Show Summary Details

Page of

PRINTED FROM OXFORD HANDBOOKS ONLINE (www.oxfordhandbooks.com). (c) Oxford University Press, 2015. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a title in Oxford Handbooks Online for personal use (for details see Privacy Policy).

date: 18 December 2017

Coastal Hazards, Risks, and Marine Extreme Events: What Are We Doing About It?

Abstract and Keywords

This article describes the threat costal hazards pose to existing life in light of climate change and natural disaster. It includes an overview of flooding, extreme waves, and other water-related stressors. The article discusses how human-induced risks in the coastal zone, resulting from mismanaged urbanization, persistent pollution, and overexploitation of resources, exacerbate matters and pose extra pressure on the environment, science, and society. Ways of measurement and reaction to these events, as well as best practices for preparedness, are discussed. Businesses, individuals, and ecosystems are under threat of destruction from these circumstances. The article also emphasizes the need to make scientific work in this field accessible and understandable to society and decisión makers.

Keywords: coast, floods, weather, climate change, IPCC, extreme weather, water, water damage

Introduction

Natural hazards are naturally occurring events that might have a negative effect on people or the environment (earthquakes, storm surges, hurricanes, landslides, floods, etc.). An expanded definition of the concept of natural hazards is used here, to include the threats that are also human induced or potentiated, because human beings are part of nature, and not doing so seems too anthropocentric.

Globally, 200 million people live on coastal floodplains, and about $1 trillion worth of assets lie within 1 meter of mean sea level. Over 1.2 billion people globally live within 100 km of the coast and less than 100 m above mean sea level, the area most directly affected by changing sea levels. Increased flood frequency or severity in these vulnerable regions would impact economic and social systems. The population densities in coastal regions are about three times higher than the global average, with increasing growth rates. The attractiveness of the coast has resulted in disproportionately rapid expansion of economic activity, settlements, urban centers, and tourist resorts. Migration of people to coastal regions is common in both developed and developing nations. Sixty percent of the world’s thirty-nine metropolises with a population of over five million are located within 100 km of the coast, including twelve of the world’s sixteen cities with populations greater than ten million. Rapid urbanization has many consequences, for example, enlargement of natural coastal inlets and dredging of waterways for navigation, port facilities, and pipelines exacerbating saltwater intrusion into surface and ground waters, according to the Intergovernmental Panel on Climate Change (IPCC, 2007; Meehl et al., 2007).

The Coastal Zone is a region where interaction of the sea, the atmosphere, and land processes occurs in a very complex and dynamic way (Zhang et al. 2004), with the extra effect of societal pressures on a changing environment. Thus, due to the high complexity and dynamism of such a region, assessment studies are of great importance when dealing with Coastal Hazards in this environment of ecological and socially high relevance.

Almost certainly the more extreme weather is a result of climate change, and severe weather is likely to become more common in the future. Natural hazards and extreme events in the coastal zone are becoming more frequent and energetic, and in future scenarios this tendency will remain or even increase due to climate changes (IPCC, 2012). These events pose a challenge to both science and society. While science has been traditionally driven by curiosity, from the second half of the twentieth century other objectives have influenced the direction of scientific research, and not always with prior consideration of societal needs. In terms of bridging science and society, partnership between stakeholders and researchers could generate mutual benefits.

Because of our failure to adapt to climate change, not least the rise in greenhouse gas emissions, linked to enhanced and persistent extreme weather events, or due to unprecedented geophysical destruction as a result of earthquakes, tsunamis, etc., the coastal zone is threatened and, when a given coastal hazard occurs, we face the event usually unprepared (IPCC, 2014). To exacerbate matters, human-induced risks in the coastal zone, resulting from mismanaged urbanization, persistent pollution, and overexploitation of resources, pose extra pressure on the environment, science, and society.

One of the greatest challenges to understand the risk associated with coastal hazards is the evaluation of forecast uncertainties, in which assumptions need to be clearly considered and presented. Therefore, knowledge of uncertainties is crucial in preparing to face coastal hazards.

Preparedness is the main action society has to take against such hazards, while acknowledging that existing measures could fail in the face of extreme geophysical disasters of unparalleled magnitude such as earthquakes, volcanic activity, landslides, hurricanes, or tsunamis (also realizing that we are not prepared for the unprecedented or very unusual).

Reliable modeling and prediction of extreme marine events are essential in order to quantify the risk to humans and the environment, as well as to measure the economic viability of activities such as marine transportation, oil and gas exploitation, and several types of coastal infrastructure. Extreme marine events are caused by a variety of factors, and even if science has made significant advances in recent years, not all the generating causes, and thus the propagating patterns, are fully known, and they vary spatially in the global ocean. However, on complex dynamical systems, it is not always wise to be too concerned about finding cause–effect relationships, because further results could be of great importance even if the causes behind the full dynamics remain unknown. The search for a specific cause may be futile when dealing with a nonlinear system, with complex and unknown feedback mechanisms, because every link in the feedback loop is both cause and effect.

Numerical modeling and prediction of extreme marine events, such as extreme sea levels, extreme ocean waves, and extreme ocean currents, are becoming more precise, but they are still not accurate enough. Improvements in methodologies for the assessment of changes in the frequency and severity of extreme marine events under greenhouse warming are of great importance.

The Network

In bridging science and society together, partnership between stakeholders and researchers could generate mutual benefits, which could then be “translated” into more easy language for the whole of society.

Considering the complexity of the dynamical problem and the feedbacks among scales, in both time and space, cooperative scientific work has much to offer regarding natural hazards and marine extreme events.

The LRF Lloyd's Register Foundation Global Network to Improve Prediction of Extreme Marine Events (LRF-GNIPEME), with nodes in Canada (Dalhousie University), the United Kingdom (University of Southampton—National Oceanography Centre), Australia (University of Melbourne), and the Institute of Astronomy and Geophysics of the São Paulo University together with the Center of Marine Studies of the University of Paraná, Brazil, brings together researchers in ocean and climate physics, with the support of global stakeholders (e.g., The Lloyd’s Register Foundation—LRF). The main objectives are to improve short-term forecasts of extreme marine events that threaten coastal communities and marine activities such as shipping and oil and gas installations; to estimate the frequency of extreme marine events over the coming decades with realistic measures of uncertainty; present the results in a way that is useful for scientists, users, and the general public; and to help in matters of social preparedness. The challenge was to provide accurate simulations of such extreme events by combining coastal observations, improving forecasts, reducing the uncertainty, and informing risk assessment practitioners. The key issues considered are related to better estimation and further reduction of a model’s uncertainties, differentiating precision from accuracy (Fig. 1), and translating from the “science speak” to a more user friendly language. This is an ethical obligation of geoscientists (Marone and Marone, 2014; http://www.geoethics.org/).

Coastal Hazards, Risks, and Marine Extreme EventsWhat Are We Doing About It?Click to view larger

Figure 1 According to ISO 5725-1, Accuracy consists of Trueness (proximity of measurement results to the true value—or reference value) and Precision (repeatability or reproducibility of the measurement—given by several observations or model outputs).

Furthermore, when trying to explain results to the general public, scientists usually focus more on forecasts than on the prediction errors. When using numerical modeling combined with field observations to predict better marine extreme events, we focus equally on the forecasts as well as on the variability and uncertainties of our results, because they are neither obvious nor irrelevant (Marone et al., 2015).

Climate Changes and Coastal Hazards

According to the IPCC, the warming of the climate system is unequivocal, and since the 1950s many of the observed changes are unprecedented over decades to millennia (IPCC, 2014). The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, and sea level has risen. Changes in many extreme weather and climate events have been observed since about 1950. Some of these changes have been linked to human influences, including a decrease in cold temperature extremes, an increase in warm temperature extremes, an increase in extreme high sea levels, and an increase in the number of heavy precipitation events in a number of regions (Chambers et al., 2014). With increasing warming, some physical and ecological systems are at risk of abrupt and/or irreversible changes, and there are signs that both warm-water coral reefs and Arctic ecosystems are already experiencing irreversible regime shifts.

IPCC results show that the amount of extreme events (cyclones, hurricanes, storm surges, high maritime agitation, etc.) will increase in the coming years and that those and other weather events (cold fronts, storms, sea agitation, drift, and return currents) will intensify (IPCC, 2007, 2012, 2014).

Not only are the coastline, weather, coastal and near-shore currents, and sea level affected by changes and the more frequent natural hazards, but also ecotones such as dunes, corals, mangroves, and marshes, which would not be resilient enough to migrate at the same pace. Besides the ecological loss (since such environments are important habitats and nurseries for animals and plants in the coastal zone), the loss of such systems causes extra damage to the coastline, which would migrate easily without the physical barrier that the ecosystems represent. The resilience of these systems should be better established, particularly in regard to those ecotones located at the points where the risk assessments indicate critical setups.

It is also necessary to mention the potential losses with the destruction of aquifers and river mouths, which are “contaminated” by the saline wedge as well as the threat to coastal ecosystems that would need room to move landward. The increasing human activities in the near-shore and continental platform (such as fisheries, maritime transportation, and oil exploitation) will be also exposed by these rising threats.

Climate changes have been studied mostly by focusing on the tendency and changes in mean climate (LaDochy et al., 2007; White et al., 2014). In terms of climate model output, these changes are more robust than changes in climate variability. However, by concentrating on changes in climate means, the full impacts of climate change on biological and human systems are probably being seriously underestimated (Thornton et al., 2014). Analyzing changes in the dynamical regime of the global system and its shifts seems to be more appropriate for understanding these impacts (Overland et al., 2008; Servain et al., 2014). Large-scale transitions between alternative states are known as regime shifts, which are abrupt changes encompassing a multitude of physical properties and variables, which lead to new regime conditions (Conversi et al., 2010).

Among different coastal hazards that affect the oceans, the most conspicuous is flooding in coastal areas. Flooding can arise through extreme weather events (Qi et al., 2006; Vianna et al., 2010; Grinsted et al., 2013), storm surges (Bernardara et al., 2011; Marcos et al., 2011), sea level rise (Woodworth and Blackman, 2002; Bernier and Thompson, 2006; Rahmstorf, 2007; Letetrel et al., 2010; Torres and Tsimplis, 2011, 2012; Pickering et al., 2012; Zhang and Sheng, 2013), extreme waves (Wang and Swail, 2001, 2002; Xie et al., 2008; Waseda et al., 2011; Young et al., 2011), changes in rainfall patterns (Chambers et al., 2014), removal of protective coastal mangroves and vegetation, and subsidence caused by groundwater extraction. Storm surges, which are temporary increases in sea level caused by low atmospheric pressure combined with strong winds, are the most common coastal hazard on the Southwestern Atlantic Ocean coasts, as in other places (Browning and Goodwin, 2013; Dowdy et al., 2013), and induce the sea level to rise even further. Extreme coastal flooding often occurs when storm surges coincide with large spring tides and high tides, and storm waves (Hemer et al., 2013; Semedo et al., 2013; Feng et al., 2014a, b) can combine with storm surges to cause extreme flooding, a risk which will increase because global average sea level is expected to rise by approximately a meter by 2100 (IPCC, 2012). Safeguarding more effectively the environment and the coastal population from storm surges (Shan et al., 2011), by informing decision makers where and when protection is needed, is of extreme importance and requires rigorous and complex scientifically grounded work.

Extreme waves are a hazard both along the coast and at sea in the region. Waves from winter storms significantly impact our coastlines, with potential for coastal erosion and flooding, endangering maritime operations, damaging oil platforms, and overtopping sea-defenses in extreme conditions. The largest waves in the region are found on the coasts arriving from S-E and N-E directions; these can propagate over large fetches from the ocean. Many factors affect the height of the waves in the region, but the persistence and strength of the winds over the Southern Atlantic are particularly important.

Some extreme events already recorded, such as the South Atlantic tropical cyclone Catarina, increased coastal flooding as well as reinforced strong winds and high precipitation levels over the coastal area, which in turn increased river runoff, producing flooding in the coastal zone, where the increasingly high sea level served as a hydraulic dam, worsening the inundation effects (Pezza and Simmonds, 2005; Veiga et al., 2008). Hurricane Catarina destroyed 1,500 homes and damaged around 40,000 others (Pezza et al., 2009). Agriculture was also severely damaged, as 85% of the banana crops and 40% of the rice crops were lost in the storm. Despite the lack of adequate storm warning, only three people were confirmed to have perished and 75 others were injured. Damages from the storm amounted to $350 million (in 2004 USD; $437 million in 2015 USD).

The Brazilian Node Background Drive

The Brazilian coastal zone lies between latitudes 6°N and 34°S, harboring the higher population densities, thus extending from equatorial latitudes and weather dynamics to the temperate ones in its southern regions. The extensive human settlements in the coastal zone have induced economic activities to concentrate most GDP on fields such as maritime transportation, the offshore oil and gas industry, and tourism.

As mentioned, natural hazards and extreme events in the coastal zone are becoming more frequent and energetic, and this escalation is linked to climate change. The Brazilian coast is not immune from such coastal hazards.

The Brazilian node of the LRF Network is also addressing the question of whether the frequency and intensity of marine hazards such as coastal flooding due to storm surges will increase as the climate grows warmer. The objective is not just to produce scientific papers in peer review journals, but also to enhance national and global preparedness for the sort of hazards that particularly affect coastal communities, homes, industry, agriculture, and infrastructure.

Accurate predictions of extreme marine events are required to improve human capacities of response (preparedness). This is a complex technical problem involving the local evolution of large-scale weather systems, as well as their interaction with the oceans and ice caps. In addition, sea level rise and the increase of cyclone activity due to climate change make prediction of extreme events even more complicated. In order to improve humankind’s ability to respond to extreme marine events in the short term, and also to plan effectively for climate change over the long term, the international research team of the LRF-GNIPEME focuses on: (1) short- and medium-term forecasting (hours to weeks) of extreme marine events; (2) projecting the frequency of occurrence of extreme events over coming decades and the next century; and (3) estimating the precision and accuracy of the obtained results. Specifically, this project aims to study the changes in extreme sea levels, waves, and currents as a consequence of mean sea level rise and possible changes in frequency, intensity, and track of storms in different regions.

Other aspects the Network will indirectly explore the influence of climate variability, for example, the North Atlantic Oscillation, El Niño-Southern Oscillation and global (or regional) mean temperatures, and the Inter-Tropical Convergence Zone activity in the Atlantic and Pacific Oceans. Apart from extreme sea level (Tsimplis and Blackman, 1997), Network researchers are also studying the climatology of extreme ocean waves, which are also a concern for the safety of ocean and coastal activities (Schiller et al., 2008).

The study area of the Brazilian node encompasses the whole Southeast Brazilian coast, including the South Brazil Bight, the most populated and developed coastal region of Brazil, extending to the south of the Rio de la Plata. Moreover, this region includes the Brazil-Malvinas Confluence (BMC), a zone of permanent SST gradients with frequent atmospheric cyclogenesis that can increase many of the cold fronts that hit the Brazilian coast every year.

With sea level rise and tropical storms becoming stronger and more frequent, the scenarios of the local impact of sea level rise and storm surges, causing loss of lives, environmental damage, and socioeconomic stress, need to be examined and properly communicated.

Numerical Modeling

In the first stage of this study, a number of numerical experiments with large-scale initial conditions (covering the whole South Atlantic Ocean) have been run to reconstruct the effect of basin-scale atmospheric forcing at ½ degree resolution (wind and air pressure fields) on the coastal area, using NCEP/NCAR-Reanalysis (Kalnay et al., 1996). These experiments were run by using the Princeton Ocean Model (POM) (Blumberg and Mellor, 1987) and have provided an historical reconstruction of the hourly sea levels for the 1948 to 2010 period, including tides. The project is focused on verifying the model’s outputs of extreme sea levels by using data recorded by tide gauges along the southeast Brazilian coast, and its results are online, updated in an almost operational way. The LRF network is working on a similar approach, at global and regional scales, on the other nodes in Canada, Australia, and the United Kingdom (Global Ocean-Atmosphere Prediction and Predictability—GOAPP, Bureau of Meteorology—BoM and Commonwealth Scientific and Industrial Research Organisation—CSIRO, National Oceanography Centre—NOC, etc.).

The POM is a simple-to-run yet powerful ocean modeling code that is able to simulate a wide range of problems: circulation and mixing processes in rivers, estuaries, shelf and slope, lakes, semienclosed seas, and open and global ocean. The POM is a sigma coordinate (terrain-following), free-surface ocean model with embedded turbulence, wave submodels, and wet-dry capability. It was developed in the late 1970s by Blumberg and Mellor, with subsequent contributions from other people.

Numerical experiments with large-scale initial conditions (South Atlantic Ocean) from reanalysis have been run, to reconstruct the effect of basin-scale atmospheric forcing (wind, air pressure fields, and tides) on the coastal area. These experiments provided a reconstruction of the hourly sea levels for the 1948 to 2010 period, and the following step focused on verifying the model’s outputs of extreme sea levels (Fig. 2). The validation of the model results is made by comparing them with observations recorded by tide gauges along the southeast Brazilian coast.

Coastal Hazards, Risks, and Marine Extreme EventsWhat Are We Doing About It?Click to view larger

Figure 2 Hourly sea level heights, observed (blue) and modelled (red), from the Cananéia site (1955–2006).

Atmospheric-Related Hazards on the Brazilian Coast

The cyclogenetic characteristic of the western portion of the South Atlantic is a recognized feature associated with mid-latitude and subtropical influence, which drives extreme marine events, and this identification and investigation was one of the focuses of the Brazilian node.

The key questions to be addressed in this part of the work were:

  • Can we properly simulate the Southwestern Atlantic extreme events?

  • What are the impacts of these extreme events on the surface ocean properties?

The research work was based on the intensive use of ocean and atmosphere numerical modeling: the POM and the Regional Atmospheric Model System (RAMS)) (Tremback, 1990), among others.

Similar to the POM simulation tool for the oceans, already mentioned, the atmospheric model RAMS is constructed around the full set of primitive dynamical equations that govern atmospheric motions. These equations are supplemented with optional parameterizations for turbulent diffusion, solar and terrestrial radiation, moist processes including the formation and interaction of clouds and precipitating liquid and ice hydrometeors, sensible and latent heat exchange between the atmosphere, multiple soil layers, a vegetation canopy, surface water, the kinematic effects of terrain, and cumulus convection. The RAMS is fundamentally a limited-area model, but may be configured to cover an area as large as a planetary hemisphere for simulating mesoscale and large-scale atmospheric systems. There is no lower limit to the domain size or to the mesh cell size of the model’s finite difference grid: Microscale phenomena, such as tornadoes and boundary layer eddies, as well as submicroscale turbulent flow over buildings and in a wind tunnel, have been simulated with this code. Two-way, interactive grid nesting in the RAMS allows local fine-mesh grids to resolve compact atmospheric systems such as thunderstorms, while simultaneously modeling the large scale environment of the systems on a coarser grid.

Both models were implemented in regional scales for the Southern Atlantic, focusing mostly on testing different resolutions and initial conditions of atmospheric fields (wind and atmospheric pressure) as well as tides. Most of the work has been concentrated on fine tuning the models to improve hindcasted scenarios (reproducing the past), in order to represent better real situations observed in former times. This action has the advantage of allowing the model results to be contrasted with real observed data, thus providing a powerful tool to improve model performance by fine tuning it in an iterative way as much as necessary, to make the model fit better with the observed natural variability.

Changes in the frequency, intensity, and tracks of tropical and extra-tropical cyclones would not only cause changes in storm surges, but also in ocean swells and waves climatology. Some likely consequences are changes in the pattern of shore erosion and flood return periods (Rego and Li, 2010). These events are referred to as extreme events. An extreme event is defined as an event that produces a value of an atmospheric or oceanic variable above (or below) a threshold value near the upper (or lower) ends of the range of observed values of the variable. The values of a variable at a high percentile (e.g., 99th) over a certain period are also usually used to represent the extremes, but other criteria could be used, depending on the targeted objective.

In March 2004, as mentioned, the southeast Brazilian coast was hit by the first South Atlantic hurricane ever recorded in the satellite era: Hurricane Catarina (Fig. 3). This unusual event, whose evolution was examined by many researchers, caused three fatalities, much damage, and left thousands of people homeless. As a consequence of this unexpected phenomenon, besides the importance of the Brazil-Malvinas Confluence and the cyclogenesis area, the Brazilian node is focused on studying the air–sea interaction and predicting extreme marine events in the southwestern Atlantic.

The second stage consisted of running new numerical experiments at 1/12 degree resolution (see Fig. 3, bottom panel), focused on a coastal area from Cabo Frío to the south of Rio de la Plata, in order to improve the resolution and spatial circulation patterns associated with the oceanic response of the continental shelf to extreme atmospheric forcing. In particular, an objective of the node is to study the extreme currents in the oil-rich Campos and Santos Basins off the southeast Brazilian coast. To this end, a statistical analysis (Franco, 2009) and modeling of extreme winds, also studying the response of surface waves in this area, is being carried out, including long-term integrations of the numerical model Wave Watch—III forced by NCEP/NCAR-Reanalysis.

Coastal Hazards, Risks, and Marine Extreme EventsWhat Are We Doing About It?Click to view larger

Figure 3 Left top: Image showing Hurricane Catarina hitting the Brazilian coast, in March 2004 (Image courtesy NASA/JPL Seaflux). Right top: Modeled SST in the southeast Brazilian coast, showing the temperature contrast in the Brazil-Malvinas confluence (BMC). Bottom panel: Example of observed and modeled sea elevation in Cananéia, southeast Brazil coast.

On the other hand, emphasis has also been placed on time series analysis, from the perspective of regime shifts and analysis of extremes. Natural phenomena show patterns of behavior or regimes, which tend to change over time at different scales. Ocean and atmosphere oscillations (sea level, pressure field, etc.) are many variables acting against each other, for which our main goal is to identify and, when possible, to separate those that have oscillatory characteristic from sporadic ones.

Uncertainties

As mentioned before, knowledge of uncertainties is of extreme importance when dealing with natural hazards. This is particularly relevant when working with numerical models to simulate the dynamics of oceans and atmosphere. The numerical models are a physics–mathematical approximation to the real world, used to forecast the future, which is unknown. However, as with any approximation, the prediction of marine natural disaster using such tools is not yet accurate enough due to still insufficient understanding, and thus representation, of the physical processes that drive the complex interactions between the land, the atmosphere, and the ocean forcings. One of the methods to verify the precision of a given numerical model is to wait and check future outputs against the observed reality. However, this way is not very efficient to prevent natural disasters, or to help the population to be better prepared to deal with them.

Although the numerical models were originally developed to forecast (in the sense of predict the future of some events), they also allow to “predict back in time” with a technique called hindcast. This technique is an imaginary trip back to the past, to a time when enough observational weather and ocean data began to be regularly collected, allowing us to start up the model in, say, 1950, and simulate the ocean and atmosphere dynamics to the present. Then, by comparing the model’s outputs (numerical world) against the observed data (the real world), we are able to estimate the errors our model has and, most importantly, to fine tune the model as many times as necessary to “force” it to represent nature more effectively. Even if it seems we are “cheating,” manipulating the model to better fit the observations, this is an acceptable and very robust way to improve its performance. It is the same procedure a goalkeeper once used to win the soccer World Cup: By studying the history of the entire opposition, a statistical model could be developed for each player, to predict, based on past data, which side of the goal he most frequently targeted.

The obtained results have shown that, in spite of the great differences in absolute numbers between the observed and the modeled data, the model represents the variability of data quite well. More importantly, a key finding of the first comparisons is that the model is representing the “colors” of the real sea level spectrum. Also, the interannual regime shifts observed in nature are mostly replicated (see Fig. 2). It is inside this variability that will be the focus of the next steps, fine tuning the model to better represent the observed data. Once able to well reproduce the past dynamic, the probability of future scenarios and forecasts being accurate enough will increase.

Regime Shifts

Regime shifts, considered as large-scale singular events (Conversi et al., 2010), have been detected in recent times, and the most commonly cited is the rapid change of the North Pacific climate in 1977 (Kerr, 1992). In terms of paleoclimatic works, the most cited are the Younger Dryas, a period of cold climatic conditions and drought that occurred between approximately 12,800 and 11,500 years bp (Berger, 1990) and the Akkadian Collapse, around 4,200 years bp (Weiss et al, 1993).

As the risks of abrupt or irreversible changes increase due to increases in warming, analyzing climate data for regime shifts is also an imperative (IPCC, 2014).

The analysis of sea level data, in conjunction with time series of climate and ocean indexes, was used to develop studies on regime shifts (Easterling and Peterson, 1995; Lanzante, 1996; Rodionov, 2004), and a similar approach has been used by several investigators in identifying regime shifts on local or regional climate and ecosystem dynamics (Overland et al, 2008; Hong et al., 2014; Werner et al. 2014; White et al., 2014). The authors also studied observed sea level data for extreme events, producing the statistic and recurrence times of those events. The intention was to analyze them against the numerical model outputs in the same way, in order to help validate the model; not just to examine the plain outputs (error between the modeled and observed data), but also their variability and to what extent the shifts in the sea level regimes are well represented by the model.

Rodionov’s Regime Shift Indexes analysis (Rodionov, 2006) was performed over the full set of data. The method is based on the sequential application of Student’s t-test, which is used in the spirit of exploratory, rather than confirmatory, data analysis. The Rodionov’s sequential algorithm allows for early detection of a regime shift and subsequent monitoring of changes in its magnitude over time. The algorithm can handle the incoming data regardless of whether they are presented in the form of anomalies or absolute values. It should be noted that the methodology allows the user to analyze the changes over the mean or on the variance of the data. Additionally, it is possible to select the cut-off length, which is the time scale for a given shift to be detected. As the cut-off length is reduced, the time scale of detected regimes becomes shorter. Additionally, the statistical significance level of the test and the Huber’s Weight Parameter can be selected and/or adjusted. The analysis was performed for shifts over the mean using the algorithms that take into account the autocorrelation (or red noise estimation by least squares) with prewhitening and a Hubert’s weight parameter of 1, and, also, the shifts on the variance of the series. Both analyses used significance levels of 0.1 and 0.01 and cut-off lengths of 1, 2.5, 5, and 10 years.

An interesting result of these experiments is the confirmation that the model is closely replicating the shift regime in the observed mean sea level recorded on the southeast coast of Brazil. This conclusion was obtained after analyzing and comparing modeled and observed data in Cananéia sea level station (Southern Brazil) after applying regime shift indexes. In spite of the fact that the modeled data had lower energy in comparison with observed data, both datasets had similar spectra (not shown) and detected the regime shifts in 1980, 1998, and 2003 (Fig. 4).

Coastal Hazards, Risks, and Marine Extreme EventsWhat Are We Doing About It?Click to view larger

Figure 4 The observed yearly mean sea level at Cananéia station shown regime shifts at the four years cut-off period around 1960, 1980, 1998 and 2003 (blue bars), while the modelled sea level heights (yearly averaged), also shown shifts (red lines) at almost the same times (except for 1960).

Future Research

Results obtained by the node have shown that the seasonal sea level cycle and long-term variation of tides contribute significantly to extreme sea levels. This kind of study is worth extending to the other coastal waters of the Atlantic and Pacific Oceans, as more research is needed on the long-term changes of extreme sea levels and the interaction between its components (such as tide–surge interaction).

Ongoing research of the long-term prediction of extreme events is not only focusing on extreme sea levels, but also extreme waves and ocean currents. Furthermore, the long-term prediction of extreme events, integrating the full Network efforts, will be extended from the northwestern Atlantic to southwestern Atlantic, Pacific Ocean, and North Sea, which are also threatened by severe marine events. In order to improve the accuracy of short-term and long-term prediction of extreme sea level, more physics will be added in ocean modeling. Circulation models will be run at higher resolution with unstructured or nested-grid, to resolve tide–surge interaction and other physical processes. Additionally, a 3D baroclinic circulation model and an ocean wave model (e.g., Wave Watch III) should be used in the study. Finally, wind forcing and sea level pressure fields with higher spatial and temporal resolutions for the past and the future are required, not only to represent the general structure of a tropical storm or hurricane during the landfall, but also to assess the potential risk caused by extreme sea levels due to climate change.

Final Remarks

We scientists have to learn to express our discoveries in plain words, but it is also necessary to review the education and social models today. For science to have a positive impact on society, deep changes are required in educational systems, thus preparing people to deal with concepts of scientific prediction and forecasts, the errors associated with any scientific procedure, and concepts like probability, recurrence times, resilience, preparedness, scenarios, precision, accuracy, precautionary principles, morals, and ethics, although not with a patronizing approach.

Scientists, on the other hand, have an ethical duty to communicate their findings, in less academic language, in order to help society safeguard lives, goods, and the environment.

Acknowledgements

This research is funded by Lloyd’s Register Foundation (LRF), which helps to protect life and property by supporting engineering-related education, public engagement, and the application of research.

References

Berger, W. H. (1990). “The Younger Dryas Cold Spell—A Quest for Causes.” Global Planet Change 3(3): 219–237. doi: 10.1016/0921-8181(90)90018-8Find this resource:

Bernardara, P., Andreewsky, M., and Benoit, M. (2011). Application of Regional Frequency Analysis to the Estimation of Extreme Storm Surges. J Geophys Res 116, C02008. doi: 10.1029/2010JC006229.Find this resource:

Bernier, N. B., and Thompson, K. R. (2006). Predicting the Frequency of Storm Surges and Extreme Sea Levels in the Northwest Atlantic. J Geophys. Res 111, C10009. doi: 10.1029/2005JC003168.Find this resource:

Blumberg, A. F., and Mellor, G. L. (1987). A Description of a Three-Dimensional Coastal Ocean Circulation Model. In Heaps N. (Ed.), Three-Dimensional Coastal Ocean Models (pp. 1–16) Washington, DC: American Geophysical Union.Find this resource:

Browning, S. A., and Goodwin, I. D. (2013). Large-scale Influences on the Evolution of Winter Subtropical Maritime Cyclones Affecting Australia’s East Coast. Mon Weather Rev 141: 2416–2431.Find this resource:

Chambers, C. R. S., Brassington, G. B., Simmonds, I., and Walsh, K. (2014). Precipitation Changes Due to the Introduction of Eddy-Resolved Sea Surface Temperatures into Simulations of the “Pasha Bulker” Australian East Coast Low of June 2007. Meteorol Atmos Phys 125(1–2): 1–15.Find this resource:

Conversi, A., Fonda Umani, S., Peluso, T., Molinero, J. C., and Santojanni, A. (2010). The Mediterranean Sea Regime Shift at the End of the 1980s, and Intriguing Parallelisms with Other European Basins. PLoS ONE 5(5): e10633. doi: 10.1371/journal.pone.0010633Find this resource:

Dowdy, A. J., Mills, G. A., Timbal, B., and Wang, Y. (2013). Changes in the Risk of Extratropical Cyclones in Eastern Australia. J Clim 26: 1403–1417.Find this resource:

Easterling, D. R., and Peterson, T. C. (1995). A New Method for Detecting Undocumented Discontinuities in Climatological Time Series. Int J Climatol 15: 369–377.Find this resource:

Feng, X., Tsimplis, M. N., Quartly, G. D., and Yelland, M. J. (2014a). Wave Height Analysis from 10 years of Observations in the Norwegian Sea. Cont Shelf Res 72: 47–56.Find this resource:

Feng, X., Tsimplis, M. N., Yelland, M. J., and Quartly, G. D. (2014b). Changes in Significant and Maximum Wave Heights in the Norwegian Sea. Global Planet Change 113: 68–76.Find this resource:

Franco, A. S. (2009). Marés: Fundamentos, Análise e Previsão. Niterói, RJ: D.H.N. 344 pp.Find this resource:

Grinsted, A., Moore, J. C., and Jevrejeva, S. (2013). Projected Atlantic Hurricane Surge Threat from Rising Temperatures. Proc Natl Acad Sci U S A 110(14): 5369–5373.Find this resource:

Hemer, M. A., Fan, Y., Mori, N., Semedo, A., and Wang, X. L. (2013). Projected Changes in Wave Climate from a Multi-Model Ensemble. Nat Clim Change 3: 471–476. doi: 10.1038/nclimate1791Find this resource:

Hong, C., Wu, Y., Li, T., and Chang C. (2014). The Climate Regime Shift over the Pacific during 1996/1997. Clim Dyn 43: 435–446. doi: 10.1007/s00382-013-1867-9Find this resource:

IPCC; Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Millerm H. L. (Eds.) (2007). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.Find this resource:

IPCC; Field, C. B., Barros, V., Stocker, T. F., Qin, D., Dokken, D. J., Ebi, K. L., Mastrandrea, M. D., Mach, K. J., Plattner, G.-K., Allen, S. K., Tignorm, M., and Midgleym, P. M. (Eds.). (2012). Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.Find this resource:

IPCC. (2014). IPCC Fifth Assessment Synthesis Report, WMO-UNEP. p. 116.Find this resource:

Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woolen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetma, A., Reynolds, R., Jenne, R., and Joseph, D. (1996). The NCEP/NCAR 40-Year Reanalysis Project. B Am Meteorol Soc 77(3): 437–471.Find this resource:

Kerr, R. A. (1992). Unmasking a Shifty Climate System. Science 255: 1508–1510.Find this resource:

LaDochy, S., Medina, R., and Patzert, W. (2007). Recent California Climate Variability: Spatial and Temporal Patterns in Temperature Trends. Clim Res 33: 159–169.Find this resource:

Lanzante, J. R. (1996). Resistant, Robust and Non-parametric Techniques for the Analysis of Climate Data: Theory and Examples, Including Applications to Historical Radiosonde Station Data. Int J Climatol 16: 1197–1226.Find this resource:

Letetrel, C., Marcos, M., Martin Miguez, B., and Woppelmann, G. (2010). Sea Level Extremes in Marseille (NW Mediterranean) during 1885–2008. Cont Shelf Res 30, 1267–1274. doi: 10.1016/j.csr.2010.04.003Find this resource:

Marcos, M., Jorda, G., Gomis, D., and Perez, B. (2011). Changes in Storm Surges in Southern Europe from a Regional Model under Climate Change Scenarios. Global Planet Change 77: 116–128. doi: 10.1016/j.gloplacha.2011.04.002Find this resource:

Marone, E., Carneiro, J. C., Cintra, M., Ribeiro, A., Cardoso, D., and Stellfeld, C. (2015). Extreme Sea Level Events, Coastal Risks, and Climate Changes: Informing the Players. In Wyss, M., and Peppoloni, S. (Eds.), Geoethics: Ethical Challenges and Case Studies in Earth Sciences (Vol. 1; pp. 123–138) London: Geological Society.Find this resource:

Marone, E., and Marone, L. (2014). A Road Map for a Deontological Code for Geoscientists Dealing with Natural Hazards. In Lollino, G., Arattano, M., Giardino, M., Oliveira, R., and Peppoloni, S. (Eds.), Engineering Geology for Society and Territory (Vol. 7; pp. 45–48) Laussane: Springer International Publishing.Find this resource:

Meehl, G. A., Stocker, T. F., Collins, P., Friedlingstein, A., Gaye, J., Gregory, A., Kitoh, R., and Knutti, A. (2007). Global Climate Projections. In Solomon, S., Manning, D. Q. M., and Chen, Z. (Eds.), Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.Find this resource:

Overland, J., Rodionov, S., Minobe, S., and Bond, N. (2008). North Pacific Regime Shifts: Definitions, Issues and Recent Transitions. Prog Oceanogr 77: 92–102.Find this resource:

Pezza, A. B., and Simmonds, I. (2005). The First South Atlantic Hurricane: Unprecedented Blocking, Low Shear and Climate Change. Geophysical Research Letters 32: L15712. doi: 10.1029/2005GL023390Find this resource:

Pezza, A. B., Simmonds, I., and Pereira Filho, A. (2009). Climate Perspective on the Large Scale Associated with the Transition of the first South Atlantic Hurricane. Int J Climatol 29: 1116–1130. doi: 10.1002/joc17.57Find this resource:

Pickering, M. D., Well, N. C., Horsburgh K. J., and Green, J. A. M. (2012). The Impact of Future Sea-Level Rise on the European Shelf Tides. Cont Shelf Res 35: 1–15. doi: 10.1016/j.csr.2011.11.011Find this resource:

Qi, L., Leslie, L., and Speer, M. (2006). Climatology of Cyclones over the Southwest Pacific: 1992-2001. Meteor Atmos Phys 91: 201–209.Find this resource:

Rahmstorf, S. (2007). A Semi-empirical Approach to Projecting Future Sea-Level Rise, Science 315: 368–370. doi: 10.1126/science.1135456Find this resource:

Rego, J. L., and Li, C. (2010). Nonlinear Terms in Storm Surge Predictions: Effect of Tide and Shelf Geometry with Case Study from Hurricane Rita. J Geophys Res 115: C06020. doi: 10.1029/2009JC005285Find this resource:

Rodionov, S. N. (2004). A Sequential Algorithm for Testing Climate Regime Shifts. Geophys Res Lett 31: L09204. doi: 10.1029/2004GL019448Find this resource:

Rodionov, S. N. (2006). The Use of Prewhitening in Climate Regime Shift Detection. Geophys Res Lett 31, L12707. doi: 101029/2006GL025904Find this resource:

Schiller, A., Oke, P. R., Brassington, G. B., Entel, M., Fiedler, R., Griffin, D. A., and Mansbridge, J. V. (2008). Eddy-Resolving Ocean Circulation in the Asian-Australian Region Inferred from an Ocean Reanalysis Effort. Prog Oceanogr 76: 334–365.Find this resource:

Semedo, A., Weisse, R., Behrens, A., Sterl, A., Bengtsson, L., and Günther, H. (2013). Projection of Global Wave Climate Change toward the End of the Twenty-First Century. J Climate 26(21): 8269–8288. doi: 10.1175/JCLI-D-12-00658.1Find this resource:

Servain, J., Caniaux, G., Kouadio, Y. K., McPhaden, M. J., and Araujo M. (2014). Recent Climatic Trends in the Tropical Atlantic. Clim Dyn doi: 10.1007/s00382-014-2168-7Find this resource:

Shan, S., Sheng, J., Thompson, K. R., and Greenberg, D. A. (2011). Simulating the Three-Dimensional Circulation and Hydrography of Halifax Harbour Using a Multi-nested Coastal Ocean Circulation Model. Ocean Dyn 61: 951–976. doi: 10.1007/s10236-011-0398-3Find this resource:

Thornton, P. K., Ericksen, P. J., Herrero, M., and Challinor, A. J. (2014). Climate Variability and Vulnerability to Climate Change: A Review. Global Change Biol 20: 3313–3328. doi: 10.1111/gcb.12581Find this resource:

Torres, R. R., and Tsimplis, M. N. (2011). Tides and Long-Term Modulations in the Caribbean Sea. J Geophys Res 116: C10022. doi: 10.1029/2011JC006973Find this resource:

Torres, R. R., and Tsimplis, M. N. (2012). Seasonal Sea Level Cycle in the Caribbean Sea. J Geophys Res 117: C07011. doi: 10.1029/2012JC008159Find this resource:

Tremback, C. J. (1990). Numerical Simulation of a Mesoscale Convective Complex: Model Development and Numerical Results. Ph.D. dissertation, Atmos. Sci. Paper No. 465, Department of Atmospheric Science, Colorado State University, Fort Collins, CO, 247 pp.Find this resource:

Tsimplis, M. N., and Blackman, D. (1997). Extreme Sea-Level Distribution and Return Periods in the Aegean and Ionian Seas. Estuar Coast Shelf Sci 44: 79–89. doi: 10.1006/ecss.1996.0126Find this resource:

Veiga, J. A. P., Pezza, A. B., Simmonds, I., and Silva Dias, P. L. (2008). An Analysis of the Environmental Energetics Associated with the Transition of the First South Atlantic Hurricane. Geophys Res Lett 35: L15806. doi: 10.1029/2008GL034511Find this resource:

Vianna, M. L., Menezes, V. V., Pezza, A. B.; and Simmonds, I. (2010). Interactions between Hurricane Catarina (2004) and Warm Core Rings in the South Atlantic Ocean. J Geophys Res 115: C07002. doi: 10.1029/2009JC005974, 1-19Find this resource:

Wang, X. L., and Swail, V. R. (2001). Changes of Extreme Wave Heights in Northern Hemisphere Oceans and Related Atmospheric Circulation Regimes. J Clim 14: 2204–2221. http://dx.doi.org/ 10.1175/1520-0442(2001)014<2204:COEWHI>2.0.CO;2Find this resource:

Wang, X. L., and Swail, V. R. (2002). Trends of Atlantic Wave Extremes as Simulated in a 40-Year Wave Hindcast Using Kinematically Reanalyzed Wind Fields. J Clim 15, 1020–1035. http://dx.doi.org/ /10.1175/1520-0442(2002)015<1020:TOAWEA>2.0.CO;2Find this resource:

Waseda, T., Hallerstig, M., Ozaki, K., and Tomita, H. (2011). Enhanced Freak Wave Occurrence with Narrow Directional Spectrum in the North Sea. Geophys Res Lett 38: L13605.Find this resource:

Weiss, H., Courty, M. A., Wetterstrom, W., Guichard, F., Senior, L., Meadow, R., and Curnow, A. (1993). The Genesis and Collapse of Third Millenium North Millenium Mesopotamian Civilization. Science 261(5124): 995–1004.Find this resource:

Werner, E. E., Davis, C. J., Skelly, D. K., Relyea, R. A., Benard, M. F., and McCauley, S. J. (2014). Rodionov Regime Shift Analysis of Pond Occupancy for the Chorus Frog. PLOS ONE doi: 10.1371/journal.pone.0097387.t002Find this resource:

White, N. J., Haigh, I. D., Church, J. A., Koen, T., Watson, C. S., Pritchard, T. R., Watson, P. J., Burgette, R. J., McInnes, K. L., You, Z., Xuebin, Z., and Tregoning, P. (2014). Australian Sea Levels—Trends, Regional Variability and Influencing Factors. Earth-Sci Rev 136: 155–174. http://dx.doi.org/10.1016/j.earscirev.2014.05.011Find this resource:

Woodworth, P. L., and Blackman, D. L. (2002). Changes in Extreme High Waters at Liverpool Since 1768. Int J Climatol 22: 697–714. doi: 10.1002/joc.761Find this resource:

Xie, L; Liu, H., and Peng, M. (2008). The Effect of Wave–Current Interactions on the Storm Surge and Inundation in Charleston Harbor during Hurricane Hugo 1989. Ocean Model 20: 252–269. doi: 10.1016/j.ocemod.2007.10.001Find this resource:

Young, I. R., Zieger, S., and Babanin, V. (2011). Global Trends in Wind Speed and Wave Height. Science 332(6028): 451–455. doi: 10.1126/science.1197219Find this resource:

Zhang, K., Douglas, B. C., and Leatherman, S. P. (2004). Global Warming and Coastal Erosion. Climatic Change 64: 41–58.Find this resource:

Zhang, H., and Sheng, J. (2013). Estimation of Extreme Sea Levels over the Eastern Continental Shelf of North America. J Geophys Res Oceans 118: 2605–2613. doi: 10.1002/2013/JC009160Find this resource: