A model reconstruction of the Antarctic sea ice thickness and volume changes over 1980–2008 using data assimilation

Ship-based observations of sea ice thickness using the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol provide information on ice thickness distribution at relatively low cost. This protocol uses a simple formula to calculate the mass of ice in ridges based on surface observations. We present two new formulae and compare these with results from the “Original” formula using data obtained in the Ross Sea in autumn and winter. The new “r-star” formula uses a more realistic ratio of sail and keel areas to transform dimensions of sails to estimates of mean keel areas. As a result, estimates of “equivalent thickness” (i.e. mean thickness of ice in ridged areas) increased by over 200%. The new “Probability” formula goes one step further, by incorporating the probability that a sail is associated with a keel underwater, and the probability that keels may be found under level surfaces. This resulted in estimates of equivalent thickness comparable with the Original formula. Estimates of equivalent thickness at one or two degree latitude resolution are sufficiently accurate for validating sea ice models. Although ridges are small features in the Ross Sea, we have shown that they constitute a significant fraction of the total ice mass.

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A comparison between Envisat and ICESat sea ice thickness in the Antarctic

10.5194/tc-2020-48 ◽

2020 ◽

Author(s):

Jinfei Wang ◽

Chao Min ◽

Robert Ricker ◽

Qinghua Yang ◽

Qian Shi ◽

...

Keyword(s):

Sea Ice ◽

European Space Agency ◽

Weddell Sea ◽

Ice Thickness ◽

Radar Altimeter ◽

Average Deviation ◽

Absolute Deviation ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

The Antarctic

Abstract. The crucial role that Antarctic sea ice plays in the global climate system is strongly linked to its thickness. While in situ observations are too sparse in the Antarctic to determine long-term trends of the Antarctic sea ice thickness on a global scale, satellite radar altimetry data can be applied with a promising prospect. A newly released Envisat-derived product from the European Space Agency Sea Ice Climate Change Initiative (ESA SICCI), including sea ice freeboard and sea ice thickness, covers the entire Antarctic year-round from 2002 to 2012. In this study, the SICCI Envisat sea ice thickness in the Antarctic is firstly compared with a conceptually new proposed ICESat ice thickness that has been derived from an algorithm employing modified ice density. Both data sets have been validated with the Weddell Sea upward looking sonar measurements (ULS), indicating that ICESat agrees better with field observations. The inter-comparisons are conducted for three seasons except winter based on the ICESat operating periods. According to the results, the deviations between Envisat and ICESat sea ice thickness are different considering different seasons, years and regions. More specifically, the smallest average deviation between Envisat and ICESat sea ice thickness exists in spring by −0.03 m while larger deviations exist in summer and autumn by 0.86 m and 0.62 m, respectively. Although the smallest absolute deviation occurs in spring 2005 by 0.02 m, the largest correlation coefficient appears in autumn 2004 by 0.77. The largest positive deviation occurs in the western Weddell Sea by 1.03 m in summer while the largest negative deviation occurs in the Eastern Antarctic by −0.25 m in spring. Potential reasons for those deviations mainly deduce from the limitations of Envisat radar altimeter affected by the weather conditions and the surface roughness as well as the different retrieval algorithms. The better performance in spring of Envisat has a potential relation with relative humidity.

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Farewell to IceBridge: 10 years of polar sea ice remote sensing

10.5194/egusphere-egu2020-12019 ◽

2020 ◽

Author(s):

Linette Boisvert ◽

Joseph MacGregor ◽

Brooke Medley ◽

Nathan Kurtz ◽

Ron Kwok ◽

...

Keyword(s):

Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

Ice Drift ◽

Laser Altimeters ◽

Sea Ice Drift ◽

To Come ◽

The Antarctic

NASA&#8217;s Operation IceBridge (OIB) was a multi-year, multi-platform, airborne mission which took place between 2009-2019. OIB was designed and implemented to continue monitoring the changing sea ice and ice sheets in both the Arctic and Antarctic by &#8216;bridging the gap&#8217; between NASA&#8217;s ICESat (2003&#8211;2009) and ICESat-2 (launched September 2018) satellite missions. OIB&#8217;s instrument suite most often consisted of laser altimeters, radar sounders, gravimeters and multi-spectral imagers. These instruments were selected to study polar sea ice thickness, ice sheet elevation, snow and ice thickness, surface temperature and bathymetry. With the launch of ICESat-2, the final year of OIB consisted of three campaigns designed to under fly the satellite: 1) the end of the Arctic growth season (spring), 2) during the Arctic summer to capture many different types of melting surfaces, and 3) the Antarctic spring to cover an entirely new area of East Antarctica. Over this ten-year period a coherent picture of Arctic and Antarctic sea ice and snow thickness and other properties have been produced and monitored. Specifically, OIB has changed the community&#8217;s perspective of snow on sea ice in the Arctic. Over the decade, OIB has also been used to validate other satellite altimeter missions like ESA&#8217;s CryoSat-2. Since the launch of ICESat-2, coincident OIB under flights with the satellite were crucial for measuring sea ice properties. With sea ice constantly in motion, and the differences in OIB aircraft and ICESat-2 ground speed, there can substantial drift in the sea ice pack over the same ground track distance being measured.Therefore, we had to design and implement sea ice drift trajectories based on low level winds measured from the aircraft in flight, adjusting our plane&#8217;s path accordingly so we could measure the same sea ice as ICESat-2. This was implemented in both the Antarctic 2018 and Arctic 2019 campaigns successfully. Specifically, the Spring Arctic 2019 campaign allowed for validation of ICESat-2 freeboards with OIB ATM freeboards proving invaluable to the success of ICESat-2 and the future of sea ice research to come from these missions.&#160;

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Sensitivity of the Antarctic sea-ice distribution to oceanic heat flux in a coupled atmosphere-sea-ice model

Annals of Glaciology ◽

10.3189/172756401781818545 ◽

2001 ◽

Vol 33 ◽

pp. 577-584 ◽

Cited By ~ 3

Author(s):

Xingren Wu ◽

W. F. Budd ◽

A. P. Worby ◽

Ian Allison

Keyword(s):

Heat Flux ◽

Sea Ice ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

Oceanic Heat Flux ◽

Ice Concentration ◽

Microwave Radiometers ◽

The Antarctic ◽

Ice Model

AbstractA coupled atmosphere-sea-ice model is used to study the sensitivity of the Antarctic sea-ice distribution to oceanic heat flux (OHF). Remote sensing of sea ice from microwave radiometers provides data on ice extent and ice concentration. The ice-thickness data used are from ship-based observations. Our simulations suggest that OHF values of 0−5 W m−2 will cause sea ice to be too thick in the model. A value of 20−25 Wm−2 throughout the year causes sea ice to be too thin in the model. The model results indicate that a seasonally varying OHF is required to match the modelled thickness with observations. Values of 5−30 Wm with an annual mean of 10−15 Wm−2, give a reasonable distribution of sea-ice thickness. This agrees with the limited observations of OHF available for the Antarctic. The model results also indicate that the OHF should be varied spatially. When a seasonally and spatially variable OHF is applied to the coupled atmosphere-sea-ice model a still better simulation of the sea-ice distribution is obtained. Our results also suggest that the role of ice advection is very important in the determination of the sea-ice distribution, and it can be quantified by the model.

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Bacterial Community Structure, Function and Diversity in Antarctic Sea Ice

10.26686/wgtn.16992562 ◽

2021 ◽

Author(s):

◽

Rebecca Olivia MacLennan Cowie

Keyword(s):

Community Structure ◽

Bacterial Community ◽

Sea Ice ◽

Structure Function ◽

Bacterial Communities ◽

Bacterial Community Structure ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

The Antarctic

Antarctic sea ice is an important feature of the southern ocean where at its maximum it can cover 8 % of the Southern Hemisphere. It provides a stable environment for the colonisation of diverse and highly specialised microbes which play a central role in the assimilation and regulation of energy through the Antarctic food web. Polar environments are sensitive to changes in the environment. Small changes in temperature can have large effects on sea ice thickness and extent and Antarctic sea ice cover is expected to shrink by 25 % over the next century. It is unknown how the sea ice microbiota will respond. In order to understand the effects of climate change on the sea ice ecosystem it is necessary to obtain information about the community structure, function and diversity and their reactions with the environment. Studies have focused on algal diversity and physiology in Antarctic sea ice and in comparison studies on the prokaryotic community are few. Although prokaryotic diversity has been investigated using clone libraries and culture based methods, it is likely that certain species have still not been described. Almost nothing is known about the Antarctic sea ice bacterial community spatial and temporal dynamics under changing abiotic and biotic conditions or their role in biogeochemical cycles. This is the first study linking Antarctic bacterial communities to function by using statistics to investigate the relationships between environmental variables and community structure. Bacterial community structure was investigated by extracting both the DNA and RNA from the environment to understand both the metabolically active (RNA) and total (DNA) bacterial community. The thickness of the sea ice and nutrient concentrations were key factors regulating bacterial community composition in Antarctic sea ice. Sea ice thickness is likely to have an effect on the physiological responses of algae leading to changes in photosynthate concentrations and composition of dissolved organic matter (DOM). Further investigations into the relationships between enzymatic activity and community structure revealed that the composition of the DOM drove variation between bacterial communities. There was no relationship between bacterial abundance and chlorophyll-a (as a measure of algal biomass), suggesting a un-coupling of the microbial loop. However bacteria were actively involved in the hydrolysis of polymers throughout the sea ice core. Investigations using quantitative PCR (qPCR) found that the functional genes involved in denitrification and light energy utilisation were in low abundance therefore these processes are minor in Antarctic sea ice. These results confirm that sea ice bacteria are predominantly heterotrophs and have a major role in the cycling of carbon and nitrogen through the microbial loop ...

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A comparison between Envisat and ICESat sea ice thickness in the Antarctic

10.5194/tc-2021-227 ◽

2021 ◽

Author(s):

Jinfei Wang ◽

Chao Min ◽

Robert Ricker ◽

Qian Shi ◽

Bo Han ◽

...

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Spatial Variations ◽

Weddell Sea ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Temporal And Spatial Variations ◽

Antarctic Sea Ice ◽

Temporal And Spatial ◽

The Antarctic

Abstract. The crucial role that Antarctic sea ice plays in the global climate system is strongly linked to its thickness. While field observations are too sparse in the Antarctic to determine long-term trends of the Antarctic sea ice thickness (SIT) on a hemispheric scale, satellite radar altimetry data can be applied with a promising prospect. European Space Agency Climate Change Initiative – Sea Ice Project (ESA SICCI) includes sea ice freeboard and sea ice thickness derived from Envisat, covering the entire Antarctic year-round from 2002 to 2012. In this study, the SICCI Envisat SIT in the Antarctic is first compared with a conceptually new ICESat SIT product retrieved from an algorithm employing modified ice density. Both data sets are compared to SIT estimates from upward-looking sonar (ULS) in the Weddell Sea, showing mean differences (MD) and standard deviations (SD) of 1.29 (0.65) m for Envisat-ULS, while we find 1.11 (0.81) m for ICESat-ULS, respectively. The inter-comparisons are conducted for three seasons except winter, based on the ICESat operating periods. According to the results, the differences between Envisat and ICESat SIT reveal significant temporal and spatial variations. More specifically, the smallest seasonal SIT MD (with SD shown in brackets) of 0.00 m (0.39 m) for Envisat-ICESat for the entire Antarctic is found in spring (October–November) while larger MD of 0.52 m (0.68 m) and 0.57 m (0.45 m) exist in summer (February–March) and autumn (May–June), respectively. It is also shown that from autumn to spring, mean Envisat SIT decreases while mean ICESat SIT increases. Our findings suggest that overestimation of Envisat sea ice freeboard, potentially caused by radar backscatter originating from inside the snow layer, primarily accounts for the differences between Envisat and ICESat SIT in summer and autumn, while the uncertainties of snow depth product are not the dominant cause of the differences.To get a better understanding of the characteristics of the Envisat-derived sea ice thickness product, we firstly conduct a comprehensive comparison between Envisat and ICESat-1 sea ice thickness. Their differences reveal significant temporal and spatial variations. Our findings suggest that overestimation of Envisat sea ice freeboard primarily accounts for the differences in summer and autumn, while the uncertainties of snow depth product are not the dominant cause of the differences.

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Bacterial Community Structure, Function and Diversity in Antarctic Sea Ice

10.26686/wgtn.16992562.v1 ◽

2021 ◽

Author(s):

◽

Rebecca Olivia MacLennan Cowie

Keyword(s):

Community Structure ◽

Bacterial Community ◽

Sea Ice ◽

Structure Function ◽

Bacterial Communities ◽

Bacterial Community Structure ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

The Antarctic

Antarctic sea ice is an important feature of the southern ocean where at its maximum it can cover 8 % of the Southern Hemisphere. It provides a stable environment for the colonisation of diverse and highly specialised microbes which play a central role in the assimilation and regulation of energy through the Antarctic food web. Polar environments are sensitive to changes in the environment. Small changes in temperature can have large effects on sea ice thickness and extent and Antarctic sea ice cover is expected to shrink by 25 % over the next century. It is unknown how the sea ice microbiota will respond. In order to understand the effects of climate change on the sea ice ecosystem it is necessary to obtain information about the community structure, function and diversity and their reactions with the environment. Studies have focused on algal diversity and physiology in Antarctic sea ice and in comparison studies on the prokaryotic community are few. Although prokaryotic diversity has been investigated using clone libraries and culture based methods, it is likely that certain species have still not been described. Almost nothing is known about the Antarctic sea ice bacterial community spatial and temporal dynamics under changing abiotic and biotic conditions or their role in biogeochemical cycles. This is the first study linking Antarctic bacterial communities to function by using statistics to investigate the relationships between environmental variables and community structure. Bacterial community structure was investigated by extracting both the DNA and RNA from the environment to understand both the metabolically active (RNA) and total (DNA) bacterial community. The thickness of the sea ice and nutrient concentrations were key factors regulating bacterial community composition in Antarctic sea ice. Sea ice thickness is likely to have an effect on the physiological responses of algae leading to changes in photosynthate concentrations and composition of dissolved organic matter (DOM). Further investigations into the relationships between enzymatic activity and community structure revealed that the composition of the DOM drove variation between bacterial communities. There was no relationship between bacterial abundance and chlorophyll-a (as a measure of algal biomass), suggesting a un-coupling of the microbial loop. However bacteria were actively involved in the hydrolysis of polymers throughout the sea ice core. Investigations using quantitative PCR (qPCR) found that the functional genes involved in denitrification and light energy utilisation were in low abundance therefore these processes are minor in Antarctic sea ice. These results confirm that sea ice bacteria are predominantly heterotrophs and have a major role in the cycling of carbon and nitrogen through the microbial loop ...

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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

10.5194/egusphere-egu21-8317 ◽

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

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.

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On the timescales and length scales of the Arctic sea ice thickness anomalies: a study based on 14 reanalyses

The Cryosphere ◽

10.5194/tc-13-521-2019 ◽

2019 ◽

Vol 13 (2) ◽

pp. 521-543 ◽

Cited By ~ 3

Author(s):

Leandro Ponsoni ◽

François Massonnet ◽

Thierry Fichefet ◽

Matthieu Chevallier ◽

David Docquier

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Sea Ice Concentration ◽

Length Scales ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Ice Concentration ◽

Arctic Sea Ice Thickness

Abstract. The ocean–sea ice reanalyses are one of the main sources of Arctic sea ice thickness data both in terms of spatial and temporal resolution, since observations are still sparse in time and space. In this work, we first aim at comparing how the sea ice thickness from an ensemble of 14 reanalyses compares with different sources of observations, such as moored upward-looking sonars, submarines, airbornes, satellites, and ice boreholes. Second, based on the same reanalyses, we intend to characterize the timescales (persistence) and length scales of sea ice thickness anomalies. We investigate whether data assimilation of sea ice concentration by the reanalyses impacts the realism of sea ice thickness as well as its respective timescales and length scales. The results suggest that reanalyses with sea ice data assimilation do not necessarily perform better in terms of sea ice thickness compared with the reanalyses which do not assimilate sea ice concentration. However, data assimilation has a clear impact on the timescales and length scales: reanalyses built with sea ice data assimilation present shorter timescales and length scales. The mean timescales and length scales for reanalyses with data assimilation vary from 2.5 to 5.0 months and 337.0 to 732.5 km, respectively, while reanalyses with no data assimilation are characterized by values from 4.9 to 7.8 months and 846.7 to 935.7 km, respectively.

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Better constraints on the sea-ice state using global sea-ice data assimilation

Geoscientific Model Development Discussions ◽

10.5194/gmdd-5-1627-2012 ◽

2012 ◽

Vol 5 (2) ◽

pp. 1627-1667 ◽

Cited By ~ 1

Author(s):

P. Mathiot ◽

C. König Beatty ◽

T. Fichefet ◽

H. Goosse ◽

F. Massonnet ◽

...

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

Ice Thickness ◽

Initial State ◽

Concentration Data ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Ice Concentration ◽

The Impact

Abstract. Short-term and decadal sea-ice prediction systems need a realistic initial state, generally obtained using ice-ocean model simulations with data assimilation. However, only sea-ice concentration and velocity data are currently assimilated. In this work, an Ensemble Kalman Filter system is used to assimilate observed ice concentration and freeboard (i.e. thickness of emerged sea ice) data into a global coupled ocean–sea-ice model. The impact and effectiveness of our data assimilation system is assessed in two steps: firstly, through the assimilation of synthetic data (i.e., model-generated data) and, secondly, through the assimilation of satellite data. While ice concentrations are available daily, freeboard data used in this study are only available during six one-month periods spread over 2005–2007. Our results show that the simulated Arctic and Antarctic sea-ice extents are improved by the assimilation of synthetic ice concentration data. Assimilation of synthetic ice freeboard data improves the simulated sea-ice thickness field. Using real ice concentration data enhances the model realism in both hemispheres. Assimilation of ice concentration data significantly improves the total hemispheric sea-ice extent all year long, especially in summer. Combining the assimilation of ice freeboard and concentration data leads to better ice thickness, but does not further improve the ice extent. Moreover, the improvements in sea-ice thickness due to the assimilation of ice freeboard remain visible well beyond the assimilation periods.

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