data file `CENOGRID_Loess_20.txt` contains the astronomically tuned deep-sea benthic foraminifer carbon (������C) and oxygen (�������O) isotope reference records uniformly covering the entire Cenozoic. The first column is the tuned age in Ma, the second column the ������C, and the third column the �������O record. The original calculations were performed using the CRP Toolbox for MATLAB. In order to avoid installing the toolbox and for better performance, the functions for calculating RP and RQA were here reimplemented, providing identical result. To reproduce the RPs in Fig. 2, use the script `perform_rp.m`, for reproducing the determinism values and upper confidence bounds, use the script `perform_rqa.m`. {"references": ["T. Westerhold, N. Marwan, et al: An astronomically dated record of Earth's climate and its predictability over the last 66 million years, Science, 369(6509), 1383\u20131387 (2020)"]}

But fish cognitive ecology did not begin in rivers and streams. Rather, one of the starting points for work on fish cognitive ecology was work done on the use of visual cues by homing pigeons. Prior to working with fish, Victoria Braithwaite helped to establish that homing pigeons rely not just on magnetic and olfactory cues but also on visual cues for successful return to their home loft. Simple, elegant experiments on homing established Victoria's ability to develop experimental manipulations to examine the role of visual cues in navigation by fish in familiar areas. This work formed the basis of a rich seam of work whereby a fish's ecology was used to propose hypotheses and predictions as to preferred cue use, and then cognitive abilities in a variety of fish species, from model systems (Atlantic salmon and sticklebacks) to the Panamanian Brachyraphis episcopi. Cognitive ecology in fish led to substantial work on fish pain and welfare, but was never left behind, with some of Victoria's last work addressed to determining the neural instantiation of cognitive variation. Publisher PDF Peer reviewed

Set of examples to demonstrate how to use OLTraj to implement Lagrangian analyses.

This research was made possible by a grant from The Gulf of Mexico Research Initiative. This publication is UMCES contribution No. 6045 and Ref. No. [UMCES] CBL 2022-008. This is National Marine Mammal Foundation Contribution #314 to peer-reviewed scientific literature. In the wake of the Deepwater Horizon (DWH) oil spill, a number of government agencies, academic institutions, consultants, and nonprofit organizations conducted lab- and field-based research to understand the toxic effects of the oil. Lab testing was performed with a variety of fish, birds, turtles, and vertebrate cell lines (as well as invertebrates); field biologists conducted observations on fish, birds, turtles, and marine mammals; and epidemiologists carried out observational studies in humans. Eight years after the spill, scientists and resource managers held a workshop to summarize the similarities and differences in the effects of DWH oil on vertebrate taxa and to identify remaining gaps in our understanding of oil toxicity in wildlife and humans, building upon the cross-taxonomic synthesis initiated during the Natural Resource Damage Assessment. Across the studies, consistency was found in the types of toxic response observed in the different organisms. Impairment of stress responses and adrenal gland function, cardiotoxicity, immune system dysfunction, disruption of blood cells and their function, effects on locomotion, and oxidative damage were observed across taxa. This consistency suggests conservation in the mechanisms of action and disease pathogenesis. From a toxicological perspective, a logical progression of impacts was noted: from molecular and cellular effects that manifest as organ dysfunction, to systemic effects that compromise fitness, growth, reproductive potential, and survival. From a clinical perspective, adverse health effects from DWH oil spill exposure formed a suite of signs/symptomatic responses that at the highest doses/concentrations resulted in multi-organ system failure. Publisher PDF Peer reviewed

Documentation: https://so-fronts.readthedocs.io/en/latest/ Paper: https://doi.org/10.5194/os-17-1545-2021 Preprint: https://doi.org/10.5194/os-2021-40 Presentation at AGU2021: https://doi.org/10.1002/essoar.10507114.1 Change-log: Improved the geographical plot of hard clustering vs. I-metric (Figure 4 in the paper). Changed velocity comparison from 135m to 2m depth (lowers correlation with Sobel edge detection method slightly). Made README.md more readable. Added new plots to visualise the preprocessing steps: Mean and standard deviation of profiles from the sample. Principal components in terms of their effect on the vertical profiles. Short description In the Southern Ocean, fronts delineate water masses, which correspond to upwelling and downwelling branches of the overturning circulation. Classically, oceanographers define Southern Ocean fronts as a small number of continuous linear features that encircle Antarctica. However, modern observational and theoretical developments are challenging this traditional framework to accommodate more localized views of fronts [Chapman et al. 2020]. Here we present code for implementing two related methods for calculating fronts from oceanographic data. The first method uses unsupervised classification (specifically, Gaussian Mixture Modeling or GMM) and a novel interclass metric to define fronts. This approach produces a discontinuous, probabilistic view of front location, emphasising the fact that the boundaries between water masses are not uniformly sharp across the entire Southern Ocean. The second method uses Sobel edge detection to highlight rapid changes [Hjelmervik & Hjelmervik, 2019]. This approach produces a more local view of fronts, with the advantage that it can highlight the movement of individual eddy-like features (such as the Agulhas rings). Chapman, C. C., Lea, M.-A., Meyer, A., Sallee, J.-B. & Hindell, M. Defining Southern Ocean fronts and their influence on biological and physical processes in a changing climate. Nature Climate Change (2020). https://doi.org/10.1038/s41558-020-0705-4 Maze, G. et al. Coherent heat patterns revealed by unsupervised classification of Argo temperature profiles in the North Atlantic Ocean. Progress in Oceanography (2017). https://doi.org/10.1016/j.pocean.2016.12.008, https://doi.org/10.5281/zenodo.3906236 Hjelmervik, K. B. & Hjelmervik, K. T. Detection of oceanographic fronts on variable water depths using empirical orthogonal functions. IEEE Journal of Oceanic Engineering (2019). https://doi.org/10.1109/JOE.2019.2917456 If you use this software, please cite it as below.

This version includes higher quality animations of the i-metric over the time period. The documentation is available at: https://so-fronts.readthedocs.io/ Code available here: https://github.com/so-wise/so-fronts Paper: https://doi.org/10.5194/os-17-1545-2021 Preprint: https://doi.org/10.5194/os-2021-40