Characterizing changes in storm surges and flood risk in the
presence of sea level rise: statistical approaches and challenges
Claudia TebaldiClimate and Global Dynamics Division
http://sealevel.climatecentral.org
http://sealevel.climatecentral.org
http://sealevel.climatecentral.org
http://sealevel.climatecentral.org
The risk of extreme high waters is changing, due to future Sea Level Rise
Storm surges will become more extreme as time goes by, because SLR is increasing the height of their “launching pad”.
We assess what the expected behavior of extreme high water levels will be by 2030, 2050, and further on, under a range of projections of sea level rise, for locations along the US coast.
Using gauge measurements from a set of locations along the continental US coasts, and projections of (global) sea level rise
– we estimate the magnitude of extreme storm surges nowadays through a GPD analysis;
– we estimate local sea level rise;
– we combine the two to assess changes in the frequency of extreme sea levels due to changes in mean sea level.
GPDAnalysisonObservedData
• 55gaugeswithalmostcompletehourlydataoverthe30yearperiod1979-2008andmonthlydataoverthe50yearperiod1959-2008.
• Dataateachgaugecomesastwo‘parallel’timeseries:actual andpredicted values:– actual,whatwasactuallyrecordedatthathour,thatday;– predicted,whatNOAAanticipatedonthebasisoftidalpatterns,
seasonalcycle,etc.
• WeusethepredictedonlyasasanitycheckonthechoiceofthethresholdforourPOTanalysis,makingsurethatthe percentileselectedfromthedistributionoftheactualvaluesisinexcessofthepredictedhightides.
0−1 1−2 2−3 3−4 4−5
50−year return levels (GPD), meters above MHW
Meters above MHW
Results from GPD analysis50-year Return Levels
La partie de l'image avec l'ID de relation rId2 n'a pas été trouvée dans le fichier.Results from GPD analysis100-year Return Levels
Meters above MHW
For the Sea Level Rise Component
• Semi-empirical models relating global average temperature change and global sea level rise
• IPCC projections of GSLR• NCA projections• NRC projectionsWe then downscale global sea level rise to local, gauge-specific rates on the basis of historical trends
• Kopp et al. 2014 Projections (localized and probabilistic)
Downscalingglobalsealevelrisetolocalrates
• Foreachofthescenariotrajectoriesof sealevelriseoverhistoricalperiod(e.g.,1959-2008),wecomputetheaveragerateofglobalSLR,Gh
• Duringthesameperiodwecomputetherelativerateatgaugek,Rhk.
• WedefinethelocalcomponentofSLRatgaugek,Lk, asthedifferencebetweenthetwo:
• Lk=Rhk-Gh
• InmostcasesLk istheresultoflandmovementduetoisostaticadjustment(mostlyreboundofthelandmassesaftertheretreatofthelargeicesheetsthatusedtoextendoverNorthAmerica).
• Weapplythislocalcomponent,unchanged,totheprojectedfutureratesofglobalSLR.I.e.,foreachscenariofuturetrajectoryofglobalSLR,wewillderiveafutureglobalrate,Gf andwewillmodifyitforlocationkasin
• Rfk=Gf+Lk
Current Trends (1959-2008)
mm/yr
Current Trends (1959-2008) Sea Level rise by 2050
mm/yr meters
Puttingthetwopiecestogether:StormsurgesunderfuturesealevelriseOnlyGPDuncertainty Bothuncertainties
Return Periods
Ret
urn
Leve
ls (m
eter
s ab
ove
MH
W)
1yr 2yr 5yr 10yr 20yr 30yr 50yr 75yr
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
2.25
2.50
2.75
3.00
3.25
3.50
3.75 current 2030 2050
} CI from EVA only
Storm surges in SEATTLE, PUGET SOUND, WA
Return Periods
Ret
urn
Leve
ls (m
eter
s)
1yr 2yr 5yr 10yr 20yr 30yr 50yr 75yr
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
2.25
2.50
2.75
3.00
3.25
3.50
3.75 current 2030 2050
} CI from EVA & SLR model
Storm surges in SEATTLE, PUGET SOUND, WA
Puttingthetwopiecestogether:StormsurgesunderfuturesealevelriseOnlyGPDuncertainty Bothuncertainties
Return Periods
Ret
urn
Leve
ls (m
eter
s ab
ove
MH
W)
1yr 2yr 5yr 10yr 20yr 30yr 50yr 75yr
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
2.25
2.50
2.75
3.00
3.25
3.50
3.75 current 2030 2050
} CI from EVA only
Storm surges in THE BATTERY, NEW YORK HARBOR, NY
Return Periods
Ret
urn
Leve
ls (m
eter
s)
1yr 2yr 5yr 10yr 20yr 30yr 50yr 75yr
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
2.25
2.50
2.75
3.00
3.25
3.50
3.75 current 2030 2050
} CI from EVA & SLR model
Storm surges in THE BATTERY, NEW YORK HARBOR, NY
Puttingthetwopiecestogether:StormsurgesunderfuturesealevelriseOnlyGPDuncertainty Bothuncertainties
Return Periods
Ret
urn
Leve
ls (m
eter
s ab
ove
MH
W)
1yr 2yr 5yr 10yr 20yr 30yr 50yr 75yr
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
2.25
2.50
2.75
3.00
3.25
3.50
3.75 current 2030 2050
} CI from EVA only
Storm surges in GALVESTON PLEASURE PIER, GULF OF MEXICO, TX
Return Periods
Ret
urn
Leve
ls (m
eter
s)
1yr 2yr 5yr 10yr 20yr 30yr 50yr 75yr
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
2.25
2.50
2.75
3.00
3.25
3.50
3.75 current 2030 2050
} CI from EVA & SLR model
Storm surges in GALVESTON PLEASURE PIER, GULF OF MEXICO, TX
Today’s 100-yr eventis closer to the 10-yr event by 2050
Combining SLR projections and Extreme Analysis
The big picture
How often will today’s 100-year event recur in 2050?
years
Conclusion so far
If we take the view that mean sea level change is the most significant factor for changes in storm surge characteristics, more in general for trends in extremes (read WCRP SLR2017 statement), a combination of absolute sea level change and current variability of extremes determines how “new” the future will be compared to the present.
Theproblemthenistwo-fold:estimatinglocalsealevelrise,andestimatingcurrentstatisticsofextremes.
But how confident are we in the robustness of our GPD estimates?
Wahletal.,2017,NCOMMSquantifiesuncertaintiesinpresent-dayExtremeSeaLevelestimatesfromstatisticalmodelchoice:“Whenwecomparethecombineduncertaintiesinpresent-dayESLestimatesduetodifferentEVAmethodsandstormsurgemodeloffsetstofutureregionalSLRuncertainties,wefindthattheformerdominateoverthelatterinmany(highrisk)regionssuchasEurope,EastAsia,andtheeasternandnorthwesternU.S. ”
An alternative approach:Skew-Surge-Joint-Probability Method
Batstone etal.,2013(OceanEngineering)
The recipe
CharacterizeextremeskewsurgesbyaGPD-POTestimation;
Characterizeremainingdistribution(belowthethreshold)byempiricalfit;
Convolvethedistributionofskewsurgeswiththedistributionoftidelevels(usuallyusingonly~19years);
Determine(underassumptionofindependence)probabilityofextremesealevels,independentlyofthespecificobservedco-occurrenceoftideandsurge.
In practice
Estimatesareverysensitivetodeclustering/extremalindexvalue
Inparticular,theReturnPeriodestimateforlevelx is
Where⍬(x) isthevalueoftheextremalindexforextremesofsizex,N isthenumberperyearofsuchextremes,andFSL(x) isthejointprobabilityofreachinglevelx,estimatedbyconvolvingskewsurgeprobabilitiesandtidelevelsprobabilities
How do GPD and SS-JPM compare?How do we validate the estimates?
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For longer Return Periods all we can do is compare the curves…
For longer Return Periods all we can do is compare the curves…
For longer Return Periods all we can do is compare the curves…
For longer Return Periods all we can do is compare the curves…
For longer Return Periods all we can do is compare the curves…
For longer Return Periods all we can do is compare the curves…
For longer Return Periods all we can do is compare the curves…
For longer Return Periods all we can do is compare the curves…
For longer Return Periods all we can do is compare the curves…
For longer Return Periods all we can do is compare the curves…
For longer Return Periods all we can do is compare the curves…
For longer Return Periods all we can do is compare the curves…
For longer Return Periods all we can do is compare the curves…
For longer Return Periods all we can do is compare the curves…
Efficiency
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Isitworthit?
Noclosedformforcomputingreturnlevels
Nostraightforwardwaytocomputeconfidenceintervalsorprobabilityranges…uncertaintiesingeneral
Whichofthemethodisthecorrectone,whentheydisagreesomuch?
Thequestions
Howdowedovalidationofalternativeextremevaluemodels?
Whenisuncertaintyjusttoomuchtosayanythinguseful?
