scholarly journals An analytical model for wind-driven Arctic summer sea ice drift

2015 ◽  
Vol 9 (2) ◽  
pp. 2101-2133
Author(s):  
H.-S. Park ◽  
A. L. Stewart
Keyword(s):  
Sea Ice ◽  
Analytical Model ◽  
Arctic Sea Ice ◽  
Sea Ice Concentration ◽  
Ice Drift ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Sea Ice Drift ◽  
Ice Velocity ◽  
Arctic Summer

Abstract. The authors present an approximate analytical model for wind-induced sea-ice drift that includes an ice–ocean boundary layer with an Ekman spiral in the ocean velocity. This model provides an analytically tractable solution that is most applicable to the marginal ice zone, where sea-ice concentration is substantially below 100%. The model closely reproduces the ice and upper-ocean velocities observed recently by the first ice-tethered profiler equipped with a velocity sensor (ITPV). The analytical tractability of our model allows efficient calculation of the sea-ice velocity provided that the surface wind field is known and that the ocean surface geostrophic velocity is relatively weak. The model is applied to estimate intraseasonal variations in Arctic sea ice cover due to short-timescale (around 1 week) intensification of the southerly winds. Utilizing 10 m surface winds from ERA-Interim reanalysis, the wind-induced sea-ice velocity and the associated changes in sea-ice concentration are calculated and compared with satellite observations. The analytical model captures the observed reduction of Arctic sea-ice concentration associated with the strengthening of southerlies on intraseasonal time scales. Further analysis indicates that the wind-induced surface Ekman flow in the ocean increases the sea-ice drift speed by 50% in the Arctic summer. It is proposed that the southerly wind-induced sea-ice drift, enhanced by the ocean's surface Ekman transport, can lead to substantial reduction in sea-ice concentration over a timescale of one week.

scholarly journals Improving Multiyear Sea Ice Concentration Estimates with Sea Ice Drift

2016 ◽  
Vol 8 (5) ◽  
pp. 397 ◽  
Author(s):  
Yufang Ye ◽  
Mohammed Shokr ◽  
Georg Heygster ◽  
Gunnar Spreen
Keyword(s):  
Sea Ice ◽  
Sea Ice Concentration ◽  
Ice Drift ◽  
Ice Concentration ◽  
Sea Ice Drift

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

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

<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>

scholarly journals An analytical model for wind-driven Arctic summer sea ice drift

2016 ◽  
Vol 10 (1) ◽  
pp. 227-244 ◽  
Author(s):  
H.-S. Park ◽  
A. L. Stewart
Keyword(s):  
Sea Ice ◽  
Analytical Model ◽  
Surface Wind ◽  
Open Water ◽  
Sea Ice Concentration ◽  
Efficient Calculation ◽  
Sea Ice Drift ◽  
Wind Input ◽  
Ice Floes ◽  
Arctic Summer

Abstract. The authors present an analytical model for wind-driven free drift of sea ice that allows for an arbitrary mixture of ice and open water. The model includes an ice–ocean boundary layer with an Ekman spiral, forced by transfers of wind-input momentum both through the sea ice and directly into the open water between the ice floes. The analytical tractability of this model allows efficient calculation of the ice velocity provided that the surface wind field is known and that the ocean geostrophic velocity is relatively weak. The model predicts that variations in the ice thickness or concentration should substantially modify the rotation of the velocity between the 10 m winds, the sea ice, and the ocean. Compared to recent observational data from the first ice-tethered profiler with a velocity sensor (ITP-V), the model is able to capture the dependencies of the ice speed and the wind/ice/ocean turning angles on the wind speed. The model is used to derive responses to intensified southerlies on Arctic summer sea ice concentration, and the results are shown to compare closely with satellite observations.

Gridded Arctic sea ice concentration reconstruction for the first half of the 20th century based on different proxy data

2021 ◽  
Author(s):  
Vladimir Semenov ◽  
Tatiana Matveeva
Keyword(s):  
Sea Ice ◽  
20Th Century ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
The North ◽  
Arctic Sea ◽  
Ice Concentration

<p>Global warming in the recent decades has been accompanied by a rapid recline of the Arctic sea ice area most pronounced in summer (10% per decade). To understand the relative contribution of external forcing and natural variability to the modern and future sea ice area changes, it is necessary to evaluate a range of long-term variations of the Arctic sea ice area in the period before a significant increase in anthropogenic emissions of greenhouse gases into the atmosphere. Available observational data on the spatiotemporal dynamics of Arctic sea ice until 1950s are characterized by significant gaps and uncertainties. In the recent years, there have appeared several reconstructions of the early 20<sup>th</sup> century Arctic sea ice area that filled the gaps by analogue methods or utilized combined empirical data and climate model’s output. All of them resulted in a stronger that earlier believed negative sea ice area anomaly in the 1940s concurrent with the early 20<sup>th</sup> century warming (ETCW) peak. In this study, we reconstruct the monthly average gridded sea ice concentration (SIC) in the first half of the 20th century using the relationship between the spatiotemporal features of SIC variability, surface air temperature over the Northern Hemisphere extratropical continents, sea surface temperature in the North Atlantic and North Pacific, and sea level pressure. In agreement with a few previous results, our reconstructed data also show a significant negative anomaly of the Arctic sea ice area in the middle of the 20th century, however with some 15% to 30% stronger amplitude, about 1.5 million km<sup>2</sup> in September and 0.7 million km<sup>2</sup> in March. The reconstruction demonstrates a good agreement with regional Arctic sea ice area data when available and suggests that ETWC in the Arctic has been accompanied by a concurrent sea ice area decline of a magnitude that have been exceeded only in the beginning of the 21<sup>st</sup> century.</p>

Sea-ice freeboard or thickness? Design choices in the context of data assimilation in the coupled numerical prediction system EC-Earth3 for seasonal Arctic sea ice prediction

2021 ◽  
Author(s):  
Francois Massonnet ◽  
Sara Fleury ◽  
Florent Garnier ◽  
Ed Blockley ◽  
Pablo Ortega Montilla ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
General Circulation ◽  
Seasonal Prediction ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Concentration

<p>It is well established that winter and spring Arctic sea-ice thickness anomalies are a key source of predictability for late summer sea-ice concentration. While numerical general circulation models (GCMs) are increasingly used to perform seasonal predictions, they are not systematically taking advantage of the wealth of polar observations available. Data assimilation, the study of how to constrain GCMs to produce a physically consistent state given observations and their uncertainties, remains, therefore, an active area of research in the field of seasonal prediction. With the recent advent of satellite laser and radar altimetry, large-scale estimates of sea-ice thickness have become available for data assimilation in GCMs. However, the sea-ice thickness is never directly observed by altimeters, but rather deduced from the measured sea-ice freeboard (the height of the emerged part of the sea ice floe) based on several assumptions like the depth of snow on sea ice and its density, which are both often poorly estimated. Thus, observed sea-ice thickness estimates are potentially less reliable than sea-ice freeboard estimates. Here, using the EC-Earth3 coupled forecasting system and an ensemble Kalman filter, we perform a set of sensitivity tests to answer the following questions: (1) Does the assimilation of late spring observed sea-ice freeboard or thickness information yield more skilful predictions than no assimilation at all? (2) Should the sea-ice freeboard assimilation be preferred over sea-ice thickness assimilation? (3) Does the assimilation of observed sea-ice concentration provide further constraints on the prediction? We address these questions in the context of a realistic test case, the prediction of 2012 summer conditions, which led to the all-time record low in Arctic sea-ice extent. We finally formulate a set of recommendations for practitioners and future users of sea ice observations in the context of seasonal prediction.</p>

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