Deepening of the winter mixed layer in the Canada Basin, Arctic Ocean over 2006-2017

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-Oceans 124(7), (2019): 4618-4630, doi: 10.1029/2019JC014940.The Arctic Ocean mixed layer interacts with the ice cover above and warmer, nutrient‐rich waters below. Ice‐Tethered Profiler observations in the Canada Basin of the Arctic Ocean over 2006–2017 are used to investigate changes in mixed layer properties. In contrast to decades of shoaling since at least the 1980s, the mixed layer deepened by 9 m from 2006–2012 to 2013–2017. Deepening resulted from an increase in mixed layer salinity that also weakened stratification at the base of the mixed layer. Vertical mixing alone can explain less than half of the observed change in mixed layer salinity, and so the observed increase in salinity is inferred to result from changes in freshwater accumulation via changes to ice‐ocean circulation or ice melt/growth and river runoff. Even though salinity increased, the shallowest density surfaces deepened by 5 m on average suggesting that Ekman pumping over this time period remained downward. A deeper mixed layer with weaker stratification has implications for the accessibility of heat and nutrients stored in the upper halocline. The extent to which the mixed layer will continue to deepen appears to depend primarily on the complex set of processes influencing freshwater accumulation.We gratefully acknowledge J. Toole for helpful conversations. S. Cole was supported by the National Science Foundation under grant PLR‐1602926 and J. Stadler by the Woods Hole Oceanographic Institution Summer Student Fellowship program. Profile data are available via the Ice‐Tethered Profiler program website: http://whoi.edu/itp. SSM/I ice concentration data were downloaded from the National Snow and Ice Data Center.2019-12-2

Numerical investigation of the Arctic ice–ocean boundary layer and implications for air–sea gas fluxes

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ocean Science 13 (2017): 61-75, doi:10.5194/os-13-61-2017.In ice-covered regions it is challenging to determine constituent budgets – for heat and momentum, but also for biologically and climatically active gases like carbon dioxide and methane. The harsh environment and relative data scarcity make it difficult to characterize even the physical properties of the ocean surface. Here, we sought to evaluate if numerical model output helps us to better estimate the physical forcing that drives the air–sea gas exchange rate (k) in sea ice zones. We used the budget of radioactive 222Rn in the mixed layer to illustrate the effect that sea ice forcing has on gas budgets and air–sea gas exchange. Appropriate constraint of the 222Rn budget requires estimates of sea ice velocity, concentration, mixed-layer depth, and water velocities, as well as their evolution in time and space along the Lagrangian drift track of a mixed-layer water parcel. We used 36, 9 and 2 km horizontal resolution of regional Massachusetts Institute of Technology general circulation model (MITgcm) configuration with fine vertical spacing to evaluate the capability of the model to reproduce these parameters. We then compared the model results to existing field data including satellite, moorings and ice-tethered profilers. We found that mode sea ice coverage agrees with satellite-derived observation 88 to 98 % of the time when averaged over the Beaufort Gyre, and model sea ice speeds have 82 % correlation with observations. The model demonstrated the capacity to capture the broad trends in the mixed layer, although with a significant bias. Model water velocities showed only 29 % correlation with point-wise in situ data. This correlation remained low in all three model resolution simulations and we argued that is largely due to the quality of the input atmospheric forcing. Overall, we found that even the coarse-resolution model can make a modest contribution to gas exchange parameterization, by resolving the time variation of parameters that drive the 222Rn budget, including rate of mixed-layer change and sea ice forcings.Funding for this research was provided by the NSF Arctic Natural Sciences program through Award # 1203558

Circulation of Pacific Winter Water in the western Arctic Ocean

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-Oceans 124(2), (2019):863-881, doi:10.1029/2018JC014604.Pacific Winter Water (PWW) enters the western Arctic Ocean from the Chukchi Sea; however, the physical mechanisms that regulate its circulation within the deep basin are still not clear. Here, we investigate the interannual variability of PWW with a comprehensive data set over a decade. We quantify the thickening and expansion of the PWW layer during 2002–2016, as well as its changing pathway. The total volume of PWW in the Beaufort Gyre (BG) region is estimated to have increased from 3.48 ± 0.04 × 1014 m3 during 2002–2006 to 4.11 ± 0.02 × 1014 m3 during 2011–2016, an increase of 18%. We find that the deepening rate of the lower bound of PWW is almost double that of its upper bound in the northern Canada Basin, a result of lateral flux convergence of PWW (via lateral advection of PWW from the Chukchi Borderland) in addition to the Ekman pumping. In particular, of the 70‐m deepening of PWW at its lower bound observed over 2003–2011 in the northwestern basin, 43% resulted from lateral flux convergence. We also find a redistribution of PWW in recent years toward the Chukchi Borderland associated with the wind‐driven spin‐up and westward shift of the BG. Finally, we hypothesize that a recently observed increase of lower halocline eddies in the BG might be explained by this redistribution, through a compression mechanism over the Chukchi Borderland.Three anonymous reviewers provided helpful comments and suggestions, which greatly improved this manuscript. We thank John Marshall (MIT) and Georgy Manucharyan (Caltech) for valuable discussions and inputs. We thank Peigen Lin (WHOI), Qinyu Liu, and Jinping Zhao (OUC) for helpful discussions. The Matlab wind rose toolbox is written by Daniel Pereira. This study is supported by the National Key Basic Research Program of China (Program 973) (2015CB953900; 2018YFA0605901), the Key Project of Chinese Natural Science Foundation (41330960), and the National Natural Science Foundation of China (41706211 and 41776192), the Office of Naval Research (grant N00014‐12‐1‐0112), the NSF Office of Polar Programs (PLR‐1416920, PLR‐1503298, PLR‐1602985, PLR‐1603259, ARC‐1203425, and NSF‐1602926). Wenli Zhong (201606335011) is supported by the China Scholarship Council for his studies in APL. We appreciate Andrey Proshutinsky and Rick Krishfield (WHOI) for providing the Beaufort Gyre Exploration Project data publicly at http://www.whoi.edu/website/beaufortgyre/. The Ice‐Tethered Profiler data were collected and made available by the Ice‐Tethered Profiler Program (Krishfield et al., 2008; Toole et al., 2011) based at the Woods Hole Oceanographic Institution (http://www.whoi.edu/itp). The Monthly Isopycnal/Mixed‐layer Ocean Climatology (MIMOC) data are available at https://www.pmel.noaa.gov/mimoc/. The monthly Arctic Dynamic Ocean Topography data are distributed by CPOM (http://www.cpom.ucl.ac.uk/dynamic_topography/). The IBCAO Bathymetry data are available from NASA (http://www.ngdc.noaa.gov/mgg/bathymetry/arctic/arctic.html). The Data‐Interpolating Variational Analysis method is publicly available at http://modb.oce.ulg.ac.be/mediawiki/index.php/DIVA.2019-07-1

Seasonality of the mesoscale inverse cascade as inferred from global scale-dependent eddy energy observations

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(8), (2022): 1677-1691, https://doi.org/10.1175/jpo-d-21-0269.1.Oceanic mesoscale motions including eddies, meanders, fronts, and filaments comprise a dominant fraction of oceanic kinetic energy and contribute to the redistribution of tracers in the ocean such as heat, salt, and nutrients. This reservoir of mesoscale energy is regulated by the conversion of potential energy and transfers of kinetic energy across spatial scales. Whether and under what circumstances mesoscale turbulence precipitates forward or inverse cascades, and the rates of these cascades, remain difficult to directly observe and quantify despite their impacts on physical and biological processes. Here we use global observations to investigate the seasonality of surface kinetic energy and upper-ocean potential energy. We apply spatial filters to along-track satellite measurements of sea surface height to diagnose surface eddy kinetic energy across 60–300-km scales. A geographic and scale-dependent seasonal cycle appears throughout much of the midlatitudes, with eddy kinetic energy at scales less than 60 km peaking 1–4 months before that at 60–300-km scales. Spatial patterns in this lag align with geographic regions where an Argo-derived estimate of the conversion of potential to kinetic energy is seasonally varying. In midlatitudes, the conversion rate peaks 0–2 months prior to kinetic energy at scales less than 60 km. The consistent geographic patterns between the seasonality of potential energy conversion and kinetic energy across spatial scale provide observational evidence for the inverse cascade and demonstrate that some component of it is seasonally modulated. Implications for mesoscale parameterizations and numerical modeling are discussed.This work was generously funded by NSF Grants OCE-1912302, OCE-1912125 (Drushka), and OCE-1912325 (Abernathey) as part of the Ocean Energy and Eddy Transport Climate Process Team

Internal waves in the Arctic : influence of ice concentration, ice roughness, and surface layer stratification

Author Posting. © American Geophysical Union, 2018. 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: Oceans 123 (2018): 5571-5586, doi:10.1029/2018JC014096.The Arctic ice cover influences the generation, propagation, and dissipation of internal waves, which in turn may affect vertical mixing in the ocean interior. The Arctic internal wavefield and its relationship to the ice cover is investigated using observations from Ice‐Tethered Profilers with Velocity and Seaglider sampling during the 2014 Marginal Ice Zone experiment in the Canada Basin. Ice roughness, ice concentration, and wind forcing all influenced the daily to seasonal changes in the internal wavefield. Three different ice concentration thresholds appeared to determine the evolution of internal wave spectral energy levels: (1) the initial decrease from 100% ice concentration after which dissipation during the surface reflection was inferred to increase, (2) the transition to 70–80% ice concentration when the local generation of internal waves increased, and (3) the transition to open water that was associated with larger‐amplitude internal waves. Ice roughness influenced internal wave properties for ice concentrations greater than approximately 70–80%: smoother ice was associated with reduced local internal wave generation. Richardson numbers were rarely supercritical, consistent with weak vertical mixing under all ice concentrations. On decadal timescales, smoother ice may counteract the effects of lower ice concentration on the internal wavefield complicating future predictions of internal wave activity and vertical mixing.Seagliders Grant Number: N00014‐12‐10180; Deployment and subsequent analysis efforts of the ITP‐Vs Grant Numbers: N00014‐12‐10799, N00014‐12‐10140; Joint Ocean Ice Studies cruise; Beaufort Gyre Observing System2019-02-1

Eddy stirring and horizontal diffusivity from Argo float observations : geographic and depth variability

Author Posting. © American Geophysical Union, 2015. 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 42 (2015): 3989–3997, doi:10.1002/2015GL063827.Stirring along isopycnals is a significant factor in determining the distribution of tracers within the ocean. Salinity anomalies on density surfaces from Argo float profiles are used to investigate horizontal stirring and estimate eddy mixing lengths. Eddy mixing length and velocity fluctuations from the ECCO2 global state estimate are used to estimate horizontal diffusivity at a 300 km scale in the upper 2000 m with near-global coverage. Diffusivity varies by over two orders of magnitude with latitude, longitude, and depth. In all basins, diffusivity is elevated in zonal bands corresponding to strong current regions, including western boundary current extension regions, the Antarctic Circumpolar Current, and equatorial current systems. The estimated mixing lengths and diffusivities provide an observationally based data set that can be used to test and constrain predictions and parameterizations of eddy stirring.This work was supported by the National Science Foundation under grants OCE-13-55668 and OCE-95-21468 and the Office of Naval Research under grants N00014-12-1-0336 and N00014-13-1-0484.2015-11-2

Ekman veering, internal waves, and turbulence observed under Arctic sea ice

Author Posting. © American Meteorological Society, 2014. 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 44 (2014): 1306–1328, doi:10.1175/JPO-D-12-0191.1.The ice–ocean system is investigated on inertial to monthly time scales using winter 2009–10 observations from the first ice-tethered profiler (ITP) equipped with a velocity sensor (ITP-V). Fluctuations in surface winds, ice velocity, and ocean velocity at 7-m depth were correlated. Observed ocean velocity was primarily directed to the right of the ice velocity and spiraled clockwise while decaying with depth through the mixed layer. Inertial and tidal motions of the ice and in the underlying ocean were observed throughout the record. Just below the ice–ocean interface, direct estimates of the turbulent vertical heat, salt, and momentum fluxes and the turbulent dissipation rate were obtained. Periods of elevated internal wave activity were associated with changes to the turbulent heat and salt fluxes as well as stratification primarily within the mixed layer. Turbulent heat and salt fluxes were correlated particularly when the mixed layer was closest to the freezing temperature. Momentum flux is adequately related to velocity shear using a constant ice–ocean drag coefficient, mixing length based on the planetary and geometric scales, or Rossby similarity theory. Ekman viscosity described velocity shear over the mixed layer. The ice–ocean drag coefficient was elevated for certain directions of the ice–ocean shear, implying an ice topography that was characterized by linear ridges. Mixing length was best estimated using the wavenumber of the beginning of the inertial subrange or a variable drag coefficient. Analyses of this and future ITP-V datasets will advance understanding of ice–ocean interactions and their parameterizations in numerical models.Support for this study and the overall ITP program was provided by the National Science Foundation and Woods Hole Oceanographic Institution. Support for S. Cole was partially though the Postdoctoral Scholar Program at the Woods Hole Oceanographic Institution, with funding provided by the Devonshire Foundation.2014-11-0

Evolution of the eddy field in the Arctic Ocean's Canada Basin, 2005–2015

Author Posting. © American Geophysical Union, 2016. 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 43 (2016): 8106–8114, doi:10.1002/2016GL069671.The eddy field across the Arctic Ocean's Canada Basin is analyzed using Ice-Tethered Profiler (ITP) and moored measurements of temperature, salinity, and velocity spanning 2005 to 2015. ITPs encountered 243 eddies, 98% of which were anticyclones, with approximately 70% of these having anomalously cold cores. The spatially and temporally varying eddy field is analyzed accounting for sampling biases in the unevenly distributed ITP data and caveats in detection methods. The highest concentration of eddies was found in the western and southern portions of the basin, close to topographic margins and boundaries of the Beaufort Gyre. The number of lower halocline eddies approximately doubled from 2005–2012 to 2013–2014. The increased eddy density suggests more active baroclinic instability of the Beaufort Gyre that releases available potential energy to balance the wind energy input; this may stabilize the Gyre spin-up and associated freshwater increase.National Science Foundation Division of Polar Programs Grant Number: 13500462017-02-0

Characterizing the eddy field in the Arctic Ocean halocline

Author Posting. © American Geophysical Union, 2014. 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: Oceans 119 (2014): 8800–8817, doi:10.1002/2014JC010488.Ice-Tethered Profilers (ITP), deployed in the Arctic Ocean between 2004 and 2013, have provided detailed temperature and salinity measurements of an assortment of halocline eddies. A total of 127 mesoscale eddies have been detected, 95% of which were anticyclones, the majority of which had anomalously cold cores. These cold-core anticyclonic eddies were observed in the Beaufort Gyre region (Canadian water eddies) and the vicinity of the Transpolar Drift Stream (Eurasian water eddies). An Arctic-wide calculation of the first baroclinic Rossby deformation radius Rd has been made using ITP data coupled with climatology; Rd ∼ 13 km in the Canadian water and ∼8 km in the Eurasian water. The observed eddies are found to have scales comparable to Rd. Halocline eddies are in cyclogeostrophic balance and can be described by a Rankine vortex with maximum azimuthal speeds between 0.05 and 0.4 m/s. The relationship between radius and thickness for the eddies is consistent with adjustment to the ambient stratification. Eddies may be divided into four groups, each characterized by distinct core depths and core temperature and salinity properties, suggesting multiple source regions and enabling speculation of varying formation mechanisms.Funding was provided by the National Science Foundation Polar Programs award ARC-1107623.2015-06-2

Retrieving sea ice drag coefficients and turning angles from in situ and satellite observations using an inverse modeling framework

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-Oceans 124(8), (2019): 6388-6413, doi: 10.1029/2018JC014881.For ice concentrations less than 85%, internal ice stresses in the sea ice pack are small and sea ice is said to be in free drift. The sea ice drift is then the result of a balance between Coriolis acceleration and stresses from the ocean and atmosphere. We investigate sea ice drift using data from individual drifting buoys as well as Arctic‐wide gridded fields of wind, sea ice, and ocean velocity. We perform probabilistic inverse modeling of the momentum balance of free‐drifting sea ice, implemented to retrieve the Nansen number, scaled Rossby number, and stress turning angles. Since this problem involves a nonlinear, underconstrained system, we used a Monte Carlo guided search scheme—the Neighborhood Algorithm—to seek optimal parameter values for multiple observation points. We retrieve optimal drag coefficients of CA=1.2×10−3 and CO=2.4×10−3 from 10‐day averaged Arctic‐wide data from July 2014 that agree with the AIDJEX standard, with clear temporal and spatial variations. Inverting daily averaged buoy data give parameters that, while more accurately resolved, suggest that the forward model oversimplifies the physical system at these spatial and temporal scales. Our results show the importance of the correct representation of geostrophic currents. Both atmospheric and oceanic drag coefficients are found to decrease with shorter temporal averaging period, informing the selection of drag coefficient for short timescale climate models.The scripts developed for this publication are available at the GitHub (https://github.com/hheorton/Freedrift_inverse_submit). The Neighborhood Algorithm was developed and kindly supplied by M. Sambridge (http://www.iearth.org.au/codes/NA/). Ice‐Tethered Profiler data are available via the Ice‐Tethered Profiler program website (http://whoi.edu/itp). Buoy data were collected as part of the Marginal Ice Zone program (www.apl.washington.edu/miz) funded by the U.S. Office of Naval Research. The ice drift data were kindly supplied by N. Kimura. H. H. was funded by the Natural Environment Research Council (Grants NE/I029439/1 and NE/R000263/1). M. T. was partially funded by the SKIM Mission Science Study (SKIM‐SciSoc) Project ESA RFP 3‐15456/18/NL/CT/gp. T. A. was supported at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. M. T. and H. H. thank Dr. Nicolas Brantut for early discussions on the implementation of inverse modeling techniques.2020-02-1