GRACE-REC: A reconstruction of climate-driven water storage changes over the last century

<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. &amp; 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. &amp; Gudmundsson L. (submitted). GRACE-REC: A reconstruction of climate-driven water storage changes over the last century. Earth System Science Data Discussions.<br>

<b>所有文件列表<br></b><i>说明文档</i><b><br></b> 00_readme.txt<b><br></b><i>月度格点数据——集合平均结果</i><b><br></b> 01_monthly_grids_ensemble_means_allmodels.zip<br><i>月度格点数据——集合结果，模型1至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>日度格点数据——集合平均结果，模型1至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>全球平均数据——日度与月度时间序列</i><b><br></b> 04_global_averages_allmodels.zip<br><b>说明文档内容</b><br>GRACE陆地水储量（Terrestrial Water Storage, TWS）重建数据集（GRACE_REC_v03）<br><br>本数据集包含基于两种不同GRACE解算方案与三种不同气象强迫数据集得到的陆地水储量日度与月度异常值重建时间序列。本数据集共包含6种不同模型：<br>model1 - 采用GRACE喷气推进实验室（Jet Propulsion Laboratory, JPL）质量集中（Mascons）解进行训练，以MSWEP强迫场驱动（时间范围：1979-2016年）<br>model2 - 采用GRACE JPL质量集中解进行训练，以GSWP3强迫场驱动（时间范围：1901-2014年）<br>model3 - 采用GRACE JPL质量集中解进行训练，以ERA-Interim强迫场驱动（时间范围：1979年至今）<br>model4 - 采用GRACE戈达德太空飞行中心（Goddard Space Flight Center, GSFC）质量集中解进行训练，以MSWEP强迫场驱动（时间范围：1979-2016年）<br>model5 - 采用GRACE GSFC质量集中解进行训练，以GSWP3强迫场驱动（时间范围：1901-2014年）<br>model6 - 采用GRACE GSFC质量集中解进行训练，以ERA-Interim强迫场驱动（时间范围：1979年至今）<br><br>本次重建旨在复现GRACE观测到的年代际以下气候驱动变率。季节性循环与人类活动对TWS的影响未被纳入重建范围。为方便使用，本数据集附带了基于GRACE的季节性循环数据，但需注意：实际场景中季节性循环的形态可能发生长期变化。模型校准过程中已移除长期信号（时长超过15年的趋势项），但最终数据集中仍保留了此类信号。对重建得到的长期趋势进行解释时需格外谨慎，因为重建趋势可能存在较大不确定性。<br><br>对于多数应用场景，可通过每个模型附带的100个集合成员得到不确定性范围。<br><br>格点数据以NetCDFv4格式存储，单位为毫米（mm，即kg·m⁻²）。尽管数据以0.5度网格提供，但有效空间分辨率为3度，与原始GRACE数据集的分辨率一致。将本数据集与其他数据源进行对比时，需考虑这一特性。<br><br>全球平均数据以逗号分隔值（Comma-Separated Values, CSV）格式存储，单位为亿吨水（Gt）。<br><br>使用本数据集时，请引用以下文献：<br>Humphrey V. & Gudmundsson L.（已投稿）. GRACE-REC: 近一个世纪气候驱动的水储量变化重建. 地球系统科学数据讨论（Earth System Science Data Discussions）.<br>或<br>Humphrey, V., L. Gudmundsson, 及 S. I. Seneviratne (2017), 全球气候驱动的年代际以下水储量变率重建, 《地球物理研究通讯》（Geophysical Research Letters）, 44卷, doi:10.1002/2017GL072564.<br><br>Vincent Humphrey, 2019年2月<br>苏黎世联邦理工学院（ETH Zurich）, 加州理工学院<br><br>欢迎反馈意见：<br>vincent.humphrey [at 字符] env.ethz.ch（vincent.humphrey [at 字符] bluewin.ch）<br><br><b>摘要</b><br> 陆面储水量是地球系统中水质量与能量交换的重要约束因子，在局地与大陆尺度均表现出显著的年际变率。2002年至2017年间，重力恢复与气候实验（Gravity Recovery and Climate Experiment, GRACE）卫星以空前的精度观测到了陆地水储量变化。本文采用基于GRACE观测训练得到的统计模型，利用历史及近实时气象数据集，在日度与月度尺度上重建过去的气候驱动TWS变化。与多数分别表征各水体储库（如积雪、土壤水等）且通常仅提供单模型运行结果的水文模型不同，本文提出的方法直接重建总TWS变化，并包含数百个集合成员，可用于量化预测不确定性。我们将此类数据驱动的TWS估算结果与其他独立评估数据集进行了对比，包括海平面收支、大气再分析资料得到的大尺度水平衡以及原位径流观测数据。结果表明，本方法的整体表现与当前主流的全球水文模型（水资源再分析版本2，Water Resources Reanalysis version 2）相当甚至更优。本数据集提供了1901年至今、空间分辨率0.5度的日度与月度TWS异常值重建结果，基于两种不同GRACE产品与三种不同气象强迫数据集，最终得到6组各包含100个集合成员的TWS重建数据集。潜在的用户群体与应用场景包括水文建模与模型基准测试、海平面收支研究、干旱频率长期变化评估、大地测量时间序列中的气候信号分析，以及填补GRACE与GRACE Follow-On任务之间的数据空白。更多细节与注意事项请参阅参考文献。<br><b>参考文献</b><br>Humphrey V. & Gudmundsson L.（已投稿）. GRACE-REC: 近一个世纪气候驱动的水储量变化重建. 地球系统科学数据讨论（Earth System Science Data Discussions）.<br>