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

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

10.5194/tc-2018-133 ◽

2018 ◽

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 the main source of Arctic sea ice thickness information in terms of spatio-temporal coverage, 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 fourteen 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 intent to characterize the time (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 time 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 time and length scales: reanalyses built with sea ice data assimilation present shorter time and length scales. The mean time and length scales for reanalyses with data ssimilation vary from 2.5–5.0 months and 337.0–732.5 km, respectively, while reanalyses with no data assimilation are characterized by values from 4.9–7.8 months and 846.7–935.7 km, respectively.

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Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017

Journal of Glaciology ◽

10.1017/jog.2019.55 ◽

2019 ◽

Vol 65 (253) ◽

pp. 813-821 ◽

Cited By ~ 2

Author(s):

Longjiang Mu ◽

Xi Liang ◽

Qinghua Yang ◽

Jiping Liu ◽

Fei Zheng

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Concentration Data ◽

Sea Ice Concentration ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Ice Concentration ◽

Ocean Prediction ◽

Arctic Ice

AbstractIn an effort to improve the reliability of Arctic sea-ice predictions, an ensemble-based Arctic Ice Ocean Prediction System (ArcIOPS) has been developed to meet operational demands. The system is based on a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model. A localized error subspace transform ensemble Kalman filter is used to assimilate the weekly merged CryoSat-2 and Soil Moisture and Ocean Salinity sea-ice thickness data together with the daily Advanced Microwave Scanning Radiometer 2 (AMSR2) sea-ice concentration data. The weather forecasts from the Global Forecast System of the National Centers for Environmental Prediction drive the sea ice–ocean coupled model. The ensemble mean sea-ice forecasts were used to facilitate the Chinese National Arctic Research Expedition in summer 2017. The forecasted sea-ice concentration is evaluated against AMSR2 and Special Sensor Microwave Imager/Sounder sea-ice concentration data. The forecasted sea-ice thickness is compared to the in-situ observations and the Pan-Arctic Ice-Ocean Modeling and Assimilation System. These comparisons show the promising potential of ArcIOPS for operational Arctic sea-ice forecasts. Nevertheless, the forecast bias in the Beaufort Sea calls for a delicate parameter calibration and a better design of the assimilation system.

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Impacts of Assimilating Satellite Sea Ice Concentration and Thickness on Arctic Sea Ice Prediction in the NCEP Climate Forecast System

Journal of Climate ◽

10.1175/jcli-d-17-0093.1 ◽

2017 ◽

Vol 30 (21) ◽

pp. 8429-8446 ◽

Cited By ~ 16

Author(s):

Zhiqiang Chen ◽

Jiping Liu ◽

Mirong Song ◽

Qinghua Yang ◽

Shiming Xu

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Forecast System ◽

Climate Forecast System ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Ice Concentration

Here sea ice concentration derived from the Special Sensor Microwave Imager/Sounder and thickness derived from the Soil Moisture and Ocean Salinity and CryoSat-2 satellites are assimilated in the National Centers for Environmental Prediction Climate Forecast System using a localized error subspace transform ensemble Kalman filter (LESTKF). Three ensemble-based hindcasts are conducted to examine impacts of the assimilation on Arctic sea ice prediction, including CTL (without any assimilation), LESTKF-1 (with initial sea ice assimilation only), and LESTKF-E5 (with every 5-day sea ice assimilation). Assessment with the assimilated satellite products and independent sea ice thickness datasets shows that assimilating sea ice concentration and thickness leads to improved Arctic sea ice prediction. LESTKF-1 improves sea ice forecast initially. The initial improvement gradually diminishes after ~3-week integration for sea ice extent but remains quite steady through the integration for sea ice thickness. Large biases in both the ice extent and thickness in CTL are remarkably reduced through the hindcast in LESTKF-E5. Additional numerical experiments suggest that the hindcast with sea ice thickness assimilation dramatically reduces systematic bias in the predicted ice thickness compared with sea ice concentration assimilation only or without any assimilation, which also benefits the prediction of sea ice extent and concentration due to their covariability. Hence, the corrected state of sea ice thickness would aid in the forecast procedure. Increasing the number of ensemble members or extending the integration period to generate estimates of initial model states and uncertainties seems to have small impacts on sea ice prediction relative to LESTKF-E5.

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Insights on Sea Ice Data Assimilation from Perfect Model Observing System Simulation Experiments

Journal of Climate ◽

10.1175/jcli-d-17-0904.1 ◽

2018 ◽

Vol 31 (15) ◽

pp. 5911-5926 ◽

Cited By ~ 12

Author(s):

Yong-Fei Zhang ◽

Cecilia M. Bitz ◽

Jeffrey L. Anderson ◽

Nancy Collins ◽

Jonathan Hendricks ◽

...

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Simulation Experiments ◽

Sea Ice Concentration ◽

Perfect Model ◽

Arctic Sea ◽

Ice Concentration ◽

Ice Model

Simulating Arctic sea ice conditions up to the present and predicting them several months in advance has high stakeholder value, yet remains challenging. Advanced data assimilation (DA) methods combine real observations with model forecasts to produce sea ice reanalyses and accurate initial conditions for sea ice prediction. This study introduces a sea ice DA framework for a sea ice model with a parameterization of the ice thickness distribution by resolving multiple thickness categories. Specifically, the Los Alamos Sea Ice Model, version 5 (CICE5), is integrated with the Data Assimilation Research Testbed (DART). A series of perfect model observing system simulation experiments (OSSEs) are designed to explore DA algorithms within the ensemble Kalman filter (EnKF) and the relative importance of different observation types. This study demonstrates that assimilating sea ice concentration (SIC) observations can effectively remove SIC errors, with the error of total Arctic sea ice area reduced by about 60% annually. When the impact of SIC observations is strongly localized in space, the error of total volume is also modestly improved. The largest simulation improvements are produced when sea ice thickness (SIT) and SIC are jointly assimilated, with the error of total volume decreased by more than 70% annually. Assimilating multiyear sea ice concentration (MYI) can reduce error in total volume by more than 50%. Assimilating MYI produces modest improvements in snow depth (errors are reduced by around 16%), while assimilating SIC and SIT has no obvious influence on snow depth. This study also suggests that different observation types may need different localization distances to optimize DA performance.

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Assimilating summer sea-ice concentration into a coupled ice–ocean model using a LSEIK filter

Annals of Glaciology ◽

10.3189/2015aog69a740 ◽

2015 ◽

Vol 56 (69) ◽

pp. 38-44 ◽

Cited By ~ 16

Author(s):

Qinghua Yang ◽

Svetlana N. Losa ◽

Martin Losch ◽

Jiping Liu ◽

Zhanhai Zhang ◽

...

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

General Circulation ◽

Arctic Sea Ice ◽

The Arctic ◽

In Situ Observation ◽

Concentration Data ◽

Sea Ice Concentration ◽

Arctic Sea ◽

Ice Concentration

AbstractThe decrease in summer sea-ice extent in the Arctic Ocean opens shipping routes and creates potential for many marine operations. For these activities accurate predictions of sea-ice conditions are required to maintain marine safety. In an attempt at Arctic sea-ice prediction, the summer of 2010 is selected to implement an Arctic sea-ice data assimilation (DA) study. The DA system is based on a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model (MITgcm) and a local singular evolutive interpolated Kalman (LSEIK) filter to assimilate Special Sensor Microwave Imager/Sounder (SSMIS) sea-ice concentration operational products from the US National Snow and Ice Data Center (NSIDC). Based on comparisons with both the assimilated NSIDC SSMIS concentration and concentration data from the Ocean and Sea Ice Satellite Application Facility, the forecasted sea-ice edge and concentration improve upon simulations without data assimilation. By the nature of the assimilation algorithm with multivariate covariance between ice concentration and thickness, sea-ice thickness fields are also updated, and the evaluation with in situ observation shows some improvement compared to the forecast without data assimilation.

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Improving Arctic Sea Ice Prediction Using PIOMAS Initial Sea Ice Thickness in a Coupled Ocean–Atmosphere Model

Monthly Weather Review ◽

10.1175/mwr-d-15-0097.1 ◽

2015 ◽

Vol 143 (11) ◽

pp. 4618-4630 ◽

Cited By ~ 33

Author(s):

Thomas W. Collow ◽

Wanqiu Wang ◽

Arun Kumar ◽

Jinlun Zhang

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Forecast System ◽

Climate Forecast System ◽

Sea Ice Concentration ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Ice Concentration ◽

Realistic Representation

Abstract Because sea ice thickness is known to influence future patterns of sea ice concentration, it is likely that an improved initialization of sea ice thickness in a coupled ocean–atmosphere model would improve Arctic sea ice cover forecasts. Here, two sea ice thickness datasets as possible candidates for forecast initialization were investigated: the Climate Forecast System Reanalysis (CFSR) and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Using Ice, Cloud, and Land Elevation Satellite (ICESat) data, it was shown that the PIOMAS dataset had a more realistic representation of sea ice thickness than CFSR. Subsequently, both March CFSR and PIOMAS sea ice thicknesses were used to initialize hindcasts using the Climate Forecast System, version 2 (CFSv2), model. A second set of model runs was also done in which the original model physics were modified to more physically reasonable settings—namely, increasing the number of marine stratus clouds in the Arctic region and enabling realistic representation of the ice–ocean heat flux. Hindcasts were evaluated using sea ice concentration observations from the National Aeronautics and Space Administration (NASA) Team and Bootstrap algorithms. Results show that using PIOMAS initial sea ice thickness in addition to the physics modifications yielded significant improvement in the prediction of September Arctic sea ice extent along with increased interannual predictive skill. Significant local improvements in sea ice concentration were also seen in distinct regions for the change to PIOMAS initial thickness or the physics adjustments, with the most improvement occurring when these changes were applied concurrently.

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Improving Arctic Sea Ice Prediction Though Freeboard Assimilation

10.5194/egusphere-egu21-2943 ◽

2021 ◽

Author(s):

Imke Sievers ◽

Till Rasmussen ◽

Lars Stenseng

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Surface Elevation ◽

Ice Formation ◽

Ice Thickness ◽

Sea Ice Concentration ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Ice Concentration ◽

Snow Thickness

With the presented work we aim to improve sea ice forecasts and our understanding of Arcitc sea ice formation though freeboard assimilation. Over the last years understanding Arctic sea ice changes and being able to make a reliable sea ice forecast has gained in importance. The central roll of Arctic sea ice extent in climate warming makes it a highly discussed topic in the climate research community. However a reliable Arctic sea ice forecast both on short term to seasonal time scales remains a challenge to be mastered, hinting that there are still many processes at play to be better understood. One promising approach to improve forecasts has been to assimilate satellite sea ice data into numerical sea ice models. Mainly two parameters measured by satellites have been used for assimilation: Sea ice concentration, which is competitively easy to obtain from satellites measuring passive microwave emissions as for example obtained by the SMOS satellite, and sea ice thickness, which is not directly measured, but has to be calculated from surface elevation measurements, as for example obtained by Cryosat 2. Compering the skill, of assimilation products using sea ice thickness and sea ice concentration shows that sea ice thickness has a longer memory and is over all leading to a better performance then sea ice concentration assimilation. Knowing this, sea ice thickness assimilation is far from being straight forward. Surface elevation measurements, obtained from satellite altemitry measurements, have to be separated into snow and ice freeborad, by assuming a snow thickness, to derive sea ice thickness from. Most of the time this is done using a snow thickness climatology obtained from Soviet drift stations measuring snow over multi year ice during the period 1954-1991 with adaption over first year sea ice, where this climatology has proven to be overestimating snow thickness. The technique is widely used jet known to introduce an error. To avoid errors caused by wrongly assumed snow covers the DMI and Aalborg University and DTU are at the moment collaborating on assimilating freebord instead of sea ice thickness into the CICE-NEMO modeling frame work using LARS NGen (LARS the Advanced Retracking System, Next Generation) sate of the art retracing software. In the presented work we will show first results of freeboard assimilation with a focus how this assimilation influences winter sea ice formation as well as the upper Arctic Ocean dynamics.

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A data assimilation application for improving estimates of Arctic sea ice thickness variability and change since the turn of the 21st century

10.5194/egusphere-egu21-8952 ◽

2021 ◽

Author(s):

Molly Wieringa ◽

Cecilia Bitz

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

Initial Conditions ◽

Arctic Sea Ice ◽

Prognostic Models ◽

Ice Thickness ◽

Sea Ice Concentration ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Arctic Sea Ice Thickness

Current sea ice prediction systems exhibit significant room for improvement compared to idealized estimates of sea ice predictability, a gap that could be closed by improving the initial conditions provided to prognostic models. Sea ice volume, the area-weighted integral of sea ice thickness (SIT), in particular, demonstrates long initial value predictability; in other words, accurate forecasting of Arctic sea ice requires highly accurate SIT initial conditions. Continuous records of SIT are, unfortunately, few and far between. To address this conundrum, we have explored applications of the Data Assimilation Research Testbed (DART) to constrain the Los Alamos Sea Ice Model (CICE5) within the Community Earth System Model using satellite-derived SIT observations from 2003 to present day. Our data assimilation system has been fine-tuned using new and highly accurate freeboard measurements from NASA&#8217;s ICESat-2 mission. Using SIT information alone, we generate two assimilation products: the first using DART with CICE5 and the second with an offline assimilation method. We compare these products to one another and to the community standard SIT record, PIOMAS. Future work will introduce multivariate assimilation of SIT with other sea ice variables, including sea ice concentration, sea ice skin temperature, and sea surface temperature.

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Long-term variation of sea ice and its response to thermodynamic factors in the Northwest Passage of the Canadian Arctic Archipelago

10.5194/tc-2020-215 ◽

2020 ◽

Author(s):

Xinyi Shen ◽

Yu Zhang ◽

Changsheng Chen ◽

Song Hu

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Sea Ice Concentration ◽

Sea Ice Thickness ◽

Northwest Passage ◽

Arctic Sea ◽

Ice Concentration ◽

Ice Conditions ◽

Thermodynamic Factors

Abstract. Sea ice conditions in the Canadian Arctic Archipelago (CAA) play a key role in the navigation of the Northwest Passage (NWP). Based on the observed and simulated sea ice concentration and thickness data, we studied the temporal and spatial characteristics of sea ice from 1979 to 2017 in the NWP of the CAA and evaluated the sea ice conditions along the southern and northern routes of the NWP. Against the background of the rapid retreat of Arctic sea ice, the 39-year observed sea ice concentration of the NWP exhibited a relatively large decreasing trend in summer and fall, while heavy sea ice conditions were maintained in winter and spring, with a slight increasing trend. Consistent with Arctic sea ice, the sea ice extent in the NWP displayed a decreasing trend of −2.34 %/10 a, with its minimum occurring in 2012. The sea ice thickness in most subregions of the NWP showed a decreasing trend, with the exception of Lancaster Sound. The decreasing trend of sea ice thickness in the NWP was estimated to −0.16 m/10 a. Based on the sea ice concentration and thickness, however, the sea ice conditions were heavier along the northern route than the southern route. This study considered both of these routes, and we selected and evaluated more specific pathways. The correlation results between the sea ice and atmospheric and oceanic thermodynamic factors in the NWP suggested that the thermodynamic factors had a greater impact on sea ice in the summer and fall, and the variations of sea ice concentration were more closely correlated with the thermodynamic factors than sea ice thickness. The sea surface temperature (SST) had a higher correlation with sea ice concentration than surface air temperature (SAT), while SAT exhibited a higher correlation with sea ice thickness than SST. The residual amount of sea ice concentration and thickness in the fall, associated with the fall SAT and SST, contributed to the formation of sea ice in the following winter and spring.

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