Stress factors resulting from the Arctic vernal sea-ice melt

During sea-ice melt in the Arctic, primary production by sympagic (sea-ice) algae can be exported efficiently to the seabed if sinking rates are rapid and activities of associated heterotrophic bacteria are limited. Salinity stress due to melting ice has been suggested to account for such low bacterial activity. We further tested this hypothesis by analyzing samples of sea ice and sinking particles collected from May 18 to June 29, 2016, in western Baffin Bay as part of the Green Edge project. We applied a method not previously used in polar regions—quantitative PCR coupled to the propidium monoazide DNA-binding method—to evaluate the viability of bacteria associated with sympagic and sinking algae. We also measured cis-trans isomerase activity, known to indicate rapid bacterial response to salinity stress in culture studies, as well as free fatty acids known to be produced by algae as bactericidal compounds. The viability of sympagic-associated bacteria was strong in May (only approximately 10% mortality of total bacteria) and weaker in June (average mortality of 43%; maximum of 75%), with instances of elevated mortality in sinking particle samples across the time series (up to 72%). Short-term stress reflected by cis-trans isomerase activity was observed only in samples of sinking particles collected early in the time series. Following snow melt, however, and saturating levels of photosynthetically active radiation in June, we observed enhanced ice-algal production of bactericidal compounds (free palmitoleic acid; up to 4.8 mg L–1). We thus suggest that protection of sinking sympagic material from bacterial degradation early in a melt season results from low bacterial activity due to salinity stress, while later in the season, algal production of bactericidal compounds induces bacterial mortality. A succession of bacterial stressors during Arctic ice melt helps to explain the efficient export of sea-ice algal material to the seabed.

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Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

Remote Sensing ◽

10.3390/rs13112174 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2174

Author(s):

Lijian Shi ◽

Sen Liu ◽

Yingni Shi ◽

Xue Ao ◽

Bin Zou ◽

...

Keyword(s):

Time Series ◽

Sea Ice ◽

Ocean Circulation ◽

The Arctic ◽

Retrieval Algorithm ◽

Atmospheric Effects ◽

Observation Data ◽

Long Time Series ◽

Ice Concentration ◽

Long Time

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

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Export of calcium carbonate corrosive waters from the East Siberian Sea

10.5194/bg-2016-478 ◽

2016 ◽

Author(s):

Leif G. Anderson ◽

Jörgen Ek ◽

Ylva Ericson ◽

Christoph Humborg ◽

Igor Semiletov ◽

...

Keyword(s):

Calcium Carbonate ◽

Sea Ice ◽

North Atlantic Ocean ◽

The Arctic ◽

High Pco2 ◽

Low Oxygen ◽

Saturation State ◽

Ice Melt ◽

The North ◽

Transformation Of Organic Matter

Abstract. The Siberian Shelf Seas are areas of extensive biogeochemical transformation of organic matter, both of marine and terrestrial origin. This in combination with brine production from sea ice formation results in a cold bottom water of relative high salinity and partial pressure of carbon dioxide (pCO2). Data from the SWERUS-C3 expedition compiled on the icebreaker Oden in July to September 2014 show the distribution of such waters at the outer shelf, as well as their export into the deep central Arctic basins. Very high pCO2 water, up to close to 1000 µatm, was observed associated with high nutrients and low oxygen concentrations. Consequently the saturation state of calcium carbonate was low, down to less than 0.8 for calcite and 0.5 for aragonite. Waters undersaturated in aragonite were also observed in the surface in waters at equilibrium with atmospheric CO2, however, at these conditions the cause of under-saturation was low salinity from river runoff and/or sea ice melt. The calcium carbonate corrosive water was observed all along the continental margin and well out into the deep Makarov and Canada Basins at a depth from about 50 m depth in the west to about 150 m in the east. These waters of low aragonite saturation state are traced in historic data to the Canada Basin and in the waters flowing out of the Arctic Ocean north of Greenland and in the western Fram Strait, thus potentially impacting the marine life in the North Atlantic Ocean.

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Sea ice melt pond fraction estimation from dual-polarisation C-band SAR – Part 2: Scaling in situ to Radarsat-2

The Cryosphere Discussions ◽

10.5194/tcd-8-845-2014 ◽

2014 ◽

Vol 8 (1) ◽

pp. 845-885 ◽

Cited By ~ 1

Author(s):

R. K. Scharien ◽

K. Hochheim ◽

J. Landy ◽

D. G. Barber

Keyword(s):

Sea Ice ◽

Large Scale ◽

Temporal Frequency ◽

Model Performance ◽

Free Water ◽

Light Transmittance ◽

The Arctic ◽

Ice Melt ◽

Melt Pond

Abstract. Observed changes in the Arctic have motivated efforts to understand and model its components as an integrated and adaptive system at increasingly finer scales. Sea ice melt pond fraction, an important summer sea ice component affecting surface albedo and light transmittance across the ocean-sea ice–atmosphere interface, is inadequately parameterized in models due to a lack of large scale observations. In this paper, results from a multi-scale remote sensing program dedicated to the retrieval of pond fraction from satellite C-band synthetic aperture radar (SAR) are detailed. The study was conducted on first-year sea (FY) ice in the Canadian Arctic Archipelago during the summer melt period in June 2012. Approaches to retrieve the subscale FY ice pond fraction from mixed pixels in RADARSAT-2 imagery, using in situ, surface scattering theory, and image data are assessed. Each algorithm exploits the dominant effect of high dielectric free-water ponds on the VV/HH polarisation ratio (PR) at moderate to high incidence angles (about 40° and above). Algorithms are applied to four images corresponding to discrete stages of the seasonal pond evolutionary cycle, and model performance is assessed using coincident pond fraction measurements from partitioned aerial photos. A RMSE of 0.07, across a pond fraction range of 0.10 to 0.70, is achieved during intermediate and late seasonal stages. Weak model performance is attributed to wet snow (pond formation) and synoptically driven pond freezing events (all stages), though PR has utility for identification of these events when considered in time series context. Results demonstrate the potential of wide-swath, dual-polarisation, SAR for large-scale observations of pond fraction with temporal frequency suitable for process-scale studies and improvements to model parameterizations.

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Spring Arctic Atmospheric Preconditioning: Do Not Rule Out Shortwave Radiation Just Yet

Journal of Climate ◽

10.1175/jcli-d-17-0710.1 ◽

2018 ◽

Vol 31 (11) ◽

pp. 4225-4240 ◽

Cited By ~ 3

Author(s):

Joseph Sedlar

Keyword(s):

Sea Ice ◽

Surface Albedo ◽

Atmospheric Transport ◽

High Surface ◽

Longwave Radiation ◽

Absolute Magnitude ◽

Arctic Sea Ice ◽

Shortwave Radiation ◽

The Arctic ◽

Ice Melt

Abstract Springtime atmospheric preconditioning of Arctic sea ice for enhanced or buffered sea ice melt during the subsequent melt year has received considerable research focus. Studies have identified enhanced poleward atmospheric transport of moisture and heat during spring, leading to increased emission of longwave radiation to the surface. Simultaneously, these studies ruled out the role of shortwave radiation as an effective preconditioning mechanism because of relatively weak incident solar radiation, high surface albedo from sea ice and snow, and increased clouds during spring. These conclusions are derived primarily from atmospheric reanalysis, which may not always accurately represent the Arctic climate system. Here, top-of-atmosphere shortwave radiation observations from a state-of-the-art satellite sensor are compared with ERA-Interim reanalysis to examine similarities and differences in the springtime absorbed shortwave radiation (ASR) over the Arctic Ocean. Distinct biases in regional location and absolute magnitude of ASR anomalies are found between satellite-based measurements and reanalysis. Observations indicate separability between ASR anomalies in spring corresponding to anomalously low and high ice extents in September; the reanalysis fails to capture the full extent of this separability. The causes for the difference in ASR anomalies between observations and reanalysis are considered in terms of the variability in surface albedo and cloud presence. Additionally, biases in reanalysis cloud water during spring are presented and are considered for their impact on overestimating spring downwelling longwave anomalies. Taken together, shortwave radiation should not be overlooked as a contributing mechanism to springtime Arctic atmospheric preconditioning.

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Chytrid fungi distribution and co-occurrence with diatoms correlate with sea ice melt in the Arctic Ocean

Communications Biology ◽

10.1038/s42003-020-0891-7 ◽

2020 ◽

Vol 3 (1) ◽

Author(s):

Estelle S. Kilias ◽

Leandro Junges ◽

Luka Šupraha ◽

Guy Leonard ◽

Katja Metfies ◽

...

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

The Arctic ◽

Ice Melt ◽

The Arctic Ocean

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Arctic Ocean Sea Ice Area Extent Cyclicity and Non-Stationarity

10.5194/egusphere-egu2020-5915 ◽

2020 ◽

Author(s):

Reginald Muskett ◽

Syun-Ichi Akasofu

Keyword(s):

Time Series ◽

Sea Ice ◽

Wave Pattern ◽

Arctic Sea Ice ◽

The Arctic ◽

Weather System ◽

Sea Ice Extent ◽

Temperature Variations ◽

Arctic Sea ◽

Arctic Temperature

Arctic sea ice is a key component of the Arctic hydrologic cycle. This cycle is connected to land and ocean temperature variations and Arctic snow cover variations, spatially and temporally. Arctic temperature variations from historical observations shows an early 20th century increase (i.e. warming), followed by a period of Arctic temperature decrease (i.e. cooling) since the 1940s, which was followed by another period of Arctic temperature increase since the 1970s that continues into the two decades of the 21st century. Evidence has been accumulating that Arctic sea ice extent can experience multi-decadal to centennial time scale variations as it is a component of the Arctic Geohydrological System.&#160; We investigate the multi-satellite and sensor daily values of area extent of Arctic sea ice since SMMR on Nimbus 7 (1978) to AMSR2 on GCOM-W1 (2019). From the daily time series we use the first year-cycle as a wave-pattern to compare to all subsequent years-cycles through April 2020 (in progress), and constitute a derivative time series. In this time series we find the emergence of a multi-decadal cycle, showing a relative minimum during the period of 2007 to 2014, and subsequently rising. This may be related to an 80-year cycle (hypothesis). The Earth&#8217;s weather system is principally driven the solar radiation and its variations. If the multi-decadal cycle in Arctic sea ice area extent that we interpret continues, it may be linked physically to the Wolf-Gleissberg cycle, a factor in the variations of terrestrial cosmogenic isotopes, ocean sediment layering and glacial varves, ENSO and Aurora.Our hypothesis and results give more evidence that the multi-decadal variation of Arctic sea ice area extent is controlled by natural physical processes of the Sun-Earth system.&#160;

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A simple approach to providing a more consistent Arctic sea ice extent time series from the 1950s to present

The Cryosphere ◽

10.5194/tc-6-1359-2012 ◽

2012 ◽

Vol 6 (6) ◽

pp. 1359-1368 ◽

Cited By ~ 25

Author(s):

W. N. Meier ◽

J. Stroeve ◽

A. Barrett ◽

F. Fetterer

Keyword(s):

Time Series ◽

Sea Ice ◽

Linear Trend ◽

Statistical Significance ◽

Recent Decade ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Extent ◽

Arctic Sea ◽

Combined Time Series

Abstract. Observations from passive microwave satellite sensors have provided a continuous and consistent record of sea ice extent since late 1978. Earlier records, compiled from ice charts and other sources exist, but are not consistent with the satellite record. Here, a method is presented to adjust a compilation of pre-satellite sources to remove discontinuities between the two periods and create a more consistent combined 59-yr time series spanning 1953–2011. This adjusted combined time series shows more realistic behavior across the transition between the two individual time series and thus provides higher confidence in trend estimates from 1953 through 2011. The long-term time series is used to calculate linear trend estimates and compare them with trend estimates from the satellite period. The results indicate that trends through the 1960s were largely positive (though not statistically significant) and then turned negative by the mid-1970s and have been consistently negative since, reaching statistical significance (at the 95% confidence level) by the late 1980s. The trend for September (when Arctic extent reaches its seasonal minimum) for the satellite period, 1979–2011 is −12.9% decade−1, nearly double the 1953–2011 trend of −6.8% decade−1 (percent relative to the 1981–2010 mean). The recent decade (2002–2011) stands out as a period of persistent decline in ice extent. The combined 59-yr time series puts the strong observed decline in the Arctic sea ice cover during 1979–2011 in a longer-term context and provides a useful resource for comparisons with historical model estimates.

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Time and space variability of freshwater content, heat content and seasonal ice melt in the Arctic Ocean from 1991 to 2011

Ocean Science Discussions ◽

10.5194/osd-9-2621-2012 ◽

2012 ◽

Vol 9 (4) ◽

pp. 2621-2677 ◽

Cited By ~ 6

Author(s):

M. Korhonen ◽

B. Rudels ◽

M. Marnela ◽

A. Wisotzki ◽

J. Zhao

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Mixed Layer ◽

Heat Content ◽

The Arctic ◽

Upper Ocean ◽

Heat Sources ◽

Time And Space ◽

Ice Melt ◽

The Arctic Ocean

Abstract. The Arctic Ocean gains freshwater mainly through river discharge, precipitation and the inflowing low salinity waters from the Pacific Ocean. In addition the recent reduction in sea ice volume is likely to influence the surface salinity and thus contribute to the freshwater content in the upper ocean. The present day freshwater storage in the Arctic Ocean appears to be sufficient to maintain the upper ocean stratification and to protect the sea ice from the deep ocean heat content. The recent freshening has not, despite the established strong stratification, been able to restrain the accelerating ice loss and other possible heat sources besides the Atlantic Water, such as the waters advecting from the Pacific Ocean and the solar insolation warming the Polar Mixed Layer, are investigated. Since the ongoing freshening, oceanic heat sources and the sea ice melt are closely related, this study, based on hydrographic observations, attempts to examine the ongoing variability in time and space in relation to these three properties. The largest time and space variability of freshwater content occurs in the Polar Mixed Layer and the upper halocline. The freshening of the upper ocean during the 2000s is ubiquitous in the Arctic Ocean although the most substantial increase occurs in the Canada Basin where the freshwater is accumulating in the thickening upper halocline. Whereas the salinity of the upper halocline is nearly constant, the freshwater content in the Polar Mixed Layer is increasing due to decreasing salinity. The decrease in salinity is likely to result from the recent changes in ice formation and melting. In contrast, in the Eurasian Basin where the seasonal ice melt has remained rather modest, the freshening of both the Polar Mixed Layer and the upper halocline is mainly of advective origin. While the warming of the Atlantic inflow was widespread in the Arctic Ocean during the 1990s, the warm and saline inflow events in the early 2000s appear to circulate mainly in the Nansen Basin. Nevertheless, even in the Nansen Basin the seasonal ice melt appears independent of the continuously increasing heat content in the Atlantic layer. As no other oceanic heat sources can be identified in the upper layers, it is likely that increased absorption of solar energy has been causing the ice melt prior to the observations.

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

10.5194/tc-2015-202 ◽

2016 ◽

Cited By ~ 2

Author(s):

S. Kern ◽

A. Rösel ◽

L. T. Pedersen ◽

N. Ivanova ◽

R. Saldo ◽

...

Keyword(s):

Sea Ice ◽

Open Water ◽

The Arctic ◽

Sea Ice Concentration ◽

Water Fraction ◽

Microwave Brightness Temperature ◽

Ice Melt ◽

Melt Ponds ◽

Retrieval Algorithms ◽

The Impact

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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