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Earth Rotation Parameters Prediction and Climate Change Indicators in it

Xueqing Xu

Xueqing Xu

  • Shanghai Astronomical Observatory, Chinese Academy of SciencesShanghai, China
  • Key Laboratory of Planetary Sciences, Chinese Academy of SciencesShanghai, China
  • School of Astronomy and Space Science, University of Chinese Academy of SciencesBeijing, China
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Xu, Xueqing
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Yonghong Zhou

Yonghong Zhou

  • Shanghai Astronomical Observatory, Chinese Academy of SciencesShanghai, China
  • Key Laboratory of Planetary Sciences, Chinese Academy of SciencesShanghai, China
  • School of Astronomy and Space Science, University of Chinese Academy of SciencesBeijing, China
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Zhou, Yonghong
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Cancan XU

Cancan XU

  • Shanghai Astronomical Observatory, Chinese Academy of SciencesShanghai, China
  • School of Astronomy and Space Science, University of Chinese Academy of SciencesBeijing, China
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XU, Cancan
  
05 gen 2023
Earth Rotation Parameters Prediction and Climate Change Indicators in it's Cover Image
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Pubblicato online: 05 gen 2023
Pagine: 262 - 273
Ricevuto: 28 giu 2022
Accettato: 16 ago 2022
DOI: https://doi.org/10.2478/arsa-2022-0023
Parole chiave
© 2022 Xueqing Xu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

EOP interannual, seasonal and sub-seasonal terms extracted from IERS C04 observations (black line) and convoluted from the EAM series (red line) during 1980-2022. (a) Px, (b) Py and (c) ΔLOD
EOP interannual, seasonal and sub-seasonal terms extracted from IERS C04 observations (black line) and convoluted from the EAM series (red line) during 1980-2022. (a) Px, (b) Py and (c) ΔLOD

Figure 2.

Monthly mean series of the climate-related ΔLOD and Nino 3.4 from January 1980 to April 2022. The blue square frames mark the three extreme El Nino events; the red square frame marks the latest two La Nina events during 2020–2022. (a) Climate-related ΔLOD and (b) Nino 3.4
Monthly mean series of the climate-related ΔLOD and Nino 3.4 from January 1980 to April 2022. The blue square frames mark the three extreme El Nino events; the red square frame marks the latest two La Nina events during 2020–2022. (a) Climate-related ΔLOD and (b) Nino 3.4

Figure 3.

The 1–90 days’ RMSE calculated from the LS + AR (blue dot line) and LS + Convolution (red dot line) model. (a) Px, (b) Py and (c) UT1-UTC
The 1–90 days’ RMSE calculated from the LS + AR (blue dot line) and LS + Convolution (red dot line) model. (a) Px, (b) Py and (c) UT1-UTC

Figure 4.

Climate change information extracted from ΔLOD observations and predictions: (a) 2020–2021 time span, (b) 2020–2022 time span and (c) 2020–2023 time span
Climate change information extracted from ΔLOD observations and predictions: (a) 2020–2021 time span, (b) 2020–2022 time span and (c) 2020–2023 time span

RMSE of different EOP forecast spans obtained by the LS + AR and LS + CV methods over 07_2021–03_2022

Forecast span (days) Px (mas) Py (mas) UT1-UTC (ms)
LS + AR LS + CV LS + AR LS + CV LS + AR LS + CV
1 0.70 0.058 0.87 0.84 0.20 0.18
2 1.14 1.05 1.05 1.00 0.35 0.32
3 1.61 1.54 1.32 1.27 0.50 0.47
4 2.12 2.11 1.50 1.45 0.64 0.60
5 2.54 2.54 1.67 1.64 0.76 0.71
6 2.84 2.83 1.83 1.81 0.86 0.82
7 3.02 3.02 1.88 1.86 0.91 0.89
8 3.22 3.22 1.93 1.92 0.97 0.95
9 3.44 3.43 2.01 2.01 1.03 1.01
10 3.75 3.74 2.14 2.13 1.10 1.07
20 4.87 5.15 3.01 3.06 2.83 2.90
30 5.44 5.80 3.98 4.10 4.18 4.40
40 5.35 5.85 5.23 5.53 5.98 6.46
50 4.47 5.51 6.47 7.08 8.03 8.72
60 4.32 4.82 7.33 8.12 9.98 10.85
70 5.27 5.44 8.17 9.08 11.63 12.47
80 6.00 6.22 9.09 10.04 13.20 14.18
90 6.10 6.13 9.52 10.32 15.13 16.04
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