Summer Enhancement of Arctic Sea Ice Volume Anomalies in the September-Ice Zone

2017 ◽  
Vol 30 (7) ◽  
pp. 2341-2362 ◽  
Author(s):  
Mitchell Bushuk ◽  
Rym Msadek ◽  
Michael Winton ◽  
Gabriel A. Vecchi ◽  
Rich Gudgel ◽  
...  

Because of its persistence on seasonal time scales, Arctic sea ice thickness (SIT) is a potential source of predictability for summer sea ice extent (SIE). New satellite observations of SIT represent an opportunity to harness this potential predictability via improved thickness initialization in seasonal forecast systems. In this work, the evolution of Arctic sea ice volume anomalies is studied using a 700-yr control integration and a suite of initialized ensemble forecasts from a fully coupled global climate model. This analysis is focused on the September sea ice zone, as this is the region where thickness anomalies have the potential to impact the SIE minimum. The primary finding of this paper is that, in addition to a general decay with time, sea ice volume anomalies display a summer enhancement, in which anomalies tend to grow between the months of May and July. This summer enhancement is relatively symmetric for positive and negative volume anomalies and peaks in July regardless of the initial month. Analysis of the surface energy budget reveals that the summer volume anomaly enhancement is driven by a positive feedback between the SIT state and the surface albedo. The SIT state affects surface albedo through changes in the sea ice concentration field, melt-onset date, snow coverage, and ice thickness distribution, yielding an anomaly in the total absorbed shortwave radiation between May and August, which enhances the existing SIT anomaly. This phenomenon highlights the crucial importance of accurate SIT initialization and representation of ice–albedo feedback processes in seasonal forecast systems.

2020 ◽  
Vol 14 (4) ◽  
pp. 1325-1345 ◽  
Author(s):  
Yinghui Liu ◽  
Jeffrey R. Key ◽  
Xuanji Wang ◽  
Mark Tschudi

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.


2021 ◽  
Author(s):  
Petteri Uotila ◽  
Joula Siponen ◽  
Eero Rinne ◽  
Steffen Tietsche

<p>Decadal changes in sea-ice thickness are one of the most visible signs of climate variability and change. To gain a comprehensive understanding of mechanisms involved, long time series, preferably with good uncertainty estimates, are needed. Importantly, the development of accurate predictions of sea ice in the Arctic requires good observational products. To assist this, a new sea-ice thickness product by ESA Climate Change Initiative (CCI) is compared to a set of five ocean reanalysis (ECCO-V4r4, GLORYS12V1, ORAS5 and PIOMAS).</p><p>The CCI product is based on two satellite altimetry missions, CryoSat-2 and ENVISAT, which are combined to the longest continuous satellite altimetry time series of Arctic-wide sea-ice thickness, 2002–2017. The CCI product performs well in the validation of the reanalyses: overall root-mean-square difference (RMSD) between monthly sea-ice thickness from CCI and the reanalyses ranges from 0.4–1.2 m. The differences are a sum of reanalysis biases, such as incorrect physics or forcing, as well as uncertainties in satellite altimetry, such as the snow climatology used in the thickness retrieval.</p><p>The CCI and reanalysis basin-scale sea-ice volumes have a good match in terms of year-to-year variability and long-term trends but rather different monthly mean climatologies. These findings provide a rationale to construct a multi-decadal sea-ice volume time series for the Arctic Ocean and its sub-basins from 1990–2019 by adjusting the ocean reanalyses ensemble toward CCI observations. Such a time series, including its uncertainty estimate, provides new insights to the evolution of the Arctic sea-ice volume during the past 30 years.</p>


2012 ◽  
Vol 25 (8) ◽  
pp. 3025-3038 ◽  
Author(s):  
Matthieu Chevallier ◽  
David Salas-Mélia

Abstract The intrinsic seasonal predictability of Arctic sea ice is investigated in a 400-yr-long preindustrial simulation performed with the Centre National de Recherches Météorologiques Coupled Global Climate Model, version 3.3 (CNRM-CM3.3). The skill of several predictors of the pan-Arctic sea ice area was quantified: the sea ice area itself, the pan-Arctic sea ice volume, and some areal predictors built from the subgrid ice thickness distribution (ITD). Sea ice area provides a potential predictability of about 3 months, which is consistent with previous studies using model and observation data. Sea ice volume predictive skill for winter sea ice area prediction is weak. Nevertheless, there is a higher potential to predict the September ice area with the June volume anomaly than with the June area anomaly. Using ITD-based predictors, two “regimes” of predictability were highlighted. The first one, a “persistence regime,” applies to winter/early spring sea ice seasonal predictability. The winter sea ice cover can be predicted in late fall/early winter from the amount of young ice formed since the freeze-up onset in the margins. However, sea ice area itself is potentially the best predictor of winter sea ice area at seasonal time scales. The second regime is a “memory regime.” It applies to the predictability of summer sea ice area. An ice area anomaly in September is potentially predictable up to 6 months in advance, using the area covered by ice thicker than a critical thickness lying between 0.9 and 1.5 m. Results of this study are preliminary; however, they provide information for the design of future prediction systems and highlight the need for observations and a state-of-the-art sea ice model.


2019 ◽  
Author(s):  
Jean-Claude Gascard ◽  
Jinlun Zhang ◽  
Mehrad Rafizadeh

Abstract. The drastic reduction of the Arctic sea ice over the past 40 years is the most glaring evidence of climate change on Planet Earth. Among all the variables characterizing sea ice, the sea ice volume is by far the most sensitive one for climate change since it is decaying at the highest rate compared to sea ice extent and sea ice thickness. In 40 years the Arctic Ocean has lost about 3/4 of its sea ice volume at the end of the summer season corresponding to a reduction of both sea ice extent and sea ice thickness by half on average. From more than 16 000 km3, 40 years ago, the Arctic sea ice summer minimum dropped down to less than 4000 km3 during the most recent summers. Being a combination of Arctic sea ice extent and sea ice thickness, the Arctic sea ice volume is difficult to observe directly and accurately. We estimated cumulative Freezing-Degree Days (FDD) over a 9 month freezing time period (September to May each year) based on ERA Interim surface air temperature reanalysis over the whole Arctic Ocean and for the past 38 years. Then we compared the Arctic sea ice volume based on sea ice thickness deduced from cumulative FDD with Arctic sea ice volume estimated from PIOMAS (Pan Arctic Ice Ocean Modeling and Assimilation System) and from the ESA CRYOSAT-2 satellite. The results are strikingly similar. The warming of the atmosphere is playing an important role in contributing to the Arctic sea ice volume decrease during the whole freezing season (September to May). In addition, the FDD spatial distribution exhibiting a sharp double peak-like feature is reflecting the Multi Y ear Ice (MYI) versus First Year Ice (FYI) dual disposition typical of the Arctic sea ice cover. This is indicative of a significant contribution from the vertical ocean heat fluxes throughout the ice depending on MYI versus FYI distribution and the snow layer on top of it influencing the surface air temperature accordingly. In 2018 the Arctic MYI vanished almost completely for the first time ever over the past 40 years. The quasi complete disappearance of the Arctic sea ice is more likely to happen in summer within the next 15 years with broad consequences for Arctic marine and terrestrial ecosystems, climate and weather patterns on a planetary scale and globally on human activities.


2019 ◽  
Author(s):  
Yinghui Liu ◽  
Jeffrey R. Key ◽  
Xuanji Wang ◽  
Mark Tschudi

Abstract. Arctic sea ice is a key component of the Arctic climate system, which in turn impacts 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), the Ice, Cloud, and land Elevation Satellite (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. Sea ice volume exhibits a decreasing trend of −411 km3/year from 1984 to 2018, stronger than the trends from other datasets. Of the factors affecting volume, changes in sea ice thickness from November to May contribute at least 80 %, decreasing to around 50 % in August and September. Changes in sea ice area contribute less than 30 % in all months.


2009 ◽  
Vol 22 (1) ◽  
pp. 165-176 ◽  
Author(s):  
R. W. Lindsay ◽  
J. Zhang ◽  
A. Schweiger ◽  
M. Steele ◽  
H. Stern

Abstract The minimum of Arctic sea ice extent in the summer of 2007 was unprecedented in the historical record. A coupled ice–ocean model is used to determine the state of the ice and ocean over the past 29 yr to investigate the causes of this ice extent minimum within a historical perspective. It is found that even though the 2007 ice extent was strongly anomalous, the loss in total ice mass was not. Rather, the 2007 ice mass loss is largely consistent with a steady decrease in ice thickness that began in 1987. Since then, the simulated mean September ice thickness within the Arctic Ocean has declined from 3.7 to 2.6 m at a rate of −0.57 m decade−1. Both the area coverage of thin ice at the beginning of the melt season and the total volume of ice lost in the summer have been steadily increasing. The combined impact of these two trends caused a large reduction in the September mean ice concentration in the Arctic Ocean. This created conditions during the summer of 2007 that allowed persistent winds to push the remaining ice from the Pacific side to the Atlantic side of the basin and more than usual into the Greenland Sea. This exposed large areas of open water, resulting in the record ice extent anomaly.


2021 ◽  
Author(s):  
Harry Heorton ◽  
Michel Tsamados ◽  
Paul Holland ◽  
Jack Landy

<p><span>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</span><span>. Ice growth is fastest during Autumn, in the Marginal Seas and over first year ice</span><span>. 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.</span></p>


2018 ◽  
Vol 45 (21) ◽  
pp. 11,751-11,759 ◽  
Author(s):  
Felix Bunzel ◽  
Dirk Notz ◽  
Leif Toudal Pedersen
Keyword(s):  
Sea Ice ◽  

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