Editing IPCC:AR6/WGI/Chapter-9 (section)

=== 9.6.4 Extreme Sea Levels:Tides, Surges and Waves ===

<div id="h2-24-siblings" class="h2-siblings"></div>

An extreme sea level (ESL) refers to an occurrence of exceptionally high or low local sea surface height (Box 9.1). This section focuses on oceanographic-driven changes in ESL (Box 9.1).

<div id="9.6.4.1" class="h3-container"></div>

<span id="past-changes"></span>
==== 9.6.4.1 Past Changes ====

<div id="h3-53-siblings" class="h3-siblings"></div>

The AR5 ( [[#Church--2013b|Church et al., 2013b]] ) concluded that changes in extreme still water levels (ESWL), combining RSL, tide and surge as observed by tide gauges (Box 9.1) are ''very likely'' to be caused by observed increases in RSL, but noted ''low confidence'' in region-specific results owing to the limited number of studies considering localized contributions from storm surge, tide or wave effects. Influences from dominant modes of climate variability, particularly ENSO and NAO (Annex IV), were also noted. Climate modes affect sea level extremes in many regions, as a result of both sea level anomalies (Sections 9.2.4.2 and 9.6.1.3) and changes in storminess ( [[IPCC:Wg1:Chapter:Chapter-11#11.7|Section 11.7]] ). The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) concluded with ''high confidence'' that inclusion of local processes (wave effects, storm surges, tides plus other regional morphology changes due to erosion, sedimentation and compaction) is essential for estimation of changes in ESL events.

As in AR5 and SROCC, tide gauge observations show that RSL rise ( [[#9.6.1.3|Section 9.6.1.3]] ) is the primary driver of changes in ESWL at most locations and, across tide gauges, has led to a median 165% increase in high-tide flooding over 1995–2014 relative to those over 1960–1980 ( ''high confidence'' ) (Figure 9.31). Some locations exhibit substantial differences between long-term RSL trends and ESWL ( ''high confidence'' ), particularly given decadal to multi-decadal variations of other ESWL contributors ( [[#Rashid--2020|Rashid and Wahl, 2020]] ). Since SROCC, RSL rise has been shown to be the dominant contributor to ESWL rise at most gauge sites along the Chinese coast, but, at some locations, the surge contribution dominates ( [[#Feng--2019|Feng et al., 2019]] ). Trends in the difference between ESWL and mean RSL rise can result from changes (either positive or negative) in the surge or tidal components, and can include non-linear interactions between tide, surge, and RSL ( [[#Arns--2015|Arns et al., 2015]] ; [[#Schindelegger--2018|Schindelegger et al., 2018]] ). The positive phase of the 18.6-year nodal cycle of the astronomical tide is a further consideration, contributing to an increased flood hazard relative to the long-term average ( [[#Talke--2018|Talke et al., 2018]] ; [[#Peng--2019|Peng et al., 2019]] ; [[#Baranes--2020|Baranes et al., 2020]] ). Failing to consider the non-linear interactions between tide, surge and RSL may overestimate trends in ESWL ( ''low confidence'' ) ( [[#Arns--2020|Arns et al., 2020]] ). In some regions, changes in ESWL depend more on changes in surge or tide than on sea level trends.

<div id="_idContainer082" class="Basic-Text-Frame"></div>

[[File:e84cc9a989047b79ec2dccfeaf4651e6 IPCC_AR6_WGI_Figure_9_31.png]]

'''Figure 9.31''' '''|''' '''Historical occurrences of minor extreme still water levels.''' Defined as the 99th percentile of daily observed water levels over 1995–2014. '''(a)''' Percent change in occurrences over 1995–2014 relative to those over 1960–1980. '''(b–g)''' Annual mean sea level (blue) and annual occurrences of extreme still water levels over the 1995–2014 99th percentile daily maximum (yellow) at six selected tide gauge locations. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

Ongoing development of the Global Extreme Sea Level Analysis (GESLA) tide gauge database ( [[#Woodworth--2016|Woodworth et al., 2016]] ) along with data archaeology ( [[#Talke--2013|Talke and Jay, 2013]] ) extends availability of tide gauge records back to the mid 19th century (or earlier). Dynamical datasets used to assess trends in ESL at global or regional scales – for example, tide and surge contributions from the Global Tide and Surge Reanalysis (GTSR; [[#Muis--2016|Muis et al., 2016]] , 2020), or wave setup/swash contributions from available wave hindcasts/reanalyses ( [[#Melet--2018|Melet et al., 2018]] ) – have model biases introduced with resolution and parametrization limitations, incomplete atmospheric data, and currently span only a few decades, so they are not yet long or accurate enough to assess long-term trends in ESLs. Therefore, there is ''medium confidence'' in observed trends in ESWL, but only ''low confidence'' in modelled ESL trends.

The AR5 indicated that the amplitude and phase of major tidal constituents have exhibited long-term change, but that their effects on ESL were not well understood. The SROCC ( [[#Bindoff--2019|Bindoff et al., 2019]] ) reported changes in tides (amplification and dampening) at some locations to be of comparable importance to changes in mean sea level for explaining changes in high water levels, with the sign of change being dependent on stability of shoreline position. RSL rise causes water depth-based alterations to the resonant characteristics of the basin, changes the bottom friction and increases the wave speed ( [[#Pickering--2012|Pickering et al., 2012]] ) and remains the primary hypothesis for observed tidal changes. Other contributing processes include strong localized anthropogenic drivers (e.g., port development, dredging, flood defences, land reclamation), changes in stratification associated with ocean warming ( [[#9.2.1.3|Section 9.2.1.3]] ), and changes in seabed roughness associated with ecological change (e.g., [[#Haigh--2019|Haigh et al., 2019]] ). Tide gauge data show that, although principal tidal components have varied in amplitude on the order of 2% to 10% per century ( [[#Jay--2009|Jay, 2009]] ; [[#Ray--2009|Ray, 2009]] ), identifying direct causality remains challenging ( [[#Haigh--2019|Haigh et al., 2019]] ). Combined, observations and models indicate RSL rise and direct anthropogenic factors are the primary drivers of observed tidal changes at tide gauge stations ( ''medium confidence'' ).

The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) reported variations in storm surge not related to changes in RSL, and concluded with ''high confidence'' that consideration of localized storm surge processes was essential to monitor trends in ESL. SL events driven by storm surge are a response to tropical and extratropical cyclones. While historical trends in extra-tropical cyclones are less clear ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.2.1|Section 11.7.2.1]] ), there is mounting evidence for an increasing proportion of stronger tropical cyclones globally, with an associated poleward migration ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.1.2|Section 11.7.1.2]] ). These changes are captured in the ESL record, for example, via increasing intensity and poleward shift in the location of typhoon-driven storm surges reported across 64 years (1950–2013) in the western North Pacific ( [[#Oey--2016|Oey and Chou, 2016]] ). Along the east coast of the USA, there has been an increase in frequency of ESL events due to tropical cyclone changes since 1923 that can be statistically linked to changes in global average temperature ( [[#Grinsted--2013|Grinsted et al., 2013]] ), and the signal is projected to emerge around 2030 ( [[#Lee--2017|Lee et al., 2017]] ). At century and longer time scales, geological proxies such as overwash deposits in coastal lagoons or sinkholes can be used to reconstruct past changes in storm activity (e.g., [[#Brandon--2013|Brandon et al., 2013]] ; [[#Lin--2014|Lin et al., 2014]] ) and put recent events into historical perspective (e.g., [[#Brandon--2015|Brandon et al., 2015]] ). However, there is ''low confidence'' in the current ability to quantitatively compare geological proxies with gauge data. Historical storm surge activity is being increasingly assessed with use of hydrodynamic model simulations and data-driven global reconstructions to supplement tide gauge observations to investigate historical changes at centennial to millennial time scales (e.g., [[#Ji--2020|Ji et al., 2020]] ; [[#Muis--2020|Muis et al., 2020]] ; [[#Tadesse--2020|Tadesse et al., 2020]] ). Large regional variations and limited observational data lead to ''low confidence'' in observed trends in the surge contribution to increasing ESL.

Waves contribute to ESL via wave setup, infra-gravity waves and swash processes ( [[#Dodet--2019|Dodet et al., 2019]] ), with Extreme Total Water Level (ETWL; Box 9.1) used to represent ESWL with addition of wave setup, and Extreme Coastal Water Level (ECWL; Box 9.1) also including contributions from swash. The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) reported the dependency of these processes on nearshore geomorphology and deep-water wave climate, and thus sensitivity to internal climate variability and climate change. Few long-term deployments of in situ measurements in the very dynamic surf zone means that long-term records of ETWL or ECWL are limited to a few sites; tidal gauges are typically located in sheltered locations (e.g., harbours) where wave contributions are absent ( [[#Lambert--2020|Lambert et al., 2020]] ). Consequently, trends in wave contributions to ESL are typically derived from trends in wave conditions observed offshore. On the basis of satellite altimeter observations, SROCC reported increasing extreme wave heights in the Southern and North Atlantic oceans of around 1.0 and 0.8 cm yr <sup>–1</sup> , respectively, over the period 1985–2018 ( ''medium confidence'' ). The SROCC ( [[#Collins--2019|Collins et al., 2019]] ) also identified sea ice loss in the Arctic as leading to increased wave heights over the period 1992–2014 ( ''medium confidence'' ). Since SROCC, the satellite wave record has been shown to be sensitive to alternate processing techniques, leading to important differences in reported trends ( [[#Timmermans--2020|Timmermans et al., 2020]] ). The most common observation platforms for surface waves over the past 30 years are in situ buoys. However, evolving biases associated with changing instrument type, configuration and sampling methodology introduce artificial trends (e.g., [[#Gemmrich--2011|Gemmrich et al., 2011]] ; [[#Timmermans--2020|Timmermans et al., 2020]] ). Accurate metadata is required to address these issues, and, while available locally, are only beginning to be globally coordinated ( [[#Centurioni--2019|Centurioni et al., 2019]] ). Wave reanalysis and hindcast products have also been used to investigate total water level at global scale ( [[#Melet--2018|Melet et al., 2018]] ; [[#Reguero--2019|Reguero et al., 2019]] ). Their applicability for trend analysis is limited by inhomogeneous data for assimilation ( [[#Stopa--2019|Stopa et al., 2019]] ), but they inform relationships between seasonal, interannual to inter-decadal variability of climate indices and wind-wave characteristics (A.G. [[#Marshall--2015|]] [[#Marshall--2015|Marshall et al., 2015]] , 2018; [[#Kumar--2016|Kumar et al., 2016]] ; [[#Stopa--2016|Stopa et al., 2016]] ). To summarize, satellite era trends in wave heights of order 0.5 cm yr <sup>–1</sup> have been reported, most pronounced in the Southern Ocean. However, sensitivity of processing techniques, inadequate spatial distribution of observations, and homogeneity issues in available records limit confidence in reported trends ( ''medium confidence'' ).

Only a few studies have attempted to quantify the role of anthropogenic climate change in ESL events (e.g., [[#Mori--2014|Mori et al., 2014]] ; Takayabu et al., 2015; [[#Turki--2019|Turki et al., 2019]] ). Detection and attribution of the human influence on climatic changes in surges, and waves remains a challenge ( [[#Ceres--2017|Ceres et al., 2017]] ), with ''limited evidence'' to suggest in some instances – for example, poleward migration of tropical cyclones in the Western North Pacific ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.1.2|Section 11.7.1.2]] ), changes in surges and waves can be attributed to anthropogenic climate change ( ''low confidence'' ). With RSL change being considered the primary driver of observed tidal changes, there is ''medium confidence'' that these changes can be attributed to human influence. The close relationship between local ESL and long-term RSL change, combined with the robust attribution of GMSL change ( [[#9.6.1.4|Section 9.6.1.4]] ), implies that observed global changes in ESL can be attributed, at least in part, to human-caused climate change ( ''medium confidence'' ), but reconciling regional variation in these changes is not yet possible ( [[#9.6.1.4|Section 9.6.1.4]] ).

<div id="9.6.4.2" class="h3-container"></div>

<span id="future-changes"></span>
==== 9.6.4.2 Future Changes ====

<div id="h3-54-siblings" class="h3-siblings"></div>

There are two distinct methods used to project future ESL changes: (i) The static, or mean sea level offset, approach employs historical distributions of tidal, surge and wave components and adjusts future ESL distributions for mean RSL rise; (ii) The dynamic approach employs hydrodynamic and/or wave models forced with atmospheric fields derived from general circulation models (GCMs) to project changes in tidal, storm surge and wave distributions, which are then combined with RSL projections to project future ESLs; and (iii) The dynamic approach is computationally expensive. Use of the dynamic approach on large spatial or global scales has only recently been successful to project 21st-century changes in ETWL ( [[#Vousdoukas--2017|Vousdoukas et al., 2017]] , 2018) and ECWL( [[#Melet--2020|Melet et al., 2020]] ). [[#Kirezci--2020|Kirezci et al. (2020)]] assume stationarity in global wave and storm surge simulations to assess projected 21st-century changes in episodic coastal ETWL-driven flooding under global sea level rise scenarios.

The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) presents projections of ESL derived using a static approach. Such projections often quantify changes in ESL event frequency, expressed as ‘frequency amplification factors’ ( [[#Hunter--2010|Hunter, 2010]] , 2012). Like RSL projections, frequency amplification factors increase under higher-emissions scenarios, and differences between scenarios increase over time. The SROCC concludes that even small to moderate changes in mean RSL can lead to hundred- to thousand-fold increases in the frequencies with which certain thresholds are exceeded – for example, what is currently a 1-in-100-year ESL height (1% annual probability or 0.01 expected annual events) will be expected once or even multiple times per year in future at many locations (Figure 9.32). The SROCC showed that currently rare ESL events (e.g., with an average return period of 100 years) will occur annually or more frequently at most available locations for RCP4.5 by the end of the century ( ''high confidence'' ). Results from these assessments are sensitive to the type of ESL probability distribution assumed ( [[#Buchanan--2016|Buchanan et al., 2016]] ; [[#Wahl--2017|Wahl et al., 2017]] ), as well as the magnitude and uncertainty of projected RSL change ( [[#Slangen--2017|Slangen et al., 2017]] ; [[#Wahl--2017|Wahl et al., 2017]] ; [[#Frederikse--2020a|Frederikse et al., 2020a]] ). Frequency amplification factors tend to be largest in tropical regions due in part to higher RSL rise projections, but primarily to the relative rarity of high ESLs in areas with little historical exposure to tropical or extratropical cyclones. Alternative representation of changes in ESL, such as presenting changes in exceedances per year ( [[#Sweet--2014|Sweet and Park, 2014]] ), are subject to similar sensitivities, and lead to ''medium confidence'' in projected changes of event frequency using these methods.

<div id="_idContainer084" class="Basic-Text-Frame"></div>

[[File:a53a0bb1c8611523ed4618421a73350f IPCC_AR6_WGI_Figure_9_32.png]]

'''Figure 9.32''' '''|''' '''Projected median frequency amplification factors for the 1% average annual probability extreme still water level in 2050 (a, c, e) and 2100 (b, d, f).''' Based on a peak-over-threshold (99.7%) method applied to the historical extreme still water levels of Global Extreme Sea Level Analysis version 2 (GESLA2) following Special Report on Ocean and Cryosphere in a Changing Climate (SROCC) and additionally fitting a Gumbel distribution between Mean Higher High Water (MHHW) and the threshold following [[#Buchanan--2016|Buchanan et al. (2016)]] , using the regional sea level projections of ( [[#9.6.3.3|Section 9.6.3.3]] for (a, b) SSP5-8.5, (c, d) SSP2-4.5 and (e, f) SSP1-2.6. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

Employing a similar static approach – fitting a Gumbel distribution between Mean Higher High Water (average of higher high water height of each tidal day) and a threshold following [[#Buchanan--2016|Buchanan et al. (2016)]] – this Report updates SROCC projections of ESL with the RSL projections from [[#9.6.3.3|Section 9.6.3.3]] (see also Supplementary Material 9.SM.4). By 2050, the median increase in frequency amplification factor at 634 tide gauge stations is 19 for SSP1-2.6, 22 for SSP2-4.5, and 30 for SSP5-8.5 (Figure 9.32). This means that, by 2050, a historical (1995–2014) 1% annual probability ESL will have increased to an 19–30% annual probability. The 1% historical annual probability event is expected to become an annual event at 19–31% of the 634 stations by 2050, consistent with SROCC. By 2100, the median frequency amplification factor is projected to be 163 for SSP1-2.6, 325 for SSP2-4.5, and 532 for SSP5-8.5, with respectively 60%, 71%, and 82% of the stations experiencing a currently 1% annual probability event at least yearly ( ''medium confidence'' ) (Figure 9.32).

In the dynamic approach, the low resolution of the forcing fields arising from GCMs limits the ability to resolve historical and future changes in tropical and extra-tropical storm frequency and intensity, and resolution of local geography and morphology limit ability to represent ECWL (Box 9.1). Not all relevant processes – such as river discharge – are included in the dynamic models, and ESL events are typically a combination of multiple contributing processes, which are often not independent ( [[#Jevrejeva--2019|Jevrejeva et al., 2019]] ). In both static and dynamical approaches, global assessment of the performance of modelled storm surge and wave contributions to ESL is limited by poor coverage of observations (limited to tide gauges for ESWL, [[#Muis--2020|Muis et al., 2020]] ), and unavailable for the wave dependent ETWL and ECWL estimates ( [[#Vitousek--2017|Vitousek et al., 2017]] ; [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ; [[#Kirezci--2020|Kirezci et al., 2020]] ; [[#Lambert--2020|Lambert et al., 2020]] ; [[#Melet--2020|Melet et al., 2020]] ). In studies to date, individual models are used to simulate different contributions to ESL, non-linear interactions are not well captured, and uncertainties associated with downscaling methodology are poorly resolved, leading to ''low confidence'' in available ESL projections that include these modelled wave and surge contributions.

Assessment of dynamic ETWL changes for regions is presented in Chapter 12, following the methods of [[#Vousdoukas--2018|Vousdoukas et al. (2018)]] and [[#Kirezci--2020|Kirezci et al. (2020)]] . Consistent with studies using the static approach, [[#Vousdoukas--2018|Vousdoukas et al. (2018)]] finds that by 2050 the historical 1% average annual probability ETWL will have increased to a 2–50% average annual probability for most high latitude regions, and more often (up to multiple times a year, &gt;100% annual probability) in the tropics, under both RCP4.5 and RCP8.5. For 2100, present-day 1% average annual probability extreme sea levels will be exceeded multiple times each year almost everywhere. In summary, despite waves and surges being non-negligible contributors to projected ETWL and ECWL changes ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ; [[#Melet--2020|Melet et al., 2020]] ), RSL change is expected to be the main driver in changes in future ESL return periods in most areas ( ''medium confidence'' ).

The SROCC ( [[#Bindoff--2019|Bindoff et al., 2019]] ) concluded that the majority of coastal regions will experience statistically significant changes in tidal amplitudes through the 21st century. Comprehensive high-resolution (of the order 10 km) numerical modelling studies provide evidence for spatially coherent changes in tidal amplitudes in shelf seas as a result of RSL rise ( [[#Haigh--2019|Haigh et al., 2019]] , and references therein). There is ''high confidence'' that GMSL rise will be the primary driver of global tidal amplitude increases and decreases over the next 100–200 years, changing the baseline tide that ESLs are imposed on. At local and regional scales, anthropogenic factors such as major land reclamation efforts, as in the East China Sea ( [[#Song--2013|Song et al., 2013]] ) or differing national coastal management strategies (maintaining the present coastline position or managed retreat) will locally modulate the influence of GMSL rise on tidal amplitude ( ''medium confidence'' ).

The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) concluded that the intensity of severe tropical cyclones will increase in a warmer climate ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.1|Section 11.7.1]] ), but ''low confidence'' remains in the future frequency of tropical cyclones. Changes in tropical cyclone climatology will contribute to variations in frequency and magnitude of future ESL surge events, although estimates of this contribution range widely ( [[#Lin--2012|Lin et al., 2012]] ; [[#McInnes--2014|McInnes et al., 2014]] , 2016; [[#Little--2015|Little et al., 2015]] ; [[#Garner--2017|Garner et al., 2017]] ; [[#Mori--2019|Mori et al., 2019]] ; [[#Muis--2020|Muis et al., 2020]] ). In the Gulf of Mexico, changes in ESL due to tropical cyclone activity may be as important as SLR in enhancing future flood hazards ( [[#Marsooli--2019|Marsooli et al., 2019]] ). For the Korean Peninsula, a maximum change in 100-year return height associated with typhoon-induced storm surges of 10% under 4°C warming is found ( [[#Yang--2018|Yang et al., 2018]] ). The effects of projected changes in tropical cyclone intensity may be enhanced or offset in different locations by effects of changes in tracks ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.1|Section 11.7.1]] ; [[#Garner--2017|Garner et al., 2017]] ). There is ''low confidence'' in projected changes in ESL driven by changes in tropical cyclone climatology.

Changes in surface wave conditions occur in response to changes in frequency; intensity and position of forcing winds and storms ( [[#Morim--2018|Morim et al., 2018]] , 2019); reduction in sea ice and associated changes in fetch conditions ( [[#Thomson--2014|Thomson and Rogers, 2014]] ; [[#Casas-Prat--2020|Casas-Prat and Wang, 2020]] ); and changes in coastal morphology associated with RSL rise ( [[#Wandres--2017|Wandres et al., 2017]] ; [[#Storlazzi--2018|Storlazzi et al., 2018]] ). A few studies considering the contribution of a non-stationary wave climate on future changes in ESL infer a small but non-negligible contribution ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ; [[#Melet--2020|Melet et al., 2020]] ). The SROCC presented qualitative assessments of projected changes in wave conditions. Since SROCC, a quantitative assessment of a community ensemble of global wind-wave projections ( [[#Morim--2019|Morim et al., 2019]] ) found robust projected changes of around 5–10% (positive or negative, depending on region) in annual mean significant wave height, mean wave period, and/or mean wave directions along about 52% of the world’s coastline that exceed internal climate variability under RCP8.5 by 2100. Continued retreat of sea ice cover in the Arctic will lead to more energetic wind-wave conditions ( [[#Casas-Prat--2020|Casas-Prat and Wang, 2020]] ). Wave climate modelling methods introduce up to around 50% of the ensemble variance in mean wave climate projections ( [[#Morim--2019|Morim et al., 2019]] ). GCMs do not typically resolve the higher-resolution tropical and extratropical storm features required to accurately determine the contribution of extreme waves to ESLs and individual studies have sought to improve resolution to address these issues (e.g., [[#Timmermans--2017|Timmermans et al., 2017]] ). To date, projections of wave height extremes have been constrained to single wave model configurations (e.g., [[#Timmermans--2017|Timmermans et al., 2017]] ; [[#Meucci--2020|Meucci et al., 2020]] ). In summary, there is ''medium confidence'' in projections of changes in mean wave climate but ''low confidence'' in the projected changes in extreme wave conditions due to ''limited evidence'' .

Correlations between changes in sea level-forced (mean sea level and tidal) and atmospherically-forced drivers (ocean surface waves and surges) of ESLs have only been considered in a few studies, although high surge and high waves co-occur along a majority of the world’s coastlines ( [[#Marcos--2019|Marcos et al., 2019]] ). Along the east coast fo the USA, ocean dynamic sea level change and change in power dissipation index (a proxy for North Atlantic tropical cyclone activity) are correlated across CMIP5 GCMs, resulting in an increase in ESLs relative to analyses assuming independence of these changes ( [[#Little--2015|Little et al., 2015]] ). In the Irish Sea, dynamically coupled wave-tide modelling results in high water wave heights up to 20% higher than in an uncoupled analysis ( [[#Lewis--2019|Lewis et al., 2019]] ). In the German Bight, RSL rise relaxes the breaking criterion of nearshore waves (assuming no geomorphological response), allowing larger waves to propagate closer to shore, leading to increased wave runup ( [[#Arns--2017|Arns et al., 2017]] ). In south-western Australia, the influence of projected SLR was found to exceed the influence of projected changes in forcing winds on wave characteristics at the coast ( [[#Wandres--2017|Wandres et al., 2017]] ). Thus, projections of ESL that do not consider correlations between and among sea level forced and atmospherically forced drivers can differ strongly from coupled projections ( ''medium confidence'' ).

The SROCC ( [[#Collins--2019|Collins et al., 2019]] ) highlighted compound events, or coincident occurrence of multiple hazards, as an example of ''deep uncertainty'' , and noted that failing to account for multiple factors contributing to extreme events will lead to underestimation of the probabilities of occurrence ( ''high confidence'' ) ''.'' Statistical studies have shown that high rain or streamflow often co-occurs with storm surge as examples of ‘compound’ surge-rain or surge-discharge events (Sections 11.8.1 and 12.4.5.6; [[#Wahl--2015|Wahl and Chambers, 2015]] ; [[#Moftakhari--2017|Moftakhari et al., 2017]] ; Ward et al., 2018; [[#Wu--2018|Wu et al., 2018]] ; [[#Couasnon--2020|Couasnon et al., 2020]] ). Dynamical modelling studies show that co-occurrence of flood drivers raises ESLs at some locations in estuaries, such as the Rhine Delta ( [[#Zhong--2013|Zhong et al., 2013]] ), the Netherlands ( [[#van%20den%20Hurk--2015|van den Hurk et al., 2015]] ), Taiwan, China ( [[#Chen--2016|Chen and Liu, 2016]] ), and the Hudson River, USA ( [[#Orton--2020|Orton et al., 2020]] ), particularly when hydrologic catchments are steep and cause high rainfall near the coast, such as in south-west UK ( [[#Svensson--2004|Svensson and Jones, 2004]] ). The compound effect of storm surge and rainfall contributes greater projected flood risk than climate-induced amplification ( [[#Hsiao--2021|Hsiao et al., 2021]] ). However, at other locations, co-occurrence was unimportant because streamflow timing did not coincide with the coastal peak storm surge (Hudson River, [[#Orton--2012|Orton et al., 2012]] ; Rhine delta, [[#Klerk--2015|Klerk et al., 2015]] ). The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) detailed the complexity of interactions in deltaic environments. Direct increases in flooding driven by increasing RSL and storm surge, rain, or correlations between these flood-drivers (e.g., [[#Moftakhari--2017|Moftakhari et al., 2017]] ; [[#Orton--2020|Orton et al., 2020]] ) are expected to be further accompanied by increases in flooding due to subsidence (vertical land movement) and sedimentation (RSL-driven blockage of river flows). The probability of concurrent surge, wave and precipitation events has been projected to increase by more than 25% by 2100 compared to present, with high northern latitudes displaying compound flooding becoming more than 2.5 times as frequent, and weakening in the subtropics ( [[#Bevacqua--2020|Bevacqua et al., 2020]] ). However, the number of studies on compound events is still limited and so there is ''low confidence'' in understanding the extent by which compound events of surge with rain will change in response to RSL rise and climate change.

<div id="9.7" class="h1-container"></div>

<span id="final-remarks"></span>