# Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ

## TL;DR

This paper introduces and tests the EnKF-SQ data assimilation method for integrating semi-qualitative sea ice thickness measurements into a coupled ice-ocean model, demonstrating its effectiveness over simpler approaches.

## Contribution

The paper presents the EnKF-SQ method, a novel stochastic data assimilation technique specifically designed for semi-qualitative data, applied successfully to Arctic sea ice modeling.

## Key findings

- EnKF-SQ improves assimilation of range-limited ice thickness data.
- The method's performance is robust to tuning parameter variations.
- EnKF-SQ is effective despite non-linearities and non-Gaussian variables.

## Abstract

A newly introduced stochastic data assimilation method, the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) is applied to a realistic coupled ice-ocean model of the Arctic, the TOPAZ4 configuration, in a twin experiment framework. The method is shown to add value to range-limited thin ice thickness measurements, as obtained from passive microwave remote sensing, with respect to more trivial solutions like neglecting the out-of-range values or assimilating climatology instead. Some known properties inherent to the EnKF-SQ are evaluated: the tendency to draw the solution closer to the thickness threshold, the skewness of the resulting analysis ensemble and the potential appearance of outliers. The experiments show that none of these properties prove deleterious in light of the other sub-optimal characters of the sea ice data assimilation system used here (non-linearities, non-Gaussian variables, lack of strong coupling). The EnKF-SQ has a single tuning parameter that is adjusted for best performance of the system at hand. The sensitivity tests reveal that the results do not depend critically on the choice of this tuning parameter. The EnKF-SQ makes overall a valid approach for assimilating semi-qualitative observations into high-dimensional nonlinear systems.