Earth Rotation Parameters Prediction and Climate Change Indicators in it

IF 0.7 Q4 ASTRONOMY & ASTROPHYSICS Artificial Satellites-Journal of Planetary Geodesy Pub Date : 2022-12-01 DOI:10.2478/arsa-2022-0023
Xueqing Xu, Yonghong Zhou, Cancan Xu
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引用次数: 1

Abstract

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.
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地球自转参数预测及气候变化指标
作为第二次地球方向参数预测比较运动(2nd EOP PCC)的参与者之一,我们提交了两份数据文件。一个是对未来365天地球方向参数(EOP)(位置参数Px和Py,时间参数UT1-UTC和日长变化ΔLOD)的预测,由传统的最小二乘和自回归(LS + AR)模型处理。另一种是利用波茨坦地球系统建模中心(ESMGFZ)的有效角动量(EAM),利用最小二乘和卷积组合方法(LS + convolution)进行90天的预测。结果表明,LS + Convolution方法在10天内的短期EOP预测中优于LS + AR模型,而传统LS + AR模型在10 - 90天的中期EOP预测中具有更高的准确性。此外,基于地球自转(主要是LOD年际变化)的气候变化信息,利用ΔLOD观测和长期预测对气候变化指标进行了研究。在与气候相关的ΔLOD观测中发现了2020-2022年期间的两次中期拉尼娜事件之后,在与气候相关的ΔLOD长期预测中显示了另一次更强的拉尼娜现象。
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自引率
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