# Long-Term and Decadal Sea-Level Trends of the Baltic Sea Using Along-Track Satellite Altimetry
## 1 Introduction
In addition, the SA data may contain geophysical (tropospheric and ionospheric) correction errors. These sea level trend uncertainties are directly correlated with the length of the time series, and they increase considerably when the uncertainties of VLM, the reference frame, and geoid changes are not considered [13]. In this study, these effects are considered which are explained in detail in relevant sections below. The time span examined for this study is from 1995 to 2022 and the study region is the section of the Baltic Sea where the best quality SA data (with lower land contamination and sea-ice concentration) is available (mainly over Baltic Proper area). The SA-derived sea level trend estimates are verified by using a larger amount of TG stations, as compared to previous studies (e.g., [41; 43; 44; 45; 49]). The aim of this study is to: (i) to develop a method for estimating the regional decadal and long-term (almost 3 decades) sea level linear trends from instantaneous SA measurements, (ii) compare the corresponding regional ASL and RSL trends, (iii) inter-comparison of various SA derived trend estimates from different approaches/datasets that consists of (a) investigating whether the distance to coast affects the SA trend estimation, (b) comparison of trend estimates of different SA mission orbit constellations, and (c) comparisons between the various existing gridded and averaged SA data-product (3 models) trends vs. instantaneous SA data (this study) derived sea level trend. The main challenge with the Baltic Sea level trend estimation is the seasonal and annual dynamic variations of sea level, which exhibit behaviour distinct to the open oceans. In adjacent years the annual mean level of the Baltic Sea could often change by more than 2 dm (cf. Figure 11), which complicates the trend estimations, especially over short-term periods. The research also aims to determine whether SA can serve as a viable substitute for TG observations in estimating regional trends, especially for the areas with poor TG networks or TGs with long data gaps. Also, by comparing the SA-derived ASL trend estimates with the RSL trend by TG, the VLM rates can be validated or developed over other regions. In this study, the DT values have been used both for TG and SA data which is a more realistic than the SLA used representation of sea level variability [33; 50; 51]. Another novelty of the paper is using instantaneous along-track SA data rather than gridded products which could lead to a more realistic regional sea level trend estimate especially in areas closer to the coast. This paper is organized such that the developed methodology is described in Section 2, whereas the specifics of the study area and utilized datasets are described in Sections 3 and 4, respectively. Section 5 presents the results in following order (i) long-term ASL and RSL trends (ii) decadal ASL over the past two decades, i.e., from 2000 to 2009 and from 2010 to 2019, and (iii) inter-comparison of SA-derived long-term ASL trend. The results are discussed in Section 6, whereas Section 7 concludes this paper.