Highlights from WCRP/IOC 2017 Sea Level conference - US CLIVAR · Highlights from WCRP/IOC 2017 Sea Level conference NADYA VINOGRADOVA CARMEN BOENING 2017 US CLIVAR Summit Baltimore,

Highlights from WCRP/IOC 2017 Sea Level conference

NADYA VINOGRADOVA

CARMEN BOENING

2017 US CLIVAR Summit Baltimore, MD August 8 – 10, 2017

QuesKons:

1.  WHAT ARE THE MAIN FACTORS CONTRIBUTING TO SEA LEVEL RISE?

2.  WHAT ARE THE FUTURE REQUIREMENTS FOR SEA LEVEL RESEARCH?

3.  WHAT ASPECTS OF RESEARCH RECEIVE LESS ATTENTION THAN THEY SHOULD?

storify.com/wcrp#sealevel2017@wcrp_climate

www.sealevel2017.org

our estimates of the contributions. As an example, weshow the time series for TG J, expansion L, glacier M,Greenland H, groundwater K, and reservoir L (Fig.10b). These choices give the smallest obtainable RMSfor TG J. The negative contribution from Greenland Hhelps to account for the small GMSLR trend in the earlypart of the twentieth century. It is compensated for bya large residual trend of 0.82 mm yr21. For TG J a con-stant residual trend cannot close the budget within un-certainties.

c. Coastal oceanographic effects

A possibility to explain the residual is that regionalpatterns of change in ocean density and circulation

might make the trend in coastal sea level rise exceed theglobal-mean trend, so that the observational re-constructions overestimate GMSLR. While there aredifferences associated with climate variability, no suchpersistent bias is apparent on multidecadal time scales(White et al. 2005; Prandi et al. 2009); on the other hand,model studies of the response to buoyancy forcing in-dicate that the consequent signal of sea level rise wouldpropagate rapidly around coastlines and radiate moreslowly into the ocean far offshore (Hsieh and Bryan1996; Johnson and Marshall 2002). This is not seen inhistorical AOGCM simulations (Gregory et al. 2001;White et al. 2005), but these might not adequately re-solve coastal ocean dynamics. We have no evidence forsuch an effect but cannot rule it out.

d. Contemporary changes in the gravity field and thesolid earth

Transfer of mass from land to ocean or vice versachanges the geoid (the surface of constant geopotentialthat would define sea level if the ocean were at rest)because it affects the gravitational field and the earth’srotation, and it causes an elastic deformation of thelithosphere (subsidence in places where the load in-creases and uplift where it decreases). These responsesare rapid, unlike the glacial isostatic adjustment forwhich tide gauge records are corrected (section 6).Subsidence and gravitational attraction due to an in-creased mass on land cause relative sea level nearby torise; in compensation, relative sea level falls elsewhere.Note that this phenomenon is distinct from and locallyopposed to the effect on global-mean sea level due to thechange of ocean volume. Changes in relative sea leveldue to these effects could bias the estimates of GMSLRfrom tide gauge datasets.Fiedler and Conrad (2010) point out that the water

impounded in reservoirs raises relative sea level atnearby tide gauges. They estimate that this effect leadsto an overestimate of GMSLR by an amount equal to;40% of the reservoir contribution to GMSLR. How-ever, it depends strongly on the selection of tide gaugesand is estimated to be only 0.035 mm yr21 (2%) ofGMSLR during the twentieth century in the tide gaugedataset used by Church and White (2011) and Churchet al. (2011).Changes in mass of ice on land likewise affect relative

sea level (Mitrovica et al. 2001; Tamisiea et al. 2003) andcould bias GMSLR computed from tide gauge datasets.We compute the effect on the GMSLR estimate ofChurch and White (2011) using the ‘‘fingerprints’’ onrelative sea level of changes in mass of the Antarctic icesheet (assuming this to be the source of our residualtrend), the Greenland ice sheet, and glaciers worldwide

FIG. 10. Two examples comparing an observational and a re-trended synthetic time series of global-mean sea level rise (thicklines, with 5%–95% observational uncertainty shaded), alsoshowing the contributions to the latter (thin lines), identified by thetime series initials in the key. In each panel, the observational andthe retrended synthetic time series have the same timemean during1901–90, and the latter and its components are all plotted relativeto zero in 2000. These examples are two of those shown in Fig. 8a;they do not include the adjustment of 0.05 mm yr21 applied in Fig.8b. Panel (b) shows the synthetic time series that gives the smallestRMS difference from the observational time series TG J.

1 JULY 2013 GREGORY ET AL . 4491

1. Contemporary sea level budget

Mainfactorscontribu?ngtosealevelrise:§  Thermalexpansionduetoheatuptake[vonSchukman,

Gregory,Charles]§  Masslossfromicesheets(GIS,AIS),icecaps,andglaciers[Hock,

Bamber,Davis]§  Changesinlandwaterstorageduetogroundwaterdeple?on

andreservoirconstruc?on[Yu,Slangen]

Gregoryetal.(2013)PresentedbyJ.Church

39%

27%

12%

9%13%

Mountain glaciers (1%)

Green-land11%

Antarctica88%

• Glacier contribution larger than the mass loss from both ice sheets combined

GreenlandThermal expansion

Glaciers

Antarctica

Land water storage

Ice volume Sea-level contribution

1992 - 2010, IPCC (2013)

1992-2009,IPCC2013PresentedbyR.Hock

Useofthesealevelbudgetapproach:§  Qualitymonitoring§  Constrainingenergyimbalancesanddeep-

oceanwarming§  Detec?onandATribu?on§  Climatemodelvalida?onandpredic?on

Understanding paleo records

§  Lookforevidenceofice-sheetretreatinthepasttoinformfuturesealevelprojec?ons

§  DuJon:duringtheLastInterglacials,globalsealevelwasatleast6mabovepresent,involvinglargecontribu?onfromAIS

§  Geodynamicprocessesmaycomplicatees?matesofancienticevolumesandsealevel:

1.  GlacialIsosta?cAdjustment[Mitrovica]-Earth’s3DstructureisimportanttoaccuratelymodelGIA,withimplica?onsto?de-gaugecorrec?ons

2.  Dynamictopography[Gomez,DuJon]-Surfaceeleva?onduetodynamictopographycanbeseveralmeterson100,000year?mescaleandneedstobeconsideredon“shorter”?mescales(i.e.,notjustmillennial)

Conclusions for Last Interglacial period (~125,000 yrs ago)

Dutton et al. (2015) Science

DuJonetal.(2015)

Looking ahead – sea level projecKons

 Ocean

Slangenetal.(2014)Seealso,Koppetal.2014

324 Climatic Change (2014) 124:317–332

Land ice (RCP4.5)a

c

b

d

e

g

f

Land ice (RCP8.5)

Ocean (RCP4.5) Ocean (RCP8.5)

Dynamic Ice Sheet Terrestrial water

GIA

0.00.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.00.00.0

0.0 0.1 0.2 0.3 0.4 SL change (m)

Fig. 1 Projected relative SLC patterns (m) of individual contributions over the period 1986–2005 to2081–2100; a RCP4.5 glaciers + ice sheet SMB, b RCP8.5 glaciers + ice sheet SMB, c RCP4.5 globalsteric+dynamic topography + AL, d RCP8.5 global steric+dynamic topography + AL, e Ice sheet dynam-ics (scenario-independent), f Groundwater depletion (scenario-independent), g GIA (scenario-independent).Global mean values in Table 1

United States, to a weakening meridional overturning circulation in the Atlantic Ocean.The Southern Ocean shows a dipolar pattern with below average increase in the southand above average increase to the north. Although low thermal expansion coefficients forcolder temperatures seem to motivate the meridional gradient across the Antarctic Cir-cumpolar Current [ACC], these changes also require a dynamical balance. In response todoubling CO2, climate models show that wind stress intensifies and the position of zero

324 Climatic Change (2014) 124:317–332

Land ice (RCP4.5)a

c

b

d

e

g

f

Land ice (RCP8.5)



GIA

0.00.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.00.00.0

0.0 0.1 0.2 0.3 0.4 SL change (m)



324 Climatic Change (2014) 124:317–332

Land ice (RCP4.5)a

c

b

d

e

g

f

Land ice (RCP8.5)



GIA

0.00.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.00.00.0

0.0 0.1 0.2 0.3 0.4 SL change (m)



Climatic Change (2014) 124:317–332 327

Scenario A suma Scenario B sumb

c dScenario A uncertainty (90% CL) Scenario B uncertainty (90% CL)

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 SL change (m)

Fig. 3 Combined regional SLC patterns and uncertainties over the period from 1986–2005 to 2081–2100 fora Scenario A sum (=Fig.1a+c+e+f+g, global mean is 0.54 m), b Scenario B sum (=Fig.1b+d+e+f+g, globalmean is 0.71 m), c Scenario A uncertainties at the 90 % confidence level (global mean is 0.32 m), d ScenarioB uncertainties at the 90 % confidence level (global mean is 0.48 m)

the signal is larger than the uncertainty in nearly all regions for both scenarios, particularlyat middle to low latitudes where human habitation is highest. The dynamic ice sheet con-tribution dominates the local uncertainty, followed by steric+dynamic and ice sheet SMBuncertainties, which both are of the same order of magnitude. Smaller uncertainty contri-butions result from glaciers, groundwater depletion, GIA and AL. For all components, theabsolute values for the scenario B uncertainties are larger than for scenario A, although thisis not the case for the signal-to-noise ratios.

The distribution function of the total local SLC (Fig. 4a) is slightly skewed. For bothscenarios, it reveals significant regional deviations from the global mean with a longer tailtowards lower values, and an upper limit that is set by the gravitational effect on the land iceand groundwater contributions. In both cases, the mode of the distribution function is abovethe global average. This is a consequence of the gravitational pattern associated with theland ice contribution, which yields a relatively small ocean area with low SLC values nearice loss regions, and a relatively large ocean area in the equatorial region with SLC valuesslightly above average.

The relative deviation of the local SLC with respect to the global mean is shown inFig. 4b. Locally, SLC deviates more than 10 % and 25 % from the global mean projectionfor up to 30 % and 9 % of the ocean area, respectively. Regionally, values of up to 30 %above the global mean are reached, for instance in the equatorial regions, around Australia,at the southern African coast, and around North America. We find relatively low valuesdown to only 50 % of the global mean in the Arctic region and near the coasts of SouthAmerica. Although the combined values are larger for Scenario B, the relative deviationfrom the global mean is mostly similar for both scenarios, and many regions are likely to

Climatic Change (2014) 124:317–332 327

Scenario A suma Scenario B sumb

c dScenario A uncertainty (90% CL) Scenario B uncertainty (90% CL)

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 SL change (m)

Fig. 3 Combined regional SLC patterns and uncertainties over the period from 1986–2005 to 2081–2100 fora Scenario A sum (=Fig.1a+c+e+f+g, global mean is 0.54 m), b Scenario B sum (=Fig.1b+d+e+f+g, globalmean is 0.71 m), c Scenario A uncertainties at the 90 % confidence level (global mean is 0.32 m), d ScenarioB uncertainties at the 90 % confidence level (global mean is 0.48 m)

the signal is larger than the uncertainty in nearly all regions for both scenarios, particularlyat middle to low latitudes where human habitation is highest. The dynamic ice sheet con-tribution dominates the local uncertainty, followed by steric+dynamic and ice sheet SMBuncertainties, which both are of the same order of magnitude. Smaller uncertainty contri-butions result from glaciers, groundwater depletion, GIA and AL. For all components, theabsolute values for the scenario B uncertainties are larger than for scenario A, although thisis not the case for the signal-to-noise ratios.

The distribution function of the total local SLC (Fig. 4a) is slightly skewed. For bothscenarios, it reveals significant regional deviations from the global mean with a longer tailtowards lower values, and an upper limit that is set by the gravitational effect on the land iceand groundwater contributions. In both cases, the mode of the distribution function is abovethe global average. This is a consequence of the gravitational pattern associated with theland ice contribution, which yields a relatively small ocean area with low SLC values nearice loss regions, and a relatively large ocean area in the equatorial region with SLC valuesslightly above average.

The relative deviation of the local SLC with respect to the global mean is shown inFig. 4b. Locally, SLC deviates more than 10 % and 25 % from the global mean projectionfor up to 30 % and 9 % of the ocean area, respectively. Regionally, values of up to 30 %above the global mean are reached, for instance in the equatorial regions, around Australia,at the southern African coast, and around North America. We find relatively low valuesdown to only 50 % of the global mean in the Arctic region and near the coasts of SouthAmerica. Although the combined values are larger for Scenario B, the relative deviationfrom the global mean is mostly similar for both scenarios, and many regions are likely to

  Ice  Land

§  Contribu?onfrombothstericanddynamicoceanchanges

§  Fullycoupledatmosphereandoceanmodels,e.g.,CMIP5AR5

§  CMIP6(IPCCAR6)pertainingtosealevelchangeisinprepara?on[Masson-DelmoJe]

324

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§  Changesinicemassandvolumeisbasedonexpertassessments[AR5,Bamber&Aspinall2013]

§  ISMIP6[Nowicki]:§  Worktowardsfullintegra?onof

GIS,AISwithoceanandatmosphere

§  Inves?gatetheroleoffeedbacksbetweenicesheetsandclimate

§  Landwaterstorageandredistribu?onfromlandtoocean

§  Groundwaterdeple?onwilllikelyincreasewithratespropor?onaltopopula?on[Wadaetal2012]

Sealevelchange(m)by2100

2. Future requirements for sea level research

a)  Reducinguncertain?esinsealevelprojec?ons:

§  Accoun?ngformodelspreadinpredic?ngocean’sthermostericanddynamicresponse:§  Flux-Anomaly-ForcedModelIntercomparisonProject(FAFMIP)

[Gregory]§  Detec?onandATribu?onasatooltoisolatechao?cvspredictable

variability[Charles]

§  Improvingmodelingofice-sheetsandsurroundingice-shelf(largestuncertaintyinsealevelerrorbudget):§  IceSheetSystemModels(ISSM)[Boening]§  AISstabilityandlong-termresponse[Winkelmann,Griffes]

IPCC2013PresentedbyJ.Gregory

Dynamic sea-level change '] (% of global mean thermosteric SL rise)in the CMIP5 ensemble for 2081-2100 under RCP4.5

Ensemble mean Ensemble standard deviation

Dyn

amic

sea

-leve

l cha

nge ']

(% o

f glo

bal m

ean

ther

mos

teric

SL

rise)

in th

e C

MIP

5 en

sem

ble

for 2

081-

2100

und

er R

CP

4.5

Ens

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e m

ean

Ens

embl

e st

anda

rd d

evia

tion

0%

10%

20%

50%

100%

CMIP5spread:dynamicsealevel%ofthermalexpansion

Projections for 2081-2100 under RCP4.5

The largest uncertainty

Data from IPCC WG1 AR5 Table 13.5

The largest contribution


b)  Expandingtherangeof?mescalesofsealevelprojec?ons:

§  Long-termprojec?ons§  Exploremul?-centuryhorizonsbeyond21stcentury§  Palmer:Sealevelrisebyafewmetersby2300ispossible

§  Short-termvariability:§  Characterizetheroleofinternalvariabilityanditspoten?altomasklong-termsea

leveltrendsondecadal[Thompson,Proshu\nsky]andinter-annual[Liu]?mescales

§  Accelera?on:§  Globalaccelera?onsaresmall,nearresolvingpowerofobservingsystem

[Nerem,Cazenave]§  Regionalaccelera?onscanbesignificant[e.g.USEast:1-3m/cy2,Davis]

Nauelsetal.,(2017)PresentedbyM.Palmer

RCP2.6

RCP4.5

RCP6.0

RCP8.5

Sealevelchange(m)2000-2300


c)  Movingtowards“ac?onablesealevelscience”:

§  Reducingthegapbetweenavailablescien?ficinforma?onandspecificdecision-makingcontext§  Hinkel:

§  Short-term,cost-benefitanalysisrequiresprobabilis?cinforma?on§  Lonerterm,riskaversedecisionsneedinforma?onontheuppertail-end(worstcasescenario)

§  Establishinginterac?veexercisebetweencoastalriskmanagers,decisionscien?stsandsealevelscien?sts[Sweiss,Behar,Bindschadler,Losada]

3. Aspects that needs more a[enKon

§  Defineconnec?onsbetweenthemeansealevelstateandthelocalchanges,includingextremeeventsfromstormsurgesand?des

§  Integra?onofinforma?onfromdifferentresearchfields,aswellasdifferentcomponentsoftheEarth’ssystemasafunc?onofscaleswillrequirequan?fica?onofanewcascadeofuncertain?es

Documents

Highlights from WCRP/IOC 2017 Sea Level conference - US CLIVAR · Highlights from WCRP/IOC 2017 Sea Level conference NADYA VINOGRADOVA CARMEN BOENING 2017 US CLIVAR Summit Baltimore,