Thresholds for morphological changes on an exposed sandy beach as a function of wave height

A sandy beach in the south of Portugal (Faro beach, Ria Formosa) was surveyed from the dune crest seaward to 15 m depth 20 times over a period of 26 months. Wave time‐series between surveys were analysed to obtain relationships between wave height and vertical profile variations and to define wave thresholds for important morphological changes. Results show that the active zone of the profile lies between 5 m above and 10·4 m below mean sea level, and that there are clear cross‐shore differences in the vertical variability of the profile. Based on the pattern of vertical variability, the profile was divided into four cross‐shore sectors: A (berm), 20–80 m from the profile origin; B (sub‐tidal terrace), 80–170 m; C (long‐shore bar), 170–360 m; and D, 360–700 m. The relationship between the modulus of the maximum vertical change in each sector and the 99th percentile of significant wave height between surveys was always significant. Calculated thresholds for significant wave height generating important morphological changes were 2·3 m in sector A, 3·2 m in sectors B and C, and 4·1 m in sector D. Copyright © 2010 John Wiley & Sons, Ltd.     

Directional wave measurements using an autonomous vessel

An autonomous vessel, the Offshore Sensing Sailbuoy, was used for wave measurements near the Ekofisk oil platform complex in the North Sea (56.5º N, 3.2º E, operated by ConocoPhillips) from 6 to 20 November 2015. Being 100 % wind propelled, the Sailbuoy has two-way communication via the Iridium network and has the capability for missions of 6 months or more. It has previously been deployed in the Arctic, Norwegian Sea and the Gulf of Mexico, but the present study was the first test for wave measurements. During the campaign the Sailbuoy held position about 20 km northeast of Ekofisk (on the lee side) during rough conditions. Mean wind speed measured at Ekofisk during the campaign was 9.8 m/s, with a maximum of 20.4 m/s, with wind mostly from south and southwest. A Datawell MOSE G1000 GPS-based 2 Hz wave sensor was mounted on the Sailbuoy. Mean significant wave height (H _s 1 min) measured was 3 m, whereas maximum H _s was 6 m. Mean wave period was 7.7 s, while maximum wave height, H _max, was 12.6 m. These measurements have been compared with non-directional Waverider observations at the Ekofisk complex. The agreement between the two data sets was very good, with a mean percent absolute error of 7 % and a linear correlation coefficient of 0.97. The wave frequency spectra measured by the two instruments compared very well, except for low H _s (～1 m), where the motion of the vessel seemed to influence the measurements. Nevertheless, the Sailbuoy performed well during this campaign, and results suggest that it is a suitable platform for wave measurements in a broad range of sea conditions.     

An effective method based on dynamic sampling for data assimilation in a global wave model

The ensemble Kalman filter (EnKF) performs well because that the covariance of background error is varying along time. It provides a dynamic estimate of background error and represents the reasonable statistic characters of background error. However, high computational cost due to model ensemble in EnKF is employed. In this study, two methods referred as static and dynamic sampling methods are proposed to obtain a good performance and reduce the computation cost. Ensemble adjustment Kalman filter (EAKF) method is used in a global surface wave model to examine the performance of EnKF. The 24-h interval difference of simulated significant wave height (SWH) within 1 year is used to compose the static samples for ensemble errors, and these errors are used to construct the ensemble states at each time the observations are available. And then, the same method of updating the model states in the EAKF is applied for the ensemble states constructed by a static sampling method. The dynamic sampling method employs a similar method to construct the ensemble states, but the period of the simulated SWH is changing with time. Here, 7 days before and after the observation time is used as this period. To examine the performance of three schemes, EAKF, static, or dynamic sampling method, observations from satellite Jason-2 in 2014 are assimilated into a global wave model, and observations from satellite Saral are used for validation. The results indicate that the EAKF performs best, while the static sampling method is relatively worse. The dynamic sampling method improves an assimilation effect dramatically compared to the static sampling method, and its overall performance is closed to the EAKF. In low latitudes, the dynamic sampling method has a slight advantage over the EAKF. In the dynamic or static sampling methods, only one wave model is required to run and their computational cost is reduced sharply. According to the performance of these three methods, the dynamic sampling method can treated as an effective alternative of EnKF, which could reduce the computational cost and provide a good performance of data assimilation.     

On wave radar measurement

The SAAB REX WaveRadar sensor is widely used for platform-based wave measurement systems by the offshore oil and gas industry. It offers in situ surface elevation wave measurements at relatively low operational costs. Furthermore, there is adequate flexibility in sampling rates, allowing in principle sampling frequencies from 1 to 10 Hz, but with an angular microwave beam width of 10° and an implied ocean surface footprint in the order of metres, significant limitations on the spatial and temporal resolution might be expected. Indeed there are reports that the accuracy of the measurements from wave radars may not be as good as expected. We review the functionality of a WaveRadar using numerical simulations to better understand how WaveRadar estimates compare with known surface elevations. In addition, we review recent field measurements made with a WaveRadar set at the maximum sampling frequency, in the light of the expected functionality and the numerical simulations, and we include inter-comparisons between SAAB radars and buoy measurements for locations in the North Sea.     

Climate change impact on extreme wave conditions in the North Sea: an ensemble study

An analysis of today’s mean and extreme wave conditions in the North Sea and their possible future changes due to anthropogenic climate change are presented. The sea state was simulated for the 30-year period 2071–2100 using the wave model WAM and an ensemble of wind field data sets for four climate change realizations as driving data. The wind field data sets are based on simulation outputs from two global circulation models (GCMs: HadAM3H and ECHAM4/OPYC3) for two emission scenarios (A2 and B2, Intergovernmental Panel on Climate Change, Special Report on Emission Scenarios). They were regionalized by the Swedish Meteorological and Hydrological Institute using the regional climate model RCAO. The effects of the climate realizations on the sea state statistics were assessed by analyzing the differences between the patterns in the four CGM/emission scenario combinations and those in two control simulations representing reference wave climate conditions for the 30-year period 1961–1990. The analysis of the four emission scenario/GCM combinations has shown that the future long-term 99 percentile wind speed and significant wave height increase by up to 7% and 18%, respectively, in the North Sea, except for significant wave height off the English coast and to the north in the HadAM3H-driven simulation. The climate change response in the ECHAM4/OPYC3-forced experiments is generally larger than in the HadAM3H-driven simulations. The differences in future significant wave height between the different combinations are in the same order of magnitude as those between the control runs for the two GCMs. Nevertheless, there is agreement among the four combinations that extreme wave heights may increase in large parts in the southern and eastern North Sea by about 0.25 to 0.35 m (5–8% of present values) towards the end of the twenty first century in case of global warming. All combinations also show an increase in future frequency of severe sea state.     

The El Salvador and Philippines Tsunamis of August 2012: Insights from Sea Level Data Analysis and Numerical Modeling

We studied two tsunamis from 2012, one generated by the El Salvador earthquake of 27 August (Mw 7.3) and the other generated by the Philippines earthquake of 31 August (Mw 7.6), using sea level data analysis and numerical modeling. For the El Salvador tsunami, the largest wave height was observed in Baltra, Galapagos Islands (71.1 cm) located about 1,400 km away from the source. The tsunami governing periods were around 9 and 19 min. Numerical modeling indicated that most of the tsunami energy was directed towards the Galapagos Islands, explaining the relatively large wave height there. For the Philippines tsunami, the maximum wave height of 30.5 cm was observed at Kushimoto in Japan located about 2,700 km away from the source. The tsunami governing periods were around 8, 12 and 29 min. Numerical modeling showed that a significant part of the far-field tsunami energy was directed towards the southern coast of Japan. Fourier and wavelet analyses as well as numerical modeling suggested that the dominant period of the first wave at stations normal to the fault strike is related to the fault width, while the period of the first wave at stations in the direction of fault strike is representative of the fault length.     

Inter-annual variability and longer-term changes in the wave climate of Western Australia between 1970 and 2009

Quantifying the long-term variability in wave conditions incident on a coastline is critical for predicting its resilience to future changes in the wave climate. In this study, a 40-year wave hindcast of the southern Indian Ocean has been created to assess the inter-annual variability and longer-term changes in the wave climate around Western Australia (WA) between 1970 and 2009. The model was validated against measurements from five wave buoys located along the WA coast. Changes in the mean annual significant wave height, 90th percentile wave height, peak period and mean wave direction were assessed, and the tracks of all wave events generating wave heights above 7 m were digitised and analysed for significant changes. Results show strong annual and inter-annual variability in the mean significant wave height, the 90th percentile wave height and the number of large events (wave height > 7 m) that impact the WA coastline. A significant positive trend in annual mean wave height was found in the southwest region of WA over the 40-year simulation. This appears to be due to an increase in intensity of the storm belt in the Southern Ocean which is associated with an increasing positive polarity in the Southern Annular Mode. However, no significant trends were found in the 90th percentile wave height or the number of large wave events impacting Western Australia. Although the number of large wave events in the southern Indian Ocean have increased, their potential to impact the coastal regions of Western Australia are reduced due to storm tracks being located further south, therefore balancing the number of large wave events reaching the WA coast.     

Assessing wave climate trends in the Bay of Biscay through an intercomparison of wave hindcasts and reanalyses

The Bay of Biscay, located in the Northeast Atlantic Ocean, is exposed to energetic waves coming from the open ocean that have crucial effects on the coast. Knowledge of the wave climate and trends in this region are critical to better understand the last decade’s evolution of coastal hazards and morphology and to anticipate their potential future changes. This study aims to characterize the long-term trends of the present wave climate over the second half of the twentieth century in the Bay of Biscay through a robust and homogeneous intercomparison of five-wave datasets (Corrected ERA-40 (C-ERA-40), ECMWF Reanalysis Interim (ERA-Interim), Bay Of Biscay Wave Atlas (BOBWA-10kH), ANEMOC, and Bertin and Dodet 2010)). The comparison of the quality of the datasets against offshore and nearshore measurements reveals that at offshore locations, global reanalyses slightly underestimate wave heights, while regional hindcasts overestimate wave heights, especially for the highest quantiles. At coastal locations, BOBWA-10kH is the dataset that compares the best with observations. Concerning long time-scale features, the comparison highlights that the main significant trends are similarly present in the five datasets, especially during summer for which there is an increase of significant wave heights and mean wave periods (up to +15 cm and +0.6 s over the period 1970–2001) as well as a southerly shift of wave directions (around ?0.4° year^?1). Over the same period, an increase of high quantiles of wave heights during the autumn season (around 3 cm year^?1 for 90th quantile of significant wave heights (SWH₉₀)) is also apparent. During winter, significant trends are much lower than during summer and autumn despite a slight increase of wave heights and periods during 1958–2001. These trends can be related to modifications in the wave-type occurrence. Finally, the trends common to the five datasets are discussed by analyzing the similarities with centennial trends issued from longer time-scale studies and exploring the various factors that could explain them.     

Wave climate simulation for southern region of the South China Sea

This study investigates long-term variability and wave characteristic trends in the southern region of the South China Sea (SCS). We implemented the state-of-the art WAVEWATCH III spectral wave model to simulate a 31-year wave hindcast. The simulation results were used to assess the inter-annual variability and long-term changes in the SCS wave climate for the period 1979 to 2009. The model was forced with Climate Forecast System Reanalysis winds and validated against altimeter data and limited available measurements from an Acoustic Wave and Current recorder located offshore of Terengganu, Malaysia. The mean annual significant wave height and peak wave period indicate the occurrence of higher wave heights and wave periods in the central SCS and lower in the Sunda shelf region. Consistent with wind patterns, the wave direction also shows southeasterly (northwesterly) waves during the summer (winter) monsoon. This detailed hindcast demonstrates strong inter-annual variability of wave heights, especially during the winter months in the SCS. Significant wave height correlated negatively with Niño3.4 index during winter, spring and autumn seasons but became positive in the summer monsoon. Such correlations correspond well with surface wind anomalies over the SCS during El Nino events. During El Niño Modoki, the summer time positive correlation extends northeastwards to cover the entire domain. Although significant positive trends were found at 95 % confidence levels during May, July and September, there is significant negative trend in December covering the Sunda shelf region. However, the trend appears to be largely influenced by large El Niño signals.     

European summer temperature response to annually dated volcanic eruptions over the past nine centuries

The drop in temperature following large volcanic eruptions has been identified as an important component of natural climate variability. However, due to the limited number of large eruptions that occurred during the period of instrumental observations, the precise amplitude of post-volcanic cooling is not well constrained. Here we present new evidence on summer temperature cooling over Europe in years following volcanic eruptions. We compile and analyze an updated network of tree-ring maximum latewood density chronologies, spanning the past nine centuries, and compare cooling signatures in this network with exceptionally long instrumental station records and state-of-the-art general circulation models. Results indicate post-volcanic June–August cooling is strongest in Northern Europe 2 years after an eruption (?0.52?±?0.05 °C), whereas in Central Europe the temperature response is smaller and occurs 1 year after an eruption (?0.18?±?0.07 °C). We validate these estimates by comparison with the shorter instrumental network and evaluate the statistical significance of post-volcanic summer temperature cooling in the context of natural climate variability over the past nine centuries. Finding no significant post-volcanic temperature cooling lasting longer than 2 years, our results question the ability of large eruptions to initiate long-term temperature changes through feedback mechanisms in the climate system. We discuss the implications of these findings with respect to the response seen in general circulation models and emphasize the importance of considering well-documented, annually dated eruptions when assessing the significance of volcanic forcing on continental-scale temperature variations.     

Rayleigh wave group velocity tomography of Gujarat region,Western India and its implications to mantle dynamics

In the present study, fundamental Rayleigh waves with varying period from 10 to 80 s are used to obtain group velocity maps in the northwest Deccan Volcanic Province of India. About 350 paths are obtained using 53 earthquakes (4.8 ≤ M ≥ 7.9) recorded by the SeisNetG (Seismic Network of Gujarat). Individual dispersion curves of group velocity of Rayleigh wave for each source-station path are estimated using multiple filter technique. These curves are used to determine lateral distribution of Rayleigh wave group velocity by tomographic inversion method. Our estimated Rayleigh group velocity at varying depths showed conspicuous corroboration with three tectonic blocks [Kachchh Rift Basin (KRB), Saurashtra Horst (SH), and Mainland Gujarat (MG)] in the region. The seismically active KRB with a thicker crust is characterized as a low velocity zone at a period varying from 10 to 30 s as indicative of mantle downwarping or sagging of the mantle beneath the KRB, while the SH and MG are found to be associated with higher group velocities, indicating the existence of the reduced crustal thickness. The trend of higher group velocity was found prevailed adjacent to the Narmada and Cambay rift basins that also correspond to the reduced crust, suggesting the processes of mantle upwarping or uplifting due to mantle upwelling. The low velocities at periods longer than 40 s beneath the KRB indicate thicker lithosphere. The known Moho depth correlates well with the observed velocities at a period of about 30 s in the Gujarat region. Our estimates of relatively lower group velocities at periods varying from 70 to 80 s may correspond to the asthenospheric flow beneath the region. It is interesting to image higher group velocity for the thinner crust beneath the Arabian Sea adjacent to the west coast of Gujarat at the period of 40 s that may correspond to the upwarped or upwelled mantle beneath the Arabian Sea. Our results have better resolution estimated by a radius of equivalent circular averaging area for each period.     

Climate and variability bias adjustment of climate model-derived winds for a southeast Australian dynamical wave model

Climate models are increasingly being used to force dynamical wind wave models in order to assess the potential climate change-driven variations in wave climate. In this study, an ensemble of wave model simulations have been used to assess the ability of climate model winds to reproduce the present-day (1981–2000) mean wave climate and its seasonal variability for the southeast coast of Australia. Surface wind forcing was obtained from three dynamically downscaled Coupled Model Intercomparison Project (CMIP-3) global climate model (GCM) simulations (CSIRO Mk3.5, GFDLcm2.0 and GFDLcm2.1). The downscaling was performed using CSIRO’s cubic conformal atmospheric model (CCAM) over the Australian region at approximately 60-km resolution. The wind climates derived from the CCAM downscaled GCMs were assessed against observations (QuikSCAT and NCEP Re-analysis 2 (NRA-2) reanalyses) over the 1981–2000 period and were found to exhibit both bias in mean wind conditions (climate bias) as well as bias in the variance of wind conditions (variability bias). Comparison of the modelled wave climate with over 20 years of wave data from six wave buoys in the study area indicates that direct forcing of the wave models with uncorrected CCAM winds result in suboptimal wave hindcast. CCAM winds were subsequently adjusted for climate and variability bias using a bivariate quantile adjustment which corrects both directional wind components to align in distribution to the NRA-2 winds. Forcing of the wave models with bias-adjusted winds leads to a significant improvement of the hindcast mean annual wave climate and its seasonal variability. However, bias adjustment of the CCAM winds does not improve the ability of the model to reproduce the storm wave climate. This is likely due to a combination of storm systems tracking too quickly through the wave generation zone and the performance of the NRA-2 winds used as a benchmark in this study.     

Site response implications associated with using non-unique Vs profiles from surface wave inversion in comparison with other commonly used methods of accounting for Vs uncertainty

This paper discusses variability and accuracy of site response predictions performed using shear wave velocity (Vs) profiles derived from non-unique surface wave inversions and other commonly used statistical methods of accounting for epistemic uncertainty and aleatory variability in Vs. Specifically, linear and equivalent linear site response analyses were performed on the following three classes of Vs profiles: (1) 350 Vs profiles developed by performing multiple surface wave inversions, each with a unique set of layering parameters, on a common dispersion dataset, (2) two upper/lower range base-case Vs profiles developed by systematically increasing or decreasing the solution Vs profile by 20%, and (3) 100 Vs profiles developed using the Vs randomization procedure proposed by Toro (1995) [26]. Vs profiles derived from surface wave inversions generally yielded accurate site response estimates with minimal variability, so long as their theoretical dispersion data fit the experimental dispersion data well. On the other hand, the upper/lower range and randomized Vs profiles generally produced inaccurate and highly variable site response predictions, although the inclusion of site-specific parameters in the randomization model improved the results. At real sites where substantial aleatory variability is anticipated and/or the epistemic uncertainty is quite high, the site response estimates associated with the randomized and/or upper/lower range Vs profiles may be deemed acceptable. However, if the experimental dispersion data and horizontal-to-vertical spectral ratios are shown to be consistent over the footprint of a site, it may be possible to significantly reduce the uncertainty associated with the input Vs profile and the resulting uncertainty in the site response.     

Shoreline change at an infinite jetty for wave time series

Future shoreline changes on a sandy beach with a structure such as a jetty or groin can be estimated when wave time series is known (i.e. sequence of wave height, period, and direction). This paper presents an extension of an existing solution (Pelnard-Considere, 1956) for the linearized partial differential equation for shoreline change at an infinite jetty where waves are time varying and when the angle of the shoreline is small with respect to the waves breaking at the shoreline. The novel solution provided in this paper allows the previous constant wave condition solution to be extended to the case where wave properties (i.e. wave direction, wave height, and wave period) are time varying. Example usage of the method presented shows that shorelines may be of different final plan form shape for time varying wave conditions even though the sediment transport along adjacent beaches is not spatially varying (i.e. spatially constant) from time step to time step. Although this difference in shape may have been known previously using numerical models, it could not be proved analytically. Reversals of wave height, period, and direction time series are shown to provide different final shoreline shapes even though the time series consists of the same waves although in different ordered time. The solution provided will allow one line numerical shoreline models to be tested using an analytic solution.     

Deep water observations of extreme waves with moored and free GPS buoys

Point-positioning GPS-based wave measurements were conducted by deep ocean (over 5,000 m) surface buoys moored in the North West Pacific Ocean in 2009, 2012, and 2013. The observed surface elevation bears statistical characteristics of Gaussian, spectrally narrow ocean waves. The tail of the averaged spectrum follows the frequency to the power of ?4 slope, and the significant wave height and period satisfies the Toba’s 3/2 law. The observations compare well with a numerical wave hindcast. Two large freak waves exceeding 13 m in height were observed in October 2009 and three extreme waves around 20 m in height were observed in October 2012 and in January 2013. These extreme events are associated with passages of a typhoon and a mid-latitude cyclone. Horizontal movement of the buoy revealed that the orbital motion of the waves at the peak of the wave group mostly exceed the weakly nonlinear estimate. For some cases, the orbital velocity exceeded the group velocity, which might indicate a breaking event but is not conclusive yet.     

The 21 May 2003 Tsunami in the Western Mediterranean Sea: Statistical and Wavelet Analyses

We report the statistical and wavelet analyses of the 21 May 2003 tsunami produced by an M _w 6.8–6.9 thrust earthquake in the western Mediterranean Sea using 19 tide gauge records. The largest trough-to-crest wave height was 196 cm recorded at the Sant Antoni station in the lee of the incoming tsunami wave. Except at one station, the first wave was not the largest wave at all the analyzed stations, and the largest wave arrived several hours after the first arrival. In addition, the tsunami waves persisted for more than 1 day at most stations. As the spectra of coastal tide gauge stations are strongly influenced by topographic features, special care was taken here while interpreting the results of spectral and wavelet analysis. Our wavelet analysis shows that only a peak at around 23 min is persistent for long duration, and other peaks at 14, 30, 45, and 60 min appeared at short durations. The 23-min signal is possibly associated with the width of the source fault whereas the fault length contributed to the 45-min signal. Based on these dominant periods, the tsunami source dimensions are estimated as 95 km × 45 km. The statistical and wavelet analyses performed here provide some new insights into the characteristics of the tsunami that was generated and propagated in the western Mediterranean basin.     

The 2009 Samoan and 2010 Chilean Tsunamis Recorded on the Pacific Coast of Russia

Two remote tsunamis were recorded on the Pacific coast of Russia: a relatively weak Samoan tsunami of September 29, 2009 and a much stronger Chilean tsunami of February 28, 2010. In the area of the South Kuril Islands, records were obtained using autonomous bottom pressure gauges of the Institute of Marine Geology and Geophysics (IMGG). Additionally, for the oceanic coast of the Kamchatka Peninsula, Paramushir, and Bering Islands we used data transmitted from coastal tide gauges of the Russian Tsunami Warning Service (TWS). The maximum trough-to-crest heights of the Samoan tsunami were about 30–40 cm, and were recorded about 3 h after the first tsunami arrival. The maximum Chilean tsunami trough-to-crest wave heights were 218 cm at Severo-Kurilsk, 187 cm at Tserkovnaya Bay (Shikotan Island), and 140 cm at Khodutka Bay (Kamchatka Peninsula). The time between first and maximum waves reached 4 h. Strong sea level oscillations for both events range for a long time: about 15–17 h. The Samoan tsunami induced high-frequency oscillations; a considerable increase in spectral energy in the tsunami spectrum was observed at periods of 4–20 min. In contrast, the Chilean tsunami induced low-frequency oscillations; the dominant periods were 30–80 min. A probable reason for these differences is the different extensions of the source areas (the Chilean source was much larger than the Samoan source) and the different energy radiation directions from the sources. Local topography resonant effects were the main reason of well-expressed peaks in power spectra in different areas: with a period of 10 min (Khodutka Bay), 19–20 min (Malokurilskaya and Tserkovnaya bays), 29 min (Krabovaya Inlet), and 43 min (Avachinskaya Guba and Nikolskoe).     

A Bayesian hierarchical spatio-temporal model for significant wave height in the North Atlantic

Bad weather and rough seas continue to be a major cause for ship losses and is thus a significant contributor to the risk to maritime transportation. This stresses the importance of taking severe sea state conditions adequately into account in ship design and operation. Hence, there is a need for appropriate stochastic models describing the variability of sea states, taking into account long-term trends related to climate change. Various stochastic models of significant wave height are reported in the literature, but most are based on point measurements without considering spatial variations. As far as the authors are aware, no model of significant wave height to date exploits the flexible framework of Bayesian hierarchical space-time models. This framework allows modelling of complex dependence structures in space and time and incorporation of physical features and prior knowledge, yet at the same time remains intuitive and easily interpreted. This paper presents a Bayesian hierarchical space-time model for significant wave height. The model has been fitted by significant wave height data for an area in the North Atlantic ocean. The different components of the model will be outlined, and the results from applying the model to monthly and daily data will be discussed. Different model alternatives have been tried and long-term trends in the data have been identified for all model alternatives. Overall, these trends are in reasonable agreement and also agree fairly well with previous studies. Furthermore, a discussion of possible extensions to the model, e.g. incorporating regression terms with relevant meteorological data will be presented.     

Characteristics of intra-, inter-annual and decadal sea-level variability and the role of meteorological forcing: the long record of Cuxhaven

This paper addresses the role of meteorological forcing on mean sea level (MSL) variability at the tide gauge of Cuxhaven over a period from 1871 to 2008. It is found that seasonal sea level differs significantly from annual means in both variability and trends. The causes for the observed differences are investigated by comparing to changes in wind stress, sea level pressure and precipitation. Stepwise regression is used to estimate the contribution of the different forcing factors to sea level variability. The model validation and sensitivity analyses showed that a robust and timely independent estimation of regression coefficients becomes possible if at least 60 to 80 years of data are available. Depending on the season, the models are able to explain between 54 % (spring, April to June) and 90 % (winter, January to March) of the observed variability. Most parts of the observed variability are attributed to changes in zonal wind stress, whereby the contribution of sea level pressure, precipitation and meridional wind stress is rather small but still significant. On decadal timescales, the explanatory power of local meteorological forcing is considerable weaker, suggesting that the remaining variability is attributed to remote forcing over the North Atlantic. Although meteorological forcing contributes to linear trends in some sub-periods of seasonal time series, the annual long-term trend is less affected. However, the uncertainties of trend estimation can be considerably reduced, when removing the meteorological influences. A standard error smaller than 0.5 mm/year requires 55 years of data when using observed MSL at Cuxhaven tide gauge. In contrast, a similar standard error in the meteorologically corrected residuals is reached after 32 years.