Toward Optimization of Rheology in Sea Ice Models through Data Assimilation J. N. STROH a Department of Atmospheric Science, University of Alaska Fairbanks, Fairbanks, Alaska GLEB PANTELEEV AND MAX YAREMCHUK Naval Research Laboratory, Stennis Space Center, Mississippi OCEANA FRANCIS Department of Civil and Environmental Engineering, University of Hawai‘i at Manoa, Honolulu, Hawaii RICHARD ALLARD Naval Research Laboratory, Stennis Space Center, Mississippi (Manuscript received 23 December 2018, in final form 27 June 2019) ABSTRACT Sea ice models that allow for deformation are primarily based on rheological formulations originally devel- oped in the 1970s. In both the original viscoplastic (VP) and elastic-VP schemes, the internal pressure term is modeled as a function of variable sea ice thickness and concentration with spatially and temporally constant empirical parameters for ice strength. This work considers a spatially variable extension of the rheology pa- rameters as well as wind stress in a one-dimensional VP sea ice data assimilation system. In regions of total ice cover, experiments that assimilate synthetic ice-state observations using variable rheological parameters show larger improvements than equivalent experiments using homogeneous parameters. For partially ice-covered regions where internal ice stresses are relatively unimportant, experiments assimilating synthetic sea ice velocity observations demonstrate reasonable reconstruction of spatially variable wind stresses. These results suggest practical benefits for sea ice–state reconstruction and forecasts by using sea ice velocity, thickness, and con- centration observations to optimize spatially varying rheological parameters and to improve wind stress forcing. 1. Introduction Sea ice models are an important component of any ice–ocean data assimilation (DA) system in the Arctic Ocean (AO) and the Southern Ocean. Currently, there are several DA systems that are widely applied to re- construct Arctic ice conditions in reanalysis or quasi- operational mode. For example, there are systems based on the MITgcm (Menemenlis et al. 2008; Heimbach 2008; Forget et al. 2015; Fenty et al. 2017), ROMS (Wang et al. 2013), HYCOM (Lisæter et al. 2007; Sakov et al. 2012), Pan-Arctic Ice Ocean Modeling and Assimilation Sys- tem (PIOMAS; Zhang and Rothrock 2003; Lindsay and Zhang 2006), and NEMO (Vancoppenolle et al. 2009; Massonnet et al. 2015). Several new methods of sea ice modeling have been proposed during the last decade in- cluding Lagrangian models (Rampal et al. 2016; Bouillon and Rampal 2015b) and finite element models (Danilov et al. 2015). In spite of these new technologies, practi- cally all ice–ocean DA systems implement sea ice models, such as the Los Alamos sea ice model (CICE; Hunke et al. 2010), which are based on the viscoplastic (VP) rheology proposed by Hibler (1979) or its asso- ciated elasto-VP (EVP) numerical scheme (Hunke and Dukowicz 1997). The essential advantage of these models is the efficient and relatively simple approxi- mation of sea ice dynamics in terms of the VP/EVP rheology that is based on a rigorous theoretical frame- work developed in the past century (Timoshenko and Goodier 1951; Goodier and Hodge 1958) with an adjunct practical stability analysis theory for sea ice a Current affiliation: Department of Bioengineering, University of Colorado Denver, Aurora, Colorado. Corresponding author: J. N. Stroh, jnstroh@alaska.edu DECEMBER 2019 STROH ET AL. 2365 DOI: 10.1175/JTECH-D-18-0239.1 Ó 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).