Can we extend local sea-ice measurements to satellite scale? An example from the N-ICE2015 expedition

ABSTRACTKnowledge of Arctic sea-ice conditions is of great interest for Arctic residents, as well as for commercial usage, and to study the effects of climate change. Information gained from analysis of satellite data contributes to this understanding. In the course of using in situ data in combination with remotely sensed data, the question of how representative local scale measurements are of a wider region may arise. We compare in situ total sea-ice thickness measurements from the Norwegian young sea ICE expedition in the area north of Svalbard with airborne-derived total sea-ice thickness from electromagnetic soundings. A segmented and classified synthetic aperture radar (SAR) quad-pol ALOS-2 Palsar-2 satellite scene was grouped into three simplified ice classes. The area fractions of the three classes are: 11.2% ‘thin’, 74.4% ‘level’, and 14.4% ‘deformed’. The area fractions of the simplified classes from ground- and helicopter-based measurements are comparable with those achieved from the SAR data. Thus, this study shows that there is potential for a stepwise upscaling from in situ, to airborne, to satellite data, which allow us to assess whether in situ data collected are representative of a wider region as observed by satellites.

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Assessment with Controlled In-Situ Data of the Dependence of L-Band Radiometry on Sea-Ice Thickness

Remote Sensing ◽

10.3390/rs12040650 ◽

2020 ◽

Vol 12 (4) ◽

pp. 650

Author(s):

Pablo Sánchez-Gámez ◽

Carolina Gabarro ◽

Antonio Turiel ◽

Marcos Portabella

Keyword(s):

Soil Moisture ◽

Sea Ice ◽

Brightness Temperature ◽

European Space Agency ◽

Ground Truth ◽

Ice Thickness ◽

Sea Ice Thickness ◽

In Situ Data ◽

L Band

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) missions are providing brightness temperature measurements at 1.4 GHz (L-band) for about 10 and 4 years respectively. One of the new areas of geophysical exploitation of L-band radiometry is on thin (i.e., less than 1 m) Sea Ice Thickness (SIT), for which theoretical and empirical retrieval methods have been proposed. However, a comprehensive validation of SIT products has been hindered by the lack of suitable ground truth. The in-situ SIT datasets most commonly used for validation are affected by one important limitation: They are available mainly during late winter and spring months, when sea ice is fully developed and the thickness probability density function is wider than for autumn ice and less representative at the satellite spatial resolution. Using Upward Looking Sonar (ULS) data from the Woods Hole Oceanographic Institution (WHOI), acquired all year round, permits overcoming the mentioned limitation, thus improving the characterization of the L-band brightness temperature response to changes in thin SIT. State-of-the-art satellite SIT products and the Cumulative Freezing Degree Days (CFDD) model are verified against the ULS ground truth. The results show that the L-band SIT can be meaningfully retrieved up to 0.6 m, although the signal starts to saturate at 0.3 m. In contrast, despite the simplicity of the CFDD model, its predicted SIT values correlate very well with the ULS in-situ data during the sea ice growth season. The comparison between the CFDD SIT and the current L-band SIT products shows that both the sea ice concentration and the season are fundamental factors influencing the quality of the thickness retrieval from L-band satellites.

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Sea Ice Thickness Forecast Performance in the Barents Sea

Volume 7: Polar and Arctic Sciences and Technology ◽

10.1115/omae2020-18039 ◽

2020 ◽

Author(s):

Laura Hume-Wright ◽

Emma Fiedler ◽

Nicolas Fournier ◽

Joana Mendes ◽

Ed Blockley ◽

...

Keyword(s):

Sea Ice ◽

Barents Sea ◽

Forecast Model ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Forecast Performance ◽

In Situ Data ◽

The Barents Sea ◽

Ice Conditions

Abstract The presence of sea ice has a major impact on the safety, operability and efficiency of Arctic operations and navigation. While satellite-based sea ice charting is routinely used for tactical ice management, the marine sector does not yet make use of existing operational sea ice thickness forecasting. However, data products are now freely available from the Copernicus Marine Environment Monitoring Service (CMEMS). Arctic asset managers and vessels’ crews are generally not aware of such products, or these have so far suffered from insufficient accuracy, verification, resolution and adequate format, in order to be well integrated within their existing decision-making processes and systems. The objective of the EU H2020 project “Safe maritime operations under extreme conditions: The Arctic case” (SEDNA) is to improve the safety and efficiency of Arctic navigation. This paper presents a component focusing on the validation of an adaption of the 7-day sea ice thickness forecast from the UK Met Office Forecast Ocean Assimilation Model (FOAM). The experimental forecast model assimilates the CryoSat-2 satellite’s ice freeboard daily data. Forecast skill is evaluated against unique in-situ data from five moorings deployed between 2015 and 2018 by the Barents Sea Metocean and Ice Network (BASMIN) Joint Industry Project. The study shows that the existing FOAM forecasts produce adequate results in the Barents Sea. However, while studies have shown the assimilation of CryoSat-2 data is effective for thick sea ice conditions, this did not improve forecasts for the thinner sea ice conditions of the Barents Sea.

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Using CloudSat snowfall rate observations to constrain and characterize the uncertainties of Arctic snow-on-sea-ice

10.5194/egusphere-egu2020-11436 ◽

2020 ◽

Author(s):

Alex Cabaj ◽

Paul Kushner ◽

Alek Petty ◽

Stephen Howell ◽

Christopher Fletcher

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Snow Depth ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

The Arctic Ocean ◽

Ice Model

Snow on Arctic sea ice plays multiple&#8212;and sometimes contrasting&#8212;roles in several feedbacks between sea ice and the global climate system. For example, the presence of snow on sea ice may mitigate sea ice melt by increasing the sea ice albedo and enhancing the ice-albedo feedback. Conversely, snow can inhibit sea ice growth by insulating the ice from the atmosphere during the sea ice growth season. In addition to its contribution to sea ice feedbacks, snow on sea ice also poses a challenge for sea ice observations. In particular, snow contributes to uncertainties in retrievals of sea ice thickness from satellite altimetry measurements, such as those from ICESat-2. Snow-on-sea-ice models can produce basin-wide snow depth estimates, but these models require snowfall input from reanalysis products. In-situ snowfall measurements are absent over most of the Arctic Ocean, so it can be difficult to determine which reanalysis snowfall product is best suited to be used as input for a snow-on-sea-ice model.In the absence of in-situ snowfall rate measurements, measurements from satellite instruments can be used to quantify snowfall over the Arctic Ocean. The CloudSat satellite, which is equipped with a 94 GHz Cloud Profiling Radar instrument, measures vertical radar reflectivity profiles from which snowfall rates can be retrieved. This instrument provides the most extensive high-latitude snowfall rate observation dataset currently available. CloudSat&#8217;s near-polar orbit enables it to make measurements at latitudes up to 82&#176;N, with a 16-day repeat cycle, over the time period from 2006-2016.We present a calibration of reanalysis snowfall to CloudSat observations over the Arctic Ocean, which we then apply to reanalysis snowfall input for the NASA Eulerian Snow On Sea Ice Model (NESOSIM). This calibration reduces the spread in snow depths produced by NESOSIM when different reanalysis inputs are used. In light of this calibration, we revise the NESOSIM parametrizations of wind-driven snow processes, and we characterize the uncertainties in NESOSIM-generated snow depths resulting from uncertainties in snowfall input. We then extend this analysis further to estimate the resulting uncertainties in sea ice thickness retrieved from ICESat-2 when snow depth estimates from NESOSIM are used as input for the retrieval.

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Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

Remote Sensing ◽

10.3390/rs8090698 ◽

2016 ◽

Vol 8 (9) ◽

pp. 698 ◽

Cited By ~ 18

Author(s):

Sanggyun Lee ◽

Jungho Im ◽

Jinwoo Kim ◽

Miae Kim ◽

Minso Shin ◽

...

Keyword(s):

Machine Learning ◽

Sea Ice ◽

Satellite Data ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Thickness Estimation ◽

Lead Detection ◽

Arctic Sea ◽

Arctic Sea Ice Thickness

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Recent summer sea ice thickness surveys in Fram Strait and associated ice volume fluxes

The Cryosphere ◽

10.5194/tc-10-523-2016 ◽

2016 ◽

Vol 10 (2) ◽

pp. 523-534 ◽

Cited By ~ 35

Author(s):

T. Krumpen ◽

R. Gerdes ◽

C. Haas ◽

S. Hendricks ◽

A. Herber ◽

...

Keyword(s):

Sea Ice ◽

Satellite Data ◽

Primary Source ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Fram Strait ◽

Data Set ◽

Sea Ice Thickness ◽

Airborne Electromagnetic

Abstract. Fram Strait is the main gateway for sea ice export out of the Arctic Ocean, and therefore observations there give insight into the composition and properties of Arctic sea ice in general and how it varies over time. A data set of ground-based and airborne electromagnetic ice thickness measurements collected during summer between 2001 and 2012 is presented here, including long transects well into the southern part of the Transpolar Drift obtained using fixed-wing aircrafts. The primary source of the surveyed sea ice leaving Fram Strait is the Laptev Sea and its age has decreased from 3 to 2 years between 1990 and 2012. The thickness data consistently also show a general thinning of sea ice for the last decade, with a decrease in modal thickness of second year and multiyear ice, and a decrease in mean thickness and fraction of ice thicker than 3 m. Local melting in the strait was investigated in two surveys performed in the downstream direction, showing a decrease in sea ice thickness of 0.19 m degree−1 latitude south of 81° N. Further north variability in ice thickness is more related to differences in age and deformation. The thickness observations were combined with ice area export estimates to calculate summer volume fluxes of sea ice. While satellite data show that monthly ice area export had positive trends since 1980 (10.9  × 103 km2 decade−1), the summer (July and August) ice area export is low with high uncertainties. The average volume export amounts to 16.78 km3. Naturally, the volume flux estimates are limited to the period when airborne thickness surveys are available. Nevertheless, we could show that the combination of satellite data and airborne observations can be used to determine volume fluxes through Fram Strait and as such, can be used to bridge the lack of satellite-based sea ice thickness information in summer.

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Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme

Remote Sensing ◽

10.3390/rs12071214 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1214 ◽

Cited By ~ 4

Author(s):

Marko Mäkynen ◽

Jari Haapala ◽

Giuseppe Aulicino ◽

Beena Balan-Sarojini ◽

Magdalena Balmaseda ◽

...

Keyword(s):

Sea Ice ◽

Satellite Data ◽

Initial Conditions ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Seasonal Forecasts ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Horizon 2020 ◽

Ice Conditions

The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from satellite data as well as the development of sea-ice seasonal forecasting capabilities. Our results are the outcome of the three-year (2015–2018) SPICES (Space-borne Observations for Detecting and Forecasting Sea-Ice Cover Extremes) project funded by the EU’s Horizon 2020 programme. New SPICES sea-ice products include pancake ice thickness and degree of ice ridging based on synthetic aperture radar imagery, Arctic sea-ice volume and export derived from multisensor satellite data, and melt pond fraction and sea-ice concentration using Soil Moisture and Ocean Salinity (SMOS) radiometer data. Forecasts of July sea-ice conditions from initial conditions in May showed substantial improvement in some Arctic regions after adding sea-ice thickness (SIT) data to the model initialization. The SIT initialization also improved seasonal forecasts for years with extremely low summer sea-ice extent. New SPICES sea-ice products have a demonstrable level of maturity, and with a reasonable amount of further work they can be integrated into various operational sea-ice services.

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Fluctuating Arctic Sea Ice Thickness Changes Estimated by an In Situ Learned and Empirically Forced Neural Network Model

Journal of Climate ◽

10.1175/2007jcli1787.1 ◽

2008 ◽

Vol 21 (4) ◽

pp. 716-729 ◽

Cited By ~ 19

Author(s):

G. I. Belchansky ◽

D. C. Douglas ◽

N. G. Platonov

Keyword(s):

Neural Network ◽

Sea Ice ◽

Arctic Sea Ice ◽

Global Climate Models ◽

Spatiotemporal Variability ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Ice Volume

Abstract Sea ice thickness (SIT) is a key parameter of scientific interest because understanding the natural spatiotemporal variability of ice thickness is critical for improving global climate models. In this paper, changes in Arctic SIT during 1982–2003 are examined using a neural network (NN) algorithm trained with in situ submarine ice draft and surface drilling data. For each month of the study period, the NN individually estimated SIT of each ice-covered pixel (25-km resolution) based on seven geophysical parameters (four shortwave and longwave radiative fluxes, surface air temperature, ice drift velocity, and ice divergence/convergence) that were cumulatively summed at each monthly position along the pixel’s previous 3-yr drift track (or less if the ice was <3 yr old). Average January SIT increased during 1982–88 in most regions of the Arctic (+7.6 ± 0.9 cm yr−1), decreased through 1996 Arctic-wide (−6.1 ± 1.2 cm yr−1), then modestly increased through 2003 mostly in the central Arctic (+2.1 ± 0.6 cm yr−1). Net ice volume change in the Arctic Ocean from 1982 to 2003 was negligible, indicating that cumulative ice growth had largely replaced the estimated 45 000 km3 of ice lost by cumulative export. Above 65°N, total annual ice volume and interannual volume changes were correlated with the Arctic Oscillation (AO) at decadal and annual time scales, respectively. Late-summer ice thickness and total volume varied proportionally until the mid-1990s, but volume did not increase commensurate with the thickening during 1996–2002. The authors speculate that decoupling of the ice thickness–volume relationship resulted from two opposing mechanisms with different latitudinal expressions: a recent quasi-decadal shift in atmospheric circulation patterns associated with the AO’s neutral state facilitated ice thickening at high latitudes while anomalously warm thermal forcing thinned and melted the ice cap at its periphery.

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Comparison of Arctic sea ice thickness and snow depth estimates from CFSR with in situ observations

Climate Dynamics ◽

10.1007/s00382-017-3607-z ◽

2017 ◽

Vol 50 (1-2) ◽

pp. 289-301 ◽

Cited By ~ 10

Author(s):

Kazutoshi Sato ◽

Jun Inoue

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Arctic Sea ◽

In Situ Observations ◽

Arctic Sea Ice Thickness

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Long-term variation of sea ice and its response to thermodynamic factors in the Northwest Passage of the Canadian Arctic Archipelago

10.5194/tc-2020-215 ◽

2020 ◽

Author(s):

Xinyi Shen ◽

Yu Zhang ◽

Changsheng Chen ◽

Song Hu

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Sea Ice Concentration ◽

Sea Ice Thickness ◽

Northwest Passage ◽

Arctic Sea ◽

Ice Concentration ◽

Ice Conditions ◽

Thermodynamic Factors

Abstract. Sea ice conditions in the Canadian Arctic Archipelago (CAA) play a key role in the navigation of the Northwest Passage (NWP). Based on the observed and simulated sea ice concentration and thickness data, we studied the temporal and spatial characteristics of sea ice from 1979 to 2017 in the NWP of the CAA and evaluated the sea ice conditions along the southern and northern routes of the NWP. Against the background of the rapid retreat of Arctic sea ice, the 39-year observed sea ice concentration of the NWP exhibited a relatively large decreasing trend in summer and fall, while heavy sea ice conditions were maintained in winter and spring, with a slight increasing trend. Consistent with Arctic sea ice, the sea ice extent in the NWP displayed a decreasing trend of −2.34 %/10 a, with its minimum occurring in 2012. The sea ice thickness in most subregions of the NWP showed a decreasing trend, with the exception of Lancaster Sound. The decreasing trend of sea ice thickness in the NWP was estimated to −0.16 m/10 a. Based on the sea ice concentration and thickness, however, the sea ice conditions were heavier along the northern route than the southern route. This study considered both of these routes, and we selected and evaluated more specific pathways. The correlation results between the sea ice and atmospheric and oceanic thermodynamic factors in the NWP suggested that the thermodynamic factors had a greater impact on sea ice in the summer and fall, and the variations of sea ice concentration were more closely correlated with the thermodynamic factors than sea ice thickness. The sea surface temperature (SST) had a higher correlation with sea ice concentration than surface air temperature (SAT), while SAT exhibited a higher correlation with sea ice thickness than SST. The residual amount of sea ice concentration and thickness in the fall, associated with the fall SAT and SST, contributed to the formation of sea ice in the following winter and spring.

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In-situ automatic observations of ice thickness of seas

Metrology and Measurement Systems ◽

10.2478/v10178-012-0051-6 ◽

2012 ◽

Vol 19 (3) ◽

pp. 583-592 ◽

Cited By ~ 1

Author(s):

Yinke Dou ◽

Xiaomin Chang

Keyword(s):

Sea Ice ◽

New Technologies ◽

Capacitive Sensor ◽

Automatic Monitoring ◽

Ice Thickness ◽

In Situ Observation ◽

Glacier Surface ◽

Sea Ice Thickness ◽

Surface Ice

Abstract Ice thickness is one of the most critical physical indicators in the ice science and engineering. It is therefore very necessary to develop in-situ automatic observation technologies of ice thickness. This paper proposes the principle of three new technologies of in-situ automatic observations of sea ice thickness and provides the findings of laboratory applications. The results show that the in-situ observation accuracy of the monitor apparatus based on the Magnetostrictive Delay Line (MDL) principle can reach ±2 mm, which has solved the “bottleneck” problem of restricting the fine development of a sea ice thermodynamic model, and the resistance accuracy of monitor apparatus with temperature gradient can reach the centimeter level and research the ice and snow substance balance by automatically measuring the glacier surface ice and snow change. The measurement accuracy of the capacitive sensor for ice thickness can also reach ±4 mm and the capacitive sensor is of the potential for automatic monitoring the water level under the ice and the ice formation and development process in water. Such three new technologies can meet different needs of fixed-point ice thickness observation and realize the simultaneous measurement in order to accurately judge the ice thickness.

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