Abstract. Sea-level observations provide information on a variety of processes occurring over different temporal and spatial scales that may contribute to coastal flooding and hazards. However, global research on sea-level extremes is restricted to hourly datasets, which prevent the quantification and analyses of processes occurring at timescales between a few minutes and a few hours. These shorter-period processes, like seiches, meteotsunamis, infragravity and coastal waves, may even dominate in low tidal basins. Therefore, a new global 1 min sea-level dataset – MISELA (Minute Sea-Level Analysis) – has been developed, encompassing quality-checked records of nonseismic sea-level oscillations at tsunami timescales (T<2 h) obtained from 331 tide-gauge sites (https://doi.org/10.14284/456, Zemunik et al., 2021b). This paper describes data quality control procedures applied to the MISELA dataset, world and regional coverage of tide-gauge sites, and lengths of time series. The dataset is appropriate for global, regional or local research of atmospherically induced high-frequency sea-level oscillations, which should be included in the overall sea-level extremes assessments.

Abstract. Sea-level observations provide information on a variety of processes occurring over different temporal and spatial scales that may contribute to coastal flooding and hazards. However, global research of sea-level extremes is restricted to hourly datasets, which prevent quantification and analyses of processes occurring at timescales between a few minutes and a few hours. These shorter period processes, like seiches, meteotsunamis, infragravity and coastal waves, may even dominate in low-tidal basins. Therefore, a new global 1-minute sea-level dataset – MISELA (Minute Sea-Level Analysis) – has been developed, encompassing quality-checked records of nonseismic sea-level oscillations at tsunami timescales (T < 2 h) obtained from 331 tide-gauge sites (https://doi.org/10.14284/456, Zemunik et al., 2021b). This paper describes data quality-control procedures applied to the MISELA dataset, world and regional coverage of tide-gauge sites and lengths of time-series. The dataset is appropriate for global, regional or local research of atmospherically-induced high-frequency sea-level oscillations, which should be included in the overall sea-level extremes assessments.

Oceanographic parameters, such as sea surface temperature, surface chlorophyll-a concentration, sea surface ice concentration, sea surface height, etc., are listed as Essential Climate Variables. Therefore, there is a crucial need for persistent and accurate measurements on a global scale. While in situ methods tend to be accurate and continuous, these qualities are difficult to scale spatially, leaving a significant portion of Earth’s oceans and seas unmonitored. To tackle this, various remote sensing techniques have been developed. One of the more prominent ways to measure the aforementioned parameters is via satellite spacecraft-mounted remote sensors. This way, spatial coverage is considerably increased while retaining significant accuracy and resolution. Unfortunately, due to the nature of electromagnetic signals, the atmosphere itself and its content (such as clouds, rain, etc.) frequently obstruct the signals, preventing the satellite-mounted sensors from measuring, resulting in gaps—missing data—in satellite recordings. One way to deal with these gaps is via various reconstruction methods developed through the past two decades. However, there seems to be a lack of review papers on reconstruction methods for satellite-derived oceanographic variables. To rectify the lack, this paper surveyed more than 130 articles dealing with the issue of data reconstruction. Articles were chosen according to two criteria: (a) the article has to feature satellite-derived oceanographic data (b) gaps in satellite data have to be reconstructed. As an additional result of the survey, a novel categorising system based on the type of input data and the usage of time series in reconstruction efforts is proposed.

We observed interannual changes in the temperature and salinity of the surface layer of the Adriatic Sea when measured during the period 2005–2020. We observed non-stationarity and a positive linear trend in the series of mixed layer depth, heat storage, and potential energy anomalies. This non-stationarity was related to the climate regime that prevailed between 2011 and 2017. We observed significant changes in the interannual variability of salinity above and below the mixed layer depth and a positive difference in the surface barrier layer. In an effort to reconstruct the cause of this phenomenon, a multi-stage investigation was conducted. The first suspected culprit was the change in wind regime over the Mediterranean and Northeast Atlantic regions in September. Using the growing neural gas algorithm, September wind fields over the past 40 years were classified into nine distinct patterns. Further analysis of the CTD data indicated an increase in heat storage, a physical property of the Adriatic Sea known to be strongly influenced by the inflow of warm water masses controlled by the bimodal oscillating system (BiOS). The observed increase in salinity confirmed the assumption that BiOS activity affects heat storage. Unexpectedly, this analysis showed that an inverse vertical salinity profile was present during the summer months of 2015, 2017, and 2020, which can only be explained by salinity changes being a dominant factor. In addition, the aforementioned wind regime caused an increase in energy loss through latent energy dissipation, contributing to an even larger increase in salinity. While changes in the depth of the mixed layer in the Adriatic are usually due to temperature changes, this phenomenon was primarily caused by abrupt changes in salinity due to a combination of BiOS and local factors. This is the first record of such an event.

&lt;p&gt;Extreme sea levels can lead to floods that cause significant damage to coastal infrastructure and put people's lives in danger. These floods are a result of physical processes occurring at various time and space scales, including sub-hourly scales. To estimate the contribution of sub-hourly sea level oscillations to extreme sea levels, raw sea level data from about 300 tide gauge stations along the European coasts, with a sampling resolution of less than 20 minutes, were collected. The data were obtained from: (1) the IOC-SLSMF website (290 stations); (2) National agencies (Portugal, Finland, Croatia &amp;#8211;24 stations). Portions of the raw dataset had various data quality issues (i.e., spikes, shifts, drifts) hence quality control procedure was required. Out of range values, values with a 50 cm difference from one neighbouring value or a 30 cm difference from both neighbouring values, were automatically removed. The automatic spike detection procedure was carried out by removing values that differed by three standard deviations from a spline fitted with the least squares method. Following the automatic quality control, all remaining data were visually examined and spurious data were removed manually.&lt;/p&gt;&lt;p&gt;The resulting data set contains sea level data from 2007. to 2021., with an average record length of approximately 7 years, however the length varies from a few months at some stations to 13 years at others. Tide gauges with longer records (&gt;10 years) are based in the Baltic region, France and Spain, whereas the ones with shorter records (&lt;3 years) are mostly based in the Eastern Mediterranean. The Western Mediterranean and western Europe have a high station coverage with records of various lengths. Tide gauges mostly provide data with a one-minute sampling frequency, however, some of them still record on a multi-minute scale (i.e., United Kingdom with 15 minutes and Norway and the Netherlands with 10 minutes sampling frequency).&lt;/p&gt;&lt;p&gt;Preliminary statistical analyses were done, resulting with spatial and temporal distribution of contribution of high-frequency sea level oscillations to total sea level extremes along the European coasts.&lt;/p&gt;