Feature-based comparison of sea-ice deformation in lead-resolving sea-ice simulations

Abstract

Abstract. The sea-ice modelling community progresses towards Pan-Arctic simulations that explicitly resolve leads in the simulated sea-ice cover. Evaluating these simulations against observations poses new challenges. A new feature-based evaluation of simulated deformation fields is introduced and the results are compared to a scaling analysis of sea ice deformation. Leads and pressure ridges – here combined into Linear Kinematic Features (LKF) – are detected and tracked automatically from deformation and drift data. LKFs in two Pan-Arctic sea-ice simulations with a horizontal grid spacing of 2 km are compared with an LKF data-set derived from the RADARSAT Geophysical Processor System (RGPS). One simulation uses a 5-class Ice Thickness Distribution (ITD). The simulated sea-ice deformation follows a multi-fractal spatial and temporal scaling as observed from RGPS. The heavy-tailed distribution of LKF lengths and the scale invariance of LKF curvature, which points to the self-similar nature of sea-ice deformation fields, is reproduced by the model. Interannual and seasonal variations of the number of LKFs, LKF densities, and LKF orientations in the ITD simulation are found to be consistent with RGPS observations. The lifetimes and growth rates follow a distribution with an exponential tail. The model overestimates the intersection angle of LKFs, which is attributed to the model's viscous-plastic rheology with an elliptical yield curve. In conclusion, the new feature-based analysis of LKF statistics is found to be useful for a comprehensive evaluation of simulated deformation features, which is required before the simulated features can be used with confidence in the context of climate studies. As such it complements the commonly used scaling analysis and provides new useful information for comparing deformation statistics. The ITD simulation is shown to reproduce LKFs sufficiently well to be used for studying the effect of directly resolved leads in climate simulations. The feature-based analysis of LKFs also identifies specific model deficits that may be address by specific parameterizations, for example, a damage parameter, a grounding scheme, and a Mohr-Coulombic yield curve.

On the multi-fractal scaling properties of sea ice deformation

“…We show here that the spatial scaling of sea ice deformation simulated in a realistic setup by neXtSIM holds down to the nominal resolution of the mesh, a result that is in agreement with previous analyses of the MEB model in idealized simulations (Dansereau et al, 2016) and realistic ones (Rampal et al, 2016). It means that neXtSIM does not need to be run at higher spatial resolution in order to reproduce the observed scalings, as, e.g., Hutter et al (2018) do when running at about 1 km resolution in order to resolve sea ice deformation at scale of about 10 km. Localizing the deformation at the nominal model resolution also means that related quantities, such as ridges, leads and linear kinematic features, are better resolved, although this is not investigated directly here.…”

“…Spreen et al (2017) and Bouchat and Tremblay (2017) have used scaling analysis to investigate their respective viscous plastic models, without going into the full details of a multi-fractal analysis or considering the temporal scaling. Hutter et al (2018), on the other hand, did a full multi-fractal analysis of the spatial and temporal scaling in a viscous plastic model. Their work shows that with a model running at ∼ 1 km resolution, they can reproduce reasonably good spatial scaling and multi-fractality down to the 10 km scale and up to 200 km; it is not shown how well the scaling holds down to the actual model resolution, and their spatial scaling does not hold beyond the 200 km scale.…”

Reassessing the Quality of Sea‐Ice Deformation Estimates Derived From the RADARSAT Geophysical Processor System and Its Impact on the Spatiotemporal Scaling Statistics

“…We now investigate the sensitivity of the deformation metrics (i.e., PDFs, spatiotemporal scaling, coupling, and structure functions) to the preprocessing method used to generate a regular 3‐day deformation field. To this end, we compare the deformation statistics for the RGPS deformation fields obtained using the Weighted‐Average preprocessing method (Hutter & Losch, 2019) and using a linear interpolation of the RGPS Lagrangian positions to regular 3‐day intervals (i.e., LinInterp data set‐ Hutchings et al., 2011; Itkin et al., 2017; Lindsay & Stern, 2003). Note that the time records of the LinInterp data set always have nonzero interpolated values for the whole January–March period, while the Weighted‐Average data set may have empty records due to insufficient coverage of the original data and our choice of time constraints to construct the data set (see section 3.2).…”

“…Here, we also showed that the deformation statistics are sensitive to the preprocessing method. To do so, we compared the deformation statistics obtained using two different preprocessing methods on the RGPS Lagrangian trajectories used in previous studies: (i) the Weighted‐Average method proposed by Hutter and Losch (2019) and (ii) a linear interpolation of the trajectories in time ( LinInterp method) as in, for example, Lindsay and Stern (2003), Hutchings et al. (2011), and Itkin et al.…”

“…Bouillon and Rampal (2015a) show that applying their smoothing procedure on triangle cells in fact reduces the spatial scaling exponents by about 50% compared to performing no smoothing, suggesting that boundary‐definition errors can have a significant impact on the sea‐ice spatiotemporal scaling statistics. However, the smoothing procedure suggested by Bouillon and Rampal (2015a) has typically not been used in recent studies investigating sea‐ice deformation statistics and the boundary‐definition error is usually not taken into account (e.g., Hutter & Losch, 2019; Rampal et al., 2019).…”