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Sea level dynamics in the coastal zone can differ significantly from that in the open ocean. The presence of the continental slope, shallow waters and the coastlines give rise to a variety of processes that mediate the response of coastal sea level to open-ocean changes and produce distinct spatiotemporal sea level patterns. Yet how exactly this interplay occurs and, more importantly, the extent to what coastal sea level variations differ from open-ocean variability remain poorly understood. In this work, we use coastal altimetry observations in combination with tide gauge data to determine patterns of coherent coastal sea level variations and the degree of decoupling between such variations and open-ocean changes.In a first step, we apply Bayesian mixture models to identify clusters of correlated tide gauge observations that explain a significant fraction of the coastal sea level variability. Using altimetry data, we find high regional coherency of along-shore coastal sea level variations, indicating common underlying mechanisms that cause these correlations.In light of previous research, we confirm that the correlation structures of these coherent patterns are often confined to the continental slopes, particularly in extratropical regions. In regions like the northeastern US continental shelf, correlations decrease with increasing water depth, indicating a decoupling of shelf sea and open-ocean variability. We investigate how these differences between coastal and open ocean sea level variations change as a function of time scale, i.e., from monthly or interannual variations to long-term trends, and validate these results against tide gauge observations. We derive across-shore correlation length scales that provide insights into the space scales of coastal sea level dynamics and are useful to understand how well gridded products can resolve such processes.We discuss possible causes of the coherent sea level fluctuations, such as wind forcing, coastally trapped waves, and large scale climate modes. The results motivate further research to better understand the driving mechanisms behind these coherent sea level variations, as well as the pathways linking remote forcing to coastal changes.

For 25 years, geodesists have inferred that the displacement of the "geocenter" estimated from (SLR) satellite laser ranging represents fluctuation of Earth's fluid envelope relative to solid Earth. However, SLR determines the displacement of the (CN) center of network of geodetic sites relative to the (CM) center of mass of Earth, consisting of solid Earth, the oceans, the atmosphere, and continental water, snow, and ice. Because solid Earth's surface is deforming in elastic response to the changing load of continental water, atmosphere and oceans, CN only roughly approximates the (CE) center of mass of solid Earth. In this study, estimate the velocity of CM relative to the (CE) center of mass of Earth by first correcting SLR site displacements (estimated by the International Laser Ranging Service 2020) for their elastic response relative to CE produced by fluctuations of continental water, atmosphere and oceans. We maintain that by correcting for loading displacements relative to CE, we arrive at an estimate of the displacement of CE. We find that transforming the SLR series from CN to CE reduces the discrepancy between the seasonal oscillation of Earth's fluid envelope estimated by SLR and that assumed by GRACE (using the technique of Sun et al. 2017) by 40 per cent. In both SLR and GRACE, a total of 0.5 x 1016 kg of mass moves between hemispheres from southern oceans in August to snow-covered areas in North America and Europe (in particular in Canada and Siberia). The primary remaining difference between the two techniques is that mass in the northern hemisphere is maximum on February 5 in SLR, 20 days before it is maximum on Feb 25 in GRACE. Knowing the total transfer of the mass of between hemispheres places a boundary constraint on global models of circulation of water on land and in the oceans and atmospheres (that may be applied to forecasting extreme events such as flooding and drought).

The GRACE Follow-On mission, a partnership between NASA (US) and GFZ (Germany), will end its nominal mission lifetime in May 2023 of five years and continues the essential climate data record of mass change in the Earth system initiated in 2002 by the GRACE mission. The combined GRACE & GRACE-FO data records now span over 21 years and provide unique observations of monthly to decadal global mass changes and transport in the Earth system derived from temporal variations in the Earth’s gravity field. These observations have become indispensable for climate-related studies that enable process understanding of the evolving global water cycle, including ocean dynamics, polar ice mass changes, and near-surface and global ground water changes.In this presentation, we will discuss (1) some recent GRACE/GRACE-FO science and applications highlights, (2) key data processing and calibration approaches on GRACE-FO, with a particular focus on the accelerometer data, and also (3) the GRACE-FO mission plan to operate and collect high-quality science data through the intensifying solar cycle 25, aiming for continuity with future mass change missions.

We discuss the state of mass balance of glaciers and ice sheets from 2002 to present using data from GRACE/GRACE-FO missions, after filling the gap between missions. We compare data processed by different centers (JPL, CSR, GFZ) and evaluate various Glacial Isotatic Adjustment models. In Greenland, the data indicate a persistent mass loss at 251 Gt/yr, with an acceleration of 3 Gt/yr/yr, and large summer losses (400-600 Gt) in 2012, 2017, 2019. The mass balance regime has been evolving significantly in recent years, especially in the North, which holds the largest potential for rapid sea level rise. In Antarctica, ongoing mass losses in the Amundsen Sea Embayment of West Antarctica (122 Gt/yr), Antarctic Peninsula (26 Gt/yr), and Wilkes Land in East Antarctica (33 Gt/yr) dominate a small but significant increase in snowfall in the Queen Maud Land sector of East Antarctica since 2009 (47 Gt/yr). For the GIC, the mass loss averages 274 Gt/yr, with an acceleration of 4 Gt/yr/yr. The largest contributors are in the Arctic: Canadian Archipelago (70 Gt/yr), Alaska (72 Gt/yr), Russian Arctic (21 Gt/yr), Svalbard and Iceland (29 Gt/yr)) versus the southern hemisphere which is dominated by Patagonia (35 Gt/yr). High Mountain Asia averages 22 Gt/yr mass loss, with a large inter-annual variability. In regions not dominated by ice dynamics, the GRACE results compare better every year with output products from regional climate models (MAR, RACMO) forced by ERA5 and with global models such as NASA’s MERRA-2, which offers interesting perspectives for model development.

The Gravity Recovery and Climate Experiment Follow-On (GRACE-FO), launched in May 2018, provides invaluable information about mass change in the Earth system, continuing the legacy of GRACE. Fundamental requirements for successful mass change recovery are precise orbit determination and inter-satellite ranging, determination of the relative clock alignment of the ultra-stable oscillators (USOs), precise attitude determination, and accelerometry. NASA/Caltech Jet Propulsion Laboratory is the official Level-1 data processing and analysis center, and is currently processing using software version 04. Here we present analysis of the aforementioned GRACE-FO sensor data, as well an overview of the current GRACE reprocessing, and a discussion of measurement system performance. The research presented in this abstract has been carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.

This talk will provide a status update on Level-2 data processing at JPL. Included will be an overview of data products currently delivered to the community, including gravity fields with the updated accelerometer transplant product. Assessments of data quality and error levels, along with detailed discussions of the solution strategy will be included. A comparison of LRI and KBR gravity fields will be provided.. A look towards an RL07 reprocessing of GRACE and GRACE-FO will additionally be provided. The reprocessing plans to incorporate the estimation of a full noise covariance model of the satellite observations in the gravity estimates. Preliminary results incorporating this strategy will be shown and discussed.

Quantification of uncertainty in surface mass change signals derived from GNSS measurements poses challenges, especially when dealing with large datasets with continental or global coverage. Our aim is to assign weights to GNSS estimates of vertical land displacement (VLD), which will be used in a future joint solution with GRACE observations. Thus, we study the structure and quantify the uncertainty present in VLD estimates derived from 3045 GNSS stations distributed across the continental US. Monthly means of daily positions are available for 15 years. First, we remove outliers by performing a 3σ test, and data screening via a number of correlation metrics between the input GNSS VLD estimates and external validation datasets (i.e., VLD predicted from GRACE/GRACE-FO and hydrology models). Afterwards, we employ various processing schemes to characterize the uncertainty of VLD through stochastic modeling and quantification of the spatially correlated errors. In particular, we test for white, colored and spatially correlated noise. When only white noise is considered nearly 30% of the stations exhibit noise level < 2 mm, 65% noise between 2-4 mm and 5% noise > 4 mm. In case of colored noise, error smaller than 2 mm, between 2-4 mm and >4mm, is mapped in 20%, 60% and 20% of the stations, respectively. Spatially correlated noise is in family but slightly smaller in magnitude compared to colored noise.

Knowledge of Earth system mass change is limited to spatial scales of approximately 300 km x 300 km, as this is near the native resolution of the data collected from the GRACE and GRACE-FO (G/GFO) satellite gravimetry missions. Measurements of surface height change at much finer spatial scales can additionally be exploited to derive mass change, albeit with the proper treatment of the data. Here, we jointly invert inter-satellite range-rate measurements from GRACE and GRACE-FO, surface height changes measured by in-situ GNSS receivers, and surface elevation changes from a multi-mission synthesis of radar and laser satellite altimeters to estimate mass change at spatial scales of 100 km x 100 km within a Bayesian framework over the time period 2002 - 2021. In this talk, we will focus on results over North America, where nearly 3,000 in-situ GNSS measurements are processed in combination with G/GFO, and over Antarctica, where Envisat, ICESat, CryoSat-2, and ICESat-2 measurements have been processed in combination with G/GFO. We find agreement between the geodetic data combination solution and the G/GFO-only solution at large basin scales, but unique knowledge of mass change is gained with the geodetic data combination solution at smaller spatial scales not observed by satellite gravimetry alone.

The Gravity Recovery and Climate Experiment Follow-On (GRACE-FO), launched in May 2018, provides invaluable information about mass change in the Earth system, continuing the legacy of GRACE. Fundamental requirements for successful mass change recovery are precise orbit determination and inter-satellite ranging, determination of the relative clock alignment of the ultra-stable oscillators (USOs), precise attitude determination, and accelerometry. NASA/Caltech Jet Propulsion Laboratory is the official Level-1 data processing and analysis center, and is currently processing software version 04. Here we present analysis of the aforementioned GRACE-FO sensor data, as well a preview of an upcoming GRACE reprocessing, and a discussion of measurement performance.

We will present a high-level overview of the GRACE Follow-On mission status, science and instrument performance, and future plans. GRACE-FO has now delivered over two years of monthly gravity field and mass changes map since Jun-2018. These unique observations extend the global mass change climate data record to almost two decades, and allow detailed quantification and process studies of ice melt, sea level changes, and land water storage variations.

The GRACE missions have changed the way that we measure mass changes of ice sheets, glaciers and ice caps, with quantied uncertainties that factor processing errors, atmospheric and oceanic corrections, and removal of glacial isostatic adjustment (GIA). We present GRACE/GRACE-FO estimates of mass balance over the Greenland and Antarctic Ice Sheets and the World’s glaciers and ice caps (GIC). The data gap between missions is filled with GRACE-calibrated results from the input-output method for ice sheets and surface mass balance (SMB) reconstructions from regional atmospheric climate models and MERRA-2 reanalysis data for the glaciers and ice caps. Over Greenland, we report low losses during the cold years of 2017-2018 followed by record melt in 2019 and an onset of rapid melt for 2020. As warm air and ocean masses get blocked over Greenland more frequently because of interactions between the wobbling jet stream and topography, we observe more high melt events in this decade than recorded in prior centuries. In Antarctica, the ongoing rapid loss in West Antarctica dominates the mass balance, but we observe a steady increase in snowfall in the Atlantic sector of East Antarctica. The exercise provides a mass balance record that can be continuously improved with better corrections and improved processing, with reduced errors, so that we can provide better constraints for ice sheet models in charge of sea level projections and improve the validation of various Earth system models and global climate models.

On May 22, 2020, the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO), will celebrate two years of successful in-orbit operation. The primary goal of this satellite mission is to provide information about time variations of the Earth’s gravity field. This is possible due to precise orbit determination and inter-satellite ranging by determining the relative clock alignment of the USOs, precise attitude determination and accelerometry. High quality satellite observations are one of the fundamental requirements for successful gravity field recovery. NASA/Caltech Jet Propulsion Laboratory is the official Level-1 data processing and analysis center. The GRACE-FO Level-1 data are currently being processed with software version V04. This software will be used also for final reprocessing of the GRACE (2002-2017) Level-1 data. Here we present the analysis of two years of GRACE-FO sensor data as well as a preview of the reprocessed GRACE data, and discuss the measurement performance.

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) &nbsp