Thu Apr 10, F. Flechtner
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Yu, Hongjuan, Chen, Qiujie, Sun, Yu, and Sosnica, Krzysztof, 2021. Geophysical Signal Detection in the Earth's Oblateness Variation and Its Climate-Driven Source Analysis. Remote Sensing, 13(10):2004, doi:10.3390/rs13102004.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2021RemS...13.2004Y,
author = {{Yu}, Hongjuan and {Chen}, Qiujie and {Sun}, Yu and {Sosnica}, Krzysztof},
title = "{Geophysical Signal Detection in the Earth's Oblateness Variation and Its Climate-Driven Source Analysis}",
journal = {Remote Sensing},
keywords = {earth's oblateness, satellite laser ranging, singular spectrum analysis, geophysical model, Lomb-Scargle periodogram, grace, climate-driven source},
year = 2021,
month = may,
volume = {13},
number = {10},
eid = {2004},
pages = {2004},
abstract = "{This study analyzes the geophysical signals in J$_{2}$ 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 {\ensuremath{\pm}} 0.08) {\texttimes}
10$^{-11}$/yr and an obvious quadratic rate of (2.38
{\ensuremath{\pm}} 0.02) {\texttimes} 10$^{-13}$/yr$^{2}$.
Besides, the annual, semi-annual, and 10.6-year signals with
determining for the first time its amplitude of 5.01
{\texttimes} 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 {\ensuremath{\Delta}}J$_{2}$ 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
{\ensuremath{\Delta}}J$_{2}$ from SLR in terms of the amplitude
and phase. About 81.5\% of observed {\ensuremath{\Delta}}J$_{2}$
can be explained by the reconstructed value. ATM, TWS, and OBP
are the most significant contributing sources for seasonal
signals in {\ensuremath{\Delta}}J$_{2}$ time series, explaining
up to 40.1\%, 31.9\%, and 26.3\% of the variances of observed
{\ensuremath{\Delta}}J$_{2}$. These three components contribute
to the annual and semi-annual variations of the observed
{\ensuremath{\Delta}}J$_{2}$ up to 30.1\% and 1.6\%, 30.8\% and
1.0\%, as well as 25.4\% and 0.7\%, respectively. GRE, ANT, and
GLA have \raisebox{-0.5ex}\textasciitilde3 to
\raisebox{-0.5ex}\textasciitilde7-year periodic fluctuations and
a positive linear trend, excluding GIA.}",
doi = {10.3390/rs13102004},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021RemS...13.2004Y},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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