scholarly journals Incorporation of satellite-derived thin-ice data into a global OGCM simulation

2019 ◽  
Vol 53 (11) ◽  
pp. 7113-7130
Author(s):  
Takahiro Toyoda ◽  
Katsushi Iwamoto ◽  
L. Shogo Urakawa ◽  
Hiroyuki Tsujino ◽  
Hideyuki Nakano ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Model Simulation ◽  
Mixed Layer Depth ◽  
The Arctic ◽  
Sea Ice Thickness ◽  
Ice Volume ◽  
The Arctic Ocean ◽  
Polar Ocean ◽  
Ice Production

Abstract The presence of thin sea ice is indicative of active freezing conditions in the polar ocean. We propose a simple yet effective method to incorporate information of thin-ice category into coupled ocean–sea-ice model simulations. In our approach, the thin-ice distribution restricts thick-ice extent and constrains atmosphere–ocean heat exchange through the sea ice. Our model simulation with the incorporation of satellite-derived thin-ice data for the Arctic Ocean showed much improved representation of sea-ice and upper-ocean fields, including sea-ice thickness in the Canadian Archipelago and the region north of Greenland, mixed-layer depth over the Central Arctic, and surface-layer salinity over the open ocean. Enhanced sea-ice production by the thin-ice data constraint increased the total sea-ice volume of the Arctic Ocean by $$5 \times 10^{3}$$ 5 × 10 3 –$$10 \times 10^{3}$$ 10 × 10 3  km3. Subsequent sea-ice melting was also enhanced, leading to the greater amplitude of the seasonal cycle by approximately $$2 \times 10^{3}$$ 2 × 10 3  km3 (15% of the baseline value from the experiment without the thin-ice data incorporation). Overall, our results demonstrate that the incorporation of satellite-derived information on thin sea ice has great potential for the improvement of coupled ocean–sea-ice simulations.

Sea ice thickness distribution in the Arctic Ocean

1987 ◽  
Vol 13 (3) ◽  
pp. 259-280 ◽  
Author(s):  
Robert H. Bourke ◽  
Robert P. Garrett
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Thickness Distribution ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
The Arctic Ocean

scholarly journals Improved mapping of sea ice production in the Arctic Ocean using AMSR-E thin ice thickness algorithm

2014 ◽  
Vol 119 (6) ◽  
pp. 3574-3594 ◽  
Author(s):  
Katsushi Iwamoto ◽  
Kay I. Ohshima ◽  
Takeshi Tamura
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
The Arctic ◽  
Ice Thickness ◽  
The Arctic Ocean ◽  
Ice Production

Sea Ice Enhancements to Polar WRF*

2015 ◽  
Vol 143 (6) ◽  
pp. 2363-2385 ◽  
Author(s):  
Keith M. Hines ◽  
David H. Bromwich ◽  
Lesheng Bai ◽  
Cecilia M. Bitz ◽  
Jordan G. Powers ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Surface Model ◽  
Sea Ice Thickness ◽  
Near Surface ◽  
The Arctic Ocean

Abstract The Polar Weather Research and Forecasting Model (Polar WRF), a polar-optimized version of the WRF Model, is developed and made available to the community by Ohio State University’s Polar Meteorology Group (PMG) as a code supplement to the WRF release from the National Center for Atmospheric Research (NCAR). While annual NCAR official releases contain polar modifications, the PMG provides very recent updates to users. PMG supplement versions up to WRF version 3.4 include modified Noah land surface model sea ice representation, allowing the specification of variable sea ice thickness and snow depth over sea ice rather than the default 3-m thickness and 0.05-m snow depth. Starting with WRF V3.5, these options are implemented by NCAR into the standard WRF release. Gridded distributions of Arctic ice thickness and snow depth over sea ice have recently become available. Their impacts are tested with PMG’s WRF V3.5-based Polar WRF in two case studies. First, 20-km-resolution model results for January 1998 are compared with observations during the Surface Heat Budget of the Arctic Ocean project. Polar WRF using analyzed thickness and snow depth fields appears to simulate January 1998 slightly better than WRF without polar settings selected. Sensitivity tests show that the simulated impacts of realistic variability in sea ice thickness and snow depth on near-surface temperature is several degrees. The 40-km resolution simulations of a second case study covering Europe and the Arctic Ocean demonstrate remote impacts of Arctic sea ice thickness on midlatitude synoptic meteorology that develop within 2 weeks during a winter 2012 blocking event.

Unprecedented decline of sea ice thickness in Fram Strait in 2017-2018

2021 ◽  
Author(s):  
Hiroshi Sumata ◽  
Laura de Steur ◽  
Dmitry Divine ◽  
Olga Pavlova ◽  
Sebastian Gerland
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Barents Sea ◽  
North Atlantic Ocean ◽  
The Arctic ◽  
Ice Thickness ◽  
Fram Strait ◽  
Sea Ice Thickness ◽  
Drift Speed ◽  
The Arctic Ocean

<p><span><span>Fram Strait is the major gateway connecting the Arctic Ocean and the northern North Atlantic Ocean where about 80 to 90% of sea ice outflow from the Arctic Ocean takes place. Long-term observations from the Fram Strait Arctic Outflow Observatory maintained by the Norwegian Polar Institute captured an unprecedented decline<!-- should we somehow add information that this statement is limited to the time since the early 1990s? --><!-- Reply to Sebastian Gerland (2021/01/12, 15:45): "..." I slightly modified the sentence to mention this. --> of sea ice thickness in 2017 – 2018 since comprehensive observations started in the early 1990s. Four Ice Profiling Sonars moored in the East Greenland Current in Fram Strait simultaneously recorded 50 – 70 cm decline of annual mean ice thickness in comparison with preceding years. A backward trajectory analysis revealed that the decline was attributed to an anomalous sea level pressure pattern from 2017 autumn to 2018 summer. Southerly wind associated with a dipole pressure anomaly between Greenland and the Barents Sea prevented southward motion of ice floes north of Fram Strait. Hence ice pack was exposed to warm Atlantic Water in the north of Fram Strait 2 – 3 times longer than the average year, allowing more melt <!-- should also slower freezing or reduced freezing rates mentioned here during winter and spring (in addition to melt in summer and autumn)? --><!-- Reply to Sebastian Gerland (2021/01/12, 15:46): "..." I would like to keep this sentence as it is, since the analysis implies sea ice melt occurred in the vicinity of Fram Strait in winter (probably due to ocean heat flux), though we don’t have direct measurements of 2018 event. This could be an interesting implications of this study, and seeds for further investigation. -->to happen. At the same time, the dipole anomaly was responsible for the slowest observed annual mean ice drift speed in Fram Strait in the last two decades. As a consequence of the record minimum of ice thickness and the slowest drift speed, the sea ice volume transport through the Fram Strait dropped by more than 50% in comparison with the 2010 – 2017 average.</span></span></p>

Using CloudSat snowfall rate observations to constrain and characterize the uncertainties of Arctic snow-on-sea-ice

2020 ◽  
Author(s):  
Alex Cabaj ◽  
Paul Kushner ◽  
Alek Petty ◽  
Stephen Howell ◽  
Christopher Fletcher
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
The Arctic Ocean ◽  
Ice Model

<p><span>Snow on Arctic sea ice plays multiple—and sometimes contrasting—roles in several feedbacks between sea ice and the global climate </span><span>system.</span><span> For example, the presence of snow on sea ice may mitigate sea ice melt by</span><span> increasing the sea ice albedo </span><span>and enhancing the ice-albedo feedback. Conversely, snow can</span><span> in</span><span>hibit sea ice growth by insulating the ice from the atmosphere during the </span><span>sea ice </span><span>growth season. </span><span>In addition to its contribution to sea ice feedbacks, snow on sea ice also poses a challenge for sea ice observations. </span><span>In particular, </span><span>snow </span><span>contributes to uncertaint</span><span>ies</span><span> in retrievals of sea ice thickness from satellite altimetry </span><span>measurements, </span><span>such as those from ICESat-2</span><span>. </span><span>Snow-on-sea-ice models can</span><span> produce basin-wide snow depth estimates, but these models require snowfall input from reanalysis products. In-situ snowfall measurements are a</span><span>bsent</span><span> over most of the Arctic Ocean, so it can be difficult to determine which reanalysis </span><span>snowfall</span><span> product is b</span><span>est</span><span> suited to be used as</span><span> input for a snow-on-sea-ice model.</span></p><p><span>In the absence of in-situ snowfall rate measurements, </span><span>measurements from </span><span>satellite instruments can be used to quantify snowfall over the Arctic Ocean</span><span>. </span><span>The CloudSat satellite, which is equipped with a 94 GHz Cloud Profiling Radar instrument, measures vertical radar reflectivity profiles from which snowfall rate</span><span>s</span><span> can be retrieved. </span> <span>T</span><span>his instrument</span><span> provides the most extensive high-latitude snowfall rate observation dataset currently available. </span><span>CloudSat’s near-polar orbit enables it to make measurements at latitudes up to 82°N, with a 16-day repeat cycle, </span><span>over the time period from 2006-2016.</span></p><p><span>We present a calibration of reanalysis snowfall to CloudSat observations over the Arctic Ocean, which we then apply to reanalysis snowfall input for the NASA Eulerian Snow On Sea Ice Model (NESOSIM). This calibration reduces the spread in snow depths produced by NESOSIM w</span><span>hen</span><span> different reanalysis inputs </span><span>are used</span><span>. </span><span>In light of this calibration, we revise the NESOSIM parametrizations of wind-driven snow processes, and we characterize the uncertainties in NESOSIM-generated snow depths resulting from uncertainties in snowfall input. </span><span>We then extend this analysis further to estimate the resulting uncertainties in sea ice thickness retrieved from ICESat-2 when snow depth estimates from NESOSIM are used as input for the retrieval.</span></p>

Sign in / Sign up

Export Citation Format

Share Document