# Long-Term and Decadal Sea-Level Trends of the Baltic Sea Using Along-Track Satellite Altimetry
## 1 Introduction
However, there are certain challenges in using SA data to retrieve spatiotemporal distribution of sea level data over coastal areas, closer than about 30 km to the land. In coastal zones, the quality of the range measurements is degraded because the radar pulses are reflected partly from land and partly from the sea. It is also more difficult to compute accurate range corrections, since the tidal patterns are more complex to model [33]. Therefore, SA observations closer than ~30 km to the coast are only seldom used in sea-level trend studies. Also, the majority of sea-level trend investigations use monthly gridded SA data based on regular grids with a spatial resolution of 6-25 km. Furthermore, many studies use the sea-level anomaly (SLA, which is a deviation of the sea level from the mean sea surface) for studying the sea-level changes at the regional scale. MSS is not an equipotential surface due to semi-persistent external forces that affect the mean sea-level variability in a spatiotemporal domain [34]. In addition, MSS models possess uncertainties due to averaging conventions. SA-based MSS models also may contain error-modelling deficiencies, e.g., dynamic atmospheric correction (DAC), especially at sub-polar latitudes. In addition, temporal interpolation of SA data for the MSS determination is vague considering the SA revisiting cycle (varies from 10-35 days). An alternative to SLA is dynamic topography (DT), which is the separation of sea surface height (SSH), and a geoid model (a static equipotential of earth's surface). Accurate DT is the most reliable source to represent sea level dynamics which plays an important role in understanding the oceans and sea level changes. Utilizing the SA-determined DT enables representing both mean and time-varying dynamics of the ocean so that sub-mesoscale dynamics can potentially be captured [33; 35; 36; 37]. Therefore, this study uses SA-determined instantaneous DT to represent more reliable sea level changes at a regional scale. Both TG and SA observations are employed for investigating various aspects of sea level trends (e.g., [38]). TG stations provide sea level records that are referred to geoid-based chart datums. These can serve for the validation of SA data. Nevertheless, TG stations are predominantly located nearshore, and their measurements are relative to land-bounded benchmarks. Consequently, to acquire ASL from the TG observation within vertical land motion (VLM) regions it is necessary to correct the TG records, i.e., ASL = RSL + VLM. Accordingly, the developed regional sea trend estimation methodology is tested in the Baltic Sea region, which is affected by the post-glacial rebound. The Baltic Sea is surrounded by nine countries with a high density of marine traffic and coastal activities where more than 84 million people live in the Baltic catchment area [39]. Therefore, predicting future sea level changes, forecasting ocean phenomena, and responding to marine disasters are needed to conduct research on sea level trends in the Baltic Sea. A realistic analysis of sea level trends and their uncertainty over the Baltic Sea region is very topical and highly important, which could help to make a reliable prediction of the long-term trend in sea level over this region. Previous studies have shown the Baltic Sea RSL trend between 1-3 mm/yr [40; 41; 42; 43; 44; 45; 46] and ASL rates between 3-6 mm/yr [29; 44; 45; 46; 47]. Thus, there is a wide range of variation amongst studies for different time spans and locations are utilized. Quantifying sea level trend uncertainties is also required to judge the reliability of sea level observations and prevent misinterpretations of artifacts arising from the limitations of the sea level observing systems [10]. The sea level trend estimate uncertainties may occur due to possible systematic effects such as inconsistencies in the international reference frame (ITRF) realizations, which can reach \(\pm\)0.1 mm/yr for GMSL trend estimation [48]. The VLM correction uncertainty also can exceed 0.05 mm/yr [13].