Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)

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.

Improving the Met Office's Forecast Ocean Assimilation Model (FOAM) with the assimilation of satellite‐derived sea‐ice thickness data from CryoSat‐2 and SMOS in the Arctic

“…CryoSat-2 measures thick ice with relatively good accuracy, but it has limitations in representing thinner ice (Ricker et al, 2014). Therefore, consistent with Fiedler et al (2022), the control's poor performance in simulating the thick ice pack in the central Arctic is notably improved by CryoSat-2 assimilation relative to airborne measurements from NASA Operation IceBridge. However, when only CryoSat-2 is assimilated, the positive impacts in thick ice regions are counteracted by an SIT overestimation in the Arctic marginal seas (see also Hume-Wright et al, 2020).…”

“…The 10 km radius is chosen since it is similar to the size of the 1/4 • ORCA tripolar grid cell in the Arctic used by FOAM, as well as to the resolution of the assimilated SMOS gridded product. Unlike Fiedler et al (2022), we use the mean rather than the median for super-obbing CryoSat-2 SITs. Whilst the mean is negatively affected by outliers, the median is negatively impacted by small sample sizes, which is the case for the much lower number of CryoSat-2 observations near the ice edge compared to higher latitudes (Figure 1).…”

“…As mentioned previously, slightly negative freeboards are permitted in the data which form the super-observations. These may result in negative SITs, which are rejected in Fiedler et al (2022), but accounted for here in the super-obbing to allow for random noise, particularly over thin ice regions, as suggested by Tilling et al (2018). After the averaging, any remaining negative SIT super-observations are rejected before the assimilation.…”

“…This is particularly clear in the Barents and Kara Seas, where both satellites can have relatively low observation uncertainties, but CryoSat-2 SITs can be up to ∼1 m larger when compared to SMOS SITs. It should be emphasised that the parametrization applied by Fiedler et al (2022) to derive CryoSat-2 uncertainties does not consider the location of the observations, only the magnitude of their SITs. Therefore, if CryoSat-2 observations are in areas of thin ice but their SITs lie within 1.5-3 m, their uncertainties will be quite low, and they will be given more weight in the analysis.…”

“…It is worth mentioning that CryoSat-2 and SMOS SIT increments are generated and applied together in Figure 3b. In order to maintain the initial volume distribution of ice between CICE categories, the SIT increments are applied proportionally to each of the five sub-grid categories based on their initial volume distribution, if the SIC within that category is above 1% (Blockley and Peterson, 2018;Fiedler et al, 2022). Additionally, they are only applied to grid cells where the model SIC, aggregated over all categories, is greater than 15%.…”