Articles | Volume 12, issue 11
https://doi.org/10.5194/tc-12-3671-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-12-3671-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Nov 2018
Research article |
| 26 Nov 2018
| 26 Nov 2018
Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis
Nansen Environmental and Remote Sensing Center, Bergen N5006, Norway
François Counillon
Nansen Environmental and Remote Sensing Center, Bergen N5006, Norway
Bjerknes Center for Climate Research, Bergen, Norway
Laurent Bertino
Nansen Environmental and Remote Sensing Center, Bergen N5006, Norway
Bjerknes Center for Climate Research, Bergen, Norway
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This study developed a new method to estimate Arctic sea ice thickness from 1992 to 2010 using a combination of machine learning and data assimilation. By training a machine learning model on data from 2011–2022, past errors in sea ice thickness can be corrected, leading to improved estimations. This approach provides insights into historical changes on sea ice thickness, showing a notable decline from 1992 to 2022, and offers a valuable resource for future studies.
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Sea ice melt, together with other freshwater sources, has effects on the Arctic environment. Sea surface salinity (SSS) plays a key role in representing water mixing. Recently the satellite SSS from SMOS was developed in the Arctic region. In this study, we first evaluate the impact of assimilating these satellite data in an Arctic reanalysis system. It shows that SSS errors are reduced by 10–50 % depending on areas, encouraging its use in a long-time reanalysis to monitor the Arctic water cycle.
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Sukun Cheng, Yumeng Chen, Ali Aydoğdu, Laurent Bertino, Alberto Carrassi, Pierre Rampal, and Christopher K. R. T. Jones
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This work studies a novel application of combining a Lagrangian sea ice model, neXtSIM, and data assimilation. It uses a deterministic ensemble Kalman filter to incorporate satellite-observed ice concentration and thickness in simulations. The neXtSIM Lagrangian nature is handled using a remapping strategy on a common homogeneous mesh. The ensemble is formed by perturbing air–ocean boundary conditions and ice cohesion. Thanks to data assimilation, winter Arctic sea ice forecasting is enhanced.
Jiping Xie, Roshin P. Raj, Laurent Bertino, Justino Martínez, Carolina Gabarró, and Rafael Catany
Ocean Sci., 19, 269–287, https://doi.org/10.5194/os-19-269-2023, https://doi.org/10.5194/os-19-269-2023, 2023
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Sea ice melt, together with other freshwater sources, has effects on the Arctic environment. Sea surface salinity (SSS) plays a key role in representing water mixing. Recently the satellite SSS from SMOS was developed in the Arctic region. In this study, we first evaluate the impact of assimilating these satellite data in an Arctic reanalysis system. It shows that SSS errors are reduced by 10–50 % depending on areas, encouraging its use in a long-time reanalysis to monitor the Arctic water cycle.
Fabio Mangini, Léon Chafik, Antonio Bonaduce, Laurent Bertino, and Jan Even Ø. Nilsen
Ocean Sci., 18, 331–359, https://doi.org/10.5194/os-18-331-2022, https://doi.org/10.5194/os-18-331-2022, 2022
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Justino Martínez, Carolina Gabarró, Antonio Turiel, Verónica González-Gambau, Marta Umbert, Nina Hoareau, Cristina González-Haro, Estrella Olmedo, Manuel Arias, Rafael Catany, Laurent Bertino, Roshin P. Raj, Jiping Xie, Roberto Sabia, and Diego Fernández
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Measuring salinity from space is challenging since the sensitivity of the brightness temperature to sea surface salinity is low, but the retrieval of SSS in cold waters is even more challenging. In 2019, the ESA launched a specific initiative called Arctic+Salinity to produce an enhanced Arctic SSS product with better quality and resolution than the available products. This paper presents the methodologies used to produce the new enhanced Arctic SMOS SSS product.
Ingo Bethke, Yiguo Wang, François Counillon, Noel Keenlyside, Madlen Kimmritz, Filippa Fransner, Annette Samuelsen, Helene Langehaug, Lea Svendsen, Ping-Gin Chiu, Leilane Passos, Mats Bentsen, Chuncheng Guo, Alok Gupta, Jerry Tjiputra, Alf Kirkevåg, Dirk Olivié, Øyvind Seland, Julie Solsvik Vågane, Yuanchao Fan, and Tor Eldevik
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Amy Solomon, Céline Heuzé, Benjamin Rabe, Sheldon Bacon, Laurent Bertino, Patrick Heimbach, Jun Inoue, Doroteaciro Iovino, Ruth Mottram, Xiangdong Zhang, Yevgeny Aksenov, Ronan McAdam, An Nguyen, Roshin P. Raj, and Han Tang
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Freshwater in the Arctic Ocean plays a critical role in the global climate system by impacting ocean circulations, stratification, mixing, and emergent regimes. In this review paper we assess how Arctic Ocean freshwater changed in the 2010s relative to the 2000s. Estimates from observations and reanalyses show a qualitative stabilization in the 2010s due to a compensation between a freshening of the Beaufort Gyre and a reduction in freshwater in the Amerasian and Eurasian basins.
Sourav Chatterjee, Roshin P. Raj, Laurent Bertino, Sebastian H. Mernild, Meethale Puthukkottu Subeesh, Nuncio Murukesh, and Muthalagu Ravichandran
The Cryosphere, 15, 1307–1319, https://doi.org/10.5194/tc-15-1307-2021, https://doi.org/10.5194/tc-15-1307-2021, 2021
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Sea ice in the Greenland Sea (GS) is important for its climatic (fresh water), economical (shipping), and ecological contribution (light availability). The study proposes a mechanism through which sea ice concentration in GS is partly governed by the atmospheric and ocean circulation in the region. The mechanism proposed in this study can be useful for assessing the sea ice variability and its future projection in the GS.
Roshin P. Raj, Sourav Chatterjee, Laurent Bertino, Antonio Turiel, and Marcos Portabella
Ocean Sci., 15, 1729–1744, https://doi.org/10.5194/os-15-1729-2019, https://doi.org/10.5194/os-15-1729-2019, 2019
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In this study we investigated the variability of the Arctic Front (AF), an important biologically productive region in the Norwegian Sea, using a suite of satellite data, atmospheric reanalysis and a regional coupled ocean–sea ice data assimilation system. We show evidence of the two-way interaction between the atmosphere and the ocean at the AF. The North Atlantic Oscillation is found to influence the strength of the AF and may have a profound influence on the region's biological productivity.
Jiping Xie, Roshin P. Raj, Laurent Bertino, Annette Samuelsen, and Tsuyoshi Wakamatsu
Ocean Sci., 15, 1191–1206, https://doi.org/10.5194/os-15-1191-2019, https://doi.org/10.5194/os-15-1191-2019, 2019
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Two gridded sea surface salinity (SSS) products have been derived from the European Space Agency’s Soil Moisture and Ocean Salinity mission. The uncertainties of these two products in the Arctic are quantified against two SSS products in the Copernicus Marine Environment Monitoring Services, two climatologies, and other in situ data. The results compared with independent in situ data clearly show a common challenge for the six SSS products to represent central Arctic freshwater masses (<24 psu).
Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino
Nonlin. Processes Geophys., 26, 143–162, https://doi.org/10.5194/npg-26-143-2019, https://doi.org/10.5194/npg-26-143-2019, 2019
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This paper describes an innovative way to use data assimilation to infer the dynamics of a physical system from its observation only. The method can operate with noisy and partial observation of the physical system. It acts as a deep learning technique specialised to dynamical models without the need for machine learning tools. The method is successfully tested on chaotic dynamical systems: the Lorenz-63, Lorenz-96, and Kuramoto–Sivashinski models and a two-scale Lorenz model.
Julien Brajard, Alberto Carrassi, Marc Bocquet, and Laurent Bertino
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-136, https://doi.org/10.5194/gmd-2019-136, 2019
Revised manuscript not accepted
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We explore the possibility of combining data assimilation with machine learning. We introduce a new hybrid method for a two-fold scope: (i) emulating hidden, possibly chaotic, dynamics and (ii) predicting its future states. Numerical experiments have been carried out using the chaotic Lorenz 96 model, proving both the convergence of the hybrid method and its statistical skills including short-term forecasting and emulation of the long-term dynamics.
Takuya Nakanowatari, Jun Inoue, Kazutoshi Sato, Laurent Bertino, Jiping Xie, Mio Matsueda, Akio Yamagami, Takeshi Sugimura, Hironori Yabuki, and Natsuhiko Otsuka
The Cryosphere, 12, 2005–2020, https://doi.org/10.5194/tc-12-2005-2018, https://doi.org/10.5194/tc-12-2005-2018, 2018
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Medium-range predictability of early summer sea ice thickness in the East Siberian Sea was examined, based on TOPAZ4 forecast data. Statistical examination indicates that the estimate drops abruptly at 4 days, which is related to dynamical process controlled by synoptic-scale atmospheric fluctuations such as an Arctic cyclone. For longer lead times (> 4 days), the thermodynamic melting process takes over, which represents most of the remaining prediction.
Fabrice Ardhuin, Yevgueny Aksenov, Alvise Benetazzo, Laurent Bertino, Peter Brandt, Eric Caubet, Bertrand Chapron, Fabrice Collard, Sophie Cravatte, Jean-Marc Delouis, Frederic Dias, Gérald Dibarboure, Lucile Gaultier, Johnny Johannessen, Anton Korosov, Georgy Manucharyan, Dimitris Menemenlis, Melisa Menendez, Goulven Monnier, Alexis Mouche, Frédéric Nouguier, George Nurser, Pierre Rampal, Ad Reniers, Ernesto Rodriguez, Justin Stopa, Céline Tison, Clément Ubelmann, Erik van Sebille, and Jiping Xie
Ocean Sci., 14, 337–354, https://doi.org/10.5194/os-14-337-2018, https://doi.org/10.5194/os-14-337-2018, 2018
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The Sea surface KInematics Multiscale (SKIM) monitoring mission is a proposal for a future satellite that is designed to measure ocean currents and waves. Using a Doppler radar, the accurate measurement of currents requires the removal of the mean velocity due to ocean wave motions. This paper describes the main processing steps needed to produce currents and wave data from the radar measurements. With this technique, SKIM can provide unprecedented coverage and resolution, over the global ocean.
Matthias Rabatel, Pierre Rampal, Alberto Carrassi, Laurent Bertino, and Christopher K. R. T. Jones
The Cryosphere, 12, 935–953, https://doi.org/10.5194/tc-12-935-2018, https://doi.org/10.5194/tc-12-935-2018, 2018
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Large deviations still exist between sea ice forecasts and observations because of both missing physics in models and uncertainties on model inputs. We investigate how the new sea ice model neXtSIM is sensitive to uncertainties in the winds. We highlight and quantify the role of the internal forces in the ice on this sensitivity and show that neXtSIM is better at predicting sea ice drift than a free-drift (without internal forces) ice model and is a skilful tool for search and rescue operations.
Kristoffer Aalstad, Sebastian Westermann, Thomas Vikhamar Schuler, Julia Boike, and Laurent Bertino
The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018, https://doi.org/10.5194/tc-12-247-2018, 2018
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We demonstrate how snow cover data from satellites can be used to constrain estimates of snow distributions at sites in the Arctic. In this effort, we make use of data assimilation to combine the information contained in the snow cover data with a simple snow model. By comparing our snow distribution estimates to independent observations, we find that this method performs favorably. Being modular, this method could be applied to other areas as a component of a larger reanalysis system.
Jiping Xie, Laurent Bertino, François Counillon, Knut A. Lisæter, and Pavel Sakov
Ocean Sci., 13, 123–144, https://doi.org/10.5194/os-13-123-2017, https://doi.org/10.5194/os-13-123-2017, 2017
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The Arctic Ocean plays an important role in the global climate system, but the concerned interpretation about its changes is severely hampered by the sparseness of the observations of sea ice and ocean. The focus of this study is to provide a quantitative assessment of the performance of the TOPAZ4 reanalysis for ocean and sea ice variables in the pan-Arctic region (north of 63 °N) in order to guide the user through its skills and limitations.
Jiping Xie, François Counillon, Laurent Bertino, Xiangshan Tian-Kunze, and Lars Kaleschke
The Cryosphere, 10, 2745–2761, https://doi.org/10.5194/tc-10-2745-2016, https://doi.org/10.5194/tc-10-2745-2016, 2016
Short summary
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As a potentially operational daily product, the SMOS-Ice can improve the statements of sea ice thickness and concentration. In this study, focusing on the SMOS-Ice data assimilated into the TOPAZ system, the quantitative evaluation for the impacts and the concerned comparison with the present observation system are valuable to understand the further improvement of the accuracy of operational ocean forecasting system.
A. S. A. Ferreira, H. Hátún, F. Counillon, M. R. Payne, and A. W. Visser
Biogeosciences, 12, 3641–3653, https://doi.org/10.5194/bg-12-3641-2015, https://doi.org/10.5194/bg-12-3641-2015, 2015
Short summary
Short summary
Our main objective was to assess which bottom-up processes can best predict changes in phytoplankton surface spring blooms in the North Atlantic. We applied new phenology algorithms to satellite-derived data and compared four different metrics based on physical drivers of phytoplankton. We show that there is a dominant physical mechanism - mixed layer shoaling - and that different regions are governed by different physical phenomena.
D. Mignac, C. A. S. Tanajura, A. N. Santana, L. N. Lima, and J. Xie
Ocean Sci., 11, 195–213, https://doi.org/10.5194/os-11-195-2015, https://doi.org/10.5194/os-11-195-2015, 2015
Related subject area
Discipline: Sea ice | Subject: Data Assimilation
Bounded and categorized: targeting data assimilation for sea ice fractional coverage and nonnegative quantities in a single-column multi-category sea ice model
Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean–sea ice model
Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model
Towards improving short-term sea ice predictability using deformation observations
Assimilating CryoSat-2 freeboard to improve Arctic sea ice thickness estimates
The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system
Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020
Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)
A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations
Estimating parameters in a sea ice model using an ensemble Kalman filter
Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system
Estimation of sea ice parameters from sea ice model with assimilated ice concentration and SST
Molly M. Wieringa, Christopher Riedel, Jeffrey L. Anderson, and Cecilia M. Bitz
The Cryosphere, 18, 5365–5382, https://doi.org/10.5194/tc-18-5365-2024, https://doi.org/10.5194/tc-18-5365-2024, 2024
Short summary
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Statistically combining models and observations with data assimilation (DA) can improve sea ice forecasts but must address several challenges, including irregularity in ice thickness and coverage over the ocean. Using a sea ice column model, we show that novel, bounds-aware DA methods outperform traditional methods for sea ice. Additionally, thickness observations at sub-grid scales improve modeled ice estimates of both thick and thin ice, a finding relevant for forecasting applications.
Marina Durán Moro, Ann Kristin Sperrevik, Thomas Lavergne, Laurent Bertino, Yvonne Gusdal, Silje Christine Iversen, and Jozef Rusin
The Cryosphere, 18, 1597–1619, https://doi.org/10.5194/tc-18-1597-2024, https://doi.org/10.5194/tc-18-1597-2024, 2024
Short summary
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Individual satellite passes instead of daily means of sea ice concentration are used to correct the sea ice model forecast in the Barents Sea. The use of passes provides a significantly larger improvement of the forecasts even after a 7 d period due to the more precise information on temporal and spatial variability contained in the passes. One major advantage of the use of satellite passes is that there is no need to wait for the daily mean availability in order to update the forecast.
Keguang Wang, Alfatih Ali, and Caixin Wang
The Cryosphere, 17, 4487–4510, https://doi.org/10.5194/tc-17-4487-2023, https://doi.org/10.5194/tc-17-4487-2023, 2023
Short summary
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A simple, efficient. and accurate data assimilation method, local analytical optimal nudging (LAON), is introduced to assimilate high-resolution sea ice concentration in a pan-Arctic high-resolution coupled ocean and sea ice model. The method provides a new vision by nudging the model evolution to the optimal estimate forwardly, continuously, and smoothly. This method is applicable to the general nudging theory and applications in physics, Earth science, psychology, and behavior sciences.
Anton Korosov, Pierre Rampal, Yue Ying, Einar Ólason, and Timothy Williams
The Cryosphere, 17, 4223–4240, https://doi.org/10.5194/tc-17-4223-2023, https://doi.org/10.5194/tc-17-4223-2023, 2023
Short summary
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It is possible to compute sea ice motion from satellite observations and detect areas where ice converges (moves together), forms ice ridges or diverges (moves apart) and opens leads. However, it is difficult to predict the exact motion of sea ice and position of ice ridges or leads using numerical models. We propose a new method to initialise a numerical model from satellite observations to improve the accuracy of the forecasted position of leads and ridges for safer navigation.
Imke Sievers, Till A. S. Rasmussen, and Lars Stenseng
The Cryosphere, 17, 3721–3738, https://doi.org/10.5194/tc-17-3721-2023, https://doi.org/10.5194/tc-17-3721-2023, 2023
Short summary
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The satellite CryoSat-2 measures freeboard (FB), which is used to derive sea ice thickness (SIT) under the assumption of hydrostatic balance. This SIT comes with large uncertainties due to errors in the observed FB, sea ice density, snow density and snow thickness. This study presents a new method to derive SIT by assimilating the FB into the sea ice model, evaluates the resulting SIT against in situ observations and compares the results to the CryoSat-2-derived SIT without FB assimilation.
Nicholas Williams, Nicholas Byrne, Daniel Feltham, Peter Jan Van Leeuwen, Ross Bannister, David Schroeder, Andrew Ridout, and Lars Nerger
The Cryosphere, 17, 2509–2532, https://doi.org/10.5194/tc-17-2509-2023, https://doi.org/10.5194/tc-17-2509-2023, 2023
Short summary
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Observations show that the Arctic sea ice cover has reduced over the last 40 years. This study uses ensemble-based data assimilation in a stand-alone sea ice model to investigate the impacts of assimilating three different kinds of sea ice observation, including the novel assimilation of sea ice thickness distribution. We show that assimilating ice thickness distribution has a positive impact on thickness and volume estimates within the ice pack, especially for very thick ice.
Sukun Cheng, Yumeng Chen, Ali Aydoğdu, Laurent Bertino, Alberto Carrassi, Pierre Rampal, and Christopher K. R. T. Jones
The Cryosphere, 17, 1735–1754, https://doi.org/10.5194/tc-17-1735-2023, https://doi.org/10.5194/tc-17-1735-2023, 2023
Short summary
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This work studies a novel application of combining a Lagrangian sea ice model, neXtSIM, and data assimilation. It uses a deterministic ensemble Kalman filter to incorporate satellite-observed ice concentration and thickness in simulations. The neXtSIM Lagrangian nature is handled using a remapping strategy on a common homogeneous mesh. The ensemble is formed by perturbing air–ocean boundary conditions and ice cohesion. Thanks to data assimilation, winter Arctic sea ice forecasting is enhanced.
Emma K. Fiedler, Matthew J. Martin, Ed Blockley, Davi Mignac, Nicolas Fournier, Andy Ridout, Andrew Shepherd, and Rachel Tilling
The Cryosphere, 16, 61–85, https://doi.org/10.5194/tc-16-61-2022, https://doi.org/10.5194/tc-16-61-2022, 2022
Short summary
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Sea ice thickness (SIT) observations derived from CryoSat-2 satellite measurements have been successfully used to initialise an ocean and sea ice forecasting model (FOAM). Other centres have previously used gridded and averaged SIT observations for this purpose, but we demonstrate here for the first time that SIT measurements along the satellite orbit track can be used. Validation of the resulting modelled SIT demonstrates improvements in the model performance compared to a control.
William Gregory, Isobel R. Lawrence, and Michel Tsamados
The Cryosphere, 15, 2857–2871, https://doi.org/10.5194/tc-15-2857-2021, https://doi.org/10.5194/tc-15-2857-2021, 2021
Short summary
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Satellite measurements of radar freeboard allow us to compute the thickness of sea ice from space; however attaining measurements across the entire Arctic basin typically takes up to 30 d. Here we present a statistical method which allows us to combine observations from three separate satellites to generate daily estimates of radar freeboard across the Arctic Basin. This helps us understand how sea ice thickness is changing on shorter timescales and what may be causing these changes.
Yong-Fei Zhang, Cecilia M. Bitz, Jeffrey L. Anderson, Nancy S. Collins, Timothy J. Hoar, Kevin D. Raeder, and Edward Blanchard-Wrigglesworth
The Cryosphere, 15, 1277–1284, https://doi.org/10.5194/tc-15-1277-2021, https://doi.org/10.5194/tc-15-1277-2021, 2021
Short summary
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Sea ice models suffer from large uncertainties arising from multiple sources, among which parametric uncertainty is highly under-investigated. We select a key ice albedo parameter and update it by assimilating either sea ice concentration or thickness observations. We found that the sea ice albedo parameter is improved by data assimilation, especially by assimilating sea ice thickness observations. The improved parameter can further benefit the forecast of sea ice after data assimilation stops.
Sindre Fritzner, Rune Graversen, Kai H. Christensen, Philip Rostosky, and Keguang Wang
The Cryosphere, 13, 491–509, https://doi.org/10.5194/tc-13-491-2019, https://doi.org/10.5194/tc-13-491-2019, 2019
Short summary
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In this work, a coupled ocean and sea-ice ensemble-based assimilation system is used to assess the impact of different observations on the assimilation system. The focus of this study is on sea-ice observations, including the use of satellite observations of sea-ice concentration, sea-ice thickness and snow depth for assimilation. The study showed that assimilation of sea-ice thickness in addition to sea-ice concentration has a large positive impact on the coupled model.
Siva Prasad, Igor Zakharov, Peter McGuire, Desmond Power, and Martin Richard
The Cryosphere, 12, 3949–3965, https://doi.org/10.5194/tc-12-3949-2018, https://doi.org/10.5194/tc-12-3949-2018, 2018
Short summary
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A numerical sea ice model, CICE, was used along with data assimilation to derive sea ice parameters in the region of Baffin Bay, Hudson Bay and Labrador Sea. The modelled ice parameters were compared with parameters estimated from remote-sensing data. The ice concentration, thickness and freeboard estimates from the model assimilated with both ice concentration and SST were found to be within the uncertainty of the observations except during March.
Cited articles
Allard, R. A., Farrell, S. L., Hebert, D. A., Johnston, W. F., Li, L., Kurtz,
N. T., Phelps, M. W., Posey, P. G., Tilling, R., Ridout, A., and Wallcraft,
A. J.: Utilizing CryoSat-2 sea ice thickness to initialize a coupled
ice-ocean modeling system, Adv. Space Res., 62, 1265–1280,
https://doi.org/10.1016/j.asr.2017.12.030, 2018.
Bathiany, S., Notz, D., Mauritsen, T., Raedel, G., and Brovkin, V.: On the
potential for abrupt Arctic winter sea ice loss. J.
Climate, 29, 2703–2719, https://doi.org/10.1175/JCLI-D-15-0466.1, 2016.
Bertino, L. and Lisæter, K. A.: The TOPAZ monitoring and prediction
system for the Atlantic and Arctic Oceans, J. Oper. Oceanogr., 1, 15–19,
https://doi.org/10.1080/1755876X.2008.11020098, 2008
Bentsen, M., Evensen, G., Drange, H., and Jenkins, A. D.: Coordinate
transformation on a sphere using conformal mapping, Mon. Weather Rev., 127,
2733–2740, https://doi.org/10.1175/1520-0493(1999)127<2733:CTOASU>2.0.CO:2, 1999.
Bouillon, S., Fichefet, T., Legat, V., and Madec, G.: The
elastic-viscous-plastic method revised, Ocean Modell., 7, 2–12,
https://doi.org/10.1016/j.ocemod.2013.05.013, 2013.
Budikova, D.: Role of Arctic sea ice in global atmospheric circulation: A
review, Global Planet. Change, 68, 149–163,
https://doi.org/10.1016/j.gloplacha.2009.04.001, 2009.
Cardinali, C., Pezzulli, S., and Andersson, E.: Influence-matrix diagnostic
of a data assimilation system, Q. J. Roy. Meteor. Soc., 130, 2767–2786,
https://doi.org/10.1256/qj.03.205, 2004.
Chassignet, E. P., Smith, L. T., and Halliwell, G. R.: North Atlantic
Simulations with the Hybrid Coordinate Ocean Model (HYCOM): Impact of the
vertical coordinate choice, reference pressure, and thermobaricity, J. Phys.
Oceanogr., 33, 2504–2526,
https://doi.org/10.1175/1520-0485(2003)033<2504:NASWTH>2.0.CO:2, 2003.
Comiso, J. C., Parkinson, C. L., Gersten, R., and Stock, L.: Accelerated
decline in the Arctic sea ice cover, Geophys. Res. Lett., 35, L01703,
https://doi.org/10.1029/2007GL031972, 2008.
Counillon, F. and Bertino, L.: High-resolution ensemble forecasting for the
Gulf of Mexico eddies and fronts, Ocean Dynam., 59, 83–95,
https://doi.org/10.1007/s10236-008-0167-0, 2009.
Day, J. J., Hawkins, E., and Tietsche S.: Will Arctic sea ice thickness
initialization improve seasonal forecast skill?, Geophys. Res. Lett., 41,
7566–7575, https://doi.org/10.1002/2014GL061694, 2014.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, I., Biblot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Greer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Kohler, M.,
Matricardi, M., McNally, A. P., Mong-Sanz, B. M., Morcette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart,
F.: The ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597,
https://doi.org/10.1002/qj.828, 2011.
Desroziers, G., Berre, L., and Poli, P.: Diagnosis of observation,
background and analysis-error statistics in observation space, Q. J. Roy.
Meteor. Soc., 131, 3385–3396, https://doi.org/10.1256/qj.05.108, 2005.
Docquier, D., Massonnet, F., Barthélemy, A., Tandon, N. F., Lecomte, O.,
and Fichefet, T.: Relationships between Arctic sea ice drift and strength
modelled by NEMO-LIM3.6, The Cryosphere, 11, 2829–2846,
https://doi.org/10.5194/tc-11-2829-2017, 2017.
Drange, H. and Simonsen, K.: Formulation of air-sea fluxes in the ESOP2
version of MICOM, Technical Report No. 125 of Nansen Environmental and Remote
Sensing Center, 1996.
Ferreira, A. S. A., Hátún, H., Counillon, F., Payne, M. R., and Visser,
A. W.: Synoptic-scale analysis of mechanisms driving surface chlorophyll
dynamics in the North Atlantic, Biogeosciences, 12, 3641–3653,
https://doi.org/10.5194/bg-12-3641-2015, 2015.
Finck, N., Counillon, F., Bertino,
L., Bouillon, S. and Rampal, P.: Validation of sea ice quantities of
TOPAZ for the period 1990-2010, Technical Report No. 332 of Nansen
Environmental and Remote Sensing Center, 2013.
Guemas, V., Blanchard-Wrigglesworth, E., Chevallier, M., Day, J. J., Déqué, M.,
Doblas-Reyes, F. J., Fuckar, N. S., Germe, A., Hawkins, E., Keeley, S.,
Koenigk, T., Salas y Mélia, D., and Tietsche, S.:
A review on Arctic
sea-ice predictability and prediction on seasonal to decadal time scales, Q.
J. Roy. Meteor. Soc., 142, 546–561, https://doi.org/10.1002/qj.2401, 2014.
Heygster, G., Hendricks, S., Kaleschke, L., Maass, N., Mills, P., Stammer, D., Tonboe, R. T., and Haas, C.: L-Band
Radiometry for Sea-Ice Applications, Final Report for ESA ESTEC Contract
21130/08/NL/EL, Institute of Environmental Physics, University of Bremen, 219
pp., available at:
http://www.iup.uni-bremen.de/iuppage/psa/documents/smos_final.pdf (last
access: 22 November 2018) 2009.
Hibler III, W. D.: A dynamic thermodynamic sea ice model, J. Phys. Oceanogr., 9, 817–846,
https://doi.org/10.1175/1520-0485(1979)009<0815:ADTSIM>2.0.CO;2, 1979.
Hibler III, W. D.: Ice dynamics. chap. 9, The Geophysics of Sea Ice,
edited by: Untersteiner, N., NATO ASI Series B: Physics, Plenum Press,
577–640, 1986.
Hunke, E. C. and Dukowicz, J. K.: An elastic-viscous-plastic model for sea
ice dynamics, J. Phys. Oceanogr., 27, 1849–1867,
https://doi.org/10.1175/1520-0485(1997)027<1849:AEVPMF>2.0.CO;2, 1997.
Johannessen, J. A., Raj, R. P., Nilesen, J. E. Ø., Pripp, T., Knudsen, P., Counillon, F., Stammer, D., Bertino, L., Andersen, O. B., Serra, N., and Koldunov,
N.:
Toward improved estimation of the dynamic
topography and ocean circulation in the high latitude and Arctic Ocean: The
importance of GOCE, Surv. Geophys., 35, 661–679,
https://doi.org/10.1007/s10712-013-9270-y, 2014.
Johannessen, O. M., Shalina, E. V., and Miles, M. W.: Satellite evidence for
an Arctic Sea ice cover in transformation, Science, 286, 1937–1939,
https://doi.org/10.1126/science.286.5446.1937, 1999.
Johnson, M., Proshutinsky, A., Aksenov, Y., Nguyen, A. T., Lindsay, R., Haas, C.,
Zhang, J., Diansky, N., Kwok, R., and Maslowski, W.: Evaluation of Arctic sea
ice thickness simulated by Arctic Ocean Model Intercomparison Project models,
J. Geophys. Res., 117, C00D31, https://doi.org/10.1029/2011JC007257, 2012.
Kaleschke, L., Maaß, N., Haas, C., Hendricks, S., Heygster, G., and
Tonboe, R. T.: A sea–ice thickness retrieval model for 1.4 GHz radiometry
and application to airborne measurements over low salinity sea-ice, The
Cryosphere, 4, 583–592, https://doi.org/10.5194/tc-4-583-2010, 2010.
Kaleschke, L., Tian-Kunze, X., Maaß, N., Ricker, R., Hendricks, S., and
Drusch, M.: Improved retrieval of sea ice thickness from SMOS and Cryosat-2.
Proceedings of 2015 International Geoscience and Remote Sensing Symposium
IGARSS, https://doi.org/10.1109/IGARSS.2015.7327014, 2015.
Karspeck, A. R.: An ensemble approach for the estimation of observational
error illustrated for a nominal 1 global ocean model, Mon. Weather Rev., 144,
1713–1728, https://doi.org/10.1175/MWR-D-14-00336.1, 2016.
Kern, S., Khvorostovsky, K., Skourup, H., Rinne, E., Parsakhoo, Z. S., Djepa,
V., Wadhams, P., and Sandven, S.: The impact of snow depth, snow density and
ice density on sea ice thickness retrieval from satellite radar altimetry:
results from the ESA-CCI Sea Ice ECV Project Round Robin Exercise, The
Cryosphere, 9, 37–52, https://doi.org/10.5194/tc-9-37-2015, 2015.
Khvorostovsky, K. and Rampal, P.: On retrieving sea ice freeboard from ICESat
laser altimeter, The Cryosphere, 10, 2329–2346,
https://doi.org/10.5194/tc-10-2329-2016, 2016.
Kimmritz, M., Counillon, F., Bitz, C. M., Massonnet, F., Bethke, I., and Gao,
Y.: Optimising assimilation of sea ice concentration in an Earth system model
with a multicategory sea ice model., Tellus A, 70, 1435945,
https://doi.org/10.1080/16000870.2018.1435945, 2018.
King, J., Howell, S., Derksen, C., Rutter, N., Toose, P., Beckers, J. F.,
Haas, C., Kurtz, N., and Richter-Menge, J.: Evaluation of Operation IceBridge
quick-look snow depth estimates on sea ice, Geophys. Res. Lett., 42,
9302–9310, https://doi.org/10.1002/2015GL066389, 2015.
Kinnard, C., Zdanowicz, C. M., Fisher, D. A., Isaksson, E., Vernal, A., and
Thompson, L. G.: Reconstructed changes in Arctic sea ice over the past 1,450
years, Nature, 479, 509–512, https://doi.org/10.1038/nature10581, 2011.
Kwok, R. and Rothrock, D.: Decline in Arctic sea ice thickness from
submarine and ICESat records: 1958–2008, Geophys. Res. Lett., 36, L15501,
https://doi.org/10.1029/2009GL039035, 2009.
Kurtz, N. T., Farrell, S. L., Studinger, M., Galin, N., Harbeck, J. P.,
Lindsay, R., Onana, V. D., Panzer, B., and Sonntag, J. G.: Sea ice thickness,
freeboard, and snow depth products from Operation IceBridge airborne data,
The Cryosphere, 7, 1035–1056, https://doi.org/10.5194/tc-7-1035-2013, 2013.
Laxon, S., Peacock, N., and Smith, D.: High interannual variability of sea
ice thickness in the Arctic region, Nature, 425, 947–950,
https://doi.org/10.1038/nature02050, 2003.
Lavergne, T., Eastwood, S., Teffah, Z., Schyberg, H., and Breivik, L.-A.:
Sea ice motion from low resolution satellite sensors: an alternative method
and its validation in the Arctic, J. Geophys. Res., 115, C10032,
https://doi.org/10.1029/2009JC005958, 2010.
Levermann, A., Mignot, J., Nawrath, S., and Rahmstorf, S.: The role of Northern
sea ice cover for the weakening of the thermohaline circulation under global
warming, J. Climate, 20, 4160–4171, https://doi.org/10.1175/JCLI4232.1, 2007.
Lindsay, R. and Schweiger, A.: Arctic sea ice thickness loss determined using
subsurface, aircraft, and satellite observations, The Cryosphere, 9,
269–283, https://doi.org/10.5194/tc-9-269-2015, 2015.
Lisæter, K. A., Rosanova, J. J., and Evensen, G.: Assimilation of ice
concentration in a coupled ice ocean model, using the Ensemble Kalman filter,
Ocean Dynam., 53, 368–388, https://doi.org/10.1007/s10236-003-0049-4, 2003.
Lisæter, K. A., Evensen, G., and Laxon, S.: Assimilating synthetic
CryoSat sea ice thickness in a coupled ice-ocean model, J. Geophys. Res.,
112, C07023, https://doi.org/10.1029/2006JC003786, 2007.
Martin, S., Drucker, R., Kwok, R., and Holt, B.: Estimation of the thin ice
thickness and heat flux for the Chukchi Sea Alaskan coast polynya from
Special Sensor Microwave/Imager data, 1990–2001, J. Geophys. Res., 109,
C10012, https://doi.org/10.1029/2004JC002428, 2004.
Massonnet, F., Goosse, H., Fichefet, T., and Counillon, F.: Calibration of
sea ice dynamic parameters in an ocean-sea ice model using an ensemble Kalman
filter, J. Geophys. Res., 119, 4168–4184,
https://doi.org/10.1002/2013JC009705, 2014.
Mathiot, P., König Beatty, C., Fichefet, T., Goosse, H., Massonnet, F., and
Vancoppenolle, M.: Better constraints on the sea-ice state using global
sea-ice data assimilation, Geosci. Model Dev., 5, 1501–1515,
https://doi.org/10.5194/gmd-5-1501-2012, 2012.
Melia, N., Haines, K., and Hawkins, E.: Improved Arctic sea ice thickness
projections using bias-corrected CMIP5 simulations, The Cryosphere, 9,
2237–2251, https://doi.org/10.5194/tc-9-2237-2015, 2015.
Metzger, E. J., Smedstad, O. M., Thoppil, P. G., Hurlburt, H. E., Cummings,
J. A., Wallcraft, A. J., Zamudio, L., Franklin, D. S., Posey, P. G., Phelps,
M. W., and Hogan, P. J.: US Navy operational global ocean and Arctic ice
prediction systems, Oceanography, 27, 32–43,
https://doi.org/10.5670/oceanog.2014.66, 2014.
Mu, L., Yang, Q., Losch, M., Losa, S. N., Ricker, R., Nerger, L., and Liang,
X.: Improving sea ice thickness estimates by assimilating CryoSat-2 and SMOS
sea ice thickness data simultaneously, Q. J. Roy. Meteor. Soc., 144,
529–538, https://doi.org/10.1002/qj.3225, 2018.
Oke, P. R. and Sakov, P.: Representation error of oceanic observations for
data assimilation, J. Atmos. Ocean. Tech., 25, 1004–1017,
https://doi.org/10.1175/2007JTECHO558.1, 2008.
Oki, T. and Sud, Y. C.: Design of Total Runoff Integrating Pathways
(TRIP) – A Global River Channel Network, Earth Interact., 2, 1–37,
https://doi.org/10.1175/1087-3562(1998)002<0001:DOTRIP>2.3.CO;2, 1998.
Olason, E. and Notz, D.: Drivers of variability in Arctic sea-ice drift
speed, J. Geophys. Res.-Oceans, 119, 5755–5775, https://doi.org/10.1002/2014JC009897,
2014.
Penny, G., Akella, S. R., Frolov, S., Fujii, Y., Karspeck, A., Peña, M.,
Subramanian, A., Tardif, R., Wu, X., Anderson, J., Kalnay, E., Kleist, D. T.,
and Todling, R.: Coupled Data Assimilation for Integrated Earth System
Analysis and Prediction: Goals, Challenges, and Recommendations, Technical
Report, available at: https://ntrs.nasa.gov/search.jsp?R=20170007430
(last access: 22 November 2018), 2017.
Perovich, D. K. and Richter-Menge, J. A.: From points to Poles:
extrapolating point measurements of sea-ice mass balance, Ann. Glaciol.,
44, 188–192, https://doi.org/10.3189/172756406781811204, 2006.
Posey, P. G., Metzger, E. J., Wallcraft, A. J., Hebert, D. A., Allard, R. A.,
Smedstad, O. M., Phelps, M. W., Fetterer, F., Stewart, J. S., Meier, W. N.,
and Helfrich, S. R.: Improving Arctic sea ice edge forecasts by assimilating
high horizontal resolution sea ice concentration data into the US Navy's ice
forecast systems, The Cryosphere, 9, 1735–1745,
https://doi.org/10.5194/tc-9-1735-2015, 2015.
Rampal, P., Weiss, J., and Marsan, D.: Positive trend in the mean speed
and deformation rate of Arctic sea ice, 1979–2007, J. Geophys. Res., 114,
C05013 https://doi.org/10.1029/2008JC005066, 2009.
Rampal, P., Bouillon, S., Ólason, E., and Morlighem, M.: neXtSIM: a new
Lagrangian sea ice model, The Cryosphere, 10, 1055–1073,
https://doi.org/10.5194/tc-10-1055-2016, 2016.
Ricker, R., Hendricks, S., Helm, V., Skourup, H., and Davidson, M.:
Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on
radar-waveform interpretation, The Cryosphere, 8, 1607–1622,
https://doi.org/10.5194/tc-8-1607-2014, 2014.
Ricker, R., Hendricks, S., Kaleschke, L., Tian-Kunze, X., King, J., and Haas,
C.: A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and
SMOS satellite data, The Cryosphere, 11, 1607–1623,
https://doi.org/10.5194/tc-11-1607-2017, 2017.
Rodgers, C.: Inverse methods for atmospheres: theory and practice, World
Scientific, 2000.
Rodwell, M. J., Lang, S. T. K., Ingleby, N. B., Bormann, N., Hólm, E.,
Rabier, F., Richardson, D. S., and Yamaguchi, M.: Reliability in ensemble
data assimilation, Q. J. Roy. Meteor. Soc., 142, 443–454,
https://doi.org/10.1002/qj.2663, 2016.
Sakov, P. and Oke, P. R.: A deterministic formulation of the ensemble
Kalman Filter: an alternative to ensemble square root filters, Tellus A, 60,
361–371, https://doi.org/10.1111/j.1600-0870.2007.00299.x, 2008.
Sakov, P., Counillon, F., Bertino, L., Lisæter, K. A., Oke, P. R., and
Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North
Atlantic and Arctic, Ocean Sci., 8, 633–656,
https://doi.org/10.5194/os-8-633-2012, 2012.
Schofield, O., Ducklow, H. W., Martinson, D. G., Meredith, M. P., Moline, M.
A., and Fraser, W. R.: How Do Polar Marine Ecosystems Respond to Rapid
Climate Change?, Science, 5985, 1520–1523, https://doi.org/10.1126/science.1185779,
2011.
Schweiger, A., Lindsay, R., Zhang, J., Steels, M., Stern, H., and Kwok, R.:
Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res., 116, C00D06,
https://doi.org/10.1029/2011JC007084, 2012.
Smith, G. C., Roy, F., Reszka, M. , Surcel Colan, D., He, Z., Deacu, D.,
Belanger, J., Skachko, S., Liu, Y., Dupont, F., Lemieux, J., Beaudoin, C.,
Tranchant, B., Drévillon, M., Garric, G., Testut, C., Lellouche, J.,
Pellerin, P., Ritchie, H., Lu, Y., Davidson, F., Buehner, M., Caya, A. and
Lajoie, M.: Sea ice forecast verification in the Canadian Global Ice Ocean
Prediction System, Q. J. Roy. Meteor. Soc., 142, 659–671,
https://doi.org/10.1002/qj.2555, 2015.
Stark, J. D., J. Ridley, M. Martin, M., and Hines, A.: Sea ice concentration
and motion assimilation in a sea ice–ocean model, J. Geophys. Res., 113,
C05S91, https://doi.org/10.1029/2007JC004224, 2008.
Stonebridge, G., Scott, K. A., and Buehner, M.: Impacts on sea ice analyses
from the assumption of uncorrelated ice thickness observation errors:
Experiments using a 1D toy model, Tellus A, 70, 1445379,
https://doi.org/10.1080/16000870.2018.1445379, 2018.
Stroeve, J. C., Serreze, M. C., Holland, M. M., Kay, J. E., Malanik, J., and Barrett, A. P.: The Arctic's rapidly
shrinking sea ice cover: a research synthesis, Climatic Change, 10,
1005–1027, https://doi.org/10.1007/s10584-011-0101-1, 2012.
Sumata, H., Lavergne, T., Girard-Ardhuin, F., Kimura, N., Tschudi, M. A.,
Kauker, F., Karcher, M., and Gerdes, R.: An intercomparison of Arctic ice
drift products to deduce uncertainty estimates, J. Geophys. Res.-Oceans, 119,
4887–4921, https://doi.org/10.1002/ 2013JC009724, 2014.
Tian-Kunze, X., Kaleschke, L., Maaß, N., Mäkynen, M., Serra, N.,
Drusch, M., and Krumpen, T.: SMOS-derived thin sea ice thickness: algorithm
baseline, product specifications and initial verification, The Cryosphere, 8,
997–1018, https://doi.org/10.5194/tc-8-997-2014, 2014.
Tilling, R. L., Ridout, A., and Shepherd, A.: Near-real-time Arctic sea ice
thickness and volume from CryoSat-2, The Cryosphere, 10, 2003–2012,
https://doi.org/10.5194/tc-10-2003-2016, 2016.
Tilling, R. L., Ridout, A., and Sheperd, A.: Estimating Arctic sea ice
thickness and volume using CryoSat-2 radar altimeter data, Adv. Space Res.,
62, 1203–1225, https://doi.org/10.1016/j.asr.2017.10.051, 2018.
Uotila, P., Goosse, H., Haines, K., Chevallier, M., Barthélemy, A.,
Bricaud, C., Carton, J., Fučkar, N., Garric, G., Iovino, D., Kauker, F.,
Korhonen, M., Lien, V. S., Marnela, M., Massonnet, F., Mignac, D., Peterson,
A., Sadikn, R., Shi, L., Tietsche, S., Toyoda, T., Xie, J., and Zhang, Z.: An
assessment of ten ocean reanalyses in the polar regions, Clim. Dynam.,
https://doi.org/10.1007/s00382-018-4242-z, online first, 2018.
Wang, Q., Ilicak, M., Gerdes, R., Drange, H., Aksenov, Y., Bailey, D. A.,
and Yeager, S. G.: An assessment of the Arctic Ocean in a suite of
interannual CORE-II simulations. Part I: Sea ice and solid freshwater, Ocean
Modell., 99, 110–132, https://doi.org/10.1016/J.OCEMOD.2015.12.008, 2016.
Wang, X., Key, J., Kwok, R., and Zhang, J.: Comparison of Arctic sea ice
thickness from satellites, aircraft, and PIOMAS data, Remote Sensing, 8,
1–17, https://doi.org/10.3390/rs8090713, 2016.
Woodgate, R., Aagaard, K., and Weingartner, T.: Monthly temperature,
salinity, and transport variability of the Bering Strait through flow,
Geophys. Res. Lett., 32, L04601, https://doi.org/10.1029/2004GL021880, 2005.
Xie, J., Bertino, L., Counillon, F., Lisæter, K. A., and Sakov, P.:
Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period
1991–2013, Ocean Sci., 13, 123–144, https://doi.org/10.5194/os-13-123-2017,
2017.
Xie, J., Bertino, L., Cardellach, E., Semmling, M., and Wickert, J.: An OSSE
evaluation of the GNSS-R altimetery data for the GEROS-ISS mission as a
complement to the existing observational networks, Remote Sens. Environ.,
209, 152–165, https://doi.org/10.1016/j.rse.2018.02.053, 2018.
Xie, J., Counillon, F., Bertino, L., Tian-Kunze, X., and Kaleschke, L.:
Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ
system, The Cryosphere, 10, 2745–2761,
https://doi.org/10.5194/tc-10-2745-2016, 2016.
Yang, Q., Losa, S. N., Losch, M., Tian-Kunze, X., Nerger, L., Liu, J.,
Kaleschke, L., and Zhang, Z.: Assimilating SMOS sea ice thickness into a
coupled ice-ocean model using a local SEIK filter, J. Geophys. Res., 119,
6680–6692, https://doi.org/10.1002/2014JC009963, 2014.
Short summary
We use the winter sea-ice thickness dataset CS2SMOS merged from two satellites SMOS and CryoSat-2 for assimilation into an ice–ocean reanalysis of the Arctic, complemented by several other ocean and sea-ice measurements, using an Ensemble Kalman Filter. The errors of sea-ice thickness are reduced by 28% and the improvements persists through the summer when observations are unavailable. Improvements of ice drift are however limited to the Central Arctic.
We use the winter sea-ice thickness dataset CS2SMOS merged from two satellites SMOS and...
