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Earth System Data Record of mass transport from time-variable gravity

data

Victor Zlotnicki1, Matthieu Talpe2, F. Lemoine3, R. Steven Nerem2, Felix Landerer1, Michael Watkins1.

1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA

2Univ. of Colorado, Boulder, CO.3NASA Goddard Space Flight Ctr, Greenbelt, MD

JPL Document Clearance CL#14-3086

Gravity changes track water storage changes

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Everything has mass, hence use gravity changes to track Water Storage changes

Groundwater storage

Soil Moisture

Reservoirs

Snow Glaciers

Ice Sheets

Sea Level

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Four ways to measure gravity from space• satell tracking from

earth (laser, doppler)(since 1975)

• high low sat sat tracking: GPS(since 1992)

• low-low sat-sat tracking GRACE(since 2002)

• gradiometry GOCE (mostly time-mean)(2009-2013)

oldest

newest

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GRACE is best, why bother with others?GRACE has provided the highest resolution and accuracy estimates of time changes in water/ice, but need:

• to extend the time series in time before GRACE launch

• to patch a possible gap between GRACE and GRACE Follow-on

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GPS + laser satellite tracking 1

M.Weigelt, T. Van Dam, et al, 2014, GRACE STM Mtg.

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GPS + laser satellite tracking 2

M.Weigelt, T. Van Dam, et al, 2014, GRACE STM Mtg.

wavelength ~ 40,000km/l

4,000 2,000 1,333 1,000 800 667 ~wavelength (km)

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GPS + laser satellite tracking 3

K. Sosnica, A. Jaggi, M.Weigelt et al, 2014, GRACE STM Mtg.

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DORIS + Laser satellite tracking 1

F. Lemoine, GSFC, 2014

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DORIS + Laser satellite tracking EOF reconstruction

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DORIS + Laser satellite tracking EOF reconstruction

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DORIS + Laser satellite tracking EOF reconstruction

Greenland

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Differences in GRACE processingno instrument bias differences between different satellites, main diff is spatial resolution

But, differences due to processing choices can have spatial patterns.

Example: RMS difference between two GRACE processing centers, using the same underlying GRACE data

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How to validate mass fluxes 1Validation

• OCEAN– comp to bottom pressure recorders (Boening, GRL 2008)– comp to altimetry-argo (Chambers, Bonin 2012)– comp to ocean+data model (Chambers, Bonin 2012)

• ICE– comp Greenland to SMB (Velicogna 2014)– comp Amundsen SMB, Icesat, Envisat (Velicogna 2015)

• LAND– comp to GPS deformation in Calif. (Argus & Landerer,

2015)– comp to well data (Swenson, 2008)

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How to validate mass fluxes 2

Amundsen Sea Embayment, WAIS.1) GRACE2) mass budget method3) Envisat radar altimetry4) Icesat and airborne OIB

laser altimetry

Sutterley, Velicogna, et al GRL 2015

Bottom Pressure Recorders, 8S, 125W(Zlotnicki, Williams, Hughes, Boening, 2013)

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Summary, Discussion• GRACE is the most accurate method to

determine time-variable mass flux globally 2003 to 2015+, >300-500km. GFO in 2017.

• The time series can be extended with GPS+SLR or Doris+SLR (or all 3), at lower spatial resolution > ~2,000-8,000km

• There are no biases introduced by the different satellites, but there can be systematic differences due to processing choices

• There are several methods to validate gravity-derived mass fluxes, but limited in space and time

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BACKUP

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Gravity data measures mass flux• The longest wavelength, J2,

measured since 1976. Glacial isostatic adjustment + Greenland and Antarctica ice sheet melt, measured by SLR.

• Greenland, Antarctica, glaciers ice mass loss measured by GRACE since 2003

• Land total water content: GRACE

• Ocean mass and bottom pressure: GRACE

J2: Cheng, Tapley JGR 2013

Velicogna & Wahr, GRL 2013

Greenland ice mass

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DORIS + Laser satellite tracking EOF reconstruction

• Magnitude of orbit perturbations (hence sensitivity) diminishes with altitude.• Sensitivity is not uniform across all coefficients of a given degree, especially given only SLR or DORIS tracking.• Envisat has sensitivity to many terms at order 1 – but not all terms are separable from SLR+DORIS data alone on that satellite.

(mm)

Orbit Perturbations (mm) for SLR+DORIS satellites from effects of Time-Variable Gravity (Cryosat2, Envisat, Jason-2)

Jason-2EnvisatCryosat-2

F. Lemoine, GSFC, 2014

Correlations of SLR+DORIS 5x5 solution with GRGS GRACE+Lageos solution (2003-2012)

C coefficientsS coefficients

• C20, C22, sectorals, match GRACE solutions well;

• For C31, C32 and coefficients at L=5, the agreement becomes less good.

F. Lemoine, GSFC, 2014