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As the sea‐ice modeling community is shifting to advanced numerical frameworks, developing new sea‐ice rheologies, and increasing model spatial resolution, ubiquitous deformation features in the Arctic sea ice are now being resolved by sea‐ice models. Initiated at the Forum for Arctic Modeling and Observational Synthesis, the Sea Ice Rheology Experiment (SIREx) aims at evaluating state‐of‐the‐art sea‐ice models using existing and new metrics to understand how the simulated deformation fields are affected by different representations of sea‐ice physics (rheology) and by model configuration. Part 1 of the SIREx analysis is concerned with evaluation of the statistical distribution and scaling properties of sea‐ice deformation fields from 35 different simulations against those from the RADARSAT Geophysical Processor System (RGPS). For the first time, the viscous‐plastic (and the elastic‐viscous‐plastic variant), elastic‐anisotropic‐plastic, and Maxwell‐elasto‐brittle rheologies are compared in a single study. We find that both plastic and brittle sea‐ice rheologies have the potential to reproduce the observed RGPS deformation statistics, including multi‐fractality. Model configuration (e.g., numerical convergence, atmospheric representation, spatial resolution) and physical parameterizations (e.g., ice strength parameters and ice thickness distribution) both have effects as important as the choice of sea‐ice rheology on the deformation statistics. It is therefore not straightforward to attribute model performance to a specific rheological framework using current deformation metrics. In light of these results, we further evaluate the statistical properties of simulated Linear Kinematic Features in a SIREx Part 2 companion paper. Plain Language Summary: The ice in the Arctic Ocean is not continuous: it is broken into individual pieces of ice (floes). As the winds and ocean currents continually move these ice floes, they get piled up together or pushed away from each other, forming regions of increased ice thickness (ridges) or regions of open water (leads). These leads and ridges (ice deformations) are important features of the Arctic pack ice because they control the amount of energy that can be exchanged between the atmosphere and the ocean. Current climate models cannot simulate individual ice floes and their deformations. Instead, various methods are used to represent the movement and deformation of the Arctic sea‐ice cover. The goal of the Sea Ice Rheology Experiment (SIREx) is to compare these different methods and evaluate the ability of a large number of sea‐ice models to reproduce observed sea‐ice deformations from satellite imagery. SIREx is divided in two parts. In Part 1 (this study), we evaluate how the intensity of ice deformations varies in space and time. In Part 2 (companion paper), we track and evaluate the occurrence of specific deformation features. With this work, we show how to improve sea‐ice models for realistic simulations of sea‐ice deformations. Key Points: Power law scaling and multi‐fractality of deformations in space and time can be achieved by both plastic and brittle sea‐ice rheologies Scaling statistics of simulated sea‐ice deformation fields depend on the model configuration and physical parameterizations Finite‐difference plastic models need to be run at higher resolution than observations to agree with the observed deformation statistics

We incorporate a physically derived parameterization of gravity drainage, in terms of a convective upwelling velocity, into a one-dimensional, thermodynamic sea- ice model of the kind currently used in coupled climate models. Our parameterization uses a local Rayleigh number to represent the important feedback between ice salinity, porosity, permeability and desalination rate. It allows us to determine salt fluxes from sea ice and the corresponding evolution of the bulk salinity of the ice, in contrast to older, established models that prescribe the ice salinity. This improves the predictive power of climate models in terms of buoyancy fluxes to the polar oceans, and also the thermal properties of sea ice, which depend on its salinity. We analyze the behaviour of exist- ing fixed-salinity models, elucidate the physics by which changing salinity affects ice growth and compare against our dynamic-salinity model, both in terms of laboratory experiments and also deep-ocean calculations. These comparisons explain why the direct effect of ice salinity on growth is relatively small (though not always negligible, and sometimes dif- ferent from previous studies), and also highlight substantial differences in the qualita- tive pattern and quantitative magnitude of salt fluxes into the polar oceans. Our study is particularly relevant to growing first-year ice, when gravity drainage is the dominant mechanism by which ice desalinates. We expect that our dynamic model, which respects the underlying physics of brine drainage, should be more robust to changes in polar cli- mate and more responsive to rapid changes in oceanic and atmospheric forcing.

Southern Ocean sea ice can exhibit widespread flooding and subsequent snow-ice formation, due to relatively thick snow covers compared to the total ice thickness. Considerable subkilometer scale variability in snow and ice thickness causes poorly constrained uncertainties in determining the amount of flooding that occurs. Using datasets of snow depth and ice thickness acquired in the Weddell Sea during austral winter 2013 (AWECS campaign) from three floes, we demonstrate large spatial variability of a factor 10 and 5 for snow and combined snow and ice thickness, respectively. The temporal evolution after the floe visit was recorded by automatic weather station and ice mass balance buoys. Using a physics-based, multi-layer snow/sea ice model in a one-dimensional and distributed mode to simulate the thermodynamic processes, we show that the distributed simulations, modeling flooding across the entire heterogeneous floe, produced vastly different amounts of flooding than one-dimensional single point simulations. Three times the flooding is produced in the one-dimensional simulation for the buoy location than distributed (floe-averaged) simulations. The latter is in close agreement with buoy observations. The results suggest that using point observations or one-dimensional simulations to extrapolate processes on the floe-scale can overestimate the amount of flooding and snow-ice formation.