Satellite gravimetry as realised by GRACE (Gravity Recovery And Climate Experiment;Tapley et al., 2004) and GRACE-FO (GRACE-Follow-On;Landerer et al., 2020) allows for the first time to track changes in the surface mass distribution globally from space. The combined data record provides quantitative estimates on the severity of the ongoing global changes (Tapley et al., 2019), as manifested by persistent ice-mass loss in Greenland and
Abstract. Observations of changes in terrestrial water storage (TWS) obtained from the satellite mission GRACE (Gravity Recovery and Climate Experiment) have frequently been used for water cycle studies and for the improvement of hydrological models by means of calibration and data assimilation. However, due to a low spatial resolution of the gravity field models, spatially localized water storage changes, such as those occurring in lakes and reservoirs, cannot properly be represented in the GRACE estimates. As surface storage changes can represent a large part of total water storage, this leads to leakage effects and results in surface water signals becoming erroneously assimilated into other water storage compartments of neighbouring model grid cells. As a consequence, a simple mass balance at grid/regional scale is not sufficient to deconvolve the impact of surface water on TWS. Furthermore, non-hydrology-related phenomena contained in the GRACE time series, such as the mass redistribution caused by major earthquakes, hamper the use of GRACE for hydrological studies in affected regions. In this paper, we present the first release (RL01) of the global correction product RECOG (REgional COrrections for GRACE), which accounts for both the surface water (lakes and reservoirs, RECOG-LR) and earthquake effects (RECOG-EQ). RECOG-LR is computed from forward-modelling surface water volume estimates derived from satellite altimetry and (optical) remote sensing and allows both a removal of these signals from GRACE and a relocation of the mass change to its origin within the outline of the lakes/reservoirs. The earthquake correction, RECOG-EQ, includes both the co-seismic and post-seismic signals of two major earthquakes with magnitudes above Mw9. We discuss that applying the correction dataset (1) reduces the GRACE signal variability by up to 75 % around major lakes and explains a large part of GRACE seasonal variations and trends, (2) avoids the introduction of spurious trends caused by leakage signals of nearby lakes when calibrating/assimilating hydrological models with GRACE, and (3) enables a clearer detection of hydrological droughts in areas affected by earthquakes. A first validation of the corrected GRACE time series using GPS-derived vertical station displacements shows a consistent improvement of the fit between GRACE and GNSS after applying the correction. Data are made available on an open-access basis via the Pangaea database (RECOG-LR: Deggim et al., 2020a, https://doi.org/10.1594/PANGAEA.921851; RECOG-EQ: Gerdener et al., 2020b, https://doi.org/10.1594/PANGAEA.921923).
Abstract. Observations of changes in terrestrial water storage obtained from the satellite mission GRACE (Gravity Recovery and Climate Experiment) have frequently been used for water cycle studies and for the improvement of hydrological models by means of calibration and data assimilation. However, due to a low spatial resolution of the gravity field models spatially localized water storage changes, such as those occurring in lakes and reservoirs, cannot properly be represented in the GRACE estimates. As surface storage changes can represent a large part of total water storage, this leads to leakage effects and results in surface water signals becoming erroneously assimilated into other water storage compartments of neighboring model grid cells. As a consequence, a simple mass balance at grid/regional scale is not sufficient to deconvolve the impact of surface water on TWS. Furthermore, non-hydrology related phenomena contained in the GRACE time series, such as the mass redistribution caused by major earthquakes, hamper the use of GRACE for hydrological studies in affected regions. In this paper, we present the first release (RL01) of the global correction product RECOG (REgional COrrections for GRACE), which accounts for both the surface water (lakes & reservoirs, RECOG-LR) and earthquake effects (RECOG-EQ). RECOG-LR is computed from forward-modelling surface water volume estimates derived from satellite altimetry and (optical) remote sensing and allows both a removal of these signals from GRACE and a re-location of the mass change to its origin within the outline of the lakes/reservoirs. The earthquake correction RECOG-EQ includes both the co-seismic and post-seismic signals of two major earthquakes with magnitudes above 9 Mw. We can show that applying the correction dataset (1) reduces the GRACE signal variability by up to 75 % around major lakes and explains a large part of GRACE seasonal variations and trends, (2) avoids the introduction of spurious trends caused by leakage signals of nearby lakes when calibrating/assimilating hydrological models with GRACE, even in neighboring river basins, and (3) enables a clearer detection of hydrological droughts in areas affected by earthquakes. A first validation of the corrected GRACE time series using GPS-derived vertical station displacements shows a consistent improvement of the fit between GRACE and GNSS after applying the correction. Data are made available as open access via the Pangea database (RECOG-LR: Deggim et al. (2020a) https://doi.org/10.1594/PANGAEA.921851; RECOG-EQ: Gerdener et al. (2020b, under revision), https://doi.pangaea.de/10.1594/PANGAEA.921923).
<p>Global coupled climate models are important for predicting future climate conditions. Due to sometimes large and often systematic model uncertainties, it is crucial to evaluate the outcome of model experiments against independent observations. Changes in the distribution and availability of terrestrial water storage (TWS), which can be measured by the satellite gravimetry missions GRACE and GRACE-FO, represent an important part of the climate system. However, the use of satellite gravity data for the evaluation of coupled climate models has only very recently become feasible. Challenges arise, e.g., from the still rather short time series of satellite data and from signal separation issues related to GRACE/-FO observing all mass change including non-water related variations such as glacial isostatic adjustment. Apart from climate model uncertainties, these challenges might be the reason for a disagreement between the direction of linear water storage trends of models and observations in several regions of the world, one of them located in Eastern Canada.</p> <p>This presentation will highlight the latest results achieved from our ongoing research on climate model evaluation based on the analysis of an ensemble of models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We will focus on long-term wetting and drying conditions in TWS. Using an ensemble of 52 GIA models that differ in the applied ice history, solid Earth rheology, and numerical code, this presentation will discuss how GIA modeling uncertainty does influence (i) the determination of water storage trends from GRACE/FO data, and (ii) the (dis-)agreement between drying/wetting trends in satellite gravimetry and CMIP6 climate models. We will show that the apparent disagreement between observations and models in highly GIA-affected regions in North America crucially depend on the particular model chosen for reducing the GIA effect from the GRACE satellite data.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.