Abstract
Abstract. The feasibility of assimilating sea ice thickness (SIT) observations derived from CryoSat-2 along-track measurements of sea ice freeboard is successfully demonstrated using a 3D-Var assimilation scheme, NEMOVAR, within the Met Office's global, coupled ocean–sea-ice model, Forecast Ocean Assimilation Model (FOAM). The CryoSat-2 Arctic freeboard measurements are produced by the Centre for Polar Observation and Modelling (CPOM) and are converted to SIT within FOAM using modelled snow depth. This is the first time along-track observations of SIT have been used in this way, with other centres assimilating gridded and temporally averaged observations. The assimilation leads to improvements in the SIT analysis and forecast fields generated by FOAM, particularly in the Canadian Arctic. Arctic-wide observation-minus-background assimilation statistics for 2015–2017 show improvements of 0.75 m mean difference and 0.41 m root-mean-square difference (RMSD) in the freeze-up period and 0.46 m mean difference and 0.33 m RMSD in the ice break-up period. Validation of the SIT analysis against independent springtime in situ SIT observations from NASA Operation IceBridge (OIB) shows improvement in the SIT analysis of 0.61 m mean difference (0.42 m RMSD) compared to a control without SIT assimilation. Similar improvements are seen in the FOAM 5 d SIT forecast. Validation of the SIT assimilation with independent Beaufort Gyre Exploration Project (BGEP) sea ice draft observations does not show an improvement, since the assimilated CryoSat-2 observations compare similarly to the model without assimilation in this region. Comparison with airborne electromagnetic induction (Air-EM) combined measurements of SIT and snow depth shows poorer results for the assimilation compared to the control, despite covering similar locations to the OIB and BGEP datasets. This may be evidence of sampling uncertainty in the matchups with the Air-EM validation dataset, owing to the limited number of observations available over the time period of interest. This may also be evidence of noise in the SIT analysis or uncertainties in the modelled snow depth, in the assimilated SIT observations, or in the data used for validation. The SIT analysis could be improved by upgrading the observation uncertainties used in the assimilation. Despite the lack of CryoSat-2 SIT observations available for assimilation over the summer due to the detrimental effect of melt ponds on retrievals, it is shown that the model is able to retain improvements to the SIT field throughout the summer months due to prior, wintertime SIT assimilation. This also results in regional improvements to the July modelled sea ice concentration (SIC) of 5 % RMSD in the European sector, due to slower melt of the thicker sea ice.
Highlights
These results demonstrate that, using Operation IceBridge (OIB) sea ice thickness (SIT) as a reference for validation, the CryoSat-2 SIT observations are more reliable than the model without SIT assimilation
The assimilation results in improvements to the SIT analysis and forecast fields generated by Forecast Ocean Assimilation Model (FOAM) compared to a control without SIT assimilation, validated using SIT observation-minus-background mean difference and root-mean-square difference (RMSD) assimilation statistics
Comparison with independent springtime in situ SIT observations from NASA Operation IceBridge (OIB; Kurtz et al, 2019) indicates that the assimilation results in substantial improvements to the model SIT, of 0.61 m mean difference and 0.42 m RMSD
Summary
Arctic sea ice cover has undergone a considerable reduction in both thickness and extent (e.g. Comiso et al, 2008; Kwok et al, 2009; Lindsay and Schweiger, 2015; Stroeve and Notz, 2018; Meredith et al, 2019), which has the potential to impact weather and climate at lower latitudes (e.g. Koenigk et al, 2016; Screen, 2017), to alter the ecosystem and living environment (e.g. Meier et al, 2014), and to change the nature of Arctic shipping by opening up new sea routes (e.g. Smith and Stephenson, 2013; Wei et al, 2020; Zeng et al, 2020). A number of studies have emphasised the importance of accurate initialisation of SIT fields for seasonal predictions of sea ice concentration and extent: Day et al (2014), Massonnet et al (2015), Collow et al (2015) and Dirkson et al (2017); and CryoSat-2 and/or SMOS SIT observations have been used to initialise seasonal sea ice forecasts by Blockley and Peterson (2018), Yang et al (2019) and Allard et al (2020). Several studies have demonstrated the impact of using satellite SIT observations in addition to SIC to initialise short-term operational sea ice forecasts: e.g.
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