Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis

Abstract

Abstract. Accurately forecasting the sea-ice thickness (SIT) in the Arctic is a major challenge. The new SIT product (referred to as CS2SMOS) merges measurements from the CryoSat-2 and SMOS satellites on a weekly basis during the winter. The impact of assimilating CS2SMOS data is tested for the TOPAZ4 system – the Arctic component of the Copernicus Marine Environment Monitoring Services (CMEMS). TOPAZ4 currently assimilates a large set of ocean and sea-ice observations with the Deterministic Ensemble Kalman Filter (DEnKF). Two parallel reanalyses are conducted without (Official run) and with (Test run) assimilation of CS2SMOS data from 19 March 2014 to 31 March 2015. Since only mapping errors were provided in the CS2SMOS observation, an arbitrary term was added to compensate for the missing errors, but was found a posteriori too large. The SIT bias (too thin) is reduced from 16 to 5 cm and the standard errors decrease from 53 to 38 cm (by 28 %) when compared to the assimilated SIT. When compared to independent SIT observations, the error reduction is 24 % against the ice mass balance (IMB) buoy 2013F and by 12.5 % against SIT data from the IceBridge campaigns. The improvement of sea-ice volume persists through the summer months in the absence of CS2SMOS data. Comparisons to sea-ice drift from the satellites show that dynamical adjustments reduce the drift errors around the North Pole by about 8 %–9 % in December 2014 and February 2015. Finally, using the degrees of freedom for signal (DFS), we find that CS2SMOS makes the prime source of information in the central Arctic and in the Kara Sea. We therefore recommend the assimilation of C2SMOS for Arctic reanalyses in order to improve the ice thickness and the ice drift.

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

“…The assimilation showed significant effects on the model state; both improvements to the modelled SIT and multivariate effects on SIC, ocean temperature and ocean salinity were found. Yang et al (2014) used the localised singular evolutive interpolated Kalman filter (Pham, 2001) to assimilate the SMOS SIT observations into the Massachusetts Institute of Technology general circulation model (Marshall et al, 1997). In this study, an improved thickness forecast was found when assimilating SMOS observations and some improvements to the SIC forecasts.…”

From Observation to Information and Users: The Copernicus Marine Service Perspective

“…Numerous OSSEs have been carried out for sea-ice remote sensing, in particular for sea-ice thickness products from CryoSat-2 (Lisaeter et al, 2007;Blockley and Peterson, 2018), as well as for thin ice thickness from SMOS (Yang et al, 2014;Xie et al, 2016) and both satellites together (Allard et al, 2018;Mu et al, 2018;Xie et al, 2018). Assimilation of combined CryoSat-2 and SMOS sea-ice thickness products in the Arctic MFC has a very positive impact by reducing sea-ice thickness errors by 12 to 24% (Xie et al, 2018). Improvements in sea-ice concentrations data have also been assessed by OSSEs (Posey et al, 2015).…”

Assessment of contemporary satellite sea ice thickness products for Arctic sea ice

“…Kaleschke et al, 2012;Key and Wang, 2015;Ricker et al, 2017a). Recent studies have demonstrated the utility of initializing numerical models with satellite-derived estimates of sea ice thickness to improve model predictions (Yang et al, 2014;Allard et al, 2018;Blockley and Peterson, 2018;Stroeve et al, 2018;Xie et al, 2018).…”