scholarly journals Extreme sea levels at different global warming levels

2021 ◽  
Vol 11 (9) ◽  
pp. 746-751
Author(s):  
Claudia Tebaldi ◽  
Roshanka Ranasinghe ◽  
Michalis Vousdoukas ◽  
D. J. Rasmussen ◽  
Ben Vega-Westhoff ◽  
...  
Keyword(s):  
Global Warming ◽  
Sea Level ◽  
Global Climate ◽  
Climate Mitigation ◽  
Frequency Change ◽  
Sea Levels ◽  
Wide Range ◽  
Extreme Sea Levels ◽  
Multimethod Approach ◽  
The Tropics

AbstractThe Paris agreement focused global climate mitigation policy on limiting global warming to 1.5 or 2 °C above pre-industrial levels. Consequently, projections of hazards and risk are increasingly framed in terms of global warming levels rather than emission scenarios. Here, we use a multimethod approach to describe changes in extreme sea levels driven by changes in mean sea level associated with a wide range of global warming levels, from 1.5 to 5 °C, and for a large number of locations, providing uniform coverage over most of the world’s coastlines. We estimate that by 2100 ~50% of the 7,000+ locations considered will experience the present-day 100-yr extreme-sea-level event at least once a year, even under 1.5 °C of warming, and often well before the end of the century. The tropics appear more sensitive than the Northern high latitudes, where some locations do not see this frequency change even for the highest global warming levels.

scholarly journals GESLA Version 3: A major update to the global higher-frequency sea-level dataset

2021 ◽  
Author(s):  
Ivan Haigh ◽  
Marta Marcos ◽  
Stefan Talke ◽  
Philip Woodworth ◽  
John Hunter ◽  
...  
Keyword(s):  
Sea Level ◽  
Storm Surges ◽  
Coastal Engineering ◽  
Multiple Sources ◽  
Sea Levels ◽  
The Third ◽  
Wide Range ◽  
The World ◽  
Extreme Sea Levels ◽  
Level Analysis

This paper describes a major update to the quasi-global, higher-frequency sea-level dataset known as GESLA (Global Extreme Sea Level Analysis). Versions 1 (released 2009) and 2 (released 2016) of the dataset have been used in many published studies, across a wide range of oceanographic and coastal engineering-related investigations concerned with evaluating tides, storm surges, extreme sea levels and other related processes. The third version of the dataset (released 2021), presented here, contains twice the number of years of data (91,021), and nearly four times the number of records (5,119), compared to version 2. The dataset consists of records obtained from multiple sources around the world. This paper describes the assembly of the dataset, its processing and its format, and outlines potential future improvements. The dataset is available from https://www.gesla.org.

Estimating contribution of high-frequency sea-level oscillations to the extreme sea levels in the Adriatic Sea

2021 ◽  
Author(s):  
Krešimir Ruić ◽  
Jadranka Šepić ◽  
Maja Karlović ◽  
Iva Međugorac
Keyword(s):  
Time Series ◽  
Sea Level ◽  
High Frequency ◽  
Adriatic Sea ◽  
Tide Gauge ◽  
Data Series ◽  
Sea Levels ◽  
Short Period ◽  
Extreme Sea Levels ◽  
Sea Level Oscillations

<p>Extreme sea levels are known to hit the Adriatic Sea and to occasionally cause floods that produce severe material damage. Whereas the contribution of longer-period (T > 2 h) sea-level oscillations to the phenomena has been well researched, the contribution of the shorter period (T < 2 h) oscillations is yet to be determined. With this aim, data of 1-min sampling resolution were collected for 20 tide gauges, 10 located at the Italian (north and west) and 10 at the Croatian (east) Adriatic coast. Analyses were done on time series of 3 to 15 years length, with the latest data coming from 2020, and with longer data series available for the Croatian coast. Sea level data were thoroughly checked, and spurious data were removed. </p><p>For each station, extreme sea levels were defined as events during which sea level surpasses its 99.9 percentile value. The contribution of short-period oscillations to extremes was then estimated from corresponding high-frequency (T < 2 h) series. Additionally, for four Croatian tide gauge stations (Rovinj, Bakar, Split, and Dubrovnik), for period of 1956-2004, extreme sea levels were also determined from the hourly sea level time series, with the contribution of short-period oscillations visually estimated from the original tide gauge charts.  </p><p>Spatial and temporal distribution of contribution of short-period sea-level oscillations to the extreme sea level in the Adriatic were estimated. It was shown that short-period sea-level oscillation can significantly contribute to the overall extremes and should be considered when estimating flooding levels. </p>

The northern and the eastern Adriatic Sea floods: connecting the extreme sea-levels to cyclone pathways 

2021 ◽  
Author(s):  
Tihana Dević ◽  
Jadranka Šepić ◽  
Darko Koračin
Keyword(s):  
Sea Level ◽  
Tide Gauge ◽  
Middle Eastern ◽  
Origin Time ◽  
Northern Adriatic ◽  
Sea Levels ◽  
North West ◽  
Mediterranean Cyclones ◽  
Extreme Sea Levels ◽  
The Moment

<p>An objective method for tracking pathways of cyclone centres over Europe was developed and applied to the ERA-Interim reanalysis atmospheric data (1979-2014). The method was used to determine trajectories of those Mediterranean cyclones which generated extreme sea levels along the northern and the eastern Adriatic coast during the period from 1979 to 2014. Extreme events were defined as periods during which sea level was above 99.95 percentile value of time series of hourly sea-level data measured at the Venice (northern Adriatic), Split (middle eastern Adriatic) and Dubrovnik (south-eastern Adriatic) tide-gauge stations. The cyclone pathways were tracked backwards from the moment closest to the moment of maximum sea level up to the cyclone origin time, or at most, up to 72 hours prior the occurrence of the sea-level maximum.</p><p>Our results point out that extreme sea levels in Venice normally appear during synoptic situations in which a cyclone centre is located to the south-west and north-west of Venice, i.e., when it can be found over the Gulf of Genoa, or the Alps. On the contrary, extreme sea levels in Dubrovnik are usually associates with cyclone centres above the middle Adriatic, whereas floods in Split seem to appear during both above-described types of situations.</p><p>Occurrence times and intensity of cyclones and extreme sea-levels was further associated with the NAO index. It has been shown that the deepest cyclones and corresponding extreme floods tend to occur during the negative NAO phase.   </p>

Multiple drivers of extreme sea levels in the northern Adriatic Sea

2021 ◽  
Author(s):  
Christian Ferrarin ◽  
Piero Lionello ◽  
Mirko Orlic ◽  
Fabio Raicich ◽  
Gianfausto Salvadori
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Adriatic Sea ◽  
Driving Factors ◽  
Northern Adriatic Sea ◽  
Relative Sea Level ◽  
Northern Adriatic ◽  
Sea Levels ◽  
Extreme Sea Levels ◽  
Relative Sea Level Rise

<p><span><span>Extreme sea levels at the coast result from the combination of astronomical tides with atmospherically forced fluctuations at multiple time scales. Seiches, river floods, waves, inter-annual and inter-decad</span></span><span><span>al dynamics and relative sea-level rise can also contribute to the total sea level. While tides are usually well described and predicted, the effect of the different atmospheric contributions to the sea level and their trends are still not well understood. Meso-scale atmospheric disturbances, synoptic-scale phenomena and planetary atmospheric waves (PAW) act at different temporal and spatial scales and thus generate sea-level disturbances at different frequencies. In this study, we analyze the 1872-2019 sea-level time series in Venice (northern Adriatic Sea, Italy) to investigate the relative role of the different driving factors in the extreme sea levels distribution. The adopted approach consists in 1) isolating the different contributions to the sea level by applying least-squares fitting and Fourier decomposition; 2) performing a multivariate statistical analysis which enables the dependencies among driving factors and their joint probability of occurrence to be described; 3) analyzing temporal changes in extreme sea levels and extrapolating possible future tendencies. The results highlight the fact that the most extreme sea levels are mainly dominated by the non-tidal residual, while the tide plays a secondary role. The non-tidal residual of the extreme sea levels is attributed mostly to PAW surge and storm surge, with the latter component becoming dominant for the most extreme events. The results of temporal evolution analysis confirm previous studies according to which the relative sea-level rise is the major driver of the increase in the frequency of floods in Venice over the last century. However, also long term variability in the storm activity impacted the frequency and intensity of extreme sea levels and have contributed to an increase of floods in Venice during the fall and winter months of the last three decades.</span></span></p>

scholarly journals Atmospheric circulation changes and their impact on extreme sea levels around Australia

2019 ◽  
Vol 19 (5) ◽  
pp. 1067-1086 ◽  
Author(s):  
Frank Colberg ◽  
Kathleen L. McInnes ◽  
Julian O'Grady ◽  
Ron Hoeke
Keyword(s):  
Sea Level ◽  
Large Scale ◽  
Climate Models ◽  
Tide Gauge ◽  
Austral Summer ◽  
Sea Level Changes ◽  
Sea Level Variability ◽  
Sea Levels ◽  
Extreme Sea Levels ◽  
Coastal Sea

Abstract. Projections of sea level rise (SLR) will lead to increasing coastal impacts during extreme sea level events globally; however, there is significant uncertainty around short-term coastal sea level variability and the attendant frequency and severity of extreme sea level events. In this study, we investigate drivers of coastal sea level variability (including extremes) around Australia by means of historical conditions as well as future changes under a high greenhouse gas emissions scenario (RCP 8.5). To do this, a multi-decade hindcast simulation is validated against tide gauge data. The role of tide–surge interaction is assessed and found to have negligible effects on storm surge characteristic heights over most of the coastline. For future projections, 20-year-long simulations are carried out over the time periods 1981–1999 and 2081–2099 using atmospheric forcing from four CMIP5 climate models. Changes in extreme sea levels are apparent, but there are large inter-model differences. On the southern mainland coast all models simulated a southward movement of the subtropical ridge which led to a small reduction in sea level extremes in the hydrodynamic simulations. Sea level changes over the Gulf of Carpentaria in the north are largest and positive during austral summer in two out of the four models. In these models, changes to the northwest monsoon appear to be the cause of the sea level response. These simulations highlight a sensitivity of this semi-enclosed gulf to changes in large-scale dynamics in this region and indicate that further assessment of the potential changes to the northwest monsoon in a larger multi-model ensemble should be investigated, together with the northwest monsoon's effect on extreme sea levels.

Statistical Downscaling of daily extreme Sea Level with Random Forest: Examples from South-East Asia and the Baltic Sea

2020 ◽  
Author(s):  
Svenja Bierstedt ◽  
Eduardo Zorita ◽  
Birgit Hünicke
Keyword(s):  
Random Forest ◽  
Baltic Sea ◽  
East Asia ◽  
Sea Level ◽  
Large Scale ◽  
South East Asia ◽  
The Baltic Sea ◽  
Sea Levels ◽  
Extreme Sea Levels ◽  
The Baltic

<p>The coastlines of the Baltic Sea and Indonesia are both relatively complex, so that the estimation of extreme sea levels caused by the atmospheric forcing becomes complex with conventional methods. Here, we explore whether Machine Learning methods can provide a model surrogate to compute more rapidly daily extremes in sea level from large-scale atmosphere-ocean fields. We investigate the connections between the atmospheric and ocean drivers of local extreme sea level in South East Asia and along the Baltic Sea based on statistical analysis by Random Forest Models, driven by large-scale meteorological predictors and daily extreme sea level measured by tide-gauge records over the last few decades.</p><p>First results show that in some Indonesian areas extremes are driven by large-scale climate fields; in other areas they are incoherently driven by local processes. An area where random forest predicted extremes show good correspondence to observed extremes is found to be the Malaysian coastline. For the Indonesian coasts, the Random Forest Algorithm was unable to predict extreme sea levels in line with observations. Along the Baltic Sea, in contrast, the Random Forest model is able to produce reasonable estimations of extreme sea levels based on the large-scale atmospheric fields. An analysis of the interrelations of extreme sea levels in the South Asia regions suggests that either the data quality may be compromised in some regions or that other forcing factors, distinct from the large-scale atmospheric fields, may also be involved.</p>

Simulating extreme sea levels at the Baltic Sea coast from synthetic cyclones

2020 ◽  
Author(s):  
Jani Särkkä ◽  
Jani Räihä ◽  
Matti Kämäräinen ◽  
Kirsti Jylhä
Keyword(s):  
Baltic Sea ◽  
Sea Level ◽  
Water Volume ◽  
The Baltic Sea ◽  
Data Set ◽  
Sea Levels ◽  
Extreme Sea Levels ◽  
Level Model ◽  
The Baltic ◽  
Sea Coast

<p>Coastal areas are under rapid changes. Management to face flooding hazards in changing climate is of great significance due to the major impact of flooding events in densely populated coastal regions, where also important and vulnerable infrastructure is located. The sea level of the Baltic Sea is affected by internal fluctuations caused by wind, air pressure and seiche oscillations, and by variations of the water volume due to the water exchange between the Baltic Sea and the North Sea through the Danish Straits. The highest sea level extremes are caused by cyclones moving over the region. The most vulnerable locations are at the ends of the bays. St. Petersburg, located at the eastern end of the Gulf of Finland, has experienced major sea floods in 1777, 1824 and 1924.</p><p>In order to study the effects of the depths and tracks of cyclones on the extreme sea levels, we have developed a method to generate cyclones for numerical sea level studies. A cyclone is modelled as a two-dimensional Gaussian function with adjustable horizontal size and depth. The cyclone moves through the Baltic Sea region with given direction and velocity. The output of this method is the gridded data set of mean sea level pressure and wind components which are used as an input for the sea level model. The internal variations of the Baltic Sea are calculated with a numerical barotropic sea level model, and the water volume variations are evaluated using a statistical sea level model based on wind speeds near the Danish Straits. The sea level model simulations allow us to study extremely rare but physically plausible sea level events that have not occurred during the observation period at the Baltic Sea coast. The simulation results are used to investigate extreme sea levels that could occur at selected sites at the Finnish coastline.</p>

Supporting European coastal sectors to adapt to changes in extreme sea levels with climate change

2020 ◽  
Author(s):  
Sanne Muis ◽  
Maialen Irazoqui Apecechea ◽  
Job Dullaart ◽  
Joao de Lima Rego ◽  
Kristine S. Madsen ◽  
...  
Keyword(s):  
Climate Change ◽  
Sea Level ◽  
Storm Surges ◽  
Climate Impact ◽  
Climate Impacts ◽  
Water Levels ◽  
Climate Data ◽  
Local Climate ◽  
Sea Levels ◽  
Extreme Sea Levels

<p>Climate change will lead to increases in the flood risk in low-lying coastal areas. Understanding the magnitude and impact of such changes is vital to design adaptive strategies and create awareness. In  the  context  of  the  CoDEC  project  (Coastal  Dataset  for  Evaluation  of  Climate  impact),  we  developed a consistent European dataset of extreme sea levels, including climatic changes from 1979 to 2100. To simulate extreme sea levels, we apply the Global Tide and Surge Model v3.0 (GTSMv3.0), a 2D hydrodynamic model with global coverage. GTSM has a coastal resolution of 2.5 km globally and 1.25 km in Europe, and incorporates dynamic interactions between sea-level  rise,  tides  and  storm surges. Validation of the dataset shows a good performance with a mean bias of 0-.04 m for the 1 in 10-year water levels. When analyzing changes in extreme sea levels for the future climate scenarios, it is projected that by the end of the century the 1 in 10-year water levels are likely to increase up to 0.5 m. This change is largely driven by the increase in mean sea levels, although locally changes in storms surge and interaction with tides can amplify the impacts of sea-level rise with changes up to 0.2 m in the 1 in 10-year water level.</p><p>The CoDEC dataset will be made accessible through a web portal on Copernicus Climate Data Store (C3S). The dataset includes a set of Climate Impact Indicators (CII’s) and new tools designed to evaluate the impacts of climate change on different sectors and industries. This data service will support European coastal sectors to adapt to changes in sea levels associated with climate change. In this presentation we will also demonstrate how the C3S coastal service can be used to enhance the understanding of local climate impacts.</p>

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