Sea-Level Change Driven by Recent Cryospheric and Hydrological Mass Flux Mark Tamisiea...
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Transcript of Sea-Level Change Driven by Recent Cryospheric and Hydrological Mass Flux Mark Tamisiea...
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:
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
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)
Ice sheet melts -- or --
River basin loses water
Load Changes
• More water in ocean• Crust and sea surface adjust to the changing mass load
UniformMelting
Meier, 1984
Melting Scenarios
RSL Fingerprints from Melting Ice
Sheets and Glaciers
Antarctica
Greenland Mountain Glaciers
1.0 corresponds to value of globally-averaged sea level rise.
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.
First Example: Small Number of Tide Gauges
Mitrovica et al., 2001Tamisiea et al., 2001
Douglas, 1997
Select Set of Tide Gauges
Raw Tide Gauge Data
GIA CorrectedTide Gauge Data
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
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
1. Fingerprint ImprovementsUniform Melting
Mass balance scenario adapted by James and Ivins, 1997 from Jacobs, 1992.
Tamisiea et al., 2001
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
Glacier model based on Arendt et al., Science, 2002
Alaska – Earth Model Dependence
mm/yr
Effects of Earth Model on Sea Surface and RSL
Tamisiea et al., 2003
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.
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]
Example: Annual SignalLaDWorld Hydrology Dataset
• Long time series• Predicted GMSL close to observed
Milly and Shmakin, 2002Milly, Cazenave, and Gennero, 2003
[Figure removed]
Variance Reduction of Tide Gauge Data
• Hydrology model time series removed from residual time series (TG-OM-IB)
• Variance reduced
[Figure removed]
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.