Modelling the accumulation and transport of microplastics by Arctic sea ice

Author(s):  
Miguel Angel Morales Maqueda ◽  
Alethea Sara Mountford

<p>The presence of microplastics in the Arctic sea ice cover and water column, as well as on land, has raised the already high concerns about the dispersion of litter in the global environment. We present a 50-year simulation carried out with the NEMO ocean general circulation model of the dispersion of buoyant and neutrally buoyant microplastics in the global ocean that includes a simple formulation of microplastic accumulation in, and advection by, sea ice. Microplastics enter the Arctic predominantly through the Barents Sea, with a smaller input through the Bering Strait, although the simulation also takes into account small plastic sources along the Arctic coastline. Microplastics become trapped in newly formed sea ice chiefly on the Eurasian shelves and the Chukchi Sea, but a still significant amount is transferred from the mixed layer to the ice base through congelation in the central Arctic, where microplastics congregate nearer to the surface than elsewhere in the global ocean due to the strong stratification and the relatively small levels of vertical turbulence underneath multiyear sea ice. In the model, the maximum average residence time of sea ice in the Arctic is about six years, and this is also, therefore, the typical timescale for maximum microplastic accumulation in the ice cover. Plastics trapped in sea ice undergo a seasonal cycle of accumulation and release in consonance with the freeze and melt sea ice cycle but ultimately are release back into the ocean in the Greenland and Labrador seas, from where they will be subsequently transported into the North Atlantic.</p>

2013 ◽  
Vol 26 (16) ◽  
pp. 6092-6104 ◽  
Author(s):  
Matthieu Chevallier ◽  
David Salas y Mélia ◽  
Aurore Voldoire ◽  
Michel Déqué ◽  
Gilles Garric

Abstract An ocean–sea ice model reconstruction spanning the period 1990–2009 is used to initialize ensemble seasonal forecasts with the Centre National de Recherches Météorologiques Coupled Global Climate Model version 5.1 (CNRM-CM5.1) coupled atmosphere–ocean general circulation model. The aim of this study is to assess the skill of fully initialized September and March pan-Arctic sea ice forecasts in terms of climatology and interannual anomalies. The predictions are initialized using “full field initialization” of each component of the system. In spite of a drift due to radiative biases in the coupled model during the melt season, the full initialization of the sea ice cover on 1 May leads to skillful forecasts of the September sea ice extent (SIE) anomalies. The skill of the prediction is also significantly high when considering anomalies of the SIE relative to the long-term linear trend. It confirms that the anomaly of spring sea ice cover in itself plays a role in preconditioning a September SIE anomaly. The skill of predictions for March SIE initialized on 1 November is also encouraging, and it can be partly attributed to persistent features of the fall sea ice cover. The present study gives insight into the current ability of state-of-the-art coupled climate systems to perform operational seasonal forecasts of the Arctic sea ice cover up to 5 months in advance.


2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Mats Brockstedt Olsen Huserbråten ◽  
Elena Eriksen ◽  
Harald Gjøsæter ◽  
Frode Vikebø

Abstract The Arctic amplification of global warming is causing the Arctic-Atlantic ice edge to retreat at unprecedented rates. Here we show how variability and change in sea ice cover in the Barents Sea, the largest shelf sea of the Arctic, affect the population dynamics of a keystone species of the ice-associated food web, the polar cod (Boreogadus saida). The data-driven biophysical model of polar cod early life stages assembled here predicts a strong mechanistic link between survival and variation in ice cover and temperature, suggesting imminent recruitment collapse should the observed ice-reduction and heating continue. Backtracking of drifting eggs and larvae from observations also demonstrates a northward retreat of one of two clearly defined spawning assemblages, possibly in response to warming. With annual to decadal ice-predictions under development the mechanistic physical-biological links presented here represent a powerful tool for making long-term predictions for the propagation of polar cod stocks.


2016 ◽  
Vol 29 (2) ◽  
pp. 889-902 ◽  
Author(s):  
Rasmus A. Pedersen ◽  
Ivana Cvijanovic ◽  
Peter L. Langen ◽  
Bo M. Vinther

Abstract Reduction of the Arctic sea ice cover can affect the atmospheric circulation and thus impact the climate beyond the Arctic. The atmospheric response may, however, vary with the geographical location of sea ice loss. The atmospheric sensitivity to the location of sea ice loss is studied using a general circulation model in a configuration that allows combination of a prescribed sea ice cover and an active mixed layer ocean. This hybrid setup makes it possible to simulate the isolated impact of sea ice loss and provides a more complete response compared to experiments with fixed sea surface temperatures. Three investigated sea ice scenarios with ice loss in different regions all exhibit substantial near-surface warming, which peaks over the area of ice loss. The maximum warming is found during winter, delayed compared to the maximum sea ice reduction. The wintertime response of the midlatitude atmospheric circulation shows a nonuniform sensitivity to the location of sea ice reduction. While all three scenarios exhibit decreased zonal winds related to high-latitude geopotential height increases, the magnitudes and locations of the anomalies vary between the simulations. Investigation of the North Atlantic Oscillation reveals a high sensitivity to the location of the ice loss. The northern center of action exhibits clear shifts in response to the different sea ice reductions. Sea ice loss in the Atlantic and Pacific sectors of the Arctic cause westward and eastward shifts, respectively.


2017 ◽  
Vol 30 (11) ◽  
pp. 3945-3962 ◽  
Author(s):  
James A. Screen

Abstract The loss of Arctic sea ice is already having profound environmental, societal, and ecological impacts locally. A highly uncertain area of scientific research, however, is whether such Arctic change has a tangible effect on weather and climate at lower latitudes. There is emerging evidence that the geographical location of sea ice loss is critically important in determining the large-scale atmospheric circulation response and associated midlatitude impacts. However, such regional dependencies have not been explored in a thorough and systematic manner. To make progress on this issue, this study analyzes ensemble simulations with an atmospheric general circulation model prescribed with sea ice loss separately in nine regions of the Arctic, to elucidate the distinct responses to regional sea ice loss. The results suggest that in some regions, sea ice loss triggers large-scale dynamical responses, whereas in other regions sea ice loss induces only local thermodynamical changes. Sea ice loss in the Barents–Kara Seas is unique in driving a weakening of the stratospheric polar vortex, followed in time by a tropospheric circulation response that resembles the North Atlantic Oscillation. For October–March, the largest spatial-scale responses are driven by sea ice loss in the Barents–Kara Seas and the Sea of Okhotsk; however, different regions assume greater importance in other seasons. The atmosphere responds very differently to regional sea ice losses than to pan-Arctic sea ice loss, and the response to pan-Arctic sea ice loss cannot be obtained by the linear addition of the responses to regional sea ice losses. The results imply that diversity in past studies of the simulated response to Arctic sea ice loss can be partly explained by the different spatial patterns of sea ice loss imposed.


2016 ◽  
Vol 49 (3) ◽  
pp. 1107-1136 ◽  
Author(s):  
Matthieu Chevallier ◽  
Gregory C. Smith ◽  
Frédéric Dupont ◽  
Jean-François Lemieux ◽  
Gael Forget ◽  
...  

2016 ◽  
Vol 49 (3) ◽  
pp. 1137-1138 ◽  
Author(s):  
Matthieu Chevallier ◽  
Gregory C. Smith ◽  
Frédéric Dupont ◽  
Jean-François Lemieux ◽  
Gael Forget ◽  
...  

2019 ◽  
Vol 32 (13) ◽  
pp. 4039-4053 ◽  
Author(s):  
Mark England ◽  
Alexandra Jahn ◽  
Lorenzo Polvani

Abstract Over the last half century, the Arctic sea ice cover has declined dramatically. Current estimates suggest that, for the Arctic as a whole, nearly one-half of the observed loss of summer sea ice cover is not due to anthropogenic forcing but rather is due to internal variability. Using the 40 members of the Community Earth System Model Large Ensemble (CESM-LE), our analysis provides the first regional assessment of the role of internal variability on the observed sea ice loss. The CESM-LE is one of the best available models for such an analysis, because it performs better than other CMIP5 models for many metrics of importance. Our study reveals that the local contribution of internal variability has a large range and strongly depends on the month and region in question. We find that the pattern of internal variability is highly nonuniform over the Arctic, with internal variability accounting for less than 10% of late summer (August–September) East Siberian Sea sea ice loss but more than 60% of the Kara Sea sea ice loss. In contrast, spring (April–May) sea ice loss, notably in the Barents Sea, has so far been dominated by internal variability.


2006 ◽  
Vol 19 (7) ◽  
pp. 1109-1125 ◽  
Author(s):  
Joy S. Singarayer ◽  
Jonathan L. Bamber ◽  
Paul J. Valdes

Abstract A steady decline in Arctic sea ice has been observed over recent decades. General circulation models predict further decreases under increasing greenhouse gas scenarios. Sea ice plays an important role in the climate system in that it influences ocean-to-atmosphere fluxes, surface albedo, and ocean buoyancy. The aim of this study is to isolate the climate impacts of a declining Arctic sea ice cover during the current century. The Hadley Centre Atmospheric Model (HadAM3) is forced with observed sea ice from 1980 to 2000 (obtained from satellite passive microwave radiometer data derived with the Bootstrap algorithm) and predicted sea ice reductions until 2100 under one moderate scenario and one severe scenario of ice decline, with a climatological SST field and increasing SSTs. Significant warming of the Arctic occurs during the twenty-first century (mean increase of between 1.6° and 3.9°C), with positive anomalies of up to 22°C locally. The majority of this is over ocean and limited to high latitudes, in contrast to recent observations of Northern Hemisphere warming. When a climatological SST field is used, statistically significant impacts on climate are only seen in winter, despite prescribing sea ice reductions in all months. When correspondingly increasing SSTs are incorporated, changes in climate are seen in both winter and summer, although the impacts in summer are much smaller. Alterations in atmospheric circulation and precipitation patterns are more widespread than temperature, extending down to midlatitude storm tracks. Results suggest that areas of Arctic land ice may even undergo net accumulation due to increased precipitation that results from loss of sea ice. Intensification of storm tracks implies that parts of Europe may experience higher precipitation rates.


2011 ◽  
Vol 57 (202) ◽  
pp. 231-237 ◽  
Author(s):  
David Marsan ◽  
Jérôme Weiss ◽  
Jean-Philippe Métaxian ◽  
Jacques Grangeon ◽  
Pierre-François Roux ◽  
...  

AbstractWe report the detection of bursts of low-frequency waves, typically f = 0.025 Hz, on horizontal channels of broadband seismometers deployed on the Arctic sea-ice cover during the DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies) experiment in spring 2007. These bursts have amplitudes well above the ambient ice swell and a lower frequency content. Their typical duration is of the order of minutes. They occur at irregular times, with periods of relative quietness alternating with periods of strong activity. A significant correlation between the rate of burst occurrences and the ice-cover deformation at the ∼400 km scale centered on the seismic network suggests that these bursts are caused by remote, episodic deformation involving shearing across regional-scale leads. This observation opens the possibility of complementing satellite measurements of ice-cover deformation, by providing a much more precise temporal sampling, hence a better characterization of the processes involved during these deformation events.


2012 ◽  
Vol 25 (5) ◽  
pp. 1431-1452 ◽  
Author(s):  
Alexandra Jahn ◽  
Kara Sterling ◽  
Marika M. Holland ◽  
Jennifer E. Kay ◽  
James A. Maslanik ◽  
...  

To establish how well the new Community Climate System Model, version 4 (CCSM4) simulates the properties of the Arctic sea ice and ocean, results from six CCSM4 twentieth-century ensemble simulations are compared here with the available data. It is found that the CCSM4 simulations capture most of the important climatological features of the Arctic sea ice and ocean state well, among them the sea ice thickness distribution, fraction of multiyear sea ice, and sea ice edge. The strongest bias exists in the simulated spring-to-fall sea ice motion field, the location of the Beaufort Gyre, and the temperature of the deep Arctic Ocean (below 250 m), which are caused by deficiencies in the simulation of the Arctic sea level pressure field and the lack of deep-water formation on the Arctic shelves. The observed decrease in the sea ice extent and the multiyear ice cover is well captured by the CCSM4. It is important to note, however, that the temporal evolution of the simulated Arctic sea ice cover over the satellite era is strongly influenced by internal variability. For example, while one ensemble member shows an even larger decrease in the sea ice extent over 1981–2005 than that observed, two ensemble members show no statistically significant trend over the same period. It is therefore important to compare the observed sea ice extent trend not just with the ensemble mean or a multimodel ensemble mean, but also with individual ensemble members, because of the strong imprint of internal variability on these relatively short trends.


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