scholarly journals Regional-scale phytoplankton dynamics and their association with glacier meltwater runoff in Svalbard

2021 ◽  
Author(s):  
Thorben Dunse ◽  
Kaixing Dong ◽  
Kjetil Schanke Aas ◽  
Leif Christian Stige
Keyword(s):  
Sea Ice ◽  
Chlorophyll A ◽  
Freshwater Discharge ◽  
The Arctic ◽  
Phytoplankton Production ◽  
Phytoplankton Dynamics ◽  
Glacier Meltwater ◽  
Meltwater Runoff ◽  
Glacier Runoff

Abstract. Arctic amplification of global warming has accelerated mass loss of Arctic land ice over the past decades and lead to increased freshwater discharge into glacier fjords and adjacent seas. Glacier freshwater discharge is typically associated with high sediment loads which limits the euphotic depth, but may also provide surface waters with essential nutrients, thus having counter-acting effects on marine productivity. In-situ observations from a few measured fjords across the Arctic indicate that glacier fjords dominated by marine-terminating glaciers are typically more productive than those with only land-terminating glaciers. Here we combine chlorophyll a from satellite ocean colour, an indicator of phytoplankton biomass, with glacier meltwater runoff from climatic mass-balance modelling to establish a statistical model of summertime-phytoplankton dynamics in Svalbard (mid-June to September). Statistical analysis reveals positive spatiotemporal association of chlorophyll a with glacier runoff for 7 out of 14 primary hydrological regions. These regions consist predominantly of the major fjord systems of Svalbard. The adjacent land areas are characterized by a wide range of total glacier coverage (35.5 % to 81.2 %) and fraction of marine-terminating glacier area (40.2 % to 87.4 %). We find that an increase in specific glacier-runoff rate of 10 mm water equivalent per 8-day timeperiod raises summertime chlorophyll a concentrations by 5.2 % to 20.0 %, depending on region. During the annual peak discharge we estimate that glacier runoff contributes to 13.1 % to 50.2 % increase in chlorophyll a compared to situations with no runoff. This suggest that glacier runoff is an important factor sustaining summertime phytoplankton production in Svalbard fjords, in line with findings from several fjords in Greenland. In contrast, for regions bordering open coasts, and beyond 10 km distance from the shore, we do not find significant association of chlorophyll a with runoff. In these regions, physical ocean and sea ice variables control chlorophyll a, pointing at the importance of a late sea ice breakup in northern Svalbard, as well as the advection of Atlantic water masses along the West Spitsbergen Current for summertime phytoplankton dynamics. Our method allows for investigation and monitoring of glacier-runoff effects on primary production throughout the summer season and is applicable on a Pan-Arctic scale, thus complementing valuable but scarce in-situ measurements in both space and time.

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

2014 ◽  
Vol 8 (1) ◽  
pp. 845-885 ◽  
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.

scholarly journals First in situ estimations of small phytoplankton carbon and nitrogen uptake rates in the Kara, Laptev, and East Siberian seas

2018 ◽  
Author(s):  
Bhavya P. Sadanandan ◽  
Jang Han Lee ◽  
Ho Won Lee ◽  
Jae Joong Kaang ◽  
Jae Hyung Lee ◽  
...  
Keyword(s):  
Sea Ice ◽  
Nitrogen Uptake ◽  
N Uptake ◽  
Total Carbon ◽  
The Arctic ◽  
Carbon And Nitrogen ◽  
Uptake Rates ◽  
Wide Range ◽  
Small Phytoplankton

Abstract. Carbon and nitrogen uptake rates by small phytoplankton (0.7–5 μm) in the Kara, Laptev, and East Siberian seas in the Arctic Ocean were quantified using in situ isotope labelling experiments for the first time as part of the NABOS (Nansen and Amundsen Basins Observational System) program during August 21 to September 22, 2013. The depth integrated C, NO3−, and NH4+ uptake rates by small phytoplankton showed a wide range from 0.54 to 15.96 mg C m−2 h−1, 0.05 to 1.02 and 0.11 to 3.73 mg N m−2 h−1, respectively. The contributions of small phytoplankton towards the total C, NO3−, and NH4+ was varied from 24 to 89 %, 32 to 89 %, and 28 to 89 %, respectively. The turnover times for NO3− and NH4+ by small phytoplankton during the present study point towards the longer residence times (years) of the nutrients in the deeper waters, particularly for NO3−. Relatively, higher C and N uptake rates by small phytoplankton obtained during the present study at locations with less sea ice concentrations points towards the possibility of small phytoplankton thrive under sea ice retreat under warming conditions. The high contributions of small phytoplankton towards the total carbon and nitrogen uptake rates suggest capability of small size autotrophs to withstand in the adverse hydrographic conditions introduced by climate change.

scholarly journals Tensile fracture of a single crack in first-year sea ice

2018 ◽  
Vol 376 (2129) ◽  
pp. 20170346 ◽  
Author(s):  
J. P. Dempsey ◽  
D. M. Cole ◽  
S. Wang
Keyword(s):  
Sea Ice ◽  
Field Trips ◽  
Cohesive Crack ◽  
The Arctic ◽  
Tensile Fracture ◽  
First Year ◽  
Crack Model ◽  
Separation Curve ◽  
Ice Phenomena

The break-up of sea ice in the Arctic and Antarctic has been studied during three field trips in the spring of 1993 at Resolute, NWT, and the fall of 2001 and 2004 on McMurdo Sound via in situ cyclic loading and fracture experiments. In this paper, the back-calculated fracture information necessary to the specification of an accurate viscoelastic fictitious (cohesive) crack model is presented. In particular, the changing shape of the stress separation curve with varying conditions and loading scenarios is revealed. This article is part of the theme issue ‘Modelling of sea-ice phenomena’.

scholarly journals Reflective properties of melt ponds on sea ice

2018 ◽  
Vol 12 (6) ◽  
pp. 1921-1937 ◽  
Author(s):  
Aleksey Malinka ◽  
Eleonora Zege ◽  
Larysa Istomina ◽  
Georg Heygster ◽  
Gunnar Spreen ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Sea Ice ◽  
Atmospheric Correction ◽  
Scattering Coefficient ◽  
Spherical Particles ◽  
The Arctic ◽  
Melt Pond ◽  
Melt Ponds ◽  
Bottom Albedo

Abstract. Melt ponds occupy a large part of the Arctic sea ice in summer and strongly affect the radiative budget of the atmosphere–ice–ocean system. In this study, the melt pond reflectance is considered in the framework of radiative transfer theory. The melt pond is modeled as a plane-parallel layer of pure water upon a layer of sea ice (the pond bottom). We consider pond reflection as comprising Fresnel reflection by the water surface and multiple reflections between the pond surface and its bottom, which is assumed to be Lambertian. In order to give a description of how to find the pond bottom albedo, we investigate the inherent optical properties of sea ice. Using the Wentzel–Kramers–Brillouin approximation approach to light scattering by non-spherical particles (brine inclusions) and Mie solution for spherical particles (air bubbles), we conclude that the transport scattering coefficient in sea ice is a spectrally independent value. Then, within the two-stream approximation of the radiative transfer theory, we show that the under-pond ice spectral albedo is determined by two independent scalar values: the transport scattering coefficient and ice layer thickness. Given the pond depth and bottom albedo values, the bidirectional reflectance factor (BRF) and albedo of a pond can be calculated with analytical formulas. Thus, the main reflective properties of the melt pond, including their spectral dependence, are determined by only three independent parameters: pond depth z, ice layer thickness H, and transport scattering coefficient of ice σt.The effects of the incident conditions and the atmosphere state are examined. It is clearly shown that atmospheric correction is necessary even for in situ measurements. The atmospheric correction procedure has been used in the model verification. The optical model developed is verified with data from in situ measurements made during three field campaigns performed on landfast and pack ice in the Arctic. The measured pond albedo spectra were fitted with the modeled spectra by varying the pond parameters (z, H, and σt). The coincidence of the measured and fitted spectra demonstrates good performance of the model: it is able to reproduce the albedo spectrum in the visible range with RMSD that does not exceed 1.5 % for a wide variety of melt pond types observed in the Arctic.

scholarly journals Impact of sea ice on the marine iron cycle and phytoplankton productivity

2014 ◽  
Vol 11 (17) ◽  
pp. 4713-4731 ◽  
Author(s):  
S. Wang ◽  
D. Bailey ◽  
K. Lindsay ◽  
J. K. Moore ◽  
M. Holland
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Phytoplankton Production ◽  
Iron Cycle ◽  
Surface Ocean ◽  
Antarctic Sea Ice ◽  
Iron Concentrations

Abstract. Iron is a key nutrient for phytoplankton growth in the surface ocean. At high latitudes, the iron cycle is closely related to the dynamics of sea ice. In recent decades, Arctic sea ice cover has been declining rapidly and Antarctic sea ice has exhibited large regional trends. A significant reduction of sea ice in both hemispheres is projected in future climate scenarios. In order to adequately study the effect of sea ice on the polar iron cycle, sea ice bearing iron was incorporated in the Community Earth System Model (CESM). Sea ice acts as a reservoir for iron during winter and releases the trace metal to the surface ocean in spring and summer. Simulated iron concentrations in sea ice generally agree with observations in regions where iron concentrations are relatively low. The maximum iron concentrations simulated in Arctic and Antarctic sea ice are much lower than observed, which is likely due to underestimation of iron inputs to sea ice or missing mechanisms. The largest iron source to sea ice is suspended sediments, contributing fluxes of iron of 2.2 × 108 mol Fe month−1 in the Arctic and 4.1 × 106 mol Fe month−1 in the Southern Ocean during summer. As a result of the iron flux from ice, iron concentrations increase significantly in the Arctic. Iron released from melting ice increases phytoplankton production in spring and summer and shifts phytoplankton community composition in the Southern Ocean. Results for the period of 1998 to 2007 indicate that a reduction of sea ice in the Southern Ocean will have a negative influence on phytoplankton production. Iron transport by sea ice appears to be an important process bringing iron to the central Arctic. The impact of ice to ocean iron fluxes on marine ecosystems is negligible in the current Arctic Ocean, as iron is not typically the growth-limiting nutrient. However, it may become a more important factor in the future, particularly in the central Arctic, as iron concentrations will decrease with declining sea ice cover and transport.

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

2012 ◽  
Vol 9 (2) ◽  
pp. 1009-1043 ◽  
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.

scholarly journals Impacts of sea ice on the marine iron cycle and phytoplankton productivity

2014 ◽  
Vol 11 (2) ◽  
pp. 2383-2418 ◽  
Author(s):  
S. Wang ◽  
D. Bailey ◽  
K. Lindsay ◽  
K. Moore ◽  
M. Holland
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Phytoplankton Production ◽  
Iron Cycle ◽  
Surface Ocean ◽  
Antarctic Sea Ice ◽  
Arctic Sea ◽  
Iron Concentrations

Abstract. Iron is a key nutrient for phytoplankton growth in the surface ocean. At high latitudes, the iron cycle is closely related to sea ice. In recent decades, Arctic sea ice cover has been declining rapidly and Antarctic sea ice has exhibited large regional trends. A significant reduction of sea ice in both hemispheres is projected in future climate scenarios. To study impacts of sea ice on the iron cycle, iron sequestration in ice is incorporated to the Biogeochemical Elemental Cycling (BEC) model. Sea ice acts as a reservoir of iron during winter and releases iron to the surface ocean in spring and summer. Simulated iron concentrations in sea ice generally agree with observations, in regions where iron concentrations are lower. The maximum iron concentrations simulated in the Arctic sea ice and the Antarctic sea ice are 192 nM and 134 nM, respectively. These values are much lower than observed, which is likely due to missing biological processes in sea ice. The largest iron source to sea ice is suspended sediments, contributing fluxes of iron of 2.2 × 108 mol Fe month−1 to the Arctic and 4.1 × 106 mol Fe month−1 to the Southern Ocean during summer. As a result of the iron flux from ice, iron concentrations increase significantly in the Arctic. Iron released from melting ice increases phytoplankton production in spring and summer and shifts phytoplankton community composition in the Southern Ocean. Simulation results for the period of 1998 to 2007 indicate that a reduction of sea ice in the Southern Ocean will have a negative influence on phytoplankton production. Iron transport by sea ice appears to be an important process bringing iron to the central Arctic. Impacts of iron fluxes from ice to ocean on marine ecosystems are negligible in the current Arctic Ocean, as iron is not typically the growth-limiting nutrient. However, it may become a more important factor in the future, particularly in the central Arctic, as iron concentrations will decrease with declining sea ice cover and transport.

scholarly journals First in situ estimations of small phytoplankton carbon and nitrogen uptake rates in the Kara, Laptev, and East Siberian seas

2018 ◽  
Vol 15 (18) ◽  
pp. 5503-5517 ◽  
Author(s):  
P. Sadanandan Bhavya ◽  
Jang Han Lee ◽  
Ho Won Lee ◽  
Jae Joong Kang ◽  
Jae Hyung Lee ◽  
...  
Keyword(s):  
Sea Ice ◽  
Nitrogen Uptake ◽  
Isotope Labeling ◽  
N Uptake ◽  
The Arctic ◽  
Carbon And Nitrogen ◽  
Uptake Rates ◽  
Small Phytoplankton ◽  
C And N

Abstract. Carbon and nitrogen uptake rates by small phytoplankton (0.7–5 µm) in the Kara, Laptev, and East Siberian seas in the Arctic Ocean were quantified using in situ isotope labeling experiments; this research, which was novel and part of the NABOS (Nansen and Amundsen Basins Observational System) program, took place from 21 August to 22 September 2013. The depth-integrated carbon (C), nitrate (NO3-), and ammonium (NH4+) uptake rates by small phytoplankton ranged from 0.54 to 15.96 mg C m−2 h−1, 0.05 to 1.02 mg C m−2 h−1, and 0.11 to 3.73 mg N m−2 h−1, respectively. The contributions of small phytoplankton towards the total C, NO3-, and NH4+ varied from 25 % to 89 %, 31 % to 89 %, and 28 % to 91 %, respectively. The turnover times for NO3- and NH4+ by small phytoplankton found in the present study indicate the longer residence times (years) of the nutrients in the deeper waters, particularly for NO3-. Additionally, the relatively higher C and N uptake rates by small phytoplankton obtained in the present study from locations with less sea ice concentration indicate the possibility that small phytoplankton thrive under the retreat of sea ice as a result of warming conditions. The high contributions of small phytoplankton to the total C and N uptake rates suggest the capability of small autotrophs to withstand the adverse hydrographic conditions introduced by climate change.

scholarly journals Ocean acidification state in western Antarctic surface waters: drivers and interannual variability

2013 ◽  
Vol 10 (5) ◽  
pp. 7879-7916 ◽  
Author(s):  
M. Mattsdotter Björk ◽  
A. Fransson ◽  
M. Chierici
Keyword(s):  
Surface Water ◽  
Sea Ice ◽  
Interannual Variability ◽  
Chlorophyll A ◽  
Total Alkalinity ◽  
Full Description ◽  
Carbonate System ◽  
Amundsen Sea ◽  
Water Carbonate

Abstract. Each December during four years from 2006 to 2010, the surface water carbonate system was measured and investigated in the Amundsen Sea and Ross Sea, western Antarctica as part of the Oden Southern Ocean expeditions (OSO). The I/B Oden started in Punta Arenas in Chile and sailed southwest, passing through different regimes such as, the marginal/seasonal ice zone, fronts, coastal shelves, and polynyas. Discrete surface water was sampled underway for analysis of total alkalinity (AT), total dissolved inorganic carbon (CT) and pH. Two of these parameters were used together with sea-surface temperature (SST), and salinity to obtain a full description of the surface water carbonate system, including pH in situ and calcium carbonate saturation state of aragonite (ΩAr) and calcite (ΩCa). Multivariate analysis was used to investigate interannual variability and the major controls (sea-ice concentration, SST, salinity and chlorophyll a) on the variability in the carbonate system and Ω. This analysis showed that SST and chlorophyll a were the major drivers of the Ω variability in both the Amundsen and Ross seas. In 2007, the sea-ice edge was located further south and the area of the open polynya was relatively small compared to 2010. We found the lowest pH in situ (7.932) and Ω = 1 values in the sea-ice zone and in the coastal Amundsen Sea, nearby marine out flowing glaciers. In 2010, the sea-ice coverage was the largest and the areas of the open polynyas were the largest for the whole period. This year we found the lowest salinity and AT, coinciding with highest chl a. This implies that the highest ΩAr in 2010 was likely an effect of biological CO2 drawdown, which out-competed the dilution of carbonate ion concentration due to large melt water volumes. We predict and discuss future Ω values, using our data and reported rates of oceanic uptake of anthropogenic CO2, suggesting that the Amundsen Sea will become undersaturated with regard to aragonite about 20 yr sooner than predicted by models.

scholarly journals Advances in altimetric snow depth estimates using bi-frequency SARAL/CryoSat-2 Ka/Ku measurements

2021 ◽  
Author(s):  
Florent Garnier ◽  
Sara Fleury ◽  
Gilles Garric ◽  
Jérôme Bouffard ◽  
Michel Tsamados ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ocean Modeling ◽  
The Arctic ◽  
Validation Data ◽  
Band Frequency ◽  
In Situ Data ◽  
Wide Range ◽  
Assimilation System

Abstract. Although snow depth on sea ice is a key parameter for Sea Ice Thickness (SIT), there currently does not exist reliable estimations. In Arctic, nearly all SIT products use a snow depth climatology (the Warren-99 modified climatology, W99m) constructed from in-situ data obtained prior to the first significant impacts of climate change. In Antarctica, the lack of information on snow depth remains a major obstacle in the development of reliable SIT products. In this study, we present the latest version of the Altimetric Snow Depth (ASD) product computed over both hemispheres from the difference of the radar penetration into the snow pack between the CryoSat-2 Ku-band and the SARAL Ka-band frequency radars. The ASD solution is compared against a wide range of snow depth products including model data (Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) or its equivalent in Antarctica the Global Ice-Ocean Modeling and Assimilation System (GIOMAS), the MERCATOR model and NASA's Eulerian Snow On Sea Ice Model (NESOSIM, only in Arctic)), the Advanced Microwave Scanning Radiometer 2 (AMSR-2) passive radiometer data, and the Dual-altimeter Snow Thickness (DuST) Ka-Ku product (only in Arctic). It is validated in the Arctic against in-situ and airborne validation data. These comparisons demonstrate that ASD provide a consistent snow depth solution, with space and time patterns comparable with those of the alternative Ka-Ku DuST product, but with a mean bias of about 6.5 cm. We also demonstrate that ASD is consistent with the validation data. Comparisons with Operation Ice Bridge's (OIB) airborne snow radar in Arctic during the period of 2014–2018 show a correlation of 0.66 and a RMSE of about 6 cm. Furthermore, a first-guess monthly climatology has been constructed in Arctic from the ASD product, which shows a good agreement with OIB during 2009–2012. This climatology is shown to provide a better solution than the W99m climatology when compared with validation data. Finally, we have characterised the SIT uncertainty due to the snow depth from an ensemble of SIT solutions computed for the Arctic by using the different snow depth products previously used in the comparison with the ASD product. During the period of 2013–2019, we found a spatially averaged SIT mean standard deviation of 20 cm. Deviations between SIT estimations due to different snow depths can reach up to 77 cm. Using the ASD data instead of W99m to estimate SIT over this time period leads to a reduction of the average SIT of about 30 cm.

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