Long-term statistics of potentially hazardous sea states in the North Sea 1958–2014

Significant wave height and mean wave period are two of the most commonly used parameters to describe wave climate, wave climate variability, and their potential long-term changes. While these parameters are generally useful to characterize the distribution of waves within a given sea state, they provide less information about potentially high-risk situations. Over the recent years, a number of criteria were suggested that are considered to better characterize high-risk situations and which could bear a potential for the development of safety warning indices. Based on a multi-decadal high-resolution wind-wave hindcast, a climatology of such parameters is developed for the North Sea covering the years 1958–2014. More specifically, average conditions, inter-annual variability and long-term changes for unusually steep, rapidly developing and crossing sea states are considered. Generally, there are pronounced spatial variations in the frequency of such sea states, while over time, there is some seasonal and inter-annual variability but no substantial long-term trend could be identified.     

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.     

North Sea wave conditions: an analysis of four transient future climate realizations

The multi-decadal wave conditions in the North Sea can be influenced by anthropogenic climate change. That may lead to the intensification of wave extremes in the future and consequently increase risks for the coastal areas as well as for on- and offshore human activities. Potential changes caused by alteration of atmospheric patterns are investigated. Four transient climate projections (1961–2100), reflecting two IPCC emission scenarios (A1B and B1) and two different initial states, are used to simulate the wave scenarios. The potential wind-induced changes in waves are studied by comparing future statistics (2001–2100) with the corresponding reference conditions (1961–2000). Generally, there is a small increase in future 99th percentile significant wave height for most eastern parts of the North Sea towards the end of the twenty-first century. This small increase is superimposed by a strong variability of the annual extremes on time scales of decades. Opposite to the differences in wave height, the change in wave direction to more waves propagating east shows less decadal variability and is more uniform among all realizations. Nevertheless, temporal and spatial differences of the wave height in the four climate projections point to the uncertainties in the climate change signals.     

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.     

Comparison of numerical hindcasted severe waves with Doppler radar measurements in the North Sea

Severe sea states in the North Sea present a challenge to wave forecasting systems and a threat to offshore installations such as oil and gas platforms and offshore wind farms. Here, we study the ability of a third-generation spectral wave model to reproduce winter sea states in the North Sea. Measured and modeled time series of integral wave parameters and directional wave spectra are compared for a 12-day period in the winter of 2013–2014 when successive severe storms moved across the North Atlantic and the North Sea. Records were obtained from a Doppler radar and wave buoys. The hindcast was performed with the WAVEWATCH III model (Tolman 2014) with high spectral resolution both in frequency and direction. A good general agreement was obtained for integrated parameters, but discrepancies were found to occur in spectral shapes.     

A long-term nearshore wave hindcast for Ireland: Atlantic and Irish Sea coasts (1979–2012)

The Northeast Atlantic possesses some of the highest wave energy levels in the world. The recent years have witnessed a renewed interest in harnessing this vast energy potential. Due to the complicated geomorphology of the Irish coast, there can be a significant variation in both the wave and wind climate. Long-term hindcasts with high spatial resolution, properly calibrated against available measurements, provide vital information for future deployments of ocean renewable energy installations. These can aid in the selection of adequate locations for potential deployment and for the planning and design of those marine operations. A 34-year (from 1979 to 2012), high-resolution wave hindcast was performed for Ireland including both the Atlantic and Irish Sea coasts, with a particular focus on the wave energy resource. The wave climate was estimated using the third-generation spectral wave model WAVEWATCH III®; version 4.11, the unstructured grid formulation. The wave model was forced with directional wave spectral data and 10-m winds from the European Centre for Medium Range Weather Forecasts (ECMWF) ERA-Interim reanalysis, which is available from 1979 to the present. The model was validated against available observed satellite altimeter and buoy data, particularly in the nearshore, and was found to be excellent. A strong spatial and seasonal variability was found for both significant wave heights, and the wave energy flux, particularly on the north and west coasts. A strong correlation between the North Atlantic Oscillation (NAO) teleconnection pattern and wave heights, wave periods, and peak direction in winter and also, to a lesser extent, in spring was identified.     

The wave climate of Liverpool Bay—observations and modelling

Directional wave measurements have been made in Liverpool Bay by means of wave buoys and acoustic instruments within the footprint of a phased-array high frequency (HF) radar system, which measures currents and waves. Several years of data have now been collected and are supplemented by an 11-year wave model hindcast. Wave parameters have been derived from the various instruments and compared: the directional waverider buoy is taken to provide the ground truth, confirming the good observations obtained from the ADCP; the HF radar wave data have a positive bias, while the model data have a negative bias. The variation of wave climate over various time-scales from seasonal and inter-annual to inter-decadal is examined. Significant wave–current interactions may occur in this area of shallow water and high tidal range and the measurements provide a good test of coupled hydrodynamic-wave models. The waves are mainly fetch-limited: largest events are due to depressions, which track across the UK from SW, generating westerly and WNW winds in the right rear quadrant. Hence, the future extreme wave events will be closely related to future North Atlantic storm tracks. Projections of 50-year return period wave heights differ between different instruments and model datasets. The future wave climate of Liverpool Bay is not expected to change much from the present day; although a slight increase in the severity of the most extreme events is projected, the frequency of extreme wind and wave events in general is slightly reduced. There is evidence for variability on decadal time-scales, with some correlation with the North Atlantic oscillation.     

A method for finding the optimal predictor indices for local wave climate conditions

In this study, a method to obtain local wave predictor indices that take into account the wave generation process is described and applied to several locations. The method is based on a statistical model that relates significant wave height with an atmospheric predictor, defined by sea level pressure fields. The predictor is composed of a local and a regional part, representing the sea and the swell wave components, respectively. The spatial domain of the predictor is determined using the Evaluation of Source and Travel-time of wave Energy reaching a Local Area (ESTELA) method. The regional component of the predictor includes the recent historical atmospheric conditions responsible for the swell wave component at the target point. The regional predictor component has a historical temporal coverage (n-days) different to the local predictor component (daily coverage). Principal component analysis is applied to the daily predictor in order to detect the dominant variability patterns and their temporal coefficients. Multivariate regression model, fitted at daily scale for different n-days of the regional predictor, determines the optimum historical coverage. The monthly wave predictor indices are selected applying a regression model using the monthly values of the principal components of the daily predictor, with the optimum temporal coverage for the regional predictor. The daily predictor can be used in wave climate projections, while the monthly predictor can help to understand wave climate variability or long-term coastal morphodynamic anomalies.     

Identifying storm-induced wave origins using SAR wave mode data

Because of weak dissipation effects, swells generated by fierce storms can propagate across an entire ocean basin; therefore, observing swell generation and decay and retrieving storm characteristics from a swell by satellite remote sensing are possible. In this study, based on the dispersion relation and geometrical optics principle, we used SAR wave mode data from 2003 to 2010 provided by GlobWave to track swells with peak wavelengths of more than 300 m to locate a storm-generated far-traveling swell and present the swell field related to this “static” origin. Through a comparison with ECMWF wind datasets, we conducted validations and explored some conditions that cause misjudgments in swell origins. Finally, we obtained the spatiotemporal distribution characteristics of satellite-observed swell origins (i.e., the fierce wind condition) and their evolution. This work can be used as a reference for wave models, providing early swell warnings, determining air-sea surface interactions, and determining global climate change.     

A high-resolution satellite-derived sea surface temperature climatology for the western North Atlantic Ocean

Long-term and high-resolution (∼1.2 km) satellite-derived sea surface temperature (SST) fields of a monthly mean time series for the 1985–1999 period, and a daily climatology have been calculated for the North West Atlantic Ocean. The SST fields extend from 78°W to 41°W in longitude, and 30°N to 56°N in latitude, encompassing the region off Cape Hatteras, North Carolina, to the southern Labrador Sea. The monthly mean time series, consists of 180 cloud-masked monthly mean SST fields, derived from a full-resolution NOAA/NASA Pathfinder SST data set for the 1985–1999 period. The satellite-derived monthly mean SST fields, as compared with in situ monthly mean near-surface ocean temperatures from buoys located in the western North Atlantic, yield an overall RMS difference of 1.15 °C. The daily climatology, which consists of 365 fields, was derived by applying a least-squares harmonic regression technique on the monthly mean SST time series for the full study period. The monthly mean and daily climatological SST fields will be useful for studying inter-annual variability related to climate variability of SST over the study domain.     

Future wave climate over the west-European shelf seas

In this paper, we investigate changes in the wave climate of the west-European shelf seas under global warming scenarios. In particular, climate change wind fields corresponding to the present (control) time-slice 1961–2000 and the future (scenario) time-slice 2061–2100 are used to drive a wave generation model to produce equivalent control and scenario wave climate. Yearly and seasonal statistics of the scenario wave climates are compared individually to the corresponding control wave climate to identify relative changes of statistical significance between present and future extreme and prevailing wave heights. Using global, regional and linked global–regional wind forcing over a set of nested computational domains, this paper further demonstrates the sensitivity of the results to the resolution and coverage of the forcing. It suggests that the use of combined forcing from linked global and regional climate models of typical resolution and coverage is a good option for the investigation of relative wave changes in the region of interest of this study. Coarse resolution global forcing alone leads to very similar results over regions that are highly exposed to the Atlantic Ocean. In contrast, fine resolution regional forcing alone is shown to be insufficient for exploring wave climate changes over the western European waters because of its limited coverage. Results obtained with the combined global–regional wind forcing showed some consistency between scenarios. In general, it was shown that mean and extreme wave heights will increase in the future only in winter and only in the southwest of UK and west of France, north of about 44–45° N. Otherwise, wave heights are projected to decrease, especially in summer. Nevertheless, this decrease is dominated by local wind waves whilst swell is found to increase. Only in spring do both swell and local wind waves decrease in average height.     

The effect of climate change on extreme waves in front of the Dutch coast

Coastal safety may be influenced by climate change, as changes in wave conditions (height, period, direction) may increase the vulnerability of dunes and other coastal defences. Dune erosion depends on mean water level, storm surge height and wave conditions. In this paper, we investigate the change in wave conditions in the North Sea in a changing climate. Until now, the effect of climate change on annual maximum wave conditions has been investigated, while events with higher return periods are actually most damaging for the coast (e.g. severe dune erosion). Here, we use the 17-member Ensemble SimulationS of Extreme weather under Non-linear Climate changeE (ESSENCE) change of climate change simulations, to analyse A1b-induced changes in the mean wave climate, the annual maxima and wave conditions with return periods of up to 1:10,000?years in front of the Dutch coast. The mean wave climate is not projected to differ between 1961–1990 and 2071–2100, with both wave height (H _s) and wave period (T _m) remaining unaltered. In the annual maximum conditions, a decrease is projected; especially, the annual T _m maximum decreases significantly by 0.3 to 0.6?s over the whole study area. Furthermore, we find that the direction of the annual maximum wave conditions shifts from north and north-west to west and south-west for both H _s and T _m. This is induced by a similar shift in the direction of the extreme wind speeds. Despite the decrease in annual maximum conditions, the return H _s and T _m are not projected to change significantly as a result of climate change in front of the Dutch coast for the period 2071–2100 relative to 1961–1990.     

Climate change and North Sea storm surge extremes: an ensemble study of storm surge extremes expected in a changed climate projected by four different regional climate models

The coastal zones are facing the prospect of changing storm surge statistics due to anthropogenic climate change. In the present study, we examine these prospects for the North Sea based on numerical modelling. The main tool is the barotropic tide-surge model TRIMGEO (Tidal Residual and Intertidal Mudflat Model) to derive storm surge climate and extremes from atmospheric conditions. The analysis is carried out by using an ensemble of four 30-year atmospheric regional simulations under present-day and possible future-enhanced greenhouse gas conditions. The atmospheric regional simulations were prepared within the EU project PRUDENCE (Prediction of Regional scenarios and Uncertainties for Defining EuropeaN Climate change risks and Effects). The research strategy of PRUDENCE is to compare simulations of different regional models driven by the same global control and climate change simulations. These global conditions, representative for 1961–1990 and 2071–2100 were prepared by the Hadley Center based on the IPCC A2 SRES scenario. The results suggest that under future climatic conditions, storm surge extremes may increase along the North Sea coast towards the end of this century. Based on a comparison between the results of the different ensemble members as well as on the variability estimated from a high-resolution storm surge reconstruction of the recent decades it is found that this increase is significantly different from zero at the 95% confidence level for most of the North Sea coast. An exception represents the East coast of the UK which is not affected by this increase of storm surge extremes.     

Storm observations by remote sensing and influences of gustiness on ocean waves and on generation of rogue waves

The impact of the gustiness on surface waves under storm conditions is investigated with focus on the appearance of wave groups with extreme high amplitude and wavelength in the North Sea. During many storms characterized by extremely high individual waves measured near the German coast, especially in cold air outbreaks, the moving atmospheric open cells are observed by optical and radar satellites. According to measurements, the footprint of the cell produces a local increase in the wind field at sea surface, moving as a consistent system with a propagation speed near to swell wave-traveling speed. The optical and microwave satellite data are used to connect mesoscale atmospheric turbulences and the extreme waves measured. The parameters of open cells observed are used for numerical spectral wave modeling. The North Sea with horizontal resolution of 2.5?km and with focus on the German Bight was simulated. The wind field “storm in storm,” including moving organized mesoscale eddies with increased wind speed, was generated. To take into account the rapid moving gust structure, the input wind field was updated each 5?min. The test cases idealized with one, two, and four open individual cells and, respectively, with groups of open cells, with and without preexisting sea state, as well the real storm conditions, are simulated. The model results confirm that an individual-moving open cell can cause the local significant wave height increase in order of meters within the cell area and especially in a narrow area of 1–2?km at the footprint center of a cell (the cell's diameter is 40–90?km). In a case of a traveling individual open cell with 15?m·s^?1 over a sea surface with a preexisting wind sea of and swell, a local significant wave height increase of 3.5?m is produced. A group of cells for a real storm condition produces a local increase of significant wave height of more than 6?m during a short time window of 10–20?min (cell passing). The sea surface simulation from modeled wave spectra points out the appearance of wave groups including extreme individual waves with a period of about 25?s and a wavelength of more than 350?m under the cell's footprint. This corresponds well with measurement of a rogue wave group with length of about 400?m and a period of near 25?s. This has been registered at FiNO-1 research platform in the North Sea during Britta storm on November 1, 2006 at 04:00 UTC. The results can explain the appearance of rogue waves in the German Bight and can be used for ship safety and coastal protection. Presently, the considered mesoscale gustiness cannot be incorporated in present operational wave forecasting systems, since it needs an update of the wind field at spatial and temporal scales, which is still not available for such applications. However, the scenario simulations for cell structures with appropriate travel speed, observed by optical and radar satellites, can be done and applied for warning messages.     

Climate and extrema of ocean waves in the East China Sea

Wave climate plays an important role in the air-sea interaction over marginal seas. Extreme wave height provides fundamental information for various ocean engineering practices, such as hazard mitigation, coastal structure design, and risk assessment. In this paper, we implement a third generation wave model and conduct a high-resolution wave hindcast over the East China Sea to reconstruct a 15-year wave field from 1988 to 2002 for derivation of monthly mean wave parameters and analysis of extreme wave conditions. The numerical results of the wave field are validated through comparison with satellite altimetry measurements, low-resolution reanalysis, and the ocean wave buoy record. The monthly averaged wave height and wave period show seasonal variation and refined spatial patterns of surface waves in the East China Sea. The climatological significant wave height and mean wave period decrease from the open ocean in the southeast toward the continental area in the northwest, with the pattern generally following the bathymetry. Extreme analysis on the significant wave height at the buoy station indicates the hindcast data underestimate the extreme values relative to the observations. The spatial pattern of extreme wave height shows single peak emerges at the southwest of Ryukyu Island although a wind forcing with multi-core structure at the extreme is applied.     

What dominates sea level at the coast: a case study for the Gulf of Guinea

Sea level variations and extreme events are a major threat for coastal zones. This threat is expected to worsen with time because low-lying coastal areas are expected to become more vulnerable to flooding and land loss as sea level rises in response to climate change. Sea level variations in the coastal ocean result from a combination of different processes that act at different spatial and temporal scales. In this study, the relative importance of processes causing coastal sea level variability at different time-scales is evaluated. Contributions from the altimetry-derived sea-level (including the sea level rise due to the ocean warming and land ice loss in response to climate change), dynamical atmospheric forcing induced sea level (surges), wave-induced run-up and set-up, and astronomical tides are estimated from observational datasets and reanalyses. As these processes impact the coast differently, evaluating their importance is essential for assessment of the local coastline vulnerability. A case study is developed in the Gulf of Guinea over the 1993–2012 period. The leading contributors to sea level variability off Cotonou differ depending on the time-scales considered. The trend is largely dominated by processes included in altimetric data and to a lesser extent by swell-waves run-up. The latter dominates interannual variations. Swell-waves run-up and tides dominate subannual variability. Extreme events are due to the conjunction of high tides and large swell run-up, exhibiting a clear seasonal cycle with more events in boreal summer and a trend mostly related to the trend in altimetric-derived sea-level.     

Sea-level rise: A review of recent past and near-future trends

Global mean sea level is a potentially sensitive indicator of climate change. Global warming will contribute to worldwide sea-level rise (SLR) from thermal expansion of ocean water, melting of mountain glaciers and polar ice sheets. A number of studies, mostly using tide-gauge data from the Permanent Service for Mean Sea Level, Bidston Observatory, England, have obtained rates of global SLR within the last 100 years that range between 0·3 and 3 mm yr^?1, with most values concentrated between 1 and 2 mm yr^?1. However, the reliability of these results has been questioned because of problems with data quality and physical processes that introduce a high level of spatial and temporal variability. Sources of uncertainty in the sea-level data include variations in winds, ocean currents, river runoff, vertical earth movements, and geographically uneven distribution of long-term records. Crustal motions introduce a major source of error. To a large extent, these can be filtered by employing palaeo-sea-level proxies, and geophysical modelling to remove glacio-isostatic changes. Ultimately, satellite geodesy will help resolve the inherent ambiguity between the land and ocean level changes recorded by tide gauges. Future sea level is expected to rise by ～ 1 m, with a ‘best-guess’ value of 48 cm by the year 2100. Such rates represent an acceleration of four to seven times over present rates. Local land subsidence could substantially increase the apparent SLR. For example, Louisiana is currently experiencing SLR trends nearly 10 times the global mean rate. These recently reduced SLR estimates are based on climate models that predict a zero to negative contribution to SLR from Antarctica. Most global climate models (GCMs) indicate an ice accumulation over Antarctica, because in a warmer world, precipitation will exceed ablation/snow-melt. However, the impacts of attritional processes, such as thinning of the ice shelves, have been downplayed according to some experts. Furthermore, not all climate models are in agreement. Opposite conclusions may be drawn from the results of other GCMs. In addition, the West Antarctic Ice Sheet is potentially subject to dynamic and volcanic instabilities that are difficult to predict. Because of the great uncertainty in SLR projections, careful monitoring of future sea-level trends by upgraded tide-gauge networks and satellite geodesy will become essential. Finally, because of the high spatial variability in crustal subsidence rates, wave climates and tidal regimes, it will be the set of local conditions (especially the relative sea-level rise), rather than a single global mean sea-level trend, that will determine each locality's vulnerability to future SLR.