sea-level change driven by recent cryospheric and hydrological mass flux
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Sea-Level Change Driven by Recent Cryospheric and Hydrological Mass Flux. Mark Tamisiea Harvard-Smithsonian Center for Astrophysics. Thanks to:. Extracting Source Information From Geographic Sea Level Variations. Introduction Terminology Physics - PowerPoint PPT PresentationTRANSCRIPT
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Sea-Level Change Driven by Recent Cryospheric and Hydrological Mass Flux
Mark TamisieaHarvard-Smithsonian Center for Astrophysics
James DavisEmma HillErik IvinsGlenn Milne
Jerry MitrovicaHans-Peter PlagRui PonteBert Vermeersen
Thanks to:
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Extracting Source InformationFrom Geographic Sea Level Variations
• Introduction– Terminology– Physics– Patterns for Greenland, Antarctica and glaciers
• Obtaining Greenland and Antarctic Ice Mass Balance– Select set of tide gauges– Binning of many tide gauges
• Future Directions– Improvements to fingerprints– Focus on near field
• New data types• Geoid better discriminator?
– Integration with ocean modeling• Large oceanic variability• Hydrological example
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Introduction Sea Level Variations Due to Loads
References:• Farrell and Clark [1976]• Clark and Primus [1987]• Nakiboglu and Lambeck [1991]• Conrad and Hager [1997]• Mitrovica et al. [2001]• Plag and Jüttner [2001]
Load
Ocean
Possible Loads:• Ice Sheets• Glaciers• Water Stored on the Continents
Assumptions:• Static Ocean Response• Elastic Earth (generally)
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Ice sheet melts -- or --
River basin loses water
Load Changes
• More water in ocean• Crust and sea surface adjust to the changing mass load
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UniformMelting
Meier, 1984
Melting Scenarios
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RSL Fingerprints from Melting Ice
Sheets and Glaciers
Antarctica
Greenland Mountain Glaciers
1.0 corresponds to value of globally-averaged sea level rise.
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Obtaining Greenland and Antarctic Ice Mass Balance
ΔRSL (at a given point) = Contributions from Glacial Isostatic Adjustment (GIA)+ Antarctica + Greenland + Glaciers + Steric Effects + Atmospheric Effects + Currents + Hydrology + Tectonics + Sedimentary Loads + …
Adding up the Contributions
Assume large spatial scales and long time scales leave only a few contributions.
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First Example: Small Number of Tide Gauges
Mitrovica et al., 2001Tamisiea et al., 2001
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Douglas, 1997
Select Set of Tide Gauges
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Raw Tide Gauge Data
GIA CorrectedTide Gauge Data
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Second Example: Binning of Many Tide Gauges
• Tide gauge data binned• Numerous regression
estimates generated by varying binning resolution, GIA model, and steric model
Results:Antarctic Contribution: 0.4 ± 0.2 mm/yrGreenland Contribution: 0.10 ± 0.05 mm/yrGlobal Average: 1.05 ± 0.75 mm/yr10 to 15% Variance Reduction
Plag, 2006.
Also, see poster by C.-Y. Kuo and C.K. Shum
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Future Directions
1. Improvements to fingerprints
2. Focus on near field– New data types– Geoid better discriminator?
3. Integration with ocean modeling– Large oceanic variability– Hydrological example
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1. Fingerprint ImprovementsUniform Melting
Mass balance scenario adapted by James and Ivins, 1997 from Jacobs, 1992.
Tamisiea et al., 2001
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2. Focus on Near Field
• The impact of different melting scenarios greatest in near field.
• Saltmarsh proxy records with uncertainties of 0.25 mm/yr would still resolve difference in models to the right.
Milne and Long
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Glacier model based on Arendt et al., Science, 2002
Alaska – Earth Model Dependence
mm/yr
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Effects of Earth Model on Sea Surface and RSL
Tamisiea et al., 2003
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3. Integration with Ocean Modeling• Interannual variability large• Incorporate fingerprinting technique into models to
perform integrated analysis
MIT/AER ECCO-GODAE solution
range (0-10 cm)
Altimeter
Source: Ponte et al.
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Comparison of Tide Gauge Time Series with Ocean Model
Hill, Ponte, and Davis, 2006A combined time series including
a) Inverted barometer time series [Ponte, 2006]
b) Ocean model time series [courtesy of D. Stammer]
were compared to the time series of 380 globally-distributed PSMSL tide gauges
While removing the model time series significantly reduces the mean global variance, an annual signals remains.
Example time series for stations with high variance reduction(red=tide gauge, blue=model)
[Figure removed]
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Example: Annual SignalLaDWorld Hydrology Dataset
• Long time series• Predicted GMSL close to observed
Milly and Shmakin, 2002Milly, Cazenave, and Gennero, 2003
[Figure removed]
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Variance Reduction of Tide Gauge Data
• Hydrology model time series removed from residual time series (TG-OM-IB)
• Variance reduced
[Figure removed]
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Conclusions
• Fingerprinting offers another method of constraining the sources of sea level rise.
• Large regional effects could provide more effective test of regional mass variation scenarios.
• Inclusion into dynamic ocean models should improve the ability to recover these static signals from the tide gauge and altimetry data.