Explore the vast range of titles available.

We assess the accuracy of global‐gridded terrestrial water storage (TWS) estimates derived from temporal gravity field variations observed by the Gravity Recovery and Climate Experiment (GRACE) satellites. The TWS data set has been corrected for signal modification due to filtering and truncation. Simulations of terrestrial water storage variations from land‐hydrology models are used to infer relationships between regional time series representing different spatial scales. These relationships, which are independent of the actual GRACE data, are used to extrapolate the GRACE TWS estimates from their effective spatial resolution (length scales of a few hundred kilometers) to finer spatial scales (∼100 km). Gridded, scaled data like these enable users who lack expertise in processing and filtering the standard GRACE spherical harmonic geopotential coefficients to estimate the time series of TWS over arbitrarily shaped regions. In addition, we provide gridded fields of leakage and GRACE measurement errors that allow users to rigorously estimate the associated regional TWS uncertainties. These fields are available for download from the GRACE project website (available at http://grace.jpl.nasa.gov). Three scaling relationships are examined: a single gain factor based on regionally averaged time series, spatially distributed (i.e., gridded) gain factors based on time series at each grid point, and gridded‐gain factors estimated as a function of temporal frequency. While regional gain factors have typically been used in previously published studies, we find that comparable accuracies can be obtained from scaled time series based on gridded gain factors. In regions where different temporal modes of TWS variability have significantly different spatial scales, gain factors based on the first two methods may reduce the accuracy of the scaled time series. In these cases, gain factors estimated separately as a function of frequency may be necessary to achieve accurate results. Key Points We present gridded gain factors and error maps for GRACE Measurement and leakage errors are taken into account The new method does not require the use of spherical harmonics by the users

As the dominant reservoir of heat uptake in the climate system, the world’s oceans provide a critical measure of global climate change. Here, we infer deep-ocean warming in the context of global sea-level rise and Earth’s energy budget between January 2005 and December 2013. Direct measurements of ocean warming above 2,000 m depth explain about 32% of the observed annual rate of global mean sea-level rise. Over the entire water column, independent estimates of ocean warming yield a contribution of 0.77 ± 0.28 mm yr −1 in sea-level rise and agree with the upper-ocean estimate to within the estimated uncertainties. Accounting for additional possible systematic uncertainties, the deep ocean (below 2,000 m) contributes −0.13 ± 0.72 mm yr −1 to global sea-level rise and −0.08 ± 0.43 W m −2 to Earth’s energy balance. The net warming of the ocean implies an energy imbalance for the Earth of 0.64 ± 0.44 W m −2 from 2005 to 2013. The global ocean is a major heat reservoir of the climate system. This study investigates ocean warming for 2005–2013 in the context of global sea-level rise and Earth’s energy budget, and finds that the deep ocean (below 2,000 m) has contributed negligibly to both.

Freshwater availability is changing worldwide. Here we quantify 34 trends in terrestrial water storage (TWS) observed by the Gravity Recovery and Climate Experiment (GRACE) satellites during 2002-2016 and categorize their drivers as natural interannual variability, unsustainable groundwater consumption, or climate change. Several of these trends had been lacking thorough investigation and attribution, including massive changes in northwestern China and the Okavango delta. Others are consistent with climate model predictions. This observation-based assessment of how the world's water landscape is responding to human impacts and climate variations provides a blueprint for evaluating and predicting emerging threats to water and food security.

Regional sea-level changes are caused by several physical processes that vary both in space and time. As a result of these processes, large regional departures from the long-term rate of global mean sea-level rise can occur. Identifying and understanding these processes at particular locations is the first step toward generating reliable projections and assisting in improved decision making. Here we quantify to what degree contemporary ocean mass change, sterodynamic effects, and vertical land motion influence sea-level rise observed by tide-gauge locations around the contiguous U.S. from 1993 to 2018. We are able to explain tide gauge-observed relative sea-level trends at 47 of 55 sampled locations. Locations where we cannot explain observed trends are potentially indicative of shortcomings in our coastal sea-level observational network or estimates of uncertainty.

In Fig. 2 of this Analysis, the tick-mark labels on the colour bars in the second and third images from the top were inadvertently swapped. In addition, the citation at the end of the sentence, “On a monthly basis GRACE can resolve TWS changes with sufficient accuracy over scales that range from approximately 200,000 km 2 at low latitudes to about 90,000 km 2 near the poles” should be to ref. 4 not ref. 1 . These errors have been corrected online.

The Atlantic Meridional Overturning Circulation (AMOC) is a key mechanism for large-scale northward heat transport and thus plays an important role for global climate. Relatively warm water is transported northward in the upper layers of the North Atlantic Ocean and, after cooling at subpolar latitudes, sinks down and is transported back south in the deeper limb of the AMOC. The utility of in situ ocean bottom pressure (OBP) observations to infer AMOC changes at single latitudes has been characterized in the recent literature using output from ocean models. We extend the analysis and examine the utility of space-based observations of time-variable gravity and the inversion for ocean bottom pressure to monitor AMOC changes and variability between 20 and 60° N. Consistent with previous results, we find a strong correlation between the AMOC signal and OBP variations, mainly along the western slope of the Atlantic Basin. We then use synthetic OBP data – smoothed and filtered to resemble the resolution of the GRACE (Gravity Recovery and Climate Experiment) gravity mission, but without errors – and reconstruct geostrophic AMOC transport. Due to the coarse resolution of GRACE-like OBP fields, we find that leakage of signal across the step slopes of the ocean basin is a significant challenge at certain latitudes. Transport signal rms is of a similar order of magnitude as error rms for the reconstructed time series. However, the interannual AMOC anomaly time series can be recovered from 20 years of monthly GRACE-like OBP fields with errors less than 1 sverdrup in many locations.

Observations from permanent Global Navigation Satellite System (GNSS) stations are commonly used to correct tide-gauge observations for vertical land motion (VLM). We combine GRACE (Gravity Recovery and Climate Experiment) observations and an ensemble of glacial isostatic adjustment (GIA) predictions to assess and evaluate the impact of solid-Earth deformation (SED) due to contemporary mass redistribution and GIA on VLM trends derived from GNSS stations. This mass redistribution causes relative sea-level (RSL) and SED patterns that not only vary in space but also exhibit large interannual variability signals. We find that for many stations, including stations in coastal locations, this deformation causes VLM trends on the order of 1 mm yr−1 or higher. In multiple regions, including the Amazon Basin and large parts of Australia, the SED trend flips sign between the first half and second half of the 15-year GRACE record. GNSS records often only span a few years, and due to these interannual variations SED causes substantial biases when the linear trends in these short records are extrapolated back in time. We propose a new method to avoid this potential bias in the VLM-corrected tide-gauge record: instead of correcting tide-gauge records for the observed VLM trend, we first remove the effects from GIA and contemporary mass redistributions from the VLM observations before computing the VLM trend. This procedure reduces the extrapolation bias induced by SED, and it also avoids the bias due to sea-floor deformation: SED includes net sea-floor deformation, which is ignored in global-mean sea-level reconstructions based on VLM-corrected tide-gauge data. We apply this method to 8166 GNSS stations. With this separation, we are able to explain a large fraction of the discrepancy between observed sea-level trends at multiple long tide-gauge records and the global-mean sea-level trend from recent reconstructions.

The Gravity Recovery and Climate Experiment (GRACE) mission data have an important, if not revolutionary, impact on how scientists quantify the water transport on the Earth's surface. The transport phenomena include land hydrology, physical oceanography, atmospheric moisture flux, and global cryospheric mass balance. The mass transport observed by the satellite system also includes solid Earth motions caused by, for example, great subduction zone earthquakes and glacial isostatic adjustment (GIA) processes. When coupled with altimetry, these space gravimetry data provide a powerful framework for studying climate-related changes on decadal timescales, such as ice mass loss and sea-level rise. As the changes in the latter are significant over the past two decades, there is a concomitant self-attraction and loading phenomenon generating ancillary changes in gravity, sea surface, and solid Earth deformation. These generate a finite signal in GRACE and ocean altimetry, and it may often be desirable to isolate and remove them for the purpose of understanding, for example, ocean circulation changes and post-seismic viscoelastic mantle flow, or GIA, occurring beneath the seafloor. Here we perform a systematic calculation of sea-level fingerprints of on-land water mass changes using monthly Release-06 GRACE Level-2 Stokes coefficients for the span April 2002 to August 2016, which result in a set of solutions for the time-varying geoid, sea-surface height, and vertical bedrock motion. We provide both spherical harmonic coefficients and spatial maps of these global field variables and uncertainties therein (https://doi.org/10.7910/DVN/8UC8IR; Adhikari et al., 2019). Solutions are provided for three official GRACE data processing centers, namely the University of Texas Austin's Center for Space Research (CSR), GeoForschungsZentrum Potsdam (GFZ), and Jet Propulsion Laboratory (JPL), with and without rotational feedback included and in both the center-of-mass and center-of-figure reference frames. These data may be applied for either study of the fields themselves or as fundamental filter components for the analysis of ocean-circulation- and earthquake-related fields or for improving ocean tide models.

This paper analyzes regional sea level changes in a climate change simulation using the Max Planck Institute for Meteorology (MPI) coupled atmosphere–ocean general circulation model ECHAM5/MPI-OM. The climate change scenario builds on observed atmospheric greenhouse gas (GHG) concentrations from 1860 to 2000, followed by the International Panel on Climate Change (IPCC) A1B climate change scenario until 2100; from 2100 to 2199, GHG concentrations are fixed at the 2100 level. As compared with the unperturbed control climate, global sea level rises 0.26 m by 2100, and 0.56 m by 2199 through steric expansion; eustatic changes are not included in this simulation. The model’s sea level evolves substantially differently among ocean basins. Sea level rise is strongest in the Arctic Ocean, from enhanced freshwater input from precipitation and continental runoff, and weakest in the Southern Ocean, because of compensation of steric changes through dynamic sea surface height (SSH) adjustments. In the North Atlantic Ocean (NA), a complex tripole SSH pattern across the subtropical to subpolar gyre front evolves, which is consistent with a northward shift of the NA current. On interannual to decadal time scales, the SSH difference between Bermuda and the Labrador Sea correlates highly with the combined baroclinic gyre transport in the NA but only weakly with the meridional overturning circulation (MOC) and, thus, does not allow for estimates of the MOC on these time scales. Bottom pressure increases over shelf areas by up to 0.45 m (water column equivalent) and decreases over the Atlantic section in the Southern Ocean by up to 0.20 m. The separate evaluation of thermosteric and halosteric sea level changes shows that thermosteric anomalies are positive over most of the World Ocean. Because of increased atmospheric moisture transport from low to high latitudes, halosteric anomalies are negative in the subtropical NA and partly compensate thermosteric anomalies, but are positive in the Arctic Ocean and add to thermosteric anomalies. The vertical distribution of thermosteric and halosteric anomalies is highly nonuniform among ocean basins, reaching deeper than 3000 m in the Southern Ocean, down to 2200 m in the North Atlantic, and only to depths of 500 m in the Pacific Ocean by the end of the twenty-first century.

Quantification of uncertainty in surface mass change signals derived from Global Positioning System (GPS) measurements poses challenges, especially when dealing with large datasets with continental or global coverage. We present a new GPS station displacement dataset that reflects surface mass load signals and their uncertainties. We assess the structure and quantify the uncertainty of vertical land displacement derived from 3045 GPS stations distributed across the continental US. Monthly means of daily positions are available for 15 years. We list the required corrections to isolate surface mass signals in GPS estimates and screen the data using GRACE(-FO) as external validation. Evaluation of GPS time series is a critical step, which identifies (a) corrections that were missed, (b) sites that contain non-elastic signals (e.g., close to aquifers), and (c) sites affected by background modeling errors (e.g., errors in the glacial isostatic model). Finally, we quantify uncertainty of GPS vertical displacement estimates through stochastic modeling and quantification of spatially correlated errors. Our aim is to assign weights to GPS estimates of vertical displacements, which will be used in a joint solution with GRACE(-FO). We prescribe white, colored, and spatially correlated noise. To quantify spatially correlated noise, we build on the common mode imaging approach by adding a geophysical constraint (i.e., surface hydrology) to derive an error estimate for the surface mass signal. We study the uncertainty of the GPS displacement time series and find an average noise level between 2 and 3 mm when white noise, flicker noise, and the root mean square (rms) of residuals about a seasonality and trend fit are used to describe uncertainty. Prescribing random walk noise increases the error level such that half of the stations have noise > 4 mm, which is systematic with the noise level derived through modeling of spatially correlated noise. The new dataset is available at https://doi.org/10.5281/zenodo.8184285 (Peidou et al., 2023) and is suitable for use in a future joint solution with GRACE(-FO)-like observations.