# Long-Term and Decadal Sea-Level Trends of the Baltic Sea Using Along-Track Satellite Altimetry
## 6 Discussion
The larger trends observed could potentially be attributed to increased westerly winds, augmented precipitation, and fluctuations in air pressure, as suggested in studies such as by [PERSON] et al. (2009) and [PERSON] et al. (2019). Further exploration of the possible reasons for these decadal variations may be necessary. Also, it should be noted that the calculated decadal trends diverge significantly from the long-term trend (1995-2022), showcasing variations that nearly triple in some stations. Furthermore, each decade displayed its unique pattern of variation, as outlined in Table 5. These results demonstrate that the interpretation of decadal trends can be more complex, with many more variations existing for several different reasons. In this study, the inter-comparison of multi-SA missions with different orbit constellations showed that low-inclination satellites appear to give slightly better results for the trend estimation across the studied sites in the Baltic Sea for the period of nineteen years of overlapped data (2003-2022). Also, the superiority of instantaneous SA-derived DT for the sea-level trend estimates, rather than gridded SLA datasets, is illustrated in Figure 12. Although the gridded data product results are comparable to TG and along-track SA data, they may suffer from issues of replicating data close to the coast, and this made the trend estimation unavailable for some stations. So, in conclusion, the along-track data are better for representing the coastal sea-level trends. By comparing the results of Figure 12 and Table 8, which include the trend value during 1995-2022 (triangles) and 1995-2019 (circles), the trend derived from averaging instantaneous SA appears to have better performance than TG observations (lower RMSE value between these years). This could be due to averaging the SA-derived DT within the specific radius (50 km) of the TG station. The TG-derived trend is more sensitive to the time-series length, and changing the time window of TG data could affect the trend estimations in larger values. This was also shown by comparing the \(DT_{TG\mathcal{SA}\text{ \emph{cycle}}}\) and \(DT_{SA\text{\emph{RTG}}}\) trends over a decadal time frame. Also, TGs are located at the coast, so they may also be influenced by other processes, such as wave set-up and set-down ([PERSON] et al., 2017), which may not always be captured by the SA. This may suggest that TG may not be the ideal dataset to represent sea-level variation, especially with trend estimates. However, the ability to precisely compare the variability in sea level between TG and along-track SA at various distances from the coast provides a valuable insight into ocean dynamics. It also highlights that in areas without the presence of TGs, along-track SA can offer an alternative that may yield even more accurate results. As a result, the utilization of the high-resolution geoid for SA to determine DT, coupled with geoid-referenced TG data, enabled a realistic quantification of these differences. Future research could further examine the comparison of TG and along-track SA in coastal areas and the possible identification of coastal processes.