DOI: 10.6084/m9.figshare.7670849.v1
title: GRACE-REC: A reconstruction of climate-driven water storage changes over the last century

description: <b>List of all files<br></b><i>Readme file</i><b><br></b> 00_readme.txt<b><br></b><i>Monthly grids - ensemble means</i><b><br></b> 01_monthly_grids_ensemble_means_allmodels.zip<br><i>Monthly grids - ensembles, model 1 to 6</i><b><br></b> 02_monthly_grids_ensemble_model1.zip 02_monthly_grids_ensemble_model2.zip 02_monthly_grids_ensemble_model3.zip 02_monthly_grids_ensemble_model4.zip 02_monthly_grids_ensemble_model5.zip 02_monthly_grids_ensemble_model6.zip<i>Daily grids - ensemble means, model 1 to 6</i><b><br></b> 03_daily_grids_ensemble_means_model1.zip 03_daily_grids_ensemble_means_model2.zip 03_daily_grids_ensemble_means_model3.zip 03_daily_grids_ensemble_means_model4.zip 03_daily_grids_ensemble_means_model5.zip 03_daily_grids_ensemble_means_model6.zip<i>Global averages - daily and monthly time series</i><b><br></b> 04_global_averages_allmodels.zip<br><b>Content of readme</b><br>GRACE TWS Reconstruction (GRACE_REC_v03)<br><br>The dataset contains reconstructed time series of daily and monthly anomalies of terrestrial water storage (TWS) based on two different GRACE solutions and three different meteorological forcing datasets. There is a total of 6 different models:<br>model1 - trained with GRACE JPL mascons, forced with MSWEP forcing (1979-2016)<br>model2 - trained with GRACE JPL mascons, forced with GSWP3 forcing (1901-2014)<br>model3 - trained with GRACE JPL mascons, forced with ERA-Interim forcing (1979-present)<br>model4 - trained with GRACE GSFC mascons, forced with MSWEP forcing (1979-2016)<br>model5 - trained with GRACE GSFC mascons, forced with GSWP3 forcing (1901-2014)<br>model6 - trained with GRACE GSFC mascons, forced with ERA-Interim forcing (1979-present)<br><br>The reconstruction aims at reproducing the sub-decadal climate-driven variability observed in the GRACE data. Seasonal cycle and human impacts on TWS are not reconstructed. A GRACE-based seasonal cycle is provided for convenience and should be used with the awareness that in reality long-term changes in the shape of the seasonal cycle might potentially occur. Long-term signals (trends over a period &gt;15 years) are removed during the model calibration procedure but are still present in the final dataset. The interpretation of the reconstructed long-term trends should be done with particular caution and the awareness that there can be a large uncertainty in the reconstructed trends.<br><br>For most applications, uncertainty ranges can be derived from the 100 ensemble members available for each model.<br><br>The grids are stored in NetCDFv4 files in units of mm (kg m^-2). Although the data is provided on a 0.5 degrees grid, the effective spatial resolution is 3 degrees, similar to the original resolution of the GRACE datasets. This should be taken into account when comparing this dataset against other sources.<br><br>The global means are stored as csv files in units of Gt of water.<br><br>When using this dataset, please cite:<br>Humphrey V. & Gudmundsson L. (submitted). GRACE-REC: A reconstruction of climate-driven water storage changes over the last century. Earth System Science Data Discussions.<br>or<br>Humphrey, V., L. Gudmundsson, and S. I. Seneviratne (2017), A global reconstruction of climate-driven sub-decadal water storage variability, Geophys. Res. Lett., 44, doi:10.1002/2017GL072564.<br><br>Vincent Humphrey, February 2019<br>ETH Zurich, California Institute of Technology<br><br>Your feedback is always welcome:<br>vincent.humphrey [at character] env.ethz.ch (vincent.humphrey [at character] bluewin.ch) <br><br><b>Abstract</b><br> The amount of water stored on continents is an important constraint for water mass and energy exchanges in the Earth system and exhibits large inter-annual variability at both local and continental scales. From 2002 to 2017, the satellites of the Gravity Recovery and Climate Experiment mission (GRACE) have observed changes in terrestrial water storage (TWS) with an unprecedented level of accuracy. In this paper, we use a statistical model trained with GRACE observations to reconstruct past climate-driven changes in TWS from historical and near real time meteorological datasets at daily and monthly scales. Unlike most hydrological models which represent water reservoirs individually (e.g. snow, soil moisture, etc.) and usually provide a single model run, the presented approach directly reconstructs total TWS changes and includes hundreds of ensemble members which can be used to quantify predictive uncertainty. We compare these data-driven TWS estimates with other independent evaluation datasets such as the sea level budget, large-scale water balance from atmospheric reanalysis and in-situ streamflow measurements. We find that the presented approach performs overall as well or better than a set of state-of-the-art global hydrological models (Water Resources Reanalysis version 2). We provide reconstructed TWS anomalies at a spatial resolution of 0.5°, at both daily and monthly scales over the period 1901 to present, based on two different GRACE products and three different meteorological forcing datasets, resulting in 6 reconstructed TWS datasets of 100 ensemble members each. Possible user groups and applications include hydrological modelling and model benchmarking, sea level budget studies, assessments of long-term changes in the frequency of droughts, the analysis of climate signals in geodetic time series and the interpretation of the data gap between the GRACE and the GRACE Follow-On mission.Check reference for additional details and caveats.<br><b>Reference</b>Humphrey V. & Gudmundsson L. (submitted). GRACE-REC: A reconstruction of climate-driven water storage changes over the last century. Earth System Science Data Discussions.<br>