Articles | Volume 12, issue 11
https://doi.org/10.5194/tc-12-3419-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-12-3419-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Oct 2018
Research article |
| 30 Oct 2018
| 30 Oct 2018
Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness
Edward W. Blockley
CORRESPONDING AUTHOR
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
K. Andrew Peterson
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
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near-surface temperatures and salinities and the sea ice all matching the
observations more closely.
Jeff K. Ridley, Edward W. Blockley, Ann B. Keen, Jamie G. L. Rae, Alex E. West, and David Schroeder
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Jamie G. L. Rae, Alexander D. Todd, Edward W. Blockley, and Jeff K. Ridley
The Cryosphere, 11, 3023–3034, https://doi.org/10.5194/tc-11-3023-2017, https://doi.org/10.5194/tc-11-3023-2017, 2017
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Several studies have highlighted links between Arctic summer storms and September sea ice extent in observations. Here we use model and reanalysis data to investigate the sensitivity of such links to the analytical methods used, in order to determine their robustness. The links were found to depend on the resolution of the model and dataset, the method used to identify storms and the time period used in the analysis. We therefore recommend caution when interpreting the results of such studies.
J. K. Ridley, R. A. Wood, A. B. Keen, E. Blockley, and J. A. Lowe
The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-28, https://doi.org/10.5194/tc-2016-28, 2016
Revised manuscript has not been submitted
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The internal variability in model projections of Arctic sea ice extent is high. As a consequence an ensemble of projections from a single model can show considerable scatter in the range of dates for an "ice-free" Arctic. This paper investigates if the scatter can be reduced for a variety of definitions of "ice-free". Daily GCM data reveals that only a high emissions scenario results in the optimal definition of five conservative years in with ice extent is below one million square kilometer.
E. W. Blockley, M. J. Martin, A. J. McLaren, A. G. Ryan, J. Waters, D. J. Lea, I. Mirouze, K. A. Peterson, A. Sellar, and D. Storkey
Geosci. Model Dev., 7, 2613–2638, https://doi.org/10.5194/gmd-7-2613-2014, https://doi.org/10.5194/gmd-7-2613-2014, 2014
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Davi Mignac, Jennifer Waters, Daniel J. Lea, Matthew J. Martin, James While, Anthony T. Weaver, Arthur Vidard, Catherine Guiavarc'h, Dave Storkey, David Ford, Edward W. Blockley, Jonathan Baker, Keith Haines, Martin R. Price, Michael J. Bell, and Richard Renshaw
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Colin G. Jones, Fanny Adloff, Ben B. B. Booth, Peter M. Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene T. Hewitt, Hazel A. Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm J. Roberts, Benjamin M. Sanderson, Roland Séférian, Samuel Somot, Pier Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Julien Boé, Tom Bracegirdle, Pascale Braconnot, Victor Brovkin, Carlo Buontempo, Francisco Doblas-Reyes, Markus Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Frölicher, Neven S. Fučkar, Matthew J. Gidden, Helge F. Goessling, Rune Grand Graversen, Silvio Gualdi, José M. Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris D. Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Mélia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard A. Wood, Shuting Yang, and Sönke Zaehle
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This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of 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 on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jane P. Mulcahy, Colin G. Jones, Steven T. Rumbold, Till Kuhlbrodt, Andrea J. Dittus, Edward W. Blockley, Andrew Yool, Jeremy Walton, Catherine Hardacre, Timothy Andrews, Alejandro Bodas-Salcedo, Marc Stringer, Lee de Mora, Phil Harris, Richard Hill, Doug Kelley, Eddy Robertson, and Yongming Tang
Geosci. Model Dev., 16, 1569–1600, https://doi.org/10.5194/gmd-16-1569-2023, https://doi.org/10.5194/gmd-16-1569-2023, 2023
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Recent global climate models simulate historical global mean surface temperatures which are too cold, possibly to due to excessive aerosol cooling. This raises questions about the models' ability to simulate important climate processes and reduces confidence in future climate predictions. We present a new version of the UK Earth System Model, which has an improved aerosols simulation and a historical temperature record. Interestingly, the long-term response to CO2 remains largely unchanged.
Alex West, Edward Blockley, and Matthew Collins
The Cryosphere, 16, 4013–4032, https://doi.org/10.5194/tc-16-4013-2022, https://doi.org/10.5194/tc-16-4013-2022, 2022
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In this study we explore a method of examining model differences in ice volume by looking at the seasonal ice growth and melt. We use simple physical relationships to judge how model differences in key variables affect ice growth and melt and apply these to three case study models with ice volume ranging from very thin to very thick. Results suggest that differences in snow and melt pond cover in early summer are most important in causing the sea ice differences for these models.
Emma K. Fiedler, Matthew J. Martin, Ed Blockley, Davi Mignac, Nicolas Fournier, Andy Ridout, Andrew Shepherd, and Rachel Tilling
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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.
Andrew Yool, Julien Palmiéri, Colin G. Jones, Lee de Mora, Till Kuhlbrodt, Ekatarina E. Popova, A. J. George Nurser, Joel Hirschi, Adam T. Blaker, Andrew C. Coward, Edward W. Blockley, and Alistair A. Sellar
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The ocean plays a key role in modulating the Earth’s climate. Understanding this role is critical when using models to project future climate change. Consequently, it is necessary to evaluate their realism against the ocean's observed state. Here we validate UKESM1, a new Earth system model, focusing on the realism of its ocean physics and circulation, as well as its biological cycles and productivity. While we identify biases, generally the model performs well over a wide range of properties.
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
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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.
Alex West, Mat Collins, and Ed Blockley
Geosci. Model Dev., 13, 4845–4868, https://doi.org/10.5194/gmd-13-4845-2020, https://doi.org/10.5194/gmd-13-4845-2020, 2020
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This study calculates sea ice energy fluxes from data produced by ice mass balance buoys (devices measuring ice elevation and temperature). It is shown how the resulting dataset can be used to evaluate a coupled climate model (HadGEM2-ES), with biases in the energy fluxes seen to be consistent with biases in the sea ice state and surface radiation. This method has potential to improve sea ice model evaluation, so as to better understand spread in model simulations of sea ice state.
Malcolm J. Roberts, Alex Baker, Ed W. Blockley, Daley Calvert, Andrew Coward, Helene T. Hewitt, Laura C. Jackson, Till Kuhlbrodt, Pierre Mathiot, Christopher D. Roberts, Reinhard Schiemann, Jon Seddon, Benoît Vannière, and Pier Luigi Vidale
Geosci. Model Dev., 12, 4999–5028, https://doi.org/10.5194/gmd-12-4999-2019, https://doi.org/10.5194/gmd-12-4999-2019, 2019
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We investigate the role that horizontal grid spacing plays in global coupled climate model simulations, together with examining the efficacy of a new design of simulation experiments that is being used by the community for multi-model comparison. We found that finer grid spacing in both atmosphere and ocean–sea ice models leads to a general reduction in bias compared to observations, and that once eddies in the ocean are resolved, several key climate processes are greatly improved.
Alex West, Mat Collins, Ed Blockley, Jeff Ridley, and Alejandro Bodas-Salcedo
The Cryosphere, 13, 2001–2022, https://doi.org/10.5194/tc-13-2001-2019, https://doi.org/10.5194/tc-13-2001-2019, 2019
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This study presents a framework for examining the causes of model errors in Arctic sea ice volume, using HadGEM2-ES as a case study. Simple models are used to estimate how much of the error in energy arriving at the ice surface is due to error in key Arctic climate variables. The method quantifies how each variable affects sea ice volume balance and shows that for HadGEM2-ES an annual mean low bias in ice thickness is likely due to errors in surface melt onset.
Jeff K. Ridley and Edward W. Blockley
The Cryosphere, 12, 3355–3360, https://doi.org/10.5194/tc-12-3355-2018, https://doi.org/10.5194/tc-12-3355-2018, 2018
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The climate change conference held in Paris in 2016 made a commitment to limiting global-mean warming since the pre-industrial era to well below 2 °C and to pursue efforts to limit the warming to 1.5 °C. Since global warming is already at 1 °C, the 1.5 °C can only be achieved at considerable cost. It is thus important to assess the risks associated with the higher target. This paper shows that the decline of Arctic sea ice, and associated impacts, can only be halted with the 1.5 °C target.
Ann Keen and Ed Blockley
The Cryosphere, 12, 2855–2868, https://doi.org/10.5194/tc-12-2855-2018, https://doi.org/10.5194/tc-12-2855-2018, 2018
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As the climate warms during the 21st century, our model shows extra melting at the top and the base of the Arctic sea ice. The reducing ice cover affects the impact these processes have on the sea ice volume budget, where the largest individual change is a reduction in the amount of growth at the base of existing ice. Using different forcing scenarios we show that, for this model, changes in the volume budget depend on the evolving ice area but not on the speed at which the ice area declines.
David Storkey, Adam T. Blaker, Pierre Mathiot, Alex Megann, Yevgeny Aksenov, Edward W. Blockley, Daley Calvert, Tim Graham, Helene T. Hewitt, Patrick Hyder, Till Kuhlbrodt, Jamie G. L. Rae, and Bablu Sinha
Geosci. Model Dev., 11, 3187–3213, https://doi.org/10.5194/gmd-11-3187-2018, https://doi.org/10.5194/gmd-11-3187-2018, 2018
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We document the latest version of the shared UK global configuration of the
NEMO ocean model. This configuration will be used as part of the climate
models for the UK contribution to the IPCC 6th Assessment Report.
30-year integrations forced with atmospheric forcing show that the new
configurations have an improved simulation in the Southern Ocean with the
near-surface temperatures and salinities and the sea ice all matching the
observations more closely.
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
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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.
Jamie G. L. Rae, Alexander D. Todd, Edward W. Blockley, and Jeff K. Ridley
The Cryosphere, 11, 3023–3034, https://doi.org/10.5194/tc-11-3023-2017, https://doi.org/10.5194/tc-11-3023-2017, 2017
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Several studies have highlighted links between Arctic summer storms and September sea ice extent in observations. Here we use model and reanalysis data to investigate the sensitivity of such links to the analytical methods used, in order to determine their robustness. The links were found to depend on the resolution of the model and dataset, the method used to identify storms and the time period used in the analysis. We therefore recommend caution when interpreting the results of such studies.
J. K. Ridley, R. A. Wood, A. B. Keen, E. Blockley, and J. A. Lowe
The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-28, https://doi.org/10.5194/tc-2016-28, 2016
Revised manuscript has not been submitted
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The internal variability in model projections of Arctic sea ice extent is high. As a consequence an ensemble of projections from a single model can show considerable scatter in the range of dates for an "ice-free" Arctic. This paper investigates if the scatter can be reduced for a variety of definitions of "ice-free". Daily GCM data reveals that only a high emissions scenario results in the optimal definition of five conservative years in with ice extent is below one million square kilometer.
E. W. Blockley, M. J. Martin, A. J. McLaren, A. G. Ryan, J. Waters, D. J. Lea, I. Mirouze, K. A. Peterson, A. Sellar, and D. Storkey
Geosci. Model Dev., 7, 2613–2638, https://doi.org/10.5194/gmd-7-2613-2014, https://doi.org/10.5194/gmd-7-2613-2014, 2014
Related subject area
Discipline: Sea ice | Subject: Sea Ice
Spring 2021 sea ice transport in the southern Beaufort Sea occurred during coastal-lead opening events
National Weather Service Alaska Sea Ice Program: gridded ice concentration maps for the Alaskan Arctic
Improving Seasonal Arctic Sea Ice Predictions with the Combination of Machine Learning and Earth System Model
Estimation of duration and its changes in Lagrangian observations relying on ice floes in the Arctic Ocean utilizing sea ice motion product
Seasonal evolution of the sea ice floe size distribution in the Beaufort Sea from 2 decades of MODIS data
Suitability of the CICE sea ice model for seasonal prediction and positive impact of CryoSat-2 ice thickness initialization
Seasonality and scenario dependence of rapid Arctic sea ice loss events in CMIP6 simulations
A large-scale high-resolution numerical model for sea-ice fragmentation dynamics
Experimental modelling of the growth of tubular ice brinicles from brine flows under sea ice
Why is summertime Arctic sea ice drift speed projected to decrease?
Developing a deep learning forecasting system for short-term and high-resolution prediction of sea ice concentration
Impact of atmospheric rivers on Arctic sea ice variations
The impacts of anomalies in atmospheric circulations on Arctic sea ice outflow and sea ice conditions in the Barents and Greenland seas: case study in 2020
Atmospheric highs drive asymmetric sea ice drift during lead opening from Point Barrow
Spatial characteristics of frazil streaks in the Terra Nova Bay Polynya from high-resolution visible satellite imagery
Modelling the evolution of Arctic multiyear sea ice over 2000–2018
A quasi-objective single-buoy approach for understanding Lagrangian coherent structures and sea ice dynamics
Linking scales of sea ice surface topography: evaluation of ICESat-2 measurements with coincident helicopter laser scanning during MOSAiC
Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring
A collection of wet beam models for wave–ice interaction
First results of Antarctic sea ice type retrieval from active and passive microwave remote sensing data
Probabilistic spatiotemporal seasonal sea ice presence forecasting using sequence-to-sequence learning and ERA5 data in the Hudson Bay region
Predictability of Arctic sea ice drift in coupled climate models
Recovering and monitoring the thickness, density, and elastic properties of sea ice from seismic noise recorded in Svalbard
Influences of changing sea ice and snow thicknesses on simulated Arctic winter heat fluxes
Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model
A new state-dependent parameterization for the free drift of sea ice
Arctic sea ice sensitivity to lateral melting representation in a coupled climate model
Retrieval and parameterisation of sea-ice bulk density from airborne multi-sensor measurements
A generalized stress correction scheme for the Maxwell elasto-brittle rheology: impact on the fracture angles and deformations
Wave dispersion and dissipation in landfast ice: comparison of observations against models
The influence of snow on sea ice as assessed from simulations of CESM2
Meltwater sources and sinks for multiyear Arctic sea ice in summer
An X-ray micro-tomographic study of the pore space, permeability and percolation threshold of young sea ice
Calibration of sea ice drift forecasts using random forest algorithms
Multiscale variations in Arctic sea ice motion and links to atmospheric and oceanic conditions
The flexural strength of bonded ice
Interannual variability in Transpolar Drift summer sea ice thickness and potential impact of Atlantification
An inter-comparison of the mass budget of the Arctic sea ice in CMIP6 models
Refining the sea surface identification approach for determining freeboards in the ICESat-2 sea ice products
Surface-based Ku- and Ka-band polarimetric radar for sea ice studies
Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations
Satellite-based sea ice thickness changes in the Laptev Sea from 2002 to 2017: comparison to mooring observations
Modeling the annual cycle of daily Antarctic sea ice extent
Changes of the Arctic marginal ice zone during the satellite era
An enhancement to sea ice motion and age products at the National Snow and Ice Data Center (NSIDC)
Accuracy and inter-analyst agreement of visually estimated sea ice concentrations in Canadian Ice Service ice charts using single-polarization RADARSAT-2
Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks
Variability scaling and consistency in airborne and satellite altimetry measurements of Arctic sea ice
Sea ice volume variability and water temperature in the Greenland Sea
MacKenzie E. Jewell, Jennifer K. Hutchings, and Angela C. Bliss
The Cryosphere, 19, 1413–1430, https://doi.org/10.5194/tc-19-1413-2025, https://doi.org/10.5194/tc-19-1413-2025, 2025
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We quantify the contribution of sea ice drift during coastal-lead events to seasonal ice transport in the Beaufort Sea. Data from GPS tracker buoys deployed as part of the Sea Ice Dynamic Experiment (SIDEx) in spring 2021 show that sea ice in the southern Beaufort Sea became more responsive to wind forcing during fracturing events detected from satellite observations. Nearly all the spring 2021 sea ice transport occurred during these events, highlighting their importance at seasonal timescales.
Astrid Pacini, Michael Steele, and Mary-Beth Schreck
The Cryosphere, 19, 1391–1411, https://doi.org/10.5194/tc-19-1391-2025, https://doi.org/10.5194/tc-19-1391-2025, 2025
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While sea ice concentration data are critically important for climate research, obtaining high-resolution data remains a challenge. Here we present and validate the US National Weather Service Alaska Sea Ice Program (ASIP) ice maps. These maps are shown to be highly accurate when compared to in situ observations and to outperform a passive-microwave-based product, especially at low concentrations. Therefore, ASIP data provide an exciting new tool to study ice conditions in the Pacific Arctic.
Zikang He, Yiguo Wang, Julien Brajard, Xidong Wang, and Zheqi Shen
EGUsphere, https://doi.org/10.5194/egusphere-2024-4092, https://doi.org/10.5194/egusphere-2024-4092, 2025
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Declining Arctic sea ice presents both risks and opportunities for ecosystems, communities, and economic activities. To address prediction errors in dynamical models, we apply machine learning for error correction during prediction (online) or post-processing (offline). Our results show both methods enhance sea ice predictions, particularly from September to January, with offline corrections outperforming online corrections.
Fanyi Zhang, Ruibo Lei, Meng Qu, Na Li, Ying Chen, and Xiaoping Pang
EGUsphere, https://doi.org/10.5194/egusphere-2024-2723, https://doi.org/10.5194/egusphere-2024-2723, 2024
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We reconstructed sea ice drift trajectories and identified optimal deployment areas for Lagrangian observations. It revealed a preference for ice advection towards the Transpolar Drift region over the Beaufort Gyre, with endpoints influenced by large-scale atmospheric circulation patterns. This study could help the future ice camp/buoy deployment strategies, ensuring the sustainability of crucial Arctic observations in the face of changing environmental conditions.
Ellen M. Buckley, Leela Cañuelas, Mary-Louise Timmermans, and Monica M. Wilhelmus
The Cryosphere, 18, 5031–5043, https://doi.org/10.5194/tc-18-5031-2024, https://doi.org/10.5194/tc-18-5031-2024, 2024
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Arctic sea ice cover evolves seasonally from large plates separated by long, linear leads in the winter to a mosaic of smaller sea ice floes in the summer. Here, we present a new image segmentation algorithm applied to thousands of images and identify over 9 million individual pieces of ice. We observe the characteristics of the floes and how they evolve throughout the summer as the ice breaks up.
Shan Sun and Amy Solomon
The Cryosphere, 18, 3033–3048, https://doi.org/10.5194/tc-18-3033-2024, https://doi.org/10.5194/tc-18-3033-2024, 2024
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The study brings to light the suitability of CICE for seasonal prediction being contingent on several factors, such as initial conditions like sea ice coverage and thickness, as well as atmospheric and oceanic conditions including oceanic currents and sea surface temperature. We show there is potential to improve seasonal forecasting by using a more reliable sea ice thickness initialization. Thus, data assimilation of sea ice thickness is highly relevant for advancing seasonal prediction skills.
Annelies Sticker, François Massonnet, Thierry Fichefet, Patricia DeRepentigny, Alexandra Jahn, David Docquier, Christopher Wyburn-Powell, Daphne Quint, Erica Shivers, and Makayla Ortiz
EGUsphere, https://doi.org/10.5194/egusphere-2024-1873, https://doi.org/10.5194/egusphere-2024-1873, 2024
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Our study analyses rapid Arctic sea ice loss events (RILEs), which are significant reductions in sea ice extent. RILEs are expected throughout the year, varying in frequency and duration with the seasons. Our research gives a year-round analysis of their characteristics in climate models and suggests that summer RILEs could begin before the mid-century. Understanding these events is crucial as they can have profound impacts on the Arctic environment.
Jan Åström, Fredrik Robertsen, Jari Haapala, Arttu Polojärvi, Rivo Uiboupin, and Ilja Maljutenko
The Cryosphere, 18, 2429–2442, https://doi.org/10.5194/tc-18-2429-2024, https://doi.org/10.5194/tc-18-2429-2024, 2024
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The HiDEM code has been developed for analyzing the fracture and fragmentation of brittle materials and has been extensively applied to glacier calving. Here, we report on the adaptation of the code to sea-ice dynamics and breakup. The code demonstrates the capability to simulate sea-ice dynamics on a 100 km scale with an unprecedented resolution. We argue that codes of this type may become useful for improving forecasts of sea-ice dynamics.
Sergio Testón-Martínez, Laura M. Barge, Jan Eichler, C. Ignacio Sainz-Díaz, and Julyan H. E. Cartwright
The Cryosphere, 18, 2195–2205, https://doi.org/10.5194/tc-18-2195-2024, https://doi.org/10.5194/tc-18-2195-2024, 2024
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Brinicles are tubular ice structures that grow under the sea ice in cold regions. This happens because the salty water going downwards from the sea ice is colder than the seawater. We have successfully recreated an analogue of these structures in our laboratory. Three methods were used, producing different results. In this paper, we explain how to use these methods and study the behaviour of the brinicles created when changing the flow of water and study the importance for natural brinicles.
Jamie L. Ward and Neil F. Tandon
The Cryosphere, 18, 995–1012, https://doi.org/10.5194/tc-18-995-2024, https://doi.org/10.5194/tc-18-995-2024, 2024
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Over the long term, the speed at which sea ice in the Arctic moves has been increasing during all seasons. However, nearly all climate models project that sea ice motion will decrease during summer. This study aims to understand the mechanisms responsible for these projected decreases in summertime sea ice motion. We find that models produce changes in winds and ocean surface tilt which cause the sea ice to slow down, and it is realistic to expect such changes to also occur in the real world.
Are Frode Kvanum, Cyril Palerme, Malte Müller, Jean Rabault, and Nick Hughes
EGUsphere, https://doi.org/10.5194/egusphere-2023-3107, https://doi.org/10.5194/egusphere-2023-3107, 2024
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Recent studies have shown that machine learning models are effective at predicting sea ice concentration, yet few have explored the development of such models in an operational context. In this study, we present the development of a machine learning forecasting system which can predict sea ice concentration at 1 km resolution, up to 3 days ahead using real time operational data. The developed forecasts predict the sea ice edge position with a better accuracy than physical and baseline forecasts.
Linghan Li, Forest Cannon, Matthew R. Mazloff, Aneesh C. Subramanian, Anna M. Wilson, and Fred Martin Ralph
The Cryosphere, 18, 121–137, https://doi.org/10.5194/tc-18-121-2024, https://doi.org/10.5194/tc-18-121-2024, 2024
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We investigate how the moisture transport through atmospheric rivers influences Arctic sea ice variations using hourly atmospheric ERA5 for 1981–2020 at 0.25° × 0.25° resolution. We show that individual atmospheric rivers initiate rapid sea ice decrease through surface heat flux and winds. We find that the rate of change in sea ice concentration has significant anticorrelation with moisture, northward wind and turbulent heat flux on weather timescales almost everywhere in the Arctic Ocean.
Fanyi Zhang, Ruibo Lei, Mengxi Zhai, Xiaoping Pang, and Na Li
The Cryosphere, 17, 4609–4628, https://doi.org/10.5194/tc-17-4609-2023, https://doi.org/10.5194/tc-17-4609-2023, 2023
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Atmospheric circulation anomalies lead to high Arctic sea ice outflow in winter 2020, causing heavy ice conditions in the Barents–Greenland seas, subsequently impeding the sea surface temperature warming. This suggests that the winter–spring Arctic sea ice outflow can be considered a predictor of changes in sea ice and other marine environmental conditions in the Barents–Greenland seas, which could help to improve our understanding of the physical connections between them.
MacKenzie E. Jewell, Jennifer K. Hutchings, and Cathleen A. Geiger
The Cryosphere, 17, 3229–3250, https://doi.org/10.5194/tc-17-3229-2023, https://doi.org/10.5194/tc-17-3229-2023, 2023
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Sea ice repeatedly fractures near a prominent Alaskan headland as winds move ice along the coast, challenging predictions of sea ice drift. We find winds from high-pressure systems drive these fracturing events, and the Alaskan coastal boundary modifies the resultant ice drift. This observational study shows how wind patterns influence sea ice motion near coasts in winter. Identified relations between winds, ice drift, and fracturing provide effective test cases for dynamic sea ice models.
Katarzyna Bradtke and Agnieszka Herman
The Cryosphere, 17, 2073–2094, https://doi.org/10.5194/tc-17-2073-2023, https://doi.org/10.5194/tc-17-2073-2023, 2023
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The frazil streaks are one of the visible signs of complex interactions between the mixed-layer dynamics and the forming sea ice. Using high-resolution visible satellite imagery we characterize their spatial properties, relationship with the meteorological forcing, and role in modifying wind-wave growth in the Terra Nova Bay Polynya. We provide a simple statistical tool for estimating the extent and ice coverage of the region of high ice production under given wind speed and air temperature.
Heather Regan, Pierre Rampal, Einar Ólason, Guillaume Boutin, and Anton Korosov
The Cryosphere, 17, 1873–1893, https://doi.org/10.5194/tc-17-1873-2023, https://doi.org/10.5194/tc-17-1873-2023, 2023
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Multiyear ice (MYI), sea ice that survives the summer, is more resistant to changes than younger ice in the Arctic, so it is a good indicator of sea ice resilience. We use a model with a new way of tracking MYI to assess the contribution of different processes affecting MYI. We find two important years for MYI decline: 2007, when dynamics are important, and 2012, when melt is important. These affect MYI volume and area in different ways, which is important for the interpretation of observations.
Nikolas O. Aksamit, Randall K. Scharien, Jennifer K. Hutchings, and Jennifer V. Lukovich
The Cryosphere, 17, 1545–1566, https://doi.org/10.5194/tc-17-1545-2023, https://doi.org/10.5194/tc-17-1545-2023, 2023
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Coherent flow patterns in sea ice have a significant influence on sea ice fracture and refreezing. We can better understand the state of sea ice, and its influence on the atmosphere and ocean, if we understand these structures. By adapting recent developments in chaotic dynamical systems, we are able to approximate ice stretching surrounding individual ice buoys. This illuminates the state of sea ice at much higher resolution and allows us to see previously invisible ice deformation patterns.
Robert Ricker, Steven Fons, Arttu Jutila, Nils Hutter, Kyle Duncan, Sinead L. Farrell, Nathan T. Kurtz, and Renée Mie Fredensborg Hansen
The Cryosphere, 17, 1411–1429, https://doi.org/10.5194/tc-17-1411-2023, https://doi.org/10.5194/tc-17-1411-2023, 2023
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Information on sea ice surface topography is important for studies of sea ice as well as for ship navigation through ice. The ICESat-2 satellite senses the sea ice surface with six laser beams. To examine the accuracy of these measurements, we carried out a temporally coincident helicopter flight along the same ground track as the satellite and measured the sea ice surface topography with a laser scanner. This showed that ICESat-2 can see even bumps of only few meters in the sea ice cover.
Ludovic Moreau, Léonard Seydoux, Jérôme Weiss, and Michel Campillo
The Cryosphere, 17, 1327–1341, https://doi.org/10.5194/tc-17-1327-2023, https://doi.org/10.5194/tc-17-1327-2023, 2023
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In the perspective of an upcoming seasonally ice-free Arctic, understanding the dynamics of sea ice in the changing climate is a major challenge in oceanography and climatology. It is therefore essential to monitor sea ice properties with fine temporal and spatial resolution. In this paper, we show that icequakes recorded on sea ice can be processed with artificial intelligence to produce accurate maps of sea ice thickness with high temporal and spatial resolutions.
Sasan Tavakoli and Alexander V. Babanin
The Cryosphere, 17, 939–958, https://doi.org/10.5194/tc-17-939-2023, https://doi.org/10.5194/tc-17-939-2023, 2023
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We have tried to develop some new wave–ice interaction models by considering two different types of forces, one of which emerges in the ice and the other of which emerges in the water. We have checked the ability of the models in the reconstruction of wave–ice interaction in a step-wise manner. The accuracy level of the models is acceptable, and it will be interesting to check whether they can be used in wave climate models or not.
Christian Melsheimer, Gunnar Spreen, Yufang Ye, and Mohammed Shokr
The Cryosphere, 17, 105–126, https://doi.org/10.5194/tc-17-105-2023, https://doi.org/10.5194/tc-17-105-2023, 2023
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It is necessary to know the type of Antarctic sea ice present – first-year ice (grown in one season) or multiyear ice (survived one summer melt) – to understand and model its evolution, as the ice types behave and react differently. We have adapted and extended an existing method (originally for the Arctic), and now, for the first time, daily maps of Antarctic sea ice types can be derived from microwave satellite data. This will allow a new data set from 2002 well into the future to be built.
Nazanin Asadi, Philippe Lamontagne, Matthew King, Martin Richard, and K. Andrea Scott
The Cryosphere, 16, 3753–3773, https://doi.org/10.5194/tc-16-3753-2022, https://doi.org/10.5194/tc-16-3753-2022, 2022
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Machine learning approaches are deployed to provide accurate daily spatial maps of sea ice presence probability based on ERA5 data as input. Predictions are capable of predicting freeze-up/breakup dates within a 7 d period at specific locations of interest to shipping operators and communities. Forecasts of the proposed method during the breakup season have skills comparing to Climate Normal and sea ice concentration forecasts from a leading subseasonal-to-seasonal forecasting system.
Simon Felix Reifenberg and Helge Friedrich Goessling
The Cryosphere, 16, 2927–2946, https://doi.org/10.5194/tc-16-2927-2022, https://doi.org/10.5194/tc-16-2927-2022, 2022
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Using model simulations, we analyze the impact of chaotic error growth on Arctic sea ice drift predictions. Regarding forecast uncertainty, our results suggest that it matters in which season and where ice drift forecasts are initialized and that both factors vary with the model in use. We find ice velocities to be slightly more predictable than near-surface wind, a main driver of ice drift. This is relevant for future developments of ice drift forecasting systems.
Agathe Serripierri, Ludovic Moreau, Pierre Boue, Jérôme Weiss, and Philippe Roux
The Cryosphere, 16, 2527–2543, https://doi.org/10.5194/tc-16-2527-2022, https://doi.org/10.5194/tc-16-2527-2022, 2022
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As a result of global warming, the sea ice is disappearing at a much faster rate than predicted by climate models. To better understand and predict its ongoing decline, we deployed 247 geophones on the fast ice in Van Mijen Fjord in Svalbard, Norway, in March 2019. The analysis of these data provided a precise daily evolution of the sea-ice parameters at this location with high spatial and temporal resolution and accuracy. The results obtained are consistent with the observations made in situ.
Laura L. Landrum and Marika M. Holland
The Cryosphere, 16, 1483–1495, https://doi.org/10.5194/tc-16-1483-2022, https://doi.org/10.5194/tc-16-1483-2022, 2022
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High-latitude Arctic wintertime sea ice and snow insulate the relatively warmer ocean from the colder atmosphere. As the climate warms, wintertime Arctic conductive heat fluxes increase even when the sea ice concentrations remain high. Simulations from the Community Earth System Model Large Ensemble (CESM1-LE) show how sea ice and snow thicknesses, as well as the distribution of these thicknesses, significantly impact large-scale calculations of wintertime surface heat budgets in the Arctic.
Yunhe Wang, Xiaojun Yuan, Haibo Bi, Mitchell Bushuk, Yu Liang, Cuihua Li, and Haijun Huang
The Cryosphere, 16, 1141–1156, https://doi.org/10.5194/tc-16-1141-2022, https://doi.org/10.5194/tc-16-1141-2022, 2022
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We develop a regional linear Markov model consisting of four modules with seasonally dependent variables in the Pacific sector. The model retains skill for detrended sea ice extent predictions for up to 7-month lead times in the Bering Sea and the Sea of Okhotsk. The prediction skill, as measured by the percentage of grid points with significant correlations (PGS), increased by 75 % in the Bering Sea and 16 % in the Sea of Okhotsk relative to the earlier pan-Arctic model.
Charles Brunette, L. Bruno Tremblay, and Robert Newton
The Cryosphere, 16, 533–557, https://doi.org/10.5194/tc-16-533-2022, https://doi.org/10.5194/tc-16-533-2022, 2022
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Sea ice motion is a versatile parameter for monitoring the Arctic climate system. In this contribution, we use data from drifting buoys, winds, and ice thickness to parameterize the motion of sea ice in a free drift regime – i.e., flowing freely in response to the forcing from the winds and ocean currents. We show that including a dependence on sea ice thickness and taking into account a climatology of the surface ocean circulation significantly improves the accuracy of sea ice motion estimates.
Madison M. Smith, Marika Holland, and Bonnie Light
The Cryosphere, 16, 419–434, https://doi.org/10.5194/tc-16-419-2022, https://doi.org/10.5194/tc-16-419-2022, 2022
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Climate models represent the atmosphere, ocean, sea ice, and land with equations of varying complexity and are important tools for understanding changes in global climate. Here, we explore how realistic variations in the equations describing how sea ice melt occurs at the edges (called lateral melting) impact ice and climate. We find that these changes impact the progression of the sea-ice–albedo feedback in the Arctic and so make significant changes to the predicted Arctic sea ice.
Arttu Jutila, Stefan Hendricks, Robert Ricker, Luisa von Albedyll, Thomas Krumpen, and Christian Haas
The Cryosphere, 16, 259–275, https://doi.org/10.5194/tc-16-259-2022, https://doi.org/10.5194/tc-16-259-2022, 2022
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Sea-ice thickness retrieval from satellite altimeters relies on assumed sea-ice density values because density cannot be measured from space. We derived bulk densities for different ice types using airborne laser, radar, and electromagnetic induction sounding measurements. Compared to previous studies, we found high bulk density values due to ice deformation and younger ice cover. Using sea-ice freeboard, we derived a sea-ice bulk density parameterisation that can be applied to satellite data.
Mathieu Plante and L. Bruno Tremblay
The Cryosphere, 15, 5623–5638, https://doi.org/10.5194/tc-15-5623-2021, https://doi.org/10.5194/tc-15-5623-2021, 2021
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We propose a generalized form for the damage parameterization such that super-critical stresses can return to the yield with different final sub-critical stress states. In uniaxial compression simulations, the generalization improves the orientation of sea ice fractures and reduces the growth of numerical errors. Shear and convergence deformations however remain predominant along the fractures, contrary to observations, and this calls for modification of the post-fracture viscosity formulation.
Joey J. Voermans, Qingxiang Liu, Aleksey Marchenko, Jean Rabault, Kirill Filchuk, Ivan Ryzhov, Petra Heil, Takuji Waseda, Takehiko Nose, Tsubasa Kodaira, Jingkai Li, and Alexander V. Babanin
The Cryosphere, 15, 5557–5575, https://doi.org/10.5194/tc-15-5557-2021, https://doi.org/10.5194/tc-15-5557-2021, 2021
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We have shown through field experiments that the amount of wave energy dissipated in landfast ice, sea ice attached to land, is much larger than in broken ice. By comparing our measurements against predictions of contemporary wave–ice interaction models, we determined which models can explain our observations and which cannot. Our results will improve our understanding of how waves and ice interact and how we can model such interactions to better forecast waves and ice in the polar regions.
Marika M. Holland, David Clemens-Sewall, Laura Landrum, Bonnie Light, Donald Perovich, Chris Polashenski, Madison Smith, and Melinda Webster
The Cryosphere, 15, 4981–4998, https://doi.org/10.5194/tc-15-4981-2021, https://doi.org/10.5194/tc-15-4981-2021, 2021
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As the most reflective and most insulative natural material, snow has important climate effects. For snow on sea ice, its high reflectivity reduces ice melt. However, its high insulating capacity limits ice growth. These counteracting effects make its net influence on sea ice uncertain. We find that with increasing snow, sea ice in both hemispheres is thicker and more extensive. However, the drivers of this response are different in the two hemispheres due to different climate conditions.
Don Perovich, Madison Smith, Bonnie Light, and Melinda Webster
The Cryosphere, 15, 4517–4525, https://doi.org/10.5194/tc-15-4517-2021, https://doi.org/10.5194/tc-15-4517-2021, 2021
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During summer, Arctic sea ice melts on its surface and bottom and lateral edges. Some of this fresh meltwater is stored on the ice surface in features called melt ponds. The rest flows into the ocean. The meltwater flowing into the upper ocean affects ice growth and melt, upper ocean properties, and ocean ecosystems. Using field measurements, we found that the summer meltwater was equal to an 80 cm thick layer; 85 % of this meltwater flowed into the ocean and 15 % was stored in melt ponds.
Sönke Maus, Martin Schneebeli, and Andreas Wiegmann
The Cryosphere, 15, 4047–4072, https://doi.org/10.5194/tc-15-4047-2021, https://doi.org/10.5194/tc-15-4047-2021, 2021
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As the hydraulic permeability of sea ice is difficult to measure, observations are sparse. The present work presents numerical simulations of the permeability of young sea ice based on a large set of 3D X-ray tomographic images. It extends the relationship between permeability and porosity available so far down to brine porosities near the percolation threshold of a few per cent. Evaluation of pore scales and 3D connectivity provides novel insight into the percolation behaviour of sea ice.
Cyril Palerme and Malte Müller
The Cryosphere, 15, 3989–4004, https://doi.org/10.5194/tc-15-3989-2021, https://doi.org/10.5194/tc-15-3989-2021, 2021
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Methods have been developed for calibrating sea ice drift forecasts from an operational prediction system using machine learning algorithms. These algorithms use predictors from sea ice concentration observations during the initialization of the forecasts, sea ice and wind forecasts, and some geographical information. Depending on the calibration method, the mean absolute error is reduced between 3.3 % and 8.0 % for the direction and between 2.5 % and 7.1 % for the speed of sea ice drift.
Dongyang Fu, Bei Liu, Yali Qi, Guo Yu, Haoen Huang, and Lilian Qu
The Cryosphere, 15, 3797–3811, https://doi.org/10.5194/tc-15-3797-2021, https://doi.org/10.5194/tc-15-3797-2021, 2021
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Our results show three main sea ice drift patterns have different multiscale variation characteristics. The oscillation period of the third sea ice transport pattern is longer than the other two, and the ocean environment has a more significant influence on it due to the different regulatory effects of the atmosphere and ocean environment on sea ice drift patterns on various scales. Our research can provide a basis for the study of Arctic sea ice dynamics parameterization in numerical models.
Andrii Murdza, Arttu Polojärvi, Erland M. Schulson, and Carl E. Renshaw
The Cryosphere, 15, 2957–2967, https://doi.org/10.5194/tc-15-2957-2021, https://doi.org/10.5194/tc-15-2957-2021, 2021
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The strength of refrozen floes or piles of ice rubble is an important factor in assessing ice-structure interactions, as well as the integrity of an ice cover itself. The results of this paper provide unique data on the tensile strength of freeze bonds and are the first measurements to be reported. The provided information can lead to a better understanding of the behavior of refrozen ice floes and better estimates of the strength of an ice rubble pile.
H. Jakob Belter, Thomas Krumpen, Luisa von Albedyll, Tatiana A. Alekseeva, Gerit Birnbaum, Sergei V. Frolov, Stefan Hendricks, Andreas Herber, Igor Polyakov, Ian Raphael, Robert Ricker, Sergei S. Serovetnikov, Melinda Webster, and Christian Haas
The Cryosphere, 15, 2575–2591, https://doi.org/10.5194/tc-15-2575-2021, https://doi.org/10.5194/tc-15-2575-2021, 2021
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Summer sea ice thickness observations based on electromagnetic induction measurements north of Fram Strait show a 20 % reduction in mean and modal ice thickness from 2001–2020. The observed variability is caused by changes in drift speeds and consequential variations in sea ice age and number of freezing-degree days. Increased ocean heat fluxes measured upstream in the source regions of Arctic ice seem to precondition ice thickness, which is potentially still measurable more than a year later.
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
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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.
Ron Kwok, Alek A. Petty, Marco Bagnardi, Nathan T. Kurtz, Glenn F. Cunningham, Alvaro Ivanoff, and Sahra Kacimi
The Cryosphere, 15, 821–833, https://doi.org/10.5194/tc-15-821-2021, https://doi.org/10.5194/tc-15-821-2021, 2021
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Rasmus Tonboe, Stefan Hendricks, Robert Ricker, James Mead, Robbie Mallett, Marcus Huntemann, Polona Itkin, Martin Schneebeli, Daniela Krampe, Gunnar Spreen, Jeremy Wilkinson, Ilkka Matero, Mario Hoppmann, and Michel Tsamados
The Cryosphere, 14, 4405–4426, https://doi.org/10.5194/tc-14-4405-2020, https://doi.org/10.5194/tc-14-4405-2020, 2020
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This study provides a first look at the data collected by a new dual-frequency Ka- and Ku-band in situ radar over winter sea ice in the Arctic Ocean. The instrument shows potential for using both bands to retrieve snow depth over sea ice, as well as sensitivity of the measurements to changing snow and atmospheric conditions.
Leandro Ponsoni, François Massonnet, David Docquier, Guillian Van Achter, and Thierry Fichefet
The Cryosphere, 14, 2409–2428, https://doi.org/10.5194/tc-14-2409-2020, https://doi.org/10.5194/tc-14-2409-2020, 2020
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The continuous melting of the Arctic sea ice observed in the last decades has a significant impact at global and regional scales. To understand the amplitude and consequences of this impact, the monitoring of the total sea ice volume is crucial. However, in situ monitoring in such a harsh environment is hard to perform and far too expensive. This study shows that four well-placed sampling locations are sufficient to explain about 70 % of the inter-annual changes in the pan-Arctic sea ice volume.
H. Jakob Belter, Thomas Krumpen, Stefan Hendricks, Jens Hoelemann, Markus A. Janout, Robert Ricker, and Christian Haas
The Cryosphere, 14, 2189–2203, https://doi.org/10.5194/tc-14-2189-2020, https://doi.org/10.5194/tc-14-2189-2020, 2020
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The validation of satellite sea ice thickness (SIT) climate data records with newly acquired moored sonar SIT data shows that satellite products provide modal rather than mean SIT in the Laptev Sea region. This tendency of satellite-based SIT products to underestimate mean SIT needs to be considered for investigations of sea ice volume transports. Validation of satellite SIT in the first-year-ice-dominated Laptev Sea will support algorithm development for more reliable SIT records in the Arctic.
Mark S. Handcock and Marilyn N. Raphael
The Cryosphere, 14, 2159–2172, https://doi.org/10.5194/tc-14-2159-2020, https://doi.org/10.5194/tc-14-2159-2020, 2020
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Traditional methods of calculating the annual cycle of sea ice extent disguise the variation of amplitude and timing (phase) of the advance and retreat of the ice. We present a multiscale model that explicitly allows them to vary, resulting in a much improved representation of the cycle. We show that phase is the dominant contributor to the variability in the cycle and that the anomalous decay of Antarctic sea ice in 2016 was due largely to a change of phase.
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
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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.
Mark A. Tschudi, Walter N. Meier, and J. Scott Stewart
The Cryosphere, 14, 1519–1536, https://doi.org/10.5194/tc-14-1519-2020, https://doi.org/10.5194/tc-14-1519-2020, 2020
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A new version of a set of data products that contain the velocity of sea ice and the age of this ice has been developed. We provide a history of the product development and discuss the improvements to the algorithms that create these products. We find that changes in sea ice motion and age show a significant shift in the Arctic ice cover, from a pack with a high concentration of older ice to a sea ice cover dominated by younger ice, which is more susceptible to summer melt.
Angela Cheng, Barbara Casati, Adrienne Tivy, Tom Zagon, Jean-François Lemieux, and L. Bruno Tremblay
The Cryosphere, 14, 1289–1310, https://doi.org/10.5194/tc-14-1289-2020, https://doi.org/10.5194/tc-14-1289-2020, 2020
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Sea ice charts by the Canadian Ice Service (CIS) contain visually estimated ice concentration produced by analysts. The accuracy of manually derived ice concentrations is not well understood. The subsequent uncertainty of ice charts results in downstream uncertainties for ice charts users, such as models and climatology studies, and when used as a verification source for automated sea ice classifiers. This study quantifies the level of accuracy and inter-analyst agreement for ice charts by CIS.
Young Jun Kim, Hyun-Cheol Kim, Daehyeon Han, Sanggyun Lee, and Jungho Im
The Cryosphere, 14, 1083–1104, https://doi.org/10.5194/tc-14-1083-2020, https://doi.org/10.5194/tc-14-1083-2020, 2020
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In this study, we proposed a novel 1-month sea ice concentration (SIC) prediction model with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). The proposed CNN model was evaluated and compared with the two baseline approaches, random-forest and simple-regression models, resulting in better performance. This study also examined SIC predictions for two extreme cases in 2007 and 2012 in detail and the influencing factors through a sensitivity analysis.
Shiming Xu, Lu Zhou, and Bin Wang
The Cryosphere, 14, 751–767, https://doi.org/10.5194/tc-14-751-2020, https://doi.org/10.5194/tc-14-751-2020, 2020
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Sea ice thickness parameters are key to polar climate change studies and forecasts. Airborne and satellite measurements provide complementary observational capabilities. The study analyzes the variability in freeboard and snow depth measurements and its changes with scale in Operation IceBridge, CryoVEx, CryoSat-2 and ICESat. Consistency between airborne and satellite data is checked. Analysis calls for process-oriented attribution of variability and covariability features of these parameters.
Valeria Selyuzhenok, Igor Bashmachnikov, Robert Ricker, Anna Vesman, and Leonid Bobylev
The Cryosphere, 14, 477–495, https://doi.org/10.5194/tc-14-477-2020, https://doi.org/10.5194/tc-14-477-2020, 2020
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This study explores a link between the long-term variations in the integral sea ice volume in the Greenland Sea and oceanic processes. We link the changes in the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) regional sea ice volume with the mixed layer, depth and upper-ocean heat content derived using the ARMOR dataset.
Cited articles
Allard, R. A., Farrell, S. L., Hebert, D. A., Johnston, W. F., Li, L., Kurtz,
N. T., Phelps, M. W., Posey, P. G., Tilling, R., Ridout, A., and Wallcraft,
A. J.: Utilizing CryoSat-2 sea ice thickness to initialize a coupled
ice–ocean modeling system, Adv. Space Res., 62, 1265–1280,
https://doi.org/10.1016/j.asr.2017.12.030, 2018.
Balmaseda, M. A., Ferranti, L., Molteni, F., and Palmer, T. N.: Impact of
2007 and 2008 Arctic ice anomalies on the atmospheric circulation:
Implications for long-range predictions, Q. J. Roy. Meteorol. Soc., 136:
1655-1664, https://doi.org/10.1002/qj.661, 2010.
Balmaseda, M. A., Hernandez, F., Storto, A., Palmer, M. D., Alves, O., Shi,
L., Smith, G. C., Toyoda, T., Valdivieso, M., Barnier, B., Behringer, D.,
Boyer, T., Chang, Y.-S., Chepurin, G. A., Ferry, N., Forget, G., Fujii, Y.,
Good, S., Guinehut, S., Haines, K., Ishikawa, Y., Keeley, S., Köhl, A.,
Lee, T., Martin, M., Masina, S., Masuda, S., Meyssignac, B., Mogensen, K.,
Parent, L., Peterson, K. A., Tang, Y. M., Yin, Y., Vernieres, G., Wang, X.,
Waters, J., Wedd, R., Wang, O., Xue, Y., Chevallier, M., Lemieux, J.-F.,
Dupont, F., Kuragano, T., Kamachi, M., Awaji, T., Caltabiano, A.,
Wilmer-Becker, K., and Gaillard, F.: The Ocean Reanalyses Intercomparison
Project (ORA-IP), J. Oper. Oceanogr., 8, s80–s97,
https://doi.org/10.1080/1755876X.2015.1022329, 2015.
Bauer, P., Magnusson, L., Thépaut, J.-N., and Hamill, T. M.: Aspects of
ECMWF model performance in polar areas, Q. J. Roy. Meteorol. Soc., 142,
583–596, https://doi.org/10.1002/qj.2449, 2016.
Blanchard-Wrigglesworth, E., Armour, K. C., Bitz, C. M., and DeWeaver, E.:
Persistence and inherent predictability of Arctic sea ice in a GCM ensemble
and observations, J. Climate, 24, 231–250, https://doi.org/10.1175/2010JCLI3775.1, 2011.
Blanchard-Wrigglesworth, E. and Bitz, C. M.: Characteristics of Arctic
Sea-Ice Thickness Variability in GCMs, J. Climate, 27, 8244–8258,
https://doi.org/10.1175/JCLI-D-14-00345.1, 2014.
Blanchard-Wrigglesworth, E., Cullather, R. I., Wang, W., Zhang, J., and Bitz,
C. M.: Model forecast skill and sensitivity to initial conditions in the
seasonal Sea Ice Outlook, Geophys. Res. Lett., 42, 8042–8048,
https://doi.org/10.1002/2015GL065860, 2015.
Blanchard-Wrigglesworth, E., Barthélemy, A, Chevallier, M., Cullather,
R., Fučkar, N., Massonnet, F., Posey, P., Wang, W., Zhang, J., Ardilouze,
C., Bitz, C. M., Vernieres, G., Wallcraft, A., and Wang, M.: Multi-model
seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and
regional scales, Clim. Dynam., 49, 1399–1410, https://doi.org/10.1007/s00382-016-3388-9,
2017.
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.
Blockley, E., Lea, D., Martin, M ., McLaren, A., Mirouze, I., Ryan, A.,
Siddorn, J., Storkey, D., and Waters, J.: Recent development and assessment
of the Met Office operational global ocean forecasting system (FOAM), in:
Operational Oceanography for Sustainable Blue Growth, Proceedings of the
Seventh EuroGOOS International Conference, 28–30 October 2014, Lisbon,
Portugal, edited by: Buch, E., Antoniou, Y., Eparkhina, D., and Nolan, G.,
425–428, 2015.
Bloom, S. C., Takacs, L. L., da Silva, A. M., and Ledvina, D.: Data
assimilation using incremental analysis updates, Mon. Weather Rev., 124,
1256–1271, 1996.
Brown, A., Milton, S., Cullen, M., Golding, B., Mitchell, J., and Shelly, A.:
Unified Modeling and Prediction of Weather and Climate: A 25-Year Journey, B.
Am. Meteorol. Soc., 93, 1865–1877, https://doi.org/10.1175/BAMS-D-12-00018.1, 2012.
Chevallier, M., Salas y Mélia, D., Voldoire, A., Déqué, M., and
Garric, G.: Seasonal forecasts of the pan-Arctic sea ice extent using a
GCM-based seasonal prediction system, J. Climat, 26, 6092–6104, 2013.
Chevallier, M., Smith, G. C., Dupont, F., et al.: Intercomparison of the
Arctic sea ice cover in global ocean-sea ice reanalyses from the ORA-IP
project, Clim. Dynam., 49, 1107–1136, https://doi.org/10.1007/s00382-016-2985-y, 2017.
Collins, M., Knutti, R., Arblaster, J., Dufresne, J.-L., Fichefet, T.,
Friedlingstein, P., Gao, X., Gutowski, W. J., Johns, T., Krinner, G.,
Shongwe, M., Tebaldi, C., Weaver, A. J., and Wehner, M.: Long-term Climate
Change: Projections, Commitments and Irreversibility. In: Climate Change
2013: The Physical Science Basis, Contribution of Working Group I to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
Cambridge University Press, Cambridge, UK, New York, NY, USA, 2013.
Collow, T. W., Wang, W., Kumar, A., and Zhang, J.: Improving Arctic Sea Ice
Prediction Using PIOMAS Initial Sea Ice Thickness in a Coupled
Ocean-Atmosphere Model, Mon. Weather. Rev., 143, 4618–4630,
https://doi.org/10.1175/MWR-D-15-0097.1, 2015.
Cummings, J. A. and Smedstad, O. M.: Ocean Data Impacts in Global HYCOM, J.
Atmos. Ocean. Tech., 31, 1771–1791, https://doi.org/10.1175/JTECH-D-14-00011.1, 2014.
Day, J. J., Hawkins, E., and Tietsche, S.: Will Arctic sea ice thickness
initialization improve seasonal forecast skill?, Geophys. Res. Lett., 41,
7566–7575, https://doi.org/10.1002/2014GL061694, 2014.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, I., Biblot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Greer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Kohler, M.,
Matricardi, M., McNally, A. P., Mong-Sanz, B. M., Morcette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart,
F.: The ERA-Interim reanalysis: Configuration and performance of the data
assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597,
https://doi.org/10.1002/qj.828, 2011.
Deser, C., Walsh, J. E., and Timlin, M. S.: Arctic sea ice variability in the
context of recent atmospheric circulation trends, J. Climate, 13, 617–633,
https://doi.org/10.1175/1520-0442(2000)013<0617:ASIVIT>2.0.CO;2, 2000.
Eicken, H.: Arctic sea ice needs better forecasts, Nature, 497, 431–433,
https://doi.org/10.1038/497431a, 2013.
Fetterer, F., Knowles, K., Meier, W., Savoie, M., and Windnagel, A.: Sea Ice
Index, Version 2, updated daily, Boulder, Colorado, USA, NSIDC: National Snow
and Ice Data Center, https://doi.org/10.7265/N5736NV7, 2016.
Francis, J. A. and S. J. Vavrus: Evidence linking Arctic amplification to
extreme weather in mid-latitudes, Geophys. Res. Lett., 39, L06801,
https://doi.org/10.1029/2012GL051000, 2012.
Goessling, H. F., Tietsche, S., Day, J. J., Hawkins, E., and Jung, T.:
Predictability of the Arctic sea ice edge, Geophys. Res. Lett., 43,
1642–1650, https://doi.org/10.1002/2015GL067232, 2016.
Goosse, H., Arzel, O., Bitz, C. M., de Montety, A., and Vancoppenolle, M.:
Increased variability of the Arctic summer ice extent in a warmer climate,
Geophys. Res. Lett., 36, L23702, https://doi.org/10.1029/2009GL040546, 2009.
Guemas, V., Blanchard-Wrigglesworth, E., Chevallier, M., Day, J. J.,
Déqué, M., Doblas-Reyes, F. J., Fučkar, N. S., Germe, A.,
Hawkins, E., Keeley, S., Koenigk, T., Salas y Mélia, D., and Tietsche,
S.: A review on Arctic sea-ice predictability and prediction on seasonal to
decadal time-scales, Q. J. Roy. Meteorol. Soc., 142, 546–561,
https://doi.org/10.1002/qj.2401, 2016.
Guerreiro, K., Fleury, S., Zakharova, E., Kouraev, A., Rémy, F., and
Maisongrande, P.: Comparison of CryoSat-2 and ENVISAT radar freeboard over
Arctic sea ice: toward an improved Envisat freeboard retrieval, The
Cryosphere, 11, 2059–2073, https://doi.org/10.5194/tc-11-2059-2017, 2017.
Guo, D., Gao, Y., Bethke, I., Gong, D., Johannessen, O. M., and Wang, H.:
Mechanism on how the spring Arctic sea ice impacts the East Asian summer
monsoon, Theor. Appl. Climatol., 115, 107–119,
https://doi.org/10.1007/s00704-013-0872-6, 2014.
Hewitt, H. T., Copsey, D., Culverwell, I. D., Harris, C. M., Hill, R. S. R.,
Keen, A. B., McLaren, A. J., and Hunke, E. C.: Design and implementation of
the infrastructure of HadGEM3: the next-generation Met Office climate
modelling system, Geosci. Model Dev., 4, 223–253,
https://doi.org/10.5194/gmd-4-223-2011, 2011.
Holland, M. M., Bailey, D. A., and Vavrus, S.: Inherent sea ice
predictability in the rapidly changing Arctic environment of the Community
Climate System Model, version 3, Clim. Dynam., 36, 1239,
https://doi.org/10.1007/s00382-010-0792-4, 2011.
Hunke, E. C., Lipscomb, W. H., Turner, A. K., Jeffery, N., and Elliott, S.:
CICE: the Los Alamos sea ice model documentation and software user's manual
version 5.1, (LA-CC-06-012), Los Alamos National Laboratory, N.M., 2015.
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.
Jackson, L. C., Peterson, K. A., Roberts, C. D., and Wood, R. A.: Recent
slowing of Atlantic overturning circulation as a recovery from earlier
strengthening, Nat. Geosci, 9, 518–522, https://doi.org/10.1038/ngeo2715, 2015.
Jung, T., Gordon, N. D., Bauer, P., Bromwich, D. H., Chevallier, M., Day, J.
J., Dawson, J., Doblas-Reyes, F., Fairall, C., Goessling, H. F., Holland, M.,
Inoue, J., Iversen, T., Klebe, S., Lemke, P., Losch, M., Makshtas, A., Mills,
B., Nurmi, P., Perovich, D., Reid, P., Renfrew, I. A., Smith, G., Svensson,
G., Tolstykh, M., and Yang, Q.: Advancing Polar Prediction Capabilities on
Daily to Seasonal Time Scales, B. Amer. Meteorol. Soc., 97, 1631–1647,
https://doi.org/10.1175/BAMS-D-14-00246.1, 2016.
Kaleschke, L., Tian-Kunze, X., Maaß, N., Beitsch, A., Wernecke, A.,
Miernecki, M., Müller, G., Fock, B. H., Gierisch, A. M., Schlünzen,
K. H., Pohlmann, T., Dobrynin, M., Hendricks, S., Asseng, J., Gerdes, R.,
Jochmann, P., Reimer, N., Holfort, J., Melsheimer, C., Heygster, G., Spreen,
G., Gerland, S., King, J., Skou, N., Søbjærg, S. S., Haas, C.,
Richter, F., and Casal, T.: SMOS sea ice product: Operational application and
validation in the Barents Sea marginal ice zone, Remote Sens. Environ., 180,
264–273, https://doi.org/10.1016/j.rse.2016.03.009, 2016.
Kauker, F., Kaminski, T., Karcher, M., Giering, R., Gerdes, R., and
Voßbeck, M.: Adjoint analysis of the 2007 all time Arctic sea-ice
minimum, Geophys. Res. Lett., 36, L03707, https://doi.org/10.1029/2008GL036323, 2009.
Koenigk, T., Caian, M., Nikulin, G., and Schimanke, S.: Regional Arctic sea
ice variations as predictor for winter climate conditions, Clim. Dynam., 46,
317–337, https://doi.org/10.1007/s00382-015-2586-1, 2016.
Kwok, R. and Cunningham, G. F.: ICESat over Arctic sea ice: Estimation of
snow depth and ice thickness, J. Geophys. Res., 113, C08010,
https://doi.org/10.1029/2008JC004753, 2008.
Kwok, R., Cunningham, G. F., Wensnahan, M., Rigor, I., Zwally, H. J., and Yi,
D.: Thinning and volume loss of the Arctic Ocean sea ice cover: 2003–2008,
J. Geophys. Res., 114, C07005, https://doi.org/10.1029/2009JC005312, 2009.
Laxon, S., Peacock, N., and Smith, D.: High interannual variability of sea
ice thickness in the Arctic region, Nature, 425, 947–950,
https://doi.org/10.1038/nature02050, 2003.
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., Farrell, S., and Davidson, M.: CryoSat-2 estimates
of Arctic sea ice thickness and volume, Geophys. Res. Lett., 40, 732–737,
https://doi.org/10.1002/grl.50193, 2013.
Lindsay, R. and Schweiger, A.: Arctic sea ice thickness loss determined using
subsurface, aircraft, and satellite observations, The Cryosphere, 9,
269–283, https://doi.org/10.5194/tc-9-269-2015, 2015.
MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D.,
Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp,
J., Xavier, P., and Madec, G.: Global Seasonal Fore cast System version 5
(GloSea5): a high resolution seasonal forecast system, Q. J. Roy. Meteor.
Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2014.
Madec, G.: NEMO ocean engine, Note du Pôle de modélisation, Institut
Pierre-Simon Laplace (IPSL), France, No. 27, 2008.
Martin, M. J., Balmaseda, M., Bertino, L., Brasseur, P., Brassington, G.,
Cummings, J., Fujii, Y., Lea, D. J., Lellouche, J.-M., Mogensen, K., Oke, P.
R., Smith, G. C., Testut, C.-E., Waagbø, G. A., Waters, J., and Weaver, A.
T.: Status and future of data assimilation in operational oceanography, J.
Oper. Oceanogr., 8, s28–s48, https://doi.org/10.1080/1755876X.2015.1022055, 2015.
Meier, W. N., Hovelsrud, G. K., van Oort, B. E. H., Key, J. R., Kovacs, K.
M., Michel, C., Haas, C., Granskog, M. A., Gerland, S., Perovich, D. K.,
Makshtas, A., and Reist, J. D.: Arctic sea ice in transformation: A review of
recent observed changes and impacts on biology and human activity, Rev.
Geophys., 52, 185–217, https://doi.org/10.1002/2013RG000431, 2014.
Msadek, R., Vecchi, G., Winton, M., and Gudgel, R: Importance of initial
conditions in seasonal predictions of Arctic sea ice extent, Geophys. Res.
Lett., 41, 5208–5215, https://doi.org/10.1002/2014GL060799, 2014.
Mu, L., Yang, Q., Losch, M., Losa, S. N., Ricker, R., Nerger, L., and Liang,
X.: Improving sea ice thickness estimates by assimilating CryoSat-2 and SMOS
sea ice thickness data simultaneously, Q. J. Roy. Meteorol. Soc., 144,
529–538, https://doi.org/10.1002/qj.3225, 2018.
Notz, D.: Sea-ice extent and its trend provide limited metrics of model
performance, The Cryosphere, 8, 229–243,
https://doi.org/10.5194/tc-8-229-2014, 2014.
Notz, D. and Stroeve, J.: Observed Arctic sea-ice loss directly follows
anthropogenic CO2 emission, Science, 354, 747–750,
https://doi.org/10.1126/science.aag2345, 2016.
OSI-SAF: EUMETSAT Ocean and Sea Ice Satellite Application Facility, Global
sea ice concentration climate data record 1979–2015 (v2.0), Norwegian and
Danish Meteorological Institutes, https://doi.org/10.15770/EUM_SAF_OSI_0008, 2017.
Peterson, K. A., Arribas, A., Hewitt, H. T., Keen, A. B., Lea, D. J., and
McLaren, A. J.: Assessing the forecast skill of Arctic sea ice extent in the
GloSea4 seasonal prediction system, Clim. Dynam., 44, 147–162,
https://doi.org/10.1007/s00382-014-2190-9, 2015.
Petoukhov, V. and Semenov, V. A.: A link between reduced Barents-Kara sea ice
and cold winter extremes over northern continents, J. Geophys. Res., 115,
D21111, https://doi.org/10.1029/2009JD013568, 2010.
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L.
V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea
surface temperature, sea ice, and night marine air temperature since the late
nineteenth century, J. Geophys. Res., 108, 4407 https://doi.org/10.1029/2002JD002670,
2003.
Ricker, R., Hendricks, S., Helm, V., Skourup, H., and Davidson, M.:
Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on
radar-waveform interpretation, The Cryosphere, 8, 1607–1622,
https://doi.org/10.5194/tc-8-1607-2014, 2014.
Ricker, R., Hendricks, S., Kaleschke, L., Tian-Kunze, X., King, J., and Haas,
C.: A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and
SMOS satellite data, The Cryosphere, 11, 1607–1623,
https://doi.org/10.5194/tc-11-1607-2017, 2017.
Ridley, J. K., Blockley, E. W., Keen, A. B., Rae, J. G. L., West, A. E., and
Schroeder, D.: The sea ice model component of HadGEM3-GC3.1, Geosci. Model
Dev., 11, 713–723, https://doi.org/10.5194/gmd-11-713-2018, 2018.
Roberts, C. D., Waters, J., Peterson, K. A., Palmer, M. D., McCarthy, G. D.,
Frajka-Williams, E., Haines, K., Lea, D. J., Martin, M. J., Storkey, D.,
Blockley, E. W., and Zuo, H.: Atmosphere drives recent interannual
variability of the Atlantic meridional overturning circulation at
26.5∘ N, Geophys. Res. Lett., 40, 5164–5170 https://doi.org/10.1002/grl.50930,
2013.
Scaife, A. A., Arribas, A., Blockley, E., Brookshaw, A., Clark, R. T.,
Dunstone, N., Eade, R., Fereday, D., Folland, C. K., Gordon, M., Hermanson,
L., Knight, J. R., Lea, D. J., MacLachlan, C., Maidens, A., Martin, M.,
Peterson, A. K., Smith, D., Vellinga, M., Wallace, E., Waters, J., and
Williams, A.: Skillful long-range prediction of European and North American
winters, Geophys. Res. Lett., 41, 2514–2519, https://doi.org/10.1002/2014GL059637, 2014.
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.:
Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res., 116, C00D06,
https://doi.org/10.1029/2011JC007084, 2011.
Screen, J. A.: Influence of Arctic sea ice on European summer precipitation,
Environ. Res. Lett., 8, 044015, https://doi.org/10.1088/1748-9326/8/4/044015, 2013.
Screen, J. A., Simmonds, I., Deser, C., and Tomas, R.: The Atmospheric
Response to Three Decades of Observed Arctic Sea Ice Loss, J. Climate, 26,
1230–1248, https://doi.org/10.1175/JCLI-D-12-00063.1, 2013.
Semmler, T., Jung, T., and Serrar, S.: Fast atmospheric response to a sudden
thinning of Arctic sea ice, Clim. Dynam. 46, 1015–1025,
https://doi.org/10.1007/s00382-015-2629-7, 2016.
Sigmond, M., Fyfe, J. C., Flato G. M., Kharin, V. V., and Merryfield, W. J.:
Seasonal forecast skill of Arctic sea ice area in a dynamical forecast
system, Geophys. Res. Lett., 40, 529–534, https://doi.org/10.1002/grl.50129, 2013.
Slingo, J. and Palmer, T.: Uncertainty in weather and climate prediction,
Philos. T. Roy. Soc. A, 369, 4751–4767, https://doi.org/10.1098/rsta.2011.0161, 2011.
Smith, L. C. and Stephenson, S. R.: New Trans-Arctic shipping routes
navigable by midcentury, P. Natl. Acad. Sci. USA, 110, E1191–E1195,
https://doi.org/10.1073/pnas.1214212110, 2013.
Stark, J. D., Ridley, J., Martin, M., and Hines, A.: Sea ice concentration
and motion assimilation in a sea ice–ocean model, J. Geophys. Res., 113,
C05S91, https://doi.org/10.1029/2007JC004224, 2008.
Storkey, D., Blaker, A. T., Mathiot, P., Megann, A., Aksenov, Y., Blockley,
E. W., Calvert, D., Graham, T., Hewitt, H. T., Hyder, P., Kuhlbrodt, T., Rae,
J. G. L., and Sinha, B.: UK Global Ocean GO6 and GO7: a traceable hierarchy
of model resolutions, Geosci. Model Dev., 11, 3187–3213,
https://doi.org/10.5194/gmd-11-3187-2018, 2018.
Stroeve, J., Hamilton, L. C., Bitz, C. M., and Blanchard-Wrigglesworth, E.:
Predicting September sea ice: Ensemble skill of the SEARCH Sea Ice Outlook
2008–2013, Geophys. Res. Lett., 41, 2411–2418, https://doi.org/10.1002/2014GL059388,
2014.
Stroeve, J. C., Schroder, D., Tsamados, M., and Feltham, D.: Warm winter,
thin ice?, The Cryosphere, 12, 1791–1809, https://doi.org/10.5194/tc-12-1791-2018,
2018.
Tietsche, S., Day, J. J., Guemas, V., Hurlin, W. J., Keeley, S. P. E., Matei,
D., Msadek, R., Collins, M., and Hawkins, E.: Seasonal to interannual arctic
sea ice predictability in current global climate models, Geophys. Res. Lett.,
41, 1035–1043, https://doi.org/10.1002/2013GL058755, 2014.
Tilling, R. L., Ridout, A., Shepherd, A., and Wingham, D. J.: Increased
Arctic sea ice volume after anomalously low melting in 2013, Nat. Geosci., 8,
643–646, 2015.
Tilling, R. L., Ridout, A., and Shepherd, A.: Near-real-time Arctic sea ice
thickness and volume from CryoSat-2, The Cryosphere, 10, 2003–2012,
https://doi.org/10.5194/tc-10-2003-2016, 2016.
Tonani, M., Balmaseda, M., Bertino, L., Blockley, E. W., Brassington, G.,
Davidson, F., Drillet, Y., Hogan, P., Kuragano, T., Lee, T., Mehra, A.,
Paranathara, F., Tanajura, C. A. S., and Wang, H.: Status and future of
global and regional ocean prediction systems, J. Oper. Oceanogr., 8,
s201–s220, https://doi.org/10.1080/1755876X.2015.1049892, 2015.
Tsamados, M., Feltham, D. L., and Wilchinsky, A. V.: Impact of a new
anisotropic rheology on simulations of Arctic sea ice, J. Geophys.
Res.-Oceans, 118, 91–107, https://doi.org/10.1029/2012JC007990, 2013.
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, https://doi.org/10.1175/JPO-D-13-0215.1, 2014.
Uotila, P., Goosse, H., Haines, K., et al.: An assessment of ten ocean
reanalyses in the polar regions, Clim. Dynam.,
https://doi.org/10.1007/s00382-018-4242-z, online first, 2018.
Vaughan, D. G., Comiso, J. C., Allison, I., Carrasco, J., Kaser, G., Kwok, R.
Mote, P., Murray, T., Paul, F., Ren, J., Rignot, E., Solomina, O., Steffen,
K., and Zhang, T.: Observations: Cryosphere. In: Climate Change 2013: The
Physical Science Basis, Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge
University Press, Cambridge, UK, New York, NY, USA, 2013.
Vihma, T.: Effects of Arctic Sea Ice Decline on Weather and Climate: A
Review, Surv. Geophys., 35, 1175, https://doi.org/10.1007/s10712-014-9284-0, 2014.
Walters, D., Baran, A., Boutle, I., Brooks, M., Earnshaw, P., Edwards, J.,
Furtado, K., Hill, P., Lock, A., Manners, J., Morcrette, C., Mulcahy, J.,
Sanchez, C., Smith, C., Stratton, R., Tennant, W., Tomassini, L., Van
Weverberg, K., Vosper, S., Willett, M., Browse, J., Bushell, A., Dalvi, M.,
Essery, R., Gedney, N., Hardiman, S., Johnson, B., Johnson, C., Jones, A.,
Mann, G., Milton, S., Rumbold, H., Sellar, A., Ujiie, M., Whitall, M.,
Williams, K., and Zerroukat, M.: The Met Office Unified Model Global
Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations, Geosci. Model
Dev. Discuss., https://doi.org/10.5194/gmd-2017-291, in review, 2017.
Waters, J., Lea, D. J., Martin, M. J., Mirouze, I., Weaver, A., and While,
J.: Implementing a variational data assimilation system in an operational
1∕4 degree global ocean model, Q. J. Roy. Meteor. Soc., 141, 333–349,
https://doi.org/10.1002/qj.2388, 2015.
Wang W., Chen M., and Kumar, A.: Seasonal prediction of Arctic sea ice extent
from a coupled dynamical forecast system, Mon. Weather Rev., 141, 1375–1394,
https://doi.org/10.1175/MWR-D-12-00057.1, 2013.
Williams, K. D., Copsey, D., Blockley, E. W., Bodas-Salcedo, A., Calvert, D.,
Comer, R., Davis, P., Graham, T., Hewitt, H. T., Hill, R., Hyder, P., Ineson,
S., Johns, T. C., Keen, A. B., Lee, R. W., Megann, A., Milton, S. F., Rae, J.
G. L., Roberts, M. J., Scaife, A. A., Schiemann, R., Storkey, D., Thorpe, L.,
Watterson, I. G., Walters, D. N., West, A., Wood, R. A., Woollings, T., and
Xavier, P. K.: The Met Office Global Coupled model 3.0 and 3.1 (GC3.0 &
GC3.1) configurations, J. Adv. Model. Eearth Syst., 10, 357–380,
https://doi.org/10.1002/2017MS001115, 2017.
Wu, B., Zhang, R., D'Arrigo, R., and Su, J.: On the Relationship between
Winter Sea Ice and Summer Atmospheric Circulation over Eurasia, J. Climate,
26, 5523–5536, https://doi.org/10.1175/JCLI-D-12-00524.1, 2013.
Xie, J., Counillon, F., Bertino, L., Tian-Kunze, X., and Kaleschke, L.:
Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ
system, The Cryosphere, 10, 2745–2761,
https://doi.org/10.5194/tc-10-2745-2016, 2016.
Yang, Q., Losa, S. N., Losch, M., Tian-Kunze, X., Nerger, L., Liu, J.,
Kaleschke, L., and Zhang, Z.: Assimilating SMOS sea ice thickness into a
coupled ice-ocean model using a local SEIK filter, J. Geophys. Res.-Ocean,
119, 6680–6692, https://doi.org/10.1002/2014JC009963, 2014.
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, 681–697, 2003.
Short summary
Arctic sea-ice prediction on seasonal time scales is becoming increasingly more relevant to society but the predictive capability of forecasting systems is low. Several studies suggest initialization of sea-ice thickness (SIT) could improve the skill of seasonal prediction systems. Here for the first time we test the impact of SIT initialization in the Met Office's GloSea coupled prediction system using CryoSat-2 data. We show significant improvements to Arctic extent and ice edge location.
Arctic sea-ice prediction on seasonal time scales is becoming increasingly more relevant to...
