Remote sensing of sea ice: advances during the DAMOCLES project

Abstract. In the Arctic, global warming is particularly pronounced so that we need to monitor its development continuously. On the other hand, the vast and hostile conditions make in situ observation difficult, so that available satellite observations should be exploited in the best possible way to extract geophysical information. Here, we give a résumé of the sea ice remote sensing efforts of the European Union's (EU) project DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies). In order to better understand the seasonal variation of the microwave emission of sea ice observed from space, the monthly variations of the microwave emissivity of first-year and multi-year sea ice have been derived for the frequencies of the microwave imagers like AMSR-E (Advanced Microwave Scanning Radiometer on EOS) and sounding frequencies of AMSU (Advanced Microwave Sounding Unit), and have been used to develop an optimal estimation method to retrieve sea ice and atmospheric parameters simultaneously. In addition, a sea ice microwave emissivity model has been used together with a thermodynamic model to establish relations between the emissivities from 6 GHz to 50 GHz. At the latter frequency, the emissivity is needed for assimilation into atmospheric circulation models, but is more difficult to observe directly. The size of the snow grains on top of the sea ice influences both its albedo and the microwave emission. A method to determine the effective size of the snow grains from observations in the visible range (MODIS) is developed and demonstrated in an application on the Ross ice shelf. The bidirectional reflectivity distribution function (BRDF) of snow, which is an essential input parameter to the retrieval, has been measured in situ on Svalbard during the DAMOCLES campaign, and a BRDF model assuming aspherical particles is developed. Sea ice drift and deformation is derived from satellite observations with the scatterometer ASCAT (62.5 km grid spacing), with visible AVHRR observations (20 km), with the synthetic aperture radar sensor ASAR (10 km), and a multi-sensor product (62.5 km) with improved angular resolution (Continuous Maximum Cross Correlation, CMCC method) is presented. CMCC is also used to derive the sea ice deformation, important for formation of sea ice leads (diverging deformation) and pressure ridges (converging). The indirect determination of sea ice thickness from altimeter freeboard data requires knowledge of the ice density and snow load on sea ice. The relation between freeboard and ice thickness is investigated based on the airborne Sever expeditions conducted between 1928 and 1993.

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Remote sensing of sea ice: advances during the DAMOCLES project

The Cryosphere Discussions ◽

10.5194/tcd-6-37-2012 ◽

2012 ◽

Vol 6 (1) ◽

pp. 37-88

Author(s):

G. Heygster ◽

V. Alexandrov ◽

G. Dybkjær ◽

F. Girard-Ardhuin ◽

W. von Hoyningen-Huene ◽

...

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Estimation Method ◽

Satellite Observations ◽

The Arctic ◽

Visible Range ◽

Ice Thickness ◽

In Situ Observation ◽

Microwave Emissivity

Abstract. In the Arctic, global warming is particularly pronounced so that we need to monitor its development continuously. On the other hand, the vast and hostile conditions make in situ observation difficult, so that available satellite observations should be exploited in the best possible way to extract geophysical information. Here, we give a résumé of the sea ice remote sensing efforts of the EU project DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies). The monthly variation of the microwave emissivity of first-year and multiyear sea ice has been derived for the frequencies of the microwave imagers like AMSR-E and sounding frequencies of AMSU, and has been used to develop an optimal estimation method to retrieve sea ice and atmospheric parameters simultaneously. A sea ice microwave emissivity model has been used together with a thermodynamic model to establish relations between the emisivities at 6 GHz and 50 GHz. At the latter frequency, the emissivity is needed for assimilation into atmospheric circulation models, but more difficult to observe directly. A method to determine the effective size of the snow grains from observations in the visible range (MODIS) is developed and applied. The bidirectional reflectivity distribution function (BRDF) of snow, which is an essential input parameter to the retrieval, has been measured in situ on Svalbard during the DAMOCLES campaign, and a BRDF model assuming aspherical particles is developed. Sea ice drift and deformation is derived from satellite observations with the scatterometer ASCAT (62.5 km grid spacing), with visible AVHRR observations (20 km), with the synthetic aperture radar sensor ASAR (10 km), and a multi-sensor product (62.5 km) with improved angular resolution (Continuous Maximum Cross Correlation, CMCC method) is presented. CMCC is also used to derive the sea ice deformation, important for formation of sea ice leads (diverging deformation) and pressure ridges (converging). The indirect determination of sea ice thickness from altimeter freeboard data requires knowledge of the ice density and snow load on sea ice. The relation between freeboard and ice thickness is investigated based on the airborne Sever expeditions conducted between 1928 and 1993.

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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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Arctic surface temperatures from Metop AVHRR compared to in situ ocean and land data

Ocean Science Discussions ◽

10.5194/osd-9-1009-2012 ◽

2012 ◽

Vol 9 (2) ◽

pp. 1009-1043 ◽

Cited By ~ 1

Author(s):

G. Dybkjær ◽

R. Tonboe ◽

J. Høyer

Keyword(s):

Sea Ice ◽

Surface Temperature ◽

Temperature Data ◽

Coupled Systems ◽

The Arctic ◽

In Situ Observation ◽

Clear Sky ◽

Ice Surface ◽

Ice Surface Temperature

Abstract. The ice surface temperature (IST) is an important boundary condition for both atmospheric and ocean and sea ice models and for coupled systems. An operational ice surface temperature product using satellite Metop AVHRR infra-red data was developed for MyOcean. The IST can be mapped in clear sky regions using a split window algorithm specially tuned for sea ice. Clear sky conditions are prevailing during spring in the Arctic while persistent cloud cover limits data coverage during summer. The cloud covered regions are detected using the EUMETSAT cloud mask. The Metop IST compares to 2 m temperature at the Greenland ice cap Summit within STD error of 3.14 °C and to Arctic drifting buoy temperature data within STD error of 3.69 °C. A case study reveal that the in situ radiometer data versus satellite IST STD error can be much lower (0.73 °C) and that the different in situ measures complicates the validation. Differences and variability between Metop IST and in situ data are analysed and discussed. An inter-comparison of Metop IST, numerical weather prediction temperatures and in situ observation indicates large biases between the different quantities. Because of the scarcity of conventional surface temperature or surface air temperature data in the Arctic the satellite IST data with its relatively good coverage can potentially add valuable information to model analysis for the Arctic atmosphere.

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A microphysics guide to cirrus – Part 2: Climatologies of clouds and humidity from observations

Atmospheric Chemistry and Physics ◽

10.5194/acp-20-12569-2020 ◽

2020 ◽

Vol 20 (21) ◽

pp. 12569-12608 ◽

Cited By ~ 3

Author(s):

Martina Krämer ◽

Christian Rolf ◽

Nicole Spelten ◽

Armin Afchine ◽

David Fahey ◽

...

Keyword(s):

Remote Sensing ◽

Asian Monsoon ◽

Satellite Observations ◽

Cirrus Clouds ◽

The Arctic ◽

Ice Crystal ◽

Data Set ◽

Geographical Regions ◽

The Tropics

Abstract. This study presents airborne in situ and satellite remote sensing climatologies of cirrus clouds and humidity. The climatologies serve as a guide to the properties of cirrus clouds, with the new in situ database providing detailed insights into boreal midlatitudes and the tropics, while the satellite-borne data set offers a global overview. To this end, an extensive, quality-checked data archive, the Cirrus Guide II in situ database, is created from airborne in situ measurements during 150 flights in 24 campaigns. The archive contains meteorological parameters, ice water content (IWC), ice crystal number concentration (Nice), ice crystal mean mass radius (Rice), relative humidity with respect to ice (RHice), and water vapor mixing ratio (H2O) for each of the flights. Depending on the parameter, the database has been extended by about a factor of 5–10 compared to earlier studies. As one result of our investigation, we show that the medians of Nice, Rice, and RHice have distinct patterns in the IWC–T parameter space. Lookup tables of these variables as functions of IWC and T can be used to improve global model cirrus representation and remote sensing retrieval methods. Another outcome of our investigation is that across all latitudes, the thicker liquid-origin cirrus predominate at lower altitudes, while at higher altitudes the thinner in situ-origin cirrus prevail. Further, examination of the radiative characteristics of in situ-origin and liquid-origin cirrus shows that the in situ-origin cirrus only slightly warm the atmosphere, while liquid-origin cirrus have a strong cooling effect. An important step in completing the Cirrus Guide II is the provision of the global cirrus Nice climatology, derived by means of the retrieval algorithm DARDAR-Nice from 10 years of cirrus remote sensing observations from satellite. The in situ measurement database has been used to evaluate and improve the satellite observations. We found that the global median Nice from satellite observations is almost 2 times higher than the in situ median and increases slightly with decreasing temperature. Nice medians of the most frequently occurring cirrus sorted by geographical regions are highest in the tropics, followed by austral and boreal midlatitudes, Antarctica, and the Arctic. Since the satellite climatologies enclose the entire spatial and temporal Nice occurrence, we could deduce that half of the cirrus are located in the lowest, warmest (224–242 K) cirrus layer and contain a significant amount of liquid-origin cirrus. A specific highlight of the study is the in situ observations of cirrus and humidity in the Asian monsoon anticyclone and the comparison to the surrounding tropics. In the convectively very active Asian monsoon, peak values of Nice and IWC of 30 cm−3 and 1000 ppmv are detected around the cold point tropopause (CPT). Above the CPT, ice particles that are convectively injected can locally add a significant amount of water available for exchange with the stratosphere. We found IWCs of up to 8 ppmv in the Asian monsoon in comparison to only 2 ppmv in the surrounding tropics. Also, the highest RHice values (120 %–150 %) inside of clouds and in clear sky are observed around and above the CPT. We attribute this to the high H2O mixing ratios (typically 3–5 ppmv) observed in the Asian monsoon compared to 1.5 to 3 ppmv found in the tropics. Above the CPT, supersaturations of 10 %–20 % are observed in regions of weak convective activity and up to about 50 % in the Asian monsoon. This implies that the water available for transport into the stratosphere might be higher than the expected saturation value.

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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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SVALBARD 2006 EXPERIMENTAL OIL SPILL UNDER ICE: REMOTE SENSING, OIL WEATHERING UNDER ARCTIC CONDITIONS AND ASSESSMENT OF OIL REMOVAL BY IN-SITUBURNING

International Oil Spill Conference Proceedings ◽

10.7901/2169-3358-2008-1-681 ◽

2008 ◽

Vol 2008 (1) ◽

pp. 681-688 ◽

Cited By ~ 11

Author(s):

David Dickins ◽

Per Johan Brandvik ◽

John Bradford ◽

Liv-Guri Faksness ◽

Lee Liberty ◽

...

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Field Experiments ◽

New Technologies ◽

Time Window ◽

The Arctic ◽

Small Scale ◽

Oil Removal ◽

Arctic Conditions

ABSTRACT This paper describes the findings from an experimental spill of 3,400 liters of Statfjord crude under first-year sea ice in Svalbard, Norway in March 2006. The objectives were to:1. Test commercially available radar and acoustics systems for detecting oil spilled under ice.2. Document the weathering processes governing crude oil behaviour in ice.3. Confirm the effectiveness of in-situ burning as an oil removal strategy. The results of this project will be used in planning new Arctic oil exploration and development programs. With the growing awareness of the Arctic basin as a potentially important province for new oil and gas discoveries, there is a critical need to: (1) develop new technologies to detect and map spills under ice; (2) increase the understanding of oil behaviour in ice and: (3) continue to demonstrate the capabilities of in-situ burning as an important and safe Arctic response tool. Tank tests conducted in 2004 (Dickins et al., 2005) showed that radar systems could detect and map oil pools as thin as 2 to 3 cm under controlled conditions under model sea ice up to 40 cm thick. This field experiment created a much larger-scale spill under thicker 65 cm natural sea ice to further evaluate potential remote sensing systems as practical operational spill response tools. The findings of the 2006 experiment: (1) demonstrated for the first time the ability of ground penetrating radar to detect and map oil under natural sea ice from the surface; (2) documented oil weathering with a relatively warm ice sheet under spring conditions; and (3) confirmed the effectiveness of in situ burning as a primary oil removal strategy under Arctic conditions. Oil weathering results are discussed and compared with small-scale field experiments performed on Svalbard during the period 2003–2006. Low temperatures and lack of waves in ice act to reduce oil spreading, evaporation, emulsification and dispersion. As a result, the operational time window for several spill response strategies such as dispersants and in-situ burning may be significantly extended compared to oil spills in open water.

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Algorithm for Flat First-Year Ice Draft Using AMSR2 Data in the Arctic Ocean

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-18-0034.1 ◽

2018 ◽

Vol 35 (11) ◽

pp. 2147-2157

Author(s):

E. Yoshizawa ◽

K. Shimada ◽

K. H. Cho

Keyword(s):

Sea Ice ◽

Spectral Characteristics ◽

The Arctic ◽

Ice Thickness ◽

First Year ◽

Brightness Temperatures ◽

Freezing Period ◽

Gradient Ratio ◽

Multiyear Ice

AbstractFirst-year ice has replaced multiyear ice in the Northern Sea Route area since 2008. In this area, sea ice survival during summer substantially depends on first-year ice thickness at melt onset, and thus monitoring of first-year ice thickness in the freezing period is a key to forecasting sea ice distributions in the following summer. In this paper we introduce a new algorithm to estimate flat first-year ice draft using brightness temperature data measured by the Advanced Microwave Scanning Radiometer-2 (AMSR2). The algorithm uses a gradient ratio (GR) of 18- and 36-GHz vertically polarized brightness temperatures based on decreases in sea ice emissivity in higher AMSR2 frequency channels with thermodynamic growth associated with an increase in volume scattering. Such spectral characteristics of the emissivity are examined by comparing GR values with flat first-year ice draft extracted by mode values of in situ draft data measured by a moored ice profiling sonar. The accuracy of the daily draft estimated from GR values after applying proper noise filters is about 10 cm for a draft range of 0.4–1.2 m.

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An Evaluation of Antarctic Sea Ice Thickness from the Global Ice-Ocean Modeling and Assimilation System based on In-situ and Satellite Observations

10.5194/tc-2021-228 ◽

2021 ◽

Author(s):

Sutao Liao ◽

Hao Luo ◽

Jinfei Wang ◽

Qian Shi ◽

Jinlun Zhang ◽

...

Keyword(s):

Sea Ice ◽

Satellite Observations ◽

Ocean Modeling ◽

Ice Thickness ◽

Earth System ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

The Earth ◽

Assimilation System

Abstract. Antarctic sea ice is an important component of the Earth system. However, its role in the Earth system is still not very clear due to limited Antarctic sea ice thickness (SIT) data. A reliable sea ice reanalysis can be useful to study Antarctic SIT and its role in the Earth system. Among various Antarctic sea ice reanalysis products, the Global Ice-Ocean Modeling and Assimilation System (GIOMAS) output is widely used in the research of Antarctic sea ice. As more Antarctic SIT observations with quality control are released, a further evaluation of Antarctic SIT from GIOMAS is conducted in this study based on in-situ and satellite observations. Generally, though only sea ice concentration is assimilated, GIOMAS can basically reproduce the observed variability of sea ice volume and its changes in the trend before and after 2013, indicating that GIOMAS is a good option to study the long-term variation of Antarctic sea ice. However, due to deficiencies in model and asymmetric changes in SIT caused by assimilation, GIOMAS underestimates Antarctic SIT especially in deformed ice regions, which has an impact on not only the mean state of SIT but also the variability. Thus, besides the further development of model, assimilating additional sea ice observations (e.g., SIT and sea ice drift) with advanced assimilation methods may be conducive to a more accurate estimation of Antarctic SIT.

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Implications of surface flooding on airborne estimates of snow depth on sea ice

The Cryosphere ◽

10.5194/tc-15-2819-2021 ◽

2021 ◽

Vol 15 (6) ◽

pp. 2819-2833

Author(s):

Anja Rösel ◽

Sinead Louise Farrell ◽

Vishnu Nandan ◽

Jaqueline Richter-Menge ◽

Gunnar Spreen ◽

...

Keyword(s):

Sea Ice ◽

Validation Studies ◽

Snow Depth ◽

The Arctic ◽

Airborne Radar ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Thickness Measurements ◽

Snow And Ice

Abstract. Snow depth observations from airborne snow radars, such as the NASA's Operation IceBridge (OIB) mission, have recently been used in altimeter-derived sea ice thickness estimates, as well as for model parameterization. A number of validation studies comparing airborne and in situ snow depth measurements have been conducted in the western Arctic Ocean, demonstrating the utility of the airborne data. However, there have been no validation studies in the Atlantic sector of the Arctic. Recent observations in this region suggest a significant and predominant shift towards a snow-ice regime caused by deep snow on thin sea ice. During the Norwegian young sea Ice, Climate and Ecosystems (ICE) expedition (N-ICE2015) in the area north of Svalbard, a validation study was conducted on 19 March 2015. This study collected ground truth data during an OIB overflight. Snow and ice thickness measurements were obtained across a two-dimensional (2-D) 400 m × 60 m grid. Additional snow and ice thickness measurements collected in situ from adjacent ice floes helped to place the measurements obtained at the gridded survey field site into a more regional context. Widespread negative freeboards and flooding of the snowpack were observed during the N-ICE2015 expedition due to the general situation of thick snow on relatively thin sea ice. These conditions caused brine wicking into and saturation of the basal snow layers. This causes the airborne radar signal to undergo more diffuse scattering, resulting in the location of the radar main scattering horizon being detected well above the snow–ice interface. This leads to a subsequent underestimation of snow depth; if only radar-based information is used, the average airborne snow depth was 0.16 m thinner than that measured in situ at the 2-D survey field. Regional data within 10 km of the 2-D survey field suggested however a smaller deviation between average airborne and in situ snow depth, a 0.06 m underestimate in snow depth by the airborne radar, which is close to the resolution limit of the OIB snow radar system. Our results also show a broad snow depth distribution, indicating a large spatial variability in snow across the region. Differences between the airborne snow radar and in situ measurements fell within the standard deviation of the in situ data (0.15–0.18 m). Our results suggest that seawater flooding of the snow–ice interface leads to underestimations of snow depth or overestimations of sea ice freeboard measured from radar altimetry, in turn impacting the accuracy of sea ice thickness estimates.

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The microwave emissivity variability of snow covered first-year sea ice from late winter to early summer: a model study

The Cryosphere ◽

10.5194/tc-8-891-2014 ◽

2014 ◽

Vol 8 (3) ◽

pp. 891-904 ◽

Cited By ~ 21

Author(s):

S. Willmes ◽

M. Nicolaus ◽

C. Haas

Keyword(s):

Sea Ice ◽

Early Summer ◽

Weddell Sea ◽

Microwave Emission ◽

The Arctic ◽

Late Winter ◽

Emission Model ◽

Regional Patterns ◽

Brightness Temperatures ◽

Microwave Emissivity

Abstract. Satellite observations of microwave brightness temperatures between 19 GHz and 85 GHz are the main data sources for operational sea-ice monitoring and retrieval of ice concentrations. However, microwave brightness temperatures depend on the emissivity of snow and ice, which is subject to pronounced seasonal variations and shows significant hemispheric contrasts. These mainly arise from differences in the rate and strength of snow metamorphism and melt. We here use the thermodynamic snow model SNTHERM forced by European Re-Analysis (ERA) interim data and the Microwave Emission Model of Layered Snowpacks (MEMLS), to calculate the sea-ice surface emissivity and to identify the contribution of regional patterns in atmospheric conditions to its variability in the Arctic and Antarctic. The computed emissivities reveal a pronounced seasonal cycle with large regional variability. The emissivity variability increases from winter to early summer and is more pronounced in the Antarctic. In the pre-melt period (January–May, July–November) the standard deviations in surface microwave emissivity due to diurnal, regional and inter-annual variability of atmospheric forcing reach up to Δε = 0.034, 0.043, and 0.097 for 19 GHz, 37 GHz and 85 GHz channels, respectively. Between 2000 and 2009, small but significant positive emissivity trends were observed in the Weddell Sea during November and December as well as in Fram Strait during February, potentially related to earlier melt onset in these regions. The obtained results contribute to a better understanding of the uncertainty and variability of sea-ice concentration and snow-depth retrievals in regions of high sea-ice concentrations.

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