Phytoplankton Bloom Changes under Extreme Geophysical Conditions in the Northern Bering Sea and the Southern Chukchi Sea

The northern Bering Sea and the southern Chukchi Sea are undergoing rapid regional biophysical changes in connection with the recent extreme climate change in the Arctic. The ice concentration in 2018 was the lowest since observations began in the 1970s, due to the unusually warm southerly wind in winter, which continued in 2019. We analyzed the characteristics of spring phytoplankton biomass distribution under the extreme environmental conditions in 2018 and 2019. Our results show that higher phytoplankton biomass during late spring compared to the 18-year average was observed in the Bering Sea in both years. Their spatial distribution is closely related to the open water extent following winter-onset sea ice retreat in association with dramatic atmospheric conditions. However, despite a similar level of shortwave heat flux, the 2019 springtime biomass in the Chukchi Sea was lower than that in 2018, and was delayed to summer. We confirmed that this difference in bloom timing in the Chukchi Sea was due to changes in seawater properties, determined by a combination of northward oceanic heat flux modulation by the disturbance from more extensive sea ice in winter and higher surface net shortwave heat flux than usual.

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Arctic Fog Detection Using Infrared Spectral Measurements

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-18-0100.1 ◽

2019 ◽

Vol 36 (8) ◽

pp. 1643-1656

Author(s):

Li Yi ◽

King-Fai Li ◽

Xianyao Chen ◽

Ka-Kit Tung

Keyword(s):

Sea Ice ◽

Chukchi Sea ◽

Open Water ◽

Detection Algorithm ◽

Probability Of Detection ◽

Satellite Observations ◽

The Arctic ◽

Water Surface Area ◽

Infrared Spectral ◽

Fog Detection

AbstractThe rapid increase in open-water surface area in the Arctic, resulting from sea ice melting during the summer likely as a result of global warming, may lead to an increase in fog [defined as a cloud with a base height below 1000 ft (~304 m)], which may imperil ships and small aircraft transportation in the region. There is a need for monitoring fog formation over the Arctic. Given that ground-based observations of fog over Arctic open water are very sparse, satellite observations may become the most effective way for Arctic fog monitoring. We developed a fog detection algorithm using the temperature difference between the cloud top and the surface, called ∂T in this work. A fog event is said to be detected if ∂T is greater than a threshold, which is typically between −6 and −12 K, depending on the time of the day (day or night) and the surface types (open water or sea ice). We applied this method to the coastal regions of Chukchi Sea and Beaufort Sea near Barrow, Alaska (now known as Utqiaġvik), during the months of March–October. Training with satellite observations between 2007 and 2014 over this region, the ∂T method can detect Arctic fog with an optimal probability of detection (POD) between 74% and 90% and false alarm rate (FAR) between 5% and 17%. These statistics are validated with data between 2015 and 2016 and are shown to be robust from one subperiod to another.

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Impact of a warm anomaly in the Pacific Arctic region derived from time-series export fluxes

PLoS ONE ◽

10.1371/journal.pone.0255837 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0255837

Author(s):

Catherine Lalande ◽

Jacqueline M. Grebmeier ◽

Andrew M. P. McDonnell ◽

Russell R. Hopcroft ◽

Stephanie O’Daly ◽

...

Keyword(s):

Sea Ice ◽

Bering Sea ◽

Chukchi Sea ◽

Arctic Region ◽

Ice Cover ◽

Biological Pump ◽

Northern Bering Sea ◽

Pacific Waters ◽

The Pacific ◽

The Impact

Unusually warm conditions recently observed in the Pacific Arctic region included a dramatic loss of sea ice cover and an enhanced inflow of warmer Pacific-derived waters. Moored sediment traps deployed at three biological hotspots of the Distributed Biological Observatory (DBO) during this anomalously warm period collected sinking particles nearly continuously from June 2017 to July 2019 in the northern Bering Sea (DBO2) and in the southern Chukchi Sea (DBO3), and from August 2018 to July 2019 in the northern Chukchi Sea (DBO4). Fluxes of living algal cells, chlorophyll a (chl a), total particulate matter (TPM), particulate organic carbon (POC), and zooplankton fecal pellets, along with zooplankton and meroplankton collected in the traps, were used to evaluate spatial and temporal variations in the development and composition of the phytoplankton and zooplankton communities in relation to sea ice cover and water temperature. The unprecedented sea ice loss of 2018 in the northern Bering Sea led to the export of a large bloom dominated by the exclusively pelagic diatoms Chaetoceros spp. at DBO2. Despite this intense bloom, early sea ice breakup resulted in shorter periods of enhanced chl a and diatom fluxes at all DBO sites, suggesting a weaker biological pump under reduced ice cover in the Pacific Arctic region, while the coincident increase or decrease in TPM and POC fluxes likely reflected variations in resuspension events. Meanwhile, the highest transport of warm Pacific waters during 2017–2018 led to a dominance of the small copepods Pseudocalanus at all sites. Whereas the export of ice-associated diatoms during 2019 suggested a return to more typical conditions in the northern Bering Sea, the impact on copepods persisted under the continuously enhanced transport of warm Pacific waters. Regardless, the biological pump remained strong on the shallow Pacific Arctic shelves.

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Airborne Lidar Observations of a Spring Phytoplankton Bloom in the Western Arctic Ocean

Remote Sensing ◽

10.3390/rs13132512 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2512

Author(s):

James H. Churnside ◽

Richard D. Marchbanks ◽

Nathan Marshall

Keyword(s):

Arctic Ocean ◽

Phytoplankton Bloom ◽

Chukchi Sea ◽

Open Water ◽

Horizontal Distribution ◽

Airborne Lidar ◽

The Arctic ◽

Spring Phytoplankton Bloom ◽

Vertical And Horizontal Distribution ◽

Western Arctic

One of the most notable effects of climate change is the decrease in sea ice in the Arctic Ocean. This is expected to affect the distribution of phytoplankton as the ice retreats earlier. We were interested in the vertical and horizontal distribution of phytoplankton in the Chukchi Sea in May. Measurements were made with an airborne profiling lidar that allowed us to cover large areas. The lidar profiles showed a uniform distribution of attenuation and scattering from the surface to the limit of lidar penetration at a depth of about 30 m. Both parameters were greater in open water than under the ice. Depolarization of the lidar decreased as attenuation and scattering increased. A cluster analysis of the 2019 data revealed four distinct clusters based on depolarization and lidar ratio. One cluster was associated with open water, one with pack ice, one with the waters along the land-fast ice, and one that appeared to be scattered throughout the region. The first three were likely the result of different assemblages of phytoplankton, while the last may have been an artifact of thin fog in the atmosphere.

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The recent amplifying seasonal cycle of the Arctic sea ice extent related to the subsurface cooling in the Bering Sea

10.5194/tc-2019-37 ◽

2019 ◽

Cited By ~ 1

Author(s):

Xiao-Yi Yang ◽

Guihua Wang

Keyword(s):

Sea Ice ◽

Bering Sea ◽

Seasonal Cycle ◽

Recent Decade ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Extent ◽

Northern Bering Sea ◽

Arctic Sea ◽

Arctic Sea Ice Extent

Abstract. After an unprecedented and accelerated retreat, the total Arctic sea ice cover in recent decade is characterized with low extent and large amplitude of annual cycle. This study investigated the spatial-temporal variation of the Arctic sea ice extent and the potential factors accounting for its amplifying seasonal cycle. The results show that the Chukchi-Bering sector of Arctic exhibits a contrasting decadal variation of sea ice extent between the different seasons: The sea ice in recent decade decreased in summer-autumn seasons but increased significantly in spring, leading an amplifying seasonal cycle. This decadal expansion of spring Chukchi-Bering sea ice may be attributed to the significant subsurface cooling in the northern Bering Sea.

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Processes controlling aggregate formation and distribution during the Arctic phytoplankton spring bloom in Baffin Bay

Elem Sci Anth ◽

10.1525/elementa.2021.00001 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Jordan Toullec ◽

Brivaëla Moriceau ◽

Dorothée Vincent ◽

Lionel Guidi ◽

Augustin Lafond ◽

...

Keyword(s):

Sea Ice ◽

Spring Bloom ◽

Phytoplankton Bloom ◽

Light Availability ◽

Open Water ◽

The Arctic ◽

Phytoplankton Production ◽

Baffin Bay ◽

Migration Pathway ◽

Phytoplankton Cells

In the last decades, the Arctic Ocean has been affected by climate change, leading to alterations in the sea ice cover that influence the phytoplankton spring bloom, its associated food web, and therefore carbon sequestration. During the Green Edge 2016 expedition in the central Baffin Bay, the phytoplankton spring bloom and its development around the ice edge was followed along 7 transects from open water to the ice-pack interior. Here, we studied some of the processes driving phytoplankton aggregation, using aggregate and copepod distribution profiles obtained with an underwater vision profiler deployed at several stations along the transects. Our results revealed a sequential pattern during sea ice retreat in phytoplankton production and in aggregate production and distribution. First, under sea ice, phytoplankton started to grow, but aggregates were not formed. Second, after sea ice melting, phytoplankton (diatoms and Phaeocystis spp. as the dominant groups) benefited from the light availability and stratified environment to bloom, and aggregation began coincident with nutrient depletion at the surface. Third, maxima of phytoplankton aggregates deepened in the water column and phytoplankton cells at the surface began to degrade. At most stations, silicate limitation began first, triggering aggregation of the phytoplankton cells; nitrate limitation came later. Copepods followed aggregates at the end of the phytoplankton bloom, possibly because aggregates provided higher quality food than senescing phytoplankton cells at the surface. These observations suggest that aggregation is involved in 2 export pathways constituting the biological pump: the gravitational pathway through the sinking of aggregates and fecal pellets and the migration pathway when zooplankton follow aggregates during food foraging.

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A comparison of factors that led to the extreme sea ice minima in the 21st century in the Arctic Ocean

Journal of Climate ◽

10.1175/jcli-d-21-0199.1 ◽

2021 ◽

pp. 1-56

Keyword(s):

Heat Flux ◽

Sea Ice ◽

Arctic Ocean ◽

Atmospheric Circulation ◽

21St Century ◽

Open Water ◽

The Arctic ◽

Area Loss ◽

The Arctic Ocean ◽

The Difference

Abstract The extreme Arctic sea ice minima in the 21st century have been attributed to multiple factors, such as anomalous atmospheric circulation, excess solar radiation absorbed by open ocean, and thinning sea ice in a warming world. Most likely it is the combination of these factors that drive the extreme sea ice minima, but it has not been quantified, how the factors rank in setting the conditions for these events. To address this question, the sea ice budget of an Arctic regional sea ice-ocean model forced by atmospheric reanalysis data is analyzed to assess the development of the observed sea ice minima. Results show that the ice area difference in the years 2012, 2019, and 2007 is driven to over 60% by the difference in summertime sea ice area loss due to air-ocean heat flux over open water. Other contributions are small. For the years 2012 and 2020 the situation is different and more complex. The air-ice heat flux causes more sea ice area loss in summer 2020 than in 2012 due to warmer air temperatures, but this difference in sea ice area loss is compensated by reduced advective sea ice loss out of the Arctic Ocean mainly caused by the relaxation of the Arctic Dipole. The difference in open water area in early August leads to different air-ocean heat fluxes, which distinguishes the sea ice minima in 2012 and 2020. Further, sensitivity experiments indicate that both the atmospheric circulation associated with the Arctic Dipole and extreme storms are essential conditions for a new low record of sea ice extent.

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Variability in spring phytoplankton blooms associated with ice retreat timing in the Pacific Arctic from 2003–2019

PLoS ONE ◽

10.1371/journal.pone.0261418 ◽

2021 ◽

Vol 16 (12) ◽

pp. e0261418

Author(s):

Hisatomo Waga ◽

Hajo Eicken ◽

Toru Hirawake ◽

Yasushi Fukamachi

Keyword(s):

Sea Ice ◽

Phytoplankton Biomass ◽

Phytoplankton Bloom ◽

Chukchi Sea ◽

Gaussian Function ◽

Phytoplankton Blooms ◽

The Pacific ◽

Marginal Ice Zone ◽

Ice Retreat ◽

Sea Ice Retreat

The Arctic is experiencing rapid changes in sea-ice seasonality and extent, with significant consequences for primary production. With the importance of accurate monitoring of spring phytoplankton dynamics in a changing Arctic, this study further examines the previously established critical relationship between spring phytoplankton bloom types and timing of the sea-ice retreat for broader temporal and spatial coverages, with a particular focus on the Pacific Arctic for 2003–2019. To this end, time-series of satellite-retrieved phytoplankton biomass were modeled using a parametric Gaussian function, as an effective approach to capture the development and decay of phytoplankton blooms. Our sensitivity analysis demonstrated accurate estimates of timing and presence/absence of peaks in phytoplankton biomass even with some missing values, suggesting the parametric Gaussian function is a powerful tool for capturing the development and decay of phytoplankton blooms. Based on the timing and presence/absence of a peak in phytoplankton biomass and following the classification developed by the previous exploratory work, spring bloom types are classified into three groups (under-ice blooms, probable under-ice blooms, and marginal ice zone blooms). Our results showed that the proportion of under-ice blooms was higher in the Chukchi Sea than in the Bering Sea. The probable under-ice blooms registered as the dominant bloom types in a wide area of the Pacific Arctic, whereas the marginal ice zone bloom was a relatively minor bloom type across the Pacific Arctic. Associated with a shift of sea-ice retreat timing toward earlier dates, we confirmed previous findings from the Chukchi Sea of recent shifts in phytoplankton bloom types from under-ice blooms to marginal ice zone blooms and demonstrated that this pattern holds for the broader Pacific Arctic sector for the time period 2003–2019. Overall, the present study provided additional evidence of the changing sea-ice retreat timing that can drive variations in phytoplankton bloom dynamics, which contributes to addressing the detection and consistent monitoring of the biophysical responses to the changing environments in the Pacific Arctic.

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N2O dynamics in the western Arctic Ocean during the summer of 2017

Scientific Reports ◽

10.1038/s41598-021-92009-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jang-Mu Heo ◽

Seong-Su Kim ◽

Sung-Ho Kang ◽

Eun Jin Yang ◽

Ki-Tae Park ◽

...

Keyword(s):

Arctic Ocean ◽

Environmental Changes ◽

Chukchi Sea ◽

Hot Spot ◽

Open Water ◽

Northern Region ◽

The Arctic ◽

Western Arctic Ocean ◽

Deep Layers ◽

Western Arctic

AbstractThe western Arctic Ocean (WAO) has experienced increased heat transport into the region, sea-ice reduction, and changes to the WAO nitrous oxide (N2O) cycles from greenhouse gases. We investigated WAO N2O dynamics through an intensive and precise N2O survey during the open-water season of summer 2017. The effects of physical processes (i.e., solubility and advection) were dominant in both the surface (0–50 m) and deep layers (200–2200 m) of the northern Chukchi Sea with an under-saturation of N2O. By contrast, both the surface layer (0–50 m) of the southern Chukchi Sea and the intermediate (50–200 m) layer of the northern Chukchi Sea were significantly influenced by biogeochemically derived N2O production (i.e., through nitrification), with N2O over-saturation. During summer 2017, the southern region acted as a source of atmospheric N2O (mean: + 2.3 ± 2.7 μmol N2O m−2 day−1), whereas the northern region acted as a sink (mean − 1.3 ± 1.5 μmol N2O m−2 day−1). If Arctic environmental changes continue to accelerate and consequently drive the productivity of the Arctic Ocean, the WAO may become a N2O “hot spot”, and therefore, a key region requiring continued observations to both understand N2O dynamics and possibly predict their future changes.

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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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Observations and Simulations of Meteorological Conditions over Arctic Thick Sea Ice in Late Winter during the Transarktika 2019 Expedition

Atmosphere ◽

10.3390/atmos12020174 ◽

2021 ◽

Vol 12 (2) ◽

pp. 174

Author(s):

Günther Heinemann ◽

Sascha Willmes ◽

Lukas Schefczyk ◽

Alexander Makshtas ◽

Vasilii Kustov ◽

...

Keyword(s):

Sea Ice ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Open Water ◽

The Arctic ◽

Late Winter ◽

Good Representation ◽

Hourly Temperature ◽

Ice Model

The parameterization of ocean/sea-ice/atmosphere interaction processes is a challenge for regional climate models (RCMs) of the Arctic, particularly for wintertime conditions, when small fractions of thin ice or open water cause strong modifications of the boundary layer. Thus, the treatment of sea ice and sub-grid flux parameterizations in RCMs is of crucial importance. However, verification data sets over sea ice for wintertime conditions are rare. In the present paper, data of the ship-based experiment Transarktika 2019 during the end of the Arctic winter for thick one-year ice conditions are presented. The data are used for the verification of the regional climate model COSMO-CLM (CCLM). In addition, Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for the comparison of ice surface temperature (IST) simulations of the CCLM sea ice model. CCLM is used in a forecast mode (nested in ERA5) for the Norwegian and Barents Seas with 5 km resolution and is run with different configurations of the sea ice model and sub-grid flux parameterizations. The use of a new set of parameterizations yields improved results for the comparisons with in-situ data. Comparisons with MODIS IST allow for a verification over large areas and show also a good performance of CCLM. The comparison with twice-daily radiosonde ascents during Transarktika 2019, hourly microwave water vapor measurements of first 5 km in the atmosphere and hourly temperature profiler data show a very good representation of the temperature, humidity and wind structure of the whole troposphere for CCLM.

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