Assimilating Arctic sea ice observations into a coupled ice-ocean model with a local SEIK filter and different uncertainty estimates

• Main Author: Yang, Qinghua
• Other Authors: Jung, Thomas, Lemke, Peter
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: Universität Bremen 2015
• Subjects: sea ice; data assimilation; Arctic; ensemble Kalman filter; ensemble forecast; 530; 530 Physics; ddc:530; Arctic Ocean; Arktis; Arktis*; National Snow and Ice Data Center; Sea ice
• Online Access: https://media.suub.uni-bremen.de/handle/elib/858; https://nbn-resolving.org/urn:nbn:de:gbv:46-00104541-11

Decrease of summer sea ice extent in the Arctic Ocean opens interesting shipping routes and creates potential for many marine operations. For these activities, accurate predictions of sea ice conditions are required to maintain marine safety. In an effort towards Arctic sea ice prediction, the Arctic sea ice data assimilation (DA) system is developed, based on a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model (MITgcm) and a local Singular Evolutive Interpolated Kalman (LSEIK) filter to assimilate Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration operational products from the National Snow and Ice Data Center (NSIDC). The summer of 2010 is selected to implement a DA study. Based on comparisons with both the assimilated NSIDC SSMIS concentration and concentration data from the Ocean and Sea Ice Satellite Application Facility (OSISAF), the forecasted sea-ice edge and concentration are improved over simulations without data assimilation. By nature of the assimilation algorithm with multivariate covariance between ice concentration and thickness, sea ice thickness also shows some improvement over the forecast without data assimilation. The LSEIK system is further extended to investigate the impact of assimilating sea ice thickness data derived from ESA s Soil Moisture and Ocean Salinity (SMOS) satellite together with SSMIS sea ice concentration data. A period of three months from November 1st, 2011 to January 31st, 2012 is selected to assess the forecast skill of the assimilation system. For comparison, the assimilation is repeated only with the SSMIS sea ice concentrations. By running two different assimilation experiments, and making comparison among the unassimilated model, independent satellite derived data and in-situ observation, it is shown that the SMOS ice thickness assimilation leads to improved thickness forecasts. With SMOS thickness data, the sea ice concentration forecasts also have a better agreement with observations, although ...