Assimilating satellite concentration data into an Arctic sea ice mass balance model, 1979-1985

1996 ◽  
Vol 101 (C9) ◽  
pp. 20849-20868 ◽  
Author(s):  
D. Thomas ◽  
S. Martin ◽  
D. Rothrock ◽  
M. Steele
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
Arctic Sea Ice ◽  
Balance Model ◽  
Mass Balance Model ◽  
Concentration Data ◽  
Arctic Sea ◽  
Ice Mass Balance

scholarly journals Estimating the snow depth, the snow–ice interface temperature, and the effective temperature of Arctic sea ice using Advanced Microwave Scanning Radiometer 2 and ice mass balance buoy data

2019 ◽  
Vol 13 (4) ◽  
pp. 1283-1296 ◽  
Author(s):  
Lise Kilic ◽  
Rasmus Tage Tonboe ◽  
Catherine Prigent ◽  
Georg Heygster
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
Effective Temperature ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Interface Temperature ◽  
Microwave Frequencies ◽  
Arctic Sea ◽  
Ice Mass Balance

Abstract. Mapping sea ice concentration (SIC) and understanding sea ice properties and variability is important, especially today with the recent Arctic sea ice decline. Moreover, accurate estimation of the sea ice effective temperature (Teff) at 50 GHz is needed for atmospheric sounding applications over sea ice and for noise reduction in SIC estimates. At low microwave frequencies, the sensitivity to the atmosphere is low, and it is possible to derive sea ice parameters due to the penetration of microwaves in the snow and ice layers. In this study, we propose simple algorithms to derive the snow depth, the snow–ice interface temperature (TSnow−Ice) and the Teff of Arctic sea ice from microwave brightness temperatures (TBs). This is achieved using the Round Robin Data Package of the ESA sea ice CCI project, which contains TBs from the Advanced Microwave Scanning Radiometer 2 (AMSR2) collocated with measurements from ice mass balance buoys (IMBs) and the NASA Operation Ice Bridge (OIB) airborne campaigns over the Arctic sea ice. The snow depth over sea ice is estimated with an error of 5.1 cm, using a multilinear regression with the TBs at 6, 18, and 36 V. The TSnow−Ice is retrieved using a linear regression as a function of the snow depth and the TBs at 10 or 6 V. The root mean square errors (RMSEs) obtained are 2.87 and 2.90 K respectively, with 10 and 6 V TBs. The Teff at microwave frequencies between 6 and 89 GHz is expressed as a function of TSnow−Ice using data from a thermodynamical model combined with the Microwave Emission Model of Layered Snowpacks. Teff is estimated from the TSnow−Ice with a RMSE of less than 1 K.

scholarly journals Estimating the snow depth, the snow-ice interface temperature, and the effective temperature of Arctic sea ice using Advanced Microwave Scanning Radiometer 2 and Ice Mass Balance buoys data

2018 ◽  
Author(s):  
Lise Kilic ◽  
Rasmus Tage Tonboe ◽  
Catherine Prigent ◽  
Georg Heygster
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
Effective Temperature ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Interface Temperature ◽  
Microwave Frequencies ◽  
Arctic Sea ◽  
Ice Mass Balance

Abstract. Mapping Sea Ice Concentration (SIC) and understanding sea ice properties and variability is important especially today with the recent Arctic sea ice decline. Moreover, accurate estimation of the sea ice effective temperature (Teff) at 50 GHz is needed for atmospheric sounding applications over sea ice and for noise reduction in SIC estimates. At low microwave frequencies, the sensitivity to atmosphere is low, and it is possible to derive sea ice parameters due to the penetration of microwaves in the snow and ice layers. In this study, we propose simple algorithms to derive the snow depth, the snow-ice interface temperature (TSnow-Ice) and the Teff of Arctic sea ice from microwave brightness temperatures (TBs). This is achieved using the Round Robin Data Package of the ESA sea ice CCI project, which contains TBs from the Advanced Microwave Scanning Radiometer 2 (AMSR2) collocated with measurements from Ice Mass Balance (IMB) buoys and the NASA Operation Ice Bridge (OIB) airborne campaigns over the Arctic sea ice. The snow depth over sea ice is estimated with an error of ~ 6 cm using a multilinear regression with the TBs at 6 V, 18 V, and 36 V. The TSnow-Ice is retrieved using a linear regression as a function of the snow depth and the TBs at 10 V or 6 V. The Root Mean Square Errors (RMSEs) obtained are 1.69 and 1.95 K respectively, with the 10 V and 6 V TBs. The Teff at microwave frequencies between 6 and 89 GHz is expressed as a function of TSnow-Ice using data from a thermodynamical model combined with the Microwave Emission Model of Layered Snow-packs. Teffs are estimated from the TSnow-Ice with a RMSE of less than 1 K.

scholarly journals Using Arctic ice mass balance buoys for evaluation of modelled ice energy fluxes

2020 ◽  
Vol 13 (10) ◽  
pp. 4845-4868
Author(s):  
Alex West ◽  
Mat Collins ◽  
Ed Blockley
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
Arctic Sea Ice ◽  
Surface Radiation ◽  
Rapid Decline ◽  
Energy Fluxes ◽  
Arctic Sea ◽  
Imperfect Knowledge ◽  
Ice Mass Balance ◽  
Arctic Ice

Abstract. A new method of sea ice model evaluation is demonstrated. Data from the network of Arctic ice mass balance buoys (IMBs) are used to estimate distributions of vertical energy fluxes over sea ice in two densely sampled regions – the North Pole and Beaufort Sea. The resulting dataset captures seasonal variability in sea ice energy fluxes well, and it captures spatial variability to a lesser extent. The dataset is used to evaluate a coupled climate model, HadGEM2-ES (Hadley Centre Global Environment Model, version 2, Earth System), in the two regions. The evaluation shows HadGEM2-ES to simulate too much top melting in summer and too much basal conduction in winter. These results are consistent with a previous study of sea ice state and surface radiation in this model, increasing confidence in the IMB-based evaluation. In addition, the IMB-based evaluation suggests an additional important cause for excessive winter ice growth in HadGEM2-ES, a lack of sea ice heat capacity, which was not detectable in the earlier study. Uncertainty in the IMB fluxes caused by imperfect knowledge of ice salinity, snow density and other physical constants is quantified (as is inaccuracy due to imperfect sampling of ice thickness) and in most cases is found to be small relative to the model biases discussed. Hence the IMB-based evaluation is shown to be a valuable tool with which to analyse sea ice models and, by extension, better understand the large spread in coupled model simulations of the present-day ice state. Reducing this spread is a key task both in understanding the current rapid decline in Arctic sea ice and in constraining projections of future Arctic sea ice change.

scholarly journals Snow contribution to first-year and second-year Arctic sea ice mass balance north of Svalbard

2017 ◽  
Vol 122 (3) ◽  
pp. 2539-2549 ◽  
Author(s):  
Mats A. Granskog ◽  
Anja Rösel ◽  
Paul A. Dodd ◽  
Dmitry Divine ◽  
Sebastian Gerland ◽  
...  
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
Arctic Sea Ice ◽  
First Year ◽  
Arctic Sea ◽  
Ice Mass Balance ◽  
Second Year

scholarly journals Ice mass-balance buoys: a tool for measuring and attributing changes in the thickness of the Arctic sea-ice cover

2006 ◽  
Vol 44 ◽  
pp. 205-210 ◽  
Author(s):  
Jacqueline A. Richter-Menge ◽  
Donald K. Perovich ◽  
Bruce C. Elder ◽  
Keran Claffey ◽  
Ignatius Rigor ◽  
...  
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
North Pole ◽  
The Arctic ◽  
Central Basin ◽  
The North ◽  
Arctic Sea ◽  
Ice Mass Balance

AbstractRecent observational and modeling studies indicate that the Arctic sea-ice cover is undergoing significant climate-induced changes, affecting both its extent and thickness. The thickness or, more precisely, the mass balance of the ice cover is a key climate-change indicator since it is an integrator of both the surface heat budget and the ocean heat flux. Accordingly, efforts are underway to develop and deploy in situ observing systems which, when combined with satellite remote-sensing information and numerical models, can effectively monitor and attribute changes in the mass balance of the Arctic sea-ice cover. As part of this effort, we have developed an autonomous ice mass-balance buoy (IMB), which is equipped with sensors to measure snow accumulation and ablation, ice growth and melt, and internal ice temperature, plus a satellite transmitter. The IMB is unique in its ability to determine whether changes in the thickness of the ice cover occur at the top or bottom of the ice cover, and hence provide insight into the driving forces behind the change. Since 2000, IMBs have been deployed each spring from the North Pole Environmental Observatory and in several other areas, including a few in the Beaufort Sea and Central Basin. At this point, the collective time series is too short to draw significant and specific conclusions regarding interannual and regional variability in ice mass balance. Comparisons of available data indicate that ice surface ablation is greater in the Beaufort region (67–80 cm), relative to the North Pole (0–30 cm), consistent with a longer period of melt in the more southerly location. Ablation at the bottom of the ice (22 cm), maximum ice thickness (235 cm) and maximum snow depth (28 cm) were comparable in the two regions.

Sea ice representation in sea ice model of CICE and Icepack

2020 ◽  
Author(s):  
Caixin Wang ◽  
Mats A. Granskog ◽  
Jens Boldingh Debernard ◽  
Keguang Wang
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
National Laboratory ◽  
Arctic Sea Ice ◽  
Surface Radiation ◽  
Radiation Balance ◽  
Momentum Exchange ◽  
Spectral Radiation ◽  
Ice Mass Balance ◽  
Ice Model

<p>Sea ice is a critical component of the Earth system, playing an important role in high-latitude<br>surface radiation balance and heat, moisture and momentum exchange between atmosphere<br>and ocean. In recent years, rapid changes have been occurring in Arctic sea ice, including<br>decline in ice extent/area, decreasing in ice thickness and volume, and shifting towards a first-<br>year ice (FYI) dominated, rather than multi-year ice (MYI) dominated ice pack. These are one<br>of the most well-known and striking examples of climate change. However, representing<br>these changes in the model is still in question since most of our knowledge is based on MYI.<br>CICE is a sea ice model developed at Los Alamos National Laboratory since 1994. It is<br>widely used to simulate the growth, melt and movement of sea ice, and to resolve the<br>biogeochemical processes. Its column version, Icepack, has been separated from CICE after<br>CICE V5.1.2, which provides additional opportunity for simulating the evolution of drifting<br>sea ice floes. How about the representation of sea ice in a column model (Icepack) and a 3d<br>model (CICE)? In 2012, an ice mass balance buoy (IMB) and a Spectral Radiation Buoy<br>(SRB) were deployed on FYI near the North Pole, and later drifted towards Fram Strait. These<br>buoys collected a complete summer melt season of in-band (350-800 nm) spectral solar<br>radiation and sea ice mass balance data. In this study, we apply the Icepack (version 1.1.1)<br>and CICE (version 5.1.2) to investigate the seasonal evolution of sea ice in 2012 in these two models, and<br>assess how well the physical processes are represented in CICE and Icepack, with the focus<br>on the surface changes.</p>

Winter Arctic sea ice bottom evolution detected by thermistor string-based ice mass balance buoys (SIMBA)

2020 ◽  
Author(s):  
Bin Cheng ◽  
Timo Vihma ◽  
Zeling Liao ◽  
Ruibo Lei ◽  
Mario Hoppmann ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Mass Balance ◽  
Arctic Sea Ice ◽  
Heating Element ◽  
The Arctic ◽  
Ice Thickness ◽  
The Arctic Ocean ◽  
Ice Mass Balance ◽  
Snow And Ice

<p>A thermistor-string-based Snow and Ice Mass Balance Array (SIMBA) has been developed in recent years and used for monitoring snow and ice mass balance in the Arctic Ocean. SIMBA measures vertical environment temperature (ET) profiles through the air-snow-sea ice-ocean column using a thermistor string (5 m long, sensor spacing 2cm). Each thermistor sensor equipped with a small identical heating element. A small voltage was applied to the heating element so that the heat energy liberated in the vicinity of each sensor is the same. The heating time intervals lasted 60 s and 120 s, respectively. The heating temperatures (HT) after these two intervals were recorded. The ET was measured 4 times a day and once per day for the HT.</p><p>A total 15 SIMBA buoys have been deployed in the Arctic Ocean during the Chinese National Arctic Research Expedition (CHINARE) 2018 and the Nansen and Amundsen Basins Observational System (NABOS) 2018 field expeditions in late autumn. We applied a recently developed SIMBA algorithm to retrieve snow and ice thickness using SIMBA ET and HT temperature data. We focus particularly on sea ice bottom evolution during Arctic winter.</p><p>In mid-September 2018, 5 SIMBA buoys were deployed in the East Siberian Sea (NABOS2018) where snow was in practical zero cm and ice thickness ranged between 1.8 m – 2.6 m. By the end of May, those SIMBA buoys were drifted in the central Arctic where snow and ice thicknesses were around 0.05m - 0.2m and 2.6m – 3.2m, respectively. For those 10 SIMBA buoys deployed by the CHINARE2018 in the Chukchi Sea and Canadian Basin, the initial snow and ice thickness were ranged between 0.05m – 0.1cm and 1.5m – 2.5m, respectively.  By the end of May, those SIMBA buoys were drifted toward the north of Greenland where snow and ice thicknesses were around 0.2m - 0.3m and 2.0m – 3.5m, respectively. The ice bottom evolution derived by SIMBA algorithm agrees well with SIMBA HT identified ice-ocean interfaces. We also perform a preliminary investigation of sea ice bottom evolution measured by several SIMBA buoys deployed during the MOSAiC leg1 field campaign in winter 2019/2020.  </p>

scholarly journals Meltwater sources and sinks for multiyear Arctic sea ice in summer

2021 ◽  
Vol 15 (9) ◽  
pp. 4517-4525
Author(s):  
Don Perovich ◽  
Madison Smith ◽  
Bonnie Light ◽  
Melinda Webster
Keyword(s):  
Sea Ice ◽  
Heat Budget ◽  
Arctic Sea Ice ◽  
Sources And Sinks ◽  
Ice Melt ◽  
Ice Surface ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Ice Mass Balance ◽  
Surface Heat Budget

Abstract. On Arctic sea ice, the melt of snow and sea ice generate a summertime flux of fresh water to the upper ocean. The partitioning of this meltwater to storage in melt ponds and deposition in the ocean has consequences for the surface heat budget, the sea ice mass balance, and primary productivity. Synthesizing results from the 1997–1998 SHEBA field experiment, we calculate the sources and sinks of meltwater produced on a multiyear floe during summer melt. The total meltwater input to the system from snowmelt, ice melt, and precipitation from 1 June to 9 August was equivalent to a layer of water 80 cm thick over the ice-covered and open ocean. A total of 85 % of this meltwater was deposited in the ocean, and only 15 % of this meltwater was stored in ponds. The cumulative contributions of meltwater input to the ocean from drainage from the ice surface and bottom melting were roughly equal.

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