Observations of recent Arctic sea ice volume loss and its impact on ocean-atmosphere energy exchange and ice production

Abstract Clouds play an important role in the Arctic surface radiative budget, impacting the seasonal evolution of Arctic sea-ice cover. We explore the large-scale impacts of springtime and early summer (March through July) cloud and radiative fluxes on sea ice by comparing these fluxes to seasonal ice volume losses over the central Arctic basin, calculated for available observational years 2004–2007 (ICESat) and 2011–2017 (CryoSat-2). We also supplement observation data with sea-ice volume computed from the Pan-Arctic Ice–Ocean Modeling and Assimilation System (PIOMAS) during summer months. We find that the volume of sea ice lost over the melt season is most closely related to observed downwelling longwave radiation in March and early summer (June and July) longwave cloud radiative forcing, which together explain a large fraction of interannual variability in seasonal sea-ice volume loss (R2 = 0.71, p = 0.007). We show that downwelling longwave fluxes likely impact the timing of melt onset near the sea-ice edge, and can limit the magnitude of ice thickening from March to April. Radiative fluxes in June and July are likely critical to seasonal volume loss because modeled data show the greatest ice volume reductions occur during these months.

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Arctic sea ice volume budget decomposition satellite product for the CryoSat-2 (2010-2020) period

10.5194/egusphere-egu21-12079 ◽

2021 ◽

Author(s):

Harry Heorton ◽

Michel Tsamados ◽

Paul Holland ◽

Jack Landy

Keyword(s):

Sea Ice ◽

Climate Model ◽

Volume Growth ◽

Arctic Sea Ice ◽

First Year ◽

Sea Ice Concentration ◽

Marginal Seas ◽

Ice Volume ◽

Arctic Sea ◽

Ice Concentration

We combine satellite-derived observations of sea ice concentration, drift, and thickness to provide the first observational decomposition of the dynamic (advection/divergence) and thermodynamic (melt/growth) drivers of wintertime Arctic sea ice volume change. Ten winter growth seasons are analyzed over the CryoSat-2 period between October 2010 and April 2020. Sensitivity to several observational products is performed to provide an estimated uncertainty of the budget calculations. The total thermodynamic ice volume growth and dynamic ice losses are calculated with marked seasonal, inter-annual and regional variations. Ice growth is fastest during Autumn, in the Marginal Seas and over first year ice.&#160;Our budget decomposition methodology can help diagnose the processes confounding climate model predictions of sea ice. We make our product and code available to the community in monthly pan-Arctic netcdft files for the entire October 2010 to April 2020 period.

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Retrievals of Arctic Sea-Ice Volume and Its Trend Significantly Affected by Interannual Snow Variability

Geophysical Research Letters ◽

10.1029/2018gl078867 ◽

2018 ◽

Vol 45 (21) ◽

pp. 11,751-11,759 ◽

Cited By ~ 7

Author(s):

Felix Bunzel ◽

Dirk Notz ◽

Leif Toudal Pedersen

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Ice Volume ◽

Arctic Sea

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Achieving realistic Arctic-midlatitude teleconnections in a climate model through stochastic process representation

10.5194/wcd-2021-61 ◽

2021 ◽

Author(s):

Kristian Strommen ◽

Stephan Juricke

Keyword(s):

Sea Ice ◽

Climate Models ◽

Climate Model ◽

Arctic Sea Ice ◽

Stochastic Perturbations ◽

Model Studies ◽

Process Representation ◽

Arctic Sea ◽

Ocean Atmosphere ◽

Winter Circulation

Abstract. The extent to which interannual variability in Arctic sea ice influences the midlatitude circulation has been extensively debated. While observational data supports the existence of a teleconnection between November sea ice in the Barents-Kara region and the subsequent winter circulation, climate models do not consistently reproduce such a link, with only very weak inter-model consensus. We show, using the EC-Earth3 climate model, that while a deterministic ensemble of coupled simulations shows no evidence of such a teleconnection, the inclusion of stochastic parameterizations to the ocean and sea ice component of EC-Earth3 results in the emergence of a robust teleconnection comparable in magnitude to that observed. We show that this can be accounted for entirely by an improved ice-ocean-atmosphere coupling due to the stochastic perturbations. In particular, the inconsistent signal in existing climate model studies may be due to model biases in surface coupling, with stochastic parameterizations being one possible remedy.

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Multidecadal Arctic sea ice thickness and volume derived from ice age

The Cryosphere ◽

10.5194/tc-14-1325-2020 ◽

2020 ◽

Vol 14 (4) ◽

pp. 1325-1345 ◽

Cited By ~ 3

Author(s):

Yinghui Liu ◽

Jeffrey R. Key ◽

Xuanji Wang ◽

Mark Tschudi

Keyword(s):

Sea Ice ◽

Global Climate ◽

Arctic Sea Ice ◽

Ice Age ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Ice Volume ◽

Arctic Sea ◽

Arctic Sea Ice Thickness

Abstract. Sea ice is a key component of the Arctic climate system, and has impacts on global climate. Ice concentration, thickness, and volume are among the most important Arctic sea ice parameters. This study presents a new record of Arctic sea ice thickness and volume from 1984 to 2018 based on an existing satellite-derived ice age product. The relationship between ice age and ice thickness is first established for every month based on collocated ice age and ice thickness from submarine sonar data (1984–2000) and ICESat (2003–2008) and an empirical ice growth model. Based on this relationship, ice thickness is derived for the entire time period from the weekly ice age product, and the Arctic monthly sea ice volume is then calculated. The ice-age-based thickness and volume show good agreement in terms of bias and root-mean-square error with submarine, ICESat, and CryoSat-2 ice thickness, as well as ICESat and CryoSat-2 ice volume, in February–March and October–November. More detailed comparisons with independent data from Envisat for 2003 to 2010 and CryoSat-2 from CPOM, AWI, and NASA GSFC (Goddard Space Flight Center) for 2011 to 2018 show low bias in ice-age-based thickness. The ratios of the ice volume uncertainties to the mean range from 21 % to 29 %. Analysis of the derived data shows that the ice-age-based sea ice volume exhibits a decreasing trend of −411 km3 yr−1 from 1984 to 2018, stronger than the trends from other datasets. Of the factors affecting the sea ice volume trends, changes in sea ice thickness contribute more than changes in sea ice area, with a contribution of at least 80 % from changes in sea ice thickness from November to May and nearly 50 % in August and September, while less than 30 % is from changes in sea ice area in all months.

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Arctic Sea Ice Volume Variability over 1901–2010: A Model-Based Reconstruction

Journal of Climate ◽

10.1175/jcli-d-19-0008.1 ◽

2019 ◽

Vol 32 (15) ◽

pp. 4731-4752 ◽

Cited By ~ 12

Author(s):

Axel J. Schweiger ◽

Kevin R. Wood ◽

Jinlun Zhang

Keyword(s):

Twentieth Century ◽

Sea Ice ◽

Arctic Sea Ice ◽

Ocean Modeling ◽

The Arctic ◽

Error Statistics ◽

Atlantic Sector ◽

Ice Volume ◽

Sea Ice Decline ◽

Arctic Sea

Abstract PIOMAS-20C, an Arctic sea ice reconstruction for 1901–2010, is produced by forcing the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) with ERA-20C atmospheric data. ERA-20C performance over Arctic sea ice is assessed by comparisons with measurements and data from other reanalyses. ERA-20C performs similarly with respect to the annual cycle of downwelling radiation, air temperature, and wind speed compared to reanalyses with more extensive data assimilation such as ERA-Interim and MERRA. PIOMAS-20C sea ice thickness and volume are then compared with in situ and aircraft remote sensing observations for the period of ~1950–2010. Error statistics are similar to those for PIOMAS. We compare the magnitude and patterns of sea ice variability between the first half of the twentieth century (1901–40) and the more recent period (1980–2010), both marked by sea ice decline in the Arctic. The first period contains the so-called early-twentieth-century warming (ETCW; ~1920–40) during which the Atlantic sector saw a significant decline in sea ice volume, but the Pacific sector did not. The sea ice decline over the 1979–2010 period is pan-Arctic and 6 times larger than the net decline during the 1901–40 period. Sea ice volume trends reconstructed solely from surface temperature anomalies are smaller than PIOMAS-20C, suggesting that mechanisms other than warming, such as changes in ice motion and deformation, played a significant role in determining sea ice volume trends during both periods.

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Autumn atmospheric response to the 2007 low Arctic sea ice extent in coupled ocean–atmosphere hindcasts

Climate Dynamics ◽

10.1007/s00382-011-1169-z ◽

2011 ◽

Vol 38 (11-12) ◽

pp. 2437-2448 ◽

Cited By ~ 74

Author(s):

Yvan J. Orsolini ◽

Retish Senan ◽

Rasmus E. Benestad ◽

Arne Melsom

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Atmospheric Response ◽

Sea Ice Extent ◽

Arctic Sea ◽

Ocean Atmosphere ◽

Low Arctic ◽

Arctic Sea Ice Extent

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Increased Arctic sea ice volume after anomalously low melting in 2013

Nature Geoscience ◽

10.1038/ngeo2489 ◽

2015 ◽

Vol 8 (8) ◽

pp. 643-646 ◽

Cited By ~ 96

Author(s):

Rachel L. Tilling ◽

Andy Ridout ◽

Andrew Shepherd ◽

Duncan J. Wingham

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Ice Volume ◽

Arctic Sea

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Atmospheric Response to Arctic and Antarctic Sea Ice: The Importance of Ocean–Atmosphere Coupling and the Background State

Journal of Climate ◽

10.1175/jcli-d-16-0564.1 ◽

2017 ◽

Vol 30 (12) ◽

pp. 4547-4565 ◽

Cited By ~ 58

Author(s):

Doug M. Smith ◽

Nick J. Dunstone ◽

Adam A. Scaife ◽

Emma K. Fiedler ◽

Dan Copsey ◽

...

Keyword(s):

Sea Ice ◽

North Atlantic ◽

Arctic Sea Ice ◽

Atmospheric Response ◽

The North ◽

Antarctic Sea Ice ◽

Arctic Sea ◽

Ocean Atmosphere ◽

The North Atlantic ◽

Background State

The atmospheric response to Arctic and Antarctic sea ice changes typical of the present day and coming decades is investigated using the Hadley Centre global climate model (HadGEM3). The response is diagnosed from ensemble simulations of the period 1979 to 2009 with observed and perturbed sea ice concentrations. The experimental design allows the impacts of ocean–atmosphere coupling and the background atmospheric state to be assessed. The modeled response can be very different to that inferred from statistical relationships, showing that the response cannot be easily diagnosed from observations. Reduced Arctic sea ice drives a local low pressure response in boreal summer and autumn. Increased Antarctic sea ice drives a poleward shift of the Southern Hemisphere midlatitude jet, especially in the cold season. Coupling enables surface temperature responses to spread to the ocean, amplifying the atmospheric response and revealing additional impacts including warming of the North Atlantic in response to reduced Arctic sea ice, with a northward shift of the Atlantic intertropical convergence zone and increased Sahel rainfall. The background state controls the sign of the North Atlantic Oscillation (NAO) response via the refraction of planetary waves. This could help to resolve differences in previous studies, and potentially provides an “emergent constraint” to narrow the uncertainties in the NAO response, highlighting the need for future multimodel coordinated experiments.

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Limitations of a coupled regional climate model in the reproduction of the observed Arctic sea-ice retreat

The Cryosphere Discussions ◽

10.5194/tcd-6-1269-2012 ◽

2012 ◽

Vol 6 (2) ◽

pp. 1269-1306 ◽

Cited By ~ 1

Author(s):

W. Dorn ◽

K. Dethloff ◽

A. Rinke

Keyword(s):

Sea Ice ◽

Climate Model ◽

Regional Climate ◽

Arctic Sea Ice ◽

Ice Volume ◽

Arctic Sea ◽

Melting Period ◽

Ice Retreat ◽

Sea Ice Retreat ◽

Model Variability

Abstract. The effects of internal model variability on the simulation of Arctic sea-ice extent and volume have been examined with the aid of a seven-member ensemble with a coupled regional climate model for the period 1948–2008. Beyond general weaknesses related to insufficient representation of feedback processes, it is found that the model's ability to reproduce observed summer sea-ice retreat depends mainly on two factors: the correct simulation of the atmospheric circulation during the summer months and the sea-ice volume at the beginning of the melting period. Since internal model variability shows its maximum during the summer months, the ability to reproduce the observed atmospheric summer circulation is limited. In addition, the atmospheric circulation during summer also significantly affects the sea-ice volume over the years, leading to a limited ability to start with reasonable sea-ice volume into the melting period. Furthermore, the sea-ice volume pathway shows notable decadal variability which amplitude varies among the ensemble members. The scatter is particularly large in periods when the ice volume increases, indicating limited skill in reproducing high-ice years.

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