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

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.

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

Annals of Glaciology ◽

10.3189/172756406781811727 ◽

2006 ◽

Vol 44 ◽

pp. 205-210 ◽

Cited By ~ 85

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.

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Mechanisms for and Predictability of a Drastic Reduction in the Arctic Sea Ice: APPOSITE Data with Climate Model MIROC

Journal of Climate ◽

10.1175/jcli-d-18-0195.1 ◽

2019 ◽

Vol 32 (5) ◽

pp. 1361-1380 ◽

Cited By ~ 3

Author(s):

J. Ono ◽

H. Tatebe ◽

Y. Komuro

Keyword(s):

Sea Ice ◽

Climate Model ◽

Arctic Sea Ice ◽

The Arctic ◽

Ensemble Prediction ◽

Forecast Errors ◽

Drastic Reduction ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Arctic Sea

Abstract The mechanisms for and predictability of a drastic reduction in the Arctic sea ice extent (SIE) are investigated using the Model for Interdisciplinary Research on Climate (MIROC) version 5.2. Here, a control (CTRL) with forcing fixed at year 2000 levels and perfect-model ensemble prediction (PRED) experiments are conducted. In CTRL, three (model years 51, 56, and 57) drastic SIE reductions occur during a 200-yr-long integration. In year 56, the sea ice moves offshore in association with a positive phase of the summer Arctic dipole anomaly (ADA) index and melts due to heat input through the increased open water area, and the SIE drastically decreases. This provides the preconditioning for the lowest SIE in year 57 when the Arctic Ocean interior is in a warm state and the spring sea ice volume has a large negative anomaly due to drastic ice reduction in the previous year. Although the ADA is one of the key mechanisms behind sea ice reduction, it does not always cause a drastic reduction. Our analysis suggests that wind direction favoring offshore ice motion is a more important factor for drastic ice reduction events. In years experiencing drastic ice reduction events, the September SIE can be skillfully predicted in PRED started from July, but not from April. This is because the forecast errors for the July sea level pressure and those for the sea ice concentration and sea ice thickness along the ice edge are large in PRED started from April.

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Gridded Arctic sea ice concentration reconstruction for the first half of the 20th century based on different proxy data

10.5194/egusphere-egu21-3106 ◽

2021 ◽

Author(s):

Vladimir Semenov ◽

Tatiana Matveeva

Keyword(s):

Sea Ice ◽

20Th Century ◽

Climate Model ◽

Surface Air Temperature ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Concentration ◽

The North ◽

Arctic Sea ◽

Ice Concentration

Global warming in the recent decades has been accompanied by a rapid recline of the Arctic sea ice area most pronounced in summer (10% per decade). To understand the relative contribution of external forcing and natural variability to the modern and future sea ice area changes, it is necessary to evaluate a range of long-term variations of the Arctic sea ice area in the period before a significant increase in anthropogenic emissions of greenhouse gases into the atmosphere. Available observational data on the spatiotemporal dynamics of Arctic sea ice until 1950s are characterized by significant gaps and uncertainties. In the recent years, there have appeared several reconstructions of the early 20th century Arctic sea ice area that filled the gaps by analogue methods or utilized combined empirical data and climate model&#8217;s output. All of them resulted in a stronger that earlier believed negative sea ice area anomaly in the 1940s concurrent with the early 20th century warming (ETCW) peak. In this study, we reconstruct the monthly average gridded sea ice concentration (SIC) in the first half of the 20th century using the relationship between the spatiotemporal features of SIC variability, surface air temperature over the Northern Hemisphere extratropical continents, sea surface temperature in the North Atlantic and North Pacific, and sea level pressure. In agreement with a few previous results, our reconstructed data also show a significant negative anomaly of the Arctic sea ice area in the middle of the 20th century, however with some 15% to 30% stronger amplitude, about 1.5 million km2 in September and 0.7 million km2 in March. The reconstruction demonstrates a good agreement with regional Arctic sea ice area data when available and suggests that ETWC in the Arctic has been accompanied by a concurrent sea ice area decline of a magnitude that have been exceeded only in the beginning of the 21st century.

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The sensitivity of the Arctic sea ice to orbitally induced insolation changes: a study of the mid-Holocene Paleoclimate Modelling Intercomparison Project 2 and 3 simulations

Climate of the Past ◽

10.5194/cp-9-969-2013 ◽

2013 ◽

Vol 9 (2) ◽

pp. 969-982 ◽

Cited By ~ 18

Author(s):

M. Berger ◽

J. Brandefelt ◽

J. Nilsson

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

The Arctic ◽

Eastern Coast ◽

Sea Ice Extent ◽

The North ◽

Arctic Sea ◽

North Eastern ◽

Intercomparison Project ◽

Insolation Change

Abstract. In the present work the Arctic sea ice in the mid-Holocene and the pre-industrial climates are analysed and compared on the basis of climate-model results from the Paleoclimate Modelling Intercomparison Project phase 2 (PMIP2) and phase 3 (PMIP3). The PMIP3 models generally simulate smaller and thinner sea-ice extents than the PMIP2 models both for the pre-industrial and the mid-Holocene climate. Further, the PMIP2 and PMIP3 models all simulate a smaller and thinner Arctic summer sea-ice cover in the mid-Holocene than in the pre-industrial control climate. The PMIP3 models also simulate thinner winter sea ice than the PMIP2 models. The winter sea-ice extent response, i.e. the difference between the mid-Holocene and the pre-industrial climate, varies among both PMIP2 and PMIP3 models. Approximately one half of the models simulate a decrease in winter sea-ice extent and one half simulates an increase. The model-mean summer sea-ice extent is 11 % (21 %) smaller in the mid-Holocene than in the pre-industrial climate simulations in the PMIP2 (PMIP3). In accordance with the simple model of Thorndike (1992), the sea-ice thickness response to the insolation change from the pre-industrial to the mid-Holocene is stronger in models with thicker ice in the pre-industrial climate simulation. Further, the analyses show that climate models for which the Arctic sea-ice responses to increasing atmospheric CO2 concentrations are similar may simulate rather different sea-ice responses to the change in solar forcing between the mid-Holocene and the pre-industrial. For two specific models, which are analysed in detail, this difference is found to be associated with differences in the simulated cloud fractions in the summer Arctic; in the model with a larger cloud fraction the effect of insolation change is muted. A sub-set of the mid-Holocene simulations in the PMIP ensemble exhibit open water off the north-eastern coast of Greenland in summer, which can provide a fetch for surface waves. This is in broad agreement with recent analyses of sea-ice proxies, indicating that beach-ridges formed on the north-eastern coast of Greenland during the early- to mid-Holocene.

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

10.5194/egusphere-egu2020-22171 ◽

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

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.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.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 &#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 &#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 &#8211; 0.1cm and 1.5m &#8211; 2.5m, respectively. &#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 &#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.&#160;&#160;

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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

10.5194/tc-2021-297 ◽

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.

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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

The Cryosphere ◽

10.5194/tc-13-1283-2019 ◽

2019 ◽

Vol 13 (4) ◽

pp. 1283-1296 ◽

Cited By ~ 5

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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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

10.5194/tc-2018-223 ◽

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.

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The relative roles of Arctic and Antarctic sea-ice loss in the response to greenhouse warming

10.5194/egusphere-egu21-16310 ◽

2021 ◽

Author(s):

Stephanie Hay ◽

Paul Kusnher

Keyword(s):

Sea Ice ◽

Climate Models ◽

Climate Model ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Extent ◽

Antarctic Sea Ice ◽

Arctic Sea ◽

Climate Model Simulations ◽

Arctic Sea Ice Loss

Antarctic sea ice has gradually increased in extent over the forty-year-long satellite record, in contrast with the clear decrease in sea-ice extent seen in the Arctic over the same time period. However, state-of-the-art climate models ubiquitously project Antarctic sea-ice to decrease over the coming century, much as they do for Arctic sea-ice. Several recent years have also seen record low Antarctic sea-ice. It is therefore of interest to understand what the climate response to Antarctic sea-ice loss will be.&#160;We have carried out new fully coupled climate model simulations to assess the response to sea-ice loss in either hemisphere separately or coincidentally under different albedo parameter settings to determine the relative importance of each. By perturbing the albedo of the snow overlying the sea ice and the albedo of the bare sea ice, we obtain a suite of simulations to assess the linearity and additivity of sea-ice loss. We find the response to sea-ice loss in each hemisphere exhibits a high degree of additivity, and can simply be decomposed into responses due to loss in each hemisphere separately.&#160;We find that the response to Antarctic sea-ice loss exceeds that of Arctic sea-ice loss in the tropics, and that Antarctic sea-ice loss leads to statistically significant Arctic warming, while the opposite is not true.With these new simulations and one in which CO2 is instantaneously doubled , we can further characterize the response to sea-ice loss from each hemisphere using an extension to classical pattern scaling that includes three controlling parameters. This allows us to simultaneously compute the sensitivity patterns to Arctic sea-ice loss, Antarctic sea-ice loss, and to tropical warming. The statistically significant response to Antarctic sea-ice loss in the Northern Hemisphere extratropics is found to be mediated by tropical warming and small amounts of Arctic sea-ice loss.

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

The Cryosphere ◽

10.5194/tc-12-3017-2018 ◽

2018 ◽

Vol 12 (9) ◽

pp. 3017-3032 ◽

Cited By ~ 18

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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