Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic

Rabatel, Matthias; Rampal, Pierre; Carrassi, Alberto; Bertino, Laurent; Jones, Christopher K. R. T.

doi:10.5194/tc-12-935-2018

Cited by 35 publications

(60 citation statements)

References 59 publications

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“…The expected outcome is to improve the location and timing of lead and ridge formation, as well as the large-scale drift pattern itself. A framework to produce an ensemble forecast with neXtSIM-F is also being developed (as a follow-up of the work of Rabatel et al, 2018), with the ultimate aim of using the Ensemble Kalman Filter (EnKF) assimilation method. Work on using EnKF with models running on adaptive meshes (like neXtSIM) is being developed in parallel at NERSC (Aydogdu et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

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

Williams¹,

Korosov²,

Rampal³

et al. 2019

Preprint

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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: Discussionmentioning

confidence: 99%

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

Williams¹,

Korosov²,

Rampal³

et al. 2019

Preprint

Self Cite

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“…This makes neXtSIM a 20 powerful research numerical tool to study polar climate processes but also for operational applications as e.g. to assist search and rescue operations in ice-infested waters in the polar regions (Rabatel et al, 2018). neXtSIM is solved on a 2-dimensional unstructured triangular adaptive moving mesh based on a Lagrangian solver that propagates the mesh of the model in time along with the motion of the sea ice (Bouillon and Rampal, 2015).…”

Section: Data Assimilation For Adaptive Mesh Models: the Issuementioning

confidence: 99%

Data assimilation using adaptive, non-conservative, moving mesh models

Aydoğdu¹,

Carrassi²,

Guider³

et al. 2019

Preprint

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Numerical models solved on adaptive moving meshes have become increasingly prevalent in recent years. Motivating problems include the study of fluids in a Lagrangian frame and the presence of highly localized structures such as shock waves or interfaces. In the former case, Lagrangian solvers move the nodes of the mesh with the dynamical flow; in the latter, mesh resolution is increased in the proximity of the localized structure. Mesh adaptation can include remeshing, a procedure that adds or 5 removes mesh nodes according to specific rules reflecting constraints in the numerical solver. In this case, the number of mesh nodes will change during the integration and, as a result, the dimension of the model's state vector will not be conserved. This work presents a novel approach to the formulation of ensemble data assimilation for models with this underlying computational structure. The challenge lies in the fact that remeshing entails a different state space dimension across members of the ensemble, thus impeding the usual computation of consistent ensemble-based statistics. Our methodology adds one forward and one 10 backward mapping step before and after the EnKF analysis respectively. This mapping takes all the ensemble members onto a fixed, uniform, reference mesh where the EnKF analysis can be performed. We consider a high-(HR) and a low-resolution (LR) fixed uniform reference mesh, whose resolutions are determined by the remeshing tolerances. This way the reference meshes embed the model numerical constraints and also are upper and lower uniform meshes bounding the resolutions of the individual ensemble meshes. Numerical experiments are carried out using 1D prototypical models: Burgers and Kuramoto-15 Sivashinsky equations, and both Eulerian and Lagrangian synthetic observations. While the HR strategy generally outperforms that of LR, their skill difference can be reduced substantially by an optimal tuning of the data assimilation parameters. The LR case is appealing in high-dimensions because of its lower computational burden. Lagrangian observations are shown to be very effective in that fewer of them are able to keep the analysis error at a level comparable to the more numerous observers for the Eulerian case. This study is motivated by the development of suitable EnKF strategies for 2D models of the sea-ice that are 20 numerically solved on a Lagrangian mesh with remeshing.and Lagrangian (where observation locations move with the flow) observations. We test the strategy for two well-known PDEs:the viscous Burgers and Kuramoto-Sivashinsky equation, whose associated computational models we refer to as BGM and KSM, respectively. The Burgers' equation, which can be viewed as modeling a one-dimensional fluid, is a canonical example for which a localized structure, in this case a shock wave, develops and an adaptive moving mesh will get denser near the shock front. The Kuramoto-Sivashinsky equation exhibits chaotic behavior and this provides a natural test-bed for DA in a dynamical Nonlin. Processes Geophys. ...

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“…The variability of sea ice emerges as the result of many processes acting on different time scales. The energy budget involving incoming and outgoing radiation [13,14,15], the melting phase transition [16,17], the transport of water through ice porous structure [18,19,20,21], the rheology of internal stresses [22,23,24,25], the transport forced by couplings with ocean and atmosphere [26,27,28,29,30,31], all these make sea ice an extremely complex system and its theoretical modelling a challenge [1,2,33,34]. An important role in the ice-albedo feedback is played by the presence, on the ice surface, of melt ponds [32,35]: during summer both the snow cover and the upper surface of sea ice melt and, as a consequence, meltwater may accumulate in depressions of the ice topography (thus forming ponds).…”

Section: Introductionmentioning

confidence: 99%

Modelling Sea Ice and Melt Ponds Evolution: Sensitivity to Microscale Heat Transfer Mechanisms

Scagliarini¹,

Calzavarini²,

Mansutti³

et al. 2020

Springer INdAM Series

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We present a mathematical model describing the evolution of sea ice and meltwater during summer. The system is described by two coupled partial differential equations for the ice thickness h and pond depth w fields. We test the sensitivity of the model to variations of parameters controlling fluid-dynamic processes at the pond level, namely the variation of turbulent heat flux with pond depth and the lateral melting of ice enclosing a pond. We observe that different heat flux scalings determine different rates of total surface ablations, while the system is relatively robust in terms of probability distributions of pond surface areas. Finally, we study pond morphology in terms of fractal dimensions, showing that the role of lateral melting is minor, whereas there is evidence of an impact from the initial sea ice topography.

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Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic

Cited by 35 publications

References 59 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

Data assimilation using adaptive, non-conservative, moving mesh models

Modelling Sea Ice and Melt Ponds Evolution: Sensitivity to Microscale Heat Transfer Mechanisms

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