Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F

Abstract

Abstract. The neXtSIM-F (neXtSIM forecast) forecasting system consists of a stand-alone sea ice model, neXtSIM (neXt-generation Sea Ice Model), forced by the TOPAZ ocean forecast and the ECMWF atmospheric forecast, combined with daily data assimilation of sea ice concentration. It uses the novel brittle Bingham–Maxwell (BBM) sea ice rheology, making it the first forecast based on a continuum model not to use the viscous–plastic (VP) rheology. It was tested in the Arctic for the time period November 2018–June 2020 and was found to perform well, although there are some shortcomings. Despite drift not being assimilated in our system, the sea ice drift is good throughout the year, being relatively unbiased, even for longer lead times like 5 d. The RMSE in speed and the total RMSE are also good for the first 3 or so days, although they both increase steadily with lead time. The thickness distribution is relatively good, although there are some regions that experience excessive thickening with negative implications for the summertime sea ice extent, particularly in the Greenland Sea. The neXtSIM-F forecasting system assimilates OSI SAF sea ice concentration products (both SSMIS and AMSR2) by modifying the initial conditions daily and adding a compensating heat flux to prevent removed ice growing back too quickly. The assimilation greatly improves the sea ice extent for the forecast duration.

An Evaluation of the Performance of Sea Ice Thickness Forecasts to Support Arctic Marine Transport

“…Elastic Viscous Plastic (EVP) [25] Viscous Plastic (VP) [26] EVP [25] Brittle Bingham-Maxwell (BBM) [27] Thermodynamics Zero-layer model [28] Three-layer [28] Three-layer [28] Three category model [29] Time [30]. The model runs on an adaptive triangular mesh with an average side length of 10 km (with the distance from each point to the opposite side being about 7.5 km) and is interpolated to a regular 3 km grid for distribution [27]. The overall scheme of the model, including information on forcing and data assimilation, is depicted in Figure 2.…”

“…The overall scheme of the model, including information on forcing and data assimilation, is depicted in Figure 2. SIC data are assimilated as a weighted average of the Advanced Microwave Scanning Radiometer 2 (AMSR2) and Special Sensor Microwave Imager/Sounder (SSMIS) products as described in Williams et al ( 2021) [27]. The model was initialised in October 2018 using SIT from the merged CryoSat-2 and Soil Moisture and Ocean Salinity (CS2SMOS) product from AWI [31].…”

“…The model was initialised in October 2018 using SIT from the merged CryoSat-2 and Soil Moisture and Ocean Salinity (CS2SMOS) product from AWI [31]. The neXtSIM-F model scheme [27]. Both SSMIS and AMSR2 concentration data from Ocean and Sea Ice Satellite Application Facility (OSI-SAF) are assimilated.…”

“…An ensemble Kalman Filter data as-Figure 2. The neXtSIM-F model scheme [27]. Both SSMIS and AMSR2 concentration data from Ocean and Sea Ice Satellite Application Facility (OSI-SAF) are assimilated.…”

Novel Arctic sea ice data assimilation combining ensemble Kalman filter with a Lagrangian sea ice model

“…This study presents a novel application of the ensemble Kalman filter (EnKF, Evensen, 2003) used to assimilate satellitebased SIC and SIT data in the Lagrangian neXt generation Sea Ice Model (neXtSIM, Rampal et al, 2016bRampal et al, , 2019. Our work builds upon and extends the preliminary DA study with neXtSIM from Williams et al (2021). They introduced the deterministic forecasting platform neXtSIM-F whereby the OSI-SAF SIC observations (both brightness temperatures measured by the Special Sensor Microwave Imager Sounder -SSMIS and Advanced Microwave Scanning Radiometer 2 -AMSR2) were assimilated by a simple DA method -"direct insertion".…”