Articles | Volume 18, issue 4
https://doi.org/10.5194/tc-18-1597-2024
© Author(s) 2024. 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-18-1597-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Apr 2024
Research article |
| 08 Apr 2024
| 08 Apr 2024
Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean–sea ice model
Marina Durán Moro
CORRESPONDING AUTHOR
Remote sensing and data management department, Norwegian Meteorological Institute, Oslo, Norway
Ann Kristin Sperrevik
Ocean and ice department, Norwegian Meteorological Institute, Oslo, Norway
Thomas Lavergne
Remote sensing and data management department, Norwegian Meteorological Institute, Oslo, Norway
Laurent Bertino
Nansen Environmental and Remote Sensing Center, 5007 Bergen, Norway
Yvonne Gusdal
Ocean and ice department, Norwegian Meteorological Institute, Oslo, Norway
Silje Christine Iversen
Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway
Jozef Rusin
Remote sensing and data management department, Norwegian Meteorological Institute, Oslo, Norway
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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.
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High-resolution clear-sky optical satellite imagery has rarely been used to evaluate satellite passive microwave sea-ice concentration products beyond case-study level. By comparing 10 such products with sea-ice concentration estimated from > 350 such optical images in both hemispheres, we expand results of earlier evaluation studies for these products. Results stress the need to look beyond precision and accuracy and to discuss the evaluation data’s quality and filters applied in the products.
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
Ocean Sci., 17, 1081–1102, https://doi.org/10.5194/os-17-1081-2021, https://doi.org/10.5194/os-17-1081-2021, 2021
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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.
Thomas Lavergne, Montserrat Piñol Solé, Emily Down, and Craig Donlon
The Cryosphere, 15, 3681–3698, https://doi.org/10.5194/tc-15-3681-2021, https://doi.org/10.5194/tc-15-3681-2021, 2021
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Pushed by winds and ocean currents, polar sea ice is on the move. We use passive microwave satellites to observe this motion. The images from their orbits are often put together into daily images before motion is measured. In our study, we measure motion from the individual orbits directly and not from the daily images. We obtain many more motion vectors, and they are more accurate. This can be used for current and future satellites, e.g. the Copernicus Imaging Microwave Radiometer (CIMR).
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.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, and Rasmus Tonboe
The Cryosphere, 14, 2469–2493, https://doi.org/10.5194/tc-14-2469-2020, https://doi.org/10.5194/tc-14-2469-2020, 2020
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Arctic sea-ice concentration (SIC) estimates based on satellite passive microwave observations are highly inaccurate during summer melt. We compare 10 different SIC products with independent satellite data of true SIC and melt pond fraction (MPF). All products disagree with the true SIC. Regional and inter-product differences can be large and depend on the MPF. An inadequate treatment of melting snow and melt ponds in the products’ algorithms appears to be the main explanation for our findings.
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.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, Rasmus Tage Tonboe, Roberto Saldo, and Atle MacDonald Sørensen
The Cryosphere, 13, 3261–3307, https://doi.org/10.5194/tc-13-3261-2019, https://doi.org/10.5194/tc-13-3261-2019, 2019
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A systematic evaluation of 10 global satellite data products of the polar sea-ice area is performed. Inter-product differences in evaluation results call for careful consideration of data product limitations when performing sea-ice area trend analyses and for further mitigation of the effects of sensor changes. We open a discussion about evaluation strategies for such data products near-0 % and near-100 % sea-ice concentration, e.g. with the aim to improve high-concentration evaluation accuracy.
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.
Thomas Lavergne, Atle Macdonald Sørensen, Stefan Kern, Rasmus Tonboe, Dirk Notz, Signe Aaboe, Louisa Bell, Gorm Dybkjær, Steinar Eastwood, Carolina Gabarro, Georg Heygster, Mari Anne Killie, Matilde Brandt Kreiner, John Lavelle, Roberto Saldo, Stein Sandven, and Leif Toudal Pedersen
The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, https://doi.org/10.5194/tc-13-49-2019, 2019
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The loss of polar sea ice is an iconic indicator of Earth’s climate change. Many satellite-based algorithms and resulting data exist but they differ widely in specific sea-ice conditions. This spread hinders a robust estimate of the future evolution of sea-ice cover.
In this study, we document three new climate data records of sea-ice concentration generated using satellite data available over the last 40 years. We introduce the novel algorithms, the data records, and their uncertainties.
Jiping Xie, François Counillon, and Laurent Bertino
The Cryosphere, 12, 3671–3691, https://doi.org/10.5194/tc-12-3671-2018, https://doi.org/10.5194/tc-12-3671-2018, 2018
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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.
Anton Andreevich Korosov, Pierre Rampal, Leif Toudal Pedersen, Roberto Saldo, Yufang Ye, Georg Heygster, Thomas Lavergne, Signe Aaboe, and Fanny Girard-Ardhuin
The Cryosphere, 12, 2073–2085, https://doi.org/10.5194/tc-12-2073-2018, https://doi.org/10.5194/tc-12-2073-2018, 2018
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A new algorithm for estimating sea ice age in the Arctic is presented. The algorithm accounts for motion, deformation, melting and freezing of sea ice and uses daily sea ice drift and sea ice concentration products. The major advantage of the new algorithm is the ability to generate individual ice age fractions in each pixel or, in other words, to provide a frequency distribution of the ice age. Multi-year ice concentration can be computed as a sum of all ice fractions older than 1 year.
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
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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. K. Sperrevik, K. H. Christensen, and J. Röhrs
Ocean Sci., 11, 237–249, https://doi.org/10.5194/os-11-237-2015, https://doi.org/10.5194/os-11-237-2015, 2015
Related subject area
Discipline: Sea ice | Subject: Data Assimilation
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Towards improving short-term sea ice predictability using deformation observations
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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
Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Jiping Xie, François Counillon, and Laurent Bertino
The Cryosphere, 12, 3671–3691, https://doi.org/10.5194/tc-12-3671-2018, https://doi.org/10.5194/tc-12-3671-2018, 2018
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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.
Cited articles
AMAP: Arctic Climate Change Update 2021: Key Trends and Impacts, Arctic Monitoring and Assessment Programme (AMAP), Tromsø, Norway, viii+148pp, ISBN 978-82-7971-201-5, 2021. a
Arango, H. G., Levin, J., Wilkin, J., and Moore, A. M.: 4D-Var data assimilation in a nested model of the Mid-Atlantic Bight, Ocean Model., 184, 102201, https://doi.org/10.1016/j.ocemod.2023.102201, 2023. a
Barton, B. I., Lenn, Y.-D., and Lique, C.: Observed Atlantification of the Barents Sea causes the Polar Front to limit the expansion of winter sea ice, J. Phys. Oceanogr., 48, 1849–1866, https://doi.org/10.1175/JPO-D-18-0003.1, 2018. a
Blockley, E., Vancoppenolle, M., Hunke, E., Bitz, C., Feltham, D., Lemieux, J.-F., Losch, M., Maisonnave, E., Notz, D., Rampal, P., Tietsche, S., Tremblay, B., Turner, A., Massonnet, F., Ólason, E., Roberts, A., Aksenov, Y., Fichefet, T., Garric, G., Iovino, D., Madec, G., Rousset, C., y Melia, D. S., and Schroeder, D.: The future of sea ice modeling: where do we go from here?, B. Am. Meteorol. Soc., 101, E1304–E1311, https://doi.org/10.1175/BAMS-D-20-0073.1, 2020. a
Brankart, J.-M., Candille, G., Garnier, F., Calone, C., Melet, A., Bouttier, P.-A., Brasseur, P., and Verron, J.: A generic approach to explicit simulation of uncertainty in the NEMO ocean model, Geosci. Model Dev., 8, 1285–1297, https://doi.org/10.5194/gmd-8-1285-2015, 2015. a
Burgers, G., Leeuwen, P. J. V., and Evensen, G.: Analysis scheme in the Ensemble Kalman Filter, Mon. Weather Rev., 126, 1719–1724, https://doi.org/10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2, 1998. a
Candille, G., Brankart, J.-M., and Brasseur, P.: Assessment of an ensemble system that assimilates Jason-1/Envisat altimeter data in a probabilistic model of the North Atlantic ocean circulation, Ocean Sci., 11, 425–438, https://doi.org/10.5194/os-11-425-2015, 2015. a
Cao, Y., Liang, S., Sun, L., Liu, J., Cheng, X., Wang, D., Chen, Y., Yu, M., and Feng, K.: Trans-Arctic shipping routes expanding faster than the model projections, Global Environmental Change, 73, 102488, https://doi.org/10.1016/j.gloenvcha.2022.102488, 2022. a
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. a
Cheng, S., Chen, Y., Aydoğdu, A., Bertino, L., Carrassi, A., Rampal, P., and Jones, C. K. R. T.: Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020, The Cryosphere, 17, 1735–1754, https://doi.org/10.5194/tc-17-1735-2023, 2023. a
Debernard, J., Kristensen, N. M., Maartensson, S., Wang, K., Hedstrom, K., Brændshøi, J., and Szapiro, N.: metno/metroms: Version 0.4.1, Zenodo [code], https://doi.org/10.5281/zenodo.5067164, 2021. a, b, c
Deng, J. and Dai, A.: Sea ice–air interactions amplify multidecadal variability in the North Atlantic and Arctic region, Nat. Commun., 13, 2100, https://doi.org/10.1038/s41467-022-29810-7, 2022. a
Denis, B., Côté, J., and Laprise, R.: Spectral decomposition of two-dimensional atmospheric fields on limited-area domains using the Discrete Cosine Transform (DCT), Mon. Weather Rev., 130, 1812–1829, https://doi.org/10.1175/1520-0493(2002)130<1812:SDOTDA>2.0.CO;2, 2002. a
Duarte, P., Brændshøi, J., Shcherbin, D., Barras, P., Albretsen, J., Gusdal, Y., Szapiro, N., Martinsen, A., Samuelsen, A., Wang, K., and Debernard, J. B.: Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling system, Geosci. Model Dev., 15, 4373–4392, https://doi.org/10.5194/gmd-15-4373-2022, 2022. a
Durán Moro, M.: SIRANO Data Assimilation dataset, Norwegian Meteorological Institute [data set], https://thredds.met.no/thredds/catalog/sirano/catalog.html, last access: 1 December 2023a. a
Durán Moro, M.: Configuration setup for the SIRANO data assimilation experiments, Zenodo [code], https://doi.org/10.5281/zenodo.8300801, 2023b. a
Egbert, G. D. and Erofeeva, S. Y.: Efficient inverse modeling of barotropic ocean tides, J. Atmos. Ocean. Tech., 19, 183–204, https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2, 2002. a
E.U. Copernicus Marine Service Information (CMEMS): Arctic Ocean Physics Analysis and Forecast, Marine Data Store (MDS) [data set], https://doi.org/10.48670/moi-00001, last access: 15 June 2022. a, b
E.U. Copernicus Marine Service Information (CMEMS): Arctic Ocean – Sea Ice Concentration Charts – Svalbard and Greenland, Marine Data Store (MDS) [data set], https://doi.org/10.48670/moi-00128, last access: 1 March 2023. a, b, c
Evensen, G.: Inverse methods and data assimilation in nonlinear ocean models, Physica D, 77, 108–129, https://doi.org/10.1016/0167-2789(94)90130-9, 1994. a
Evensen, G.: The Ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, https://doi.org/10.1007/s10236-003-0036-9, 2003. a
Fritzner, S., Graversen, R., Christensen, K. H., Rostosky, P., and Wang, K.: Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system, The Cryosphere, 13, 491–509, https://doi.org/10.5194/tc-13-491-2019, 2019. a
Fritzner, S., Graversen, R., and Christensen, K. H.: Assessment of high-resolution dynamical and machine learning models for prediction of sea ice concentration in a regional application, J. Geophys. Res.-Oceans, 125, e2020JC016277, https://doi.org/10.1029/2020JC016277, 2020. a, b, c
Garuba, O. A., Singh, H. A., Hunke, E., and Rasch, P. J.: Disentangling the Coupled Atmosphere-Ocean-Ice Interactions Driving Arctic Sea Ice Response to CO2 Increases, J. Adv. Model. Earth Sy., 12, e2019MS001902, https://doi.org/10.1029/2019MS001902, 2020. a
Houtekamer, P. and Mitchell, H.: Data assimilation using an ensemble Kalman filter technique, Mon. Weather Rev., 126, 796–811, https://doi.org/10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2, 1998. a
Hunke, E., Lipscomb, W., Jones, P., Turner, A., Jeffery, N., and Elliott, S.: CICE, The Los Alamos Sea Ice Model, Version 00, https://www.osti.gov//servlets/purl/1364126 (last access: 15 September 2021), 2017. a
Isaksen, K., Nordli, Ø., Ivanov, B., Køltzow, M. A., Aaboe, S., Gjelten, H. M., Mezghani, A., Eastwood, S., Førland, E., Benestad, R. E., Hanssen-Bauer, I., Brækkan, R., Sviashchennikov, P., Demin, V., Revina, A., and Karandasheva, T.: Exceptional warming over the Barents area, Scientific Reports, 12, 9371–9389, https://doi.org/10.1038/s41598-022-13568-5, 2022. a
Ivanova, N., Pedersen, L. T., Tonboe, R. T., Kern, S., Heygster, G., Lavergne, T., Sørensen, A., Saldo, R., Dybkjær, G., Brucker, L., and Shokr, M.: Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations, The Cryosphere, 9, 1797–1817, https://doi.org/10.5194/tc-9-1797-2015, 2015. a
Iversen, S. C., Sperrevik, A. K., and Goux, O.: Improving sea surface temperature in a regional ocean model through refined sea surface temperature assimilation, Ocean Sci., 19, 729–744, https://doi.org/10.5194/os-19-729-2023, 2023. a
JCOMM: Expert Team on Sea Ice. Sea ice information services of the world, Edition 2017, Geneva, Switzerland, World Meteorological Organization, (WMO-No.574), 103 pp. https://doi.org/10.25607/OBP-1325, 2017. a, b
Jones, E. P.: Circulation in the Arctic Ocean, Polar Res., 20, 139–146, https://doi.org/10.3402/polar.v20i2.6510, 2001. a
Jørgensen, L. L., Primicerio, R., Ingvaldsen, R. B., Fossheim, M., Strelkova, N., Thangstad, T. H., Manushin, I., and Zakharov, D.: Impact of multiple stressors on sea bed fauna in a warming Arctic, Mar. Ecol. Prog. Ser., 608, 1–12, https://doi.org/10.3354/meps12803, 2022. a
Kalman, R. E.: A new approach to linear filtering and prediction problems, Journal of Basic Engineering, 82, 35–45, https://doi.org/10.1115/1.3662552, 1960. a
Kern, S., Lavergne, T., Notz, D., Pedersen, L. T., Tonboe, R. T., Saldo, R., and Sørensen, A. M.: Satellite passive microwave sea-ice concentration data set intercomparison: closed ice and ship-based observations, The Cryosphere, 13, 3261–3307, https://doi.org/10.5194/tc-13-3261-2019, 2019. a
Kristensen, N. M., JensBDebernard, SebastianMaartensson, Wang, K., and Hedstrom, K.: metno/metroms: Version 0.3 – before merge, Zenodo [code], https://doi.org/10.5281/zenodo.1046114, 2017. a
Larson, J., Jacob, R., and Ong, E.: The model coupling toolkit: a new FORTRAN90 toolkit for building multiphysics parallel coupled models, Int. J. High Perform. C., 19, 277–292, https://doi.org/10.1177/1094342005056115, 2005. a
Lavergne, T., Sørensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkjær, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, 2019. a
Lind, S., Ingvaldsen, R. B., and Furevik, T.: Arctic warming hotspot in the northern Barents Sea linked to declining sea-ice import, Nat. Clim. Change, 8, 634–639, https://doi.org/10.1038/s41558-018-0205-y, 2018. a
Lindström, G., Pers, C., Rosberg, J., Strömqvist, J., and Arheimer, B.: Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales, Hydrol. Res., 41, 295–319, https://doi.org/10.2166/nh.2010.007, 2010. a
Lisæter, K. A., Rosanova, 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. a
Loeng, H.: Features of the physical oceanographic conditions of the Barents Sea, Polar Res., 10, 5–18, https://doi.org/10.3402/polar.v10i1.6723, 1991. a
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.-Oceans, 119, 4168–4184, https://doi.org/10.1002/2013JC009705, 2014. a
Min, C., Yang, Q., Chen, D., Yang, Y., Zhou, X., Shu, Q., and Liu, J.: The Emerging Arctic Shipping Corridors, Geophys. Res. Lett., 49, e2022GL099157, https://doi.org/10.1029/2022GL099157, 2022. a
Mohamed, B., Nilsen, F., and Skogseth, R.: Interannual and decadal variability of sea surface temperature and sea ice concentration in the Barents Sea, Remote Sensing, 14, 4413, https://doi.org/10.3390/rs14174413, 2022. a
Müller, M., Batrak, Y., Kristiansen, J., Køltzow, M. A. O., Noer, G., and Korosov, A.: Characteristics of a Convective-Scale Weather Forecasting System for the European Arctic, Mon. Weather Rev., 145, 4771–4787, https://doi.org/10.1175/MWR-D-17-0194.1, 2017a. a
Müller, M., Homleid, M., Ivarsson, K.-I., Køltzow, M. A. Ø., Lindskog, M., Midtbø, K. H., Andrae, U., Aspelien, T., Berggren, L., Bjørge, D., Dahlgren, P., Kristiansen, J., Randriamampianina, R., Ridal, M., and Vignes, O.: AROME-MetCoOp: A Nordic Convective-Scale Operational Weather Prediction Model, Weather Forecast., 32, 609–627, https://doi.org/10.1175/WAF-D-16-0099.1, 2017b. a
Naughten, K. A., Meissner, K. J., Galton-Fenzi, B. K., England, M. H., Timmermann, R., Hellmer, H. H., Hattermann, T., and Debernard, J. B.: Intercomparison of Antarctic ice-shelf, ocean, and sea-ice interactions simulated by MetROMS-iceshelf and FESOM 1.4, Geosci. Model Dev., 11, 1257–1292, https://doi.org/10.5194/gmd-11-1257-2018, 2018. a
Owens, R. G. and Hewson, T.: ECMWF Forecast User Guide, ECMWF, https://doi.org/10.21957/m1cs7h, 2018. a
Parkinson, C. L.: Arctic sea ice coverage from 43 years of satellite passive-microwave observations, Frontiers in Remote Sensing, 3, https://doi.org/10.3389/frsen.2022.1021781, 2022. a
Ren, L., Nash, S., and Hartnett, M.: Forecasting of surface currents via correcting wind stress with assimilation of high-frequency radar data in a three-dimensional model, Adv. Meteorol., 2016, 8950378, https://doi.org/10.1155/2016/8950378, 2016. a
Ricard, D., Lac, C., Riette, S., Legrand, R., and Mary, A.: Kinetic energy spectra characteristics of two convection-permitting limited-area models AROME and Meso-NH, Q. J. Roy. Meteorol. Soc., 139, 1327–1341, https://doi.org/10.1002/qj.2025, 2013. a
Röhrs, J., Gusdal, Y., Rikardsen, E. S. U., Durán Moro, M., Brændshøi, J., Kristensen, N. M., Fritzner, S., Wang, K., Sperrevik, A. K., Idžanović, M., Lavergne, T., Debernard, J. B., and Christensen, K. H.: Barents-2.5km v2.0: an operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard, Geosci. Model Dev., 16, 5401–5426, https://doi.org/10.5194/gmd-16-5401-2023, 2023. a, b, c, d, e
Sakov, P.: EnKF-C user guide, arXiv, https://doi.org/10.48550/ARXIV.1410.1233, 2014 (code available at: https://github.com/sakov/EnKF-C.git, last access: 8 July 2021). a, b, c
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. a
Sakov, P., Evensen, G., and Bertino, L.: Asynchronous data assimilation with the EnKF, Tellus A, 62, 24–29, https://doi.org/10.1111/j.1600-0870.2009.00417.x, 2010. a, b
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. a, b
Shchepetkin, A. F. and McWilliams, J. C.: The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model, Ocean Model., 9, 347–404, https://doi.org/10.1016/j.ocemod.2004.08.002, 2005. a
Thomas, D. N., Arévalo-Martínez, D. L., Crocket, K. C., Grosse, F., Grosse, J., Schulz, K., Sühring, R., and Tessin, A.: A changing Arctic Ocean, Ambio, 51, 293–297, https://doi.org/10.1007/s13280-021-01677-w, 2022. a
Urrego-Blanco, J. R., Urban, N. M., Hunke, E. C., Turner, A. K., and Jeffery, N.: Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model, J. Geophys. Res.-Oceans, 121, 2709–2732, https://doi.org/10.1002/2015JC011558, 2016. a
Våge, S., Basedow, S., Tande, K., and Zhou, M.: Physical structure of the Barents Sea Polar Front near Storbanken in August 2007, J. Marine Syst., 130, 256–262, https://doi.org/10.1016/j.jmarsys.2011.11.019, 2014. a
Short summary
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.
Individual satellite passes instead of daily means of sea ice concentration are used to correct...
