Articles | Volume 17, issue 6
https://doi.org/10.5194/tc-17-2509-2023
© Author(s) 2023. 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-17-2509-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Jun 2023
Research article |
| 27 Jun 2023
| 27 Jun 2023
The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system
Nicholas Williams
CORRESPONDING AUTHOR
Centre for Polar Observation and Modelling, Department of Meteorology, University of Reading, Earley Gate, PO Box 243, Reading, RG6 6BB, UK
Nicholas Byrne
Department of Meteorology, University of Reading, Earley Gate, PO Box 243, Reading, RG6 6BB, UK
Daniel Feltham
Centre for Polar Observation and Modelling, Department of Meteorology, University of Reading, Earley Gate, PO Box 243, Reading, RG6 6BB, UK
Peter Jan Van Leeuwen
Department of Meteorology, University of Reading, Earley Gate, PO Box 243, Reading, RG6 6BB, UK
Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, USA
Ross Bannister
Department of Meteorology, University of Reading, Earley Gate, PO Box 243, Reading, RG6 6BB, UK
David Schroeder
Centre for Polar Observation and Modelling, Department of Meteorology, University of Reading, Earley Gate, PO Box 243, Reading, RG6 6BB, UK
Andrew Ridout
Centre for Polar Observation and Modelling, Department of Earth Sciences, University College London, London, UK
Lars Nerger
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Related authors
No articles found.
Frauke Bunsen, Judith Hauck, Lars Nerger, and Sinhué Torres-Valdés
EGUsphere, https://doi.org/10.5194/egusphere-2024-1750, https://doi.org/10.5194/egusphere-2024-1750, 2024
Short summary
Short summary
Computer models are used to derive estimates of the ocean CO2 uptake. Because such idealized models don't always correspond precisely to the real-world, we combine real-world observations of ocean temperature and salinity with a model, and study the effect on the modeled air-sea CO2 flux (2010–2020). The corrections of temperature and salinity have their largest effect on regional CO2 fluxes in the Southern Ocean during winter, but a comparatively small effect on the global ocean CO2 uptake.
Yumeng Chen, Lars Nerger, and Amos S. Lawless
EGUsphere, https://doi.org/10.5194/egusphere-2024-1078, https://doi.org/10.5194/egusphere-2024-1078, 2024
Short summary
Short summary
In this paper, we present pyPDAF, a Python interface to the parallel data assimilation framework (PDAF) allowing for coupling with Python-based models. We demonstrate the capability and efficiency of pyPDAF under a coupled data assimilation setup.
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024, https://doi.org/10.5194/gmd-17-4433-2024, 2024
Short summary
Short summary
This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiment results underscore the effectiveness of the WRF-PDAF system.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-78, https://doi.org/10.5194/gmd-2024-78, 2024
Preprint under review for GMD
Short summary
Short summary
To accurately characterize the spatiotemporal distribution of PM2.5 chemical components, we developed a hybrid nonlinear data assimilation system (NAQPMS-PDAF v2.0), which is optimal for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has superior computing efficiency and excels when used a small ensemble size. The one-month assimilation experiments show that NAQPMS-PDAF v2.0 can significantly improve the simulation performance of chemical components.
Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, https://doi.org/10.5194/gmd-17-3559-2024, 2024
Short summary
Short summary
We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
Ross Noel Bannister and Chris Wilson
EGUsphere, https://doi.org/10.5194/egusphere-2024-655, https://doi.org/10.5194/egusphere-2024-655, 2024
Short summary
Short summary
Prior information is essential for the top-down estimation of CH4 surface fluxes. Errors in the prior are correlated in time/space, but accounting for correlations can be costly. We report on an efficient scheme to represent correlations in the inverse modelling system, INVICAT. The method is tested by assimilating CH4 observations using the scheme. Our findings show that accounting for spatio-temporal correlations improve CH4 flux estimates, demonstrating that the method should be further used.
Alexander T. Archibald, Bablu Sinha, Maria Russo, Emily Matthews, Freya Squires, N. Luke Abraham, Stephane Bauguitte, Thomas Bannan, Thomas Bell, David Berry, Lucy Carpenter, Hugh Coe, Andrew Coward, Peter Edwards, Daniel Feltham, Dwayne Heard, Jim Hopkins, James Keeble, Elizabeth C. Kent, Brian King, Isobel R. Lawrence, James Lee, Claire R. Macintosh, Alex Megann, Ben I. Moat, Katie Read, Chris Reed, Malcolm Roberts, Reinhard Schiemann, David Schroeder, Tim Smyth, Loren Temple, Navaneeth Thamban, Lisa Whalley, Simon Williams, Huihui Wu, and Ming-Xi Yang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-405, https://doi.org/10.5194/essd-2023-405, 2024
Preprint under review for ESSD
Short summary
Short summary
Here we present an overview of the data generated as part of the North Atlantic Climate System Integrated Studies (ACSIS) programme which are available through dedicated repositories at the Centre for Environmental Data Analysis (CEDA, www.ceda.ac.uk) and the British Oceanographic Data Centre (BODC, bodc.ac.uk). ACSIS data cover the full North Atlantic System comprising: the North Atlantic Ocean, the atmosphere above it including its composition, Arctic Sea Ice and the Greenland Ice Sheet.
Ieuan Higgs, Jozef Skákala, Ross Bannister, Alberto Carrassi, and Stefano Ciavatta
Biogeosciences, 21, 731–746, https://doi.org/10.5194/bg-21-731-2024, https://doi.org/10.5194/bg-21-731-2024, 2024
Short summary
Short summary
A complex network is a way of representing which parts of a system are connected to other parts. We have constructed a complex network based on an ecosystem–ocean model. From this, we can identify patterns in the structure and areas of similar behaviour. This can help to understand how natural, or human-made, changes will affect the shelf sea ecosystem, and it can be used in multiple future applications such as improving modelling, data assimilation, or machine learning.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-238, https://doi.org/10.5194/gmd-2023-238, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through “data assimilation”, which blends numerical models and observations. Here, we present EAT, a flexible and efficient testbed that allows any scientist to explore and further develop the state-of-the-art in data assimilation.
Jiangshan Zhu and Ross Noel Bannister
Geosci. Model Dev., 16, 6067–6085, https://doi.org/10.5194/gmd-16-6067-2023, https://doi.org/10.5194/gmd-16-6067-2023, 2023
Short summary
Short summary
We describe how condensation and evaporation are included in the existing (otherwise dry) simplified ABC model. The new model (Hydro-ABC) includes transport of vapour and condensate within a dynamical core, and it transitions between these two phases via a micro-physics scheme. The model shows the development of an anvil cloud and excitation of atmospheric waves over many frequencies. The covariances that develop between variables are also studied together with indicators of convective motion.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
EGUsphere, https://doi.org/10.5194/egusphere-2023-1731, https://doi.org/10.5194/egusphere-2023-1731, 2023
Short summary
Short summary
This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used for UK contributions to CMIP7. Documentation of physical science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details of how SI3 was adapted to work with Met Office coupling methodology, along with thorough documentation of coupling processes in the model.
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
Geosci. Model Dev., 16, 4233–4247, https://doi.org/10.5194/gmd-16-4233-2023, https://doi.org/10.5194/gmd-16-4233-2023, 2023
Short summary
Short summary
Traditional Kalman smoothers are expensive to apply in large global ocean operational forecast and reanalysis systems. We develop a cost-efficient method to overcome the technical constraints and to improve the performance of existing reanalysis products.
Rebecca Caitlin Frew, Daniel Feltham, David Schroeder, and Adam William Bateson
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-91, https://doi.org/10.5194/tc-2023-91, 2023
Preprint under review for TC
Short summary
Short summary
As summer Arctic sea ice extent has retreated, the marginal ice zone (MIZ) has been widening and making up an increasing percentage of the summer sea ice. The MIZ is projected to become a larger percentage of the summer ice cover, as the Arctic transitions to ice free summers. Using a sea ice model we find that the processes and timing of sea ice loss differ in the MIZ to the rest of the sea cover.
Maria Vittoria Guarino, Louise C. Sime, Rachel Diamond, Jeff Ridley, and David Schroeder
Clim. Past, 19, 865–881, https://doi.org/10.5194/cp-19-865-2023, https://doi.org/10.5194/cp-19-865-2023, 2023
Short summary
Short summary
We investigate the response of the atmosphere, ocean, and ice domains to the release of a large volume of glacial meltwaters thought to have occurred during the Last Interglacial period. We show that the signal that originated in the North Atlantic travels over great distances across the globe. It modifies the ocean gyre circulation in the Northern Hemisphere as well as the belt of westerly winds in the Southern Hemisphere, with consequences for Antarctic sea ice.
Joshua Chun Kwang Lee, Javier Amezcua, and Ross Noel Bannister
Geosci. Model Dev., 15, 6197–6219, https://doi.org/10.5194/gmd-15-6197-2022, https://doi.org/10.5194/gmd-15-6197-2022, 2022
Short summary
Short summary
In this article, we implement a novel data assimilation method for the ABC–DA system which combines traditional data assimilation approaches in a hybrid approach. We document the technical development and test the hybrid approach in idealised experiments within a tropical framework of the ABC–DA system. Our findings indicate that the hybrid approach outperforms individual traditional approaches. Its potential benefits have been highlighted and should be explored further within this framework.
Adam William Bateson, Daniel L. Feltham, David Schröder, Yanan Wang, Byongjun Hwang, Jeff K. Ridley, and Yevgeny Aksenov
The Cryosphere, 16, 2565–2593, https://doi.org/10.5194/tc-16-2565-2022, https://doi.org/10.5194/tc-16-2565-2022, 2022
Short summary
Short summary
Numerical models are used to understand the mechanisms that drive the evolution of the Arctic sea ice cover. The sea ice cover is formed of pieces of ice called floes. Several recent studies have proposed variable floe size models to replace the standard model assumption of a fixed floe size. In this study we show the need to include floe fragmentation processes in these variable floe size models and demonstrate that model design can determine the impact of floe size on size ice evolution.
Hao-Cheng Yu, Yinglong Joseph Zhang, Lars Nerger, Carsten Lemmen, Jason C. S. Yu, Tzu-Yin Chou, Chi-Hao Chu, and Chuen-Teyr Terng
EGUsphere, https://doi.org/10.5194/egusphere-2022-114, https://doi.org/10.5194/egusphere-2022-114, 2022
Preprint archived
Short summary
Short summary
We develop a new data assimilative approach by combining two parallel frameworks: PDAF and ESMF. This allows maximum flexibility and easy implementation of data assimilation for fully coupled earth system model applications. It is also validated by using a simple benchmark and applied to a realistic case simulation around Taiwan. The real case test shows significant improvement for temperature, velocity and surface elevation before, during and after typhoon events.
Beatriz M. Monge-Sanz, Alessio Bozzo, Nicholas Byrne, Martyn P. Chipperfield, Michail Diamantakis, Johannes Flemming, Lesley J. Gray, Robin J. Hogan, Luke Jones, Linus Magnusson, Inna Polichtchouk, Theodore G. Shepherd, Nils Wedi, and Antje Weisheimer
Atmos. Chem. Phys., 22, 4277–4302, https://doi.org/10.5194/acp-22-4277-2022, https://doi.org/10.5194/acp-22-4277-2022, 2022
Short summary
Short summary
The stratosphere is emerging as one of the keys to improve tropospheric weather and climate predictions. This study provides evidence of the role the stratospheric ozone layer plays in improving weather predictions at different timescales. Using a new ozone modelling approach suitable for high-resolution global models that provide operational forecasts from days to seasons, we find significant improvements in stratospheric meteorological fields and stratosphere–troposphere coupling.
Sagar K. Tamang, Ardeshir Ebtehaj, Peter Jan van Leeuwen, Gilad Lerman, and Efi Foufoula-Georgiou
Nonlin. Processes Geophys., 29, 77–92, https://doi.org/10.5194/npg-29-77-2022, https://doi.org/10.5194/npg-29-77-2022, 2022
Short summary
Short summary
The outputs from Earth system models are optimally combined with satellite observations to produce accurate forecasts through a process called data assimilation. Many existing data assimilation methodologies have some assumptions regarding the shape of the probability distributions of model output and observations, which results in forecast inaccuracies. In this paper, we test the effectiveness of a newly proposed methodology that relaxes such assumptions about high-dimensional models.
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
Short summary
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.
Rachel Diamond, Louise C. Sime, David Schroeder, and Maria-Vittoria Guarino
The Cryosphere, 15, 5099–5114, https://doi.org/10.5194/tc-15-5099-2021, https://doi.org/10.5194/tc-15-5099-2021, 2021
Short summary
Short summary
The Hadley Centre Global Environment Model version 3 (HadGEM3) is the first coupled climate model to simulate an ice-free summer Arctic during the Last Interglacial (LIG), 127 000 years ago, and yields accurate Arctic surface temperatures. We investigate the causes and impacts of this extreme simulated ice loss and, in particular, the role of melt ponds.
Nicholas J. Kedzuf, J. Christine Chiu, V. Chandrasekar, Sounak Biswas, Shashank S. Joshil, Yinghui Lu, Peter Jan van Leeuwen, Christopher Westbrook, Yann Blanchard, and Sebastian O'Shea
Atmos. Meas. Tech., 14, 6885–6904, https://doi.org/10.5194/amt-14-6885-2021, https://doi.org/10.5194/amt-14-6885-2021, 2021
Short summary
Short summary
Ice clouds play a key role in our climate system due to their strong controls on precipitation and the radiation budget. However, it is difficult to characterize co-existing ice species using radar observations. We present a new method that separates the radar signals of pristine ice embedded in snow aggregates and retrieves their respective abundances and sizes for the first time. The ability to provide their quantitative microphysical properties will open up many research opportunities.
Anne Braakmann-Folgmann, Andrew Shepherd, and Andy Ridout
The Cryosphere, 15, 3861–3876, https://doi.org/10.5194/tc-15-3861-2021, https://doi.org/10.5194/tc-15-3861-2021, 2021
Short summary
Short summary
We investigate the disintegration of the B30 iceberg using satellite remote sensing and find that the iceberg lost 378 km3 of ice in 6.5 years, corresponding to 80 % of its initial volume. About two thirds are due to fragmentation at the sides, and one third is due to melting at the iceberg’s base. The release of fresh water and nutrients impacts ocean circulation, sea ice formation, and biological production. We show that adding a snow layer is important when deriving iceberg thickness.
Concetta Di Mauro, Renaud Hostache, Patrick Matgen, Ramona Pelich, Marco Chini, Peter Jan van Leeuwen, Nancy K. Nichols, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 4081–4097, https://doi.org/10.5194/hess-25-4081-2021, https://doi.org/10.5194/hess-25-4081-2021, 2021
Short summary
Short summary
This study evaluates how the sequential assimilation of flood extent derived from synthetic aperture radar data can help improve flood forecasting. In particular, we carried out twin experiments based on a synthetically generated dataset with controlled uncertainty. Our empirical results demonstrate the efficiency of the proposed data assimilation framework, as forecasting errors are substantially reduced as a result of the assimilation.
Sagar K. Tamang, Ardeshir Ebtehaj, Peter J. van Leeuwen, Dongmian Zou, and Gilad Lerman
Nonlin. Processes Geophys., 28, 295–309, https://doi.org/10.5194/npg-28-295-2021, https://doi.org/10.5194/npg-28-295-2021, 2021
Short summary
Short summary
Data assimilation aims to improve hydrologic and weather forecasts by combining available information from Earth system models and observations. The classical approaches to data assimilation usually proceed with some preconceived assumptions about the shape of their probability distributions. As a result, when such assumptions are invalid, the forecast accuracy suffers. In the proposed methodology, we relax such assumptions and demonstrate improved performance.
Ann Keen, Ed Blockley, David A. Bailey, Jens Boldingh Debernard, Mitchell Bushuk, Steve Delhaye, David Docquier, Daniel Feltham, François Massonnet, Siobhan O'Farrell, Leandro Ponsoni, José M. Rodriguez, David Schroeder, Neil Swart, Takahiro Toyoda, Hiroyuki Tsujino, Martin Vancoppenolle, and Klaus Wyser
The Cryosphere, 15, 951–982, https://doi.org/10.5194/tc-15-951-2021, https://doi.org/10.5194/tc-15-951-2021, 2021
Short summary
Short summary
We compare the mass budget of the Arctic sea ice in a number of the latest climate models. New output has been defined that allows us to compare the processes of sea ice growth and loss in a more detailed way than has previously been possible. We find that that the models are strikingly similar in terms of the major processes causing the annual growth and loss of Arctic sea ice and that the budget terms respond in a broadly consistent way as the climate warms during the 21st century.
Lu Zhou, Julienne Stroeve, Shiming Xu, Alek Petty, Rachel Tilling, Mai Winstrup, Philip Rostosky, Isobel R. Lawrence, Glen E. Liston, Andy Ridout, Michel Tsamados, and Vishnu Nandan
The Cryosphere, 15, 345–367, https://doi.org/10.5194/tc-15-345-2021, https://doi.org/10.5194/tc-15-345-2021, 2021
Short summary
Short summary
Snow on sea ice plays an important role in the Arctic climate system. Large spatial and temporal discrepancies among the eight snow depth products are analyzed together with their seasonal variability and long-term trends. These snow products are further compared against various ground-truth observations. More analyses on representation error of sea ice parameters are needed for systematic comparison and fusion of airborne, in situ and remote sensing observations.
Masa Kageyama, Louise C. Sime, Marie Sicard, Maria-Vittoria Guarino, Anne de Vernal, Ruediger Stein, David Schroeder, Irene Malmierca-Vallet, Ayako Abe-Ouchi, Cecilia Bitz, Pascale Braconnot, Esther C. Brady, Jian Cao, Matthew A. Chamberlain, Danny Feltham, Chuncheng Guo, Allegra N. LeGrande, Gerrit Lohmann, Katrin J. Meissner, Laurie Menviel, Polina Morozova, Kerim H. Nisancioglu, Bette L. Otto-Bliesner, Ryouta O'ishi, Silvana Ramos Buarque, David Salas y Melia, Sam Sherriff-Tadano, Julienne Stroeve, Xiaoxu Shi, Bo Sun, Robert A. Tomas, Evgeny Volodin, Nicholas K. H. Yeung, Qiong Zhang, Zhongshi Zhang, Weipeng Zheng, and Tilo Ziehn
Clim. Past, 17, 37–62, https://doi.org/10.5194/cp-17-37-2021, https://doi.org/10.5194/cp-17-37-2021, 2021
Short summary
Short summary
The Last interglacial (ca. 127 000 years ago) is a period with increased summer insolation at high northern latitudes, resulting in a strong reduction in Arctic sea ice. The latest PMIP4-CMIP6 models all simulate this decrease, consistent with reconstructions. However, neither the models nor the reconstructions agree on the possibility of a seasonally ice-free Arctic. Work to clarify the reasons for this model divergence and the conflicting interpretations of the records will thus be needed.
Lars Nerger, Qi Tang, and Longjiang Mu
Geosci. Model Dev., 13, 4305–4321, https://doi.org/10.5194/gmd-13-4305-2020, https://doi.org/10.5194/gmd-13-4305-2020, 2020
Short summary
Short summary
Data assimilation combines observations with numerical models to get an improved estimate of the model state. This work discusses the technical aspects of how a coupled model that simulates the ocean and the atmosphere can be augmented by data assimilation functionality provided in generic form by the open-source software PDAF (Parallel Data Assimilation Framework). A very efficient program is obtained that can be executed on high-performance computers.
Ross Noel Bannister
Geosci. Model Dev., 13, 3789–3816, https://doi.org/10.5194/gmd-13-3789-2020, https://doi.org/10.5194/gmd-13-3789-2020, 2020
Short summary
Short summary
Forecasting models start from initial conditions, and data assimilation (DA) is the way that initial conditions are found from a combination of previous model data and latest observations. The ABC model is a simplified convective-scale model developed previously, and ABC-DA is the version of this system that includes the DA capability. This system is described in the present paper, and its performance is demonstrated with a range of options that control how the data assimilation is done.
Rebecca J. Rolph, Daniel L. Feltham, and David Schröder
The Cryosphere, 14, 1971–1984, https://doi.org/10.5194/tc-14-1971-2020, https://doi.org/10.5194/tc-14-1971-2020, 2020
Short summary
Short summary
It is well known that the Arctic sea ice extent is declining, and it is often assumed that the marginal ice zone (MIZ), the area of partial sea ice cover, is consequently increasing. However, we find no trend in the MIZ extent during the last 40 years from observations that is consistent with a widening of the MIZ as it moves northward. Differences of MIZ extent between different satellite retrievals are too large to provide a robust basis to verify model simulations of MIZ extent.
Adam W. Bateson, Daniel L. Feltham, David Schröder, Lucia Hosekova, Jeff K. Ridley, and Yevgeny Aksenov
The Cryosphere, 14, 403–428, https://doi.org/10.5194/tc-14-403-2020, https://doi.org/10.5194/tc-14-403-2020, 2020
Short summary
Short summary
The Arctic sea ice cover has been observed to be decreasing, particularly in summer. We use numerical models to gain insight into processes controlling its seasonal and decadal evolution. Sea ice is made of pieces of ice called floes. Previous models have set these floes to be the same size, which is not supported by observations. In this study we show that accounting for variable floe size reveals the importance of sea ice regions close to the open ocean in driving seasonal retreat of sea ice.
Maria-Vittoria Guarino, Louise C. Sime, David Schroeder, Grenville M. S. Lister, and Rosalyn Hatcher
Geosci. Model Dev., 13, 139–154, https://doi.org/10.5194/gmd-13-139-2020, https://doi.org/10.5194/gmd-13-139-2020, 2020
Short summary
Short summary
When the same weather or climate simulation is run on different high-performance computing (HPC) platforms, model outputs may not be identical for a given initial condition. Here, we investigate the behaviour of the Preindustrial simulation prepared by the UK Met Office for the forthcoming CMIP6 under different computing environments. Discrepancies between the means of key climate variables were analysed at different timescales, from decadal to centennial.
Christopher Horvat, Lettie A. Roach, Rachel Tilling, Cecilia M. Bitz, Baylor Fox-Kemper, Colin Guider, Kaitlin Hill, Andy Ridout, and Andrew Shepherd
The Cryosphere, 13, 2869–2885, https://doi.org/10.5194/tc-13-2869-2019, https://doi.org/10.5194/tc-13-2869-2019, 2019
Short summary
Short summary
Changes in the floe size distribution (FSD) are important for sea ice evolution but to date largely unobserved and unknown. Climate models, forecast centres, ship captains, and logistic specialists cannot currently obtain statistical information about sea ice floe size on demand. We develop a new method to observe the FSD at global scales and high temporal and spatial resolution. With refinement, this method can provide crucial information for polar ship routing and real-time forecasting.
David Schröder, Danny L. Feltham, Michel Tsamados, Andy Ridout, and Rachel Tilling
The Cryosphere, 13, 125–139, https://doi.org/10.5194/tc-13-125-2019, https://doi.org/10.5194/tc-13-125-2019, 2019
Short summary
Short summary
This paper uses sea ice thickness data (CryoSat-2) to identify and correct shortcomings in simulating winter ice growth in the widely used sea ice model CICE. Adding a model of snow drift and using a different scheme for calculating the ice conductivity improve model results. Sensitivity studies demonstrate that atmospheric winter conditions have little impact on winter ice growth, and the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season.
Sammie Buzzard, Daniel Feltham, and Daniela Flocco
The Cryosphere, 12, 3565–3575, https://doi.org/10.5194/tc-12-3565-2018, https://doi.org/10.5194/tc-12-3565-2018, 2018
Short summary
Short summary
Surface lakes on ice shelves can not only change the amount of solar energy the ice shelf receives, but may also play a pivotal role in sudden ice shelf collapse such as that of the Larsen B Ice Shelf in 2002.
Here we simulate current and future melting on Larsen C, Antarctica’s most northern ice shelf and one on which lakes have been observed. We find that should future lakes occur closer to the ice shelf front, they may contain sufficient meltwater to contribute to ice shelf instability.
Isobel R. Lawrence, Michel C. Tsamados, Julienne C. Stroeve, Thomas W. K. Armitage, and Andy L. Ridout
The Cryosphere, 12, 3551–3564, https://doi.org/10.5194/tc-12-3551-2018, https://doi.org/10.5194/tc-12-3551-2018, 2018
Short summary
Short summary
In this paper we estimate the thickness of snow cover on Arctic sea ice from space. We use data from two radar altimeter satellites, AltiKa and CryoSat-2, that have been operating synchronously since 2013. We produce maps of monthly average snow depth for the four growth seasons (October to April): 2012–2013, 2013–2014, 2014–2015, and 2015–2016. Snow depth estimates are essential for the accurate retrieval of sea ice thickness from satellite altimetry.
Julienne C. Stroeve, David Schroder, Michel Tsamados, and Daniel Feltham
The Cryosphere, 12, 1791–1809, https://doi.org/10.5194/tc-12-1791-2018, https://doi.org/10.5194/tc-12-1791-2018, 2018
Short summary
Short summary
This paper looks at the impact of the warm winter and anomalously low number of total freezing degree days during winter 2016/2017 on thermodynamic ice growth and overall thickness anomalies. The approach relies on evaluation of satellite data (CryoSat-2) and model output. While there is a negative feedback between rapid ice growth for thin ice, with thermodynamic ice growth increasing over time, since 2012 that relationship is changing, in part because the freeze-up is happening later.
Jeff K. Ridley, Edward W. Blockley, Ann B. Keen, Jamie G. L. Rae, Alex E. West, and David Schroeder
Geosci. Model Dev., 11, 713–723, https://doi.org/10.5194/gmd-11-713-2018, https://doi.org/10.5194/gmd-11-713-2018, 2018
Short summary
Short summary
The sea ice component of the Met Office coupled climate model, HadGEM3-GC3.1, is presented and evaluated. We determine that the mean state of the sea ice is well reproduced for the Arctic; however, a warm sea surface temperature bias over the Southern Ocean results in a low Antarctic sea ice cover.
ABC model: a non-hydrostatic toy model for use in convective-scale data assimilation investigations
Ruth Elizabeth Petrie, Ross Noel Bannister, and Michael John Priestley Cullen
Geosci. Model Dev., 10, 4419–4441, https://doi.org/10.5194/gmd-10-4419-2017, https://doi.org/10.5194/gmd-10-4419-2017, 2017
Short summary
Short summary
The model and experiments in this paper are to study atmospheric flows on small (kilometre) scales. Compared to larger-scale flows, kilometre-scale motion is more difficult to predict, and geophysical balances are less valid. For these reasons, data assimilation (or DA, the task of using observations to initialise models) is more difficult, as the character of forecast errors (which have to be corrected by DA) is more difficult to represent. This model will be used to study small-scale DA.
Ross Noel Bannister, Stefano Migliorini, Alison Clare Rudd, and Laura Hart Baker
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-260, https://doi.org/10.5194/gmd-2017-260, 2017
Revised manuscript has not been submitted
Short summary
Short summary
An ensemble of weather forecasts (i.e. multiple forecasts) contains useful information that a traditional single forecast does not have. Most existing forecast ensembles though have few members (ensemble too small), meaning that the information that they contain is noisy. This paper shows how more ensemble members can be generated from an existing (small) ensemble, and how the value added by the extra members can be assessed in a quantitative way.
Thomas W. K. Armitage, Sheldon Bacon, Andy L. Ridout, Alek A. Petty, Steven Wolbach, and Michel Tsamados
The Cryosphere, 11, 1767–1780, https://doi.org/10.5194/tc-11-1767-2017, https://doi.org/10.5194/tc-11-1767-2017, 2017
Short summary
Short summary
We present a new 12-year record of geostrophic currents at monthly resolution in the ice-covered and ice-free Arctic Ocean and characterise their seasonal to decadal variability. We also present seasonal climatologies of eddy kinetic energy, and examine the changing location of the Beaufort Gyre. Geostrophic current variability highlights the complex interplay between seasonally varying forcing and sea ice conditions, changing ice–ocean coupling and increasing ocean surface stress in the 2000s.
Rachel L. Tilling, Andy Ridout, and Andrew Shepherd
The Cryosphere, 10, 2003–2012, https://doi.org/10.5194/tc-10-2003-2016, https://doi.org/10.5194/tc-10-2003-2016, 2016
Short summary
Short summary
We use CryoSat-2 satellite data to provide the first near-real-time (NRT) measurements of absolute sea ice thickness across the entire Northern Hemisphere. We analyse our NRT sea-ice-thickness data for one sea ice growth season, from October 2014 to April 2015. Over that time period a NRT thickness measurement was delivered, on average, within 14, 7 and 6 km of each location in the Arctic every 2, 14 and 28 days respectively.
Alek A. Petty, Michel C. Tsamados, Nathan T. Kurtz, Sinead L. Farrell, Thomas Newman, Jeremy P. Harbeck, Daniel L. Feltham, and Jackie A. Richter-Menge
The Cryosphere, 10, 1161–1179, https://doi.org/10.5194/tc-10-1161-2016, https://doi.org/10.5194/tc-10-1161-2016, 2016
Short summary
Short summary
This study presents an analysis of Arctic sea ice topography using high-resolution, three-dimensional surface elevation data from the Airborne Topographic Mapper (ATM) laser altimeter, flown as part of NASA's Operation IceBridge mission. We describe and implement a newly developed sea ice surface feature-picking algorithm and derive novel information regarding the height, volume and geometry of surface features over the western Arctic sea ice cover.
Daniela Flocco, Daniel L. Feltham, David Schroeder, and Michel Tsamados
The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-118, https://doi.org/10.5194/tc-2016-118, 2016
Preprint withdrawn
Short summary
Short summary
Melt ponds form over the sea ice cover in the Arctic and impact the surface albedo inducing a positive feedback leading to further melting.
While they refreeze, ponds delay basal sea ice growth in Autumn impacting the internal sea ice temperature and therefore its basal growth rate. By using a numerical model we estimate an inhibited basal growth of up to 228 km3, which represents 25 % of the basal sea ice growth estimated by PIOMAS during the months of September and October.
Qinghua Yang, Martin Losch, Svetlana N. Losa, Thomas Jung, Lars Nerger, and Thomas Lavergne
The Cryosphere, 10, 761–774, https://doi.org/10.5194/tc-10-761-2016, https://doi.org/10.5194/tc-10-761-2016, 2016
Short summary
Short summary
We assimilate the summer SICCI sea ice concentration data with an ensemble-based Kalman Filter. Comparing with the approach using a constant data uncertainty, the sea ice concentration estimates are further improved when the SICCI-provided uncertainty are taken into account, but the sea ice thickness cannot be improved. We find the data assimilation system cannot give a reasonable ensemble spread of sea ice concentration and thickness if the provided uncertainty are directly used.
A. A. Petty, P. R. Holland, and D. L. Feltham
The Cryosphere, 8, 761–783, https://doi.org/10.5194/tc-8-761-2014, https://doi.org/10.5194/tc-8-761-2014, 2014
L. H. Baker, A. C. Rudd, S. Migliorini, and R. N. Bannister
Nonlin. Processes Geophys., 21, 19–39, https://doi.org/10.5194/npg-21-19-2014, https://doi.org/10.5194/npg-21-19-2014, 2014
Related subject area
Discipline: Sea ice | Subject: Data Assimilation
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
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
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
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.
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
Short summary
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
Short summary
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
Short summary
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.
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
Short summary
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
Short summary
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
Short summary
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
Short summary
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
Short summary
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
Short summary
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
Short summary
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.
Cited articles
Barber, D. G., Hop, H., Mundy, C. J., Else, B., Dmitrenko, I. A., Tremblay, J.-E., Ehn, J. K., Assmy, P., Daase, M., Candlish, L. M., and Rysgaard, S.:
Selected physical, biological and biogeochemical implications of a rapidly changing Arctic Marginal Ice Zone, Prog. Oceanogr., 139, 122–150, 2015. a
Beaven, S. G.:
Sea ice radar backscatter modeling, measurements, and the fusion of active and passive microwave data, University of Kansas, 1995. a
Bishop, C. H.:
Data assimilation strategies for state-dependent observation error variances, Q. J. Roy. Meteor. Soc., 145, 217–227, 2019. a
Bitz, C. M. and Lipscomb, W. H.:
An energy-conserving thermodynamic model of sea ice, J. Geophys. Res.-Oceans, 104, 15669–15677, 1999. a
Blockley, E. W. and Peterson, K. A.:
Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness, The Cryosphere, 12, 3419–3438, https://doi.org/10.5194/tc-12-3419-2018, 2018. a
Blockley, E. W., Martin, M. J., McLaren, A. J., Ryan, A. G., Waters, J., Lea, D. J., Mirouze, I., Peterson, K. A., Sellar, A., and Storkey, D.:
Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts, Geosci. Model Dev., 7, 2613–2638, https://doi.org/10.5194/gmd-7-2613-2014, 2014. a
Briegleb, B. and Light, B.:
A Delta-Eddington multiple scattering parameterization for solar radiation in the sea ice component of the Community Climate System Model, NCAR technical note, 1–108, 2007. a
Brusdal, K., Brankart, J.-M., Halberstadt, G., Evensen, G., Brasseur, P., van Leeuwen, P. J., Dombrowsky, E., and Verron, J.:
A demonstration of ensemble-based assimilation methods with a layered OGCM from the perspective of operational ocean forecasting systems, J. Marine Syst., 40, 253–289, 2003. a
Bunzel, F., Notz, D., and Pedersen, L. T.:
Retrievals of Arctic sea-ice volume and its trend significantly affected by interannual snow variability, Geophys. Res. Lett., 45, 11–751, 2018. a
Cavalieri, D. J.:
NASA sea ice validation program for the Defense Meteorological Satellite Program special sensor microwave imager, J. Geophys. Res.-Oceans, 96, 21969–21970, 1991. a
Chevallier, M., Smith, G. C., Dupont, F., Lemieux, J.-F., Forget, G., Fujii, Y., Hernandez, F., Msadek, R., Peterson, K. A., Storto, A., Toyoda, T., Validivieso, M., Vernieres, G., Zuo, H., Balmaseda, M., Chang, Y.-S., Ferry, N., Garric, G., Haines, K., Keeley, S., Kovach, R., Kuragano, T., Masina, S., Tang, Y., Tsujino, H., and Wang, X.:
Intercomparison of the Arctic sea ice cover in global ocean–sea ice reanalyses from the ORA-IP project, Clim. Dynam., 49, 1107–1136, 2017. a
Comiso, J. C. and Kwok, R.:
Surface and radiative characteristics of the summer Arctic sea ice cover from multisensor satellite observations, J. Geophys. Res.-Oceans, 101, 28397–28416, 1996. a
Comiso, J. C., Grenfell, T. C., Lange, M., Lohanick, A. W., Moore, R. K., and Wadhams, P.:
Microwave remote sensing of the Southern Ocean ice cover, American Geophysical Union Geophysical Monograph Series, Vol. 68, 243–259, Washington, DC, 1992. a
Dai, A., Luo, D., Song, M., and Liu, J.:
Arctic amplification is caused by sea-ice loss under increasing CO2, Nat. Commun., 10, 1–13, 2019. a
Farrell, S. L., Kurtz, N., Connor, L. N., Elder, B. C., Leuschen, C., Markus, T., McAdoo, D. C., Panzer, B., Richter-Menge, J., and Sonntag, J. G.:
A first assessment of IceBridge snow and ice thickness data over Arctic sea ice, IEEE T. Geosci. Remote, 50, 2098–2111, 2011. a
Ferry, N., Masina, S., Storto, A., Haines, K., Valdivieso, M., Barnier, B., and Molines, J.-M.:
Product User Manual GLOBALREANALYSISPHYS-001-004-a and b, MyOcean, 2011. a
Fiedler, E. K., Martin, M. J., Blockley, E., Mignac, D., Fournier, N., Ridout, A., Shepherd, A., and Tilling, R.:
Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM), The Cryosphere, 16, 61–85, https://doi.org/10.5194/tc-16-61-2022, 2022. a, b, c
Flocco, D., Feltham, D. L., and Turner, A. K.:
Incorporation of a physically based melt pond scheme into the sea ice component of a climate model, J. Geophys. Res.-Oceans, 115, C8, https://doi.org/10.1029/2009JC005568, 2010. 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, b, c
Gaspari, G. and Cohn, S. E.:
Construction of correlation functions in two and three dimensions, Q. J. Roy. Meteor. Soc., 125, 723–757, 1999. a
Giles, K. A., Laxon, S. W., and Ridout, A. L.:
Circumpolar thinning of Arctic sea ice following the 2007 record ice extent minimum, Geophys. Res. Lett., 35, L22502, https://doi.org/10.1029/2008GL035710,
2008. a
Hebert, D. A., Allard, R. A., Metzger, E. J., Posey, P. G., Preller, R. H., Wallcraft, A. J., Phelps, M. W., and Smedstad, O. M.:
Short-term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the US Navy's Arctic Cap Nowcast/Forecast System, J. Geophys. Res.-Oceans, 120, 8327–8345, 2015. a
Houtekamer, P. L. and Mitchell, H. L.:
Data assimilation using an ensemble Kalman filter technique, Mon. Weather Rev., 126, 796–811, 1998. a
Hunke, E., Lipscomb, W., Turner, A., Jeffery, N., and Elliott, S.:
CICE: The Los Alamos sea ice model documentation and software user's manual 1568 version 5.1, Tech. rep., Los Alamos National Laboratory, 2015 (code available at: https://github.com/CICE-Consortium/CICE-svn-trunk/tree/cice-5.1.2, last access: 23 June 2023). a
Hunt, B. R., Kostelich, E. J., and Szunyogh, I.:
Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica D, 230, 112–126, 2007. 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
Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.-K., Hnilo, J., Fiorino, M., and Potter, G.:
NCEP–DOE AMIP-II Reanalysis (R-2), B. Am. Meteorol. Soc., 83, 1631–1644, 2002. a
Krishfield, R. A., Proshutinsky, A., Tateyama, K., Williams, W. J., Carmack, E. C., McLaughlin, F. A., and Timmermans, M.-L.:
Deterioration of perennial sea ice in the Beaufort Gyre from 2003 to 2012 and its impact on the oceanic freshwater cycle, J. Geophys. Res.-Oceans, 119, 1271–1305, 2014. a
Kurtz, N., Studinger, M., Harbeck, J., Onana, V., and Farrell, S.:
IceBridge sea ice freeboard, snow depth, and thickness, Digital media, NASA Distributed Active Archive Center at the National Snow and Ice Data Center, Boulder, Colorado, USA, http://nsidc.org/data/idcsi2 html (last access: 6 December 2013), 2012. a
Kurtz, N., M. Studinger, J., Harbeck, V., Onana, V., and Yi, D.:
IceBridge L4 Sea Ice Freeboard, Snow Depth, and Thickness, Version 1, https://doi.org/10.5067/G519SHCKWQV6, 2015. a
Kurtz, N., M. Studinger, J., Harbeck, V., Onana, V., and Yi, D.:
IceBridge Sea Ice Freeboard, Snow Depth, and Thickness Quick Look, Version 1, https://doi.org/10.5067/GRIXZ91DE0L9, 2016. a
Kurtz, N. T. and Farrell, S. L.:
Large-scale surveys of snow depth on Arctic sea ice from Operation IceBridge, Geophys. Res. Lett., 38, L20505, 2011. a
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. a
Laxon, S. W., Giles, K. A., Ridout, A. L., Wingham, D. J., Willatt, R., Cullen, R., Kwok, R., Schweiger, A., Zhang, J., Haas, C., Hendricks, S., Krishfield, R., Kurtz, N., Farrel, S., and Davidson, M.:
CryoSat-2 estimates of Arctic sea ice thickness and volume, Geophys. Res. Lett., 40, 732–737, 2013. a
Lipscomb, W. H.:
Remapping the thickness distribution in sea ice models, J. Geophys. Res.-Oceans, 106, 13989–14000, 2001. a
Lipscomb, W. H. and Hunke, E. C.:
Modeling sea ice transport using incremental remapping, Mon. Weather Rev., 132, 1341–1354, 2004. a
Lipscomb, W. H., Hunke, E. C., Maslowski, W., and Jakacki, J.:
Ridging, strength, and stability in high-resolution sea ice models, J. Geophys. Res.-Oceans, 112, C03S91,
https://doi.org/10.1029/2005JC003355, 2007. a
Lisæter, K., Evensen, G., and Laxon, S.:
Assimilating synthetic CryoSat sea ice thickness in a coupled ice-ocean model, J. Geophys. Res.-Oceans, 112, C07023,
https://doi.org/10.1029/2006JC003786, 2007. a
Markus, T. and Cavalieri, D. J.:
An enhancement of the NASA Team sea ice algorithm, IEEE T. Geosci. Remote, 38, 1387–1398, 2000. a
Markus, T., Neumann, T., Martino, A., Abdalati, W., Brunt, K., Csatho, B., Farrell, S., Fricker, H., Gardner, A., Harding, D., Jasinski, M., Kwok, R., Magruder, L., Lubin, D., Luthcke, S., Morison, J., Nelson, R., Neuenschwander, A., Palm, S., Popescu, S., and Zwally, J.:
The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): science requirements, concept, and implementation, Remote Sens. Environ., 190, 260–273, 2017. a
Massonnet, F., Fichefet, T., Goosse, H., Vancoppenolle, M., Mathiot, P., and König Beatty, C.:
On the influence of model physics on simulations of Arctic and Antarctic sea ice, The Cryosphere, 5, 687–699, https://doi.org/10.5194/tc-5-687-2011, 2011. a
Meier, W. N., Maslanik, J. A., and Fowler, C. W.:
Error analysis and assimilation of remotely sensed ice motion within an Arctic sea ice model, J. Geophys. Res.-Oceans, 105, 3339–3356, 2000. a
Mignac, D., Martin, M., Fiedler, E., Blockley, E., and Fournier, N.:
Improving the Met Office's Forecast Ocean Assimilation Model (FOAM) with the assimilation of satellite-derived sea-ice thickness data from CryoSat-2 and SMOS in the Arctic, Q. J. Roy. Meteor. Soc., 1144–1167,
https://doi.org/10.1002/qj.4252, 2022. a
Mu, L., Nerger, L., Tang, Q., Loza, S. N., Sidorenko, D., Wang, Q., Semmler, T., Zampieri, L., Losch, M., and Goessling, H. F.:
Toward a data assimilation system for seamless sea ice prediction based on the AWI climate model, J. Adv. Model. Earth Sy., 12, e2019MS001937, https://doi.org/10.1029/2019MS001937, 2020. a
Nerger, L. and Hiller, W.:
Software for ensemble-based data assimilation systems–Implementation strategies and scalability, Comput. Geosci., 55, 110–118, 2013 (data available at: https://pdaf.awi.de/trac/wiki, last access: 23 June 2023). a
Pham, D. T., Verron, J., and Roubaud, M. C.:
A singular evolutive extended Kalman filter for data assimilation in oceanography, J. Marine Syst., 16, 323–340, 1998. a
Pringle, D., Eicken, H., Trodahl, H., and Backstrom, L.:
Thermal conductivity of landfast Antarctic and Arctic sea ice, J. Geophys. Res.-Oceans, 112, C04017,
https://doi.org/10.1029/2006JC003641, 2007. a
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. a
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. a
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom, S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster, R., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, Meta, and Woollen, J.:
MERRA: NASA's modern-era retrospective analysis for research and applications, J. Climate, 24, 3624–3648, 2011. a
Rostosky, P., Spreen, G., Farrell, S. L., Frost, T., Heygster, G., and Melsheimer, C.:
Snow depth retrieval on Arctic sea ice from passive microwave radiometers—Improvements and extensions to multiyear ice using lower frequencies, J. Geophys. Res.-Oceans, 123, 7120–7138, 2018. a
Rothrock, D., Zhang, J., and Yu, Y.:
The arctic ice thickness anomaly of the 1990s: A consistent view from observations and models, J. Geophys. Res.-Oceans, 108, 3083,
https://doi.org/10.1029/2001JC001208, 2003. a
Rothrock, D. A.:
The energetics of the plastic deformation of pack ice by ridging, J. Geophys. Res., 80, 4514–4519, 1975. a
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
Schröder, D., Feltham, D. L., Tsamados, M., Ridout, A., and Tilling, R.:
New insight from CryoSat-2 sea ice thickness for sea ice modelling, The Cryosphere, 13, 125–139, https://doi.org/10.5194/tc-13-125-2019, 2019. a, b, c
Schutz, B. E., Zwally, H. J., Shuman, C. A., Hancock, D., and DiMarzio, J. P.:
Overview of the ICESat mission, Geophys. Res. Lett., 32, L21S01,
https://doi.org/10.1029/2005GL024009, 2005. a
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.:
Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res.-Oceans, 116, C00D06,
https://doi.org/10.1029/2011JC007084, 2011. a
Serreze, M. C. and Barry, R. G.:
Processes and impacts of Arctic amplification: A research synthesis, Global Planet. Change, 77, 85–96, 2011. a
Serreze, M. C. and Hurst, C. M.:
Representation of mean Arctic precipitation from NCEP–NCAR and ERA reanalyses, J. Climate, 13, 182–201, 2000. a
Smith, G. C., Roy, F., Reszka, M., Surcel Colan, D., He, Z., Deacu, D., Belanger, J.-M., Skachko, S., Liu, Y., Dupont, F., Lemieux, J.-F., Beaudoin, C., Tranchant, B., Drevillon, M., Garric, G., Testut, C.-E., Lellouche J.-M., 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, 2016. a
Smith, M. M., Holland, M. M., Petty, A. A., Light, B., and Bailey, D. A.:
Effects of Increasing the Category Resolution of the Sea Ice Thickness Distribution in a Coupled Climate Model on Arctic and Antarctic Sea Ice Mean State, J. Geophys. Res.-Oceans, 127, C00D06,
https://doi.org/10.1029/2011JC007084, 2022. a
Stroeve, J., Nandan, V., Willatt, R., Tonboe, R., Hendricks, S., Ricker, R., Mead, J., Mallett, R., Huntemann, M., Itkin, P., Schneebeli, M., Krampe, D., Spreen, G., Wilkinson, J., Matero, I., Hoppmann, M., and Tsamados, M.:
Surface-based Ku- and Ka-band polarimetric radar for sea ice studies, The Cryosphere, 14, 4405–4426, https://doi.org/10.5194/tc-14-4405-2020, 2020. a
Sundfjord, A., Fer, I., Kasajima, Y., and Svendsen, H.:
Observations of turbulent mixing and hydrography in the marginal ice zone of the Barents Sea, J. Geophys. Res.-Oceans, 112, e2022JC019044,
https://doi.org/10.1029/2022JC019044, 2007. a
Thomas, D. and Rothrock, D.:
The Arctic Ocean ice balance: A Kalman smoother estimate, J. Geophys. Res.-Oceans, 98, 10053–10067, 1993. a
Tsamados, M., Feltham, D. L., and Wilchinsky, A.:
Impact of a new anisotropic rheology on simulations of Arctic sea ice, J. Geophys. Res.-Oceans, 118, 91–107, 2013. a
Tsamados, M., Feltham, D. L., Schroeder, D., Flocco, D., Farrell, S. L., Kurtz, N., Laxon, S. W., and Bacon, S.:
Impact of variable atmospheric and oceanic form drag on simulations of Arctic sea ice, J. Phys. Oceanogr., 44, 1329–1353, 2014. a
Warren, S. G., Rigor, I. G., Untersteiner, N., Radionov, V. F., Bryazgin, N. N., Aleksandrov, Y. I., and Colony, R.:
Snow depth on Arctic sea ice, J. Climate, 12, 1814–1829, 1999. a
Wingham, D., Francis, C., Baker, S., Bouzinac, C., Brockley, D., Cullen, R., de Chateau-Thierry, P., Laxon, S., Mallow, U., Mavrocordatos, C., Phalippou, L., Ratier, G., Rey, L., Rostan, F., Viau, P., and Wallis, D. W.:
CryoSat: A mission to determine the fluctuations in Earth's land and marine ice fields, Adv. Space Res., 37, 841–871, 2006. a
Zhang, J. and Rothrock, D. A.:
Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates, Mon. Weather Rev., 131, 845–861, 2003. a
Zhang, Y.-F., Bushuk, M., Winton, M., Hurlin, B., Yang, X., Delworth, T., and Jia, L.:
Assimilation of satellite-retrieved sea ice concentration and prospects for september predictions of Arctic sea ice, J. Climate, 34, 2107–2126, 2021. a
Zuo, H., Balmaseda, M. A., and Mogensen, K.:
The new eddy-permitting ORAP5 ocean reanalysis: description, evaluation and uncertainties in climate signals, Clim. Dynam., 49, 791–811, 2017. a
Short summary
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.
Observations show that the Arctic sea ice cover has reduced over the last 40 years. This study...
