Abstract. EOT20 is the latest in a series of empirical ocean tide (EOT) models derived using residual tidal analysis of multi-mission satellite altimetry at DGFI-TUM. The amplitudes and phases of 17 tidal constituents are provided on a global 0.125∘ grid based on empirical analysis of seven satellite altimetry missions and four extended missions. The EOT20 model shows significant improvements compared to the previous iteration of the global model (EOT11a) throughout the ocean, particularly in the coastal and shelf regions, due to the inclusion of more recent satellite altimetry data as well as more missions, the use of the updated FES2014 tidal model as a reference to estimated residual signals, the inclusion of the ALES retracker and improved coastal representation. In the validation of EOT20 using tide gauges and ocean bottom pressure data, these improvements in the model compared to EOT11a are highlighted with the root sum square (RSS) of the eight major tidal constituents improving by ∼ 1.4 cm for the entire global ocean with the major improvement in RSS (∼ 2.2 cm) occurring in the coastal region. Concerning the other global ocean tidal models, EOT20 shows an improvement of ∼ 0.2 cm in RSS compared to the closest model (FES2014) in the global ocean. Variance reduction analysis was conducted comparing the results of EOT20 with FES2014 and EOT11a using the Jason-2, Jason-3 and SARAL satellite altimetry missions. From this analysis, EOT20 showed a variance reduction for all three satellite altimetry missions with the biggest improvement in variance occurring in the coastal region. These significant improvements, particularly in the coastal region, provide encouragement for the use of the EOT20 model as a tidal correction for satellite altimetry in sea-level research. All ocean and load tide data from the model can be freely accessed at https://doi.org/10.17882/79489 (Hart-Davis et al., 2021). The tide gauges from the TICON dataset used in the validation of the tide model, are available at https://doi.org/10.1594/PANGAEA.896587 (Piccioni et al., 2018a).

Since the launch of the first altimetry satellites, ocean tide models have been improved dramatically for deep and shallow waters. However, issues are still found for areas of great interest for climate change investigations: the coastal regions. The purpose of this study is to analyze the influence of the ALES coastal retracker on tide modeling in these regions with respect to a standard open ocean retracker. The approach used to compute the tidal constituents is an updated and along-track version of the Empirical Ocean Tide model developed at DGFI-TUM. The major constituents are derived from a least-square harmonic analysis of sea level residuals based on the FES2014 tide model. The results obtained with ALES are compared with the ones estimated with the standard product. A lower fitting error is found for the ALES solution, especially for distances closer than 20 km from the coast. In comparison with in situ data, the root mean squared error computed with ALES can reach an improvement larger than 2 cm at single locations, with an average impact of over 10% for tidal constituents K 2 , O 1 , and P 1. For Q 1 , the improvement is over 25%. It was observed that improvements to the root-sum squares are larger for distances closer than 10 km to the coast, independently on the sea state. Finally, the performance of the solutions changes according to the satellite's flight direction: for tracks approaching land from open ocean root mean square differences larger than 1 cm are found in comparison to tracks going from land to ocean.

The TICON (TIdal CONstants) dataset contains harmonic constants of 40 tidal constituents computed for 1,145 tide gauges distributed globally. The tidal estimations are based on publicly available sea level records of the second version of the Global Extreme Sea Level Analysis (GESLA) project and were derived through a least squares‐based harmonic analysis on the single time series. A preliminary screening was performed on all records to exclude doubtful observations. Only the records containing more than 70% of valid measurements were processed, that correspond to 89.7% of the total 1,276 original public GESLA records. The results are stored in a text file, and include additional information on the position of the stations, the starting and ending years of the analysed record, the estimated error of the fit, a code that corresponds to the source of the record and additional information on the single time series. In ocean tide models, data from in situ stations are used for validation purposes, and TICON is a useful and easy‐to‐handle data set that allows the users to select the records according to different criteria most suitable for their purposes. The data are provided with DOI identification in the PANGAEA repository.

Sea level monitoring in the Arctic region has always been an extreme challenge for remote sensing, and in particular for satellite altimetry. Despite more than two decades of observations, altimetry is still limited in the inner Arctic Ocean. We have developed an updated version of the Danish Technical University's (DTU) Arctic Ocean altimetric sea level timeseries starting in 1993 and now extended up to 2015 with CryoSat-2 data. The time-series covers a total of 23 years, which allows higher accuracy in sea level trend determination. The record shows a sea level trend of 2.2 ± 1.1 mm/y for the region between 66 • N and 82 • N. In particular, a local increase of 15 mm/y is found in correspondence to the Beaufort Gyre. An early estimate of the mean sea level trend budget closure in the Arctic for the period 2005-2015 was derived by using the Equivalent Water Heights obtained from GRACE Tellus Mascons data and the steric sea level from the NOAA Global Ocean Heat and Salt Content dataset. In this first attempt, we computed the budget based on seasonally averaged values, obtaining the closure with a difference of 0.4 mm/y. This closure is clearly inside the uncertainties of the various components in the sea level trend budget.

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