Articles | Volume 18, issue 9
https://doi.org/10.5194/tc-18-4335-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-4335-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Sep 2024
Research article |
| 23 Sep 2024
| 23 Sep 2024
How many parameters are needed to represent polar sea ice surface patterns and heterogeneity?
Joseph Fogarty
Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
Mitchell Bushuk
Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, Princeton, NJ, USA
Linette Boisvert
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MA, USA
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Mohammad Allouche, Vladislav I. Sevostianov, Einara Zahn, Mark A. Zondlo, Nelson Luís Dias, Gabriel G. Katul, Jose D. Fuentes, and Elie Bou-Zeid
Atmos. Chem. Phys., 24, 9697–9711, https://doi.org/10.5194/acp-24-9697-2024, https://doi.org/10.5194/acp-24-9697-2024, 2024
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The significance of surface–atmosphere exchanges of aerosol species to atmospheric composition is underscored by their rising concentrations that are modulating the Earth's climate and having detrimental consequences for human health and the environment. Estimating these exchanges, using field measurements, and offering alternative models are the aims here. Limitations in measuring some species misrepresent their actual exchanges, so our proposed models serve to better quantify them.
Einara Zahn and Elie Bou-Zeid
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-272, https://doi.org/10.5194/essd-2024-272, 2024
Preprint under review for ESSD
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Quantifying water and CO2 exchanges through transpiration, evaporation, photosynthesis, and soil respiration are essential to understand how ecosystems function. We implemented five methods to estimate these fluxes over a five-year period across 47 sites. This is the first dataset representing such a large spatial and temporal coverage of soil and plant exchanges, and it has many potentials applications such as to examine the response of ecosystem to weather extremes and climate change.
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.
Sean Horvath, Linette Boisvert, Chelsea Parker, Melinda Webster, Patrick Taylor, and Robyn Boeke
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-297, https://doi.org/10.5194/tc-2021-297, 2021
Preprint withdrawn
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Arctic sea ice has been experiencing a dramatic decline since the late 1970s. A database is presented that combines satellite observations with daily sea ice parcel drift tracks. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states. This has multiple applications for the scientific community that can shed light on the atmosphere-snow-sea ice interactions in the changing Arctic environment.
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.
Naika Meili, Gabriele Manoli, Paolo Burlando, Elie Bou-Zeid, Winston T. L. Chow, Andrew M. Coutts, Edoardo Daly, Kerry A. Nice, Matthias Roth, Nigel J. Tapper, Erik Velasco, Enrique R. Vivoni, and Simone Fatichi
Geosci. Model Dev., 13, 335–362, https://doi.org/10.5194/gmd-13-335-2020, https://doi.org/10.5194/gmd-13-335-2020, 2020
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We developed a novel urban ecohydrological model (UT&C v1.0) that is able to account for the effects of different plant types on the urban climate and hydrology, as well as the effects of the urban environment on plant well-being and performance. UT&C performs well when compared against energy flux measurements in three cities in different climates (Singapore, Melbourne, Phoenix) and can be used to assess urban climate mitigation strategies that aim at increasing or changing urban green cover.
Alek A. Petty, Melinda Webster, Linette Boisvert, and Thorsten Markus
Geosci. Model Dev., 11, 4577–4602, https://doi.org/10.5194/gmd-11-4577-2018, https://doi.org/10.5194/gmd-11-4577-2018, 2018
Dana R. Caulton, Qi Li, Elie Bou-Zeid, Jeffrey P. Fitts, Levi M. Golston, Da Pan, Jessica Lu, Haley M. Lane, Bernhard Buchholz, Xuehui Guo, James McSpiritt, Lars Wendt, and Mark A. Zondlo
Atmos. Chem. Phys., 18, 15145–15168, https://doi.org/10.5194/acp-18-15145-2018, https://doi.org/10.5194/acp-18-15145-2018, 2018
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Mobile laboratory measurements have been widely used to quantify methane emissions from point sources such as oil and gas wells, but the emission uncertainties are poorly constrained. We designed a hierarchical measurement strategy to sample natural gas emissions in the Marcellus Shale play based upon high-resolution modeling of select sites. Our study quantifies the largest sources of error with this approach and provides guidance on how to best implement mobile laboratory sampling protocols.
Alek A. Petty, Julienne C. Stroeve, Paul R. Holland, Linette N. Boisvert, Angela C. Bliss, Noriaki Kimura, and Walter N. Meier
The Cryosphere, 12, 433–452, https://doi.org/10.5194/tc-12-433-2018, https://doi.org/10.5194/tc-12-433-2018, 2018
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There was significant scientific and media attention surrounding Arctic sea ice in 2016, due primarily to the record-warm air temperatures and low sea ice conditions observed at the start of the year. Here we quantify and assess the record-low monthly sea ice cover in winter, spring and fall, and the lack of record-low sea ice conditions in summer. We explore the primary drivers of these monthly sea ice states and explore the implications for improved summer sea ice forecasting.
Julienne C. Stroeve, John R. Mioduszewski, Asa Rennermalm, Linette N. Boisvert, Marco Tedesco, and David Robinson
The Cryosphere, 11, 2363–2381, https://doi.org/10.5194/tc-11-2363-2017, https://doi.org/10.5194/tc-11-2363-2017, 2017
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As the sea ice has declined strongly in recent years there has been a corresponding increase in Greenland melting. While both are likely a result of changes in atmospheric circulation patterns that favor summer melt, this study evaluates whether or not sea ice reductions around the Greenland ice sheet are having an influence on Greenland summer melt through enhanced sensible and latent heat transport from open water areas onto the ice sheet.
Related subject area
Discipline: Sea ice | Subject: Numerical Modelling
Exploring non-Gaussian sea ice characteristics via observing system simulation experiments
Past and future of the Arctic sea ice in High-Resolution Model Intercomparison Project (HighResMIP) climate models
Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic
Using Icepack to reproduce ice mass balance buoy observations in landfast ice: improvements from the mushy-layer thermodynamics
Understanding the influence of ocean waves on Arctic sea ice simulation: a modeling study with an atmosphere–ocean–wave–sea ice coupled model
Sea ice cover in the Copernicus Arctic Regional Reanalysis
Smoothed particle hydrodynamics implementation of the standard viscous–plastic sea-ice model and validation in simple idealized experiments
Phase-field models of floe fracture in sea ice
The effect of partial dissolution on sea-ice chemical transport: a combined model–observational study using poly- and perfluoroalkylated substances (PFASs)
Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology
Impact of atmospheric forcing uncertainties on Arctic and Antarctic sea ice simulations in CMIP6 OMIP models
Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework
Wave-triggered breakup in the marginal ice zone generates lognormal floe size distributions: a simulation study
Exploring the capabilities of electrical resistivity tomography to study subsea permafrost
Sea ice floe size: its impact on pan-Arctic and local ice mass and required model complexity
A probabilistic seabed–ice keel interaction model
The effect of changing sea ice on wave climate trends along Alaska's central Beaufort Sea coast
Arctic sea ice anomalies during the MOSAiC winter 2019/20
Edge displacement scores
Toward a method for downscaling sea ice pressure for navigation purposes
The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 1: How to obtain sea ice brightness temperatures at 6.9 GHz from climate model output
The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation
Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations
Wave energy attenuation in fields of colliding ice floes – Part 1: Discrete-element modelling of dissipation due to ice–water drag
Validation of the sea ice surface albedo scheme of the regional climate model HIRHAM–NAOSIM using aircraft measurements during the ACLOUD/PASCAL campaigns
Simulating intersection angles between conjugate faults in sea ice with different viscous–plastic rheologies
IcePAC – a probabilistic tool to study sea ice spatio-temporal dynamics: application to the Hudson Bay area
New insight from CryoSat-2 sea ice thickness for sea ice modelling
Investigating future changes in the volume budget of the Arctic sea ice in a coupled climate model
Medium-range predictability of early summer sea ice thickness distribution in the East Siberian Sea based on the TOPAZ4 ice–ocean data assimilation system
Christopher Riedel and Jeffrey Anderson
The Cryosphere, 18, 2875–2896, https://doi.org/10.5194/tc-18-2875-2024, https://doi.org/10.5194/tc-18-2875-2024, 2024
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Accurate sea ice conditions are crucial for quality sea ice projections, which have been connected to rapid warming over the Arctic. Knowing which observations to assimilate into models will help produce more accurate sea ice conditions. We found that not assimilating sea ice concentration led to more accurate sea ice states. The methods typically used to assimilate observations in our models apply assumptions to variables that are not well suited for sea ice because they are bounded variables.
Julia Selivanova, Doroteaciro Iovino, and Francesco Cocetta
The Cryosphere, 18, 2739–2763, https://doi.org/10.5194/tc-18-2739-2024, https://doi.org/10.5194/tc-18-2739-2024, 2024
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Climate models show differences in sea ice representation in comparison to observations. Increasing the model resolution is a recognized way to improve model realism and obtain more reliable future projections. We find no strong impact of resolution on sea ice representation; it rather depends on the analysed variable and the model used. By 2050, the marginal ice zone (MIZ) becomes a dominant feature of the Arctic ice cover, suggesting a shift to a new regime similar to that in Antarctica.
Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, Guillaume Boutin, and Einar Ólason
The Cryosphere, 18, 1791–1815, https://doi.org/10.5194/tc-18-1791-2024, https://doi.org/10.5194/tc-18-1791-2024, 2024
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This paper focuses on predicting Arctic-wide sea-ice thickness using surrogate modeling with deep learning. The model has a predictive power of 12 h up to 6 months. For this forecast horizon, persistence and daily climatology are systematically outperformed, a result of learned thermodynamics and advection. Consequently, surrogate modeling with deep learning proves to be effective at capturing the complex behavior of sea ice.
Mathieu Plante, Jean-François Lemieux, L. Bruno Tremblay, Adrienne Tivy, Joey Angnatok, François Roy, Gregory Smith, Frédéric Dupont, and Adrian K. Turner
The Cryosphere, 18, 1685–1708, https://doi.org/10.5194/tc-18-1685-2024, https://doi.org/10.5194/tc-18-1685-2024, 2024
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We use a sea ice model to reproduce ice growth observations from two buoys deployed on coastal sea ice and analyze the improvements brought by new physics that represent the presence of saline liquid water in the ice interior. We find that the new physics with default parameters degrade the model performance, with overly rapid ice growth and overly early snow flooding on top of the ice. The performance is largely improved by simple modifications to the ice growth and snow-flooding algorithms.
Chao-Yuan Yang, Jiping Liu, and Dake Chen
The Cryosphere, 18, 1215–1239, https://doi.org/10.5194/tc-18-1215-2024, https://doi.org/10.5194/tc-18-1215-2024, 2024
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We present a new atmosphere–ocean–wave–sea ice coupled model to study the influences of ocean waves on Arctic sea ice simulation. Our results show (1) smaller ice-floe size with wave breaking increases ice melt, (2) the responses in the atmosphere and ocean to smaller floe size partially reduce the effect of the enhanced ice melt, (3) the limited oceanic energy is a strong constraint for ice melt enhancement, and (4) ocean waves can indirectly affect sea ice through the atmosphere and the ocean.
Yurii Batrak, Bin Cheng, and Viivi Kallio-Myers
The Cryosphere, 18, 1157–1183, https://doi.org/10.5194/tc-18-1157-2024, https://doi.org/10.5194/tc-18-1157-2024, 2024
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Atmospheric reanalyses provide consistent series of atmospheric and surface parameters in a convenient gridded form. In this paper, we study the quality of sea ice in a recently released regional reanalysis and assess its added value compared to a global reanalysis. We show that the regional reanalysis, having a more complex sea ice model, gives an improved representation of sea ice, although there are limitations indicating potential benefits in using more advanced approaches in the future.
Oreste Marquis, Bruno Tremblay, Jean-François Lemieux, and Mohammed Islam
The Cryosphere, 18, 1013–1032, https://doi.org/10.5194/tc-18-1013-2024, https://doi.org/10.5194/tc-18-1013-2024, 2024
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We developed a standard viscous–plastic sea-ice model based on the numerical framework called smoothed particle hydrodynamics. The model conforms to the theory within an error of 1 % in an idealized ridging experiment, and it is able to simulate stable ice arches. However, the method creates a dispersive plastic wave speed. The framework is efficient to simulate fractures and can take full advantage of parallelization, making it a good candidate to investigate sea-ice material properties.
Huy Dinh, Dimitrios Giannakis, Joanna Slawinska, and Georg Stadler
The Cryosphere, 17, 3883–3893, https://doi.org/10.5194/tc-17-3883-2023, https://doi.org/10.5194/tc-17-3883-2023, 2023
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We develop a numerical method to simulate the fracture in kilometer-sized chunks of floating ice in the ocean. Our approach uses a mathematical model that balances deformation energy against the energy required for fracture. We study the strength of ice chunks that contain random impurities due to prior damage or refreezing and what types of fractures are likely to occur. Our model shows that crack direction critically depends on the orientation of impurities relative to surrounding forces.
Max Thomas, Briana Cate, Jack Garnett, Inga J. Smith, Martin Vancoppenolle, and Crispin Halsall
The Cryosphere, 17, 3193–3201, https://doi.org/10.5194/tc-17-3193-2023, https://doi.org/10.5194/tc-17-3193-2023, 2023
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A recent study showed that pollutants can be enriched in growing sea ice beyond what we would expect from a perfectly dissolved chemical. We hypothesise that this effect is caused by the specific properties of the pollutants working in combination with fluid moving through the sea ice. To test our hypothesis, we replicate this behaviour in a sea-ice model and show that this type of modelling can be applied to predicting the transport of chemicals with complex behaviour in sea ice.
Tobias Sebastian Finn, Charlotte Durand, Alban Farchi, Marc Bocquet, Yumeng Chen, Alberto Carrassi, and Véronique Dansereau
The Cryosphere, 17, 2965–2991, https://doi.org/10.5194/tc-17-2965-2023, https://doi.org/10.5194/tc-17-2965-2023, 2023
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We combine deep learning with a regional sea-ice model to correct model errors in the sea-ice dynamics of low-resolution forecasts towards high-resolution simulations. The combined model improves the forecast by up to 75 % and thereby surpasses the performance of persistence. As the error connection can additionally be used to analyse the shortcomings of the forecasts, this study highlights the potential of combined modelling for short-term sea-ice forecasting.
Xia Lin, François Massonnet, Thierry Fichefet, and Martin Vancoppenolle
The Cryosphere, 17, 1935–1965, https://doi.org/10.5194/tc-17-1935-2023, https://doi.org/10.5194/tc-17-1935-2023, 2023
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This study provides clues on how improved atmospheric reanalysis products influence sea ice simulations in ocean–sea ice models. The summer ice concentration simulation in both hemispheres can be improved with changed surface heat fluxes. The winter Antarctic ice concentration and the Arctic drift speed near the ice edge and the ice velocity direction simulations are improved with changed wind stress. The radiation fluxes and winds in atmospheric reanalyses are crucial for sea ice simulations.
Guillaume Boutin, Einar Ólason, Pierre Rampal, Heather Regan, Camille Lique, Claude Talandier, Laurent Brodeau, and Robert Ricker
The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, https://doi.org/10.5194/tc-17-617-2023, 2023
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Sea ice cover in the Arctic is full of cracks, which we call leads. We suspect that these leads play a role for atmosphere–ocean interactions in polar regions, but their importance remains challenging to estimate. We use a new ocean–sea ice model with an original way of representing sea ice dynamics to estimate their impact on winter sea ice production. This model successfully represents sea ice evolution from 2000 to 2018, and we find that about 30 % of ice production takes place in leads.
Nicolas Guillaume Alexandre Mokus and Fabien Montiel
The Cryosphere, 16, 4447–4472, https://doi.org/10.5194/tc-16-4447-2022, https://doi.org/10.5194/tc-16-4447-2022, 2022
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On the fringes of polar oceans, sea ice is easily broken by waves. As small pieces of ice, or floes, are more easily melted by the warming waters than a continuous ice cover, it is important to incorporate these floe sizes in climate models. These models simulate climate evolution at the century scale and are built by combining specialised modules. We study the statistical distribution of floe sizes under the impact of waves to better understand how to connect sea ice modules to wave modules.
Mauricio Arboleda-Zapata, Michael Angelopoulos, Pier Paul Overduin, Guido Grosse, Benjamin M. Jones, and Jens Tronicke
The Cryosphere, 16, 4423–4445, https://doi.org/10.5194/tc-16-4423-2022, https://doi.org/10.5194/tc-16-4423-2022, 2022
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We demonstrate how we can reliably estimate the thawed–frozen permafrost interface with its associated uncertainties in subsea permafrost environments using 2D electrical resistivity tomography (ERT) data. In addition, we show how further analyses considering 1D inversion and sensitivity assessments can help quantify and better understand 2D ERT inversion results. Our results illustrate the capabilities of the ERT method to get insights into the development of the subsea permafrost.
Adam William Bateson, Daniel L. Feltham, David Schröder, Yanan Wang, Byongjun Hwang, Jeff K. Ridley, and Yevgeny Aksenov
The Cryosphere, 16, 2565–2593, https://doi.org/10.5194/tc-16-2565-2022, https://doi.org/10.5194/tc-16-2565-2022, 2022
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Numerical models are used to understand the mechanisms that drive the evolution of the Arctic sea ice cover. The sea ice cover is formed of pieces of ice called floes. Several recent studies have proposed variable floe size models to replace the standard model assumption of a fixed floe size. In this study we show the need to include floe fragmentation processes in these variable floe size models and demonstrate that model design can determine the impact of floe size on size ice evolution.
Frédéric Dupont, Dany Dumont, Jean-François Lemieux, Elie Dumas-Lefebvre, and Alain Caya
The Cryosphere, 16, 1963–1977, https://doi.org/10.5194/tc-16-1963-2022, https://doi.org/10.5194/tc-16-1963-2022, 2022
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In some shallow seas, grounded ice ridges contribute to stabilizing and maintaining a landfast ice cover. A scheme has already proposed where the keel thickness varies linearly with the mean thickness. Here, we extend the approach by taking into account the ice thickness and bathymetry distributions. The probabilistic approach shows a reasonably good agreement with observations and previous grounding scheme while potentially offering more physical insights into the formation of landfast ice.
Kees Nederhoff, Li Erikson, Anita Engelstad, Peter Bieniek, and Jeremy Kasper
The Cryosphere, 16, 1609–1629, https://doi.org/10.5194/tc-16-1609-2022, https://doi.org/10.5194/tc-16-1609-2022, 2022
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Diminishing sea ice is impacting waves across the Arctic region. Recent work shows the effect of the sea ice on offshore waves; however, effects within the nearshore are less known. This study characterizes the wave climate in the central Beaufort Sea coast of Alaska. We show that the reduction of sea ice correlates strongly with increases in the average and extreme waves. However, found trends deviate from offshore, since part of the increase in energy is dissipated before reaching the shore.
Klaus Dethloff, Wieslaw Maslowski, Stefan Hendricks, Younjoo J. Lee, Helge F. Goessling, Thomas Krumpen, Christian Haas, Dörthe Handorf, Robert Ricker, Vladimir Bessonov, John J. Cassano, Jaclyn Clement Kinney, Robert Osinski, Markus Rex, Annette Rinke, Julia Sokolova, and Anja Sommerfeld
The Cryosphere, 16, 981–1005, https://doi.org/10.5194/tc-16-981-2022, https://doi.org/10.5194/tc-16-981-2022, 2022
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Sea ice thickness anomalies during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) winter in January, February and March 2020 were simulated with the coupled Regional Arctic climate System Model (RASM) and compared with CryoSat-2/SMOS satellite data. Hindcast and ensemble simulations indicate that the sea ice anomalies are driven by nonlinear interactions between ice growth processes and wind-driven sea-ice transports, with dynamics playing a dominant role.
Arne Melsom
The Cryosphere, 15, 3785–3796, https://doi.org/10.5194/tc-15-3785-2021, https://doi.org/10.5194/tc-15-3785-2021, 2021
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This study presents new methods to assess how well observations of sea ice expansion are reproduced by results from models. The aim is to provide information about the quality of forecasts for changes in the sea ice extent to operators in or near ice-infested waters. A test using 2 years of model results demonstrates the practical applicability and usefulness of the methods that are presented.
Jean-François Lemieux, L. Bruno Tremblay, and Mathieu Plante
The Cryosphere, 14, 3465–3478, https://doi.org/10.5194/tc-14-3465-2020, https://doi.org/10.5194/tc-14-3465-2020, 2020
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Sea ice pressure poses great risk for navigation; it can lead to ship besetting and damages. Sea ice forecasting systems can predict the evolution of pressure. However, these systems have low spatial resolution (a few km) compared to the dimensions of ships. We study the downscaling of pressure from the km-scale to scales relevant for navigation. We find that the pressure applied on a ship beset in heavy ice conditions can be markedly larger than the pressure predicted by the forecasting system.
Clara Burgard, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe
The Cryosphere, 14, 2369–2386, https://doi.org/10.5194/tc-14-2369-2020, https://doi.org/10.5194/tc-14-2369-2020, 2020
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The high disagreement between observations of Arctic sea ice makes it difficult to evaluate climate models with observations. We investigate the possibility of translating the model state into what a satellite could observe. We find that we do not need complex information about the vertical distribution of temperature and salinity inside the ice but instead are able to assume simplified distributions to reasonably translate the simulated sea ice into satellite
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Clara Burgard, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe
The Cryosphere, 14, 2387–2407, https://doi.org/10.5194/tc-14-2387-2020, https://doi.org/10.5194/tc-14-2387-2020, 2020
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The high disagreement between observations of Arctic sea ice inhibits the evaluation of climate models with observations. We develop a tool that translates the simulated Arctic Ocean state into what a satellite could observe from space in the form of brightness temperatures, a measure for the radiation emitted by the surface. We find that the simulated brightness temperatures compare well with the observed brightness temperatures. This tool brings a new perspective for climate model evaluation.
Nils Hutter and Martin Losch
The Cryosphere, 14, 93–113, https://doi.org/10.5194/tc-14-93-2020, https://doi.org/10.5194/tc-14-93-2020, 2020
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Sea ice is composed of a multitude of floes that constantly deform due to wind and ocean currents and thereby form leads and pressure ridges. These features are visible in the ice as stripes of open-ocean or high-piled ice. High-resolution sea ice models start to resolve these deformation features. In this paper we present two simulations that agree with satellite data according to a new evaluation metric that detects deformation features and compares their spatial and temporal characteristics.
Agnieszka Herman, Sukun Cheng, and Hayley H. Shen
The Cryosphere, 13, 2887–2900, https://doi.org/10.5194/tc-13-2887-2019, https://doi.org/10.5194/tc-13-2887-2019, 2019
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Sea ice interactions with waves are extensively studied in recent years, but mechanisms leading to wave energy attenuation in sea ice remain poorly understood. Close to the ice edge, processes contributing to dissipation include collisions between ice floes and turbulence generated under the ice due to velocity differences between ice and water. This paper analyses details of those processes both theoretically and by means of a numerical model.
Evelyn Jäkel, Johannes Stapf, Manfred Wendisch, Marcel Nicolaus, Wolfgang Dorn, and Annette Rinke
The Cryosphere, 13, 1695–1708, https://doi.org/10.5194/tc-13-1695-2019, https://doi.org/10.5194/tc-13-1695-2019, 2019
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The sea ice surface albedo parameterization of a coupled regional climate model was validated against aircraft measurements performed in May–June 2017 north of Svalbard. The albedo parameterization was run offline from the model using the measured parameters surface temperature and snow depth to calculate the surface albedo and the individual fractions of the ice surface subtypes. An adjustment of the variables and additionally accounting for cloud cover reduced the root-mean-squared error.
Damien Ringeisen, Martin Losch, L. Bruno Tremblay, and Nils Hutter
The Cryosphere, 13, 1167–1186, https://doi.org/10.5194/tc-13-1167-2019, https://doi.org/10.5194/tc-13-1167-2019, 2019
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We study the creation of fracture in sea ice plastic models. To do this, we compress an ideal piece of ice of 8 km by 25 km. We use two different mathematical expressions defining the resistance of ice. We find that the most common one is unable to model the fracture correctly, while the other gives better results but brings instabilities. The results are often in opposition with ice granular nature (e.g., sand) and call for changes in ice modeling.
Charles Gignac, Monique Bernier, and Karem Chokmani
The Cryosphere, 13, 451–468, https://doi.org/10.5194/tc-13-451-2019, https://doi.org/10.5194/tc-13-451-2019, 2019
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The IcePAC tool is made to estimate the probabilities of specific sea ice conditions based on historical sea ice concentration time series from the EUMETSAT OSI-409 product (12.5 km grid), modelled using the beta distribution and used to build event probability maps, which have been unavailable until now. Compared to the Canadian ice service atlas, IcePAC showed promising results in the Hudson Bay, paving the way for its usage in other regions of the cryosphere to inform stakeholders' decisions.
David Schröder, Danny L. Feltham, Michel Tsamados, Andy Ridout, and Rachel Tilling
The Cryosphere, 13, 125–139, https://doi.org/10.5194/tc-13-125-2019, https://doi.org/10.5194/tc-13-125-2019, 2019
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This paper uses sea ice thickness data (CryoSat-2) to identify and correct shortcomings in simulating winter ice growth in the widely used sea ice model CICE. Adding a model of snow drift and using a different scheme for calculating the ice conductivity improve model results. Sensitivity studies demonstrate that atmospheric winter conditions have little impact on winter ice growth, and the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season.
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.
Takuya Nakanowatari, Jun Inoue, Kazutoshi Sato, Laurent Bertino, Jiping Xie, Mio Matsueda, Akio Yamagami, Takeshi Sugimura, Hironori Yabuki, and Natsuhiko Otsuka
The Cryosphere, 12, 2005–2020, https://doi.org/10.5194/tc-12-2005-2018, https://doi.org/10.5194/tc-12-2005-2018, 2018
Short summary
Short summary
Medium-range predictability of early summer sea ice thickness in the East Siberian Sea was examined, based on TOPAZ4 forecast data. Statistical examination indicates that the estimate drops abruptly at 4 days, which is related to dynamical process controlled by synoptic-scale atmospheric fluctuations such as an Arctic cyclone. For longer lead times (> 4 days), the thermodynamic melting process takes over, which represents most of the remaining prediction.
Cited articles
Allouche, M., Katul, G. G., Fuentes, J. D., and Bou-Zeid, E.: Probability law of turbulent kinetic energy in the atmospheric surface layer, Phys. Rev. Fluids, 6, 074601, https://doi.org/10.1103/PhysRevFluids.6.074601, 2021. a
Allouche, M., Bou-Zeid, E., and Iipponen, J.: Unsteady Land-Sea Breeze Circulations in the Presence of a Synoptic Pressure Forcing, ESS Open Archive [preprint], https://doi.org/10.22541/essoar.170542134.41279506/v1, 2023b. a
Anderson, W., Barros, J. M., Christensen, K. T., and Awasthi, A.: Numerical and experimental study of mechanisms responsible for turbulent secondary flows in boundary layer flows over spanwise heterogeneous roughness, J. Fluid Mech., 768, 316–347, https://doi.org/10.1017/jfm.2015.91, 2015. a
Andreas, E. L., Horst, T. W., Grachev, A. A., Persson, P. O. G., Fairall, C. W., Guest, P. S., and Jordan, R. E.: Parametrizing turbulent exchange over summer sea ice and the marginal ice zone, Q. J. Roy. Meteor. Soc., 136, 927–943, https://doi.org/10.1002/qj.618, 2010. a
Au-Boehm, C., Tsamados, M., Manescu, P., and Takao, S.: ARISGAN: Extreme Super-Resolution of Arctic Surface Imagery using Generative Adversarial Networks, Front. Remote Sens. [preprint], 5, 1417417, https://doi.org/10.3389/frsen.2024.1417417, 2024. a
Baidya Roy, S.: Impact of land use/land cover change on regional hydrometeorology in Amazonia, J. Geophys. Res., 107, 8037, https://doi.org/10.1029/2000JD000266, 2002. a
Bates, N. R., Moran, S. B., Hansell, D. A., and Mathis, J. T.: An increasing CO2 sink in the Arctic Ocean due to sea–ice loss, Geophys. Res. Lett., 33, L23609, https://doi.org/10.1029/2006gl027028, 2006. a
Blackadar, A. K.: Boundary Layer Wind Maxima and Their Significance for the Growth of Nocturnal Inversions, B. Am. Meteorol. Soc., 38, 283–290, https://doi.org/10.1175/1520-0477-38.5.283, 1957. a
Boudreault, L.-É., Dupont, S., Bechmann, A., and Dellwik, E.: How Forest Inhomogeneities Affect the Edge Flow, Bound.-Lay. Meteorol., 162, 375–400, https://doi.org/10.1007/s10546-016-0202-5, 2017. a
Bourassa, M. A., Gille, S. T., Bitz, C., Carlson, D., Cerovecki, I., Clayson, C. A., Cronin, M. F., Drennan, W. M., Fairall, C. W., Hoffman, R. N., Magnusdottir, G., Pinker, R. T., Renfrew, I. A., Serreze, M., Speer, K., Talley, L. D., and Wick, G. A.: High-Latitude Ocean and Sea Ice Surface Fluxes: Challenges for Climate Research, B. Am. Meteorol. Soc., 94, 403–423, https://doi.org/10.1175/BAMS-D-11-00244.1, 2013. a
Bou-Zeid, E., Meneveau, C., and Parlange, M. B.: Large-eddy simulation of neutral atmospheric boundary layer flow over heterogeneous surfaces: Blending height and effective surface roughness: LES OF NEUTRAL ATMOSPHERIC BOUNDARY LAYER FLOW, Water Resour. Res., 40, W02505, https://doi.org/10.1029/2003WR002475, 2004. a, b
Bou-Zeid, E., Meneveau, C., and Parlange, M.: A scale-dependent Lagrangian dynamic model for large eddy simulation of complex turbulent flows, Phys. Fluids, 17, 025105, https://doi.org/10.1063/1.1839152, 2005. a, b, c, d
Bou-Zeid, E., Parlange, M. B., and Meneveau, C.: On the Parameterization of Surface Roughness at Regional Scales, J. Atmos. Sci., 64, 216–227, https://doi.org/10.1175/JAS3826.1, 2007. a
Bou-Zeid, E., Anderson, W., Katul, G. G., and Mahrt, L.: The Persistent Challenge of Surface Heterogeneity in Boundary-Layer Meteorology: A Review, Bound.-Lay. Meteorol, 177, 227–245, https://doi.org/10.1007/s10546-020-00551-8, 2020. a, b
Bradley, E. F.: A micrometeorological study of velocity profiles and surface drag in the region modified by a change in surface roughness, Q. J. Roy. Meteor. Soc., 94, 361–379, https://doi.org/10.1002/qj.49709440111, 1968. a
Brasseur, J. G. and Wei, T.: Designing large-eddy simulation of the turbulent boundary layer to capture law-of-the-wall scaling, Phys. Fluids, 22, 1–21, https://doi.org/10.1063/1.3319073, 2010. a
Brunsell, N. A., Mechem, D. B., and Anderson, M. C.: Surface heterogeneity impacts on boundary layer dynamics via energy balance partitioning, Atmos. Chem. Phys., 11, 3403–3416, https://doi.org/10.5194/acp-11-3403-2011, 2011. a
Brutsaert, W.: Hydrology: An Introduction, Cambridge University Press, https://doi.org/10.1017/CBO9780511808470, 2005. a, b
Casagrande, F., Stachelski, L., and De Souza, R. B.: Assessment of Antarctic sea ice area and concentration in Coupled Model Intercomparison Project Phase 5 and Phase 6 models, Int. J. Climatol., 43, 1314–1332, https://doi.org/10.1002/joc.7916, 2023. a
Computational and Information Systems Laboratory: Cheyenne: HPE/SGI ICE XA System (University Community Computing), Boulder, CO, National Center for Atmospheric Research, https://doi.org/10.5065/D6RX99HX, 2019. a
Courault, D., Drobinski, P., Brunet, Y., Lacarrere, P., and Talbot, C.: Impact of surface heterogeneity on a buoyancy-driven convective boundary layer in light winds, Bound.-Lay. Meteorol., 124, 383–403, https://doi.org/10.1007/s10546-007-9172-y, 2007. a
Cressie, N.: Statistics for Spatial Data - Revised Edition, Wiley Series in Probability and Statistics, John Wiley & Sons, Inc., ISBN 978-1-119-11515-1, https://doi.org/10.1002/9781119115151, 1993. a
Crosman, E. T. and Horel, J. D.: Sea and lake breezes: A review of numerical studies, Bound.-Lay. Meteorol., 137, 1–29, 2010. a
Cushman, S. A., McGarigal, K., and Neel, M. C.: Parsimony in landscape metrics: Strength, universality, and consistency, Ecol. Indic., 8, 691–703, https://doi.org/10.1016/j.ecolind.2007.12.002, 2008. a
de Vrese, P., Schulz, J.-P., and Hagemann, S.: On the Representation of Heterogeneity in Land-Surface–Atmosphere Coupling, Bound.-Lay. Meteorol., 160, 157–183, https://doi.org/10.1007/s10546-016-0133-1, 2016. a, b
Docquier, D. and Koenigk, T.: Observation-based selection of climate models projects Arctic ice-free summers around 2035, Commun. Earth Environ., 2, 144, https://doi.org/10.1038/s43247-021-00214-7, 2021. a
Dumont, D.: Marginal ice zone dynamics: history, definitions and research perspectives, Philos. T. Roy. Soc. A, 380, 20210253, https://doi.org/10.1098/rsta.2021.0253, 2022. a, b
Elvidge, A. D., Renfrew, I. A., Weiss, A. I., Brooks, I. M., Lachlan-Cope, T. A., and King, J. C.: Observations of surface momentum exchange over the marginal ice zone and recommendations for its parametrisation, Atmos. Chem. Phys., 16, 1545–1563, https://doi.org/10.5194/acp-16-1545-2016, 2016. a, b
Elvidge, A. D., Renfrew, I. A., Brooks, I. M., Srivastava, P., Yelland, M. J., and Prytherch, J.: Surface Heat and Moisture Exchange in the Marginal Ice Zone: Observations and a New Parameterization Scheme for Weather and Climate Models, J. Geophys. Res.-Atmos., 126, e2021JD034827, https://doi.org/10.1029/2021JD034827, 2021. a
Esau, I. N.: Amplification of turbulent exchange over wide Arctic leads: Large–eddy simulation study, J. Geophys. Res.-Atmos., 112, D08109, https://doi.org/10.1029/2006jd007225, 2007. a
Essery, R. L. H., Best, M. J., Betts, R. A., Cox, P. M., and Taylor, C. M.: Explicit Representation of Subgrid Heterogeneity in a GCM Land Surface Scheme, J. Hydrometeorol., 4, 530–543, https://doi.org/10.1175/1525-7541(2003)004<0530:EROSHI>2.0.CO;2, 2003. a
Feltham, D. L.: Sea Ice Rheology, Annu. Rev. Fluid Mech., 40, 91–112, https://doi.org/10.1146/annurev.fluid.40.111406.102151, 2008. a
Fetterer, F. and Untersteiner, N.: Observations of melt ponds on Arctic sea ice, J. Geophys. Res., 103, 24821–24835, https://doi.org/10.1029/98JC02034, 1998. a
Fetterer, F., Wilds, S., and Sloan, J.: Arctic Sea Ice Melt Pond Statistics and Maps, 1999–2001, Version 1, National Snow & Ice Data Center [data set], https://doi.org/10.7265/N5PK0D32, 2008. a, b, c
Finnigan, J. J. and Shaw, R. H.: Double-averaging methodology and its application to turbulent flow in and above vegetation canopies, Acta Geophysica, 56, 534–561, https://doi.org/10.2478/s11600-008-0034-x, 2008. a
Fogarty, J. and Bou-Zeid, E.: Large-Eddy Simulation and Statistical Metric Results for Patterned Sea Ice Surfaces, Princeton University [data set], https://doi.org/10.34770/5x2y-5485, 2023b. a
Fogarty, J., Bou-Zeid, E., Bushuk, M., Calaf, M., Allouche, M., and Ghannam, K.: Numerical Simulations of Satellite-Sensed Surface Maps in the Marginal Ice Zone, https://doi.org/10.22541/essoar.172251979.90440727/v1, 2024. a
Ghannam, K. and Bou-Zeid, E.: Baroclinicity and directional shear explain departures from the logarithmic wind profile, Q. J. Roy. Meteor. Soc., 147, 443–464, https://doi.org/10.1002/qj.3927, 2021. a
Gryschka, M., Gryanik, V. M., Lüpkes, C., Mostafa, Z., Sühring, M., Witha, B., and Raasch, S.: Turbulent Heat Exchange Over Polar Leads Revisited: A Large Eddy Simulation Study, J. Geophys. Res.-Atmos., 128, e2022JD038236, https://doi.org/10.1029/2022JD038236, 2023. a, b
Herman, A., Wenta, M., and Cheng, S.: Sizes and Shapes of Sea Ice Floes Broken by Waves–A Case Study From the East Antarctic Coast, Front. Earth Sci., 9, 655977, https://doi.org/10.3389/feart.2021.655977, 2021. a
Horvat, C.: Marginal ice zone fraction benchmarks sea ice and climate model skill, Nat. Commun., 12, 2221, https://doi.org/10.1038/s41467-021-22004-7, 2021. a
Huang, H.-Y., Margulis, S. A., Chu, C. R., and Tsai, H.-C.: Investigation of the impacts of vegetation distribution and evaporative cooling on synthetic urban daytime climate using a coupled LES-LSM model, Hydrol. Process., 25, 1574–1586, https://doi.org/10.1002/hyp.7919, 2011. a
Huang, J. and Bou-Zeid, E.: Turbulence and Vertical Fluxes in the Stable Atmospheric Boundary Layer. Part I: A Large-Eddy Simulation Study, J. Atmos. Sci., 70, 1513–1527, https://doi.org/10.1175/JAS-D-12-0167.1, 2013. a
Hwang, B. and Wang, Y.: Multi-scale satellite observations of Arctic sea ice: new insight into the life cycle of the floe size distribution, Philos. T. Roy. Soc. A, 380, 20210259, https://doi.org/10.1098/rsta.2021.0259, 2022. a
Ibidoja, O. J., Shan, F. P., Sulaiman, J., and Ali, M. K. M.: Detecting heterogeneity parameters and hybrid models for precision farming, J. Big Data, 10, 130, https://doi.org/10.1186/s40537-023-00810-8, 2023. a
Jaeger, J. A.: Landscape division, splitting index, and effective mesh size: new measures of landscape fragmentation, Landscape Ecol., 15, 115–130, https://doi.org/10.1023/A:1008129329289, 2000. a
Kleissl, J., Kumar, V., Meneveau, C., and Parlange, M. B.: Numerical study of dynamic Smagorinsky models in large-eddy simulation of the atmospheric boundary layer: Validation in stable and unstable conditions, Water Resour. Res., 42, W06D10, https://doi.org/10.1029/2005WR004685, 2006. a
Kumar, V., Kleissl, J., Meneveau, C., and Parlange, M. B.: Large-eddy simulation of a diurnal cycle of the atmospheric boundary layer: Atmospheric stability and scaling issues: LES OF A DIURNAL CYCLE OF THE ABL, Water Resour. Res., 42, https://doi.org/10.1029/2005WR004651, 2006. a
Kwok, R.: Declassified high-resolution visible imagery for Arctic sea ice investigations: An overview, Remote Sens. Environ., 142, 44–56, https://doi.org/10.1016/j.rse.2013.11.015, 2014. a
Li, H. and Reynolds, J. F.: A Simulation Experiment to Quantify Spatial Heterogeneity in Categorical Maps, Ecology, 75, 2446, https://doi.org/10.2307/1940898, 1994. a
Li, H. and Reynolds, J. F.: On Definition and Quantification of Heterogeneity, Oikos, 73, 280, https://doi.org/10.2307/3545921, 1995. a
Li, Q. and Bou-Zeid, E.: Contrasts between momentum and scalar transport over very rough surfaces, J. Fluid Mech., 880, 32–58, https://doi.org/10.1017/jfm.2019.687, 2019. a, b, c
Li, Q., Bou-Zeid, E., Anderson, W., Grimmond, S., and Hultmark, M.: Quality and reliability of LES of convective scalar transfer at high Reynolds numbers, Int. J. Heat Mass Trans., 102, 959–970, https://doi.org/10.1016/j.ijheatmasstransfer.2016.06.093, 2016. a
Liu, C., Yang, Y., Liao, X., Cao, N., Liu, J., Ou, N., Allan, R. P., Jin, L., Chen, N., and Zheng, R.: Discrepancies in Simulated Ocean Net Surface Heat Fluxes over the North Atlantic, Adv. Atmos. Sci., 39, 1941–1955, https://doi.org/10.1007/s00376-022-1360-7, 2022. a
Lu, J., Nazarian, N., Hart, M. A., Krayenhoff, E. S., and Martilli, A.: Representing the effects of building height variability on urban canopy flow, Q. J. Roy. Meteor. Soc., 150, 46–67, https://doi.org/10.1002/qj.4584, 2023. a
Lüpkes, C., Gryanik, V. M., Witha, B., Gryschka, M., Raasch, S., and Gollnik, T.: Modeling convection over arctic leads with LES and a non-eddy-resolving microscale model, J. Geophys. Res.-Oceans, 113, C09028, https://doi.org/10.1029/2007JC004099, 2008. a, b, c
Lüpkes, C., Gryanik, V. M., Hartmann, J., and Andreas, E. L.: A parametrization, based on sea ice morphology, of the neutral atmospheric drag coefficients for weather prediction and climate models, J. Geophys. Res.-Atmos., 117, D13112, https://doi.org/10.1029/2012JD017630, 2012. a, b, c
Mandelbrot, B.: How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension, Science, 156, 636–638, https://doi.org/10.1126/science.156.3775.636, 1967. a
Mandelbrot, B. B.: The fractal geometry of nature, Freeman, San Francisco, CA, https://cds.cern.ch/record/98509 (last access: 5 December 2023), 1982. a
Margairaz, F., Pardyjak, E. R., and Calaf, M.: Surface Thermal Heterogeneities and the Atmospheric Boundary Layer: The Relevance of Dispersive Fluxes, Bound.-Lay. Meteorol., 175, 369–395, https://doi.org/10.1007/s10546-020-00509-w, 2020. a
Maronga, B. and Raasch, S.: Large-Eddy Simulations of Surface Heterogeneity Effects on the Convective Boundary Layer During the LITFASS-2003 Experiment, Bound.-Lay. Meteorol., 146, 17–44, https://doi.org/10.1007/s10546-012-9748-z, 2013. a
McGarigal, K. and Marks, B. J.: FRAGSTATS: spatial pattern analysis program for quantifying landscape structure., Tech. Rep. PNW-GTR-351, U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland, OR [data set], https://doi.org/10.2737/PNW-GTR-351, 1995. a, b, c, d
Michaelis, J. and Lüpkes, C.: The Impact of Lead Patterns on Mean Profiles of Wind, Temperature, and Turbulent Fluxes in the Atmospheric Boundary Layer over Sea Ice, Atmosphere, 13, 148, https://doi.org/10.3390/atmos13010148, 2022. a
Michaelis, J., Lüpkes, C., Zhou, X., Gryschka, M., and Gryanik, V. M.: Influence of Lead width on the Turbulent Flow Over Sea Ice Leads: Modeling and Parametrization, J. Geophys. Res.-Atmos., 125, e2019JD031996, https://doi.org/10.1029/2019JD031996, 2020. a
Michaelis, J., Lüpkes, C., Schmitt, A. U., and Hartmann, J.: Modelling and parametrization of the convective flow over leads in sea ice and comparison with airborne observations, Q. J. Roy. Meteor. Soc., 147, 914–943, https://doi.org/10.1002/qj.3953, 2021. a, b
Miles, J.: Tolerance and Variance Inflation Factor, John Wiley & Sons, Ltd, ISBN 9781118445112, https://doi.org/10.1002/9781118445112.stat06593, 2014. a
Miller, N. B., Shupe, M. D., Cox, C. J., Noone, D., Persson, P. O. G., and Steffen, K.: Surface energy budget responses to radiative forcing at Summit, Greenland, The Cryosphere, 11, 497–516, https://doi.org/10.5194/tc-11-497-2017, 2017. a
Moltchanov, S., Bohbot-Raviv, Y., Duman, T., and Shavit, U.: Canopy edge flow: A momentum balance analysis, Water Resour. Res., 51, 2081–2095, https://doi.org/10.1002/2014WR015397, 2015. a
Momen, M. and Bou-Zeid, E.: Large-Eddy Simulations and Damped-Oscillator Models of the Unsteady Ekman Boundary Layer, J. Atmos. Sci., 73, 25–40, https://doi.org/10.1175/JAS-D-15-0038.1, 2016. a
Moody, A. and Woodcock, C.: Scale-dependent errors in the estimation of land-cover proportions: Implications for global land-cover datasets, Photogramm. Eng. Remote Sens., 60, 585–594, 1994. a
Moody, A. and Woodcock, C. E.: The influence of scale and the spatial characteristics of landscapes on land-cover mapping using remote sensing, Landscape Ecol., 10, 363–379, https://doi.org/10.1007/BF00130213, 1995. a
Myksvoll, M. S., Britt Sandø, A., Tjiputra, J., Samuelsen, A., Çaǧlar Yumruktepe, V., Li, C., Mousing, E. A., Bettencourt, J. P., and Ottersen, G.: Key physical processes and their model representation for projecting climate impacts on subarctic Atlantic net primary production: A synthesis, Prog. Oceanogr., 217, 103084, https://doi.org/10.1016/j.pocean.2023.103084, 2023. a
Nilsson, E. D., Rannik, É., Swietlicki, E., Leck, C., Aalto, P. P., Zhou, J., and Norman, M.: Turbulent aerosol fluxes over the Arctic Ocean: 2. Wind-driven sources from the sea, J. Geophys. Res.-Atmos., 106, 32139–32154, https://doi.org/10.1029/2000JD900747, 2001. a
Notz, D. and Community, S.: Arctic Sea Ice in CMIP6, Geophys. Res. Lett., 47, e2019GL086749, https://doi.org/10.1029/2019GL086749, 2020. a
Notz, D. and Stroeve, J.: The Trajectory Towards a Seasonally Ice-Free Arctic Ocean, Current Climate Change Reports, 4, 407–416, https://doi.org/10.1007/s40641-018-0113-2, 2018. a
Omidvar, H., Bou-Zeid, E., Li, Q., Mellado, J.-P., and Klein, P.: Plume or bubble? Mixed-convection flow regimes and city-scale circulations, J. Fluid Mech., 897, A5, https://doi.org/10.1017/jfm.2020.360, 2020. a
Orszag, S. A.: On the Elimination of Aliasing in Finite-Difference Schemes by Filtering High-Wavenumber Components, J. Atmos. Sci., 28, 1074, https://doi.org/10.1175/1520-0469(1971)028<1074:OTEOAI>2.0.CO;2, 1971. a
O'Brien, R. M.: A Caution Regarding Rules of Thumb for Variance Inflation Factors, Quality & Quantity, 41, 673–690, https://doi.org/10.1007/s11135-006-9018-6, 2007. 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
Persson, P. O. G., Fairall, C. W., Andreas, E. L., Guest, P. S., and Perovich, D. K.: Measurements near the Atmospheric Surface Flux Group tower at SHEBA: Near-surface conditions and surface energy budget, J. Geophys. Res.-Oceans, 107, SHE 21-1–SHE 21-35, https://doi.org/10.1029/2000JC000705, 2002. a
Pickett, S. T. A. and Cadenasso, M. L.: Landscape Ecology: Spatial Heterogeneity in Ecological Systems, Science, 269, 331–334, https://doi.org/10.1126/science.269.5222.331, 1995. a
Piomelli, U. and Balaras, E.: Wall-Layer Models for Large-Eddy Simulations, Annu. Rev. Fluid Mech., 34, 349–374, https://doi.org/10.1146/annurev.fluid.34.082901.144919, 2002. a
Porson, A., Steyn, D. G., and Schayes, G.: Sea-breeze scaling from numerical model simulations, part II: Interaction between the sea breeze and slope flows, Bound.-Lay. Meteorol., 122, 31–41, 2007. a
Ramudu, E., Gelderloos, R., Yang, D., Meneveau, C., and Gnanadesikan, A.: Large Eddy Simulation of Heat Entrainment Under Arctic Sea Ice, J. Geophys. Res.-Oceans, 123, 287–304, https://doi.org/10.1002/2017jc013267, 2018. a
Raupach, M. R. and Shaw, R. H.: Averaging Procedures for Flow Within Vegetation Canopies, Bound.-Lay. Meteorol., 22, 79–90, https://doi.org/10.1007/BF00128057, 1982. a, b
Ren, H., Zhang, C., and Zhao, X.: Numerical simulations on the fracture of a sea ice floe induced by waves, Appl. Ocean Res., 108, 102527, https://doi.org/10.1016/j.apor.2021.102527, 2021. a
Riitters, K. H., O'Neill, R. V., Hunsaker, C. T., Wickham, J. D., Yankee, D. H., Timmins, S. P., Jones, K. B., and Jackson, B. L.: A factor analysis of landscape pattern and structure metrics, Landscape Ecol., 10, 23–39, https://doi.org/10.1007/BF00158551, 1995. a, b
Rosenblum, E. and Eisenman, I.: Faster Arctic Sea Ice Retreat in CMIP5 than in CMIP3 due to Volcanoes, J. Climate, 29, 9179–9188, https://doi.org/10.1175/JCLI-D-16-0391.1, 2016. a
Rosenblum, E. and Eisenman, I.: Sea Ice Trends in Climate Models Only Accurate in Runs with Biased Global Warming, J. Climate, 30, 6265–6278, https://doi.org/10.1175/JCLI-D-16-0455.1, 2017. a
Salesky, S. T., Calaf, M., and Anderson, W.: Unstable turbulent channel flow response to spanwise-heterogeneous heat fluxes: Prandtl's secondary flow of the third kind, J. Fluid Mech., 934, A46, https://doi.org/10.1017/jfm.2022.15, 2022. a
Seabold, S. and Perktold, J.: statsmodels: Econometric and statistical modeling with python, in: 9th Python in Science Conference, 28 June–23 July 2010, Austin Texas, 2010. a
Šímová, P. and Gdulová, K.: Landscape indices behavior: A review of scale effects, Appl. Geogr., 34, 385–394, https://doi.org/10.1016/j.apgeog.2012.01.003, 2012. a
Stoll, R., Gibbs, J. A., Salesky, S. T., Anderson, W., and Calaf, M.: Large-Eddy Simulation of the Atmospheric Boundary Layer, Bound.-Lay. Meteorol., 177, 541–581, https://doi.org/10.1007/s10546-020-00556-3, 2020. a
Stroeve, J., Holland, M. M., Meier, W., Scambos, T., and Serreze, M.: Arctic sea ice decline: Faster than forecast, Geophys. Res. Lett., 34, L09501, https://doi.org/10.1029/2007GL029703, 2007. a
Strong, C., Foster, D., Cherkaev, E., Eisenman, I., and Golden, K. M.: On the Definition of Marginal Ice Zone Width, J. Atmos. Ocean. Tech., 34, 1565–1584, https://doi.org/10.1175/JTECH-D-16-0171.1, 2017. a
Taylor, P. C., Hegyi, B. M., Boeke, R. C., and Boisvert, L. N.: On the Increasing Importance of Air-Sea Exchanges in a Thawing Arctic: A Review, Atmosphere, 9, 41, https://doi.org/10.3390/atmos9020041, 2018. a
Tetzlaff, A., Lüpkes, C., and Hartmann, J.: Aircraft-based observations of atmospheric boundary-layer modification over Arctic leads, Q. J. Roy. Meteor. Soc., 141, 2839–2856, https://doi.org/10.1002/qj.2568, 2015. a
Tseng, Y.-H., Meneveau, C., and Parlange, M. B.: Modeling Flow around Bluff Bodies and Predicting Urban Dispersion Using Large Eddy Simulation, Environ. Sci. Technol., 40, 2653–2662, https://doi.org/10.1021/es051708m, 2006. a
Wang, Y., Holt, B., Erick Rogers, W., Thomson, J., and Shen, H. H.: Wind and wave influences on sea ice floe size and leads in the Beaufort and Chukchi Seas during the summer–fall transition 2014, J. Geophys. Res.-Oceans, 121, 1502–1525, https://doi.org/10.1002/2015JC011349, 2016. a
Wenta, M. and Herman, A.: The influence of the spatial distribution of leads and ice floes on the atmospheric boundary layer over fragmented sea ice, Ann. Glaciol., 59, 213–230, https://doi.org/10.1017/aog.2018.15, 2018. a
Wenta, M. and Herman, A.: Area-Averaged Surface Moisture Flux over Fragmented Sea Ice: Floe Size Distribution Effects and the Associated Convection Structure within the Atmospheric Boundary Layer, Atmosphere, 10, 654, https://doi.org/10.3390/atmos10110654, 2019. a
Willingham, D., Anderson, W., Christensen, K. T., and Barros, J. M.: Turbulent boundary layer flow over transverse aerodynamic roughness transitions: Induced mixing and flow characterization, Phys. Fluids, 26, 025111, https://doi.org/10.1063/1.4864105, 2014. a
Wood, N. and Mason, P.: The influence of static stability on the effective roughness lengths for momentum and heat transfer, Q. J. Roy. Meteor. Soc., 117, 1025–1056, https://doi.org/10.1002/qj.49711750108, 1991. a
Short summary
We hypothesize that using a broad set of surface characterization metrics for polar sea ice surfaces will lead to more accurate representations in general circulation models. However, the first step is to identify the minimum set of metrics required. We show via numerical simulations that sea ice surface patterns can play a crucial role in determining boundary layer structures. We then statistically analyze a set of high-resolution sea ice surface images to obtain this minimal set of parameters.
We hypothesize that using a broad set of surface characterization metrics for polar sea ice...
