SeaLevel Change Driven by Recent Cryospheric and Hydrological

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 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 Assumptions: • Static Ocean Response • Elastic Earth (generally) Load Ocean Possible Loads: • Ice Sheets • Glaciers • Water Stored on the Continents 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 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 Greenland RSL Fingerprints from Melting Ice Sheets and Glaciers 1. 0 corresponds to value of globally-averaged sea level rise. Mountain Glaciers

Obtaining Greenland 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 La. DWorld 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.