scholarly journals Simulated Atmospheric Response to Regional and Pan-Arctic Sea Ice Loss

2017 ◽  
Vol 30 (11) ◽  
pp. 3945-3962 ◽  
Author(s):  
James A. Screen
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Large Scale ◽  
Circulation Model ◽  
Arctic Sea Ice ◽  
Ecological Impacts ◽  
The Arctic ◽  
Stratospheric Polar Vortex ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss

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.

The Robustness of Midlatitude Weather Pattern Changes due to Arctic Sea Ice Loss

2016 ◽  
Vol 29 (21) ◽  
pp. 7831-7849 ◽  
Author(s):  
Hans W. Chen ◽  
Fuqing Zhang ◽  
Richard B. Alley
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Coupled System ◽  
Circulation Pattern ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Intrinsic Variability ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss

Abstract The significance and robustness of the link between Arctic sea ice loss and changes in midlatitude weather patterns is investigated through a series of model simulations from the Community Atmosphere Model, version 5.3, with systematically perturbed sea ice cover in the Arctic. Using a large ensemble of 10 sea ice scenarios and 550 simulations, it is found that prescribed Arctic sea ice anomalies produce statistically significant changes for certain metrics of the midlatitude circulation but not for others. Furthermore, the significant midlatitude circulation changes do not scale linearly with the sea ice anomalies and are not present in all scenarios, indicating that the remote atmospheric response to reduced Arctic sea ice can be statistically significant under certain conditions but is generally nonrobust. Shifts in the Northern Hemisphere polar jet stream and changes in the meridional extent of upper-level large-scale waves due to the sea ice perturbations are generally small and not clearly distinguished from intrinsic variability. Reduced Arctic sea ice may favor a circulation pattern that resembles the negative phase of the Arctic Oscillation and may increase the risk of cold outbreaks in eastern Asia by almost 50%, but this response is found in only half of the scenarios with negative sea ice anomalies. In eastern North America the frequency of extreme cold events decreases almost linearly with decreasing sea ice cover. This study’s finding of frequent significant anomalies without a robust linear response suggests interactions between variability and persistence in the coupled system, which may contribute to the lack of convergence among studies of Arctic influences on midlatitude circulation.

scholarly journals Internal Variability in Projections of Twenty-First-Century Arctic Sea Ice Loss: Role of the Large-Scale Atmospheric Circulation

2014 ◽  
Vol 27 (2) ◽  
pp. 527-550 ◽  
Author(s):  
Justin J. Wettstein ◽  
Clara Deser
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
Large Scale ◽  
Wave Train ◽  
Internal Variability ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Arctic Sea ◽  
Rossby Wave Train ◽  
Arctic Sea Ice Loss

Abstract Internal variability in twenty-first-century summer Arctic sea ice loss and its relationship to the large-scale atmospheric circulation is investigated in a 39-member Community Climate System Model, version 3 (CCSM3) ensemble for the period 2000–61. Each member is subject to an identical greenhouse gas emissions scenario and differs only in the atmospheric model component's initial condition. September Arctic sea ice extent trends during 2020–59 range from −2.0 × 106 to −5.7 × 106 km2 across the 39 ensemble members, indicating a substantial role for internal variability in future Arctic sea ice loss projections. A similar nearly threefold range (from −7.0 × 103 to −19 × 103 km3) is found for summer sea ice volume trends. Higher rates of summer Arctic sea ice loss in CCSM3 are associated with enhanced transpolar drift and Fram Strait ice export driven by surface wind and sea level pressure patterns. Over the Arctic, the covarying atmospheric circulation patterns resemble the so-called Arctic dipole, with maximum amplitude between April and July. Outside the Arctic, an atmospheric Rossby wave train over the Pacific sector is associated with internal ice loss variability. Interannual covariability patterns between sea ice and atmospheric circulation are similar to those based on trends, suggesting that similar processes govern internal variability over a broad range of time scales. Interannual patterns of CCSM3 ice–atmosphere covariability compare well with those in nature and in the newer CCSM4 version of the model, lending confidence to the results. Atmospheric teleconnection patterns in CCSM3 suggest that the tropical Pacific modulates Arctic sea ice variability via the aforementioned Rossby wave train. Large ensembles with other coupled models are needed to corroborate these CCSM3-based findings.

scholarly journals The Role of Extratropical Ocean Warming in the Coupled Climate Response to Arctic Sea Ice Loss

2018 ◽  
Vol 31 (22) ◽  
pp. 9193-9206 ◽  
Author(s):  
Russell Blackport ◽  
Paul J. Kushner
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
General Circulation ◽  
Arctic Sea Ice ◽  
Ocean Warming ◽  
The Arctic ◽  
Climate Response ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss

The role of extratropical ocean warming in the coupled climate response to Arctic sea ice loss is investigated using coupled atmosphere–ocean general circulation model (AOGCM) and uncoupled atmospheric-only (AGCM) experiments. Coupled AOGCM experiments driven by sea ice albedo reduction and greenhouse gas–dominated radiative forcing are used to diagnose the extratropical sea surface temperature (SST) response to sea ice loss. Sea ice loss is then imposed in AGCM experiments both with and without these extratropical SST changes, which are found to extend beyond the regions where sea ice is lost. Sea ice loss in isolation drives warming that is confined to the Arctic lower troposphere and only a weak atmospheric circulation response. When the extratropical SST response caused by sea ice loss is also included in the forcing, the warming extends into the Arctic midtroposphere during winter. This coincides with a stronger atmospheric circulation response, including an equatorward shift in the eddy-driven jet, a deepening of the Aleutian low, and an expansion of the Siberian high. Similar results are found whether the extratropical SST forcing is taken directly from the AOGCM driven by sea ice loss, or whether they are diagnosed using a two-parameter pattern scaling technique where tropical adjustment to sea ice loss is removed. These results suggest that AGCM experiments that are driven by sea ice loss and only local SST increases will underestimate the Arctic midtroposphere warming and atmospheric circulation response to sea ice loss, compared to AOGCM simulations and the real world.

Seasonal Forecasts of the Pan-Arctic Sea Ice Extent Using a GCM-Based Seasonal Prediction System

2013 ◽  
Vol 26 (16) ◽  
pp. 6092-6104 ◽  
Author(s):  
Matthieu Chevallier ◽  
David Salas y Mélia ◽  
Aurore Voldoire ◽  
Michel Déqué ◽  
Gilles Garric
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Global Climate Model ◽  
Circulation Model ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Seasonal Forecasts ◽  
Sea Ice Extent ◽  
Arctic Sea

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.

scholarly journals Weak Stratospheric Polar Vortex Events Modulated by the Arctic Sea‐Ice Loss

2019 ◽  
Vol 124 (2) ◽  
pp. 858-869 ◽  
Author(s):  
Kazuhira Hoshi ◽  
Jinro Ukita ◽  
Meiji Honda ◽  
Tetsu Nakamura ◽  
Koji Yamazaki ◽  
...  
Keyword(s):  
Sea Ice ◽  
Polar Vortex ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Stratospheric Polar Vortex ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss

scholarly journals The Atmospheric Response to Three Decades of Observed Arctic Sea Ice Loss

2013 ◽  
Vol 26 (4) ◽  
pp. 1230-1248 ◽  
Author(s):  
James A. Screen ◽  
Ian Simmonds ◽  
Clara Deser ◽  
Robert Tomas
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Stratospheric Ozone ◽  
Lower Troposphere ◽  
Circulation Model ◽  
Arctic Sea Ice ◽  
Atmospheric Response ◽  
Early Winter ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss

Abstract Arctic sea ice is declining at an increasing rate with potentially important repercussions. To understand better the atmospheric changes that may have occurred in response to Arctic sea ice loss, this study presents results from atmospheric general circulation model (AGCM) experiments in which the only time-varying forcings prescribed were observed variations in Arctic sea ice and accompanying changes in Arctic sea surface temperatures from 1979 to 2009. Two independent AGCMs are utilized in order to assess the robustness of the response across different models. The results suggest that the atmospheric impacts of Arctic sea ice loss have been manifested most strongly within the maritime and coastal Arctic and in the lowermost atmosphere. Sea ice loss has driven increased energy transfer from the ocean to the atmosphere, enhanced warming and moistening of the lower troposphere, decreased the strength of the surface temperature inversion, and increased lower-tropospheric thickness; all of these changes are most pronounced in autumn and early winter (September–December). The early winter (November–December) atmospheric circulation response resembles the negative phase of the North Atlantic Oscillation (NAO); however, the NAO-type response is quite weak and is often masked by intrinsic (unforced) atmospheric variability. Some evidence of a late winter (March–April) polar stratospheric cooling response to sea ice loss is also found, which may have important implications for polar stratospheric ozone concentrations. The attribution and quantification of other aspects of the possible atmospheric response are hindered by model sensitivities and large intrinsic variability. The potential remote responses to Arctic sea ice change are currently hard to confirm and remain uncertain.

Modelling the accumulation and transport of microplastics by Arctic sea ice

2020 ◽  
Author(s):  
Miguel Angel Morales Maqueda ◽  
Alethea Sara Mountford
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Barents Sea ◽  
Circulation Model ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Global Ocean ◽  
Simple Formulation ◽  
Arctic Sea

<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>

scholarly journals Mechanisms causing reduced Arctic sea ice loss in a coupled climate model

2012 ◽  
Vol 6 (4) ◽  
pp. 2653-2687 ◽  
Author(s):  
A. E. West ◽  
A. B. Keen ◽  
H. T. Hewitt
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Historical Record ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Slight Reduction ◽  
Coupled Climate Model ◽  
Arctic Sea ◽  
Ensembles Project ◽  
Arctic Sea Ice Loss

Abstract. The fully-coupled climate model HadGEM1 produces one of the most accurate simulations of the historical record of Arctic sea ice seen in the IPCC AR4 multi-model ensemble. In this study, we examine projections of sea ice decline out to 2030, produced by two ensembles of HadGEM1 with natural and anthropogenic forcings included. These ensembles project a significant slowing of the rate of ice loss to occur after 2010, with some integrations even simulating a small increase in ice area. We use an energy budget of the Arctic to examine the causes of this slowdown. A negative feedback effect by which rapid reductions in ice thickness north of Greenland reduce ice export is found to play a major role. A slight reduction in ocean-to-ice heat flux in the relevant period, caused by changes in the MOC and subpolar gyre in some integrations, is also found to play a part. Finally, we assess the likelihood of a slowdown occurring in the real world due to these causes.

Large-scale Arctic sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission

2021 ◽  
Author(s):  
Stephen Howell ◽  
Mike Brady ◽  
Alexander Komarov
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Large Scale ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
High Temporal Resolution ◽  
Sar Images ◽  
Sea Ice Extent ◽  
Arctic Sea

<p>As the Arctic’s sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of this process.  Recently, the sea ice community has entered a new era of synthetic aperture radar (SAR) satellites operating at C-band with the launch of Sentinel-1A in 2014, Sentinel-1B in 2016 and the RADARSAT Constellation Mission (RCM) in 2019. These missions represent a collection of 5 spaceborne SAR sensors that together can routinely cover Arctic sea ice with a high spatial resolution (20-90 m) but also with a high temporal resolution (1-7 days) typically associated with passive microwave sensors. Here, we used ~28,000 SAR image pairs from Sentinel-1AB together with ~15,000 SAR images pairs from RCM to generate high spatiotemporal large-scale sea ice motion products across the pan-Arctic domain for 2020. The combined Sentinel-1AB and RCM sea ice motion product provides almost complete 7-day coverage over the entire pan-Arctic domain that also includes the pole-hole. Compared to the National Snow and Ice Data Center (NSIDC) Polar Pathfinder and Ocean and Sea Ice-Satellite Application Facility (OSI-SAF) sea ice motion products, ice speed was found to be faster with the Senintel-1AB and RCM product which is attributed to the higher spatial resolution of SAR imagery. More sea ice motion vectors were detected from the Sentinel-1AB and RCM product in during the summer months and within the narrow channels and inlets compared to the NSIDC Polar Pathfinder and OSI-SAF sea ice motion products. Overall, our results demonstrate that sea ice geophysical variables across the pan-Arctic domain can now be retrieved from multi-sensor SAR images at both high spatial and temporal resolution.</p>

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