Patagonia Ice Field Melting Observed by GRACE

Joint International GSTM and DFG SPP Symposium, October 15-17, 2007 at GFZ Potsdam

J.L. Chen 1, C.R. Wilson 1,2,4, B.D. Tapley 1, D.D. Blankenship 3,4

Center for Space Research, University of Texas at Austin, USA 1

Department of Geological Sciences, University of Texas at Austin, USA 2

Institute for Geophysics, University of Texas at Austin, USA 3

Jackson School of Geosciences, University of Texas at Austin, USA 4

Image of the Patagonia Ice FieldsImage of the Patagonia Ice Fields

Motivations:Motivations:

Patagonia Ice Fields (PIF) are second largest SH ice massPatagonia Ice Fields (PIF) are second largest SH ice mass Northern Patagonia Ice Field (NPIF ), ~ 4200 km2 - Chile;

Southern Patagonia Ice Field (SPIF), ~ 13000 km2 -Chile and Argentina

Observed historical / contemporary melting / retreat..

GRACE RL04 solutions show improved spatial resolution, though GRACE RL04 solutions show improved spatial resolution, though coarse relative to ~hundred km scale of PIF. coarse relative to ~hundred km scale of PIF.

PIF mass change may be estimated from RL04 and:PIF mass change may be estimated from RL04 and: Filtering to suppress noise;

Forward modeling, using known location of the PIF;

GRACE Data and ProcessingGRACE Data and Processing

CSR GRACE RL04 solutionsCSR GRACE RL04 solutions April 2002 through December 2006;April 2002 through December 2006;

53 monthly solutions;53 monthly solutions;

FiltersFilters De-correlation filtering [Swenson and Wahr, 2006]De-correlation filtering [Swenson and Wahr, 2006]

300 km Gaussian smoothing300 km Gaussian smoothing

GRACE Data ProcessingGRACE Data Processing

Global Mass Change Rate MapGlobal Mass Change Rate Map

53 point monthly time series of surface mass change relative to mean 53 point monthly time series of surface mass change relative to mean

field (equivalent water layer thickness change relative to mean) at 1 x field (equivalent water layer thickness change relative to mean) at 1 x

1 degree grid points1 degree grid points

Least squares fit every time series with annual, semiannual, 161-day Least squares fit every time series with annual, semiannual, 161-day

S2 ocean tide alias, and linear mass rate.S2 ocean tide alias, and linear mass rate.

Examine maps of mass rate for globe and smaller areas of interest Examine maps of mass rate for globe and smaller areas of interest

such as the PIFsuch as the PIF

Global Mass Rates (cm/year): Apr 2002 - Dec 2006Global Mass Rates (cm/year): Apr 2002 - Dec 2006

GRACE Apparent Mass Rates in the PIF RegionGRACE Apparent Mass Rates in the PIF Region

GRACE Mass Change Time Series at Point AGRACE Mass Change Time Series at Point A

red curve: seasonal + 161-day tidal alias + linear trend; vertical scale is mass layer equivalent relative to mean GRACE field (not mass rate)

blue curve: time series at A after removing seasonal & 161-day tidal alias

Forward Modeling SchemeForward Modeling Scheme

Forward ModelingForward Modeling

Assume: Mass change concentrated in PIF; but limited Assume: Mass change concentrated in PIF; but limited

spatial resolution causes spatial leakage to surrounding spatial resolution causes spatial leakage to surrounding

areas.areas.

Assign mass rates to NPIF and SPIF regions, uniform Assign mass rates to NPIF and SPIF regions, uniform

over rectangular regions. Retain GRACE mass rates over rectangular regions. Retain GRACE mass rates

outside red area. outside red area.

Expand in SH to degree, order 60; apply same noise Expand in SH to degree, order 60; apply same noise

decorrelation and 300 km Gaussian smoothing filters.decorrelation and 300 km Gaussian smoothing filters.

Adjust model rates to agree with GRACE rate maps, Adjust model rates to agree with GRACE rate maps,

and sum of values over the red area to agree with and sum of values over the red area to agree with

GRACE rate maps. GRACE rate maps.

GRACE and Modeled PIF Mass RatesGRACE and Modeled PIF Mass Rates

Model: - 24.3 kmModel: - 24.3 km33/year mass /year mass rate uniform over PIFrate uniform over PIF

To Obtain an Ice Mass Rate, Other To Obtain an Ice Mass Rate, Other Signals Must Be EstimatedSignals Must Be Estimated

Land Water Storage ChangeLand Water Storage Change LadWorld land surface model;LadWorld land surface model;

April 2002 through November 2006;April 2002 through November 2006;

Estimate is ~ - 5.4 kmEstimate is ~ - 5.4 km33/year land water mass change./year land water mass change.

Postglacial Rebound (PGR)Postglacial Rebound (PGR) Regional PGR rate from Erik Ivins [Ivins and James, 2004).Regional PGR rate from Erik Ivins [Ivins and James, 2004).

PGR effect is PGR effect is sensitive to asthenosphere viscosity.

Assume 65 km lithosphere and mantle viscosity of 1.0 x 10Assume 65 km lithosphere and mantle viscosity of 1.0 x 101919 Pa s. Pa s.

Estimated PGR contribution ~ + 6.1 kmEstimated PGR contribution ~ + 6.1 km33/year./year.

Terrestrial Water Storage + PGR estimated to approximately cancel.Terrestrial Water Storage + PGR estimated to approximately cancel.

Conclusions:Conclusions:

GRACE RL04 data are of sufficient spatial resolution to be useful in GRACE RL04 data are of sufficient spatial resolution to be useful in studying features as small as the PIF.studying features as small as the PIF.

Forward modeling with known geography is a useful interpretive Forward modeling with known geography is a useful interpretive tool and provides quantitative estimates.tool and provides quantitative estimates.

Predicted water storage and PGR effects are of opposite sign and Predicted water storage and PGR effects are of opposite sign and nearly cancel. nearly cancel.

PIF ice melting rate is estimated to be 25.0 ± 9.2 kmPIF ice melting rate is estimated to be 25.0 ± 9.2 km33/year./year.

Uncertainty estimate combines least squares fit errors and 100% of predicted hydrological and PGR effects. (Note: errors could be larger than 100%!)

25.0 km25.0 km33/year agrees reasonably well with others based on /year agrees reasonably well with others based on topography and remote sensing datatopography and remote sensing data

The authors would like to thank Erik Ivins for providing the The authors would like to thank Erik Ivins for providing the PGR model data, and Chris Milly for providing PGR model data, and Chris Milly for providing

the LadWorld land water storage data.the LadWorld land water storage data.

The above analysis is being published in -

Chen, J.L., C.R. Wilson, B.D. Tapley, D.D. Blankenship, Patagonia Ice Field Melting Chen, J.L., C.R. Wilson, B.D. Tapley, D.D. Blankenship, Patagonia Ice Field Melting Observed by GRACE, Observed by GRACE, Geophys. Res. LettGeophys. Res. Lett., 2007., 2007..