Interannual variability in Transpolar Drift ice thickness and
potential impact of Atlantification

Belter, Hans Jakob; Krumpen, Thomas; Albedyll, Luisa von; Alekseeva, Tatyana N; Фролов, С. В.; Hendricks, Stefan; Herber, Andreas; Polyakov, Igor V.; Raphael, Ian; Ricker, Robert; Serovetnikov, S. S.; Webster, Melinda; Haas, Christian

doi:10.5194/tc-2020-305

Cited by 4 publications

(3 citation statements)

References 51 publications

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“…To date, pan-Arctic sea ice thickness observations from satellite laser altimetry (Petty et al, 2020), radar altimetry (Laxon et al, 2013) and Lband radiometry (Kaleschke et al, 2012) are only available for the winter months of October-April. Airborne electromagnetic (EM) sensors and moored upward-looking sonar (ULS) instruments have provided snapshots of the sea ice thickness distribution over limited areas and/or time periods (Haas and Howell, 2015;Belter et al, 2020Belter et al, , 2021. However, consistent pan-Arctic sea ice thickness observations remain elusive for the months of May-September when they would arguably be most valuable.…”

Section: Introductionmentioning

confidence: 99%

A 10-year record of Arctic summer sea ice freeboard from CryoSat-2

Dawson

Landy

Komarov

et al. 2022

Remote Sensing of Environment

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Satellite observations of pan-Arctic sea ice thickness have so far been constrained to winter months. For radar altimeters, conventional methods cannot differentiate leads from meltwater ponds that accumulate at the ice surface in summer months, which is a critical step in the ice thickness calculation. Here, we use over 350 optical and synthetic aperture radar (SAR) images from the summer months to train a 1D convolution neural network for separating CryoSat-2 radar altimeter returns from sea ice floes and leads with an accuracy >80%. This enables us to generate the first pan-Arctic measurements of sea ice radar freeboard for May-September between 2011 and 2020. Results indicate that the freeboard distributions in May and September compare closely to those from a conventional 'winter' processor in April and October, respectively. The freeboards capture expected patterns of sea ice melt over the Arctic summer, matching well to ice draft observations from the Beaufort Gyre Exploration Program (BGEP) moorings. However, compared to airborne laser scanner freeboards from Operation IceBridge and airborne EM ice thickness surveys from the Alfred Wegener Institute (AWI) IceBird program, CryoSat-2 freeboards are underestimated by 0.02-0.2 m, and ice thickness is underestimated by 0.28-1.0 m, with the largest differences being over thicker multi-year sea ice. To create the first pan-Arctic summer sea ice thickness dataset we must address primary sources of uncertainty in the conversion from radar freeboard to ice thickness.

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Section: Introductionmentioning

confidence: 99%

A 10-year record of Arctic summer sea ice freeboard from CryoSat-2

Dawson

Landy

Komarov

et al. 2022

Remote Sensing of Environment

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“…In the past few decades, sea ice has decreased both in extent (Stroeve & Notz, 2018) and thickness (Belter et al., 2020; Kwok, 2018). Numerical experiments revealed that in the 2000s, about half of the liquid freshwater content (LFWC) rise in the Beaufort Gyre (BG) could be ascribed to sea ice decline (through both the meltwater and modification of ocean surface stress and circulation) caused by atmospheric warming (Wang, Wekerle, Danilov, Koldunov, et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

Arctic Ocean Freshwater in CMIP6 Coupled Models

Wang

et al. 2022

Earth's Future

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In this study we assessed the representation of the sea surface salinity (SSS) and liquid freshwater content (LFWC) of the Arctic Ocean in the historical simulation of 31 CMIP6 models with comparison to 39 Coupled Model Intercomparison Project phase 5 (CMIP5) models, and investigated the projected changes in Arctic liquid and solid freshwater content and freshwater budget in scenarios with two different shared socioeconomic pathways (SSP2‐4.5 and SSP5‐8.5). No significant improvement was found in the SSS and LFWC simulation from CMIP5 to CMIP6, given the large model spreads in both CMIP phases. The overestimation of LFWC continues to be a common bias in CMIP6. In the historical simulation, the multi‐model mean river runoff, net precipitation, Bering Strait and Barents Sea Opening (BSO) freshwater transports are 2,928 ± 1,068, 1,839 ± 3,424, 2,538 ± 1,009, and −636 ± 553 km3/year, respectively. In the last decade of the 21st century, CMIP6 MMM projects these budget terms to rise to 4,346 ± 1,484 km3/year (3,678 ± 1,255 km3/year), 3,866 ± 2,935 km3/year (3,145 ± 2,651 km3/year), 2,631 ± 1,119 km3/year (2,649 ± 1,141 km3/year) and 1,033 ± 1,496 km3/year (449 ± 1,222 km3/year) under SSP5‐8.5 (SSP2‐4.5). Arctic sea ice is expected to continue declining in the future, and sea ice meltwater flux is likely to decrease to about zero in the mid‐21st century under both SSP2‐4.5 and SSP5‐8.5 scenarios. Liquid freshwater exiting Fram and Davis straits will be higher in the future, and the Fram Strait export will remain larger. The Arctic Ocean is projected to hold a total of 160,300 ± 62,330 km3 (141,590 ± 50,310 km3) liquid freshwater under SSP5‐8.5 (SSP2‐4.5) by 2100, about 60% (40%) more than its historical climatology.

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“…The need for a continuous dataset for ice tracking is clear (e.g. Pfirman et al 2004;Krumpen et al 2016;Williams et al 2016;Newton et al 2017;Mahoney et al 2019;Belter et al 2020). Such a dataset is also included in the Sea Ice Tracking Utility, made publicly available recently on the National Snow and Ice Data Center website (SITU, Campbell et al 2020), or the Alfred Wegener Institute ICETrack tool (Krumpen, 2018) -for educational, scientific and field expedition planning purposes.…”

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confidence: 99%

A new sea ice state dependent parameterization for the free drift of sea ice

Brunette

Tremblay

Newton

2021

Preprint

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Abstract. Free drift estimates of sea ice motion are necessary to produce a seamless observational record combining buoy and satellite-derived sea ice motion vectors. We develop a new parameterization for the free drift of sea ice based on wind forcing, wind turning angle, sea ice state variables (concentration and thickness) and ocean current (as a residual). Building on the fact that the spatially varying standard wind-ice transfer coefficient (considering only surface wind stress) has a structure as the spatial distribution of sea ice thickness, we introduce a wind-ice transfer coefficient that scales linearly with thickness. Results show a mean error of −0.5 cm/s (low-speed bias) and a root-mean-square error of 5.1 cm/s, considering daily buoy drift data as truth. This represents a 31 % reduction of the error on drift speed compared to the free drift estimates used in the Polar Pathfinder dataset. The thickness-dependent wind transfer coefficient provides an improved seasonality and long-term trend of the sea ice drift speed, with a minimum (maximum) drift speed in May (October), compared to July (January) for the constant wind transfer coefficient parameterizations which simply follow the peak in mean surface wind stresses. The trend in sea ice drift in this new model is +0.45 cm/s decade−1 compared with +0.39 cm/s decade−1 from the buoy observations, whereas there is essentially no trend in the standard free drift parameterization (−0.01 cm/s decade−1) or the Polar Pathfinder free drifts (−0.03 cm/s decade−1). The wind turning angle that minimize the cost function is equal of 25°, with a mean and root-mean square error of +2.6° and 51° on the direction of the drift, respectively. The residual from the minimization procedure (i.e. the ocean currents) resolves key large scale features such as the Beaufort Gyre and Transpolar Drift Stream, and is in good agreement with ocean state estimates from the ECCO, GLORYS and PIOMAS ice-ocean reanalyses, and geostrophic currents from dynamical ocean topography, with a root-mean-square difference of 2.4, 2.9, 2.6 and 3.8 cm/s, respectively. Finally, a repeat of the analysis on a two sub-section of the time series (pre- and post-2000) clearly shows the acceleration of the Beaufort Gyre (particularly along the Alaskan coastline) and an expansion of the gyre in the post-2000 concurrent with a thinning of the sea ice cover and observations acceleration of the ice drift speed and ocean current. This new dataset is publicly available for complementing merged observations-based sea ice drift datasets that includes satellite and buoy drift records.

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Interannual variability in Transpolar Drift ice thickness and potential impact of Atlantification

Cited by 4 publications

References 51 publications

A 10-year record of Arctic summer sea ice freeboard from CryoSat-2

A 10-year record of Arctic summer sea ice freeboard from CryoSat-2

Arctic Ocean Freshwater in CMIP6 Coupled Models

A new sea ice state dependent parameterization for the free drift of sea ice

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