The impact of melt ponds on summertime microwave brightness temperatures and sea ice concentrations

Abstract. The sea ice concentration (SIC) derived from satellite microwave brightness temperature (TB) data are known to be less accurate during summer melt conditions – in the Arctic Ocean primarily because of the impact of melt ponds on sea ice. Using data from June to August 2009, we investigate how TBs and SICs vary as a function of the ice surface fraction (ISF) computed from open water fraction and melt pond fraction both derived from satellite optical reflectance data. SIC is computed from TBs using a set of eight different retrieval algorithms and applying a consistent set of tie points. We find that TB values change during sea ice melt non-linearly and not monotonically as a function of ISF for ISF of 50 to 100 %. For derived parameters such as the polarization ratio at 19 GHz the change is monotonic but substantially smaller than theoretically expected. Changes in ice/snow radiometric properties during melt also contribute to the TB changes observed; these contributions are functions of frequency and polarization and have the potential to partly counter-balance the impact of changing ISF on the observed TBs. All investigated SIC retrieval algorithms overestimate ISF when using winter tie points. The overestimation varies among the algorithms as a function of ISF such that the SIC retrieval algorithms could be categorized into two different classes. These reveal a different degree of ISF overestimation at high ISF and an opposite development of ISF over-estimation as ISF decreases. For one class, correlations between SIC and ISF are ≥ 0.85 and the associated linear regression lines suggest an exploitable relationship between SIC and ISF if reliable summer sea ice tie points can be established. This study shows that melt ponds are interpreted as open water by the SIC algorithms, while the concentration of ice between the melt ponds is in general being overestimated. These two effects may cancel each other out and thus produce seemingly correct SIC for the wrong reasons. This cancelling effect will in general only be "correct" at one specific value of MPF. Based on our findings we recommend to not correct SIC algorithms for the impact of melt ponds as this seems to violate physical principles. Users should be aware that the SIC algorithms available at the moment retrieve a combined parameter presented by SIC in winter and ISF in summer.

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Abrupt transitions in Arctic open water area

10.5194/tc-2016-108 ◽

2016 ◽

Cited By ~ 2

Author(s):

Michael A. Goldstein ◽

Amanda H. Lynch ◽

Todd E. Arbetter ◽

Florence Fetterer

Keyword(s):

Sea Ice ◽

Structural Breaks ◽

Open Water ◽

Arctic Sea Ice ◽

Ice Age ◽

The Arctic ◽

Sea Ice Concentration ◽

Atlantic Sector ◽

Water Fraction ◽

Ice Concentration

Abstract. September open water fraction in the Arctic is analyzed using the satellite era record of ice concentration (1979–2014). This analysis suggests that there is a statistically significant breakpoint (shift in the mean) and increase in the variance around 1988 and another breakpoint around 2007 in the Pacific sector. These structural breaks are robust to the choice of algorithm used for deriving sea ice concentration from satellite data, and are also apparent in other measures of open water, such as operational ice charts and the record of navigable days from Barrow to Prudhoe Bay. Breakpoints in the Atlantic sector record of open water are evident in 1988 and 2007 but more weakly significant. The breakpoints appear to be associated with concomitant shifts in average ice age, and tend to lead change in Arctic circulation regimes. These results support the thesis that Arctic sea ice may have critical points beyond which a return to the previous state is less likely.

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Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery

The Cryosphere ◽

10.5194/tc-12-1307-2018 ◽

2018 ◽

Vol 12 (4) ◽

pp. 1307-1329 ◽

Cited By ~ 9

Author(s):

Nicholas C. Wright ◽

Chris M. Polashenski

Keyword(s):

Sea Ice ◽

Open Source ◽

Surface Coverage ◽

Open Water ◽

The Arctic ◽

Source Distribution ◽

Ice Melt ◽

Observation Area ◽

Melt Ponds ◽

Optical Imagery

Abstract. Snow, ice, and melt ponds cover the surface of the Arctic Ocean in fractions that change throughout the seasons. These surfaces control albedo and exert tremendous influence over the energy balance in the Arctic. Increasingly available meter- to decimeter-scale resolution optical imagery captures the evolution of the ice and ocean surface state visually, but methods for quantifying coverage of key surface types from raw imagery are not yet well established. Here we present an open-source system designed to provide a standardized, automated, and reproducible technique for processing optical imagery of sea ice. The method classifies surface coverage into three main categories: snow and bare ice, melt ponds and submerged ice, and open water. The method is demonstrated on imagery from four sensor platforms and on imagery spanning from spring thaw to fall freeze-up. Tests show the classification accuracy of this method typically exceeds 96 %. To facilitate scientific use, we evaluate the minimum observation area required for reporting a representative sample of surface coverage. We provide an open-source distribution of this algorithm and associated training datasets and suggest the community consider this a step towards standardizing optical sea ice imagery processing. We hope to encourage future collaborative efforts to improve the code base and to analyze large datasets of optical sea ice imagery.

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

Remote Sensing ◽

10.3390/rs13122283 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2283

Author(s):

Hyangsun Han ◽

Sungjae Lee ◽

Hyun-Cheol Kim ◽

Miae Kim

Keyword(s):

Random Forest ◽

Sea Ice ◽

Arctic Ocean ◽

Open Water ◽

The Arctic ◽

Atmospheric Effects ◽

Sea Ice Concentration ◽

Air Temperatures ◽

The Pacific ◽

Ice Concentration

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

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Cryospheric Impacts of Soviet River Diversion Schemes

Annals of Glaciology ◽

10.3189/1984aog5-1-61-68 ◽

1984 ◽

Vol 5 ◽

pp. 61-68 ◽

Cited By ~ 4

Author(s):

T. Holt ◽

P. M. Kelly ◽

B. S. G. Cherry

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

River Discharge ◽

Large Scale ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Concentration ◽

Ice Concentration ◽

The Arctic Ocean ◽

The Impact

Soviet plans to divert water from rivers flowing into the Arctic Ocean have led to research into the impact of a reduction in discharge on Arctic sea ice. We consider the mechanisms by which discharge reductions might affect sea-ice cover and then test various hypotheses related to these mechanisms. We find several large areas over which sea-ice concentration correlates significantly with variations in river discharge, supporting two particular hypotheses. The first hypothesis concerns the area where the initial impacts are likely to which is the Kara Sea. Reduced riverflow is associated occur, with decreased sea-ice concentration in October, at the time of ice formation. This is believed to be the result of decreased freshening of the surface layer. The second hypothesis concerns possible effects on the large-scale current system of the Arctic Ocean and, in particular, on the inflow of Atlantic and Pacific water. These effects occur as a result of changes in the strength of northward-flowing gradient currents associated with variations in river discharge. Although it is still not certain that substantial transfers of riverflow will take place, it is concluded that the possibility of significant cryospheric effects and, hence, large-scale climate impact should not be neglected.

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Unexpectedly high dimethyl sulfide concentration in high-latitude Arctic sea ice melt ponds

Environmental Science Processes & Impacts ◽

10.1039/c9em00195f ◽

2019 ◽

Vol 21 (10) ◽

pp. 1642-1649 ◽

Cited By ~ 2

Author(s):

Keyhong Park ◽

Intae Kim ◽

Jung-Ok Choi ◽

Youngju Lee ◽

Jinyoung Jung ◽

...

Keyword(s):

Sea Ice ◽

High Latitude ◽

Dimethyl Sulfide ◽

Arctic Sea Ice ◽

Sulfide Concentration ◽

Sea Ice Concentration ◽

Ice Melt ◽

Melt Ponds ◽

Arctic Sea ◽

Ice Concentration

Dimethyl sulfide (DMS) production in the northern Arctic Ocean has been considered to be minimal because of high sea ice concentration and extremely low productivity.

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

Annals of Glaciology ◽

10.1017/s0260305500014415 ◽

1997 ◽

Vol 25 ◽

pp. 434-438 ◽

Cited By ~ 10

Author(s):

Mark A. Tschudi ◽

Judith A. Curry ◽

James A. Maslanik

Keyword(s):

Sea Ice ◽

Open Water ◽

Video Camera ◽

Surface Feature ◽

Video Data ◽

Surface Energy Budget ◽

The Arctic ◽

Areal Coverage ◽

Melt Ponds

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

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Erroneous sea-ice concentration retrieval in the East Antarctic

Annals of Glaciology ◽

10.1017/aog.2018.1 ◽

2018 ◽

Vol 59 (76pt2) ◽

pp. 201-212 ◽

Cited By ~ 1

Author(s):

Hoi Ming Lam ◽

Gunnar Spreen ◽

Georg Heygster ◽

Christian Melsheimer ◽

Neal W. Young

Keyword(s):

Sea Ice ◽

Microwave Remote Sensing ◽

Atmospheric Effects ◽

Sea Ice Concentration ◽

Microwave Brightness Temperature ◽

Antarctic Sea Ice ◽

Optical Images ◽

Ice Concentration ◽

Retrieval Algorithms ◽

Landfast Sea Ice

ABSTRACTLarge discrepancies have been observed between satellite-derived sea-ice concentrations(IC) from passive microwave remote sensing and those derived from optical images at several locations in the East Antarctic, between February and April 2014. These artefacts, that resemble polynyas in the IC maps, appear in areas where optical satellite data show that there is landfast sea ice. The IC datasets and the corresponding retrieval algorithms are investigated together with microwave brightness temperature, air temperature, snowfall and bathymetry to understand the failure of the IC retrieval. The artefacts are the result of the application of weather filters in retrieval algorithms. These filters use the 37 and 19 GHz channels to correct for atmospheric effects on the retrieval. These channels show significant departures from typical ranges when the artefacts occur. A melt–refreeze cycle with associated snow metamorphism is proposed as the most likely cause. Together, the areas of the artefacts account for up to 0.5% of the Antarctic sea-ice area and thus cause a bias in sea-IC time series. In addition, erroneous sea ICs can adversely affect shipping operations.

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Perspectives on future sea ice and navigability in the Arctic

The Cryosphere ◽

10.5194/tc-15-5473-2021 ◽

2021 ◽

Vol 15 (12) ◽

pp. 5473-5482

Author(s):

Jinlei Chen ◽

Shichang Kang ◽

Wentao Du ◽

Junming Guo ◽

Min Xu ◽

...

Keyword(s):

Sea Ice ◽

Coupled Model ◽

Open Water ◽

Circulation Pattern ◽

The Arctic ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Transportation Accessibility ◽

Decadal Rate

Abstract. The retreat of sea ice has been found to be very significant in the Arctic under global warming. It is projected to continue and will have great impacts on navigation. Perspectives on the changes in sea ice and navigability are crucial to the circulation pattern and future of the Arctic. In this investigation, the decadal changes in sea ice parameters were evaluated by the multi-model from the Coupled Model Inter-comparison Project Phase 6, and Arctic navigability was assessed under two shared socioeconomic pathways (SSPs) and two vessel classes with the Arctic transportation accessibility model. The sea ice extent shows a high possibility of decreasing along SSP5-8.5 under current emissions and climate change. The decadal rate of decreasing sea ice extent will increase in March but decrease in September until 2060, when the oldest ice will have completely disappeared and the sea ice will reach an irreversible tipping point. Sea ice thickness is expected to decrease and transit in certain parts, declining by −0.22 m per decade after September 2060. Both the sea ice concentration and volume will thoroughly decline at decreasing decadal rates, with a greater decrease in volume in March than in September. Open water ships will be able to cross the Northern Sea Route and Northwest Passage between August and October during the period from 2045 to 2055, with a maximum navigable percentage in September. The time for Polar Class 6 (PC6) ships will shift to October–December during the period from 2021 to 2030, with a maximum navigable percentage in October. In addition, the central passage will be open for PC6 ships between September and October during 2021–2030.

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Impacts of a lengthening open water season on Alaskan coastal communities

10.5194/tc-2017-211 ◽

2017 ◽

Author(s):

Rebecca J. Rolph ◽

Andrew R. Mahoney ◽

John Walsh ◽

Philip A. Loring

Keyword(s):

Sea Ice ◽

Open Water ◽

Severity Index ◽

Economic Benefits ◽

Coastal Communities ◽

Sea Ice Concentration ◽

Subsistence Hunting ◽

Break Up ◽

Indirect Impacts ◽

The Impact

Abstract. It is often remarked that Arctic coastal communities are on the frontlines of the impacts related to the rapidly diminishing ice pack. These impacts can have direct effects on communities, such as reduced access to subsistence hunting species, or increased wave height and coastal erosion. There are also indirect effects driven by external socioeconomic systems, such as increased maritime activity, which may provide local economic benefits while increasing potential for disruption to subsistence activities. Here, we use the Historical Sea Ice Atlas (HSIA) dataset to assess the potential direct and indirect impacts from sea ice change for selected Alaska communities. The HSIA provides sea ice concentration for the Bering, Chukchi and Beaufort Seas on a 0.25-degree grid for the period 1953–2013. We estimate the timing of freeze-up and break-up, which is reported by local residents to be of critical importance for subsistence hunting activities and food security. We calculate the open water season length and extend the existing timeseries of the Barnett Severity Index (BSI), which assesses the impact of ice conditions on maritime traffic destined for the Beaufort Sea. We find consistent trends toward later freeze-up and earlier break-up, leading to a lengthened open water period. In Utqiavik (formerly Barrow), there is evidence of a navigational regime change in the 1990s when the pack ice edge started to routinely retreat beyond this most northern community.

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Temporal evolution of IP25 and other highly branched isoprenoid lipids in sea ice and the underlying water column during an Arctic melting season

Elem Sci Anth ◽

10.1525/elementa.377 ◽

2019 ◽

Vol 7 ◽

Cited By ~ 4

Author(s):

Rémi Amiraux ◽

Lukas Smik ◽

Denizcan Köseoğlu ◽

Jean-François Rontani ◽

Virginie Galindo ◽

...

Keyword(s):

Sea Ice ◽

Water Column ◽

Spring Bloom ◽

Phytoplankton Bloom ◽

Change Point Analysis ◽

The Arctic ◽

Distribution Data ◽

Ice Melt ◽

Point Analysis ◽

The Impact

In recent years, certain mono- and di-unsaturated highly branched isoprenoid (HBI) alkene biomarkers (i.e., IP25 and HBI IIa) have emerged as useful proxies for sea ice in the Arctic and Antarctic, respectively. Despite the relatively large number of sea ice reconstructions based on IP25 and HBI IIa, considerably fewer studies have addressed HBI variability in sea ice or in the underlying water column during a spring bloom and ice melt season. In this study, we quantified IP25 and various other HBIs at high temporal and vertical resolution in sea ice and the underlying water column (suspended and sinking particulate organic matter) during a spring bloom/ice melt event in Baffin Bay (Canadian Arctic) as part of the Green Edge project. The IP25 data are largely consistent with those reported from some previous studies, but also highlight: (i) the short-term variability in its production in sea ice; (ii) the release of ice algae with high sinking rates following a switch in sea ice conditions from hyper- to hyposaline within the study period; and (iii) the occurrence of an under-ice phytoplankton bloom. Outcomes from change-point analysis conducted on chlorophyll a and IP25, together with estimates of the percentage of ice algal organic carbon in the water column, also support some previous investigations. The co-occurrence of other di- and tri-unsaturated HBIs (including the pelagic biomarker HBI III) in sea ice are likely to have originated from the diatom Berkeleya rutilans and/or the Pleurosigma and Rhizosolenia genera, residing either within the sea ice matrix or on its underside. Although a possible sea ice source for HBIs such as HBI III may also impact the use of such HBIs as pelagic counterparts to IP25 in the phytoplankton marker-IP25 index, we suggest that the impact is likely to be small based on HBI distribution data.

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