The ECMWF analysis system currently assimilates Level-4 sea ice concentration (SIC) from OSTIA (the Operational SST and Sea Ice Analysis produced by the UK Met Office). Here, we evaluate
the impact of assimilating Level-3 SIC observations in the ECMWF ocean-sea ice analysis system.
Furthermore, we make use of the availability of Arctic-wide sea ice thickness (SIT) observations in
the recent years to constrain the modelled sea ice thickness. Coupled forecasts of the ocean-seaice-
wave-land-atmosphere are then initialized using the improved sea-ice initial conditions from the
above assimilation experiments, and the predictive skill of Arctic sea ice up to lead times of 7 months
is investigated in a low-resolution analogue of the currently operational ECMWF seasonal forecasting
system SEAS5. Results show that the system successfully assimilates Level-3 SIC observations from
the OSISAF (EUMETSAT Ocean and Sea Ice Satellite Applications Facility) product OSI-401-b.
Differences in the analysis are small and within the observational uncertainties, but the assimilation
of Level-3 SIC will result in increased operational reliability. The impact on coupled forecasts is
generally positive for SIC at lead month 1 and neutral for longer lead times. Statistically significant
improvements are found over the ice edge and coastal seas in the Arctic mostly in the first 2 weeks for
forecasts initialized in most calendar months, except for January starts, when the impact is neutral.
The positive impact persists up to week 4 for March, May, August, November and December start
months. For SIT and sea ice volume, the forecast impact of Level-3 SIC assimilation is neutral in all
Using SIT information from CS2-SMOS (CryoSat2-Soil Moisture and Ocean Salinity) as an additional
constraint results in substantial changes of sea ice volume and thickness in the ocean-sea ice
analysis. Forecasts started from these sea-ice initial conditions show a reduction of the positive sea
ice bias and an overall reduction of summer-time forecast errors compared to SEAS5. A slight degradation
in skill is found in the autumn sea ice forecasts initialized in July and August. While there
is improvement in the skill of autumn 2m-temperature forecast initialized in spring, a degradation in
skill is found for the October forecasts initialized in August. We conclude that the strong thinning
by CS2-SMOS initialization mitigates or enhances seasonally dependent forecast model errors in sea
ice and near surface temperatures. Hence, changes in root-mean-square errors are predominantly due
to changes in biases. Using a novel metric, the Integrated Ice Edge Error (IIEE), we find significant
improvement of up to 28% in the September sea ice extent forecast started from April. Our results
demonstrate the usefulness of new sea ice observational products in both data assimilation and
forecast verification, and strongly suggest that better initialization of SIT is crucial for improving
seasonal sea-ice forecasts.