scholarly journals Modeled Trends in Antarctic Sea Ice Thickness

2014 ◽  
Vol 27 (10) ◽  
pp. 3784-3801 ◽  
Author(s):  
Paul R. Holland ◽  
Nicolas Bruneau ◽  
Clare Enright ◽  
Martin Losch ◽  
Nathan T. Kurtz ◽  
...  
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Volume ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Order Of Magnitude ◽  
Concentration Changes

Abstract Unlike the rapid sea ice losses reported in the Arctic, satellite observations show an overall increase in Antarctic sea ice concentration over recent decades. However, observations of decadal trends in Antarctic ice thickness, and hence ice volume, do not currently exist. In this study a model of the Southern Ocean and its sea ice, forced by atmospheric reanalyses, is used to assess 1992–2010 trends in ice thickness and volume. The model successfully reproduces observations of mean ice concentration, thickness, and drift, and decadal trends in ice concentration and drift, imparting some confidence in the hindcasted trends in ice thickness. The model suggests that overall Antarctic sea ice volume has increased by approximately 30 km3 yr−1 (0.4% yr−1) as an equal result of areal expansion (20 × 103 km2 yr−1 or 0.2% yr−1) and thickening (1.5 mm yr−1 or 0.2% yr−1). This ice volume increase is an order of magnitude smaller than the Arctic decrease, and about half the size of the increased freshwater supply from the Antarctic Ice Sheet. Similarly to the observed ice concentration trends, the small overall increase in modeled ice volume is actually the residual of much larger opposing regional trends. Thickness changes near the ice edge follow observed concentration changes, with increasing concentration corresponding to increased thickness. Ice thickness increases are also found in the inner pack in the Amundsen and Weddell Seas, where the model suggests that observed ice-drift trends directed toward the coast have caused dynamical thickening in autumn and winter. Modeled changes are predominantly dynamic in origin in the Pacific sector and thermodynamic elsewhere.

scholarly journals Seasonal prediction and predictability of regional Antarctic sea ice

2021 ◽  
pp. 1-68
Author(s):  
Mitchell Bushuk ◽  
Michael Winton ◽  
F. Alexander Haumann ◽  
Thomas Delworth ◽  
Feiyu Lu ◽  
...  
Keyword(s):  
Sea Ice ◽  
Heat Content ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
The Arctic ◽  
Ice Thickness ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice

AbstractCompared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen and Bellingshausen, Indian, and West Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently-developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal timescales.

scholarly journals Sea ice export through the Fram Strait derived from a combined model and satellite data set

2019 ◽  
Vol 13 (12) ◽  
pp. 3209-3224 ◽  
Author(s):  
Chao Min ◽  
Longjiang Mu ◽  
Qinghua Yang ◽  
Robert Ricker ◽  
Qian Shi ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Fram Strait ◽  
Combined Model ◽  
Sea Ice Concentration ◽  
Data Set ◽  
Sea Ice Thickness ◽  
Ice Volume

Abstract. Sea ice volume export through the Fram Strait plays an important role in the Arctic freshwater and energy redistribution. The combined model and satellite sea ice thickness (CMST) data set assimilates CryoSat-2 and soil moisture and ocean salinity (SMOS) thickness products together with satellite sea ice concentration. The CMST data set closes the gap of stand-alone satellite-derived sea ice thickness in summer and therefore allows us to estimate sea ice volume export during the melt season. In this study, we first validate the CMST data set using field observations, and then we estimate the continuous seasonal and interannual variations in Arctic sea ice volume flux through the Fram Strait from September 2010 to December 2016. The results show that seasonal and interannual sea ice volume export vary from about -240(±40) to -970(±60) km3 and -1970(±290) to -2490(±280) km3, respectively. The sea ice volume export reaches its maximum in spring and about one-third of the yearly total volume export occurs in the melt season. The minimum monthly sea ice export is −11 km3 in August 2015, and the maximum (−442 km3) appears in March 2011. The seasonal relative frequencies of sea ice thickness and drift suggest that the Fram Strait outlet in summer is dominated by sea ice that is thicker than 2 m with relatively slow seasonal mean drift of about 3 km d−1.

scholarly journals Taking into Account Atmospheric Uncertainty Improves Sequential Assimilation of SMOS Sea Ice Thickness Data in an Ice–Ocean Model

2016 ◽  
Vol 33 (3) ◽  
pp. 397-407 ◽  
Author(s):  
Qinghua Yang ◽  
Martin Losch ◽  
Svetlana N. Losa ◽  
Thomas Jung ◽  
Lars Nerger
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
The Arctic ◽  
Ensemble Prediction ◽  
Ice Thickness ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ensemble Prediction System ◽  
Ice Concentration

AbstractThe sensitivity of assimilating sea ice thickness data to uncertainty in atmospheric forcing fields is examined using ensemble-based data assimilation experiments with the Massachusetts Institute of Technology General Circulation Model (MITgcm) in the Arctic Ocean during November 2011–January 2012 and the Met Office (UKMO) ensemble atmospheric forecasts. The assimilation system is based on a local singular evolutive interpolated Kalman (LSEIK) filter. It combines sea ice thickness data derived from the European Space Agency’s (ESA) Soil Moisture Ocean Salinity (SMOS) satellite and Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration data with the numerical model. The effect of representing atmospheric uncertainty implicit in the ensemble forcing is assessed by three different assimilation experiments. The first two experiments use a single deterministic forcing dataset and a different forgetting factor to inflate the ensemble spread. The third experiment uses 23 members of the UKMO atmospheric ensemble prediction system. It avoids additional ensemble inflation and is hence easier to implement. As expected, the model-data misfits are substantially reduced in all three experiments, but with the ensemble forcing the errors in the forecasts of sea ice concentration and thickness are smaller compared to the experiments with deterministic forcing. This is most likely because the ensemble forcing results in a more plausible spread of the model state ensemble, which represents model uncertainty and produces a better forecast.

scholarly journals Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model

2020 ◽  
Vol 13 (10) ◽  
pp. 4773-4787
Author(s):  
Eduardo Moreno-Chamarro ◽  
Pablo Ortega ◽  
François Massonnet
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
The Arctic ◽  
Global Ocean ◽  
Ice Thickness ◽  
Model Data ◽  
Atmospheric Variability ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Concentration

Abstract. This study assesses the impact of different sea ice thickness distribution (ITD) discretizations on the sea ice concentration (SIC) variability in ocean stand-alone NEMO3.6–LIM3 simulations. Three ITD discretizations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by K-means clustering both in the Arctic and Antarctica between 1979 and 2014. We focus on two seasons, winter (January–March) and summer (August–October), in which correlation coefficients across clusters in individual months are largest. In the Arctic, clusters are computed before and after detrending the series with a second-degree polynomial to separate interannual from longer-term variability. The analysis shows that, before detrending, winter clusters reflect the SIC response to large-scale atmospheric variability at both poles, while summer clusters capture the negative and positive trends in Arctic and Antarctic SIC, respectively. After detrending, Arctic clusters reflect the SIC response to interannual atmospheric variability predominantly. The cluster analysis is complemented with a model–data comparison of the sea ice extent and SIC anomaly patterns. The single-category discretization shows the worst model–data agreement in the Arctic summer before detrending, related to a misrepresentation of the long-term melting trend. Similarly, increasing the number of thin categories reduces model–data agreement in the Arctic, due to a poor representation of the summer melting trend and an overly large winter sea ice volume associated with a net increase in basal ice growth. In contrast, more thin categories improve model realism in Antarctica, and more thick ones improve it in central Arctic regions with very thick ice. In all the analyses we nonetheless identify no optimal discretization. Our results thus suggest that no clear benefit in the representation of SIC variability is obtained from increasing the number of sea ice thickness categories beyond the current standard with five categories in NEMO3.6–LIM3.

scholarly journals On the retrieval of sea-ice thickness using SMOS polarization differences

2019 ◽  
Vol 65 (251) ◽  
pp. 481-493
Author(s):  
MUKESH GUPTA ◽  
CAROLINA GABARRO ◽  
ANTONIO TURIEL ◽  
MARCOS PORTABELLA ◽  
JUSTINO MARTINEZ
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Operational Application ◽  
Ocean Salinity ◽  
Ice Concentration

ABSTRACTArctic sea ice is going through a dramatic change in its extent and volume at an unprecedented rate. Sea-ice thickness (SIT) is a controlling geophysical variable that needs to be understood with greater accuracy. For the first time, a SIT-retrieval method that exclusively uses only airborne SIT data for training the empirical algorithm to retrieve SIT from Soil Moisture Ocean Salinity (SMOS) brightness temperature (TB) at different polarization is presented. A large amount of airborne SIT data has been used from various field campaigns in the Arctic conducted by different countries during 2011–15. The algorithm attempts to circumvent the issue related to discrimination between TB signatures of thin SIT versus low sea-ice concentration. The computed SIT has a rms error of 0.10 m, which seems reasonably good (as compared to the existing algorithms) for analysis at the used 25 km grid. This new SIT retrieval product is designed for direct operational application in ice prediction/climate models.

scholarly journals Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6-LIM3 global ocean–sea ice model

2019 ◽  
Author(s):  
Eduardo Moreno-Chamarro ◽  
Pablo Ortega ◽  
François Massonnet
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
The Arctic ◽  
Ice Thickness ◽  
Model Data ◽  
Atmospheric Variability ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Concentration ◽  
Single Category

Abstract. This study assesses the impact of different sea ice thickness distribution (ITD) configurations on the sea ice concentration (SIC) variability in ocean-standalone NEMO3.6-LIM3 simulations. Three ITD configurations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by k-means clustering both in the Arctic and Antarctica between 1979 and 2014 in two seasons, January–March and August–October, which show the largest coherence across clusters in individual months. Analysis in the Arctic is done before and after detrending the series with a 2nd degree polynomial to separate interannual from longer-term variability. Before detrending, winter clusters capture SIC response to atmospheric variability at both poles and summer cluster a positive and negative trend in the Arctic and Antarctic SIC respectively. After detrending, Arctic clusters reflect SIC response to interannual atmospheric variability predominantly. Model–data cluster comparison suggests that no specific ITD configuration or category number increases realism of the simulated Arctic and Antarctic SIC variability in winter. In the Arctic summer, more thin-ice categories decrease model–data agreement without detrending but increase agreement after detrending. Overall, a single-category configuration agrees the worst with data. Direct model–data comparison of SIC anomaly fields shows that more thick-ice categories improve winter SIC variability realism in Central Arctic regions with very thick ice. By contrast, more thin-ice categories reduce model–data agreement in the Central Arctic in summer, due to an overly large simulated sea ice volume. In summary, whereas better resolving thin ice in NEMO3.6-LIM3 can hamper model realism in the Arctic but improve it in Antarctica, more thick-ice categories increase realism in the Arctic winter. And although the single-category configuration performs the worst overall, no optimal configuration is identified. Our results suggest that no clear benefit is obtained from increasing the number of sea ice thickness categories beyond the current usual standard of 5 categories in NEMO3.6-LIM3.

Development and application of NMEFC Arctic Ice-Ocean Prediction System (ArcIOPS) of CHINA

2020 ◽  
Author(s):  
Xi Liang ◽  
Fu Zhao ◽  
Chunhua Li ◽  
Lin Zhang
Keyword(s):  
Sea Ice ◽  
Early Stage ◽  
The Arctic ◽  
Ice Thickness ◽  
Prediction System ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Concentration ◽  
Ocean Prediction ◽  
Arctic Ice

<p>NMEFC provides sea ice services for the CHINARE since 2010, the products in the early stage (before 2017) include satellite-retrieved and numerical forecasts of sea ice concentration. Based on MITgcm and ensemble Kalman Filter data assimilation scheme,  the Arctic Ice-Ocean Prediction System (ArcIOPS v1.0), was established in 2017. ArcIOPS v1.0 assimilates available satellite-retrieved sea ice concentration and thickness data. Sea ice thickness forecasting products from ArcIOPS v1.0 are provided to the CHINARE8, and are believed to have played an important role in the successful passage of R/V XUELONG through the Central Arctic for the first time during the summer of 2017. In 2019, ArcIOPS v1.0 was upgraded to the latest version (ArcIOPS v1.1), which assimilates satellite-retrieved sea ice concentration, sea ice thickness, as well as sea surface temperature (SST) data in ice free areas. Comparison between outputs of the latest version of ArcIOPS and that of its previous version shows that the latest version has a substantial improvement on sea ice concentration forecasts. In the future, with more and more kinds of observations to be assimilated, the high-resolution version of ArcIOPS will be put into operational running and benefit Chinese scientific and commercial activities in the Arctic Ocean.</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>

scholarly journals Multidecadal Arctic sea ice thickness and volume derived from ice age

2020 ◽  
Vol 14 (4) ◽  
pp. 1325-1345 ◽  
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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