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 Jerry Mitrovica Hans-Peter Plag Rui Ponte Bert Vermeersen Thanks to:

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 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

Uniform Melting Meier, 1984 Melting Scenarios

RSL Fingerprints from Melting Ice Sheets and Glaciers Antarctica GreenlandMountain 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., 2001 Tamisiea et al., 2001

Douglas, 1997 Select Set of Tide Gauges

Raw Tide Gauge Data GIA Corrected Tide 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/yr Greenland Contribution: 0.10 ± 0.05 mm/yr Global Average: 1.05 ± 0.75 mm/yr 10 to 15% Variance Reduction Plag, 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, 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, 2006 A 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 Signal LaDWorld Hydrology Dataset Long time series Predicted GMSL close to observed Milly and Shmakin, 2002 Milly, 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.