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Author Posting. © American Meteorological Society, 2022. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 52(10), (2022): 2325–2341, https://doi.org/10.1175/jpo-d-21-0015.1., The ocean surface boundary layer is a gateway of energy transfer into the ocean. Wind-driven shear and meteorologically forced convection inject turbulent kinetic energy into the surface boundary layer, mixing the upper ocean and transforming its density structure. In the absence of direct observations or the capability to resolve subgrid-scale 3D turbulence in operational ocean models, the oceanography community relies on surface boundary layer similarity scalings (BLS) of shear and convective turbulence to represent this mixing. Despite their importance, near-surface mixing processes (and ubiquitous BLS representations of these processes) have been undersampled in high-energy forcing regimes such as the Southern Ocean. With the maturing of autonomous sampling platforms, there is now an opportunity to collect high-resolution spatial and temporal measurements in the full range of forcing conditions. Here, we characterize near-surface turbulence under strong wind forcing using the first long-duration glider microstructure survey of the Southern Ocean. We leverage these data to show that the measured turbulence is significantly higher than standard shear-convective BLS in the shallower parts of the surface boundary layer and lower than standard shear-convective BLS in the deeper parts of the surface boundary layer; the latter of which is not easily explained by present wave-effect literature. Consistent with the CBLAST (Coupled Boundary Layers and Air Sea Transfer) low winds experiment, this bias has the largest magnitude and spread in the shallowest 10% of the actively mixing layer under low-wind and breaking wave conditions, when relatively low levels of turbulent kinetic energy (TKE) in surface regime are easily biased by wave events., This paper is VIMS Contribution 4103. Computational resources were provided by the VIMS Ocean-Atmosphere and Climate Change Research Fund. AUSSOM was supported by the OCE Division of the National Science Foundation (1558639).

Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 33(19), (2020): 8415-8437, doi:10.1175/JCLI-D-19-0954.1., Four state-of-the-art satellite-based estimates of ocean surface latent heat fluxes (LHFs) extending over three decades are analyzed, focusing on the interannual variability and trends of near-global averages and regional patterns. Detailed intercomparisons are made with other datasets including 1) reduced observation reanalyses (RedObs) whose exclusion of satellite data renders them an important independent diagnostic tool; 2) a moisture budget residual LHF estimate using reanalysis moisture transport, atmospheric storage, and satellite precipitation; 3) the ECMWF Reanalysis 5 (ERA5); 4) Remote Sensing Systems (RSS) single-sensor passive microwave and scatterometer wind speed retrievals; and 5) several sea surface temperature (SST) datasets. Large disparities remain in near-global satellite LHF trends and their regional expression over the 1990–2010 period, during which time the interdecadal Pacific oscillation changed sign. The budget residual diagnostics support the smaller RedObs LHF trends. The satellites, ERA5, and RedObs are reasonably consistent in identifying contributions by the 10-m wind speed variations to the LHF trend patterns. However, contributions by the near-surface vertical humidity gradient from satellites and ERA5 trend upward in time with respect to the RedObs ensemble and show less agreement in trend patterns. Problems with wind speed retrievals from Special Sensor Microwave Imager/Sounder satellite sensors, excessive upward trends in trends in Optimal Interpolation Sea Surface Temperature (OISST AVHRR-Only) data used in most satellite LHF estimates, and uncertainties associated with poor satellite coverage before the mid-1990s are noted. Possibly erroneous trends are also identified in ERA5 LHF associated with the onset of scatterometer wind data assimilation in the early 1990s., FRR, JBR, and MGB acknowledge support for this investigation through the NASA Energy and Water Cycle Study (NEWS), Dr. Jared Entin, Program Manager. MS acknowledges the financial support by the EUMETSAT member states through CM SAF. The NOAA-CIRES-DOE Twentieth Century Reanalysis Project version 3 used resources of the National Energy Research Scientific Computing Center managed by Lawrence Berkeley National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract DE-AC02-05CH11231 and used resources of NOAA’s remotely deployed high-performance computing systems. Support for the Twentieth Century Reanalysis Project version 3 dataset is provided by the U.S. DOE, Office of Science Biological and Environmental Research (BER), by the NOAA Climate Program Office, and by the NOAA Physical Sciences Laboratory. RSS products are supported by funding from the NASA Earth Science Division. H. Tomita acknowledges support from JSPS Grants JP18H03726, JP18H03737, and JP19H05696 and JAXA Announcement EO-2. We gratefully acknowledge provision and institutional support for the following SST datasets: ESA CCI (http://data.ceda.ac.uk/neodc/esacci/sst/data/CDR_v2/); NOAA Optimum Interpolation 1/4 Degree Daily Sea Surface Temperature (OISST) Analysis, version 2, (https:/doi.org/10.7289/V5SQ8XB5); NOAA ERSST v5 (https:/doi.org/10.7289/V5T72FNM) and access to COBE-SST2 provided by the NOAA/OAR/ESRL PSD (boyin.huang@noaa.gov); 20CRv3 data are available at the NERSC Science Tape Gateway via portal.nersc.gov., 2021-03-01

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Clayson, C. A., Centurioni, L., Cronin, M. F., Edson, J., Gille, S., Muller-Karger, F., Parfitt, R., Riihimaki, L. D., Smith, S. R., Swart, S., Vandemark, D., Boas, A. B. V., Zappa, C. J., & Zhang, D. Super sites for advancing understanding of the oceanic and atmospheric boundary layers. Marine Technology Society Journal, 55(3), (2021): 144–145, https://doi.org/10.4031/MTSJ.55.3.11., Air‐sea interactions are critical to large-scale weather and climate predictions because of the ocean's ability to absorb excess atmospheric heat and carbon and regulate exchanges of momentum, water vapor, and other greenhouse gases. These exchanges are controlled by molecular, turbulent, and wave-driven processes in the atmospheric and oceanic boundary layers. Improved understanding and representation of these processes in models are key for increasing Earth system prediction skill, particularly for subseasonal to decadal time scales. Our understanding and ability to model these processes within this coupled system is presently inadequate due in large part to a lack of data: contemporaneous long-term observations from the top of the marine atmospheric boundary layer (MABL) to the base of the oceanic mixing layer. We propose the concept of “Super Sites” to provide multi-year suites of measurements at specific locations to simultaneously characterize physical and biogeochemical processes within the coupled boundary layers at high spatial and temporal resolution. Measurements will be made from floating platforms, buoys, towers, and autonomous vehicles, utilizing both in-situ and remote sensors. The engineering challenges and level of coordination, integration, and interoperability required to develop these coupled ocean‐atmosphere Super Sites place them in an “Ocean Shot” class., NOAA CVP TPOS, Understanding Processes Controlling Near-Surface Salinity in the Tropical Ocean Using Multiscale Coupled Modeling and Analysis, NA18OAR4310402 to CAC and JE. NSF Award PLR-1425989 and OPP-1936222, Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) to SG. NOAA, BOEM, ONR, NSF, NOPP, NASA Applied Sciences Office, Biodiversity & Ecological Forecasting Program; National Science Foundation (Co-PI J. Pearlman); OceanObs Research Coordination Network (OCE-1728913) to FM-K. NASA, SWOT program, Award # 80NSSC20K1136 to ABVB. NSF, Investigating the Air-Sea Energy Exchange in the presence of Surface Gravity Waves by Measurements of Turbulence Dissipation, Production and Transport, OCE 17-56839; NSF, A Multi-Spectral Thermal Infrared Imaging System for Air-Sea Interaction Research, OCE 20-23678; NSF, Investigating the Relationship Between Ocean Surface Gravity–Capillary Waves, Surface-Layer Hydrodynamics, and Air–Sea Momentum Flux, OCE 20-49579 to CJZ. Partially funded by NOAA/Climate Program Office and the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) under NOAA Cooperative Agreement NA15OAR4320063 to DZ.

The “OASIS for a Predicted Ocean” satellite event to the UN Decade Predicted Ocean Laboratory took place twice to account for different time zones, on Sep 16, 2021 at 1600 CET and on Sep 17, 2021 at 0100 CET. The fun event used the interactive gather.town platform (Figures 1-2), and had poster viewing (Figure 3) and socializing before and after the events. Overall, the event was fun and went smoothly due to the careful planning by the organizers, the Consortium for Ocean Leadership (COL) support team, and the gather.town architect, Sheri Schwartz. Roughly half of the team of organizers, moderators, keynote and invited speakers, and note takers were Early Career Ocean Professionals (ECOP), representing the stake they have in the future.

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Gentemann, C. L., Clayson, C. A., Brown, S., Lee, T., Parfitt, R., Farrar, J. T., Bourassa, M., Minnett, P. J., Seo, H., Gille, S. T., & Zlotnicki, V. FluxSat: measuring the ocean-atmosphere turbulent exchange of heat and moisture from space. Remote Sensing, 12(11), (2020): 1796, doi:10.3390/rs12111796., Recent results using wind and sea surface temperature data from satellites and high-resolution coupled models suggest that mesoscale ocean–atmosphere interactions affect the locations and evolution of storms and seasonal precipitation over continental regions such as the western US and Europe. The processes responsible for this coupling are difficult to verify due to the paucity of accurate air–sea turbulent heat and moisture flux data. These fluxes are currently derived by combining satellite measurements that are not coincident and have differing and relatively low spatial resolutions, introducing sampling errors that are largest in regions with high spatial and temporal variability. Observational errors related to sensor design also contribute to increased uncertainty. Leveraging recent advances in sensor technology, we here describe a satellite mission concept, FluxSat, that aims to simultaneously measure all variables necessary for accurate estimation of ocean–atmosphere turbulent heat and moisture fluxes and capture the effect of oceanic mesoscale forcing. Sensor design is expected to reduce observational errors of the latent and sensible heat fluxes by almost 50%. FluxSat will improve the accuracy of the fluxes at spatial scales critical to understanding the coupled ocean–atmosphere boundary layer system, providing measurements needed to improve weather forecasts and climate model simulations., C.L.G. was funded by NASA grant 80NSSC18K0837. C.A.C. was funded by NASA grants 80NSSC18K0778 and 80NSSC20K0662. J.T.F. was funded by NASA grants NNX17AH54G, NNX16AH76G, and 80NSSC19K1256. S.T.G. was funded by the National Science Foundation grant PLR-1425989 and by the NASA Ocean Vector Winds Science Team grant 80NSSC19K0059. M.B. was funded in part by the Ocean Observing and Monitoring Division, Climate Program Office (FundRef number 100007298), National Oceanic and Atmospheric Administration, U.S. Department of Commerce, and by the NASA Ocean Vector Winds Science Team grant through NASA/JPL. H.S. was funded by National Oceanic and Atmospheric Administration (NOAA) grant NA19OAR4310376 and the Andrew W. Mellon Foundation Endowed Fund for Innovative Research at Woods Hole Oceanographic Institution.