Abstract
AbstractTo improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.
Highlights
In contrast to the consistent decrease in Arctic sea-ice extent (SIE) during the satellite era, Antarctic SIE exhibited a gradual increase until 2015, but in recent years has abruptly declined (e.g. Stuecker and others, 2017; Turner and Comiso, 2017; Turner and others, 2017; Kusahara and others, 2018; Schlosser and others, 2018; Wang and others, 2019)
This study introduces a data assimilation system called Data Assimilation System for the Southern Ocean (DASSO) based on a regional Southern Ocean sea ice–ocean coupled model, and presents a set of assimilation experiments to assess the impact of sea-ice thickness (SIT) as well as sea-ice concentrations (SIC) observations on reproducing the sea-ice conditions and variations during the period from 15 April to 14 October, 2016
Assimilating SIC and SIT improves the simulation of Antarctic sea ice, and in particular suppresses the positive biases of SIT and SIC in the model-free run
Summary
In contrast to the consistent decrease in Arctic sea-ice extent (SIE) during the satellite era, Antarctic SIE exhibited a gradual increase until 2015, but in recent years has abruptly declined (e.g. Stuecker and others, 2017; Turner and Comiso, 2017; Turner and others, 2017; Kusahara and others, 2018; Schlosser and others, 2018; Wang and others, 2019). Sea-ice data assimilation, merging the information from observations with that from models, can provide more accurate and useful estimates of sea-ice conditions than could otherwise be obtained through either observations or models individually (Buehner and others, 2017). The SIC observation has been assimilated by adopting more advanced data assimilation methods such as ensemble Kalman filter (e.g. Massonnet and others, 2013) and 4-D variational analysis (e.g. Mazloff and others, 2010) in Antarctic sea ice–ocean models, which is likely to provide more balanced estimations of model state. With recent advances in retrieval methods, the soil moisture and ocean salinity (SMOS) satellite data have been used to derive Antarctic SIT, which appears to be more accurate in the thin ice regime (Tian-Kunze and others, 2014; Tian-Kunze and Kaleschke, 2018). As a new kind of observation, it provides an unique opportunity to investigate the constraint of SIT observations on the Antarctic sea-ice estimates, how to assimilate Antarctic SIT observation derived from SMOS is still debatable
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