Effects of snow depth forcing on Southern Ocean sea ice simulations

AbstractSnow depth on sea ice plays a critical role in the heat exchange between ocean and atmosphere because of its thermal insulation property. Furthermore, a heavy snow load on the relatively thin Southern Ocean sea-ice cover submerges the ice floes below sea level, causing snow-to-ice conversion. Snowfall is also an important freshwater source into the weakly stratified ocean. Snow-depth on sea-ice information can be used as an indirect measure of solid precipitation. Satellite passive microwave data are used to investigate the interannual and regional variability of the snow cover on sea ice. In this study we make use of 12 years (1992–2003) of Special Sensor Microwave/Imager (SSM/I) radiances to calculate average monthly snow depth on the Antarctic sea-ice cover. For the Antarctic sea-ice region as a whole, we find that September snow depth and sea-ice area are negatively correlated, which is not the case for individual regions. An analysis of the snow depth around Antarctica was undertaken. The results show an overall increase in snow depth for each of the five Antarctic sectors and the region as a whole, but only the Indian Ocean sector and the entire Southern Ocean show a statistically significant increase. There is a partial eastward propagation of maximum snow depths, which may be related to the Antarctic Circumpolar Wave. The overall trend and the variability of regional snow-depth distributions are also in agreement with cyclone density.

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On the impact of sea ice in a global ocean circulation model

Annals of Glaciology ◽

10.3189/s0260305500013884 ◽

1997 ◽

Vol 25 ◽

pp. 111-115 ◽

Cited By ~ 3

Author(s):

Achim Stössel

Keyword(s):

Southern Ocean ◽

Sea Ice ◽

Ocean Circulation ◽

General Circulation ◽

Thermohaline Circulation ◽

Circulation Model ◽

Deep Ocean ◽

Global Ocean ◽

Convective Activity ◽

The Impact

This paper investigates the long-term impact of sea ice on global climate using a global sea-ice–ocean general circulation model (OGCM). The sea-ice component involves state-of-the-art dynamics; the ocean component consists of a 3.5° × 3.5° × 11 layer primitive-equation model. Depending on the physical description of sea ice, significant changes are detected in the convective activity, in the hydrographic properties and in the thermohaline circulation of the ocean model. Most of these changes originate in the Southern Ocean, emphasizing the crucial role of sea ice in this marginally stably stratified region of the world's oceans. Specifically, if the effect of brine release is neglected, the deep layers of the Southern Ocean warm up considerably; this is associated with a weakening of the Southern Hemisphere overturning cell. The removal of the commonly used “salinity enhancement” leads to a similar effect. The deep-ocean salinity is almost unaffected in both experiments. Introducing explicit new-ice thickness growth in partially ice-covered gridcells leads to a substantial increase in convective activity, especially in the Southern Ocean, with a concomitant significant cooling and salinification of the deep ocean. Possible mechanisms for the resulting interactions between sea-ice processes and deep-ocean characteristics are suggested.

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Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

Remote Sensing ◽

10.3390/rs11232864 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2864 ◽

Cited By ~ 4

Author(s):

Jiping Liu ◽

Yuanyuan Zhang ◽

Xiao Cheng ◽

Yongyun Hu

Keyword(s):

Neural Network ◽

Sea Ice ◽

Snow Depth ◽

Deep Neural Network ◽

Arctic Basin ◽

Temporal Variations ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Thickness ◽

Arctic Sea

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

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DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations

Journal of Glaciology ◽

10.1017/jog.2021.57 ◽

2021 ◽

pp. 1-6

Author(s):

Hao Luo ◽

Qinghua Yang ◽

Longjiang Mu ◽

Xiangshan Tian-Kunze ◽

Lars Nerger ◽

...

Keyword(s):

Data Assimilation ◽

Southern Ocean ◽

Sea Ice ◽

Coupled Model ◽

Sea Ice Concentration ◽

Model Free ◽

Antarctic Sea Ice ◽

Ice Concentration ◽

Data Assimilation System ◽

Assimilation System

Abstract To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.

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Phytoplankton under Southern Ocean Sea Ice

10.1002/essoar.10507491.1 ◽

2021 ◽

Author(s):

Kelsey M Bisson ◽

B. B. Cael

Keyword(s):

Southern Ocean ◽

Sea Ice

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Energy exchange in early spring over sea ice in the Pacific sector of the Southern Ocean

Journal of Geophysical Research Atmospheres ◽

10.1029/1998jd200069 ◽

1999 ◽

Vol 104 (D4) ◽

pp. 3925-3935 ◽

Cited By ~ 5

Author(s):

Adrian Hauser ◽

Gerd Wendler ◽

Ute Adolphs ◽

Martin O. Jeffries

Keyword(s):

Southern Ocean ◽

Sea Ice ◽

Energy Exchange ◽

Early Spring ◽

The Pacific

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A Link Between CMIP5 Phytoplankton Phenology and Sea Ice in the Atlantic Southern Ocean

Geophysical Research Letters ◽

10.1029/2018gl078061 ◽

2018 ◽

Vol 45 (13) ◽

pp. 6566-6575 ◽

Cited By ~ 3

Author(s):

Mark Hague ◽

Marcello Vichi

Keyword(s):

Southern Ocean ◽

Sea Ice

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The implications of selected processing methods on satellite altimetry derived sea ice thickness state and trends in the seasonal ice zone

10.5194/egusphere-egu21-12240 ◽

2021 ◽

Author(s):

Isolde Glissenaar ◽

Jack Landy ◽

Alek Petty ◽

Nathan Kurtz ◽

Julienne Stroeve

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Satellite Altimetry ◽

Region Of Interest ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Baffin Bay ◽

The Impact

The ice cover of the Arctic Ocean is increasingly becoming dominated by seasonal sea ice. It is important to focus on the processing of altimetry ice thickness data in thinner seasonal ice regions to understand seasonal sea ice behaviour better. This study focusses on Baffin Bay as a region of interest to study seasonal ice behaviour.We aim to reconcile the spring sea ice thickness derived from multiple satellite altimetry sensors and sea ice charts in Baffin Bay and produce a robust long-term record (2003-2020) for analysing trends in sea ice thickness. We investigate the impact of choosing different snow depth products (the Warren climatology, a passive microwave snow depth product and modelled snow depth from reanalysis data) and snow redistribution methods (a sigmoidal function and an empirical piecewise function) to retrieve sea ice thickness from satellite altimetry sea ice freeboard data.The choice of snow depth product and redistribution method results in an uncertainty envelope around the March mean sea ice thickness in Baffin Bay of 10%. Moreover, the sea ice thickness trend ranges from -15 cm/dec to 20 cm/dec depending on the applied snow depth product and redistribution method. Previous studies have shown a possible long-term asymmetrical trend in sea ice thinning in Baffin Bay. The present study shows that whether a significant long-term asymmetrical trend was found depends on the choice of snow depth product and redistribution method. The satellite altimetry sea ice thickness results with different snow depth products and snow redistribution methods show that different processing techniques can lead to different results and can influence conclusions on total and spatial sea ice thickness trends. Further processing work on the historic radar altimetry record is needed to create reliable sea ice thickness products in the marginal ice zone.

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Modeling the effect of Ross Ice Shelf melting on the Southern Ocean in quasi-equilibrium

The Cryosphere ◽

10.5194/tc-12-3033-2018 ◽

2018 ◽

Vol 12 (9) ◽

pp. 3033-3044 ◽

Cited By ~ 1

Author(s):

Xiying Liu

Keyword(s):

Southern Ocean ◽

Sea Ice ◽

Ross Sea ◽

Global Ocean ◽

Freshwater Flux ◽

Quasi Equilibrium ◽

Ice Shelf ◽

Sea Ice Concentration ◽

Ross Ice Shelf ◽

Ice Concentration

Abstract. To study the influence of basal melting of the Ross Ice Shelf (BMRIS) on the Southern Ocean (ocean southward of 35∘ S) in quasi-equilibrium, numerical experiments with and without the BMRIS effect were performed using a global ocean–sea ice–ice shelf coupled model. In both experiments, the model started from a state of quasi-equilibrium ocean and was integrated for 500 years forced by CORE (Coordinated Ocean-ice Reference Experiment) normal-year atmospheric fields. The simulation results of the last 100 years were analyzed. The melt rate averaged over the entire Ross Ice Shelf is 0.25 m a−1, which is associated with a freshwater flux of 3.15 mSv (1 mSv = 103 m3 s−1). The extra freshwater flux decreases the salinity in the region from 1500 m depth to the sea floor in the southern Pacific and Indian oceans, with a maximum difference of nearly 0.005 PSU in the Pacific Ocean. Conversely, the effect of concurrent heat flux is mainly confined to the middle depth layer (approximately 1500 to 3000 m). The decreased density due to the BMRIS effect, together with the influence of ocean topography, creates local differences in circulation in the Ross Sea and nearby waters. Through advection by the Antarctic Circumpolar Current, the flux difference from BMRIS gives rise to an increase of sea ice thickness and sea ice concentration in the Ross Sea adjacent to the coast and ocean water to the east. Warm advection and accumulation of warm water associated with differences in local circulation decrease sea ice concentration on the margins of sea ice cover adjacent to open water in the Ross Sea in September. The decreased water density weakens the subpolar cell as well as the lower cell in the global residual meridional overturning circulation (MOC). Moreover, we observe accompanying reduced southward meridional heat transport at most latitudes of the Southern Ocean.

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