Abstract
Abstract. The sea ice modeling community is progressing 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 (LKFs) – 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 dataset derived from the RADARSAT Geophysical Processor System (RGPS). One simulation uses a five-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, are reproduced by the model. Interannual and seasonal variations in 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 for it 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 addressed by specific parameterizations, for example, a damage parameter, a grounding scheme, and a Mohr–Coulombic yield curve.
Highlights
Current efforts in the sea ice modeling community push sea ice models to pan-Arctic lead-permitting sea ice simulations
Our linear kinematic features (LKFs) detection and tracking algorithms (Hutter et al, 2019a) split the detection of LKFs in sea ice deformation fields into three steps: (i) the algorithm classifies pixels with locally higher deformation rates as LKF pixels; (ii) separates the LKFs in a global binary map into minimal LKFsegments; and (iii) reconnects multiple minimal segments into individual LKFs based on a probability that is determined by their distance, orientation relative to each other, and difference in deformation rates
For mean sea ice deformation the simulation with an ice thickness distribution (ITD) leads to scaling exponents closer to the ones retrieved from RADARSAT Geophysical Processor System (RGPS), i.e., sea ice deformation is more strongly localized in space and time compared to the simulation without an ITD
Summary
Current efforts in the sea ice modeling community push sea ice models to pan-Arctic lead-permitting sea ice simulations. The emergence of deformation features, which can be identified as leads and pressure ridges, calls for a proper evaluation of model simulations against observations. This is challenging because ice mechanics are nonlinear and chaotic. A direct comparison of deformation fields has similar issues to comparing eddy resolving ocean model simulations to high-resolution satellite observations (Mourre et al, 2018). It should not be attempted when accurate initial conditions (e.g., obtained by data assimilation) are not available
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