Merging Recent Mean Sea Surface Into a 2023 Hybrid Model (From Scripps, DTU, CLS, and CNES)

Abstract In this paper, we compute a new hybrid mean sea surface (MSS) model by merging three recent models, CNES_CLS22, SCRIPPS_CLS22, and DTU21, and taking advantage of their respective features. The errors associated with these models were assessed using sea level anomalies for wavelengths ranging from 15 to 100 km from Sentinel‐3A (S3A), SWOT KaRIn during its calibration phase and ICESat‐2 in the Arctic ice‐covered regions. The variance of the error associated with this new Hybrid23 MSS is estimated at 0.15 ± 0.04 cm 2 with S3A. The greatest improvements observed on S3A sea level anomalies are mainly located in coastal regions and along geodetic structures: on average, the error is reduced by 23% within 200 km along the coast and by 35% in the Indonesian region compared with SCRIPPS_CLS22. Despite these improvements, the MSS error still impacts significantly sea level anomalies computed from altimetry: it explains 15% and 18% of the S3A and SWOT KaRIn respective global variance. It becomes predominant (>30%) if we consider the shorter wavelengths ([15, 30 km]). CNES_CLS15 (Pujol et al., 2018, https://doi.org/10.1029/2017jc013503 ), older, explains up to 88% of the variance of SWOT KaRIn at these wavelengths. MSS errors have become a major limiting factor to the accuracy of sea level anomalies, and hybridization even adds sub‐mesoscale errors. SCRIPPS_CLS22 and DTU21 also remain better in certain regions of the North Atlantic above 60°N and in Arctic coastal areas. Finally, many efforts are still required to develop the MSS to a new level of precision, which we could soon achieve with SWOT KaRIn during the scientific phase.