scholarly journals Environmental factors influencing the seasonal dynamics of spring algal blooms in and beneath sea ice in western Baffin Bay

Elem Sci Anth ◽  
2019 ◽  
Vol 7 ◽  
Author(s):  
L. Oziel ◽  
P. Massicotte ◽  
A. Randelhoff ◽  
J. Ferland ◽  
A. Vladoiu ◽  
...  
Keyword(s):  
Sea Ice ◽  
Water Column ◽  
Seasonal Dynamics ◽  
Phytoplankton Bloom ◽  
Arctic Sea Ice ◽  
Series Data ◽  
Western Coast ◽  
Snow Melt ◽  
Phytoplankton Blooms ◽  
Baffin Bay

Arctic sea ice is experiencing a shorter growth season and an earlier ice melt onset. The significance of spring microalgal blooms taking place prior to sea ice breakup is the subject of ongoing scientific debate. During the Green Edge project, unique time-series data were collected during two field campaigns held in spring 2015 and 2016, which documented for the first time the concomitant temporal evolution of the sea ice algal and phytoplankton blooms in and beneath the landfast sea ice in western Baffin Bay. Sea ice algal and phytoplankton blooms were negatively correlated and respectively reached 26 (6) and 152 (182) mg of chlorophyll a per m2 in 2015 (2016). Here, we describe and compare the seasonal evolutions of a wide variety of physical forcings, particularly key components of the atmosphere–snow–ice–ocean system, that influenced microalgal growth during both years. Ice algal growth was observed under low-light conditions before the snow melt period and was much higher in 2015 due to less snowfall. By increasing light availability and water column stratification, the snow melt onset marked the initiation of the phytoplankton bloom and, concomitantly, the termination of the ice algal bloom. This study therefore underlines the major role of snow on the seasonal dynamics of microalgae in western Baffin Bay. The under-ice water column was dominated by Arctic Waters. Just before the sea ice broke up, phytoplankton had consumed most of the nutrients in the surface layer. A subsurface chlorophyll maximum appeared and deepened, favored by spring tide-induced mixing, reaching the best compromise between light and nutrient availability. This deepening evidenced the importance of upper ocean tidal dynamics for shaping vertical development of the under-ice phytoplankton bloom, a major biological event along the western coast of Baffin Bay, which reached similar magnitude to the offshore ice-edge bloom.

Physical Control of Phytoplankton Production under Sea Ice (Manitounuk Sound, Hudson Bay)

1981 ◽  
Vol 38 (11) ◽  
pp. 1385-1392 ◽  
Author(s):  
L. Legendre ◽  
R. G. Ingram ◽  
M. Poulin
Keyword(s):  
Surface Water ◽  
Sea Ice ◽  
Water Column ◽  
Phytoplankton Bloom ◽  
Phytoplankton Production ◽  
Hudson Bay ◽  
Ice Algae ◽  
Phytoplankton Blooms ◽  
Low Salinity ◽  
Light Utilization

In polar and subpolar seas, there are numerous accounts of phytoplankton blooms in the upper water column under the ice. Various mechanisms have been invoked to explain these blooms: the seeding of the underlying surface water by algal cells (epontic flora) released from the melting ice, the optimization of light utilization by the cells, and the stabilization of the upper water column by the low-salinity melting water. From studies conducted in Manitounuk Sound (Hudson Bay), it is proposed that phytoplankton blooms under the ice probably result from the simultaneous deepening of both the photic layer (seasonal light increase) and the stratified layer (low-salinity melting water). In ice-covered seas, the release of ice algae superimposes itself on the phytoplankton bloom, resulting in the observed algal increase under melting ice.Key words: phytoplankton, under-ice blooms, ice flora, stability, nutrients, Hudson Bay

scholarly journals Implications of sea-ice biogeochemistry for oceanic production and emissions of dimethyl sulfide in the Arctic

2017 ◽  
Vol 14 (12) ◽  
pp. 3129-3155 ◽  
Author(s):  
Hakase Hayashida ◽  
Nadja Steiner ◽  
Adam Monahan ◽  
Virginie Galindo ◽  
Martine Lizotte ◽  
...  
Keyword(s):  
Sea Ice ◽  
Water Column ◽  
Dimethyl Sulfide ◽  
Phytoplankton Bloom ◽  
Model Simulation ◽  
Field Measurements ◽  
Sulfur Cycle ◽  
The Arctic ◽  
Model Parameters ◽  
Ocean Ecosystem

Abstract. Sea ice represents an additional oceanic source of the climatically active gas dimethyl sulfide (DMS) for the Arctic atmosphere. To what extent this source contributes to the dynamics of summertime Arctic clouds is, however, not known due to scarcity of field measurements. In this study, we developed a coupled sea ice–ocean ecosystem–sulfur cycle model to investigate the potential impact of bottom-ice DMS and its precursor dimethylsulfoniopropionate (DMSP) on the oceanic production and emissions of DMS in the Arctic. The results of the 1-D model simulation were compared with field data collected during May and June of 2010 in Resolute Passage. Our results reproduced the accumulation of DMS and DMSP in the bottom ice during the development of an ice algal bloom. The release of these sulfur species took place predominantly during the earlier phase of the melt period, resulting in an increase of DMS and DMSP in the underlying water column prior to the onset of an under-ice phytoplankton bloom. Production and removal rates of processes considered in the model are analyzed to identify the processes dominating the budgets of DMS and DMSP both in the bottom ice and the underlying water column. When openings in the ice were taken into account, the simulated sea–air DMS flux during the melt period was dominated by episodic spikes of up to 8.1 µmol m−2 d−1. Further model simulations were conducted to assess the effects of the incorporation of sea-ice biogeochemistry on DMS production and emissions, as well as the sensitivity of our results to changes of uncertain model parameters of the sea-ice sulfur cycle. The results highlight the importance of taking into account both the sea-ice sulfur cycle and ecosystem in the flux estimates of oceanic DMS near the ice margins and identify key uncertainties in processes and rates that should be better constrained by new observations.

scholarly journals Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes

2020 ◽  
Vol 35 (3) ◽  
pp. 793-806
Author(s):  
William Gregory ◽  
Michel Tsamados ◽  
Julienne Stroeve ◽  
Peter Sollich
Keyword(s):  
Sea Ice ◽  
Time Series Data ◽  
Gaussian Process Regression ◽  
Forecast Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Series Data ◽  
Lead Times ◽  
Sea Ice Concentration ◽  
Sea Ice Extent

Abstract Reliable predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. In this study pan-Arctic and regional September mean sea ice extents are forecast with lead times of up to 3 months using a complex network statistical approach. This method exploits relationships within climate time series data by constructing regions of spatiotemporal homogeneity (i.e., nodes), and subsequently deriving teleconnection links between them. Here the nodes and links of the networks are generated from monthly mean sea ice concentration fields in June, July, and August; hence, individual networks are constructed for each respective month. Network information is then utilized within a linear Gaussian process regression forecast model, a Bayesian inference technique, in order to generate predictions of sea ice extent. Pan-Arctic forecasts capture a significant amount of the variability in the satellite observations of September sea ice extent, with detrended predictive skills of 0.53, 0.62, and 0.81 at 3-, 2-, and 1-month lead times, respectively. Regional forecasts are also performed for nine Arctic regions. On average, the highest predictive skill is achieved in the Canadian Archipelago, Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas, although the ability to accurately predict many of these regions appears to be changing over time.

scholarly journals Holocene variability in sea ice and primary productivity in the northeastern Baffin Bay

arktos ◽  
2020 ◽  
Vol 6 (1-3) ◽  
pp. 55-73 ◽  
Author(s):  
Jeetendra Saini ◽  
Ruediger Stein ◽  
Kirsten Fahl ◽  
Jens Weiser ◽  
Dierk Hebbeln ◽  
...  
Keyword(s):  
Sea Ice ◽  
Ocean Circulation ◽  
Water Discharge ◽  
Greenland Ice Sheet ◽  
Open Water ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Baffin Bay ◽  
Arctic Sea ◽  
Ice Edge

AbstractArctic sea ice is a critical component of the climate system, known to influence ocean circulation, earth’s albedo, and ocean–atmosphere heat and gas exchange. Current developments in the use of IP25 (a sea ice proxy with 25 carbon atoms only synthesized by Arctic sea ice diatoms) have proven it to be a suitable proxy for paleo-sea ice reconstructions over hundreds of thousands to even millions of years. In the NE Baffin Bay, off NW Greenland, Melville Bugt is a climate-sensitive region characterized by strong seasonal sea ice variability and strong melt-water discharge from the Greenland Ice Sheet (GIS). Here, we present a centennial-scale resolution Holocene sea ice record, based on IP25 and open-water phytoplankton biomarkers (brassicasterol, dinosterol and HBI III) using core GeoB19927-3 (73° 35.26′ N, 58° 05.66′ W). Seasonal to ice-edge conditions near the core site are documented for most of the Holocene period with some significant variability. In the lower-most part, a cold interval characterized by extensive sea ice cover and very low local productivity is succeeded by an interval (~ 9.4–8.5 ka BP) with reduced sea ice cover, enhanced GIS spring melting, and strong influence of the West Greenland Current (WGC). From ~ 8.5 until ~ 7.8 ka BP, a cooling event is recorded by ice algae and phytoplankton biomarkers. They indicate an extended sea ice cover, possibly related to the opening of Nares Strait, which may have led to an increased influx of Polar Water into NE-Baffin Bay. The interval between ~ 7.8 and ~ 3.0 ka BP is characterized by generally reduced sea ice cover with millennial-scale variability of the (late winter/early spring) ice-edge limit, increased open-water conditions (polynya type), and a dominant WGC carrying warm waters at least as far as the Melville Bugt area. During the last ~ 3.0 ka BP, our biomarker records do not reflect the late Holocene ‘Neoglacial cooling’ observed elsewhere in the Northern Hemisphere, possibly due to the persistent influence of the WGC and interactions with the adjacent fjords. Peaks in HBI III at about ~ 2.1 and ~ 1.3 ka BP, interpreted as persistent ice-edge situations, might correlate with the Roman Warm Period (RWP) and Medieval Climate Anomaly (MCA), respectively, in-phase with the North Atlantic Oscillation (NAO) mode. When integrated with marine and terrestrial records from other circum-Baffin Bay areas (Disko Bay, the Canadian Arctic, the Labrador Sea), the Melville Bugt biomarker records point to close ties with high Arctic and Northern Hemispheric climate conditions, driven by solar and oceanic circulation forcings.

scholarly journals Hidden Production: On the Importance of Pelagic Phytoplankton Blooms Beneath Arctic Sea Ice

2020 ◽  
Vol 125 (9) ◽  
Author(s):  
Jaclyn Clement Kinney ◽  
Wieslaw Maslowski ◽  
Robert Osinski ◽  
Meibing Jin ◽  
Marina Frants ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Phytoplankton Blooms ◽  
Arctic Sea

Random Walks through Climate Networks: Sea Ice Prediction with Bayesian Inference

2020 ◽  
Author(s):  
William Gregory ◽  
Michel Tsamados ◽  
Julienne Stroeve ◽  
Peter Sollich
Keyword(s):  
Bayesian Inference ◽  
Sea Ice ◽  
Time Series Data ◽  
Gaussian Process Regression ◽  
Forecast Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Series Data ◽  
Sea Ice Concentration ◽  
Spring Predictability Barrier

<p><span>Spatial predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. However, with sea ice variability likely to increase under continued anthropogenic warming, increasingly complex tools are required in order to make accurate forecasts. In this study, predictions of both Arctic and Antarctic summer sea ice extents are made using a complex network statistical approach. This method exploits statistical relationships within geo-spatial time series data in order to construct regions of spatio-temporal homogeneity -- nodes, and subsequently derive teleconnection links between them. The nodes and links of the networks here are generated from monthly sea ice concentration fields in June(November), July(December) and August(January) for Arctic(Antarctic) forecasts, hence lead times extend from 1 to 3 months. Network information is then utilised within a linear Gaussian Process Regression forecast model; a Bayesian inference technique. Network teleconnection weights are used to generate priors over functions in the form of a random walk covariance kernel; the hyperparameters of which are determined by the empirical Bayesian approach of type-II maximum likelihood. We also show predictions of all other months in order to ascertain the presence of a spring predictability barrier in observational data, for both hemispheres.</span></p>

scholarly journals An estimation of the quantitative impacts of copepod grazing on an under sea-ice spring phytoplankton bloom in western Baffin Bay, Canadian Arctic

Elem Sci Anth ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Makoto Sampei ◽  
Louis Fortier ◽  
Patrick Raimbault ◽  
Kohei Matsuno ◽  
Yoshiyuki Abe ◽  
...  
Keyword(s):  
Sea Ice ◽  
Primary Production ◽  
Phytoplankton Bloom ◽  
Grazing Rate ◽  
Grazing Pressure ◽  
Spring Phytoplankton Bloom ◽  
Baffin Bay ◽  
Chl A ◽  
Copepod Grazing ◽  
The Impact

This study aimed to quantify the impact of copepod grazing on the productivity of phytoplankton during an under sea-ice spring phytoplankton bloom (USPB) in western Baffin Bay. To quantify positive and/or negative impacts of copepod grazing on primary production and the interaction between copepod grazing and phytoplankton species, we sampled seawater and zooplankton under the landfast sea ice every 2–3 days between May 24 and July 10, 2016. Samples were analyzed for estimation of primary production, chlorophyll-a (chl-a) concentration, diatom abundance, and copepod fecal pellet (FP) production/grazing rate. Analyses of chl-a concentration, primary production, and FP production/grazing rate revealed clear temporal changes and a mismatch between primary production and copepod consumption. The FP production/grazing rate reached a maximum (9.4/31.2 mg C m–2 d–1) on June 16 before the USPB phase and suddenly decreased to 0.7/2.4 mg C m–2 d–1 on June 21, despite an increase in primary production to 74.0 mg C m–2 d–1. The copepod grazing rate (3.7 mg C m–2 d–1) was low relative to primary production (344.6 mg C m–2 d–1) during the USPB phase (after June 20). While our estimates illustrate that copepod grazing did not limit the maximum daily primary production during the USPB, the low grazing pressure (2% of primary production) may have been an additional contributor to the reduction in total primary productivity at the end of the USPB period due primarily to the low supply of regenerated nitrogen-containing nutrients to drive regenerated production.

scholarly journals 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 ◽  
2019 ◽  
Vol 7 ◽  
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.

scholarly journals Influence of Arctic sea-ice loss on the Greenland ice sheet climate

2021 ◽  
Author(s):  
Raymond Sellevold ◽  
Jan T. M. Lenaerts ◽  
Miren Vizcaino
Keyword(s):  
Sea Ice ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Humid Atmosphere ◽  
Baffin Bay ◽  
Arctic Sea ◽  
Surface Melt ◽  
The Impact

AbstractThe Arctic is the region on Earth that is warming the fastest. At the same time, Arctic sea ice is reducing while the Greenland ice sheet (GrIS) is losing mass at an accelerated pace. Here, we study the seasonal impact of reduced Arctic sea ice on GrIS surface mass balance (SMB), using the Community Earth System Model version 2.1 (CESM2), which features an advanced, interactive calculation of SMB. Addressing the impact of sea-ice reductions on the GrIS SMB from observations is difficult due to the short observational records. Also, signals detected using transient climate simulations may be aliases of other forcings. Here, we analyze dedicated simulations from the Polar Amplification Model Intercomparison Project with reduced Arctic sea ice and compare them with preindustrial sea ice simulations while keeping all other forcings constant. In response to reduced sea ice, the GrIS SMB increases in winter due to increased precipitation, driven by the more humid atmosphere and increasing cyclones. In summer, surface melt increases due to a warmer, more humid atmosphere providing increased energy transfer to the surface through the sensible and latent heat fluxes, which triggers the melt-albedo feedback. Further, warming occurs throughout the entire troposphere over Baffin Bay. This deep warming results in regional enhancement of the 500 hPa geopotential heights over the Baffin Bay and Greenland, increasing blocking and heat advection over the GrIS’ surface. This anomalous circulation pattern has been linked to recent increases in the surface melt of the GrIS.

scholarly journals A recent bifurcation in Arctic sea-ice cover

2012 ◽  
Vol 6 (4) ◽  
pp. 2621-2651 ◽  
Author(s):  
V. N. Livina ◽  
T. M. Lenton
Keyword(s):  
Sea Ice ◽  
Early Warning ◽  
Normal State ◽  
Time Series Data ◽  
Warning Signal ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Series Data ◽  
Internal Climate Variability ◽  
Arctic Sea

Abstract. There is ongoing debate over whether Arctic sea-ice has already passed a "tipping point", or whether it will do so in future, with several recent studies arguing that the loss of summer sea ice does not involve a bifurcation because it is highly reversible in models. Recently developed methods can detect and sometimes forewarn of bifurcations in time-series data, hence we applied them to satellite data for Arctic sea-ice cover. Here we show that a new low ice cover state has appeared from 2007 onwards, which is distinct from the normal state of seasonal sea ice variation, suggesting a bifurcation has occurred from one attractor to two. There was no robust early warning signal of critical slowing down prior to this bifurcation, consistent with it representing the appearance of a new ice cover state rather than the loss of stability of the existing state. The new low ice cover state has been sampled predominantly in summer-autumn and seasonal forcing combined with internal climate variability are likely responsible for triggering recent transitions between the two ice cover states. However, all early warning indicators show destabilization of the summer-autumn sea-ice since 2007. This suggests the new low ice cover state may be a transient feature and further abrupt changes in summer-autumn Arctic sea-ice cover could lie ahead; either reversion to the normal state or a yet larger ice loss.

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