Sea Level Stations Research Articles

Water Level Time Series Forecasting Using TCN Study Case in Surabaya

Climate change is causing water levels to rise, leading to detrimental effects like tidal flooding in coastal areas. Surabaya, the capital of East Java Province in Indonesia, is particularly vulnerable due to its low-lying location. According to the Meteorological, Climatological, and Geophysical Agency (BMKG), tidal flooding occurs annually in Surabaya as a result of rising water levels, highlighting the urgent need for water level forecasting models to mitigate these impacts. In this study, we employ the Temporal Convolutional Network (TCN) machine learning model for water level forecasting using data from a sea level station monitoring facility in Surabaya. We divided the training data into three scenarios: 3, 6, and 8 months to train TCN models for 14-day forecasts. The 8-month training scenario yielded the best results. Subsequently, we used the 8-month training data to forecast 1, 3, 7, and 14 days using TCN, Transformers, and the Recurrent Neural Network (RNN) models. TCN consistently outperformed other models, particularly excelling in 1-day forecasting with coefficient of determination () and RMSE values of 0.9950 and 0.0487, respectively.

Does Sea Level Change in The Strait of Istanbul, Türkiye?

Bosphorus is a narrow water passage connecting the Marmara Sea and the Black Sea through a sea level balance current flow. In this study, change points in mean and in variance and trend analysis are performed to reveal whether sea level is changing or not. Moreover, the simple linear regression is calculated to explain the relationships among sea level data in the three stations located in the Black Sea, the Marmara Sea and Bosphorus. The Turkish National Sea Level Monitoring System (TUDES) measures sea water level at each 15-minute periods in three stations in order to minimize the effect of turbulence. The sea level in the coastal area of Istanbul city is most populous region with its highly economic importance. The most important reason for this is the serious decrease in the amount of water discharged into the Black Sea due to dams and excessive water usage. The three sea level stations have no change points on the average of the whole data. The change points in variance are depicted especially in gap data years and especially around year 2018. While the difference in the sea level of the Istanbul and Sile stations contain seasonality, the difference between the other stations has a sinusoidal component. While this study lights on understanding the sea level characteristics of the Bosphorus, it also emphasizes the importance of accuracy, completeness and long-term measurement data requirement.

An ozonesonde evaluation of spaceborne observations in the Andean tropics

Satellite observations of ozone in the tropics have feedback from in situ measurements at sea level stations, but the tropical Andes is a region that is yet to be included in systematic validations. In this work, ozonesondes launched from the equatorial Andes were used to evaluate total column ozone (TCO) measured by spaceborne sensors TROPOMI/S5P (2018–2021), GOME-2/MetOp-B, OMI/Aura, and OMPS/Suomi NPP (2014–2021). Likewise, we evaluated tropospheric column ozone (TrCO) measured by the first two. Additionally, we evaluated TCO and TrCO from reanalysis products MERRA-2 and CAMS-EAC4. Results indicate that TCO observations by OMPS/Suomi NPP produce the closest comparison to ozonesondes (− 0.2% mean difference) followed by OMI/Aura (+ 1.2% mean difference). Thus, they outperform the sensor with the highest spatial resolution of current satellite measurements, namely TROPOMI/S5P (+ 3.7% mean difference). This overprediction is similar to the one encountered for GOME-2/MetOp-B (+ 3.2% mean difference). A positive bias with respect to soundings was also identified in TrCO measured by TROPOMI/S5P (+ 32.5% mean difference). It was found that the climatology used by TROPOMI overpredicts ozone in the troposphere when compared with the mean of Andes measurements, while both data sets are essentially the same in the stratosphere. Regarding reanalysis products, MERRA-2 compares better to ozonesondes than CAMS, both for TCO and TrCO (mean differences are 1.9% vs. 3.3%, and 11.5% vs. 22.9%, respectively). Identifying spaceborne ozone measurements that currently perform the best over the region is relevant given the present conditions of rapidly changing atmospheric composition. At the same time, ozonesonde data in this work offer an opportunity to improve satellite observations in the Andean tropics, a challenging region for space measurements.

Mechanism of generation and propagation characteristics of coastal trapped waves in the Black Sea

Abstract. Coastal trapped waves (CTW) are a major mechanism to distribute energy from the atmosphere in the ocean and play a significant role in large-scale, low-frequency sea-level and current variability in continental shelf and slope areas. Despite their significance for coastal dynamics, observational evidence of the influence of CTWs on the large-scale circulation is rather limited. In this study, mode-1 coastal trapped waves that were captured at sea-level stations at five locations along the southern coast of the Black Sea are examined together with sea surface height reanalysis from the Copernicus Marine Service to reveal their generation mechanisms and role in the coastal dynamics. It is found that the CTWs were formed when water accumulated on the western shelf after gale-force alongshore winds in the western Black Sea. Excited waves propagate along the Black Sea coast from west to east with a speed of 2.3–2.6 m s−1 and transport the atmospherically induced energy all over the Black Sea. The coastal current generated by the CTWs (on the order of 1 m s−1 in magnitude) and the main Black Sea current merge and flow eastward as a single structure, resulting in intensification of the Black Sea circulation during winter. Hence, we present evidence of the influence of CTWs on the large-scale circulation.

Scenario-based tsunami hazard assessment for the coast of Vietnam from the \(109^{o}\) Meridian fault zone

Located on the continental shelf and stretching approximately 1,000 km along the Central and Southern Central coasts of Vietnam, the 109o Meridian Fault system has long been recognized as seismically active. Empirical evidence of the seismic, volcanic, and submarine landslide activities also suggests that this fault might be capable of originating near-field tsunamis, which can harm the Vietnamese coastal zone. This paper investigates the possibility of the near-field tsunamis from the 109o Meridian Fault and their impact on the Vietnamese coasts. Location and segmentation of the 109o Meridian fault were determined using seismological, geophysical data, and geomorphologic evidence. The fault’s seismotectonic and geodynamic characteristics were used to model the tsunami source. The COMCOT model was used for simulating three worst-case tsunami scenarios excited by earthquakes with Mw = 8.0 originated in two different segments of the 109o Meridian Fault source. The relationship between tsunami wave height and the source-to-coast travel time is investigated in detail at the virtual sea-level stations distributed along the Vietnamese coastline. The simulation results are presented in the deep-ocean tsunami amplitude maps for the entire East Vietnam Sea region and the coastal tsunami amplitude maps for the Vietnamese coasts. The simulation results show the highest tsunami hazards concentrated along the Central and Southern Central Vietnam coastal zones (from Quang Nam province to Ba Ria-Vung Tau province), with the highest tsunami waves not exceeding 4 m observed off-shore the Quang Ngai, Phu Yen and Ninh Thuan provinces. The shortest tsunami travel time from the source to the Southern Central coast is 35 minutes. Due to its strike-slip mechanism, there is a low possibility of the 109o Meridian fault generating earthquake-triggered tsunamis. Moreover, even if they occurred, the near-field tsunamis generated from the 109o Meridian fault source can hardly cause severe damage and losses for the coastal zones of Vietnam, as shown by the simulation results. Nevertheless, the worst-case scenarios simulation results provide the highest risk that the near-field tsunamis from the 109o Meridian fault source might affect the Vietnamese coasts, which is helpful for the national tsunami warning and response purpose.

Development of Seawater Intrusion Vulnerability Assessment for Averaged Seasonality of Using Modified GALDIT Method

Climate change and anthropogenic activities are necessitating accurate diagnoses of seawater intrusion (SWI) to ensure the sustainable utilization of groundwater resources in coastal areas. Here, vulnerability to SWI was assessed by classifying the existing GALDIT into static parameters (groundwater occurrence (G), aquifer hydraulic conductivity (A), and distance from shore (D)) and dynamic parameters (height to groundwater-level above sea-level (L), impact of existing status of seawater intrusion (I), and aquifer thickness (T)). When assessing the vulnerability of SWI based on observational data (2010–2019), 10-year-averaged data of each month is used for GALDIT dynamic parameter for representing the seasonal characteristics of local water cycles. In addition, the parameter L is indicated by the data observed at the sea-level station adjacent to the groundwater level station. The existing GALDIT method has a range of scores that can be divided into quartiles to express the observed values. To sensitively reflect monthly changes in values, the range of scores is divided into deciles. The calculated GALDIT index showed that the most vulnerable month is September, due to relatively low groundwater level. The proposed method can be used to apply countermeasures to vulnerable coastal areas and build water resources management plan considering vulnerable seasons.

Regional-scale modelling for the assessment of atmospheric particulate matter concentrations at rural background locations in Europe

Abstract. The application of regional-scale air quality models is an important tool in air quality assessment and management. For this reason, the understanding of model abilities and performances is mandatory. The main objective of this research was to investigate the spatial and temporal variability of background particulate matter (PM) concentrations, to evaluate the regional air quality modelling performance in simulating PM concentrations during statically stable conditions and to investigate processes that contribute to regionally increased PM concentrations with a focus on eastern and central Europe. The temporal and spatial variability of observed PM was analysed at 310 rural background stations in Europe during 2011. Two different regional air quality modelling systems (offline coupled European Monitoring and Evaluation Programme, EMEP, and online coupled Weather Research and Forecasting with Chemistry) were applied to simulate the transport of pollutants and to further investigate the processes that contributed to increased concentrations during high-pollution episodes. Background PM measurements from rural background stations, wind speed, surface pressure and ambient temperature data from 920 meteorological stations across Europe, classified according to the elevation, were used for the evaluation of individual model performance. Among the sea-level stations (up to 200 m), the best modelling performance, in terms of meteorology and chemistry, was found for both models. The underestimated modelled PM concentrations in some cases indicated the importance of the accurate assessment of regional air pollution transport under statically stable atmospheric conditions and the necessity of further model improvements.

The Huelva (Spain) Tsunami-Ready Station and Its Interaction with Storm Emma (March 2018)

The sea level station operating since 1996 at Mazagón (Huelva, Spain) has been progressively upgraded to fit tsunami warning requirements, due to its location in one of the main regions at risk. Its radar water level sensor was complemented in 2017, with the addition of a pressure sensor. The performance of both sea level sensors and their response to sea level oscillations, at different frequencies, is assessed. Particular emphasis is put on the effect of extreme events, such as Storm Emma, when alternative methods to obtain 1-min data are tested, in contrast to the one based on arithmetic means. The overall differences are small, for the whole period of study (centered-root-mean-square-error below 1 cm, for 5-min, and hourly data; similar tidal parameters and sea level oscillations with periods between 30 s and 5 min). However, during Storm Emma, the pressure sensor presents sensibly lower readings than the radar, with the centered-root-mean-square-error rising to 80 mm on the March 2nd 2018. A new method to compute 1-min data, based on medians, reduced this value to 10 mm for the same day.

Characterization of aerosol particles at Cabo Verde close to sea level and at the cloud level – Part 1: Particle number size distribution, cloud condensation nuclei and their origins

Abstract. In the framework of the MarParCloud (Marine biological production, organic aerosol particles and marine clouds: a Process Chain) project, measurements were carried out on the islands of Cabo Verde (a.k.a. Cape Verde) to investigate the abundance, properties and sources of aerosol particles in general, and cloud condensation nuclei (CCN) in particular, both close to sea level and at the cloud level. A thorough comparison of particle number concentration (PNC), particle number size distribution (PNSD) and CCN number concentration (NCCN) at the Cape Verde Atmospheric Observatory (CVAO, sea-level station) and Monte Verde (MV, cloud-level station) reveals that during times without clouds the aerosols at CVAO and MV are similar and the boundary layer is generally well mixed. Therefore, data obtained at CVAO can be used to describe the aerosol particles at cloud level. Cloud events were observed at MV during roughly 58 % of the time, and during these events a large fraction of particles was activated to cloud droplets. A trimodal parameterization method was deployed to characterize PNC at CVAO. Based on number concentrations in different aerosol modes, four well-separable types of PNSDs were found, which were named the marine type, mixture type, dust type1 and dust type2. Aerosol particles differ depending on their origins. When the air masses came from the Atlantic Ocean, sea spray can be assumed to be one source for particles besides new particle formation. For these air masses, PNSDs featured the lowest number concentration in Aitken, accumulation and coarse modes. Particle number concentrations for sea spray aerosol (SSA, i.e., the coarse mode for these air masses) accounted for about 3.7 % of NCCN,0.30 % (CCN number concentration at 0.30 % supersaturation) and about 1.1 % to 4.4 % of Ntotal (total particle number concentration). When the air masses came from the Sahara, we observed enhanced Aitken, accumulation and coarse mode particle number concentrations and overall increased NCCN; NCCN,0.30 % during the strongest observed dust periods is about 2.5 times higher than that during marine periods. However, the particle hygroscopicity parameter κ for these two most different periods shows no significant difference and is generally similar, independent of air mass. Overall, κ averaged 0.28, suggesting the presence of organic material in particles. This is consistent with previous model work and field measurements. There is a slight increase in κ with increasing particle size, indicating the addition of soluble, likely inorganic, material during cloud processing.

Ocean Observations Required to Minimize Uncertainty in Global Tsunami Forecasts, Warnings, and Emergency Response

It is possible that no catastrophe has mobilized the global ocean science and coastal emergency management communities more than the 2004 Indian Ocean tsunami. Though the Pacific tsunami threat was recognized, and a warning system had been in place since 1965, there was no warning system in the Indian Ocean, and almost 230,000 people perished. More broadly, the event highlighted critical gaps in global tsunami science and observation systems. In 2004, real-time coastal and deep-ocean observation systems were almost nonexistent. Tsunami sources were inferred based on rough seismic parameters. Since then, tremendous strides have been made under the auspices of IOC/UNESCO toward better understanding tsunami mechanisms, deploying advanced real-time tsunami observation systems, and establishing tsunami warning and mitigation systems for the four main ocean basins at risk from tsunamis. Nevertheless, significant detection, measurement, and forecast uncertainties remain to meet emergency response and community needs. A new generation of ocean sensing capabilities presents an opportunity to address several of these uncertainties. Ocean bottom pressures can be measured over dense, multisensor grids linking stand-alone buoy systems with emerging capabilities like fiber-optic cables. The increasing number of coastal sea-level stations can better verify forecasts and account for local variability. In addition, GNSS sensors may be able to provide solid-earth data needed to define seismic tsunami sources more precisely in the short timescales required. When combined with advances in seismology, other emerging techniques, and state-of-the-art modeling and computational resources, these capabilities will enable more timely and accurate tsunami detection and measurement to meet identified user needs and save lives. Because of these advances in detection and measurement, the opportunity exists to greatly reduce and/or quantify uncertainties associated with forecasting tsunamis. Providing more timely and accurate information related to tsunami location, arrival time, height, inundation, and duration would improve public trust and confidence and fundamentally alter tsunami emergency response. Additionally, this capability could be integrated with related fields (e.g., storm surge, sea-level rise, tide predictions, and ocean forecasting) to develop and deploy one continuous, real-time, accurate depiction of the always moving boundary that separates ocean from coast, wet from dry, and, sometimes, life from death.

A 10-year characterization of the Saharan Air Layer lidar ratio in the subtropical North Atlantic

Abstract. Particle extinction-to-backscatter ratio (lidar ratio) is a key parameter for a correct interpretation of elastic lidar measurements. Of particular importance is the determination of the lidar ratio of the Saharan Air Layer mineral dust transported into the free troposphere over the North Atlantic region. The location of the two sun photometer stations managed by the Izaña Atmospheric Research Centre (IARC) on the island of Tenerife and a decade of available micropulse lidar (MPL) data allow us to determine the lidar ratio under almost pure-dust conditions. This result can be considered representative of the Saharan dust transported westward over the North Atlantic in the subtropical belt. Three different methods have been used to calculate the lidar ratio in this work: (1) using the inversion of sky radiance measurements from a sun–sky photometer installed at the Izaña Observatory (2373 m a.s.l.) under free-troposphere conditions; (2) the one-layer method, a joint determination using a micropulse lidar sited at the Santa Cruz de Tenerife sea-level station and photometric information considering one layer of aerosol characterized by a single lidar ratio; and (3) the two-layer method, a joint determination using the micropulse lidar and photometric information considering two layers of aerosol with two different lidar ratios. The one-layer method only uses data from a co-located photometer at Santa Cruz de Tenerife, while the two-layer conceptual approach incorporates photometric information at two heights from the observatories of Izaña and Santa Cruz de Tenerife. The almost pure-dust lidar ratio retrieval from the sun–sky photometer and from the two-layer method give similar results, with lidar ratios at 523 nm of 49 ± 6 and 50 ± 11 sr. These values obtained from a decade of data records are coincident with other studies in the literature reporting campaigns in the subtropical North Atlantic region. This result shows that the two-layer method is an improved conceptual approach compared to the single-layer approach, which matches the real lower-troposphere structure well. The two-layer method is able to retrieve reliable lidar ratios and therefore aerosol extinction profiles despite the inherent limitations of the elastic lidar technique. We found a lack of correlation between lidar ratio and Ångström exponent (α), which indicates that the dust lidar ratio can be considered independent of dust size distribution in this region. This finding suggests that dust is, under most atmospheric conditions, the predominant aerosol in the North Atlantic free troposphere, which is in agreement with previous studies conducted at the Izaña Observatory.

Tsunami Modelling of the 8th September 2017 Mexico Earthquake of M 8.1, Using Two Different Software

On 8th of September 2017, an earthquake of magnitude Mw 8.1 was generated offshore Mexico, Chiapas area, at 04:49 UTC, with a depth of 72 km and the following coordinates: Latitude 15.02 N, Longitude 93.81 W, 98 km away from Pijijiapan (Mexico). The fault plane solution of the event was normal plane. Maximum tsunami waves of 1.1 m were measured at Salina Cruz sea level station, following the earthquake. Tsunami modelling simulations were accomplished using the earthquake’s parameters (location, magnitude, depth) and moment tensor solutions given by 3 different agencies: United States Geological Survey (USGS), German Research Centre for Geosciences (GFZ) and Global Centroid Moment Tensor (GCMT). For every case studied, the affected locations, sea level estimates and maximum wave heights were computed. There are two software used for modelling, the Tsunami Analysis Tool (TAT), provided and developed by the Joint Research Centre, Ispra, Italy and TRIDEC Cloud, provided by the German Research Centre for Geosciences (GFZ), Potsdam, Germany. After analysing the modelling scenarios, a comparison between the results of the two software was accomplished, for the same earthquake parameters. The results show that the parameters of GCMT computed with TAT overestimate those computed with TRIDEC, of maximum 3.9 m wave height at Arista with TAT and 2.46 m with TRIDEC. For the GFZ parameters, the estimates give 3.5 m for Pasito de la Senora with TAT, compared to 1.64 m maximum waves at Puerto Madero with TRIDEC. For the USGS data set, the results are similar, maximum waves of 2.4 m at Pasito de la Senora with TAT, and 2.9 m at Puerto Madero with TRIDEC. The Salina Cruz station, where 1.1 m height waves were measured, gives results only for the simulations ran with TAT, with 1.0 m for the GFZ earthquake parameters, 0.6 m for USGS and 1.4 m for the GCMT parameters.

The horizontal motion of a water layer during the passage of tsunami waves based on data from a dense ocean-floor network of deepwater sea-level stations

A method for the retrieval the horizontal motion of a water layer during the passage of a tsunami wave is proposed based on the data from a dense network of sea-level deepwater stations. The method is applied for calculation of the horizontal velocity of the flow and displacement of water particles in the vicinity of the DONET/JAMSTEC observatories during the passage the 2011 Tohoku tsunami. It was found that the amplitude of the flow speed was ~0.01 m/s, while the amplitude of the displacement exceeded 10 m.

A Century of Sea Level Measurements at Newlyn, Southwest England

ABSTRACTThe Newlyn Tidal Observatory is the most important sea level station in the United Kingdom. It commenced operations in 1915 as part of the Second Geodetic Levelling of England and Wales, and the mean sea level determined from the tide gauge during the first six years (May 1915–April 1921) defined Ordnance Datum Newlyn (ODN) which became the national height datum for the whole of Great Britain. The 100 years of sea level data now available have contributed significantly to many studies in oceanography, geology, and climate change. This paper marks the centenary of this important station by reviewing the sea level (and, more recently, detailed land level) measurements and Newlyn's contributions to UK cartography, geodesy and sea-level science in general. Recommendations are made on how sea and land level measurements at Newlyn might be enhanced.

Protecting People from Natural Disasters: Political Institutions and Ocean-Originated Hazards

Why do some leaders protect their citizens from natural disasters while others do not? This paper argues that leaders in large coalition systems provide more protection against natural disasters than leaders in small coalition systems. Yet, autocrats also provide large-scale disaster protection if members of their winning coalition are exposed to natural hazards. The paper tests these propositions by examining cross-country variation in the number of sea-level stations as a lower bound for protection against ocean-originated disasters. Empirical evidence indicates that leaders in large coalition systems deploy more sea-level stations than their counterparts in small coalition systems. The evidence also shows that if the national capital is close to the coast, thus exposing members of the ruling coalition to ocean-originated hazards, leaders across political systems install more sea-level stations.

The GLOSS Delayed Mode Data Centre and the GLOSS Implementation Plan 2012

Abstract. The main component of the Global Sea Level Observing System (GLOSS) is the GLOSS Core Network (GCN) of 290 sea level stations. The present definition of the GCN (the definition is modified every few years) is called GLOSS10. In 2012 a new GLOSS implementation plan was developed to update technical standards for GLOSS tide gauge stations, as well as describing the basic terms and obligations for Member States participating in GLOSS. The British Oceanographic Data Centre (BODC) and the Permanent Service for Mean Sea Level (PSMSL) run the GLOSS Delayed Mode Data Centre jointly. It is responsible for assembling, quality controlling and distributing the "final" version of GLOSS sea level data sets and their supporting metadata, as well as carrying out data archaeology on historical analogue sea level records, preserving them in digital form. It also maintains the GLOSS Station Handbook and GLOSS website.

Data quality through a web-based QA/QC system: implementation for atmospheric mercury data from the global mercury observation system.

The overall goal of the on-going Global Mercury Observation System (GMOS) project is to develop a coordinated global monitoring network for mercury, including ground-based, high altitude and sea level stations. In order to ensure data reliability and comparability, a significant effort has been made to implement a centralized system, which is designed to quality assure and quality control atmospheric mercury datasets. This system, GMOS-Data Quality Management (G-DQM), uses a web-based approach with real-time adaptive monitoring procedures aimed at preventing the production of poor-quality data. G-DQM is plugged on a cyberinfrastructure and deployed as a service. Atmospheric mercury datasets, produced during the first-three years of the GMOS project, are used as the input to demonstrate the application of the G-DQM and how it identifies a number of key issues concerning data quality. The major issues influencing data quality are presented and discussed for the GMOS stations under study. Atmospheric mercury data collected at the Longobucco (Italy) station is used as a detailed case study.

Analysis of the surface temperature and wind forecast errors of the NCAR-AirDat operational CONUS 4-km WRF forecasting system

Investigating the characteristics of model-forecast errors using various statistical and object-oriented methods is necessary for providing useful guidance to end-users and model developers as well. To this end, the random and systematic errors (i.e., biases) of the 2-m temperature and 10-m wind predictions of the NCAR-AirDat weather research and forecasting (WRF)-based real-time four-dimensional data assimilation (RTFDDA) and forecasting system are analyzed. This system has been running operationally over a contiguous United States (CONUS) domain at a 4-km grid spacing with four forecast cycles daily from June 2009 to September 2010. In the result an exceptionally useful forecast dataset was generated and used for studying the error properties of the model forecasts, in terms of both a longer time period and a broader coverage of geographic regions than previously studied. Spatiotemporal characteristics of the errors are investigated based on the 24-h forecasts between June 2009 and April 2010, and the 72-h forecasts between May and September 2010. It was found that the biases of both wind and temperature forecasts vary greatly seasonally and diurnally, with dependency on the forecast length, station elevation, geographical location, and meteorological conditions. The temperature showed systematic cold biases during the daytime at all station elevations and warm biases during the nighttime above 1,000 m above sea level (ASL), while below 600 m ASL cold biases occurred during the nighttime. The forecasts of surface wind speed exhibited strong positive biases during the nighttime, while the negative biases were observed in the spring and summer afternoons. The surface wind speed was mostly over-predicted except for the stations located between 1,000 and 2,100 m ASL, for which negative biases were identified for most forecast cycles. The highest wind-speed errors were found over the high terrain and near sea-level stations. The wind-direction errors were relatively large at the high-terrain elevation in the Rocky and Appalachian mountain ranges and the western coastal areas and the error structure exhibited notable diurnal variability.

The contribution to tidal asymmetry by different combinations of tidal constituents

[1] We provide a general framework for identifying the constituents responsible for asymmetry in any tidal time series, by extending and generalizing the skewness-based approach of Nidzieko (2010) to include any number of tidal constituents. We show that this statistic has two features which greatly simplify the attribution of asymmetry to particular constituents: (1) only combinations of two or three constituents can contribute to skewness, regardless of how many constituents are significant in the time series and (2) of those combinations, only the few meeting the frequency conditions 2ω1 = ω2 or ω1 + ω2 = ω3 will give rise to long-term mean asymmetry. It is therefore relatively easy to identify every such combination, even when many constituents are present. We then go on to show how the relative contribution of each such combination can be measured and compared, based on the amplitudes, frequencies and relative phases of the constituents. We also show that there is an upper bound to the skewness generated by any such combination. The metrics are applied to data from 335 worldwide sea level stations and from a global ocean tidal model based on TOPEX/POSEIDON altimetry. Global maps are made of the patterns of tidal skewness. We identify the combinations of astronomical tides that dominate skewness in different tidal regimes and geographic locations, and explain the dependence of skewness on tidal form number.

Contribution of regional climate drivers to future winter sea-level changes in the Baltic Sea estimated by statistical methods and simulations of climate models

A statistical downscaling approach is applied to the output of five different global climate model simulations driven by twenty-first century future scenarios of greenhouse gas concentrations. The contribution of sea-level pressure (SLP) and precipitation changes to regional future winter sea-level changes is estimated for four Baltic sea-level stations by establishing statistical relationships between sea level as predictand and large-scale climate fields as predictors. Using SLP as predictor for the central and eastern Baltic Sea level stations, three climate models lead to statistically significant twenty-first century future trends in the range of the order of 1–2 mm/year. Using precipitation as predictor for the stations in the southern Baltic coast all five models lead to statistically significant trends with a range of the order of 0.4 mm/year. These numbers are smaller, but of the order of magnitude as the predicted global sea-level rise.