Quantification of the Arctic Sea Ice‐Driven Atmospheric Circulation Variability in Coordinated Large Ensemble Simulations

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.

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

Journal of Climate ◽

10.1175/jcli-d-12-00839.1 ◽

2014 ◽

Vol 27 (2) ◽

pp. 527-550 ◽

Cited By ~ 53

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.

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Model-data comparison and data assimilation of mid-Holocene Arctic sea-ice concentration

Climate of the Past Discussions ◽

10.5194/cpd-9-6515-2013 ◽

2013 ◽

Vol 9 (6) ◽

pp. 6515-6549 ◽

Cited By ~ 1

Author(s):

F. Klein ◽

H. Goosse ◽

A. Mairesse ◽

A. de Vernal

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

Atmospheric Circulation ◽

Barents Sea ◽

Arctic Sea Ice ◽

The Arctic ◽

Model Data ◽

Sea Ice Concentration ◽

Arctic Sea ◽

Ice Concentration

Abstract. The consistency between a new quantitative reconstruction of Arctic sea-ice concentration based on dinocyst assemblages and the results of climate models has been investigated for the mid-Holocene. The comparison shows that the simulated sea-ice changes are weaker and spatially more homogeneous than the recorded ones. Furthermore, although the model-data agreement is relatively good in some regions such as the Labrador Sea, the skill of the models at local scale is low. The response of the models follows mainly the increase in summer insolation at large scale. This is modulated by changes in atmospheric circulation leading to differences between regions in the models that are albeit smaller than in the reconstruction. Performing simulations with data assimilation using the model LOVECLIM amplifies those regional differences, mainly through a reduction of the southward winds in the Barents Sea and an increase in the westerly winds in the Canadian Basin of the Arctic. This leads to an increase in the ice concentration in the Barents and Chukchi Seas and a better agreement with the reconstructions. This underlines the potential role of atmospheric circulation to explain the reconstructed changes during the Holocene.

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Two leading modes of wintertime atmospheric circulation drive the recent warm Arctic-cold Eurasia temperature pattern

10.5194/egusphere-egu2020-9705 ◽

2020 ◽

Author(s):

kunhui Ye ◽

Gabriele Messori

Keyword(s):

Sea Ice ◽

Atmospheric Circulation ◽

North Atlantic ◽

Arctic Sea Ice ◽

The Arctic ◽

The North ◽

Downward Longwave Radiation ◽

North Asia ◽

Arctic Sea ◽

The North Atlantic

<p>The wintertime warm Arctic-cold Eurasia (WACE) temperature trend during 1990-2010 was characterized by accelerating warming in the Arctic region, cooling in Eurasia and accelerating autumn/winter Arctic sea ice loss. We identify two atmospheric circulation modes over the North Atlantic-Northern Eurasian sector which displayed strong upward trends over the same period and can explain a large part of the observed decadal WACE pattern. Both modes bear a close resemblance to well-known teleconnection patterns and are relatively independent from anomalies in Arctic sea-ice cover. The first mode (PC1) captures the recent negative trends in the North Atlantic Oscillation and increased Greenland blocking frequency while the second mode (PC2) is reminiscent of a Rossby wave train and reflects an increased blocking frequency over the Urals and North Asia. We find that the loss in the Arctic sea ice and the upward trends in the PC1/PC2 together account for most of the decadal Arctic warming trend (>80%). However, the decadal Eurasian cooling trends may be primarily ascribed to the two circulation modes alone: all of the cooling in Siberia is contributed to by the PC1, and 65% of the cooling in East Asia by their combination (the contribution by PC2 doubles that by PC1). Enhanced intraseasonal activity of the two circulation modes increases blocking frequencies over Greenland, the Ural region and North Asia, which drive anomalous moisture/heat flux towards the Arctic and alter the downward longwave radiation. It weakens warm advection and enhances advection of Arctic cold airmass towards Eurasia.</p>

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Two Leading Modes of Wintertime Atmospheric Circulation Drive the Recent Warm Arctic–Cold Eurasia Temperature Pattern

Journal of Climate ◽

10.1175/jcli-d-19-0403.1 ◽

2020 ◽

Vol 33 (13) ◽

pp. 5565-5587 ◽

Cited By ~ 1

Author(s):

Kunhui Ye ◽

Gabriele Messori

Keyword(s):

Sea Ice ◽

Atmospheric Circulation ◽

North Atlantic ◽

Arctic Sea Ice ◽

The Arctic ◽

The North ◽

Downward Longwave Radiation ◽

North Asia ◽

Arctic Sea ◽

The North Atlantic

AbstractThe wintertime warm Arctic–cold Eurasia (WACE) temperature trend during 1990–2010 was characterized by accelerating warming in the Arctic region, cooling in Eurasia, and accelerating autumn/winter Arctic sea ice loss. We identify two atmospheric circulation modes over the North Atlantic–northern Eurasian sector that displayed strong upward trends over the same period and can explain a large part of the observed decadal WACE pattern. Both modes bear a close resemblance to well-known teleconnection patterns and are relatively independent from variability in Arctic sea ice cover. The first mode (PC1) captures the recent negative trends in the North Atlantic Oscillation and increased Greenland blocking frequency, while the second mode (PC2) is reminiscent of a Rossby wave train and reflects an increased blocking frequency over the Urals and north Asia. We find that the loss in the Arctic sea ice and the upward trends in PC1 and PC2 together account for most of the decadal Arctic warming trend (>80%). However, the decadal Eurasian cooling trends may be primarily ascribed to the two circulation modes alone: all of the cooling in Siberia is contributed to by PC1 and 65% of the cooling in East Asia by their combination (the contribution by PC2 doubles that by PC1). Enhanced intraseasonal activity of the two circulation modes increases blocking frequencies over Greenland, the Ural region, and north Asia, which drive anomalous moisture/heat flux toward the Arctic and alter the downward longwave radiation. This also weakens warm advection and enhances advection of cold Arctic airmasses towards Eurasia.

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

Journal of Climate ◽

10.1175/jcli-d-18-0192.1 ◽

2018 ◽

Vol 31 (22) ◽

pp. 9193-9206 ◽

Cited By ~ 6

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.

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Atmospheric Circulation and Its Effect on Arctic Sea Ice in CCSM3 Simulations at Medium and High Resolution*

Journal of Climate ◽

10.1175/jcli3753.1 ◽

2006 ◽

Vol 19 (11) ◽

pp. 2415-2436 ◽

Cited By ~ 64

Author(s):

Eric DeWeaver ◽

Cecilia M. Bitz

Keyword(s):

Sea Ice ◽

Atmospheric Circulation ◽

Vertical Structure ◽

Surface Wind ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Surface Winds ◽

Arctic Sea ◽

Siberian Coast

Abstract The simulation of Arctic sea ice and surface winds changes significantly when Community Climate System Model version 3 (CCSM3) resolution is increased from T42 (∼2.8°) to T85 (∼1.4°). At T42 resolution, Arctic sea ice is too thick off the Siberian coast and too thin along the Canadian coast. Both of these biases are reduced at T85 resolution. The most prominent surface wind difference is the erroneous North Polar summer anticyclone, present at T42 but absent at T85. An offline sea ice model is used to study the effect of the surface winds on sea ice thickness. In this model, the surface wind stress is prescribed alternately from reanalysis and the T42 and T85 simulations. In the offline model, CCSM3 surface wind biases have a dramatic effect on sea ice distribution: with reanalysis surface winds annual-mean ice thickness is greatest along the Canadian coast, but with CCSM3 winds thickness is greater on the Siberian side. A significant difference between the two CCSM3-forced offline simulations is the thickness of the ice along the Canadian archipelago, where the T85 winds produce thicker ice than their T42 counterparts. Seasonal forcing experiments, with CCSM3 winds during spring and summer and reanalysis winds in fall and winter, relate the Canadian thickness difference to spring and summer surface wind differences. These experiments also show that the ice buildup on the Siberian coast at both resolutions is related to the fall and winter surface winds. The Arctic atmospheric circulation is examined further through comparisons of the winter sea level pressure (SLP) and eddy geopotential height. At both resolutions the simulated Beaufort high is quite weak, weaker at higher resolution. Eddy heights show that the wintertime Beaufort high in reanalysis has a barotropic vertical structure. In contrast, high CCSM3 SLP in Arctic winter is found in association with cold lower-tropospheric temperatures and a baroclinic vertical structure. In reanalysis, the summertime Arctic surface circulation is dominated by a polar cyclone, which is accompanied by surface inflow and a deep Ferrel cell north of the traditional polar cell. The Arctic Ferrel cell is accompanied by a northward flux of zonal momentum and a polar lobe of the zonal-mean jet. These features do not appear in the CCSM3 simulations at either resolution.

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Recent summer Arctic atmospheric circulation anomalies in a historical perspective

The Cryosphere Discussions ◽

10.5194/tcd-8-4823-2014 ◽

2014 ◽

Vol 8 (5) ◽

pp. 4823-4847 ◽

Cited By ~ 1

Author(s):

A. Belleflamme ◽

X. Fettweis ◽

M. Erpicum

Keyword(s):

Sea Ice ◽

Atmospheric Circulation ◽

Greenland Ice Sheet ◽

Arctic Region ◽

Arctic Sea Ice ◽

The Arctic ◽

Arctic Sea ◽

Circulation Anomalies ◽

Arctic Sea Ice Loss ◽

The Arctic Region

Abstract. A significant increase in the summertime occurrence of a high pressure area over the Beaufort Sea and Greenland has been observed from the beginning of the 2000's, and particularly between 2007 and 2012. These circulation anomalies are likely partly responsible for the enhanced Greenland ice sheet melt as well as the Arctic sea ice loss observed since 2007. Therefore, it is interesting to analyse whether similar conditions might have happened since the late 19th century over the Arctic region. We have used an atmospheric circulation type classification based on daily mean sea level pressure and 500 hPa geopotential height data from four reanalysis datasets (ERA-Interim, ERA-40, NCEP/NCAR, and 20CRv2) to put the recent circulation anomalies in perspective with the atmospheric circulation variability since 1871. We found that circulation conditions similar to 2007–2012 have occurred in the past, despite a higher uncertainty of the reconstructed circulation before 1940. But the recent anomalies largely exceed the interannual variability of the atmospheric circulation of the Arctic region. These circulation anomalies are linked with the North Atlantic Oscillation suggesting that they are not limited to the Arctic. Finally, they favour summertime Arctic sea ice loss.

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Impacts of Arctic Sea Ice on Cold Season Atmospheric Variability and Trends Estimated from Observations and a Multi-model Large Ensemble

Journal of Climate ◽

10.1175/jcli-d-20-0578.1 ◽

2021 ◽

pp. 1-64

Author(s):

Yu-Chiao Liang ◽

Claude Frankignoul ◽

Young-Oh Kwon ◽

Guillaume Gastineau ◽

Elisa Manzini ◽

...

Keyword(s):

Sea Ice ◽

Surface Temperature ◽

General Circulation ◽

Cold Season ◽

Arctic Sea Ice ◽

The Arctic ◽

Atmospheric Variability ◽

Large Ensemble ◽

Near Surface ◽

Arctic Sea

AbstractTo examine the atmospheric responses to Arctic sea-ice variability in the Northern Hemisphere cold season (October to following March), this study uses a coordinated set of large-ensemble experiments of nine atmospheric general circulation models (AGCMs) forced with observed daily-varying sea-ice, sea-surface temperature, and radiative forcings prescribed during the 1979-2014 period, together with a parallel set of experiments where Arctic sea ice is substituted by its climatology. The simulations of the former set reproduce the near-surface temperature trends in reanalysis data, with similar amplitude, and their multi-model ensemble mean (MMEM) shows decreasing sea-level pressure over much of the polar cap and Eurasia in boreal autumn. The MMEM difference between the two experiments allows isolating the effects of Arctic sea-ice loss, which explain a large portion of the Arctic warming trends in the lower troposphere and drives a small but statistically significant weakening of the wintertime Arctic Oscillation. The observed interannual co-variability between sea-ice extent in the Barents-Kara Seas and lagged atmospheric circulation is distinguished from the effects of confounding factors based on multiple regression, and quantitatively compared to the co-variability in MMEMs. The interannual sea-ice decline followed by a negative North Atlantic Oscillation-like anomaly found in observations is also seen in the MMEM differences, with consistent spatial structure but much smaller amplitude. This result suggests that the sea-ice impacts on trends and interannual atmospheric variability simulated by AGCMs could be underestimated, but caution is needed because internal atmospheric variability may have affected the observed relationship.

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