Articles | Volume 15, issue 8
https://doi.org/10.5194/tc-15-3989-2021
© Author(s) 2021. 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-15-3989-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Aug 2021
Research article |
| 23 Aug 2021
Calibration of sea ice drift forecasts using random forest algorithms
Development Centre for Weather Forecasting, Norwegian Meteorological Institute, Oslo, Norway
Malte Müller
Development Centre for Weather Forecasting, Norwegian Meteorological Institute, Oslo, Norway
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Cyril Palerme, Thomas Lavergne, Jozef Rusin, Arne Melsom, Julien Brajard, Are Frode Kvanum, Atle Macdonald Sørensen, Laurent Bertino, and Malte Müller
EGUsphere, https://doi.org/10.5194/egusphere-2023-2439, https://doi.org/10.5194/egusphere-2023-2439, 2023
Short summary
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Sea ice forecasts are operationally produced using physical-based models, but these forecasts are often not accurate enough for maritime operations. In this study, we developed a statistical correction technique (also called calibration) using machine learning in order to improve the skill of short-term (up to 10 days) sea ice concentration forecasts produced by the TOPAZ4 model. This technique allows to reduce the errors from the TOPAZ4 sea ice concentration forecasts by 41 % on average.
Arne Melsom, Cyril Palerme, and Malte Müller
Ocean Sci., 15, 615–630, https://doi.org/10.5194/os-15-615-2019, https://doi.org/10.5194/os-15-615-2019, 2019
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Retreating sea ice in the Arctic Ocean gives rise to increased naval traffic with shorter navigation distances. Hence, information about the position of the sea ice edge is crucial for safe navigation.
In the present study we explore methods for examining the quality of sea ice edge forecasts. We conclude that the forecast quality can be monitored with results from a set of four quantities. We also recommend the use of maps which display discrepancies in the positions of the sea ice edge.
Florentin Lemonnier, Jean-Baptiste Madeleine, Chantal Claud, Christophe Genthon, Claudio Durán-Alarcón, Cyril Palerme, Alexis Berne, Niels Souverijns, Nicole van Lipzig, Irina V. Gorodetskaya, Tristan L'Ecuyer, and Norman Wood
The Cryosphere, 13, 943–954, https://doi.org/10.5194/tc-13-943-2019, https://doi.org/10.5194/tc-13-943-2019, 2019
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Evaluation of the vertical precipitation rate profiles of CloudSat radar by comparison with two surface-based micro-rain radars (MRR) located at two antarctic stations gives a near-perfect correlation between both datasets, even though climatic and geographic conditions are different for the stations. A better understanding and reassessment of CloudSat uncertainties ranging from −13 % up to +22 % confirms the robustness of the CloudSat retrievals of snowfall over Antarctica.
Cyril Palerme, Thomas Lavergne, Jozef Rusin, Arne Melsom, Julien Brajard, Are Frode Kvanum, Atle Macdonald Sørensen, Laurent Bertino, and Malte Müller
EGUsphere, https://doi.org/10.5194/egusphere-2023-2439, https://doi.org/10.5194/egusphere-2023-2439, 2023
Short summary
Short summary
Sea ice forecasts are operationally produced using physical-based models, but these forecasts are often not accurate enough for maritime operations. In this study, we developed a statistical correction technique (also called calibration) using machine learning in order to improve the skill of short-term (up to 10 days) sea ice concentration forecasts produced by the TOPAZ4 model. This technique allows to reduce the errors from the TOPAZ4 sea ice concentration forecasts by 41 % on average.
Arne Melsom, Cyril Palerme, and Malte Müller
Ocean Sci., 15, 615–630, https://doi.org/10.5194/os-15-615-2019, https://doi.org/10.5194/os-15-615-2019, 2019
Short summary
Short summary
Retreating sea ice in the Arctic Ocean gives rise to increased naval traffic with shorter navigation distances. Hence, information about the position of the sea ice edge is crucial for safe navigation.
In the present study we explore methods for examining the quality of sea ice edge forecasts. We conclude that the forecast quality can be monitored with results from a set of four quantities. We also recommend the use of maps which display discrepancies in the positions of the sea ice edge.
Florentin Lemonnier, Jean-Baptiste Madeleine, Chantal Claud, Christophe Genthon, Claudio Durán-Alarcón, Cyril Palerme, Alexis Berne, Niels Souverijns, Nicole van Lipzig, Irina V. Gorodetskaya, Tristan L'Ecuyer, and Norman Wood
The Cryosphere, 13, 943–954, https://doi.org/10.5194/tc-13-943-2019, https://doi.org/10.5194/tc-13-943-2019, 2019
Short summary
Short summary
Evaluation of the vertical precipitation rate profiles of CloudSat radar by comparison with two surface-based micro-rain radars (MRR) located at two antarctic stations gives a near-perfect correlation between both datasets, even though climatic and geographic conditions are different for the stations. A better understanding and reassessment of CloudSat uncertainties ranging from −13 % up to +22 % confirms the robustness of the CloudSat retrievals of snowfall over Antarctica.
I. Fer, M. Müller, and A. K. Peterson
Ocean Sci., 11, 287–304, https://doi.org/10.5194/os-11-287-2015, https://doi.org/10.5194/os-11-287-2015, 2015
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Over the Yermak Plateau northwest of Svalbard there is substantial energy conversion from barotropic to internal tides. Internal tides are trapped along the topography. An approximate local conversion-to-dissipation balance is found over
shallows and also in the deep part of the sloping flanks. Dissipation of
tidal energy can be a significant contributor to turbulent mixing and cooling of the Atlantic layer in the Arctic Ocean.
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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.
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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.
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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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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.
Linghan Li, Forest Cannon, Matthew R. Mazloff, Aneesh C. Subramanian, Anna M. Wilson, and F. Martin Ralph
EGUsphere, https://doi.org/10.5194/egusphere-2022-36, https://doi.org/10.5194/egusphere-2022-36, 2022
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We investigate how moisture transport through atmospheric rivers influences Arctic sea ice variations, using ERA5 hourly atmospheric reanalysis for 1981–2020 at 0.25º x 0.25º resolution. We show that individual atmospheric rivers initiate rapid sea ice decrease through surface heat flux and winds. We find that rate of change of sea ice concentration has significant anticorrelation with moisture, northward wind, and turbulent heat flux on weather timescales almost everywhere in the Arctic Ocean.
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.
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.
Chao Min, Longjiang Mu, Qinghua Yang, Robert Ricker, Qian Shi, Bo Han, Renhao Wu, and Jiping Liu
The Cryosphere, 13, 3209–3224, https://doi.org/10.5194/tc-13-3209-2019, https://doi.org/10.5194/tc-13-3209-2019, 2019
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Sea ice volume export through the Fram Strait has been studied using varied methods, however, mostly in winter months. Here we report sea ice volume estimates that extend over summer seasons. A recent developed sea ice thickness dataset, in which CryoSat-2 and SMOS sea ice thickness together with SSMI/SSMIS sea ice concentration are assimilated, is used and evaluated in the paper. Results show our estimate is more reasonable than that calculated by satellite data only.
M. Jeffrey Mei, Ted Maksym, Blake Weissling, and Hanumant Singh
The Cryosphere, 13, 2915–2934, https://doi.org/10.5194/tc-13-2915-2019, https://doi.org/10.5194/tc-13-2915-2019, 2019
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Sea ice thickness is hard to measure directly, and current datasets are very limited to sporadically conducted drill lines. However, surface elevation is much easier to measure. Converting surface elevation to ice thickness requires making assumptions about snow depth and density, which leads to large errors (and may not generalize to new datasets). A deep learning method is presented that uses the surface morphology as a direct predictor of sea ice thickness, with testing errors of < 20 %.
Pierre Rampal, Véronique Dansereau, Einar Olason, Sylvain Bouillon, Timothy Williams, Anton Korosov, and Abdoulaye Samaké
The Cryosphere, 13, 2457–2474, https://doi.org/10.5194/tc-13-2457-2019, https://doi.org/10.5194/tc-13-2457-2019, 2019
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In this article, we look at how the Arctic sea ice cover, as a solid body, behaves on different temporal and spatial scales. We show that the numerical model neXtSIM uses a new approach to simulate the mechanics of sea ice and reproduce the characteristics of how sea ice deforms, as observed by satellite. We discuss the importance of this model performance in the context of simulating climate processes taking place in polar regions, like the exchange of energy between the ocean and atmosphere.
Alberto Alberello, Miguel Onorato, Luke Bennetts, Marcello Vichi, Clare Eayrs, Keith MacHutchon, and Alessandro Toffoli
The Cryosphere, 13, 41–48, https://doi.org/10.5194/tc-13-41-2019, https://doi.org/10.5194/tc-13-41-2019, 2019
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Existing observations do not provide quantitative descriptions of the floe size distribution for pancake ice floes. This is important during the Antarctic winter sea ice expansion, when hundreds of kilometres of ice cover around the Antarctic continent are composed of pancake floes (D = 0.3–3 m). Here, a new set of images from the Antarctic marginal ice zone is used to measure the shape of individual pancakes for the first time and to infer their size distribution.
Frédéric Laliberté, Stephen E. L. Howell, Jean-François Lemieux, Frédéric Dupont, and Ji Lei
The Cryosphere, 12, 3577–3588, https://doi.org/10.5194/tc-12-3577-2018, https://doi.org/10.5194/tc-12-3577-2018, 2018
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Ice that forms over marginal seas often gets anchored and becomes landfast. Landfast ice is fundamental to the local ecosystems, is of economic importance as it leads to hazardous seafaring conditions and is also a choice hunting ground for both the local population and large predators. Using observations and climate simulations, this study shows that, especially in the Canadian Arctic, landfast ice might be more resilient to climate change than is generally thought.
Iina Ronkainen, Jonni Lehtiranta, Mikko Lensu, Eero Rinne, Jari Haapala, and Christian Haas
The Cryosphere, 12, 3459–3476, https://doi.org/10.5194/tc-12-3459-2018, https://doi.org/10.5194/tc-12-3459-2018, 2018
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We quantify the sea ice thickness variability in the Bay of Bothnia using various observational data sets. For the first time we use helicopter and shipborne electromagnetic soundings to study changes in drift ice of the Bay of Bothnia. Our results show that the interannual variability of ice thickness is larger in the drift ice zone than in the fast ice zone. Furthermore, the mean thickness of heavily ridged ice near the coast can be several times larger than that of fast ice.
Edward W. Blockley and K. Andrew Peterson
The Cryosphere, 12, 3419–3438, https://doi.org/10.5194/tc-12-3419-2018, https://doi.org/10.5194/tc-12-3419-2018, 2018
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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.
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.
Aleksey Malinka, Eleonora Zege, Larysa Istomina, Georg Heygster, Gunnar Spreen, Donald Perovich, and Chris Polashenski
The Cryosphere, 12, 1921–1937, https://doi.org/10.5194/tc-12-1921-2018, https://doi.org/10.5194/tc-12-1921-2018, 2018
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Melt ponds occupy a large part of the Arctic sea ice in summer and strongly affect the radiative budget of the atmosphere–ice–ocean system. The melt pond reflectance is modeled in the framework of the radiative transfer theory and validated with field observations. It improves understanding of melting sea ice and enables better parameterization of the surface in Arctic atmospheric remote sensing (clouds, aerosols, trace gases) and re-evaluating Arctic climatic feedbacks at a new accuracy level.
Peng Lu, Matti Leppäranta, Bin Cheng, Zhijun Li, Larysa Istomina, and Georg Heygster
The Cryosphere, 12, 1331–1345, https://doi.org/10.5194/tc-12-1331-2018, https://doi.org/10.5194/tc-12-1331-2018, 2018
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It is the first time that the color of melt ponds on Arctic sea ice was quantitatively and thoroughly investigated. We answer the question of why the color of melt ponds can change and what the physical and optical reasons are that lead to such changes. More importantly, melt-pond color was provided as potential data in determining ice thickness, especially under the summer conditions when other methods such as remote sensing are unavailable.
Cited articles
Ahijevych, D., Pinto, J. O., Williams, J. K., and Steiner, M.: Probabilistic
Forecasts of Mesoscale Convective System Initiation Using the Random Forest
Data Mining Technique, Weather Forecast., 31, 581–599,
https://doi.org/10.1175/WAF-D-15-0113.1, 2016. a
Berkman, P. A., Fiske, G., Røyset, J.-A., Brigham, L. W., and Lorenzini, D.:
Next-Generation Arctic Marine Shipping Assessments, Springer
International Publishing, 241–268, https://doi.org/10.1007/978-3-030-25674-6_11, 2020. a
Bleck, R.: An oceanic general circulation model framed in hybrid
isopycnic-Cartesian coordinates, Ocean Model., 4, 55–88,
https://doi.org/10.1016/S1463-5003(01)00012-9, 2002. a
Cavalieri, D. J. and Parkinson, C. L.: Arctic sea ice variability and trends,
1979–2010, The Cryosphere, 6, 881–889, https://doi.org/10.5194/tc-6-881-2012, 2012. a
Chassignet, E. P., Hurlburt, H. E., Smedstad, O. M., Halliwell, G. R., Hogan,
P. J., Wallcraft, A. J., and Bleck, R.: Ocean Prediction with the Hybrid
Coordinate Ocean Model (HYCOM), Springer Netherlands,
Dordrecht, 413–426, https://doi.org/10.1007/1-4020-4028-8_16, 2006. a
Chi, J. and Kim, H.-C.: Prediction of arctic sea ice concentration using a
fully data driven deep neural network, Remote Sens., 9, 1305, https://doi.org/10.3390/rs9121305, 2017. a
Comeau, D., Giannakis, D., Zhao, Z., and Majda, A. J.: Predicting regional and
pan-Arctic sea ice anomalies with kernel analog forecasting, Clim.
Dynam., 52, 5507–5525, 2019. a
Comiso, J. C., Meier, W. N., and Gersten, R.: Variability and trends in the
Arctic Sea ice cover: Results from different techniques, J.
Geophys. Res.-Ocean., 122, 6883–6900,
https://doi.org/10.1002/2017JC012768, 2017. a, b
Eriksen, T. and Olsen, Ø.: Vessel Tracking Using Automatic Identification
System Data in the Arctic, Springer International Publishing, 115–136,
https://doi.org/10.1007/978-3-319-78425-0_7, 2018. a
Fisher, N. I. and Lee, A. J.: A correlation coefficient for circular data,
Biometrika, 70, 327–332, https://doi.org/10.1093/biomet/70.2.327, 1983. a
Fritzner, S., Graversen, R., and Christensen, K. H.: Assessment of
High-Resolution Dynamical and Machine Learning Models for Prediction of Sea
Ice Concentration in a Regional Application, J. Geophys. Res.-Ocean., 125, e2020JC016277, https://doi.org/10.1029/2020JC016277,
2020. a
Gagne II, D. J., McGovern, A., and Xue, M.: Machine Learning Enhancement
of Storm-Scale Ensemble Probabilistic Quantitative Precipitation Forecasts,
Weather Forecast., 29, 1024–1043, https://doi.org/10.1175/WAF-D-13-00108.1,
2014. a, b
Gegiuc, A., Similä, M., Karvonen, J., Lensu, M., Mäkynen, M., and Vainio,
J.: Estimation of degree of sea ice ridging based on dual-polarized C-band
SAR data, The Cryosphere, 12, 343–364, https://doi.org/10.5194/tc-12-343-2018, 2018. a
Girard-Ardhuin, F. and Ezraty, R.: Enhanced Arctic Sea Ice Drift Estimation
Merging Radiometer and Scatterometer Data, IEEE Trans. Geosci.
Remote Sens., 50, 2639–2648, https://doi.org/10.1109/TGRS.2012.2184124, 2012. a
Han, H., Im, J., Kim, M., Sim, S., Kim, J., Kim, D.-J., and Kang, S.-H.:
Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X
Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the
Chukchi Sea with Mid-Incidence Angle Data, Remote Sens., 8, 57,
https://doi.org/10.3390/rs8010057, 2016. 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 U.S. Navy's Arctic Cap Nowcast/Forecast System, J. Geophys.
Res.-Ocean., 120, 8327–8345, https://doi.org/10.1002/2015JC011283, 2015. a, b
Herman, G. R. and Schumacher, R. S.: Money Doesn't Grow on Trees, but
Forecasts Do: Forecasting Extreme Precipitation with Random Forests, Mon.
Weather Rev., 146, 1571–1600, https://doi.org/10.1175/MWR-D-17-0250.1, 2018. a
Hunke, E. C. and Dukowicz, J. K.: An Elastic–Viscous–Plastic Model for Sea
Ice Dynamics, J. Phys. Oceanogr., 27, 1849–1867,
https://doi.org/10.1175/1520-0485(1997)027<1849:AEVPMF>2.0.CO;2, 1997. a
IABP: International Arctic Buoy Programme: Buoy observations, updated periodically [data set], available at: https://iabp.apl.uw.edu/Data_Products/Daily_Full_Res_Data/Arctic/, last access: 18 August 2021.
Kim, Y. J., Kim, H.-C., Han, D., Lee, S., and Im, J.: Prediction of monthly
Arctic sea ice concentrations using satellite and reanalysis data based on
convolutional neural networks, The Cryosphere, 14, 1083–1104,
https://doi.org/10.5194/tc-14-1083-2020, 2020. a, b, c
Kwok, R.: Arctic sea ice thickness, volume, and multiyear ice coverage: losses
and coupled variability (1958–2018), Environ. Res.
Lett., 13, 105005, https://doi.org/10.1088/1748-9326/aae3ec, 2018. a
Kwok, R. and Rothrock, D. A.: Decline in Arctic sea ice thickness from
submarine and ICESat records: 1958–2008, Geophys. Res. Lett., 36, 15,
https://doi.org/10.1029/2009GL039035, 2009. a
Lavergne, T., Eastwood, S., Teffah, Z., Schyberg, H., and Breivik, L.-A.: Sea
ice motion from low-resolution satellite sensors: An alternative method and
its validation in the Arctic, J. Geophys. Res.-Ocean., 115, C10,
https://doi.org/10.1029/2009JC005958, 2010. a
Lavergne, T., Sørensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S.,
Bell, L., Dybkjær, G., Eastwood, S., Gabarro, C., Heygster, G., Killie,
M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen,
L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration
climate data records, The Cryosphere, 13, 49–78,
https://doi.org/10.5194/tc-13-49-2019, 2019. a, b
Lee, S., Im, J., Kim, J., Kim, M., Shin, M., Kim, H.-C., and Quackenbush, L.:
Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using
Machine Learning-Based Lead Detection, Remote Sens., 8, 698,
https://doi.org/10.3390/rs8090698, 2016. a
Loken, E. D., Clark, A. J., McGovern, A., Flora, M., and Knopfmeier, K.:
Postprocessing Next-Day Ensemble Probabilistic Precipitation Forecasts Using
Random Forests, Weather Forecast., 34, 2017–2044,
https://doi.org/10.1175/WAF-D-19-0109.1, 2019. a, b
Mao, Y. and Sorteberg, A.: Improving radar based precipitation nowcasts with
machine learning using an approach based on random forest, Weather
Forecast., 35, 2461–2478, https://doi.org/10.1175/WAF-D-20-0080.1, 2020. a
Miao, X., Xie, H., Ackley, S. F., Perovich, D. K., and Ke, C.: Object-based
detection of Arctic sea ice and melt ponds using high spatial resolution
aerial photographs, Cold Reg. Sci. Technol., 119, 211–222,
https://doi.org/10.1016/j.coldregions.2015.06.014, 2015. a
OSISAF: Ocean and Sea Ice Satellite Application Facility, Version 2 of the global sea ice concentration climate data record, [data set], available at: ftp://osisaf.met.no/reprocessed/ice/conc/v2p0/, last access: 18 August 2021.
Olason, E. and Notz, D.: Drivers of variability in Arctic sea-ice drift speed,
J. Geophys. Res.-Ocean., 119, 5755–5775,
https://doi.org/10.1002/2014JC009897, 2014. a, b
Palerme, C.: Calibration_of_sea_ice_drift_forecasts, GitHub [data set], available at:
https://github.com/cyrilpalerme/Calibration_of_sea_ice_drift_forecasts/, last access: 18 August 2021.
Park, J.-W., Korosov, A. A., Babiker, M., Won, J.-S., Hansen, M. W., and Kim,
H.-C.: Classification of sea ice types in Sentinel-1 synthetic aperture radar
images, The Cryosphere, 14, 2629–2645, https://doi.org/10.5194/tc-14-2629-2020, 2020. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine learning in Python, J. Mach. Learn.
Res., 12, 2825–2830, 2011. a
Petty, A. A., Kurtz, N. T., Kwok, R., Markus, T., and Neumann, T. A.: Winter
Arctic Sea Ice Thickness From ICESat-2 Freeboards, J. Geophys.
Res.-Ocean., 125, e2019JC015764,
https://doi.org/10.1029/2019JC015764, 2020. a
Rabatel, M., Rampal, P., Carrassi, A., Bertino, L., and Jones, C. K. R. T.:
Impact of rheology on probabilistic forecasts of sea ice trajectories:
application for search and rescue operations in the Arctic, The Cryosphere,
12, 935–953, https://doi.org/10.5194/tc-12-935-2018, 2018. a, b
Rampal, P., Weiss, J., and Marsan, D.: Positive trend in the mean speed and
deformation rate of Arctic sea ice, 1979–2007, J. Geophys.
Res.-Ocean., 114, C5, https://doi.org/10.1029/2008JC005066, 2009. a, b, c
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
Sakov, P., Counillon, F., Bertino, L., Lisæter, K. A., Oke, P. R., and
Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North
Atlantic and Arctic, Ocean Sci., 8, 633–656, https://doi.org/10.5194/os-8-633-2012,
2012. a, b, c
Schweiger, A. J. and Zhang, J.: Accuracy of short-term sea ice drift forecasts
using a coupled ice-ocean model, J. Geophys. Res.-Ocean.,
120, 7827–7841, https://doi.org/10.1002/2015JC011273, 2015. a, b
Spreen, G., Kwok, R., and Menemenlis, D.: Trends in Arctic sea ice drift and
role of wind forcing: 1992–2009, Geophys. Res. Lett., 38, 19,
https://doi.org/10.1029/2011GL048970, 2011.
a, b
Strobl, C., Boulesteix, A.-L., Zeileis, A., and Hothorn, T.: Bias in random
forest variable importance measures: Illustrations, sources and a solution,
BMC Bioinform., 8, 1–21, 2007. a
Tandon, N. F., Kushner, P. J., Docquier, D., Wettstein, J. J., and Li, C.:
Reassessing Sea Ice Drift and Its Relationship to Long-Term Arctic Sea Ice
Loss in Coupled Climate Models, J. Geophys. Res.-Ocean., 123,
4338–4359, https://doi.org/10.1029/2017JC013697, 2018. a, b
Tschudi, M. A., Meier, W. N., and Stewart, J. S.: An enhancement to sea ice
motion and age products at the National Snow and Ice Data Center (NSIDC), The
Cryosphere, 14, 1519–1536, https://doi.org/10.5194/tc-14-1519-2020, 2020. a, b
Wang, L., Yuan, X., and Li, C.: Subseasonal forecast of Arctic sea ice
concentration via statistical approaches, Clim. Dynam., 52, 4953–4971,
2019. a
Williams, T., Korosov, A., Rampal, P., and Ólason, E.: Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F, The
Cryosphere Discuss., The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-154, 2019. a, b, c
Xie, J., Bertino, L., Counillon, F., Lisæter, K. A., and Sakov, P.: Quality
assessment of the TOPAZ4 reanalysis in the Arctic over the
period 1991–2013, Ocean Sci., 13, 123–144, https://doi.org/10.5194/os-13-123-2017,
2017. a, b
Yu, X., Rinke, A., Dorn, W., Spreen, G., Lüpkes, C., Sumata, H., and Gryanik,
V. M.: Evaluation of Arctic sea ice drift and its dependency on near-surface
wind and sea ice conditions in the coupled regional climate model
HIRHAM–NAOSIM, The Cryosphere, 14, 1727–1746,
https://doi.org/10.5194/tc-14-1727-2020, 2020. a, b, c
Short summary
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.
Methods have been developed for calibrating sea ice drift forecasts from an operational...
