Manual point-measurements of sea ice mass balance during the MOSAiC Expedition

2021 ◽  
Author(s):  
Ian Raphael ◽  
Donald Perovich ◽  
Chris Polashenski ◽  
David Clemens-Sewall ◽  
Polona Itkin ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Mass Balance ◽  
Critical Role ◽  
The Arctic ◽  
Diverse Range ◽  
Individual Measurement ◽  
Ice Surface ◽  
Ice Mass Balance ◽  
Ocean Measurements

<p>Sea ice plays a critical role in the Arctic climate system, regulating much of the energy transfer between the ocean and the atmosphere. Repeat measurements of ice mass balance at discrete points allow us to determine the direct response of sea ice mass to environmental conditions. We installed a network of mass balance measurement sites across the MOSAiC Central Observatories, distributed over a diverse range of ice types and features. The sites were composed of gridded arrays of 9-17 hotwire thickness gauges, each paired with a surface ablation stake. Seven sites were installed on first year ice, and seven on second or multi year ice, with a total of 120+ individual measurement stations. The sites were operational over different periods throughout the year; several were destroyed or became inaccessible during ridging events. Initial ice thicknesses ranged from 0.13-3.50 m. We made measurements of ice and snow interfaces and thicknesses with 1 cm precision at each station, at intervals of 2-3 weeks during the growth season and as few as 1-2 days during the melt season. From these measurements, we infer ice growth, ice bottom melt, ice surface melt, snow deposition, snow erosion, and snow melt. The time series spans October 2019–September 2020, with a five-week measurement gap beginning mid-May 2020. We present an overview of the measurements and preliminary analysis, partitioning results by ice type and comparing mass balance to concurrent atmosphere and ocean measurements. We identify trends in the seasonal evolution of different ice types, and give particular attention to notable events in the time series. As true point-measurements, the data are especially relevant in improving one-dimensional thermodynamic sea ice models. The results also provide validation for satellite and electromagnetic induction ice-thickness measurements made during MOSAiC, which offer higher areal coverage but lower measurement- and spatial-precision.</p>

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.

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

2019 ◽  
Author(s):  
Alex West ◽  
Mat Collins ◽  
Ed Blockley
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
The North ◽  
Energy Evaluation ◽  
Ice Mass Balance ◽  
Arctic Ice

Abstract. Arctic sea ice has declined rapidly over recent decades. Models predict that the Arctic will be nearly ice-free by mid-century, but the spread in predictions of sea ice extent is currently large. The reasons for this spread are poorly understood, partly due to a lack of observations with which the processes by which Arctic atmospheric and oceanic forcing affect sea ice state can be examined. In this study, a method of estimating fluxes of top melt, top conduction, basal conduction and ocean heat flux from Arctic ice mass balance buoy elevation and temperature data is presented. The derived fluxes are used to evaluate modelled fluxes from the coupled climate model HadGEM2-ES in two densely sampled regions of the Arctic, the North Pole and Beaufort Sea. The evaluation shows the model to overestimate the magnitude of summer top melting fluxes, and winter conductive fluxes, results which are physically consistent with an independent sea ice and surface energy evaluation of the same model.

scholarly journals Sea-ice mass-balance monitoring in an Arctic fjord

2007 ◽  
Vol 46 ◽  
pp. 435-442 ◽  
Author(s):  
Sebastian Gerland ◽  
Angelika H.H. Renner
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Protective Effect ◽  
Mass Balance ◽  
Meteorological Parameters ◽  
Monitoring Study ◽  
Arctic Fjord ◽  
Fast Ice ◽  
Ice Mass Balance ◽  
Snow Thickness

AbstractA sea-ice mass-balance monitoring study including ice extent and thickness observations was started at Kongsfjorden (79˚N, 12˚E), Svalbard, in 2003. The inner part of Kongsfjorden is usually covered by seasonal fast ice <1m thick, initially forming between December and March and persisting until June. Ice extent is visually observed from the mountain Zeppelinfjellet, and documented by ice maps and photographs several times a week. Ice and snow thickness is measured regularly at four sites from drillholes. Time series of ice extent in four areas east of Ny-Ålesund (total area 120 km2) were calculated for 2003–05. By combining extent with thickness data, ice-mass time series were calculated. As also observed earlier than 2003 in other studies, the fast ice varies interannually in extent and thickness. Among the factors which control the fast-ice evolution are physical and meteorological parameters, and the geographical setting of Kongsfjorden, with its coastline and a group of islands in its inner part having a protective effect. This study is ongoing and a major aim is to identify and quantify connections between the Kongsfjorden fast-ice evolution and climate parameters.

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 Multiyear sea ice thermal regimes and oceanic heat flux derived from an ice mass balance buoy in the Arctic Ocean

2014 ◽  
Vol 119 (1) ◽  
pp. 537-547 ◽  
Author(s):  
Ruibo Lei ◽  
Na Li ◽  
Petra Heil ◽  
Bin Cheng ◽  
Zhanhai Zhang ◽  
...  
Keyword(s):  
Heat Flux ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Mass Balance ◽  
The Arctic ◽  
Oceanic Heat Flux ◽  
Thermal Regimes ◽  
The Arctic Ocean ◽  
Ice Mass Balance

scholarly journals Satellite-derived sea ice export and its impact on Arctic ice mass balance

2018 ◽  
Vol 12 (9) ◽  
pp. 3017-3032 ◽  
Author(s):  
Robert Ricker ◽  
Fanny Girard-Ardhuin ◽  
Thomas Krumpen ◽  
Camille Lique
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
North Atlantic ◽  
Large Scale ◽  
The Arctic ◽  
Fram Strait ◽  
Ice Volume ◽  
Ice Drift ◽  
Ice Mass Balance ◽  
Arctic Ice

Abstract. Sea ice volume export through the Fram Strait represents an important freshwater input to the North Atlantic, which could in turn modulate the intensity of the thermohaline circulation. It also contributes significantly to variations in Arctic ice mass balance. We present the first estimates of winter sea ice volume export through the Fram Strait using CryoSat-2 sea ice thickness retrievals and three different ice drift products for the years 2010 to 2017. The monthly export varies between −21 and −540 km3. We find that ice drift variability is the main driver of annual and interannual ice volume export variability and that the interannual variations in the ice drift are driven by large-scale variability in the atmospheric circulation captured by the Arctic Oscillation and North Atlantic Oscillation indices. On shorter timescale, however, the seasonal cycle is also driven by the mean thickness of exported sea ice, typically peaking in March. Considering Arctic winter multi-year ice volume changes, 54  % of their variability can be explained by the variations in ice volume export through the Fram Strait.

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