WAVE CLIMATE VARIABILITY AND RELATED CLIMATE INDICES

Climate change is highly expected to give significant impact on coastal hazards and environment. The future projections of wave climate under global warming scenarios have been carried out and shows changes in wave heights depending on the regions (e.g., Hemer et al., 2013). Beside the long-term trends of wave climate, annual to decadal changes are also important to understand variability. For example, the North Atlantic Oscillation (NAO) is highly correlated to monthly mean wave height along the western European coast. However, variability of wave climate is not well understood over the globe, quantitatively. Additionally, the standard coastal engineers regard stationary process for wave environment for solving coastal problems. This study analyzes global wave climate variability for the last half century based on principal component analysis of atmospheric forcing (sea surface winds U10 and sea level pressure P) and wave hindcast.

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Analysis of the Variability of Wave Energy Due to Climate Changes on the Example of the Black Sea

Energies ◽

10.3390/en11082020 ◽

2018 ◽

Vol 11 (8) ◽

pp. 2020 ◽

Cited By ~ 6

Author(s):

Yana Saprykina ◽

Sergey Kuznetsov

Keyword(s):

Black Sea ◽

Wave Energy ◽

Wave Climate ◽

Positive Phase ◽

The Black Sea ◽

Power Flux ◽

Teleconnection Patterns ◽

The North ◽

Wave Heights ◽

The North Atlantic Oscillation

An analysis of the variability of wave climate and energy within the Black Sea for the period 1960–2011 was made using field data from the Voluntary Observing Ship Program. Methods using wavelet analysis were applied. It was determined that the power flux of wave energy in the Black Sea fluctuates: the highest value is 4.2 kW/m, the lowest is 1.4 kW/m. Results indicate significant correlations among the fluctuations of the average annual wave heights, periods, the power flux of wave energy, and teleconnection patterns of the North Atlantic Oscillation (NAO), the Atlantic Multi-decadal Oscillation (AMO), the Pacific Decadal Oscillation (PDO) and the East Atlantic/West Russia (EA/WR). It was revealed that, in positive phases of long-term periods of AMO (50–60 years) as well as PDO, NAO, and AO (40 years), a decrease of wave energy was observed; however, an increase in wave energy was observed in the positive phase of a 15-year period of NAO and AO. The positive phase of changes of EA/WR for periods 50–60, 20–25, and 13 years led to an increase of wave energy. The approximation functions of the oscillations of the average annual wave heights, periods, and the power flux of wave energy for the Black Sea are proposed.

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Wave climate in the Arctic 1992–2014: seasonality and trends

10.5194/tc-2016-37 ◽

2016 ◽

Cited By ~ 3

Author(s):

Justin E. Stopa ◽

Fabrice Ardhuin ◽

Fanny Girard-Ardhuin

Keyword(s):

Sea Ice ◽

Wave Model ◽

Wave Climate ◽

Arctic Sea Ice ◽

The Arctic ◽

Wave Hindcast ◽

The North ◽

Marginal Ice Zone ◽

The North Atlantic Oscillation ◽

The Impact

Abstract. Over the past decade, the diminishing Arctic sea ice has impacted the wave field which is principally dependent on the ice-free area and wind. This study characterizes the wave climate in the Arctic using detailed sea state information from a wave hindcast and merged altimeter dataset spanning 1992–2014. The wave model uses winds from the Climate Forecast System Reanalysis and ice concentrations derived from satellites as input. The ice concentrations have a grid spacing of 12.5 km, which is sufficiently able to resolve important features in the marginal ice zone. The model performs well, verified by the altimeters and is relatively consistent for climate studies. The wave seasonality and extremes are linked to the ice coverage, wind strength, and wind direction. This creates distinct features in the wind-seas and swells. The increase in wave heights is caused by the loss of sea ice and not the wind verified by the altimeters and model. However, trends are convoluted by inter-annual climate oscillations like the North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation. The Nordic-Greenland Sea is the only region with negative trends in wind speed and wave height and is related to the NAO. Swells are becoming more prevalent and wind-sea steepness is declining which make the impact on sea ice uncertain. It is inconclusive how important wave-ice processes are within the climate system, but selected events suggest the importance of waves within the marginal ice zone.

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A multi-decadal wind-wave hindcast for the North Sea 1949–2014: coastDat2

Earth System Science Data ◽

10.5194/essd-9-955-2017 ◽

2017 ◽

Vol 9 (2) ◽

pp. 955-968 ◽

Cited By ~ 8

Author(s):

Nikolaus Groll ◽

Ralf Weisse

Keyword(s):

Climate Variability ◽

North Sea ◽

Wind Wave ◽

Wave Climate ◽

Climate Variability And Change ◽

Data Set ◽

Wave Hindcast ◽

Wave Data ◽

The North ◽

The North Sea

Abstract. Long and consistent wave data are important for analysing wave climate variability and change. Moreover, such wave data are also needed in coastal and offshore design and for addressing safety-related issues at sea. Using the third-generation spectral wave model WAM a multi-decadal wind-wave hindcast for the North Sea covering the period 1949–2014 was produced. The hindcast is part of the coastDat database representing a consistent and homogeneous met-ocean data set. It is shown that despite not being perfect, data from the wave hindcast are generally suitable for wave climate analysis. In particular, comparisons of hindcast data with in situ and satellite observations show on average a reasonable agreement, while a tendency towards overestimation of the highest waves could be inferred. Despite these limitations, the wave hindcast still provides useful data for assessing wave climate variability and change as well as for risk analysis, in particular when conservative estimates are needed. Hindcast data are stored at the World Data Center for Climate (WDCC) and can be freely accessed using the doi:10.1594/WDCC/coastDat-2_WAM–North_Sea Groll and Weisse(2016) or via the coastDat web-page http://www.coastdat.de.

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A multi-decadal wind-wave hindcast for the North Sea 1949–2014: coastDat2

10.5194/essd-2017-36 ◽

2017 ◽

Cited By ~ 1

Author(s):

Nikolaus Groll ◽

Ralf Weisse

Keyword(s):

Climate Variability ◽

North Sea ◽

Wind Wave ◽

Wave Model ◽

Wave Climate ◽

Climate Variability And Change ◽

Data Set ◽

Wave Hindcast ◽

The North ◽

The North Sea

Abstract. Long and consistent wave data are important for analysing wave climate variability and change. Moreover, such statistics are also needed in coastal and offshore design and for addressing safety-related issues at sea. Using the third-generation spectral wave model WAM a multi-decadal wind-wave hindcast for the North Sea covering the period 1949–2014 was produced. The hindcast is part of the coastDat database representing a consistent and homogenous met-ocean data set. It is shown that despite not being perfect, data from the wave hindcast are generally suitable for wave climate analysis. In particular comparisons of hindcast data with in situ and satellite observations show on average a reasonable agreement while a tendency towards overestimation of the highest waves could be inferred. Despite these limitations, the wave hindcast still provides useful data for assessing wave climate variability and change as well as for risk analysis, in particular when conservative estimates are needed. Hindcast data are stored at the World Data Center for Climate (WDCC) and can be freely accessed using the https://doi.org/10.1594/WDCC/coastDat-2_WAM-North_Sea (Groll and Weisse, 2016) or via the coastDat web-page http://www.coastdat.de.

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Dynamical Downscaling of ERA5 Data on the North-Western Mediterranean Sea: From Atmosphere to High-Resolution Coastal Wave Climate

Journal of Marine Science and Engineering ◽

10.3390/jmse9020208 ◽

2021 ◽

Vol 9 (2) ◽

pp. 208

Author(s):

Valentina Vannucchi ◽

Stefano Taddei ◽

Valerio Capecchi ◽

Michele Bendoni ◽

Carlo Brandini

Keyword(s):

Mediterranean Sea ◽

Wind Wave ◽

Dynamical Downscaling ◽

Wave Climate ◽

Western Mediterranean ◽

Limited Area ◽

Computational Domain ◽

Wave Hindcast ◽

The North ◽

Western Mediterranean Sea

A 29-year wind/wave hindcast is produced over the Mediterranean Sea for the period 1990–2018. The dataset is obtained by downscaling the ERA5 global atmospheric reanalyses, which provide the initial and boundary conditions for a numerical chain based on limited-area weather and wave models: the BOLAM, MOLOCH and WaveWatch III (WW3) models. In the WW3 computational domain, an unstructured mesh is used. The variable resolutions reach up to 500 m along the coasts of the Ligurian and Tyrrhenian seas (Italy), the main objects of the study. The wind/wave hindcast is validated using observations from coastal weather stations and buoys. The wind validation provides velocity correlations between 0.45 and 0.76, while significant wave height correlations are much higher—between 0.89 and 0.96. The results are also compared to the original low-resolution ERA5 dataset, based on assimilated models. The comparison shows that the downscaling improves the hindcast reliability, particularly in the coastal regions, and especially with regard to wind and wave directions.

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Climate influences on flood probabilities across Europe

Hydrology and Earth System Sciences ◽

10.5194/hess-23-1305-2019 ◽

2019 ◽

Vol 23 (3) ◽

pp. 1305-1322 ◽

Cited By ~ 11

Author(s):

Eva Steirou ◽

Lars Gerlitz ◽

Heiko Apel ◽

Xun Sun ◽

Bruno Merz

Keyword(s):

Large Scale ◽

Classical Model ◽

Spatial Coherence ◽

Extreme Value ◽

Climate Indices ◽

East Atlantic ◽

Gev Distribution ◽

The North ◽

Streamflow Extremes ◽

The North Atlantic Oscillation

Abstract. The link between streamflow extremes and climatology has been widely studied in recent decades. However, a study investigating the effect of large-scale circulation variations on the distribution of seasonal discharge extremes at the European level is missing. Here we fit a climate-informed generalized extreme value (GEV) distribution to about 600 streamflow records in Europe for each of the standard seasons, i.e., to winter, spring, summer and autumn maxima, and compare it with the classical GEV distribution with parameters invariant in time. The study adopts a Bayesian framework and covers the period 1950 to 2016. Five indices with proven influence on the European climate are examined independently as covariates, namely the North Atlantic Oscillation (NAO), the east Atlantic pattern (EA), the east Atlantic–western Russian pattern (EA/WR), the Scandinavia pattern (SCA) and the polar–Eurasian pattern (POL). It is found that for a high percentage of stations the climate-informed model is preferred to the classical model. Particularly for NAO during winter, a strong influence on streamflow extremes is detected for large parts of Europe (preferred to the classical GEV distribution for 46 % of the stations). Climate-informed fits are characterized by spatial coherence and form patterns that resemble relations between the climate indices and seasonal precipitation, suggesting a prominent role of the considered circulation modes for flood generation. For certain regions, such as northwestern Scandinavia and the British Isles, yearly variations of the mean seasonal climate indices result in considerably different extreme value distributions and thus in highly different flood estimates for individual years that can also persist for longer time periods.

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Influence of Modes of Climate Variability on Global Temperature Extremes

Journal of Climate ◽

10.1175/2008jcli2125.1 ◽

2008 ◽

Vol 21 (15) ◽

pp. 3872-3889 ◽

Cited By ~ 139

Author(s):

Jesse Kenyon ◽

Gabriele C. Hegerl

Keyword(s):

North America ◽

Climate Variability ◽

Large Scale ◽

Southern Oscillation ◽

Temperature Extremes ◽

Modes Of Variability ◽

The North ◽

The Pacific ◽

The North Atlantic Oscillation ◽

Modes Of Climate Variability

Abstract The influence of large-scale modes of climate variability on worldwide summer and winter temperature extremes has been analyzed, namely, that of the El Niño–Southern Oscillation, the North Atlantic Oscillation, and Pacific interdecadal climate variability. Monthly indexes for temperature extremes from worldwide land areas are used describe moderate extremes, such as the number of exceedences of the 90th and 10th climatological percentiles, and more extreme events such as the annual, most extreme temperature. This study examines which extremes show a statistically significant (5%) difference between the positive and negative phases of a circulation regime. Results show that temperature extremes are substantially affected by large-scale circulation patterns, and they show distinct regional patterns of response to modes of climate variability. The effects of the El Niño–Southern Oscillation are seen throughout the world but most clearly around the Pacific Rim and throughout all of North America. Likewise, the influence of Pacific interdecadal variability is strongest in the Northern Hemisphere, especially around the Pacific region and North America, but it extends to the Southern Hemisphere. The North Atlantic Oscillation has a strong continent-wide effect for Eurasia, with a clear but weaker effect over North America. Modes of variability influence the shape of the daily temperature distribution beyond a simple shift, often affecting cold and warm extremes and sometimes daytime and nighttime temperatures differently. Therefore, for reliable attribution of changes in extremes as well as prediction of future changes, changes in modes of variability need to be accounted for.

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Quantifying the rarity of extreme multi-decadal trends: how unusual was the late twentieth century trend in the North Atlantic Oscillation?

Climate Dynamics ◽

10.1007/s00382-021-05978-4 ◽

2021 ◽

Author(s):

R. Eade ◽

D. B. Stephenson ◽

A. A. Scaife ◽

D. M. Smith

Keyword(s):

North Atlantic Oscillation ◽

North Atlantic ◽

Serial Correlation ◽

Climate Models ◽

Climate Indices ◽

Positive Trend ◽

Climate Trends ◽

The North ◽

The North Atlantic ◽

The North Atlantic Oscillation

AbstractClimate trends over multiple decades are important drivers of regional climate change that need to be considered for climate resilience. Of particular importance are extreme trends that society may not be expecting and is not well adapted to. This study investigates approaches to assess the likelihood of maximum moving window trends in historical records of climate indices by making use of simulations from climate models and stochastic time series models with short- and long-range dependence. These approaches are applied to assess the unusualness of the large positive trend that occurred in the North Atlantic Oscillation (NAO) index between the 1960s to 1990s. By considering stochastic models, we show that the chance of extreme trends is determined by the variance of the trend process, which generally increases when there is more serial correlation in the index series. We find that the Coupled Model Intercomparison Project (CMIP5 + 6) historical simulations have very rarely (around 1 in 200 chance) simulated maximum trends greater than the observed maximum. Consistent with this, the NAO indices simulated by CMIP models were found to resemble white noise, with almost no serial correlation, in contrast to the observed NAO which exhibits year-to-year correlation. Stochastic model best fits to the observed NAO suggest an unlikely chance (around 1 in 20) for there to be maximum 31-year NAO trends as large as the maximum observed since 1860. This suggests that current climate models do not fully represent important aspects of the mechanism for low frequency variability of the NAO.

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LONG-TERM RISE OF STORMINESS OF THE BALTIC SEA NEAR POLAND; POSSIBLE ORIGIN AND CONSEQUENCES

Coastal Engineering Proceedings ◽

10.9753/icce.v32.management.1 ◽

2011 ◽

Vol 1 (32) ◽

pp. 1

Author(s):

Grzegorz Marcin Rozynski ◽

Zbigniew Pruszak

Keyword(s):

Baltic Sea ◽

Wave Climate ◽

Sandy Beaches ◽

The Baltic Sea ◽

Winter Storms ◽

The North ◽

The Baltic ◽

The North Atlantic Oscillation ◽

Autumn And Winter

Long-term growth of storminess of the Baltic Sea near Poland has been identified for autumn and winter months, particularly for January. This growth is concurrent with the increase of westerly waves in Jan., Feb. and Oct. A vivid relationship between the North Atlantic Oscillation and significant wave height Hs in Jan. suggests it can be a potential driver of storminess growth in that month. For Feb. this relationship is unstable; other months demonstrate no connection toward the NAO. The wave climate in January also exhibits a strong 8-year cycle, very likely to drive 8-year variations of shoreline position, detected previously at a study site. The influence of NAO may manifest an unfavorable regime change in which mightier winter storms will be mostly occurring above freezing in the absence of ice cover. Without that cover vulnerable sandy beaches will be exposed to accelerated erosion from direct and stronger wave attack.

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WAVE CLIMATE ANALYSIS FOR ENGINEERING PURPOSE

Coastal Engineering Proceedings ◽

10.9753/icce.v15.2 ◽

1976 ◽

Vol 1 (15) ◽

pp. 2 ◽

Cited By ~ 1

Author(s):

Hans H. Dette ◽

Alfred Fuhrboter

Keyword(s):

Wave Climate ◽

Incoming Wave ◽

Offshore Area ◽

The North ◽

Wave Measurements ◽

Wind Velocities ◽

Wave Heights ◽

The North Sea ◽

One Year ◽

Climate Analysis

The North Sea (Fig. 1) is known as a random sea with depths in the southern part between 40 m and 100 m so that in contrary to the Atlantic and Pacific coastlines deep sea wave conditions do not exist. After four years of comprehensive wave measurements in the offshore area of the Island of Sylt near the Danish border a general analysis of the wave climate in that region was possible. In this paper results and suggestions will be presented under the aspect of replacing qualitative judgements by quantitative statements which are derived from the knowledge of the adjacent wave climate. Because the wave action varies from year to year a general time unit is not advisable for the evaluation of shore processes; therefore the time scale should be substituted by the integral of incoming wave energy occurring after a certain time. The investigated method of expressing the total energy of one season or one year in the electrical unit Kilowatthour (kWh) per meter (m) width of shoreline could prove in future as a feasible way of classifying the irregular seasonal and yearly wave intensities. It is further shown that wave measurements over a period of several years can be sufficient for the investigation of correlations between the wind velocities occurring from all directions and the resulting wave heights. In case of satisfying correlation factors it will then be possible to carry out feedback operations for periods from which only records of wind velocities and directions are available and even to hindcast the wave heights for certain not yet measured wind velocities.

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