scholarly journals Profiling Sea Ice with a Multiple Altimeter Beam Experimental Lidar (MABEL)

2014 ◽  
Vol 31 (5) ◽  
pp. 1151-1168 ◽  
Author(s):  
R. Kwok ◽  
T. Markus ◽  
J. Morison ◽  
S. P. Palm ◽  
T. A. Neumann ◽  
...  
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Laser Pulses ◽  
The Arctic ◽  
Ice Cloud ◽  
Ice Conditions ◽  
Arctic Coast ◽  
Background Photon ◽  
532 Nm

AbstractThe sole instrument on the upcoming Ice, Cloud, and Land Elevation Satellite (ICESat-2) altimetry mission is a micropulse lidar that measures the time of flight of individual photons from laser pulses transmitted at 532 nm. Prior to launch, the Multiple Altimeter Beam Experimental Lidar (MABEL) serves as an airborne implementation for testing and development. This paper provides a first examination of MABEL data acquired on two flights over sea ice in April 2012: one north of the Arctic coast of Greenland and the other in the east Greenland Sea. The phenomenology of photon distributions in the sea ice returns is investigated. An approach to locate the surface and estimate its elevation in the distributions is described, and its achievable precision is assessed. Retrieved surface elevations over relatively flat leads in the ice cover suggest that precisions of several centimeters are attainable. Restricting the width of the elevation window used in the surface analysis can mitigate potential biases in the elevation estimates due to subsurface returns at 532 nm. Comparisons of nearly coincident elevation profiles from MABEL with those acquired by an analog lidar show good agreement. Discrimination of ice and open water, a crucial step in the determination of sea ice freeboard and the estimation of ice thickness, is facilitated by contrasts in the observed signal–background photon statistics. Future flight paths will sample a broader range of seasonal ice conditions for further evaluation of the year-round profiling capabilities and limitations of the MABEL instrument.

scholarly journals Determination of areal surface-feature coverage in the Beaufort Sea using aircraft video data

1997 ◽  
Vol 25 ◽  
pp. 434-438 ◽  
Author(s):  
Mark A. Tschudi ◽  
Judith A. Curry ◽  
James A. Maslanik
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Video Camera ◽  
Surface Feature ◽  
Video Data ◽  
Surface Energy Budget ◽  
The Arctic ◽  
Areal Coverage ◽  
Melt Ponds

The surface-energy budget of the Arctic Ocean depends on the distribution of various sea-ice features that form by both mechanical and thermodynamic processes. Melt ponds, new ice and open water greatly affect the determination of surface albedo. However, even basic measurements of some surface-feature characteristics, such as areal extent of melt ponds, remain rare.A method has been developed to assess the areal coverage of melt ponds, new ice and open water using video data from the Beaufort and Arctic Storms Experiment (BASE). A downward-looking video camera mounted on the underside of a Hercules C-130 aircraft provided clear images of the surface. Images acquired over multi-year ice on 21 September 1994 were analyzed using a spectral technique to determine the areal coverage of melt ponds, new ice and open water. Statistics from this analysis were then compared to previous field studies and to the Schramm and others (in press) sea-ice model.

Mission Based Ship Design Under Uncertain Arctic Sea Ice Conditions

2015 ◽  
Author(s):  
Minjoo Choi ◽  
Stein Ove Erikstad ◽  
Sören Ehlers
Keyword(s):  
Sea Ice ◽  
Route Planning ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Operational Conditions ◽  
Arctic Sea ◽  
Ice Conditions ◽  
Arctic Change ◽  
Dynamic Route

For the design of an ice-going ship, determining its ice-capability is one of the key design aspects. Excessive ice-capability increases the ship’s acquisition cost and reduces its deadweight capacity. On the other hand, less ice-capability limits its serviceable area and it decreases the probability for the ship to complete its given/expected missions successfully. The ice conditions, which the ship would encounter during its operations, are dependent on its route planning, and they become a basis for the determination of its ice-capability. For the design of an ice-going ship, which is going to be operated under constant operational conditions, static route analysis or use of historical voyage data is sufficient to estimate its required ice-capability. However, if the operational conditions change dynamically, like the Arctic sea ice conditions, a dynamic route analysis is needed. Otherwise, the required ice-capability tends to be over-estimated by the static analysis. Sea ice conditions in the Arctic change dynamically from hour-to-hour. In addition, the forecast of its operational conditions has a high uncertainty due to lack of understanding of the Arctic sea ice. Thus, for the design of a ship for Arctic operation, we carry out transit simulations in a dynamic and stochastic manner in this paper and estimate the required ice-capability from the simulations’ result.

Arctic sea ice extent and thickness

1995 ◽  
Vol 352 (1699) ◽  
pp. 301-319 ◽  
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Ice Conditions ◽  
Fast Ice ◽  
Arctic Sea Ice Extent

Current knowledge on Arctic sea ice extent and thickness variability is reviewed, and we examine whether measurements to date provide evidence for the impact of climate change. The total Arctic ice extent has shown a small but significant reduction of (2.1 ± 0.9)% during the period 1978-87, after apparently increasing from a lower level in the early 1970s. However, open water within the pack ice limit has also diminished, so that the reduction of sea ice area is only (1.8 ± 1.2)%. This stability conceals large interannual variations and trends in individual regions of the Arctic Ocean and sub-Arctic seas, which are out of phase with one another and so have little net impact on the overall hemispheric ice extent. The maximum annual global extent (occurring during the Antarctic winter) shows a more significant decrease of 5% during 1972-87. Ice thickness distribution has been measured by submarine sonar profiling, moored upward sonars, airborne laser prohlometry, airborne electromagnetic techniques and drilling. Promising new techniques include: sonar mounted on an AUV or neutrally buoyant float; acoustic tomography or thermometry; and inference from a combination of microwave sensors. In relation to climate change, the most useful measurement has been repeated submarine sonar profiling under identical parts of the Arctic, which offers some evidence of a decline in mean ice thickness in the 1980s compared to the 1970s. The link between mean ice thickness and climatic warming is complex because of the effects of dynamics and deformation. Only fast ice responds primarily to air temperature changes and one can predict thinning of fast ice and extension of the open water season in fast ice areas. Another region of increasingly mild ice conditions is the central Greenland Sea where winter thermohaline convection is triggered by cyclic growth and melt of local young ice. In recent years convection to the bottom has slowed or ceased, possibly related to moderation of ice conditions.

scholarly journals Determination of areal surface-feature coverage in the Beaufort Sea using aircraft video data

1997 ◽  
Vol 25 ◽  
pp. 434-438 ◽  
Author(s):  
Mark A. Tschudi ◽  
Judith A. Curry ◽  
James A. Maslanik
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Video Camera ◽  
Surface Feature ◽  
Video Data ◽  
Surface Energy Budget ◽  
The Arctic ◽  
Areal Coverage ◽  
Melt Ponds

The surface-energy budget of the Arctic Ocean depends on the distribution of various sea-ice features that form by both mechanical and thermodynamic processes. Melt ponds, new ice and open water greatly affect the determination of surface albedo. However, even basic measurements of some surface-feature characteristics, such as areal extent of melt ponds, remain rare.A method has been developed to assess the areal coverage of melt ponds, new ice and open water using video data from the Beaufort and Arctic Storms Experiment (BASE). A downward-looking video camera mounted on the underside of a Hercules C-130 aircraft provided clear images of the surface. Images acquired over multi-year ice on 21 September 1994 were analyzed using a spectral technique to determine the areal coverage of melt ponds, new ice and open water. Statistics from this analysis were then compared to previous field studies and to the Schramm and others (in press) sea-ice model.

Prévoir les variations saisonnières de la glace de mer arctique et leurs impacts sur le climat

2020 ◽  
pp. 024
Author(s):  
Rym Msadek ◽  
Gilles Garric ◽  
Sara Fleury ◽  
Florent Garnier ◽  
Lauriane Batté ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Arctic Sea Ice ◽  
Climate Impacts ◽  
The Arctic ◽  
Recent Effort ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Conditions ◽  
Upper Level

L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente dernières années, avec une augmentation de la température de surface environ deux fois plus rapide que pour la moyenne globale. Le déclin de la banquise arctique observé depuis le début de l'ère satellitaire et attribué principalement à l'augmentation de la concentration des gaz à effet de serre aurait joué un rôle important dans cette amplification des températures au pôle. Cette fonte importante des glaces arctiques, qui devrait s'accélérer dans les décennies à venir, pourrait modifier les vents en haute altitude et potentiellement avoir un impact sur le climat des moyennes latitudes. L'étendue de la banquise arctique varie considérablement d'une saison à l'autre, d'une année à l'autre, d'une décennie à l'autre. Améliorer notre capacité à prévoir ces variations nécessite de comprendre, observer et modéliser les interactions entre la banquise et les autres composantes du système Terre, telles que l'océan, l'atmosphère ou la biosphère, à différentes échelles de temps. La réalisation de prévisions saisonnières de la banquise arctique est très récente comparée aux prévisions du temps ou aux prévisions saisonnières de paramètres météorologiques (température, précipitation). Les résultats ayant émergé au cours des dix dernières années mettent en évidence l'importance des observations de l'épaisseur de la glace de mer pour prévoir l'évolution de la banquise estivale plusieurs mois à l'avance. Surface temperatures over the Arctic region have been increasing twice as fast as global mean temperatures, a phenomenon known as arctic amplification. One main contributor to this polar warming is the large decline of Arctic sea ice observed since the beginning of satellite observations, which has been attributed to the increase of greenhouse gases. The acceleration of Arctic sea ice loss that is projected for the coming decades could modify the upper level atmospheric circulation yielding climate impacts up to the mid-latitudes. There is considerable variability in the spatial extent of ice cover on seasonal, interannual and decadal time scales. Better understanding, observing and modelling the interactions between sea ice and the other components of the climate system is key for improved predictions of Arctic sea ice in the future. Running operational-like seasonal predictions of Arctic sea ice is a quite recent effort compared to weather predictions or seasonal predictions of atmospheric fields like temperature or precipitation. Recent results stress the importance of sea ice thickness observations to improve seasonal predictions of Arctic sea ice conditions during summer.

scholarly journals Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

2021 ◽  
Vol 13 (12) ◽  
pp. 2283
Author(s):  
Hyangsun Han ◽  
Sungjae Lee ◽  
Hyun-Cheol Kim ◽  
Miae Kim
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Open Water ◽  
The Arctic ◽  
Atmospheric Effects ◽  
Sea Ice Concentration ◽  
Air Temperatures ◽  
The Pacific ◽  
Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

scholarly journals Observations and Simulations of Meteorological Conditions over Arctic Thick Sea Ice in Late Winter during the Transarktika 2019 Expedition

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 174
Author(s):  
Günther Heinemann ◽  
Sascha Willmes ◽  
Lukas Schefczyk ◽  
Alexander Makshtas ◽  
Vasilii Kustov ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Open Water ◽  
The Arctic ◽  
Late Winter ◽  
Good Representation ◽  
Hourly Temperature ◽  
Ice Model

The parameterization of ocean/sea-ice/atmosphere interaction processes is a challenge for regional climate models (RCMs) of the Arctic, particularly for wintertime conditions, when small fractions of thin ice or open water cause strong modifications of the boundary layer. Thus, the treatment of sea ice and sub-grid flux parameterizations in RCMs is of crucial importance. However, verification data sets over sea ice for wintertime conditions are rare. In the present paper, data of the ship-based experiment Transarktika 2019 during the end of the Arctic winter for thick one-year ice conditions are presented. The data are used for the verification of the regional climate model COSMO-CLM (CCLM). In addition, Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for the comparison of ice surface temperature (IST) simulations of the CCLM sea ice model. CCLM is used in a forecast mode (nested in ERA5) for the Norwegian and Barents Seas with 5 km resolution and is run with different configurations of the sea ice model and sub-grid flux parameterizations. The use of a new set of parameterizations yields improved results for the comparisons with in-situ data. Comparisons with MODIS IST allow for a verification over large areas and show also a good performance of CCLM. The comparison with twice-daily radiosonde ascents during Transarktika 2019, hourly microwave water vapor measurements of first 5 km in the atmosphere and hourly temperature profiler data show a very good representation of the temperature, humidity and wind structure of the whole troposphere for CCLM.

Eddy covariance flux measurements of momentum, heat and carbon dioxide in Hudson Bay at the onset of sea ice melt 

2021 ◽  
Author(s):  
Richard Sims ◽  
Brian Butterworth ◽  
Tim Papakyriakou ◽  
Mohamed Ahmed ◽  
Brent Else
Keyword(s):  
Carbon Dioxide ◽  
Gas Exchange ◽  
Sea Ice ◽  
Eddy Covariance ◽  
Open Water ◽  
The Arctic ◽  
Gas Transfer ◽  
Hudson Bay ◽  
Flux Measurements ◽  
Ice Fraction

<p>Remoteness and tough conditions have made the Arctic Ocean historically difficult to access; until recently this has resulted in an undersampling of trace gas and gas exchange measurements. The seasonal cycle of sea ice completely transforms the air sea interface and the dynamics of gas exchange. To make estimates of gas exchange in the presence of sea ice, sea ice fraction is frequently used to scale open water gas transfer parametrisations. It remains unclear whether this scaling is appropriate for all sea ice regions. Ship based eddy covariance measurements were made in Hudson Bay during the summer of 2018 from the icebreaker CCGS Amundsen. We will present fluxes of carbon dioxide (CO<sub>2</sub>), heat and momentum and will show how they change around the Hudson Bay polynya under varying sea ice conditions. We will explore how these fluxes change with wind speed and sea ice fraction. As freshwater stratification was encountered during the cruise, we will compare our measurements with other recent eddy covariance flux measurements made from icebreakers and also will compare our turbulent CO<sub>2 </sub>fluxes with bulk fluxes calculated using underway and surface bottle pCO<sub>2</sub> data. </p><p> </p>

scholarly journals Importance of open-water ice growth and ice concentration evolution: a study based on FESOM-ECHAM6

2015 ◽  
Vol 6 (2) ◽  
pp. 2137-2179
Author(s):  
X. Shi ◽  
G. Lohmann
Keyword(s):  
Sea Ice ◽  
Surface Albedo ◽  
Global Climate Model ◽  
Open Water ◽  
Arctic Sea Ice ◽  
Meridional Overturning Circulation ◽  
The Arctic ◽  
Water Ice ◽  
Ice Growth ◽  
Ice Concentration

Abstract. A newly developed global climate model FESOM-ECHAM6 with an unstructured mesh and high resolution is applied to investigate to what degree the area-thickness distribution of new ice formed in open water affects the ice and ocean properties. A sensitivity experiment is performed which reduces the horizontal-to-vertical aspect ratio of open-water ice growth. The resulting decrease in the Arctic winter sea-ice concentration strongly reduces the surface albedo, enhances the ocean heat release to the atmosphere, and increases the sea-ice production. Furthermore, our simulations show a positive feedback mechanism among the Arctic sea ice, the Atlantic Meridional Overturning Circulation (AMOC), and the surface air temperature in the Arctic, as the sea ice transport affects the freshwater budget in regions of deep water formation. A warming over Europe, Asia and North America, associated with a negative anomaly of Sea Level Pressure (SLP) over the Arctic (positive phase of the Arctic Oscillation (AO)), is also simulated by the model. For the Southern Ocean, the most pronounced change is a warming along the Antarctic Circumpolar Current (ACC), especially for the Pacific sector. Additionally, a series of sensitivity tests are performed using an idealized 1-D thermodynamic model to further investigate the influence of the open-water ice growth, which reveals similar results in terms of the change of sea ice and ocean temperature. In reality, the distribution of new ice on open water relies on many uncertain parameters, for example, surface albedo, wind speed and ocean currents. Knowledge of the detailed processes is currently too crude for those processes to be implemented realistically into models. Our sensitivity experiments indicate a pronounced uncertainty related to open-water sea ice growth which could significantly affect the climate system.

Winter Arctic sea ice freeboard, snow depth and thickness variability from ICESat-2 and NESOSIM

2021 ◽  
Author(s):  
Alek Petty ◽  
Nicole Keeney ◽  
Alex Cabaj ◽  
Paul Kushner ◽  
Nathan Kurtz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Cloud ◽  
Ice Drift ◽  
Thickness Variability ◽  
Arctic Ice

<div> <div> <div> <div> <p>National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite‐ 2 (ICESat‐2) mission was launched in September 2018 and is now providing routine, very high‐resolution estimates of surface height/type (the ATL07 product) and freeboard (the ATL10 product) across the Arctic and Southern Oceans. In recent work we used snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM) together with ATL10 freeboard data to estimate sea ice thickness across the entire Arctic Ocean. Here we provide an overview of updates made to both the underlying ATL10 freeboard product and the NESOSIM model, and the subsequent impacts on our estimates of sea ice thickness including updated comparisons to the original ICESat mission and ESA’s CryoSat-2. Finally we compare our Arctic ice thickness estimates from the 2018-2019 and 2019-2020 winters and discuss possible causes of these differences based on an analysis of atmospheric data (ERA5), ice drift (NSIDC) and ice type (OSI SAF).</p> </div> </div> </div> </div>

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