ALBERT

Description: Author Posting. © American Geophysical Union, 2019. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research-Atmospheres 124 (17-18), (2019): 9773-9795, doi: 10.1029/2018JD029933.

Description: National Aeronautics and Space Administration's Orbiting Carbon Observatory‐2 (OCO‐2) satellite provides observations of total column‐averaged CO2 mole fractions (XCO2 ) at high spatial resolution that may enable novel constraints on surface‐atmosphere carbon fluxes. Atmospheric inverse modeling provides an approach to optimize surface fluxes at regional scales, but the accuracy of the fluxes from inversion frameworks depends on key inputs, including spatially and temporally dense CO2 observations and reliable representations of atmospheric transport. Since XCO2 observations are sensitive to both synoptic and mesoscale variations within the free troposphere, horizontal atmospheric transport imparts substantial variations in these data and must be either resolved explicitly by the atmospheric transport model or accounted for within the error covariance budget provided to inverse frameworks. Here, we used geostatistical techniques to quantify the imprint of atmospheric transport in along‐track OCO‐2 soundings. We compare high‐pass‐filtered (〈250 km, spatial scales that primarily isolate mesoscale or finer‐scale variations) along‐track spatial variability in XCO2 and XH2O from OCO‐2 tracks to temporal synoptic and mesoscale variability from ground‐based XCO2 and XH2O observed by nearby Total Carbon Column Observing Network sites. Mesoscale atmospheric transport is found to be the primary driver of along‐track, high‐frequency variability for OCO‐2 XH2O. For XCO2 , both mesoscale transport variability and spatially coherent bias associated with other elements of the OCO‐2 retrieval state vector are important drivers of the along‐track variance budget.

Description: The authors thank the leadership and participants of the NASA OCO‐2 mission and acknowledge financial support from NASA Award NNX15AH13G. A.D. Torres also acknowledges support from the NASA Earth and Space Science Fellowship Award 80NSSC17K0382. We thank TCCON for providing observations. We thank A. Jacobson and the National Oceanographic and Atmospheric Administration Earth System Research Laboratory in Boulder, CO, for providing CarbonTracker CT2017 data, available online (http://carbontracker.noaa.gov). We thank S. Wofsy for providing HIPPO data, funded by the National Science Foundation and NOAA and available online (https://www.eol.ucar.edu/field_projects/hippo). The TCCON Principal Investigators acknowledge funding from their national funding organizations. TCCON data were obtained from the archive at the https://tccondata.org Web site. NARR data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site (https://www.esrl.noaa.gov/psd/).

Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Chapman, A. S. A., Beaulieu, S. E., Colaco, A., Gebruk, A. V., Hilario, A., Kihara, T. C., Ramirez-Llodra, E., Sarrazin, J., Tunnicliffe, V., Amon, D. J., Baker, M. C., Boschen-Rose, R. E., Chen, C., Cooper, I. J., Copley, J. T., Corbari, L., Cordes, E. E., Cuvelier, D., Duperron, S., Du Preez, C., Gollner, S., Horton, T., Hourdez, S., Krylova, E. M., Linse, K., LokaBharathi, P. A., Marsh, L., Matabos, M., Mills, S. W., Mullineaux, L. S., Rapp, H. T., Reid, W. D. K., Rybakova (Goroslavskaya), E., Thomas, T. R. A., Southgate, S. J., Stohr, S., Turner, P. J., Watanabe, H. K., Yasuhara, M., & Bates, A. E. sFDvent: a global trait database for deep-sea hydrothermal-vent fauna. Global Ecology and Biogeography, 28(11), (2019): 1538-1551, doi: 10.1111/geb.12975.

Description: Motivation Traits are increasingly being used to quantify global biodiversity patterns, with trait databases growing in size and number, across diverse taxa. Despite growing interest in a trait‐based approach to the biodiversity of the deep sea, where the impacts of human activities (including seabed mining) accelerate, there is no single repository for species traits for deep‐sea chemosynthesis‐based ecosystems, including hydrothermal vents. Using an international, collaborative approach, we have compiled the first global‐scale trait database for deep‐sea hydrothermal‐vent fauna – sFDvent (sDiv‐funded trait database for the Functional Diversity of vents). We formed a funded working group to select traits appropriate to: (a) capture the performance of vent species and their influence on ecosystem processes, and (b) compare trait‐based diversity in different ecosystems. Forty contributors, representing expertise across most known hydrothermal‐vent systems and taxa, scored species traits using online collaborative tools and shared workspaces. Here, we characterise the sFDvent database, describe our approach, and evaluate its scope. Finally, we compare the sFDvent database to similar databases from shallow‐marine and terrestrial ecosystems to highlight how the sFDvent database can inform cross‐ecosystem comparisons. We also make the sFDvent database publicly available online by assigning a persistent, unique DOI. Main types of variable contained Six hundred and forty‐six vent species names, associated location information (33 regions), and scores for 13 traits (in categories: community structure, generalist/specialist, geographic distribution, habitat use, life history, mobility, species associations, symbiont, and trophic structure). Contributor IDs, certainty scores, and references are also provided. Spatial location and grain Global coverage (grain size: ocean basin), spanning eight ocean basins, including vents on 12 mid‐ocean ridges and 6 back‐arc spreading centres. Time period and grain sFDvent includes information on deep‐sea vent species, and associated taxonomic updates, since they were first discovered in 1977. Time is not recorded. The database will be updated every 5 years. Major taxa and level of measurement Deep‐sea hydrothermal‐vent fauna with species‐level identification present or in progress. Software format .csv and MS Excel (.xlsx).

Description: We would like to thank the following experts, who are not authors on this publication but made contributions to the sFDvent database: Anna Metaxas, Alexander Mironov, Jianwen Qiu (seep species contributions, to be added to a future version of the database) and Anders Warén. We would also like to thank Robert Cooke for his advice, time, and assistance in processing the raw data contributions to the sFDvent database using R. Thanks also to members of iDiv and its synthesis centre – sDiv – for much‐valued advice, support, and assistance during working‐group meetings: Doreen Brückner, Jes Hines, Borja Jiménez‐Alfaro, Ingolf Kühn and Marten Winter. We would also like to thank the following supporters of the database who contributed indirectly via early design meetings or members of their research groups: Malcolm Clark, Charles Fisher, Adrian Glover, Ashley Rowden and Cindy Lee Van Dover. Finally, thanks to the families of sFDvent working group members for their support while they were participating in meetings at iDiv in Germany. Financial support for sFDvent working group meetings was gratefully received from sDiv, the Synthesis Centre of iDiv (DFG FZT 118). ASAC was a PhD candidate funded by the SPITFIRE Doctoral Training Partnership (supported by the Natural Environmental Research Council, grant number: NE/L002531/1) and the University of Southampton at the time of submission. ASAC also thanks Dominic, Lesley, Lettice and Simon Chapman for their support throughout this project. AEB and VT are sponsored through the Canada Research Chair Programme. SEB received support from National Science Foundation Division of Environmental Biology Award #1558904 and The Joint Initiative Awards Fund from the Andrew W. Mellon Foundation. AC is supported by Program Investigador (IF/00029/2014/CP1230/CT0002) from Fundação para a Ciência e a Tecnologia (FCT). This study also had the support of Fundação para a Ciência e a Tecnologia, through the strategic project UID/MAR/04292/2013 granted to marine environmental sciences centre. Data compiled by AVG and EG were supported by Russian science foundation Grant 14‐50‐00095. AH was supported by the grant BPD/UI88/5805/2017 awarded by CESAM (UID/AMB/50017), which is financed by FCT/Ministério da Educação through national funds and co‐funded by fundo Europeu de desenvolvimento regional, within the PT2020 Partnership Agreement and Compete 2020. ERLL was partially supported by the MarMine project (247626/O30). JS was supported by Ifremer. Data on vent fauna from the East Scotia Ridge, Mid‐Cayman Spreading Centre, and Southwest Indian Ridge were obtained by UK natural environment research council Grants NE/D01249X/1, NE/F017774/1 and NE/H012087/1, respectively. REBR's contribution was supported by a Postdoctoral Fellowship at the University of Victoria, funded by the Canadian Healthy Oceans Network II Strategic Research Program (CHONe II). DC is supported by a post‐doctoral scholarship (SFRH/BPD/110278/2015) from FCT. HTR was supported by the Research Council of Norway through project number 70184227 and the KG Jebsen Centre for Deep Sea Research (University of Bergen). MY was partially supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (project codes: HKU 17306014, HKU 17311316).

Description: Author Posting. © American Geophysical Union, 2020. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 47 (2020): e2020GL087669, doi:10.1029/2020GL087669.

Description: We present a year‐round time series of dissolved methane (CH4), along with targeted observations during ice melt of CH4 and carbon dioxide (CO2) in a river and estuary adjacent to Cambridge Bay, Nunavut, Canada. During the freshet, CH4 concentrations in the river and ice‐covered estuary were up to 240,000% saturation and 19,000% saturation, respectively, but quickly dropped by 〉100‐fold following ice melt. Observations with a robotic kayak revealed that river‐derived CH4 and CO2 were transported to the estuary and rapidly ventilated to the atmosphere once ice cover retreated. We estimate that river discharge accounts for 〉95% of annual CH4 sea‐to‐air emissions from the estuary. These results demonstrate the importance of resolving seasonal dynamics in order to estimate greenhouse gas emissions from polar systems.

Description: All data generated by the authors that were used in this article are available on PANGAEA (https://doi.org/10.1594/PANGAEA.907159) and model code for estimating CH4 transport is available on GitHub (https://doi.org/10.5281/zenodo.3785893). We acknowledge the use of imagery from the NASA Worldview application (https://worldview.earthdata.nasa.gov), part of the NASA Earth Observing System Data and Information System (EOSDIS), and data from Ocean Networks Canada, and Environment Canada. We thank everyone involved in the fieldwork including C. Amegainik, Y. Bernard, A. Cranch, F. Emingak, S. Marriott, and A. Pedersen. Laboratory analysis and experiments were performed by A. Cranch, R. McCulloch, A. Morrison, and Z. Zheng. We thank J. Brinckerhoff, the Arctic Research Foundation, and the staff of the Canadian High Arctic Research Station for support with field logistics. Funding for the work was provided by MEOPAR NCE funding to B. Else, a WHOI Interdisciplinary Award to A. Michel., D. Nicholson. and S. Wankel, and Canadian NSERC grants to P. Tortell. and B. Else. Authors received fellowships, scholarships, and travel grants including an NSERC postdoctoral fellowship to C. Manning, an NDSEG fellowship to V. Preston, NSERC PGS‐D and Izaak Walton Killam Pre‐Doctoral scholarships to S. Jones, and Northern Scientific Training Program funds (Polar Knowledge Canada, administered by the Arctic Institute of North America, University of Calgary) to S. Jones and P. Duke. We also thank Polar Knowledge Canada (POLAR) and Nunavut Arctic College for laboratory space and field logistics support.

Description: 2020-10-23

Description: © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Davis, G. E., Baumgartner, M. F., Corkeron, P. J., Bell, J., Berchok, C., Bonnell, J. M., Thornton, J. B., Brault, S., Buchanan, G. A., Cholewiak, D. M., Clark, C. W., Delarue, J., Hatch, L. T., Klinck, H., Kraus, S. D., Martin, B., Mellinger, D. K., Moors-Murphy, H., Nieukirk, S., Nowacek, D. P., Parks, S. E., Parry, D., Pegg, N., Read, A. J., Rice, A. N., Risch, D., Scott, A., Soldevilla, M. S., Stafford, K. M., Stanistreet, J. E., Summers, E., Todd, S., & Van Parijs, S. M. Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data. Global Change Biology, (2020): 1-30, doi:10.1111/gcb.15191.

Description: Six baleen whale species are found in the temperate western North Atlantic Ocean, with limited information existing on the distribution and movement patterns for most. There is mounting evidence of distributional shifts in many species, including marine mammals, likely because of climate‐driven changes in ocean temperature and circulation. Previous acoustic studies examined the occurrence of minke (Balaenoptera acutorostrata ) and North Atlantic right whales (NARW; Eubalaena glacialis ). This study assesses the acoustic presence of humpback (Megaptera novaeangliae ), sei (B. borealis ), fin (B. physalus ), and blue whales (B. musculus ) over a decade, based on daily detections of their vocalizations. Data collected from 2004 to 2014 on 281 bottom‐mounted recorders, totaling 35,033 days, were processed using automated detection software and screened for each species' presence. A published study on NARW acoustics revealed significant changes in occurrence patterns between the periods of 2004–2010 and 2011–2014; therefore, these same time periods were examined here. All four species were present from the Southeast United States to Greenland; humpback whales were also present in the Caribbean. All species occurred throughout all regions in the winter, suggesting that baleen whales are widely distributed during these months. Each of the species showed significant changes in acoustic occurrence after 2010. Similar to NARWs, sei whales had higher acoustic occurrence in mid‐Atlantic regions after 2010. Fin, blue, and sei whales were more frequently detected in the northern latitudes of the study area after 2010. Despite this general northward shift, all four species were detected less on the Scotian Shelf area after 2010, matching documented shifts in prey availability in this region. A decade of acoustic observations have shown important distributional changes over the range of baleen whales, mirroring known climatic shifts and identifying new habitats that will require further protection from anthropogenic threats like fixed fishing gear, shipping, and noise pollution.

Description: We thank Chris Pelkie, David Wiley, Michael Thompson, Chris Tessaglia‐Hymes, Eric Matzen, Chris Tremblay, Lance Garrison, Anurag Kumar, John Hildebrand, Lynne Hodge, Russell Charif, Kathleen Dudzinski, and Ann Warde for help with project planning, field work support, and data management. For all the support and advice, thanks to the NEFSC Protected Species Branch, especially the passive acoustics group, Josh Hatch, and Leah Crowe. We thank the field and crew teams on all the ships that helped in the numerous deployments and recoveries. This research was funded and supported by many organizations, specified by projects as follows: data recordings from region 1 were provided by K. Stafford (funding: National Science Foundation #NSF‐ARC 0532611). Region 2 data: D. K. Mellinger and S. Nieukirk, National Oceanic and Atmospheric Administration (NOAA) PMEL contribution #5055 (funding: NOAA and the Office of Naval Research #N00014–03–1–0099, NOAA #NA06OAR4600100, US Navy #N00244‐08‐1‐0029, N00244‐09‐1‐0079, and N00244‐10‐1‐0047). Region 3A data: D. Risch (funding: NOAA and Navy N45 programs). Region 3 data: H. Moors‐Murphy and Fisheries and Oceans Canada (2005–2014 data), and the Whitehead Lab of Dalhousie University (eastern Scotian Shelf data; logistical support by A. Cogswell, J. Bartholette, A. Hartling, and vessel CCGS Hudson crew). Emerald Basin and Roseway Basin Guardbuoy data, deployment, and funding: Akoostix Inc. Region 3 Emerald Bank and Roseway Basin 2004 data: D. K. Mellinger and S. Nieukirk, NOAA PMEL contribution #5055 (funding: NOAA). Region 4 data: S. Parks (funding: NOAA and Cornell University) and E. Summers, S. Todd, J. Bort Thornton, A. N. Rice, and C. W. Clark (funding: Maine Department of Marine Resources, NOAA #NA09NMF4520418, and #NA10NMF4520291). Region 5 data: S. M. Van Parijs, D. Cholewiak, L. Hatch, C. W. Clark, D. Risch, and D. Wiley (funding: National Oceanic Partnership Program (NOPP), NOAA, and Navy N45). Region 6 data: S. M. Van Parijs and D. Cholewiak (funding: Navy N45 and Bureau of Ocean and Energy Management (BOEM) Atlantic Marine Assessment Program for Protected Species [AMAPPS] program). Region 7 data: A. N. Rice, H. Klinck, A. Warde, B. Martin, J. Delarue, and S. Kraus (funding: New York State Department of Environmental Conservation, Massachusetts Clean Energy Center, and BOEM). Region 8 data: G. Buchanan, and K. Dudzinski (funding: New Jersey Department of Environmental Protection and the New Jersey Clean Energy Fund) and A. N. Rice, C. W. Clark, and H. Klinck (funding: Center for Conservation Bioacoustics at Cornell University and BOEM). Region 9 data: J. E. Stanistreet, J. Bell, D. P. Nowacek, A. J. Read, and S. M. Van Parijs (funding: NOAA and US Fleet Forces Command). Region 10 data: L. Garrison, M. Soldevilla, C. W. Clark, R. A. Chariff, A. N. Rice, H. Klinck, J. Bell, D. P. Nowacek, A. J. Read, J. Hildebrand, A. Kumar, L. Hodge, and J. E. Stanistreet (funding: US Fleet Forces Command, BOEM, NOAA, and NOPP). Region 11 data: C. Berchok as part of a collaborative project led by the Fundacion Dominicana de Estudios Marinos, Inc. (Dr. Idelisa Bonnelly de Calventi; funding: The Nature Conservancy [Elianny Dominguez]) and D. Risch (funding: World Wildlife Fund, NOAA, and Dutch Ministry of Economic Affairs).