Realism of simulated internal variability in Arctic sea ice

2021 ◽  
Author(s):  
Christopher Wyburn-Powell ◽  
Alexandra Jahn ◽  
Mark England
Keyword(s):  
Sea Ice ◽  
Internal Variability ◽  
Forced Response ◽  
Arctic Sea Ice ◽  
Cmip5 Models ◽  
Edge Region ◽  
Arctic Sea ◽  
Ice Edge ◽  
Arctic Summer

<p>Arctic summer sea ice has decreased dramatically over the last few decades, particularly in the summer months. The observed decline is faster than most CMIP5 models, but if internal variability is considered, models and observations are not inconsistent. With only one realization of reality in observations, it is difficult to disentangle the role of internal variability from the forced response. We directly compare one metric of internal variability by resampling both observations and models. So far we have compared five CMIP5 models from the CLIVAR multi-model large ensemble archive (CanESM2, CESM1, CSIRO MK36, GFDL ESM2M, and MPI ESM1). For the pan-Arctic, these models were found to have higher internal variability than observed by approximately 10-50% across models and seasons. Spatially, we find the variability in ice edge region is consistently modelled well in March. In September, although the member mean of the models shows both smaller absolute declines and smaller variation of such declines with resampling, the models have at least one member consistent with observations. This allows us to conclude that the models’ representation of this specific metric of internal variability is consistent with observations.</p>

scholarly journals Nonuniform Contribution of Internal Variability to Recent Arctic Sea Ice Loss

2019 ◽  
Vol 32 (13) ◽  
pp. 4039-4053 ◽  
Author(s):  
Mark England ◽  
Alexandra Jahn ◽  
Lorenzo Polvani
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Late Summer ◽  
Internal Variability ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Cmip5 Models ◽  
East Siberian Sea ◽  
Arctic Sea

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.

scholarly journals Variability of Arctic Sea Ice Thickness Using PIOMAS and the CESM Large Ensemble

2018 ◽  
Vol 31 (8) ◽  
pp. 3233-3247 ◽  
Author(s):  
Zachary Labe ◽  
Gudrun Magnusdottir ◽  
Hal Stern
Keyword(s):  
Sea Ice ◽  
Internal Variability ◽  
Forced Response ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Spatial And Temporal Variability ◽  
Large Ensemble ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

Abstract Because of limited high-quality satellite and in situ observations, less attention has been given to the trends in Arctic sea ice thickness and therefore sea ice volume than to the trends in sea ice extent. This study evaluates the spatial and temporal variability in Arctic sea ice thickness using the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Additionally, the Community Earth System Model Large Ensemble Project (LENS) is used to quantify the forced response and internal variability in the model. A dipole spatial pattern of sea ice thickness variability is shown in both PIOMAS and LENS with opposite signs of polarity between the East Siberian Sea and near the Fram Strait. As future sea ice thins, this dipole structure of variability is reduced, and the largest interannual variability is found only along the northern Greenland coastline. Under a high-emissions scenario (RCP8.5) projection, average September sea ice thickness falls below 0.5 m by the end of the twenty-first century. However, a regional analysis shows internal variability contributes to an uncertainty of 10 to 20 years for the timing of the first September sea ice thickness less than 0.5 m in the marginal seas.

Trends in Arctic sea ice and the role of atmospheric circulation

2013 ◽  
Vol 14 (2) ◽  
pp. 97-101 ◽  
Author(s):  
Masayo Ogi ◽  
Ignatius G. Rigor
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
Arctic Sea Ice ◽  
Arctic Sea

scholarly journals Late-Twentieth-Century Simulation of Arctic Sea Ice and Ocean Properties in the CCSM4

2012 ◽  
Vol 25 (5) ◽  
pp. 1431-1452 ◽  
Author(s):  
Alexandra Jahn ◽  
Kara Sterling ◽  
Marika M. Holland ◽  
Jennifer E. Kay ◽  
James A. Maslanik ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Sea Ice ◽  
Internal Variability ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Motion Field ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Ensemble Mean

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.

Arctic sea ice variability and trends in the last four decades: role of ocean-atmospheric forcing

2021 ◽  
pp. 301-324
Author(s):  
Avinash Kumar ◽  
Juhi Yadav ◽  
Rohit Srivastava ◽  
Rahul Mohan
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Atmospheric Forcing ◽  
Arctic Sea

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 The role of cyclone activity in snow accumulation on Arctic sea ice

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
M. A. Webster ◽  
C. Parker ◽  
L. Boisvert ◽  
R. Kwok
Keyword(s):  
Sea Ice ◽  
Snow Accumulation ◽  
Arctic Sea Ice ◽  
Surface Energy Budget ◽  
The Arctic ◽  
Cyclone Activity ◽  
In Situ Data ◽  
Arctic Sea ◽  
Spatio Temporal

AbstractIdentifying the mechanisms controlling the timing and magnitude of snow accumulation on sea ice is crucial for understanding snow’s net effect on the surface energy budget and sea-ice mass balance. Here, we analyze the role of cyclone activity on the seasonal buildup of snow on Arctic sea ice using model, satellite, and in situ data over 1979–2016. On average, 44% of the variability in monthly snow accumulation was controlled by cyclone snowfall and 29% by sea-ice freeze-up. However, there were strong spatio-temporal differences. Cyclone snowfall comprised ~50% of total snowfall in the Pacific compared to 83% in the Atlantic. While cyclones are stronger in the Atlantic, Pacific snow accumulation is more sensitive to cyclone strength. These findings highlight the heterogeneity in atmosphere-snow-ice interactions across the Arctic, and emphasize the need to scrutinize mechanisms governing cyclone activity to better understand their effects on the Arctic snow-ice system with anthropogenic warming.

scholarly journals Summer predictions of Arctic sea ice edge in multi-model seasonal re-forecasts

2020 ◽  
Vol 54 (11-12) ◽  
pp. 5013-5029
Author(s):  
Lauriane Batté ◽  
Ilona Välisuo ◽  
Matthieu Chevallier ◽  
Juan C. Acosta Navarro ◽  
Pablo Ortega ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Arctic Sea ◽  
Ice Edge ◽  
Sea Ice Edge

Summer retreat of Arctic sea ice: Role of summer winds

2008 ◽  
Vol 35 (24) ◽  
Author(s):  
Masayo Ogi ◽  
Ignatius G. Rigor ◽  
Miles G. McPhee ◽  
John M. Wallace
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Arctic Sea
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