Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean-sea ice modeling system

Abstract

Abstract. The accuracy of the initial state is very important for the quality of a forecast, and data assimilation is crucial for obtaining a best possible initial state. For many years, sea-ice concentration was the only parameter used for assimilation into numerical sea-ice models. Sea-ice concentration can easily be observed by satellites, and satellite observations provide a full Arctic coverage. During the last decade, an increasing number of sea-ice related variables have become available, these include sea-ice thickness and snow depth, which are both important parameters in the numerical sea-ice models. In the present study, a coupled ocean-sea-ice model is used to asses the assimilation impact of sea-ice thickness and snow depth on the model. The model system with the assimilation of these parameters is verified by comparison with a system assimilating only ice concentration and a system having no assimilation. The observations assimilated are sea ice concentration from the Ocean and Sea Ice Satellite Application facility, thin sea ice thickness from the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity mission, thick sea ice thickness from ESA’s CryoSat satellite, and a new snow depth product derived from the National Space Agency’s Advanced Microwave Scanning Radiometers (AMSR-E/AMSR-2) satellites. The model results are verified by comparing assimilated observations and independent observations of ice concentration from AMSR-E/AMSR-2, and ice thickness and snow depth from the IceBridge campaign. It is found that the assimilation of ice thickness strongly improves ice concentration, ice thickness and snow depth, while the snow observations have a positive effect on snow thickness and ice concentration. In our study, the seasonal forecast showed that assimilating snow depth lead to a worse estimation of sea-ice extent compared to the other assimilation systems, the other three gave similar results. The improvements due to assimilation were found to last for at least 3–4 months, possibly even longer.

Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application

“…A coupled ocean and sea-ice model (Kristensen et al, 2017) with a horizontal resolution of 2.5 km is used. This model is similar to that applied in Fritzner et al (2019).…”

“…In Metroms, we use the Deterministic Ensemble Kalman Filter (DEnKF; Sakov & Oke, 2008) for assimilation; the same setup was also used in Fritzner et al (2019). The DEnKF is a version of the Ensemble Kalman Filter (EnKF; Evensen, 1994;Burgers et al, 1998) which has been applied in a wide range of geophysical models (Houtekamer & Zhang, 2016).…”

“…In this section, the dynamical model is investigated. The dynamical model system is fundamentally the same as that applied in Fritzner et al (2019)…”

“…(Caya et al, 2010;K. Wang et al, 2013;Sakov et al, 2012;Buehner et al, 2013;Yang et al, 2014;Posey et al, 2015;Shlyaeva et al, 2016;Xie et al, 2016;Mu et al, 2018;Fritzner et al, 2018Fritzner et al, , 2019. Common for many of the Arctic sea-ice models used in these studies is that the model resolution is typically coarse, on the order of 10-20 km.…”

“…(Lisaeter et al, 2003;Sakov et al, 2012;K. Wang et al, 2013;Buehner et al, 2013;Posey et al, 2015;Fritzner et al, 2018Fritzner et al, , 2019. Sea-ice concentration (SIC) is by far the most used variable in sea-ice data assimilation studies, however other types of observations have become available in recent years.…”