Influence of sea ice concentration on phytoplankton community structure in the Chukchi and East Siberian Seas, Pacific Arctic Ocean

Abstract. This study assesses the response of phytoplankton assemblages to recent climate change, especially with regard to the shrinking of sea ice in the northern Chukchi Sea of the western Arctic Ocean. Distribution patterns of phytoplankton groups in the late summers of 2008–2010 were analysed based on HPLC pigment signatures and, the following four major algal groups were inferred via multiple regression and cluster analyses: prasinophytes, diatoms, haptophytes and dinoflagellates. A remarkable interannual difference in the distribution pattern of the groups was found in the northern basin area. Haptophytes dominated and dispersed widely in warm surface waters in 2008, whereas prasinophytes dominated in cold water in 2009 and 2010. A difference in the onset date of sea ice retreat was evident among years–the sea ice retreat in 2008 was 1–2 months earlier than in 2009 and 2010. The spatial distribution of early sea ice retreat matched the areas in which a shift in algal community composition was observed. Steel-Dwass's multiple comparison tests were used to assess the physical, chemical and biological parameters of the four clusters. We found a statistically significant difference in temperature between the haptophyte-dominated cluster and the other clusters, suggesting that the change in the phytoplankton communities was related to the earlier sea ice retreat in 2008 and the corollary increase in sea surface temperatures. Longer periods of open water during the summer, which are expected in the future, may affect food webs and biogeochemical cycles in the western Arctic due to shifts in phytoplankton community structure.

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Timing of sea ice retreat can alter phytoplankton community structure in the western Arctic Ocean

Biogeosciences Discussions ◽

10.5194/bgd-10-15153-2013 ◽

2013 ◽

Vol 10 (9) ◽

pp. 15153-15180 ◽

Cited By ~ 1

Author(s):

A. Fujiwara ◽

T. Hirawake ◽

K. Suzuki ◽

I. Imai ◽

S.-I. Saitoh

Keyword(s):

Community Structure ◽

Sea Ice ◽

Arctic Ocean ◽

Phytoplankton Community ◽

Phytoplankton Community Structure ◽

Western Arctic Ocean ◽

Phytoplankton Communities ◽

Ice Retreat ◽

Western Arctic ◽

Sea Ice Retreat

Abstract. This study assesses the response of phytoplankton assemblages to recent climate change, especially with regard to the shrinking of sea ice in the northern Chukchi Sea of the western Arctic Ocean. Distribution patterns of phytoplankton groups in the late summers of 2008–2010 were analyzed based on HPLC pigment signatures and, the following four major algal groups were inferred via multiple regression and cluster analyses: prasinophytes, diatoms, haptophytes and dinoflagellates. A remarkable interannual difference in the distribution pattern of the groups was found in the northern basin area. Haptophytes dominated and dispersed widely in warm surface waters in 2008, whereas prasinophytes dominated in cold water in 2009 and 2010. A difference in the onset date of sea ice retreat was evident among years – the sea ice retreat in 2008 was 1–2 months earlier than in 2009 and 2010. The spatial distribution of early sea ice retreat matched the areas in which a shift in algal community composition was observed. Steel-Dwass's multiple comparison tests were used to assess the physical, chemical and biological parameters of the four clusters. We found a statistically significant difference in temperature between the haptophyte-dominated cluster and the other clusters, suggesting that the change in the phytoplankton communities was related to the earlier sea ice retreat in 2008 and the corollary increase in sea surface temperatures. Longer periods of open water during the summer, which are expected in the future, may affect food webs and biogeochemical cycles in the western Arctic due to shifts in phytoplankton community structure.

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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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Response of ocean phytoplankton community structure to climate change over the 21st century: partitioning the effects of nutrients, temperature and light

Biogeosciences ◽

10.5194/bg-7-3941-2010 ◽

2010 ◽

Vol 7 (12) ◽

pp. 3941-3959 ◽

Cited By ~ 96

Author(s):

I. Marinov ◽

S. C. Doney ◽

I. D. Lima

Keyword(s):

Climate Change ◽

Community Structure ◽

Sea Ice ◽

Phytoplankton Biomass ◽

Phytoplankton Community ◽

Vertical Mixing ◽

Phytoplankton Community Structure ◽

Marginal Sea ◽

Small Phytoplankton ◽

The Impact

Abstract. The response of ocean phytoplankton community structure to climate change depends, among other factors, upon species competition for nutrients and light, as well as the increase in surface ocean temperature. We propose an analytical framework linking changes in nutrients, temperature and light with changes in phytoplankton growth rates, and we assess our theoretical considerations against model projections (1980–2100) from a global Earth System model. Our proposed "critical nutrient hypothesis" stipulates the existence of a critical nutrient threshold below (above) which a nutrient change will affect small phytoplankton biomass more (less) than diatom biomass, i.e. the phytoplankton with lower half-saturation coefficient K are influenced more strongly in low nutrient environments. This nutrient threshold broadly corresponds to 45° S and 45° N, poleward of which high vertical mixing and inefficient biology maintain higher surface nutrient concentrations and equatorward of which reduced vertical mixing and more efficient biology maintain lower surface nutrients. In the 45° S–45° N low nutrient region, decreases in limiting nutrients – associated with increased stratification under climate change – are predicted analytically to decrease more strongly the specific growth of small phytoplankton than the growth of diatoms. In high latitudes, the impact of nutrient decrease on phytoplankton biomass is more significant for diatoms than small phytoplankton, and contributes to diatom declines in the northern marginal sea ice and subpolar biomes. In the context of our model, climate driven increases in surface temperature and changes in light are predicted to have a stronger impact on small phytoplankton than on diatom biomass in all ocean domains. Our analytical predictions explain reasonably well the shifts in community structure under a modeled climate-warming scenario. Climate driven changes in nutrients, temperature and light have regionally varying and sometimes counterbalancing impacts on phytoplankton biomass and structure, with nutrients and temperature dominant in the 45° S–45° N band and light-temperature effects dominant in the marginal sea-ice and subpolar regions. As predicted, decreases in nutrients inside the 45° S–45° N "critical nutrient" band result in diatom biomass decreasing more than small phytoplankton biomass. Further stratification from global warming could result in geographical shifts in the "critical nutrient" threshold and additional changes in ecology.

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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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Impact of the Assimilation of Sea Ice Concentration Data on an Atmosphere-Ocean-Sea Ice Coupled Simulation of the Arctic Ocean Climate

SOLA ◽

10.2151/sola.2011-010 ◽

2011 ◽

Vol 7 ◽

pp. 37-40 ◽

Cited By ~ 5

Author(s):

Takahiro Toyoda ◽

Toshiyuki Awaji ◽

Nozomi Sugiura ◽

Shuhei Masuda ◽

Hiromichi Igarashi ◽

...

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

The Arctic ◽

Coupled Simulation ◽

Concentration Data ◽

Sea Ice Concentration ◽

Ocean Climate ◽

Ice Concentration ◽

The Arctic Ocean

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Retrieval of Arctic sea ice freeboard from passive microwave data using deep neural network

10.5194/egusphere-egu2020-2254 ◽

2020 ◽

Author(s):

Junhwa Chi ◽

Hyun-Cheol Kim ◽

Sung Jae Lee

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Arctic Sea Ice ◽

Passive Microwave ◽

Retrieval Algorithm ◽

Sea Ice Concentration ◽

Ice Volume ◽

Arctic Sea ◽

Ice Concentration ◽

Microwave Sensors

<p>Changes in Arctic sea ice cover represent one of the most visible indicators of climate change. While changes in sea ice extent affect the albedo, changes in sea ice volume explain changes in the heat budget and the exchange of fresh water between ice and the ocean. Global climate simulations predict that Arctic sea ice will exhibit a more significant change in volume than extent. Satellite observations show a long-term negative trend in Arctic sea ice&#160; during all seasons, particularly in summer. Sea ice volume has been estimated by ICESat and CryoSat-2 satellites, and then NASA&#8217;s second-generation spaceborne lidar mission, ICESat-2 has successfully been launched in 2018. &#160;Although these sensors can measure sea ice freeboard precisely, long revisit cycles and narrow swaths are problematic for monitoring of the freeboard in the entire of Arctic ocean effectively. Passive microwave sensors are widely used in retrieval of sea ice concentration. Because of the capability of high temporal resolution and wider swaths, these sensors enable to produce daily sea ice concentration maps over the entire Arctic ocean. Brightness temperatures from passive microwave sensors are often used to estimate sea ice freeboard for first-year ice, but it is difficult to associate with physical characteristics related to sea ice height of multi-year ice. In machine learning community, deep learning has gained attention and notable success in addressing more complicated decision making using multiple hidden layers. In this study, we propose a deep learning based Arctic sea ice freeboard retrieval algorithm incorporating the brightness temperature data from the AMSR2 passive microwave data and sea ice freeboard data from the ICESat-2. The proposed retrieval algorithm enables to estimate daily freeboard for both first- and multi-year ice over the entire Arctic ocean. The estimated freeboard values from the AMSR2 are then quantitatively and qualitatively compared with other sea ice freeboard or thickness products. &#160;</p>

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The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation

10.5194/tc-2019-318 ◽

2020 ◽

Cited By ~ 1

Author(s):

Clara Burgard ◽

Dirk Notz ◽

Leif T. Pedersen ◽

Rasmus T. Tonboe

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Climate Model ◽

System Model ◽

The Arctic ◽

Sea Ice Concentration ◽

Brightness Temperatures ◽

Climate Model Evaluation ◽

Observational Uncertainty ◽

Ice Concentration

Abstract. The observational uncertainty in sea-ice-concentration estimates from remotely-sensed passive-microwave brightness temperatures is a challenge for reliable climate model evaluation and initialization. To address this challenge, we introduce a new tool: the Arctic Ocean Observation Operator (ARC3O). ARC3O allows us to simulate brightness temperatures at 6.9 GHz at vertical polarisation from standard output of an Earth System Model. We evaluate ARC3O by simulating brightness temperatures based on three assimilation runs of the MPI Earth System Model (MPI-ESM) assimilated with three different sea-ice concentration products. We then compare these three sets of simulated brightness temperatures to brightness temperatures measured by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) from space. We find that they differ up to 10 K in the period between October and June, depending on the region and the assimilation run. However, we show that these discrepancies between simulated and observed brightness temperature can be mainly attributed to the underlying observational uncertainty in sea-ice concentration and, to a lesser extent, to the data assimilation process, rather than to biases in ARC3O itself. In summer, the discrepancies between simulated and observed brightness temperatures are larger than in winter and locally reach up to 20 K. This is caused by the very large observational uncertainty in summer sea-ice concentration but also by the melt-pond parametrization in MPI-ESM, which is not necessarily realistic. ARC3O is therefore capable to realistically translate the simulated Arctic Ocean climate state into one observable quantity for a more comprehensive climate model evaluation and initialization.

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review of "Measuring sea ice concentration in the Arctic Ocean using SMOS" by Carolina Gabarro et al.

10.5194/tc-2016-175-rc1 ◽

2016 ◽

Author(s):

Anonymous

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

The Arctic ◽

Sea Ice Concentration ◽

Ice Concentration ◽

The Arctic Ocean

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Validation of remote-sensing products of sea-ice motion: a case study in the western Arctic Ocean

Journal of Glaciology ◽

10.1017/jog.2020.49 ◽

2020 ◽

Vol 66 (259) ◽

pp. 807-821

Author(s):

Dawei Gui ◽

Ruibo Lei ◽

Xiaoping Pang ◽

Jennifer K. Hutchings ◽

Guangyu Zuo ◽

...

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Arctic Ocean ◽

Sea Ice Concentration ◽

Western Arctic Ocean ◽

Ice Concentration ◽

Western Arctic ◽

Ice Floes ◽

Relative Errors

AbstractThe accuracy of sea-ice motion products provided by the National Snow and Ice Data Center (NSIDC) and the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) was validated with data collected by ice drifters that were deployed in the western Arctic Ocean in 2014 and 2016. Data from both NSIDC and OSI-SAF products exhibited statistically significant (p < 0.001) correlation with drifter data. The OSI-SAF product tended to overestimate ice speed, while underestimation was demonstrated for the NSIDC product, especially for the melt season and the marginal ice zone. Monthly Lagrangian trajectories of ice floes were reconstructed using the products. Larger spatial variability in the deviation between NSIDC and drifter trajectories was observed than that of OSI-SAF, and seasonal variability in the deviation for NSIDC was observed. Furthermore, trajectories reconstructed using the NSIDC product were sensitive to variations in sea-ice concentration. The feasibility of using remote-sensing products to characterize sea-ice deformation was assessed by evaluating the distance between two arbitrary positions as estimated by the products. Compared with the OSI-SAF product, relative errors are lower (<11.6%), and spatial-temporal resolutions are higher in the NSIDC product, which makes it more suitable for estimating sea-ice deformation.

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