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Earth Rotation Parameters Prediction and Climate Change Indicators in it Cover

Earth Rotation Parameters Prediction and Climate Change Indicators in it

By: Xueqing Xu,  Yonghong Zhou and  Cancan XU  
Open Access
|Jan 2023

Abstract

As one of the participants in the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC), we submitted two data files. One is 365 days’ predictions into the future for Earth orientation parameters (EOP) (the position parameters Px and Py, the time parameters UT1-UTC and length of day changes ΔLOD), processed by the traditional least-square and autoregressive (LS + AR) model. Another is 90 days’ predictions by the combined least-square and convolution method (LS + Convolution), with effective angular momentum (EAM) from Earth System Modelling GeoForschungsZentrum in Potsdam (ESMGFZ). Results showed that the LS + Convolution method performed better than the LS + AR model in short-term EOP predictions within 10 days, while the traditional LS + AR model presented higher accuracy in medium-term predictions over 10–90 days. Furthermore, based on the climate change information in Earth’s rotation (mainly in the interannual variations of LOD), the climate change indicators are investigated with ΔLOD observations and long-term predictions. After two intermediate La Nina events were detected in the climate-related ΔLOD observations during the period of 2020–2022, another stronger La Nina phenomenon is indicated in the climate-related ΔLOD long-term predictions.

DOI: https://doi.org/10.2478/arsa-2022-0023 | Journal eISSN: 2083-6104 | Journal ISSN: 1509-3859
Language: English
Page range: 262 - 273
Submitted on: Jun 28, 2022
Accepted on: Aug 16, 2022
Published on: Jan 5, 2023
Published by: Polish Academy of Sciences, Space Research Centre
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Xueqing Xu, Yonghong Zhou, Cancan XU, published by Polish Academy of Sciences, Space Research Centre
This work is licensed under the Creative Commons Attribution 4.0 License.