scholarly journals Contribution of snow to Arctic first‐year and multi‐year sea ice mass balance within the Last Ice Area

2021 ◽  
Author(s):  
Benjamin A. Lange ◽  
Christian Haas ◽  
Alfonso Mucci ◽  
Justin F. Beckers ◽  
J. Alec Casey ◽  
...  
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
First Year ◽  
Ice Mass Balance

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 The importance of wind-blown snow redistribution to snow accumulation on Bellingshausen Sea ice

2011 ◽  
Vol 52 (57) ◽  
pp. 271-278 ◽  
Author(s):  
Katherine C. Leonard ◽  
Ted Maksym
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
Snow Accumulation ◽  
Blowing Snow ◽  
Wind Speeds ◽  
Antarctic Sea Ice ◽  
Drifting Snow ◽  
Bellingshausen Sea ◽  
Ice Mass Balance ◽  
High Winds

AbstractSnow distribution is a dominating factor in sea-ice mass balance in the Bellingshausen Sea, Antarctica, through its roles in insulating the ice and contributing to snow-ice production. the wind has long been qualitatively recognized to influence the distribution of snow accumulation on sea ice, but the relative importance of drifting and blowing snow has not been quantified over Antarctic sea ice prior to this study. the presence and magnitude of drifting snow were monitored continuously along with wind speeds at two sites on an ice floe in the Bellingshausen Sea during the October 2007 Sea Ice Mass Balance in the Antarctic (SIMBA) experiment. Contemporaneous precipitation measurements collected on board the RVIB Nathaniel B. Palmer and accumulation measurements by automated ice mass-balance buoys (IMBs) allow us to document the proportion of snowfall that accumulated on level ice surfaces in the presence of high winds and blowing-snow conditions. Accumulation on the sea ice during the experiment averaged <0.01 m w.e. at both IMB sites, during a period when European Centre for Medium-Range Weather Forecasts analyses predicted >0.03 m w.e. of precipitation on the ice floe. Accumulation changes on the ice floe were clearly associated with drifting snow and high winds. Drifting-snow transport during the SIMBA experiment was supply-limited. Using these results to inform a preliminary study using a blowing-snow model, we show that over the entire Southern Ocean approximately half of the precipitation over sea ice could be lost to leads.

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

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

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

&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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 &amp;#8211; 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 &amp;#8211; 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 &amp;#8211; 0.1cm and 1.5m &amp;#8211; 2.5m, respectively. &amp;#160;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 &amp;#8211; 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.&amp;#160;&amp;#160;&lt;/p&gt;

scholarly journals Thin ice, deep snow and surface flooding in Kotzebue Sound: landfast ice mass balance during two anomalously warm winters and implications for marine mammals and subsistence hunting

2021 ◽  
pp. 1-15
Author(s):  
Andrew R. Mahoney ◽  
Kate E. Turner ◽  
Donna D. W. Hauser ◽  
Nathan J. M. Laxague ◽  
Jessica M. Lindsay ◽  
...  
Keyword(s):  
Heat Flux ◽  
Sea Ice ◽  
Mass Balance ◽  
Marine Mammals ◽  
Advisory Council ◽  
Subsistence Hunting ◽  
Ice Growth ◽  
Landfast Ice ◽  
Ice Mass Balance ◽  
Kotzebue Sound

Abstract The inaugural data from the first systematic program of sea-ice observations in Kotzebue Sound, Alaska, in 2018 coincided with the first winter in living memory when the Sound was not choked with ice. The following winter of 2018–19 was even warmer and characterized by even less ice. Here we discuss the mass balance of landfast ice near Kotzebue (Qikiqtaġruk) during these two anomalously warm winters. We use in situ observations and a 1-D thermodynamic model to address three research questions developed in partnership with an Indigenous Advisory Council. In doing so, we improve our understanding of connections between landfast ice mass balance, marine mammals and subsistence hunting. Specifically, we show: (i) ice growth stopped unusually early due to strong vertical ocean heat flux, which also likely contributed to early start to bearded seal hunting; (ii) unusually thin ice contributed to widespread surface flooding. The associated snow ice formation partly offset the reduced ice growth, but the flooding likely had a negative impact on ringed seal habitat; (iii) sea ice near Kotzebue during the winters of 2017–18 and 2018–19 was likely the thinnest since at least 1945, driven by a combination of warm air temperatures and a persistent ocean heat flux.

scholarly journals Fate of sea ice in the 'New Arctic': A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions

2021 ◽  
Author(s):  
Sean Horvath ◽  
Linette Boisvert ◽  
Chelsea Parker ◽  
Melinda Webster ◽  
Patrick Taylor ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Mass Balance ◽  
Air Temperature ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Ice Mass Balance ◽  
Temperature And Pressure

Abstract. Since the early 2000s, sea ice has experienced an increased rate of decline in thickness and extent and transitioned to a seasonal ice cover. This shift to thinner, seasonal ice in the 'New Arctic' is accompanied by a reshuffling of energy flows at the surface. Understanding the magnitude and nature of this reshuffling and the feedbacks therein remains limited. A novel database is presented that combines satellite observations, model output, and reanalysis data with daily sea ice parcel drift tracks produced in a Lagrangian framework. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states. Building on previous work, this dataset includes remotely sensed radiative and turbulent fluxes from which the surface energy budget can be calculated. Additionally, flags indicate when sea ice parcels travel within cyclones, recording distance and direction from the cyclone center. The database drift track was evaluated by comparison with sea ice mass balance buoys. Results show ice parcels generally remain within 100km of the corresponding buoy, with a mean distance of 82.6 km and median distance of 54 km. The sea ice mass balance buoys also provide recordings of sea ice thickness, snow depth, and air temperature and pressure which were compared to this database. Ice thickness and snow depth typically are less accurate than air temperature and pressure due to the high spatial variability of the former two quantities when compared to a point measurement. The correlations between the ice parcel and buoy data are high, which highlights the accuracy of this Lagrangian database in capturing the seasonal changes and evolution of sea ice. This database has multiple applications for the scientific community; it can be used to study the processes that influence individual sea ice parcel time series, or to explore generalized summary statistics and trends across the Arctic. Applications such as these may shed light on the atmosphere-snow-sea ice interactions in the changing Arctic environment.

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.

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