The imprints of contemporary mass redistribution on regional sea level and vertical land motion observations

Data supplement to:

The imprints of contemporary mass redistribution on regional sea level and vertical land motion observations
Thomas Frederikse, Felix Landerer, and Lambert Caron
Solid Earth, 2019

https://doi.org/10.5194/se-2018-128

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA

(c) 2019 California Institute of Technology. U.S. Government sponsorship acknowledged.
This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Please cite the accompanying paper if you find this data useful.

If you use this data, please keep in mind that this data heavily relies on work by others. Please cite these people to acknowledge their work.

The GIA data comes from:
Caron, L., Ivins, E. R., Larour, E., Adhikari, S., Nilsson, J., & Blewitt, G. (2018). GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science. Geophysical Research Letters, 45(5), 2203–2212. https://doi.org/10.1002/2017GL076644

The GRACE data:
1. Watkins, M. M., Wiese, D. N., Yuan, D.-N., Boening, C., & Landerer, F. W. (2015). Improved methods for observing Earth’s time variable mass distribution with GRACE using spherical cap mascons. Journal of Geophysical Research: Solid Earth, 120(4), 2648–2671. https://doi.org/10.1002/2014JB011547
2. Wiese, D. N., Landerer, F. W., & Watkins, M. M. (2016). Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution. Water Resources Research, 52(9), 7490–7502. https://doi.org/10.1002/2016WR019344

The separation between TWS and glaciers relies on the Randolph Glacier inventory, and the WGMS glacier mass balance estimates:
1. Pfeffer, W. T., Arendt, A. A., Bliss, A., Bolch, T., Cogley, J. G., Gardner, A. S., … The Randolph Consortium. (2014). The Randolph Glacier Inventory: A globally complete inventory of glaciers. Journal of Glaciology, 60(221), 537–552. https://doi.org/10.3189/2014JoG13J176
2. Zemp, M., Huss, M., Thibert, E., Eckert, N., McNabb, R., Huber, J., … Cogley, J. G. (2019). Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016. Nature, 568(7752), 382–386. https://doi.org/10.1038/s41586-019-1071-0

The GNSS trends heavily rely on the data archiving, proceesing work done at Nevada Geodetic Laboratory, as well as the MIDAS trend estimator.
1. Blewitt, Geoffrey, Corné Kreemer, William C. Hammond, and Julien Gazeaux. “MIDAS Robust Trend Estimator for Accurate GPS Station Velocities without Step Detection.” Journal of Geophysical Research: Solid Earth 121, no. 3 (March 2016): 2054–68. https://doi.org/10.1002/2015JB012552.
2. Blewitt, Geoffrey, William Hammond, and Corné Kreemer. “Harnessing the GPS Data Explosion for Interdisciplinary Science.” Eos 99 (September 24, 2018). https://doi.org/10.1029/2018EO104623.

Contents:
GIA_stats.nc     NetCDF:     GIA trends and uncertainties (Depicted in Figure 2)
ewh_results.nc   NetCDF:     Trends and standard errors in GRACE-observed EWH, corrected for GIA (Depicted in Figure 3)
sle_results.nc   NetCDF:     Trends, time series, and standard errors in barystatic sea-level changes and deformation, RSL, geocentric sea level from all individual processes (Parts are depicted in Figures 6,7,8)
obs_trends.txt   plain text: List of observed and corrected VLM trends and their uncertainties (Depicted in Figure 10)