Transcript Sea-Level Change Driven by Recent Cryospheric and

Sea-Level Change Driven by
Recent Cryospheric and
Hydrological Mass Flux
Mark Tamisiea
Harvard-Smithsonian Center for Astrophysics
James Davis
Emma Hill
Erik Ivins
Glenn Milne
Thanks to:
Jerry Mitrovica
Hans-Peter Plag
Rui Ponte
Bert Vermeersen
Extracting Source Information
From 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
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New data types
Geoid better discriminator?
– Integration with ocean modeling
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Large oceanic variability
Hydrological example
Introduction
Sea Level Variations Due to Loads
Assumptions:
• Static Ocean Response
• Elastic Earth (generally)
Load
Ocean
Possible Loads:
• Ice Sheets
• Glaciers
• Water Stored on
the Continents
References:
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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 Changes
Ice sheet melts
-- or -River basin loses water
• More water in ocean
• Crust and sea surface
adjust to the changing
mass load
Melting Scenarios
Uniform
Melting
Meier, 1984
Antarctica
RSL Fingerprints
from Melting Ice
Sheets and
Glaciers
Greenland
Mountain Glaciers
1.0 corresponds to value of
globally-averaged sea level rise.
Obtaining Greenland and Antarctic
Ice Mass Balance
Adding up the Contributions
ΔRSL (at a given point) = Contributions from
Glacial Isostatic Adjustment (GIA)+
Antarctica +
Greenland +
Glaciers +
Steric Effects +
Atmospheric Effects +
Currents +
Hydrology +
Tectonics +
Sedimentary Loads + …
Assume large spatial scales and long time scales
leave only a few contributions.
First Example:
Small Number of Tide Gauges
Mitrovica et al., 2001
Tamisiea et al., 2001
Select Set of Tide Gauges
Douglas, 1997
Raw Tide
Gauge Data
GIA Corrected
Tide Gauge Data
Second Example:
Binning of Many Tide Gauges
Plag, 2006.
• 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/yr
Greenland Contribution:
0.10 ± 0.05 mm/yr
Global Average:
1.05 ± 0.75 mm/yr
10 to 15% Variance Reduction
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 Improvements
Uniform Melting
Mass balance scenario
adapted by
James and Ivins, 1997
from Jacobs, 1992.
Tamisiea et al., 2001
2. Focus on Near Field
Milne and Long
• 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.
Alaska – Earth Model Dependence
mm/yr
Glacier model based on Arendt et al.,
Science, 2002
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
Altimeter
MIT/AER ECCO-GODAE solution
range (0-10 cm)
Source: Ponte et al.
Comparison of Tide Gauge Time
Series with Ocean Model
A combined time series including
Hill, Ponte, and Davis, 2006
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.
[Figure removed]
Example time series for stations with high variance reduction
(red=tide gauge, blue=model)
Example: Annual Signal
LaDWorld Hydrology Dataset
[Figure removed]
Milly and Shmakin, 2002
Milly, Cazenave, and Gennero, 2003
• Long time series
• Predicted GMSL close to observed
Variance Reduction of Tide Gauge Data
[Figure removed]
• Hydrology model time series removed from residual time series
(TG-OM-IB)
• Variance reduced
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.