Abstract
The snow depth, an essential metric of snowpacks, can modulate sea ice changes and is a necessary input parameter to obtain altimeter-derived sea ice thickness values. In this study, we propose an innovative snow depth retrieval method with the improved NASA Eulerian Snow on Sea Ice Model (INESOSIM) and the particle filter (PF) approach, namely, INESOSIM-PF. Then, we generate daily snow depth estimates with INESOSIM-PF from 2012 to 2020 at a 50-km resolution. With the use of Operation IceBridge (OIB) data, it can be revealed that compared to the NESOSIM-estimated snow depth, the INESOSIM-PF-estimated snow depth is greatly improved, with a root mean square error (RMSE) decrease of 17.97 % (RMSE: 6.73 cm) and a correlation coefficient increase of 11.85 % (r: 0.71). The INESOSIM-PF-estimated snow depth is close to the satellite-derived snow depth, which is applied in data assimilation. With the use of Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) snow buoy data, it can be verified that INESOSIM-PF performs well in the Central Arctic with an RMSE of 9.23 cm. INESOSIM-PF is robust and the snow depth determined with INESOSIM-PF is less influenced by input parameters with a snow depth uncertainty of 0.74 cm. The variations in the monthly and seasonal snow depth estimates retrieved from INESOSIM-PF agree well with those in the estimates retrieved from two other existing algorithms. Based on the presented snow depth estimates, we can retrieve the sea ice thickness and perform long-term snow depth and sea ice analysis. Snow depth estimates improve the understanding of Arctic environmental change and promote the future development of sea ice models.
Highlights
In the Arctic, snowpack on sea ice modulates variations in sea ice through the snow depth, density and distribution (Webster et al, 2014), playing an important role in Arctic hydrology, energy balance and climate (Serreze et al, 2006; Perovich and Polashenski, 2012; Handorf et al, 2015)
Compared to INESOSIM, the accuracy of the INESOSIM-particle filter (PF) snow depth has been greatly improved, namely, the root mean square error (RMSE) decreases by 9.48%, the MAE decreases 315 by 11.50%, the bias changes from -1.75 cm to -1.40 cm, and the correlation coefficient increases by 13.64% (Table 2)
The results indicate that NASA Eulerian Snow on Sea Ice Model (NESOSIM) is ineffective at snow depths smaller than 15 cm, and the deviation between the NESOSIM-estimated snow depth values and Operation IceBridge (OIB) data ranges from -10 cm to 20 cm, mostly located on the right side of the 0 line, suggesting snow depth overestimation (Fig. 5(a))
Summary
In the Arctic, snowpack on sea ice modulates variations in sea ice through the snow depth, density and distribution (Webster et al, 2014), playing an important role in Arctic hydrology, energy balance and climate (Serreze et al, 2006; Perovich and Polashenski, 2012; Handorf et al, 2015). Rapid sea ice change affects Arctic amplification (Screen and Simmonds, 2010; Screen and Francis, 2016). Arctic amplification is notable in areas where sea ice decreases sharply (Dai et al, 2019). Discussion started: 27 September 2021 c Author(s) 2021.
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