ALBERT

Description: Geobiology explores how Earth's system has changed over the course of geologic history and how living organisms on this planet are impacted by or are indeed causing these changes. For decades, geologists, paleontologists, and geochemists have generated data to investigate these topics. Foundational efforts in sedimentary geochemistry utilized spreadsheets for data storage and analysis, suitable for several thousand samples, but not practical or scalable for larger, more complex datasets. As results have accumulated, researchers have increasingly gravitated toward larger compilations and statistical tools. New data frameworks have become necessary to handle larger sample sets and encourage more sophisticated or even standardized statistical analyses. In this paper, we describe the Sedimentary Geochemistry and Paleoenvironments Project (SGP; Figure 1), which is an open, community-oriented, database-driven research consortium. The goals of SGP are to (1) create a relational database tailored to the needs of the deep-time (millions to billions of years) sedimentary geochemical research community, including assembling and curating published and associated unpublished data; (2) create a website where data can be retrieved in a flexible way; and (3) build a collaborative consortium where researchers are incentivized to contribute data by giving them priority access and the opportunity to work on exciting questions in group papers. Finally, and more idealistically, the goal was to establish a culture of modern data management and data analysis in sedimentary geochemistry. Relative to many other fields, the main emphasis in our field has been on instrument measurement of sedimentary geochemical data rather than data analysis (compared with fields like ecology, for instance, where the post-experiment ANOVA (analysis of variance) is customary). Thus, the longer-term goal was to build a collaborative environment where geobiologists and geologists can work and learn together to assess changes in geochemical signatures through Earth history. With respect to the data product, SGP is focused on assembling a well-vetted and comprehensive dataset that is tractable to multivariate statistical analyses accounting for multiple geological and methodological biases. Phase 1 of the project, which focused on the Neoproterozoic and Paleozoic, has been completed. Future phases will capture a broader range of geologic time, data types, and geography. The database contains tens of thousands of unpublished data points provided by consortium members, as well as detailed metadata that go beyond what is contained in papers. In many cases, these represent measurements that are tangential to a given published study but still of high utility to database studies; these allow the community to address questions that would be impossible to answer solely with the published data. For instance, in order to use a proxy such as Mo/TOC (total organic carbon) ratios in mudrocks deposited under a euxinic water column, the full suite of trace metal, iron speciation, and total organic carbon data is needed. Likewise, geospatial information is required to account for sampling biases, and many statistical learning approaches cannot accept, or have difficulty with, incomplete geological predictor variables. Ultimately, it is this complete data matrix that will allow for SGP’s most insightful analyses.

Description: Ionosphere delay is a key factor in the single-frequency Precise Point Positioning (SFPPP). In tradition, two SFPPP models are applied, i.e., ionosphere-corrected (IC) and ionosphere-free-half (IFH) models. The ionospheric delays are directly corrected in IC model with external ionospheric products, while they are eliminated by forming the ionosphere-free combination with code and phase in IFH model. However, almost all studies focus on the numerical performance of these two models and lack the comprehensive study on the estimability and solvability of SFPPP model with either code division multiple access (CDMA) or frequency division multiple access (FDMA) system, respectively. In this paper, we dedicate to the analytical study on SFPPP models for both CDMA and FDMA systems. To assimilate the impact of ionospheric delays on positioning, a general SFPPP model, i.e., ionosphere-weighted (IW) model, is first formulated to identify the varying situations with the different uncertainties of ionospheric constraints. Then, we mathematically show how the IC, IFH and ionosphere-float (IF) models are reduced from IW model. The numerical comparison with GPS and GLONASS data with geodetic and cost-effective receivers effectively confirms our theoretical inference on the relationship of IC, IF and IW models and indicates the best results of IW model for all situations.

Description: The GRACE and GRACE-FO gravity satellites have become an important tool for monitoring large-scale groundwater storage (GWS) changes. However, the limited spatial resolution of the monthly gravity field models obtained from GRACE/GRACE-FO and the need for separating GWS from other relevant storage changes leads to large uncertainties in monitoring GWS signals at small scales. To improve the spatial resolution and address the signal leakage, we propose a joint inversion downscaling method based on the independent component analysis method to separate the GWS changes in the North China Plain (NCP). This method combines the high spatial resolution information (0.5°) of different compartment storages simulated within WGHM and the large-scale spatio-temporal mass changes information from GRACE/GRACE-FO within a least square adjustment, and derives downscaled and isolated GWS signals. Significant interannual GWS changes are found in the results of GRACE/GRACE-FO with a long-term trend of -4.03±0.14 km〈sup〉3〈/sup〉/year from 2005 to 2022, which are confirmed by in-situ groundwater level observations. Our analysis reveals that the GWS in the NCP experienced four stages: depletion from 2005 to 2009, stability from 2010 to 2013, depletion again from 2014 to 2019, and then rapid recovery from 2020 to 2022. The recent GWS recovery occurs in both shallow and deep aquifers, due to more precipitation since 2020 in the NCP. Our results demonstrate that the method can effectively improve the spatial resolution of GRACE/GRACE-FO (increased to 50 km), enhance the ability to monitor the small-scale GWS changes, and provide important references for water resources management and decision making.

Description: To determine the terrestrial water change over the Yangtze river basin, an adaptive regularized approach is used to solve the regional mascons constructed with the GRACE level-2 product. By using the adaptive regularized approach the low-frequency terms are not regularized, the mid-frequency terms are regularized by the Tikhonov method and the high-frequency terms are regularized by the TSVD method. The sets of low-, mid-, and high-frequencies are determined with the analytical conditions that are derived based on the criteria that the introduced biases should be smaller than the reduced errors, in other words, the mean squared error (MSE) should be reduced. The observation equation with 3915 mascon parameters at 0.5° resolution is constructed using the Tongji-Grace 2018 monthly gravity field models from April 2002 to December 2016. The results show that the adaptive regularized approach outperforms the ordinary Tikhonov and TSVD regularizations, with mean MSE reductions of mascon parameters of 13.40% and 11.69%, respectively. Furthermore, the spatial resolution of the derived secular trend is significantly improved. The improved singular spectrum analysis and Lomb-Scargle power spectrum analysis are used to investigate the seasonal terrestrial water variation from the derived time series. The results show that the adaptive approach can capture more seasonal signals than ordinary approaches.

Description: 〉The improvement of GRACE-FO monthly gravity field models requires, on the one hand, updated observation data and background force models, and on the other hand refined data processing strategies. In this contribution, we investigate the impact of different accelerometer and orbit data processing strategies on the quality of gravity field models recovered from GRACE-FO L1B data using the classical dynamic approach. For accelerometer modeling, various calibration parameterizations and different transplant products, i.e., those produced by JPL (ACT and ACH) and TU Graz, are compared. Our results indicate that heavy parameterizations, e.g., daily scale parameters, tend to have unfavorable effects on gravity field solutions, especially for resonant spherical harmonics, which can be attributed to the fact that the GRACE-D accelerometer uses transplanted instead of real data. For orbit modeling, the issues of spectral sensitivities, sampling rates, optimal weights, and stochastic modeling are systematically discussed in terms of both numerical simulations and real data cases, which tries to address the long-standing concern, that is, how to optimally use orbit data in GRACE/GRACE-FO gravity field recovery.

Description: The northern South China Sea (SCS) is one of vulnerable areas under climate change scenarios and its sea levels need to be precisely monitored. In this paper, multi-mission altimeter data over 26 years (1996-2022) are first reprocessed to construct monthly sea level anomaly (SLA) time series using dedicated coastal retracking techniques. The reprocessed SLA, together with ESA CCI and CMEMS gridded data, are then validated against tide gauge records. The results demonstrate that both along-track and gridded altimeter data are of good quality with high data availability. A multiple variable linear regression (MVLR) model including the decadal variability associated with the climate modes (i.e., ENSO, PDO and NPGO) is used to estimate sea level trends and uncertainties. The results show that trends from CMEMS are typically 1-3 mm yr〈sup〉-1〈/sup〉 greater than those from reprocessed and ESA CCI datasets. Over the period 1996-2022, the mean trends over the entire study area are 3.94±0.8 mm yr〈sup〉-1〈/sup〉 from CMEMS grid points and 2.00±1.3 mm yr〈sup〉-1〈/sup〉 from reprocessed data at crossovers. The mean trends at the coast over the tide gauge period are 2.29±1.6 mm yr〈sup〉-1〈/sup〉 and 0.85±1.0 mm yr〈sup〉-1〈/sup〉 for tide gauges and reprocessed data nearby, respectively. It is also found that more than 23% (up to 62%) of nearshore trends (3-5 km to the coast) differ from offshore trends (15-20 km off the coast) by more than 2 mm yr〈sup〉-1〈/sup〉, suggesting that the impact of small-scale coastal processes on the sea level trends nearshore needs to be further investigated.

Description: To deeply understand the evolution of surface deformation in North China, the vertical displacement is determined by using the vertical time series of 20 GNSS (Global Navigation Satellite System) stations from January 2011 to November 2019. Then the hydrology-induced secular trend and seasonal driving sources of the GNSS vertical displacement are identified together with the time-variable gravity field models from Gravity Recovery and Climate Experiment (GRACE) and atmospheric loading models. To better determine the driving sources, independent component analysis (ICA) and singular spectrum analysis (SSA) are jointly employed to develop a SSA-ICA approach. The results show that significant seasonal variations exist in the GNSS displacement time series with annual amplitudes of 1.67-4.46 mm, and the long-term displacement trends induced by hydrologic mass changes highlight the severe land subsidence caused by extensive exploitation of groundwater, with the subsiding rates ranging from -0.28 to -0.03 mm/yr, especially in the east-central plain. Atmospheric loading (ATML) and hydrologic loading (HYDL) are the primary driving sources of seasonal GNSS displacements since the most significant independent components (IC1 and IC2) of GNSS are well consistent respectively with the displacements derived from external mass loading models, but with slight phase biases. After the phase biases are adjusted, the average weighted root-mean-squares reduction (WRMS) is increased by 12.14% than that before adjustment, significantly improving the consistencies between GNSS and mass loading measurements.