GRACE MONTHLY MASS GRIDS - LAND

Current data version(s):

[RL05.DSTvSCS1409] for GFZ, CSR; [RL05.DSTvSCS1411] for JPL

Please download ALL MONTHS from these new solutions

and replace previous versions to work with a consistent time series;

for more details, please read the [README file].

LAND DATA PROCESSING

- The land data are based on the RL05 spherical harmonics from CSR, JPL and GFZ
- The
**C20 (degree 2 order 0)** coefficients are replaced with the solutions from Satellite Laser Ranging [Cheng et al., 2011], because the native GRACE-C20 values have a larger uncertainty than the SLR-values.
- The degree 1 coefficients (
**geocenter**) are estimated from Swenson, Chambers, and Wahr (2008).
- A glacial isostatic adjustment (
**GIA**) correction has been applied based in the model from Geruo A and J. Wahr (2013).
- A destriping filter has been applied to the data, to minimize the effect of an error whose telltale signal are N-S stripes in GRACE monthly maps.
- A 300 km wide gaussian filter has also been applied to the data (please note: make sure that the Gain Factor file is the correct 300km version!).
- All reported data are
**anomalies relative to the 2004.0-2009.999 time-mean baseline**. Note that this baseline needs to be consistent when comparing GRACE data to other anomaly data (i.e., groundwater or sea level).

The spatial sampling of all grids is 1 degree in both latitude and longitude (approx. 111 km at the Equator). However, this does not mean that two neighboring grid cells are 'independent' because spatial smoothing has been applied. For a **detailed description of the data processing, gain factor derivation & caveats**, please see DATA PROCESSING and CAVEATS DESCRIPTION FOR **LAND** GRIDS (PDF, 3.68 MB).

LAND GRID SCALING

Due to the sampling and post-processing of GRACE observations, surface mass variations at small spatial scales tend to be attenuated. Therefore, USERS SHOULD MULTIPLY THE GRCTellus LAND DATA BY THE PROVIDED SCALING GRID. The scaling grid is a set of scaling coefficients, one for each 1 degree bin of the land grids, and are intended to restore much of the energy removed by the destriping, gaussian, and degree 60 filters to the land grids. To use these scaling coefficients, the time series at one grid (1 degree bin) location must be multiplied by the scaling factor at the same 1 degree bin position. The netcdf file with gain factors is CLM4.SCALE_FACTOR.DS.G300KM.nc in the netcdf directory , and it must be applied to the GRACE grids in the same directory (an identical grid in ascii format can be found in the ascii directory) .

The scaling coefficients were computed by applying the same filters applied to the GRACE data to a numerical land-hydrology model (NCAR's CLM4). In a nutshell, the gain coefficient is the multiplicative factor that minimizes the difference between the model's smoothed and unfiltered monthly water storage variations at any geographic location. The coefficients are independent of the GRACE data proper, hence they are provided as a separate file. Furthermore, the gain factors tend to be dominated by the annual cycles of water storage variations, and may thus not be suitable to quantify trends from the GRCTellus land data. While the dependence of the gain factors on the specific land model used is generally small, please note that inter-annual trends in particular in hydrology models are very uncertain.

For a **detailed description of the data processing, gain factor derivation & caveats**, please see DATA PROCESSING and CAVEATS DESCRIPTION FOR **LAND** GRIDS (PDF, 3.68 MB).

NOT SUITABLE FOR CRYOSPHERIC STUDIES

The current GRCTellus Land grids are not suitable to accurately quantify ice mass changes over Greenland or Antarctica, or glaciers and ice caps. These regions require region-specific averaging kernels, as well as proper treatment of signal contamination from nearby land hydrology and adjusted GIA effects. We recommend the paper by Jacob et al. (Nature 2012, full citation below), for a thorough discussion of these aspects.

UNITS, FORMAT

The units of the 'equivalent water thickness' grids are cm of water thickness. The units of the error grids are cm. The scaling factors are dimensionless. If each grid node is g(x,y,t) where x is longitude index, y is latitude index, t is time (month) index, and the scaling grid is s(x,y), then the time series is simply

g'(x,y,t) = g(x,y,t)*s(x,y)

These grids have 360 longitudes (0.5,1.5,2.5,...,359.5), and 180 latitudes (-89.5, -88.5, ..., -0.5, +0.5, ...+89.5). However, missing grid points are not included in the ascii files

The data are provided in

- NETCDF format, suitable for automatic ingestion into several software packages.
- ASCII, a plain text format (compressed with gzip)
- Error estimates due to the measurement and errors due to leakage are also provided, in separate files (ascii) or together with the scaling coefficient file (netcdf).

ERROR ESTIMATES

To compute error estimates for the scaled values, two additional grids are provided (as separate ASCII files or in the same netCDF file as the scaling coefficients).

1. The errors given in CLM4.*.DS.G200KM.txt are in centimeters (same as the GRACE data).

2. The measurement errors have already been scaled so no further multiplication is necessary.

3. The leakage errors are residual errors after filtering and rescaling, such that the total error in Total Water Storage for a given grid pixel is:

total_err_pix = sqrt(leakage_err_pix^2+measurement_err_pix^2).

4. The errors in nearby pixels are correlated. Therefore, if the total error in a region of adjacent pixels is desired, this covariance needs to be considered. Here is pseudo-code to get the total leakage (lerr) and measurement (merr) errors for a region:

var_merr = 0. ; measurement error

var_lerr = 0. ; leakage error

betam = 300. ; km ~ measurement error de-correlation length

betal = 100. ; km ~ leakage error de-correlation length

for i=0, npix-1 do begin

for j=0, npix-1 do begin

dist = sqrt((lon[i]-lon[j])*cos(lat[i]))^2.+(lat[i]-lat[j])^2.) * (pi/180) * 6371. ; lon, lat in degs, dist in km

expdbm = exp(-(dist^2.)/(2.*betam^2.))

expdbl = exp(-(dist^2.)/(2.*betal^2.))

var_merr = var_merr + merr[i] * merr[j] * expdbm

var_lerr = var_lerr + lerr[i] * lerr[j] * expdbl

endfor

endfor

sigma_merr = sqrt(var_merr)/npix

sigma_lerr = sqrt(var_lerr)/npix

TIME AVERAGE REMOVED FROM MONTHLY SOLUTIONS

Each monthly GRCTellus grid represents the surface mass deviation for that month relative to the baseline average over Jan 2004 to Dec 2009. If you compare against other data or model, it is critical that anomalies relative to the same time-average are compared. This is simple to do: for example, if the new baseline is 2004-2006, average the GRCTellus grids over 1/2004 to 12/2006, and subtract this average grid from all other monthly grids.

TIME SPAN OF GRCTellus MONTHLY SOLUTIONS

'Monthly' is used somewhat loosely: please see the

**TABLE OF ACTUAL DATA DAYS** used for each 'monthly' solution. Note that from 2011 on, the GRACE instruments are periodically turned off due to battery management.

BROWSE IMAGES and NUMERIC DATA

The LAND gridded data and browse images are available here

ACKNOWLEDGEMENT and CITATION

When using any of these data, please acknowledge

**GRACE land data were processed by Sean Swenson, supported by the
NASA MEaSUREs Program, and are available at http://grace.jpl.nasa.gov**

and cite

Landerer F.W. and S. C. Swenson, Accuracy of scaled GRACE terrestrial water storage estimates. Water Resources Research, Vol 48, W04531, 11 PP, doi:10.1029/2011WR011453 2012.

Swenson, S. C. and J. Wahr, Post-processing removal of correlated
errors in GRACE data, Geophys. Res. Lett., 33, L08402,

doi:10.1029/2005GL025285, 2006.

If you encounter any problems with the data, please contact the person listed at bottom right.

ADDITIONAL REFERENCES used above:

Cheng, M. and Tapley, B.D.: Variations in the Earth's oblateness during the past 28 years, J. Geophys Res v109, B9, 2004

Jacob T., J. Wahr, W.T.Pfeffer, and S. Swenson, Recent contributions of glaciers and ice caps to sea level rise. Nature 2012.

doi:/10.1038/nature10847
Swenson, S. C. and J. Wahr, Post-processing removal of correlated errors in GRACE data, Geophys. Res. Lett., 33, L08402, doi:10.1029/2005GL025285, 2006.

Swenson S.C , D. P. Chambers, and J. Wahr: Estimating geocenter variations from a combination of GRACE and ocean model output. J Geophys. Res.-Solid Earth, Vol 113, Issue: B8, Article B08410. 2008.

Wahr, J., M. Molenaar, and F. Bryan, Time-variability of the Earth's gravity field: Hydrological and oceanic effects and their possible detection using GRACE, J. Geophys. Res., 103, 32,20530,229, 1998.

LAST UPDATE: 2014-10-10 FWL, Zheng Qu, V.Zlotnicki. We thank Holly Maness and Sean Swenson for their contributions.