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

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

The Cryosphere Discussions ◽

10.5194/tcd-7-5711-2013 ◽

2013 ◽

Vol 7 (6) ◽

pp. 5711-5734 ◽

Cited By ~ 1

Author(s):

S. Willmes ◽

M. Nicolaus ◽

C. Haas

Keyword(s):

Sea Ice ◽

Early Summer ◽

Weddell Sea ◽

Microwave Emission ◽

Late Winter ◽

Emission Model ◽

Regional Variability ◽

Sea Ice Concentration ◽

Regional Patterns ◽

Microwave Emissivity

Abstract. Satellite observations of microwave brightness temperatures between 19 GHz and 85 GHz are the main data source for operational sea-ice monitoring. However, the sea ice microwave emissivity is subject to pronounced seasonal variations and shows significant hemispheric contrasts that mainly arise from differences in the rate and strength of snow metamorphism and melt. We use the thermodynamic snow model SNTHERM and the microwave emission model MEMLS to identify the contribution of regional patterns in atmospheric energy fluxes to surface emissivity variations on Arctic and Antarctic sea ice between 2000 and 2009. The obtained emissivity data reveal a pronounced seasonal cycle with a 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 variations in surface microwave emissivity due to diurnal, regional and inter-annual variability of atmospheric forcing reach up to 3.4%, 4.3%, and 9.7% for 19 GHz, 37 GHz and 85 GHz channels, respectively. Small but significant emissivity trends can be observed in the Weddell Sea during November and December as well as in Fram Strait during February. The obtained emissivity data lend themselves for an assessment of sea-ice concentration and snow-depth algorithm accuracies.

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The importance of diurnal processes for the Seasonal cycle of Sea-ice microwave brightness temperatures during early Summer in the Weddell Sea, Antarctica

Annals of Glaciology ◽

10.3189/172756406781811817 ◽

2006 ◽

Vol 44 ◽

pp. 297-302 ◽

Cited By ~ 21

Author(s):

Sascha Willmes ◽

Jörg Bareiss ◽

Christian Haas ◽

Marcel Nicolaus

Keyword(s):

Sea Ice ◽

Seasonal Cycle ◽

Meteorological Data ◽

Austral Summer ◽

Early Summer ◽

Arctic Sea Ice ◽

Weddell Sea ◽

Brightness Temperatures ◽

Antarctic Sea Ice ◽

Dominant Feature

AbstractOver the perennial Sea ice in the western and central Weddell Sea, Antarctica, the onset of Summer is accompanied by a Significant decrease of Sea-ice brightness temperatures (Tb) as observed by passive-microwave radiometers Such as the Special Sensor Microwave/Imager (SSM/I). The Summer-specific Tb drop is the dominant feature in the seasonal cycle of Tb data and represents a conspicuous difference to most Arctic Sea-ice regions, where the onset of Summer is mostly marked by a rise in Tb. Data from a 5 week drift Station through the western Weddell Sea in the 2004/05 austral Summer, Ice Station POLarstern (IsPOL), helped with identifying the characteristic processes for Antarctic Sea ice. In Situ glaciological and meteorological data, in combination with SSM/I Swath Satellite data, indicate that the cycle of repeated diurnal thawing and refreezing of Snow (‘freeze–thaw cycles’) is the dominant process in the Summer Season, with the absence of complete Snow wetting. The resulting metamorphous Snow with increased grain Size, as well as the formation of ice layers, leads to decreasing emissivity, enhanced volume Scattering and increased backscatter. This causes the Summer Tb drop.

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The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 1: How to obtain sea ice brightness temperatures at 6.9 GHz from climate model output

The Cryosphere ◽

10.5194/tc-14-2369-2020 ◽

2020 ◽

Vol 14 (7) ◽

pp. 2369-2386 ◽

Cited By ~ 1

Author(s):

Clara Burgard ◽

Dirk Notz ◽

Leif T. Pedersen ◽

Rasmus T. Tonboe

Keyword(s):

Sea Ice ◽

Climate Model ◽

Subsurface Layer ◽

Microwave Emission ◽

The Arctic ◽

Model Output ◽

Linear Temperature ◽

Observation Operator ◽

Brightness Temperatures ◽

Climate Model Output

Abstract. We explore the feasibility of an observation operator producing passive microwave brightness temperatures for sea ice at a frequency of 6.9 GHz. We investigate the influence of simplifying assumptions for the representation of sea ice vertical properties on the simulation of microwave brightness temperatures. We do so in a one-dimensional setup, using a complex 1D thermodynamic sea ice model and a 1D microwave emission model. We find that realistic brightness temperatures can be simulated in cold conditions from a simplified linear temperature profile and a simplified salinity profile as a function of depth in the ice. These realistic brightness temperatures can be obtained based on profiles interpolated to as few as five layers. Most of the uncertainty resulting from the simplifications is introduced by the simplification of the salinity profiles. In warm conditions, the simplified salinity profiles lead to brine volume fractions that are too high in the subsurface layer. To overcome this limitation, we suggest using a constant brightness temperature for the ice during warm conditions and treating melt ponds as water surfaces. Finally, in our setup, we cannot assess the effect of wet snow properties. As periods of snow with intermediate moisture content, typically occurring in spring and fall, locally last for less than a month, our approach allows one to estimate realistic brightness temperatures at 6.9 GHz from climate model output for most of the year.

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Remote sensing of sea ice: advances during the DAMOCLES project

The Cryosphere ◽

10.5194/tc-6-1411-2012 ◽

2012 ◽

Vol 6 (6) ◽

pp. 1411-1434 ◽

Cited By ~ 19

Author(s):

G. Heygster ◽

V. Alexandrov ◽

G. Dybkjær ◽

W. von Hoyningen-Huene ◽

F. Girard-Ardhuin ◽

...

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Estimation Method ◽

Satellite Observations ◽

Microwave Emission ◽

The Arctic ◽

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 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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Arctic summer sea-ice SAR signatures, melt-season characteristics, and melt-pond fractions

Polar Record ◽

10.1017/s003224740001442x ◽

1997 ◽

Vol 33 (185) ◽

pp. 101-112 ◽

Cited By ~ 15

Author(s):

M. O. Jeffries ◽

K. Schwartz ◽

S. Li

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Late Summer ◽

Beaufort Sea ◽

Early Summer ◽

The Arctic ◽

Late Winter ◽

Melt Pond ◽

Melt Ponds ◽

The Arctic Ocean

AbstractVariations in multiyear sea-ice backscatter from the synthetic aperture radar (SAR) aboard the ERS-1 satellite are interpreted in terms of melt-season characteristics (onset of melt in spring and of freeze-up in autumn, and the duration of the snow-decay period, the melt season, and the melt-pond season) from late winter to early autumn 1992 in two regions of the Arctic Ocean: the northeastern Beaufort Sea adjacent to the Queen Elizabeth Islands in the Canadian high Arctic and the western Beaufort Sea north of Alaska. In the northeastern Beaufort Sea, the onset of melt occurs later, and the periods of snow-cover decay and the occurrence of melt ponds are shorter than in the western Beaufort Sea. These melt-season characteristics of each area are consistent with previous observations that the northeastern Beaufort Sea has one of the most severe summer climates in the Arctic Ocean. A model, which assumes that the backscatter from multiyear floes is the sum of backscatter from bare ice and melt ponds, is used to derive the melt-pond fraction during the summer. The results show that melt-pond fractions decrease from an early-summer maximum of about 60% to a late-summer minimum around 10%. The magnitude of the melt-pond fractions and their decline during the summer is consistent with previous, more qualitative data. The SAR model, which gives melt-pond fractions with lower variability and less uncertainty than previous data, offers an improved approach to the reliable estimation of the areal extent of water on ice floes. Suggestions for further improvement of the model include accounting for the consequences of wind-speed variations, summer snowfall, and freeze/thaw cycles and their effects on melt-pond and ice-surface roughness.

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Arctic Snow Microstructure Experiment for the development of snow emission modelling

Geoscientific Instrumentation Methods and Data Systems ◽

10.5194/gi-5-85-2016 ◽

2016 ◽

Vol 5 (1) ◽

pp. 85-94 ◽

Cited By ~ 2

Author(s):

William Maslanka ◽

Leena Leppänen ◽

Anna Kontu ◽

Mel Sandells ◽

Juha Lemmetyinen ◽

...

Keyword(s):

Measurement Techniques ◽

Microwave Emission ◽

The Arctic ◽

Future Research ◽

Emission Model ◽

Horizontal Polarization ◽

Experimental Conditions ◽

Radiometric Measurements ◽

Brightness Temperatures ◽

Snow Microstructure

Abstract. The Arctic Snow Microstructure Experiment (ASMEx) took place in Sodankylä, Finland in the winters of 2013–2014 and 2014–2015. Radiometric, macro-, and microstructure measurements were made under different experimental conditions of homogenous snow slabs, extracted from the natural seasonal taiga snowpack. Traditional and modern measurement techniques were used for snow macro- and microstructure observations. Radiometric measurements of the microwave emission of snow on reflector and absorber bases were made at frequencies 18.7, 21.0, 36.5, 89.0, and 150.0 GHz, for both horizontal and vertical polarizations. Two measurement configurations were used for radiometric measurements: a reflecting surface and an absorbing base beneath the snow slabs. Simulations of brightness temperatures using two microwave emission models, the Helsinki University of Technology (HUT) snow emission model and Microwave Emission Model of Layered Snowpacks (MEMLS), were compared to observed brightness temperatures. RMSE and bias were calculated; with the RMSE and bias values being smallest upon an absorbing base at vertical polarization. Simulations overestimated the brightness temperatures on absorbing base cases at horizontal polarization. With the other experimental conditions, the biases were small, with the exception of the HUT model 36.5 GHz simulation, which produced an underestimation for the reflector base cases. This experiment provides a solid framework for future research on the extinction of microwave radiation inside snow.

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The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 1: How to obtain sea-ice brightness temperatures at 6.9 GHz from climate model output

10.5194/tc-2019-317 ◽

2020 ◽

Author(s):

Clara Burgard ◽

Dirk Notz ◽

Leif T. Pedersen ◽

Rasmus T. Tonboe

Keyword(s):

Sea Ice ◽

Climate Model ◽

Microwave Emission ◽

The Arctic ◽

Model Output ◽

Linear Temperature ◽

Observation Operator ◽

Salinity Profile ◽

Brightness Temperatures ◽

Climate Model Output

Abstract. We explore the feasibility of an observation operator producing passive microwave brightness temperatures for sea ice at a frequency of 6.9 GHz. We investigate the influence of simplifying assumptions for the representation of sea-ice vertical properties on the simulation of microwave brightness temperatures. We do so in a one-dimensional setup, using a complex 1D thermodynamic sea-ice model and a 1D microwave emission model. We find that realistic brightness temperatures can be simulated in winter from a simplified linear temperature profile and a self-similar salinity profile in the ice. These realistic brightness temperatures can be obtained based on profiles interpolated to as few as five layers. Most of the uncertainty resulting from the simplifications is introduced by the simplification of the salinity profiles. In summer, the simplified salinity profile leads to too high liquid water fractions at the surface. To overcome this limitation, we suggest using a constant brightness temperature for the ice during summer and to treat melt ponds as water surfaces. Finally, in our setup, we cannot assess the effect of snow properties during melting. As periods of melting snow with intermediate moisture content typically last for less than a month, our approach allows one to estimate reasonable brightness temperatures at 6.9 GHz from climate model output for about 11 months throughout the year.

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Snow thickness retrieval from L-band brightness temperatures: a model comparison

Annals of Glaciology ◽

10.3189/2015aog69a886 ◽

2015 ◽

Vol 56 (69) ◽

pp. 9-17 ◽

Cited By ~ 10

Author(s):

Nina Maass ◽

Lars Kaleschke ◽

Xiangshan Tian-Kunze ◽

Rasmus T. Tonboe

Keyword(s):

Sea Ice ◽

Surface Temperature ◽

Brightness Temperature ◽

Model Comparison ◽

Complex Model ◽

Microwave Emission ◽

Emission Model ◽

Brightness Temperatures ◽

L Band ◽

Snow Thickness

AbstractThe Soil Moisture and Ocean Salinity (SMOS) satellite’s L-band (1.4 GHz) measurements have been used to retrieve Snow thickness over thick sea Ice in a previous study. Here we consider brightness temperature simulations for 2.5–4.5m thick Arctic multi-year Ice and compare the results of the relatively simple emission model (M2013) used previously for the retrieval with simulations from a more complex model (T2011) that combines a sea-Ice version of the Microwave Emission Model for Layered Snowpacks (MEMLS) with a thermodynamic model. We find that L-band brightness temperature is mainly determined by Ice temperature. In the M2013 model, Ice temperature in turn is mainly determined by surface temperature and Snow thickness, and this dependence has been used previously to explain the potential for a Snow thickness retrieval. Our comparisons suggest that the M2013 retrieval model may benefit from a more sophisticated thermodynamic calculation of the Ice temperature or from using independent temperature data (e.g. from 6 GHz channels). In both models, horizontally polarized brightness temperatures increase with Snow thickness while holding surface temperature, Ice thickness and Snow density near constant. The increase in the T2011 model is steeper than in M2013, suggesting a higher sensitivity to Snow thickness than found earlier.

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Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI

Remote Sensing ◽

10.3390/rs13081457 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1457 ◽

Cited By ~ 1

Author(s):

Lele Li ◽

Haihua Chen ◽

Lei Guan

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Data Retrieval ◽

Arctic Sea Ice ◽

Microwave Emission ◽

The Arctic ◽

First Year ◽

Emission Model ◽

Momentum Exchange ◽

Multiyear Ice

Given their high albedo and low thermal conductivity, snow and sea ice are considered key reasons for amplified warming in the Arctic. Snow-covered sea ice is a more effective insulator, which greatly limits the energy and momentum exchange between the atmosphere and surface, and further controls the thermal dynamic processes of snow and ice. In this study, using the Microwave Emission Model of Layered Snowpacks (MEMLS), the sensitivities of the brightness temperatures (TBs) from the FengYun-3B/MicroWave Radiometer Imager (FY3B/MWRI) to changes in snow depth were simulated, on both first-year and multiyear ice in the Arctic. Further, the correlation coefficients between the TBs and snow depths in different atmospheric and sea ice environments were investigated. Based on the simulation results, the most sensitive factors to snow depth, including channels of MWRI and their combination form, were determined for snow depth retrieval. Finally, using the 2012–2013 Operational IceBridge (OIB) snow depth data, retrieval algorithms of snow depth were developed for the Arctic on first-year and multiyear ice, separately. Validation using the 2011 OIB data indicates that the bias and standard deviation (Std) of the algorithm are 2.89 cm and 2.6 cm on first-year ice (FYI), respectively, and 1.44 cm and 4.53 cm on multiyear ice (MYI), respectively.

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

Atmosphere ◽

10.3390/atmos12020174 ◽

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.

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