Arctic Sea Ice Concentration Assimilation in an Operational Global 1/10° Ocean Forecast System
"> Figure 1
<p>Arctic sea ice concentration from NSIDC (<b>a</b>,<b>e</b>) which is regarded as the “truth”, the biases of OSISAF (<b>b</b>,<b>f</b>) and model biases (<b>c</b>,<b>d</b>,<b>g,h</b>) of the Northern Hemisphere in March (<b>a</b>–<b>d</b>) and September (<b>e</b>–<b>h</b>), 2019.</p> "> Figure 2
<p>Annual-mean RMSE of Arctic sea ice concentration of the OSISAF observation (<b>a</b>), the numerical experiment NoSICasim (<b>b</b>), and the numerical experiment SICasim (<b>c</b>) with respect to the NSIDC observation during 2019.</p> "> Figure 3
<p>The RMSE time series of sea ice concentration with respect to the NSIDC observation of the Northern Hemisphere from 1 January to 31 December 2019. The results of experiments NoSICasim and SICasim are shown in blue and orange, respectively. The RMSE time series of OSISAF with respect to the NSIDC observation is shown in gray. Note that (<b>a</b>) is computed over the area where the NSIDC observation is larger than 15% and below 80%, and (<b>b</b>) is computed over the area where the NSIDC observation is larger than 80%. Date format for x-axis is day/month.</p> "> Figure 4
<p>Sea ice area (<b>a</b>) and IIEE (<b>b</b>) time series in the Arctic region from 1 January to 31 December 2019. The experiments without and with data assimilation are shown in blue and orange, respectively. Sea ice areas of NSIDC and OSISAF are shown in green and gray, respectively. IIEEs between NSIDC and OSISAF are shown in gray.</p> "> Figure 5
<p>Simulated and observed sea ice edge locations in March (<b>a</b>–<b>c</b>) and September 2019 (<b>d</b>). Areas where sea ice concentration in the NSIDC observation is higher than 15% are denoted by the blue patch. The ice edges in the experiments NoSICasim and SICasim are denoted by green and red lines, respectively. Note that (<b>b</b>,<b>c</b>) show the same data as (<b>a</b>), but are zoomed in on the Bering Sea and the Nordic Seas.</p> "> Figure 6
<p>Spatial distributions of SST (unit: °C) based on the GMPE SST Observation (<b>a</b>,<b>d</b>), the absolute biases of NoSICasim (<b>b</b>,<b>e</b>), and SICasim (<b>c</b>,<b>f</b>). The results for March are shown in (<b>a</b>–<b>c</b>), while for September they are shown in (<b>d</b>–<b>f</b>). Areas either with sea ice concentrations above 15% in the NSIDC observation or ice-free regions in NoSICasim are represented as white.</p> "> Figure 7
<p>Sea ice concentration RMSE (<b>a</b>) and IIEE (<b>b</b>) time series of the FIO-COM10 forecast from 1 August to 30 September 2021. The forecasts with and without sea ice concentration assimilation are shown in thick and thin lines, respectively. The RMSE and IIEE of NSIDC with respect to the OSISAF observation are shown in thick gray lines.</p> "> Figure 8
<p>Sea ice concentration RMSE between FIO-COM10 forecast and the OSISAF observation. (<b>a</b>) The Arctic domain (blue patched area), averaging region for sea ice concentration RMSE analysis. (<b>b</b>) Monthly-mean sea ice concentration RMSE from January to December 2021 over the Arctic domain. (<b>c</b>) Annual-mean RMSE of forecast and the corresponding persistence forecast for different lead time in 2021. (<b>d</b>) Mean RMSE during 1 August to 30 November 2021 of forecast and the corresponding persistence forecast for different lead time.</p> "> Figure 9
<p>The IIEE between FIO-COM10 forecast and OSISAF observation. (<b>a</b>) Monthly-mean IIEE time series from January to December 2021 in the Arctic region. (<b>b</b>) Annual-mean IIEE of forecast and the corresponding persistence forecast for different lead time in 2021. (<b>c</b>) Mean IIEE during 1 August to 30 November 2021 of forecast and the corresponding persistence forecast for different lead time.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Model Description
2.2. Sea Ice Data Assimilation Method
2.3. Data
2.3.1. Sea Ice Concentration
2.3.2. SST Dataset
2.4. Numerical Experiments of Sea Ice Concentration Assimilation
2.5. Real-Time Forecast of 2021
3. Results
3.1. Performance of Sea Ice Assimilation
3.2. Impact on SST
3.3. Impacts on Real-Time Sea Ice Forecasts
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Model Components | Model Parameters/Schemes | Values/Configurations |
---|---|---|
Surface wave model MASNUM | Horizontal resolution | 1/10° × 1/10° |
Spectral discretization | 24 directions, 25 wave numbers | |
Spatial coverage | global ocean | |
Ocean model MOM5 | Horizontal resolution | 1/10° × 1/10° |
Vertical levels | 54 levels (min: 2 m) | |
Spatial coverage | global ocean | |
Horizontal grid | Tri-polar grid with bi-polar region set to north of 65°N | |
Vertical grid | Z* coordinate configured with bottom partial cells | |
Horizontal diffusivity | Bi-harmonic, diffusive velocities of 1.96 cm/s for momentum and 0.65 cm/s for tracers | |
Vertical diffusivity | KPP + Bv | |
Air-sea fluxes | NCEP/Bulk formula | |
Model topography | ETOPO1 | |
Sea ice model SIS | Ice thickness categories | 5 |
Ice bulk salinity | 0.005 PSU | |
Snow albedo | 0.85 | |
Ice albedo | 0.72 | |
Ice/ocean drag coefficient | ||
Ice surface roughness length |
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Shao, Q.; Shu, Q.; Xiao, B.; Zhang, L.; Yin, X.; Qiao, F. Arctic Sea Ice Concentration Assimilation in an Operational Global 1/10° Ocean Forecast System. Remote Sens. 2023, 15, 1274. https://doi.org/10.3390/rs15051274
Shao Q, Shu Q, Xiao B, Zhang L, Yin X, Qiao F. Arctic Sea Ice Concentration Assimilation in an Operational Global 1/10° Ocean Forecast System. Remote Sensing. 2023; 15(5):1274. https://doi.org/10.3390/rs15051274
Chicago/Turabian StyleShao, Qiuli, Qi Shu, Bin Xiao, Lujun Zhang, Xunqiang Yin, and Fangli Qiao. 2023. "Arctic Sea Ice Concentration Assimilation in an Operational Global 1/10° Ocean Forecast System" Remote Sensing 15, no. 5: 1274. https://doi.org/10.3390/rs15051274