Satellite-Based Data Assimilation System for the Initialization of Arctic Sea Ice Concentration and Thickness Using CICE5

• Published in: Frontiers in Climate
• Main Authors: Jeong-Gil Lee, Yoo-Geun Ham
• Format: Article in Journal/Newspaper
• Language: English
• Published: Frontiers Media S.A. 2022
• Subjects: Arctic sea ice; data assimilation; satellite observation; EnOI; CICE5; geo; envir; Arctic; Arctic Ocean; Sea ice
• Online Access: https://doi.org/10.3389/fclim.2022.797733; https://doaj.org/article/36ab24b2e85343b6a2d99550e731c1b6

The satellite-derived sea ice concentration (SIC) and thickness (SIT) observation over the Arctic region are assimilated by implementing the Ensemble Optimal Interpolation (EnOI) into the Community Ice CodE version 5.1.2 (CICE5) model. The assimilated observations are derived from Special Sensor Microwave Imager/Sounder (SSMIS) for the SIC, European Space Agency's (ESA) Soil Moisture and Ocean Salinity mission (SMOS) for the SIT of the thin ice, and ESA's CryoSat-2 satellite for the SIT of the thick ice. The SIC, and SIT observations are assimilated during 2000–2019, and 2011–2019, respectively. The quality of the reanalysis is evaluated by comparing with observation and modeled data. Three data assimilation experiments are conducted: noDA without data assimilation, Ver1 with SIC assimilation, and Ver2 with SIC and SIT assimilation. The climatological bias of the SIC in noDA was reduced in Ver1 by 29% in marginal ice zones during boreal winter, and 82% in pan-Arctic ocean during boreal summer. The quality of simulating the interannual variation of sea ice extent (SIE) is improved in all months particularly during boreal summer. The root-mean-square errors (RMSEs) of SIE anomaly in August are significantly reduced compared to noDA. However, the interannual variations of SIT is unrealistic in Ver1 which requires the additional assimilation of the SIT observation. The climatological bias of SIT over the Arctic was further reduced in Ver2 by 28% during boreal winter compared to that in Ver1. The interannual variability of SIT anomalies is realistically simulated in Ver2 by reducing the RMSEs of SIT anomalies by 33% in February, and 28% in August by compared to that in Ver1. The dominant interannual variation extracted by empirical orthogonal function (EOF) of SIT anomalies in Ver2 is better simulated than Ver1. The additional assimilation of SIT improves not only SIT, but also SIC. The climatological bias of SIE and the errors in leading EOF of SIC anomalies in Ver2 is further reduced compared to those in Ver1 ...