Increased Arctic sea ice volume after anomalously low melting in 2013

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

Journal of Geophysical Research Atmospheres ◽

10.1029/2010jc006235 ◽

2011 ◽

Vol 116 (C4) ◽

Cited By ~ 49

Author(s):

N. T. Kurtz ◽

T. Markus ◽

S. L. Farrell ◽

D. L. Worthen ◽

L. N. Boisvert

Keyword(s):

Sea Ice ◽

Energy Exchange ◽

Arctic Sea Ice ◽

Volume Loss ◽

Ice Volume ◽

Arctic Sea ◽

Ocean Atmosphere ◽

Ice Production

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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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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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Benefits of sea ice thickness initialization for the Arctic decadal climate prediction skill in EC-Earth3

10.5194/gmd-2020-331 ◽

2020 ◽

Author(s):

Tian Tian ◽

Shuting Yang ◽

Mehdi Pasha Karami ◽

François Massonnet ◽

Tim Kruschke ◽

...

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Climate Prediction ◽

Arctic Sea Ice ◽

Prediction Skill ◽

The Arctic ◽

Ensemble Prediction ◽

Central Arctic Ocean ◽

Ice Volume ◽

Arctic Sea

Abstract. A substantial part of Arctic climate predictability at interannual time scales stems from the knowledge of the initial sea ice conditions. Among all the variables characterizing sea ice, sea ice volume, being a product of sea ice area/concentration (SIC) and thickness (SIT), is the most sensitive parameter for climate change. However, the majority of climate prediction systems are only assimilating the observed SIC due to lack of long-term reliable global observation of SIT. In this study the EC-Earth3 Climate Prediction System with anomaly initialization to ocean, SIC and SIT states is developed. In order to evaluate the benefits of specific initialized variables at regional scales, three sets of retrospective ensemble prediction experiments are performed with different initialization strategies: ocean-only; ocean plus SIC; and ocean plus SIC and SIT initialization. The increased skill from ocean plus SIC initialization is small in most regions, compared to ocean-only initialization. In the marginal ice zone covered by seasonal ice, skills regarding winter SIC are mainly gained from the initial ocean temperature anomalies. Consistent with previous studies, the Arctic sea ice volume anomalies are found to play a dominant role for the prediction skill of September Arctic sea ice extent. Winter preconditioning of SIT for the perennial ice in the central Arctic Ocean results in increased skill of SIC in the adjacent Arctic coastal waters (e.g. the Laptev/East Siberian/Chukchi Seas) for lead time up to a decade. This highlights the importance of initializing SIT for predictions of decadal time scale in regional Arctic sea ice. Our results suggest that as the climate warming continues and the central Arctic Ocean might become seasonal ice free in the future, the controlling mechanism for decadal predictability may thus shift from being the sea ice volume playing the major role to a more ocean-related processes.

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Arctic Sea Ice Volume Export Through Fram Strait From 1992 to 2014

Journal of Geophysical Research Oceans ◽

10.1029/2019jc016039 ◽

2020 ◽

Vol 125 (6) ◽

Cited By ~ 3

Author(s):

Gunnar Spreen ◽

Laura Steur ◽

Dmitry Divine ◽

Sebastian Gerland ◽

Edmond Hansen ◽

...

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Fram Strait ◽

Ice Volume ◽

Arctic Sea

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Retrieval of Arctic sea ice freeboard from passive microwave data using deep neural network

10.5194/egusphere-egu2020-2254 ◽

2020 ◽

Author(s):

Junhwa Chi ◽

Hyun-Cheol Kim ◽

Sung Jae Lee

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Arctic Sea Ice ◽

Passive Microwave ◽

Retrieval Algorithm ◽

Sea Ice Concentration ◽

Ice Volume ◽

Arctic Sea ◽

Ice Concentration ◽

Microwave Sensors

Changes in Arctic sea ice cover represent one of the most visible indicators of climate change. While changes in sea ice extent affect the albedo, changes in sea ice volume explain changes in the heat budget and the exchange of fresh water between ice and the ocean. Global climate simulations predict that Arctic sea ice will exhibit a more significant change in volume than extent. Satellite observations show a long-term negative trend in Arctic sea ice&#160; during all seasons, particularly in summer. Sea ice volume has been estimated by ICESat and CryoSat-2 satellites, and then NASA&#8217;s second-generation spaceborne lidar mission, ICESat-2 has successfully been launched in 2018. &#160;Although these sensors can measure sea ice freeboard precisely, long revisit cycles and narrow swaths are problematic for monitoring of the freeboard in the entire of Arctic ocean effectively. Passive microwave sensors are widely used in retrieval of sea ice concentration. Because of the capability of high temporal resolution and wider swaths, these sensors enable to produce daily sea ice concentration maps over the entire Arctic ocean. Brightness temperatures from passive microwave sensors are often used to estimate sea ice freeboard for first-year ice, but it is difficult to associate with physical characteristics related to sea ice height of multi-year ice. In machine learning community, deep learning has gained attention and notable success in addressing more complicated decision making using multiple hidden layers. In this study, we propose a deep learning based Arctic sea ice freeboard retrieval algorithm incorporating the brightness temperature data from the AMSR2 passive microwave data and sea ice freeboard data from the ICESat-2. The proposed retrieval algorithm enables to estimate daily freeboard for both first- and multi-year ice over the entire Arctic ocean. The estimated freeboard values from the AMSR2 are then quantitatively and qualitatively compared with other sea ice freeboard or thickness products. &#160;

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