The Timing of Initial Spring Melt in the Arctic from Nimbus-7 SMMR Data (Abstract)

The ablation of sea ice is an important feature in the global climate system. During the melt season in the Arctic, rapid changes occur in sea-ice surface conditions and areal extent of ice. These changes alter the albedo and vary the energy budgets. Understanding the spatial and temporal variations of melt is critical in the polar regions. This study investigates the spring onset of melt in the seasonal sea-ice zone of the Arctic Basin through the use of a melt signature derived by Anderson and others from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) data. The signature is recognized in the “gradient ratio” of the 18 and 37 GHz vertical brightness temperatures used to distinguish multi-year ice. A spuriously high fraction of multi-year ice appears rapidly during the initial melt of sea ice, when the snow-pack on the ice surface has started to melt. The brightness-temperature changes are a result of either enlarged snow crystals or incipient puddles forming at the snow/ice interface.The timing of these melt events varies geographically and with time. Within the Arctic Basin, the melt signatures are observed first in the Chukchi and Kara/Barents Seas. As the melt progresses, the location of the melt signature moves westward from the Chukchi Sea and eastward from the Kara/Barents Seas to the Laptev Sea region. The timing of the melt signal also varies with year. For example, the melt signature occurred first in the Chukchi Sea in 1979, while in 1980 the signature was first observed in the Kara Sea.There are also differences in the timing of melt for specific geographic locations between years. The melt signature varied almost 25 days in the Chukchi Sea region between 1979 and 1980. The other areas had changes in the 7–10 day range.The occurrence of these melt signatures can be used as an indicator of climate variability in the seasonal sea-ice zones of the Arctic. The timing of the microwave melt signature has also been examined in relation to melt observed on short-wave imagery. The melt events derived from the SMMR data are also related to the large-scale climate conditions.

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The Timing of Initial Spring Melt in the Arctic from Nimbus-7 SMMR Data (Abstract)

Annals of Glaciology ◽

10.1017/s0260305500000811 ◽

1987 ◽

Vol 9 ◽

pp. 244

Author(s):

Mark R. Anderson

Keyword(s):

Sea Ice ◽

Large Scale ◽

Chukchi Sea ◽

Global Climate ◽

Arctic Basin ◽

Temporal Variations ◽

The Arctic ◽

Energy Budgets ◽

Polar Regions ◽

Ice Surface

The ablation of sea ice is an important feature in the global climate system. During the melt season in the Arctic, rapid changes occur in sea-ice surface conditions and areal extent of ice. These changes alter the albedo and vary the energy budgets. Understanding the spatial and temporal variations of melt is critical in the polar regions. This study investigates the spring onset of melt in the seasonal sea-ice zone of the Arctic Basin through the use of a melt signature derived by Anderson and others from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) data. The signature is recognized in the “gradient ratio” of the 18 and 37 GHz vertical brightness temperatures used to distinguish multi-year ice. A spuriously high fraction of multi-year ice appears rapidly during the initial melt of sea ice, when the snow-pack on the ice surface has started to melt. The brightness-temperature changes are a result of either enlarged snow crystals or incipient puddles forming at the snow/ice interface. The timing of these melt events varies geographically and with time. Within the Arctic Basin, the melt signatures are observed first in the Chukchi and Kara/Barents Seas. As the melt progresses, the location of the melt signature moves westward from the Chukchi Sea and eastward from the Kara/Barents Seas to the Laptev Sea region. The timing of the melt signal also varies with year. For example, the melt signature occurred first in the Chukchi Sea in 1979, while in 1980 the signature was first observed in the Kara Sea. There are also differences in the timing of melt for specific geographic locations between years. The melt signature varied almost 25 days in the Chukchi Sea region between 1979 and 1980. The other areas had changes in the 7–10 day range. The occurrence of these melt signatures can be used as an indicator of climate variability in the seasonal sea-ice zones of the Arctic. The timing of the microwave melt signature has also been examined in relation to melt observed on short-wave imagery. The melt events derived from the SMMR data are also related to the large-scale climate conditions.

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Surface reflectivity in polar regions retrieved from TDS-1 mission data

10.5194/egusphere-egu21-13036 ◽

2021 ◽

Author(s):

Frederik Kreß ◽

Maximilian Semmling ◽

Estel Cardellach ◽

Weiqiang Li ◽

Mainul Hoque ◽

...

Keyword(s):

Remote Sensing ◽

Surface Roughness ◽

Sea Ice ◽

Large Scale ◽

Global Climate ◽

Path Loss ◽

Signal Strength ◽

Global Navigation Satellite Systems ◽

The Arctic ◽

Polar Regions

In current times of a changing global climate, a special interest is focused on the large-scale recording of sea ice. Among the existing remote sensing methods, bi- statically reflected signals of Global Navigation Satellite Systems (GNSS) could play an important role in fulfilling the task. Within this project, sensitivity of GNSS signal reflections to sea ice properties like its occurrence, sea ice thick- ness (SIT) and sea concentration (SIC) is evaluated. When getting older, sea ice tends go get thicker. Because of decreasing salinity, i.e. less permittivity, as well as relatively higher surface roughness of older ice, it can be assumed that reflected signal strength decreases with increasing SIT. The reflection data used were recorded in the years 2015 and 2016 by the TechDemoSat-1 (TDS-1) satellite over the Arctic and Antarctic. It includes a down-looking antenna for the reflected as well as an up-looking antenna dedicated to receive the direct sig- nal. The raw data, provided by the manufacturer SSTL, were pre-processed by IEEC/ICE-CSIC to derive georeferenced signal power values. The reflectivity was estimated by comparing the power of the up- and down-looking links. The project focuses on the signal link budget to apply necessary corrections. For this reason, the receiver antenna gain as well as the Free-Space Path Loss (FSPL) were calculated and applied for reflectivity correction. Differences of nadir and zenith antenna FSPL and gain show influence of up to 6 dB and &#8722;9 dB to 9 dB respectively on the recorded signal strength. All retrieved reflectivity values are compared to model predictions based on Fresnel coefficients but also to avail- able ancillary truth data of other remote sensing missions to identify possible patterns: SIT relations are investigated using Level-2 data of the Soil Moisture and Ocean Salinity (SMOS) satellite. The SIC comparison was done with an AMSR-2 product. The results show sensitivity of the reflectivity value to both SIT and SIC simultaneously, whereby the surface roughness is also likely to have an influence. This on-going study aims at the consolidation of retrieval algorithms for sea-ice observation. The resolution of different ice types and the retrieval of SIT and SIC based on satellite data is a challenge for future work in this respect.

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Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

Remote Sensing ◽

10.3390/rs11232864 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2864 ◽

Cited By ~ 4

Author(s):

Jiping Liu ◽

Yuanyuan Zhang ◽

Xiao Cheng ◽

Yongyun Hu

Keyword(s):

Neural Network ◽

Sea Ice ◽

Snow Depth ◽

Deep Neural Network ◽

Arctic Basin ◽

Temporal Variations ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Thickness ◽

Arctic Sea

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

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Arctic sea ice thickness loss determined using subsurface, aircraft, and satellite observations

The Cryosphere ◽

10.5194/tc-9-269-2015 ◽

2015 ◽

Vol 9 (1) ◽

pp. 269-283 ◽

Cited By ~ 208

Author(s):

R. Lindsay ◽

A. Schweiger

Keyword(s):

Sea Ice ◽

Large Scale ◽

Spatial Scales ◽

Arctic Basin ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Spatial And Temporal Variability ◽

Sampling Errors ◽

Sea Ice Thickness

Abstract. Sea ice thickness is a fundamental climate state variable that provides an integrated measure of changes in the high-latitude energy balance. However, observations of mean ice thickness have been sparse in time and space, making the construction of observation-based time series difficult. Moreover, different groups use a variety of methods and processing procedures to measure ice thickness, and each observational source likely has different and poorly characterized measurement and sampling errors. Observational sources used in this study include upward-looking sonars mounted on submarines or moorings, electromagnetic sensors on helicopters or aircraft, and lidar or radar altimeters on airplanes or satellites. Here we use a curve-fitting approach to determine the large-scale spatial and temporal variability of the ice thickness as well as the mean differences between the observation systems, using over 3000 estimates of the ice thickness. The thickness estimates are measured over spatial scales of approximately 50 km or time scales of 1 month, and the primary time period analyzed is 2000–2012 when the modern mix of observations is available. Good agreement is found between five of the systems, within 0.15 m, while systematic differences of up to 0.5 m are found for three others compared to the five. The trend in annual mean ice thickness over the Arctic Basin is −0.58 ± 0.07 m decade−1 over the period 2000–2012. Applying our method to the period 1975–2012 for the central Arctic Basin where we have sufficient data (the SCICEX box), we find that the annual mean ice thickness has decreased from 3.59 m in 1975 to 1.25 m in 2012, a 65% reduction. This is nearly double the 36% decline reported by an earlier study. These results provide additional direct observational evidence of substantial sea ice losses found in model analyses.

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September Arctic Sea Ice minimum prediction – a new skillful statistical approach

10.5194/esd-2018-61 ◽

2018 ◽

Author(s):

Monica Ionita ◽

Klaus Grosfeld ◽

Patrick Scholz ◽

Renate Treffeisen ◽

Gerrit Lohmann

Keyword(s):

Sea Ice ◽

Statistical Model ◽

Climate Models ◽

Global Climate ◽

Arctic Sea Ice ◽

The Arctic ◽

Polar Regions ◽

Sea Ice Extent ◽

Important Indicator ◽

Predictive Skill

Abstract. Sea ice in both Polar Regions is an important indicator for the expression of global climate change and its polar amplification. Consequently, a broad interest exists on sea ice coverage, variability and long term change. However, its predictability is complex and it depends on various atmospheric and oceanic parameters. In order to provide insights into the potential development of a monthly/seasonal signal of sea ice evolution, we developed a robust statistical model based on oceanic and different atmospheric variables to calculate an estimate of the September sea ice extent (SSIE) on monthly time scale. Although previous statistical attempts of monthly/seasonal SSIE forecasts show a relatively reduced skill, when the trend is removed, we show here that the September sea ice extent has a high predictive skill, up to 4 months ahead, based on previous months' atmospheric and oceanic conditions. Our statistical model skillfully captures the interannual variability of the SSIE and could provide a valuable tool for identifying relevant regions and atmospheric parameters that are important for the sea ice development in the Arctic and for detecting sensitive and critical regions in global coupled climate models with focus on sea ice formation.

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Hemispheric sea ice extent dynamics as observed from MSMR

MAUSAM ◽

10.54302/mausam.v60i3.1101 ◽

2021 ◽

Vol 60 (3) ◽

pp. 295-308

Author(s):

NILAY SHARMA ◽

M. K. DASH ◽

P. C. PANDEY ◽

N. K. VYAS

Keyword(s):

Sea Ice ◽

Global Climate ◽

Ice Cover ◽

The Arctic ◽

Ice Formation ◽

Spatial And Temporal Variation ◽

Polar Regions ◽

Sea Ice Extent ◽

Year 2000 ◽

Formation Phase

The ice covered regions of the polar seas influence the global climate in several ways. Any perturbation in the polar oceanic cryosphere affects the local weather and the global climate through modulation of the radiative forcing, the bottom water formation and the mass & the momentum transfer between Atmosphere-Cryosphere-Ocean System. The cold, harsh and inhospitable conditions in the polar regions prohibit the collection of extensive in situ data with sufficient spatial and temporal variation. However, satellite remote sensing is an ideal technique for studying the areas like the polar regions with synoptic and repetitive coverage. This paper discusses the analysis of the data obtained over the polar oceanic regions during the period June 1999 – September 2001 through the use of Multi-channel Scanning Microwave Radiometer (MSMR), onboard India’s first oceanographic satellite Oceansat-1. The MSMR observation shows that all the sectors in the Antarctic behave differently to the melting and formation of the sea ice. Certain peculiar features like the increase in sea ice extent during the melt season of 1999 – 2000 in the Indian Ocean sector, 15 – 20% decrease in the sea ice extent in the western Pacific sector during the ice formation period for the year 2000, melting spell within the formation phase of sea ice in B & A sector in the year 2000 were observed. On the other hand the northern polar sea ice extent is seen to be more dominated by the land characteristics. The ice formation in Kara and the Barent Sea sector is dominated by the ocean currents, where as the ice covered in the Japan and the Okhotsk Sea is dominated by the land processes. The sea ice extent in the Arctic Ocean show fluctuations from July to October and remain almost steady over other months. The global sea ice cover shows a formation phase from March to June and melting phase from November to February. In other months, i.e., from July – October the global sea ice cover is dominated by the hemispheric asymmetry of the ice growth and retreat.

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Potential Consequences Of “Dirty” Arctic Sea Ice

Annals of Glaciology ◽

10.1017/s0260305500009290 ◽

1990 ◽

Vol 14 ◽

pp. 355

Author(s):

Stephanie Pfirman ◽

Manfred A. Lange ◽

Tamara S. Ledley

Keyword(s):

Sea Ice ◽

Arctic Basin ◽

Surface Melting ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Melting ◽

Large Area ◽

Sea Ice Concentration ◽

East Greenland Current ◽

Ice Surface

Observations of high particulate loads on Eurasian Basin sea ice in 1987 raise questions of consequence for sediment budgets, ice melting, ice modeling and remote sensing. Biogenic and lithogenic particles were observed in concentrations high enough to color the ice surface brown over large area (greater than 15 × 15 km2) within the Siberian branch of the Transpolar Drift stream. The sediment is most likely incorporated when ice forms on the Siberian shelf seas, and is concentrated at the ice surface after several years of summer surface melting and biological growth within the Arctic basin. Much of the particle-laden multi-year ice appears to leave the Arctic basin via Fram Strait, depositing its sediment load along the axis of the East Greenland Current. To date, variation in sea-ice particle load has not been taken into consideration when modeling ice thickness or distribution for past or future environmental scenarios, with the exception of soot deposited from nuclear war. Naturally elevated surface-particle concentration may occur if there is increased deposition from long-range or coastal transport of aeolian material, increased sediment input into sea ice which is then exposed to surface melting, and/or increased biogenic productivity on the ice surface. Such conditions may have prevailed during the Younger Dryas. If particle loads become high enough to cause extensive sea-ice melting, changes may be expected in sea-ice concentration and distribution, sea-floor sedimentation rates, and oceanic productivity.

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Understanding the variability of the Eddy Kinetic Energy in the Arctic Ocean.

10.5194/egusphere-egu21-10314 ◽

2021 ◽

Author(s):

Camille Lique ◽

Heather Regan ◽

Gianluca Meneghello ◽

Claude Talandier

Keyword(s):

Kinetic Energy ◽

Arctic Ocean ◽

Time Scales ◽

Large Scale ◽

Small Deformation ◽

Arctic Basin ◽

Temporal Variations ◽

Eddy Kinetic Energy ◽

The Arctic ◽

The Arctic Ocean

Mesoscale activity in the Arctic Ocean remains largely unexplored, owing primarily to the challenges of i) observing eddies in this ice-covered region and ii) modelling at such small deformation radius. In this talk, we will use results from a simulation performed with a high-resolution, eddy resolving model to investigate the spatial and temporal variations of the eddy kinetic energy (EKE) in the Arctic Basin. On average and in contrast to the typical open ocean conditions, the levels of mean and eddy kinetic energy are of the same order of magnitude, and EKE is intensified along the boundary and in the subsurface. On long time scales (interannual to decadal), EKE levels do not respond as expected to changes in the large scale circulation. This can be exemplified when looking at the spin up of the gyre that occurred in response to a strong surface input of momentum in 2007-2008. On seasonal time scales, the estimation of a Lorenz energy cycle allows us to investigate the drivers behind the peculiarities of the EKE field, and to understand the relative roles played by the atmospheric forcing for them.&#160;

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Toward Quantifying the Increasing Role of Oceanic Heat in Sea Ice Loss in the New Arctic

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-13-00177.1 ◽

2015 ◽

Vol 96 (12) ◽

pp. 2079-2105 ◽

Cited By ~ 111

Author(s):

E. Carmack ◽

I. Polyakov ◽

L. Padman ◽

I. Fer ◽

E. Hunke ◽

...

Keyword(s):

Heat Flux ◽

Sea Ice ◽

Arctic Ocean ◽

Climate Models ◽

Global Climate ◽

Research Strategy ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Surface ◽

Wide Range

Abstract The loss of Arctic sea ice has emerged as a leading signal of global warming. This, together with acknowledged impacts on other components of the Earth system, has led to the term “the new Arctic.” Global coupled climate models predict that ice loss will continue through the twenty-first century, with implications for governance, economics, security, and global weather. A wide range in model projections reflects the complex, highly coupled interactions between the polar atmosphere, ocean, and cryosphere, including teleconnections to lower latitudes. This paper summarizes our present understanding of how heat reaches the ice base from the original sources—inflows of Atlantic and Pacific Water, river discharge, and summer sensible heat and shortwave radiative fluxes at the ocean/ice surface—and speculates on how such processes may change in the new Arctic. The complexity of the coupled Arctic system, and the logistic and technological challenges of working in the Arctic Ocean, require a coordinated interdisciplinary and international program that will not only improve understanding of this critical component of global climate but will also provide opportunities to develop human resources with the skills required to tackle related problems in complex climate systems. We propose a research strategy with components that include 1) improved mapping of the upper- and middepth Arctic Ocean, 2) enhanced quantification of important process, 3) expanded long-term monitoring at key heat-flux locations, and 4) development of numerical capabilities that focus on parameterization of heat-flux mechanisms and their interactions.

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Causes and consequences of Southern Ocean change: the IPCC SROCC assessment

10.5194/egusphere-egu2020-164 ◽

2020 ◽

Author(s):

Michael Meredith ◽

Martin Sommerkorn ◽

Sandra Cassotta ◽

Chris Derksen ◽

Alexey Ekaykin ◽

...

Keyword(s):

Southern Ocean ◽

Sea Ice ◽

Sea Level ◽

Ocean Circulation ◽

Global Climate ◽

Ice Sheets ◽

The Arctic ◽

Polar Regions ◽

Ice Shelves

Climate change in the polar regions exerts a profound influence both locally and over all of our planet.&#160; Physical and ecosystem changes influence societies and economies, via factors that include food provision, transport and access to non-renewable resources.&#160; Sea level, global climate and potentially mid-latitude weather are influenced by the changing polar regions, through coupled feedback processes, sea ice changes and the melting of snow and land-based ice sheets and glaciers.Reflecting this importance, the IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) features a chapter highlighting past, ongoing and future change in the polar regions, the impacts of these changes, and the possible options for response.&#160; The role of the polar oceans, both in determining the changes and impacts in the polar regions and in structuring the global influence, is an important component of this chapter.With emphasis on the Southern Ocean and through comparison with the Arctic, this talk will outline key findings from the polar regions chapter of SROCC. It will synthesise the latest information on the rates, patterns and causes of changes in sea ice, ocean circulation and properties. It will assess cryospheric driving of ocean change from ice sheets, ice shelves and glaciers, and the role of the oceans in determining the past and future evolutions of polar land-based ice. The implications of these changes for climate, ecosystems, sea level and the global system will be outlined.

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