Pierre Rampal

Positive trend in the mean speed and deformation rate of Arctic sea ice, 1979–2007

Using buoy data from the International Arctic Buoy Program, we found that the sea ice mean speed has substantially increased over the last 29 years (+17% per decade for winter and +8.5% for summer). A strong seasonal dependence of the mean speed is also revealed, with a maximum in October and a minimum in April. The sea ice mean strain rate also increased significantly over the period (+51% per decade for winter and +52% for summer). We check that these increases in both sea ice mean speed and deformation rate are unlikely to be consequences of a stronger atmospheric forcing. Instead, they suggest that sea ice kinematics play a fundamental role in the albedo feedback loop and sea ice decline: increasing deformation means stronger fracturing, hence more lead opening, and therefore a decreasing albedo. This accelerates sea ice thinning in summer and delays refreezing in early winter, therefore decreasing the mechanical strength of the cover and allowing even more fracturing, larger drifting speed and deformation, and possibly a faster export of sea ice through the Fram Strait. The September minimum sea ice extent of 2007 might be a good illustration of this interplay between sea ice deformation and sea ice shrinking, as we found that for both winter 2007 and summer 2007 exceptionally large deformation rates affected the Arctic sea ice cover.

Space and Time Scaling Laws Induced by the Multiscale Fracturing of The Arctic Sea Ice Cover

Recent analyses of in-situ and satellite observations of arctic sea ice stresses and strain-rates revealed a highly intermittent and heterogeneous dynamics characterized by temporal and spatial scaling laws, a vision at odds with the classical smooth, fluid-like modelling framework of the sea ice cover. We propose here a simple multiscale fracturing model that explains the observed scaling properties of the deformation fields from the fractal properties of fracturing.

A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data

Sea ice drift strongly influences sea ice thickness distribution and indirectly controls air-sea ice-ocean interactions. Estimating sea ice drift over a large range of spatial and temporal scales is therefore needed to characterize the properties of sea ice dynamics and to better understand the ongoing changes of the climate in the polar regions. An efficient algorithm is developed for processing SAR data based on the combination of feature tracking (FT) and pattern matching (PM) techniques. The main advantage of the combination is that the FT rapidly provides the first guess estimate of ice drift in a few unevenly distributed keypoints, and PM accurately provides drift vectors on a regular or irregular grid. Thorough sensitivity analysis of the algorithm is performed, and optimal sets of parameters are suggested for retrieval of sea ice drift on various spatial and temporal scales. The algorithm has rather high accuracy (error is below 300 m) and high speed (the time for one image pair is 1 min), which opens new opportunities for studying sea ice kinematic processes. The ice drift can now be efficiently observed in the Lagrangian coordinate system on an irregular grid and, therefore, used for pointwise evaluation of the models running on unstructured meshes or for assimilation into Lagrangian models.

Parallel implementation of a Lagrangian-based model on an adaptive mesh in C++: Application to sea-ice

Abstract We present a parallel implementation framework for a new dynamic/thermodynamic sea-ice model, called neXtSIM , based on the Elasto–Brittle rheology and using an adaptive mesh. The spatial discretisation of the model is done using the finite-element method. The temporal discretisation is semi-implicit and the advection is achieved using either a pure Lagrangian scheme or an Arbitrary Lagrangian Eulerian scheme (ALE). The parallel implementation presented here focuses on the distributed-memory approach using the message-passing library MPI. The efficiency and the scalability of the parallel algorithms are illustrated by the numerical experiments performed using up to 500 processor cores of a cluster computing system. The performance obtained by the proposed parallel implementation of the neXtSIM code is shown being sufficient to perform simulations for state-of-the-art sea ice forecasting and geophysical process studies over geographical domain of several millions squared kilometers like the Arctic region.