Wave Extremes in the North East Atlantic from Ensemble Forecasts

A method for estimating return values from ensembles of forecasts at advanced lead times is presented. Return values of significant wave height in the North-East Atlantic, the Norwegian Sea and the North Sea are computed from archived +240-h forecasts of the ECMWF ensemble prediction system (EPS) from 1999 to 2009. We make three assumptions: First, each forecast is representative of a six-hour interval and collectively the data set is then comparable to a time period of 226 years. Second, the model climate matches the observed distribution, which we confirm by comparing with buoy data. Third, the ensemble members are sufficiently uncorrelated to be considered independent realizations of the model climate. We find anomaly correlations of 0.20, but peak events (>P97) are entirely uncorrelated. By comparing return values from individual members with return values of subsamples of the data set we also find that the estimates follow the same distribution and appear unaffected by correlations in the ensemble. The annual mean and variance over the 11-year archived period exhibit no significant departures from stationarity compared with a recent reforecast, i.e., there is no spurious trend due to model upgrades. EPS yields significantly higher return values than ERA-40 and ERA-Interim and is in good agreement with the high-resolution hindcast NORA10, except in the lee of unresolved islands where EPS overestimates and in enclosed seas where it is biased low. Confidence intervals are half the width of those found for ERA-Interim due to the magnitude of the data set.Comment: 27 pp, 10 figures, J Climate (in press

Comparison of wind speed and wave height trends from twentieth-century models and satellite altimeters

The trends in marine 10-m wind speed U10 and significant wave height Hs found in two century-long reanalyses are compared against a model-only integration. Reanalyses show spurious trends due to the assimilation of an increasing number of observations over time. The comparisons between model and reanalyses show that the areas where the discrepancies in U10 and Hs trends are greatest are also the areas where there is a marked increase in assimilated observations. Large differences in the yearly averages call into question the quality of the observations assimilated by the reanalyses, resulting in unreliable U10 and Hs trends before the 1950s. Four main regions of the world’s oceans are identified where the trends between model and reanalyses deviate strongly. These are the North Atlantic, the North Pacific, the Tasman Sea, and the western South Atlantic. The trends at +24-h lead time are markedly weaker and less correlated with the observation count. A 1985–2010 comparison with an extensive dataset of calibrated satellite altimeters shows contrasting results in Hs trends but similar U10 spatial trend distributions, with general agreement between model, reanalyses, and satellite altimeters on a broad increase in wind speed over the Southern Hemisphere.publishedVersio

In situ coastal observations of wave homogeneity and coherence

A better understanding of wave homogeneity, i.e. the spatial variations of the wave characteristics, and wave coherence in coastal areas and fjords is essential for the design and analysis of sea-crossing infrastructures, such as floating bridge concepts. The wave conditions in fjords that are exposed to the open sea are complex and often characterized by a mixed swell–wind sea state. This study investigates the spatial coherence and homogeneity of ocean waves using two years of unique buoy observations from Sulafjorden – a fjord partly exposed to the open sea. We analyze both long term statistics and four selected cases with different sea states. The most exposed locations are dominated by long waves (swell), while the energy of the wind sea is comparable to the swell energy in the more sheltered locations. Despite the study area being relatively small (ca. 2 km ×1 km), the differences in wave conditions are significant because the complex fjord geometry blocks the incoming offshore waves, and changes in fetch and wind conditions affects the local wave growth. For swell waves we measured an along-crest spatial coherence (ca. 0.6) over a 1–2 km distance. The coherence between consecutive crests for swell was weaker (up to 0.3–0.4) for distances between 0.6 km and 1.3 km (up to about 5 wavelengths). Wind sea (both along crest and between crests) showed no coherence over these distances.publishedVersio

Characterization of Wind-Sea- and Swell-Induced Wave Energy along the Norwegian Coast

The necessity to reduce CO2 emissions in combination with the rising energy demand worldwide makes the extensive use of renewable energy sources increasingly important. To that end, countries with long coastlines, such as Norway, can exploit ocean wave energy to produce large amounts of power. In order to facilitate these efforts as well as to provide quantitative data on the wave energy potential of a specific area, it is essential to analyze the weather and climatic conditions detecting any variabilities. The complex physical processes and the atmosphere-wave synergetic effects make the investigation of temporal variability of wave energy a challenging issue. This work aims to shed new light on potential wave energy mapping, presenting a spatio-temporal assessment of swell- and wind-sea-induced energy flux in the Nordic Seas with a focus on the Norwegian coastline using the NORA10 hindcast for the period 1958–2017 (59 years). The results indicate high spatial and seasonal variability of the wave energy flux along the coast. The maximum wave energy flux is observed during winter, while the minimum is observed during summer. The highest coastal wave energy flux is observed in the Norwegian Sea. The majority of areas with dominant swell conditions (i.e., in the Norwegian Sea) are characterized by the highest coastal wave energy flux. The maximum values of wave energy flux in the North Sea are denoted in its northern parts in the intersection with the Norwegian Sea. In contrast to the Norwegian Sea, areas located in the North Sea and the Barents Sea show that wind sea is contributing more than swell to the total wave energy flux.publishedVersio

Nora3: A nonhydrostatic high-resolution hindcast of the North sea, the Norwegian sea, and the Barents sea

The 3-km Norwegian Reanalysis (NORA3) is a 15-yr mesoscale-permitting atmospheric hindcast of the North Sea, the Norwegian Sea, and the Barents Sea. With a horizontal resolution of 3 km, the nonhydrostatic numerical weather prediction model HARMONIE–AROME runs explicitly resolved deep convection and yields hindcast fields that realistically downscale the ERA5 reanalysis. The wind field is much improved relative to its host analysis, in particular in mountainous areas and along the improved grid-resolving coastlines. NORA3 also performs much better than the earlier hydrostatic 10-km Norwegian Hindcast Archive (NORA10) in complex terrain. NORA3 recreates the detailed structures of mesoscale cyclones with sharp gradients in wind and with clear frontal structures, which are particularly important when modeling polar lows. In extratropical windstorms, NORA3 exhibits significantly higher maximum wind speeds and compares much better to observed maximum wind than do NORA10 and ERA5. The activity of the model is much more realistic than that of NORA10 and ERA5, both over the ocean and in complex terrain.publishedVersio

The importance of wind forcing in fjord wave modelling

Accurate predictions of surface ocean waves in coastal areas are important for a number of marine activities. In complex coastlines with islands and fjords, the quality of wind forcing significantly affects the results. We investigate the role of wind forcing on wave conditions in a fjord system partly exposed to open sea. For this reason, we implemented the wave model SWAN at the west coast of Norway using four different wind forcing. Wind and wave estimates were compared with observations from five measurement sites. The best results in terms of significant wave height are found at the sites exposed to offshore conditions using a wind input that is biased slightly high compared with the buoy observations. Positively biased wind input, on the other hand, leads to significant overestimation of significant wave height in more sheltered locations. The model also shows a poorer performance for mean wave period in these locations. Statistical results are supported by two case studies which also illustrate the effect of high spatial resolution in wind forcing. Detailed wind forcing is necessary in order to obtain a realistic wind field in complex fjord terrain, but wind channelling and lee effects may have unpredictable effects on the wave simulations. Pure wave propagation (no wind forcing) is not able to reproduce the highest significant wave height in any of the locations.publishedVersio

Bias Correction of Operational Storm Surge Forecasts Using Neural Networks

Storm surges can give rise to extreme floods in coastal areas. The Norwegian Meteorological Institute produces 120-hour regional operational storm surge forecasts along the coast of Norway based on the Regional Ocean Modeling System (ROMS), using a model setup called Nordic4-SS. Despite advances in the development of models and computational capabilities, forecast errors remain large enough to impact response measures and issued alerts, in particular, during the strongest events. Reducing these errors will positively impact the efficiency of the warning systems while minimizing efforts and resources spent on mitigation. Here, we investigate how forecasts can be improved with residual learning, i.e., training data-driven models to predict the residuals in forecasts from Nordic4-SS. A simple error mapping technique and a more sophisticated Neural Network (NN) method are tested. Using the NN residual correction method, the Root Mean Square Error in the Oslo Fjord is reduced by 36% for lead times of one hour and 9% for 24 hours. Therefore, the residual NN method is a promising direction for correcting storm surge forecasts, especially on short timescales. Moreover, it is well adapted to being deployed operationally, as i) the correction is applied on top of the existing model and requires no changes to it, ii) all predictors used for NN inference are already available operationally, iii) prediction by the NNs is very fast, typically a few seconds per station, and iv) the NN correction can be provided to a human expert who may inspect it, compare it with the model output, and see how much correction is brought by the NN, allowing to capitalize on human expertise as a quality validation of the NN output. While no changes to the hydrodynamic model are necessary to calibrate the neural networks, they are specific to a given model and must be recalibrated when the numerical models are updated

Nearshore wave forecasting and hindcasting by dynamical and statistical downscaling

A high-resolution nested WAM/SWAN wave model suite aimed at rapidly establishing nearshore wave forecasts as well as a climatology and return values of the local wave conditions with Rapid Enviromental Assessment (REA) in mind is described. The system is targeted at regions where local wave growth and partial exposure to complex open-ocean wave conditions makes diagnostic wave modelling difficult. SWAN is set up on 500 m resolution and is nested in a 10 km version of WAM. A model integration of more than one year is carried out to map the spatial distribution of the wave field. The model correlates well with wave buoy observations (0.96) but overestimates the wave height somewhat (18%, bias 0.29 m). To estimate wave height return values a much longer time series is required and running SWAN for such a period is unrealistic in a REA setting. Instead we establish a direction-dependent transfer function between an already existing coarse open-ocean hindcast dataset and the high-resolution nested SWAN model. Return values are estimated using ensemble estimates of two different extreme-value distributions based on the full 52 years of statistically downscaled hindcast data. We find good agreement between downscaled wave height and wave buoy observations. The cost of generating the statistically downscaled hindcast time series is negligible and can be redone for arbitrary locations within the SWAN domain, although the sectors must be carefully chosen for each new location. The method is found to be well suited to rapidly providing detailed wave forecasts as well as hindcasts and return values estimates of partly sheltered coastal regions.Comment: 20 pages, 7 figures and 2 tables, MREA07 special issue on Marine rapid environmental assessmen

Wind and Wave Extremes over the World Oceans from Very Large Ensembles

Global return values of marine wind speed and significant wave height are estimated from very large aggregates of archived ensemble forecasts at +240-h lead time. Long lead time ensures that the forecasts represent independent draws from the model climate. Compared with ERA-Interim, a reanalysis, the ensemble yields higher return estimates for both wind speed and significant wave height. Confidence intervals are much tighter due to the large size of the dataset. The period (9 yrs) is short enough to be considered stationary even with climate change. Furthermore, the ensemble is large enough for non-parametric 100-yr return estimates to be made from order statistics. These direct return estimates compare well with extreme value estimates outside areas with tropical cyclones. Like any method employing modeled fields, it is sensitive to tail biases in the numerical model, but we find that the biases are moderate outside areas with tropical cyclones.Comment: 28 pages, 16 figure