Geophysical Signal Detection in the Earth’s Oblateness Variation and Its Climate-Driven Source Analysis
This study analyzes the geophysical signals in J2 time series from 1976 to 2020 by using singular spectrum analysis (SSA) and the Lomb-Scargle (L-S) periodogram for the first time. The results of SSA indicate that the secular trend is characterized by a superposition of the secular linear decrease w...
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ftmdpi:oai:mdpi.com:/2072-4292/13/10/2004/ 2023-08-20T04:02:27+02:00 Geophysical Signal Detection in the Earth’s Oblateness Variation and Its Climate-Driven Source Analysis Hongjuan Yu Qiujie Chen Yu Sun Krzysztof Sosnica agris 2021-05-20 application/pdf https://doi.org/10.3390/rs13102004 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs13102004 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 10; Pages: 2004 earth’s oblateness satellite laser ranging singular spectrum analysis geophysical model Lomb-Scargle periodogram grace climate-driven source Text 2021 ftmdpi https://doi.org/10.3390/rs13102004 2023-08-01T01:45:38Z This study analyzes the geophysical signals in J2 time series from 1976 to 2020 by using singular spectrum analysis (SSA) and the Lomb-Scargle (L-S) periodogram for the first time. The results of SSA indicate that the secular trend is characterized by a superposition of the secular linear decrease with a rate of approximately (−5.80 ± 0.08) × 10−11/yr and an obvious quadratic rate of (2.38 ± 0.02) × 10−13/yr2. Besides, the annual, semi-annual, and 10.6-year signals with determining for the first time its amplitude of 5.01 × 10−11, are also detected by SSA, where their stochastic behavior can be maintained to the greatest extent. The 18.6-year signal cannot be detected by SSA even when the window size of 18.6 years was selected, while L-S periodogram can detect the signal of 18.6 years after removing the 18.6-year tidal theoretical value and the linear trend, proving the existence of the tidal variations of 18.6 years in the residual time series. Nevertheless, the 10.6-year signal can be found only after removing the secular trend. This fact suggests that the advantages of different methods used will lead to different sensitivity to the particular signals hard to be detected. Finally, the reconstructed ΔJ2 time series through the sum of the climate-driven contributions from glacial isostatic adjustment (GIA), Antarctic ice sheets (ANT), atmosphere (ATM), continental glaciers (GLA), Greenland ice sheets (GRE), ocean bottom pressure (OBP), and terrestrial water storage (TWS) by using GRACE gravity field solution and geophysical models agrees very well with that of the observed ΔJ2 from SLR in terms of the amplitude and phase. About 81.5% of observed ΔJ2 can be explained by the reconstructed value. ATM, TWS, and OBP are the most significant contributing sources for seasonal signals in ΔJ2 time series, explaining up to 40.1%, 31.9%, and 26.3% of the variances of observed ΔJ2. These three components contribute to the annual and semi-annual variations of the observed ΔJ2 up to 30.1% and 1.6%, 30.8% and 1.0%, as well as ... Text Antarc* Antarctic Greenland MDPI Open Access Publishing Antarctic Greenland Remote Sensing 13 10 2004 |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
earth’s oblateness satellite laser ranging singular spectrum analysis geophysical model Lomb-Scargle periodogram grace climate-driven source |
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earth’s oblateness satellite laser ranging singular spectrum analysis geophysical model Lomb-Scargle periodogram grace climate-driven source Hongjuan Yu Qiujie Chen Yu Sun Krzysztof Sosnica Geophysical Signal Detection in the Earth’s Oblateness Variation and Its Climate-Driven Source Analysis |
topic_facet |
earth’s oblateness satellite laser ranging singular spectrum analysis geophysical model Lomb-Scargle periodogram grace climate-driven source |
description |
This study analyzes the geophysical signals in J2 time series from 1976 to 2020 by using singular spectrum analysis (SSA) and the Lomb-Scargle (L-S) periodogram for the first time. The results of SSA indicate that the secular trend is characterized by a superposition of the secular linear decrease with a rate of approximately (−5.80 ± 0.08) × 10−11/yr and an obvious quadratic rate of (2.38 ± 0.02) × 10−13/yr2. Besides, the annual, semi-annual, and 10.6-year signals with determining for the first time its amplitude of 5.01 × 10−11, are also detected by SSA, where their stochastic behavior can be maintained to the greatest extent. The 18.6-year signal cannot be detected by SSA even when the window size of 18.6 years was selected, while L-S periodogram can detect the signal of 18.6 years after removing the 18.6-year tidal theoretical value and the linear trend, proving the existence of the tidal variations of 18.6 years in the residual time series. Nevertheless, the 10.6-year signal can be found only after removing the secular trend. This fact suggests that the advantages of different methods used will lead to different sensitivity to the particular signals hard to be detected. Finally, the reconstructed ΔJ2 time series through the sum of the climate-driven contributions from glacial isostatic adjustment (GIA), Antarctic ice sheets (ANT), atmosphere (ATM), continental glaciers (GLA), Greenland ice sheets (GRE), ocean bottom pressure (OBP), and terrestrial water storage (TWS) by using GRACE gravity field solution and geophysical models agrees very well with that of the observed ΔJ2 from SLR in terms of the amplitude and phase. About 81.5% of observed ΔJ2 can be explained by the reconstructed value. ATM, TWS, and OBP are the most significant contributing sources for seasonal signals in ΔJ2 time series, explaining up to 40.1%, 31.9%, and 26.3% of the variances of observed ΔJ2. These three components contribute to the annual and semi-annual variations of the observed ΔJ2 up to 30.1% and 1.6%, 30.8% and 1.0%, as well as ... |
format |
Text |
author |
Hongjuan Yu Qiujie Chen Yu Sun Krzysztof Sosnica |
author_facet |
Hongjuan Yu Qiujie Chen Yu Sun Krzysztof Sosnica |
author_sort |
Hongjuan Yu |
title |
Geophysical Signal Detection in the Earth’s Oblateness Variation and Its Climate-Driven Source Analysis |
title_short |
Geophysical Signal Detection in the Earth’s Oblateness Variation and Its Climate-Driven Source Analysis |
title_full |
Geophysical Signal Detection in the Earth’s Oblateness Variation and Its Climate-Driven Source Analysis |
title_fullStr |
Geophysical Signal Detection in the Earth’s Oblateness Variation and Its Climate-Driven Source Analysis |
title_full_unstemmed |
Geophysical Signal Detection in the Earth’s Oblateness Variation and Its Climate-Driven Source Analysis |
title_sort |
geophysical signal detection in the earth’s oblateness variation and its climate-driven source analysis |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13102004 |
op_coverage |
agris |
geographic |
Antarctic Greenland |
geographic_facet |
Antarctic Greenland |
genre |
Antarc* Antarctic Greenland |
genre_facet |
Antarc* Antarctic Greenland |
op_source |
Remote Sensing; Volume 13; Issue 10; Pages: 2004 |
op_relation |
Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs13102004 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13102004 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
10 |
container_start_page |
2004 |
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1774712905694969856 |