SDUST2024MSS_AO: a MSS model of the Arctic Ocean derived from CryoSat-2 SAR altimeter data

Abstract. Due to the seasonal and perennial sea ice coverage in the Arctic Ocean, determining the sea surface height (SSH) is more difficult compared to mid- and low-latitude regions. This has resulted in a lack of high-precision, high-resolution mean sea surface (MSS) models for the Arctic Ocean. This paper focuses on the SSH in the ice-covered regions of the Arctic Ocean, using CryoSat-2 SAR mode altimeter data and MODIS images to develop a feature and threshold optimization method based on waveform characteristic method, combining mutual information and the F1 Score. This method detects lead observations in Baseline-E CryoSat-2 ice products with a precision of 90.79 % and a recall of 85.25 %. Using CryoSat-2 SAR mode altimeter data from July 2010 to December 2023, the lead observations are divided into 5-km grids for each month, and gross error observations in each grid are removed according to the two-sigma principle. The mean SSH for each month is calculated to establish a monthly mean SSH time series within each grid. Then, using least square estimation (LSE), a new MSS model with a grid size of 5 km is constructed, named SDUST 2024 MSS of Arctic Ocean (SDUST2024MSS_AO). The SDUST2024MSS_AO model is compared with four internationally renowned MSS models (CLS2022, DTU21, UCL13, and SDUST2020) and validated using ICESat-2 altimeter data, demonstrating the reliability of the SDUST2024MSS_AO model. The SDUST2024MSS_AO model data are available at https://doi.org/10.5281/zenodo.13624487 (Liu et al., 2024).