Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system

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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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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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

<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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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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How well can the observed Arctic sea ice summer retreat and winter advance be represented in the NCEP Climate Forecast System version 2?

Climate Dynamics ◽

10.1007/s00382-016-3417-8 ◽

2016 ◽

Vol 49 (5-6) ◽

pp. 1651-1663 ◽

Cited By ~ 1

Author(s):

Thomas W. Collow ◽

Wanqiu Wang ◽

Arun Kumar ◽

Jinlun Zhang

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Climate Forecast ◽

Forecast System ◽

Climate Forecast System ◽

Arctic Sea ◽

Ncep Climate Forecast System

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Model-data comparison and data assimilation of mid-Holocene Arctic sea-ice concentration

Climate of the Past Discussions ◽

10.5194/cpd-9-6515-2013 ◽

2013 ◽

Vol 9 (6) ◽

pp. 6515-6549 ◽

Cited By ~ 1

Author(s):

F. Klein ◽

H. Goosse ◽

A. Mairesse ◽

A. de Vernal

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

Atmospheric Circulation ◽

Barents Sea ◽

Arctic Sea Ice ◽

The Arctic ◽

Model Data ◽

Sea Ice Concentration ◽

Arctic Sea ◽

Ice Concentration

Abstract. The consistency between a new quantitative reconstruction of Arctic sea-ice concentration based on dinocyst assemblages and the results of climate models has been investigated for the mid-Holocene. The comparison shows that the simulated sea-ice changes are weaker and spatially more homogeneous than the recorded ones. Furthermore, although the model-data agreement is relatively good in some regions such as the Labrador Sea, the skill of the models at local scale is low. The response of the models follows mainly the increase in summer insolation at large scale. This is modulated by changes in atmospheric circulation leading to differences between regions in the models that are albeit smaller than in the reconstruction. Performing simulations with data assimilation using the model LOVECLIM amplifies those regional differences, mainly through a reduction of the southward winds in the Barents Sea and an increase in the westerly winds in the Canadian Basin of the Arctic. This leads to an increase in the ice concentration in the Barents and Chukchi Seas and a better agreement with the reconstructions. This underlines the potential role of atmospheric circulation to explain the reconstructed changes during the Holocene.

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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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Model–data comparison and data assimilation of mid-Holocene Arctic sea ice concentration

Climate of the Past ◽

10.5194/cp-10-1145-2014 ◽

2014 ◽

Vol 10 (3) ◽

pp. 1145-1163 ◽

Cited By ~ 4

Author(s):

F. Klein ◽

H. Goosse ◽

A. Mairesse ◽

A. de Vernal

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

Atmospheric Circulation ◽

Barents Sea ◽

Arctic Sea Ice ◽

Model Data ◽

Sea Ice Concentration ◽

Arctic Sea ◽

Ice Concentration ◽

Concentration Changes

Abstract. The consistency between new quantitative reconstructions of Arctic sea ice concentration based on dinocyst assemblages and the results of climate models has been investigated for the mid-Holocene. The response of the models mainly follows the increase in summer insolation, modulated to a limited extent by changes in atmospheric circulation. This leads to differences between regions in the models that are smaller than in the reconstruction. It is, however, impossible to precisely assess the models' skills because the sea ice concentration changes at the mid-Holocene are small in both the reconstructions and the models and of the same order of magnitude as the reconstruction uncertainty. Performing simulations with data assimilation using the model LOVECLIM amplifies the regional differences and improves the model–data agreement as expected. This is mainly achieved through a reduction of the southward winds in the Barents Sea and an increase in the westerly winds in the Canadian Basin, inducing an increase in the ice concentration in the Barents and Chukchi seas. This underlines the potential role of atmospheric circulation in explaining the reconstructed changes during the Holocene.

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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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