On the multi-fractal scaling properties of sea ice deformation

Rampal, Pierre; Dansereau, Véronique; Ólason, Einar; Bouillon, Sylvain; Williams, Timothy; Korosov, Anton; Samaké, Abdoulaye

doi:10.5194/tc-13-2457-2019

Cited by 81 publications

(174 citation statements)

References 61 publications

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“…previous version of neXtSIM including an elasto-brittle (EB) rheology as described in Bouillon and Rampal (2015), showing good agreement with observed drift and concentration in particular. More recently, Rampal et al (2019) showed the ability to reproduce the characteristic multi-fractal scalings of deformation when using the current version of neXtSIM, which now includes the MEB rheology. Rampal et al (2019) update the paper of Rampal et al (2016), evaluating the model's improved performance when using the new MEB rheology.…”

Section: The Nextsim Modelmentioning

confidence: 99%

“…More recently, Rampal et al (2019) showed the ability to reproduce the characteristic multi-fractal scalings of deformation when using the current version of neXtSIM, which now includes the MEB rheology. Rampal et al (2019) update the paper of Rampal et al (2016), evaluating the model's improved performance when using the new MEB rheology. The key addition of the MEB rheology is a viscous dissipation of stress in 5 areas where the ice is damaged, which restricts the velocity and particularly convergence in those areas.…”

Section: The Nextsim Modelmentioning

confidence: 99%

“…The code has evolved from a serial (non-parallelised) Matlab code (used by eg. Rampal et al (2016)), to a parallelised C++ code (used by Rampal et al, 2019, andpresented by Samaké et al, 2017). We use this parallelised code.…”

Section: The Nextsim Modelmentioning

confidence: 99%

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Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F

Williams¹,

Korosov²,

Rampal³

et al. 2019

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The neXtSIM-F forecast system consists of a stand-alone sea ice model, neXtSIM, forced by the TOPAZ ocean forecast and the ECMWF atmospheric forecast, combined with daily data assimilation. It was tested for the northern winter of 2018 -2019 with different data being assimilated and was found to perform well. Despite drift not being assimilated in our system, we obtain quite good agreement between observations, comparing well to more sophisticated coupled ice-ocean forecast systems. The RMSE in drift speed is around 3 km/day for the first three days, climbing to about 4 km/day for the 5 next day or two; computing the RMSE in the total drift adds about 1 km/day to the error in speed. The drift bias remains close to zero over the whole period from Nov 2018 -Apr 2019. The neXtSIM-F forecast system assimilates OSISAF sea ice concentration products (both SSMI and AMSR2) and SMOS sea ice thickness by modifying the initial conditions daily and adding a compensating heat flux to prevent removed ice growing back too quickly. This greatly improved the agreement of these quantities with observations for the first 3 -4 days of the forecast.(Svalbard) after getting into trouble with sea ice. The crew had to be rescued by the Norwegian Coast Guard icebreaker K.V.Svalbard, who then had to drain 300kL of diesel from the damaged vessel. 2 Thus sea ice forecasting is becoming increasingly important. As well as search and rescue/accident prevention, other applications are optimized ship (icebreaker) routing based on forecasts (Kaleschke et al., 2016) and support of research activities -e.g. Schweiger and Zhang (2015) give an example of scheduling of high-resolution SAR images in order to follow the drift 5 of some ice-mass balance (IMB) buoys by using the PIOMAS/MIZMAS forecast from the University of Washington. The planned year-long drift of the Polarstern from September 2019 (part of the MOSAIC project) will also rely heavily on sea ice and weather forecasts.The sea ice forecast system neXtSIM-F is based on a stand-alone version of the sea ice model neXtSIM (Rampal et al., 2016. The dynamical core of this model is based on the Maxwell-Elasto-Brittle (MEB) rheology as developed for sea ice and 10 originally presented in Dansereau et al. (2016), which showed its capabilities at reproducing the main spatial characteristics of sea ice mechanics and deformation: strain localization and scaling (Marsan et al., 2004;Rampal et al., 2008; Stern and Lindsay, 2009). With the implementation of this rheology in neXtSIM (along with accompanying thermodynamics and general model infrastructure: Rampal et al., 2019), MEB was able to be assessed over longer simulations, and it was found that it could also reproduce the observed temporal deformation scalings, in addition to the spatial ones. In particular, the results show strong 15 multifractality, meaning that higher deformations are more localised in space and more intermittent in time than smaller ones.These properties have strong implications for things like distribution and size of lead op...

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Section: The Nextsim Modelmentioning

confidence: 99%

Section: The Nextsim Modelmentioning

confidence: 99%

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Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F

Williams¹,

Korosov²,

Rampal³

et al. 2019

Preprint

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“…In this study, we compare the properties of lead fraction statistics calculated from passive microwave satellite data to that simulated by a continuum sea ice model. The model used, neXtSIM, is the first model that was shown to reproduce the observed multifractality of sea ice deformation rates in both space and time (Rampal et al, 2019). 60 Section 2.1 presents briefly the model setup, as well as the data and the methodology of the scaling analysis performed in this study.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, the probability distribution functions 50 (PDF) of open water densities, floe sizes, and deformation rates share the common property of a "heavy tail" (Rothrock and Thorndike, 1984;Matsushita, 1985; Stern et al, 2018;Weiss, 2003;Marsan et al, 2004;Marcq and Weiss, 2012) that is a sign of scale-invariance. Deformation rates Hutchings et al, 2011;Bouillon and Rampal, 2015a;Rampal et al, 2019) and open water densities (Weiss and Marsan, 2004) have been shown to display multifractality in the space domain and, in the case of deformation rates, in the time domain also (Weiss and Dansereau, 2017;Rampal et al, 2019). This 55 2 https://doi.…”

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On the statistical properties of sea ice lead fraction and heat fluxes in the Arctic

Ólason¹,

Rampal²,

Dansereau³

2020

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Abstract. In this paper we explore several statistical properties of the observed and simulated Arctic sea-ice lead-fraction, as well as the statistics of simulated Arctic ocean-atmosphere heat fluxes. We first show that the probability density function (PDF) and the monofractal spatial scaling of the observed lead fraction in the Central Arctic are both well represented by our model, neXtSIM. Given that the heat flux through leads may be up to two orders of magnitude larger than that through unbroken ice we then explore the statistical properties (PDF and spatial scaling) of the heat fluxes simulated by neXtSIM. We demonstrate that the modelled heat fluxes present a multifractal scaling in the Central Arctic, where heat fluxes through leads dominate the high-flux tail of the PDF. In the Central Arctic, the high-flux tail of the PDF is dominated by an exponential decay, which we attribute to the presence of coastal polynyas. Finally, we show that the scaling of simulated lead fraction and heat fluxes depend weakly on the model resolution and discuss the role sub-grid scale parameterisations of the ice heterogeneity may have in improving this result.

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A new brittle rheology and numerical framework for large-scale sea-ice models

Ólason

Boutin

Korosov

et al. 2021

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On the multi-fractal scaling properties of sea ice deformation

Cited by 81 publications

References 61 publications

Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F

Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F

On the statistical properties of sea ice lead fraction and heat fluxes in the Arctic

A new brittle rheology and numerical framework for large-scale sea-ice models

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