Regional atmospheric influence on the Chandler wobble

Continental and oceanic AAM contributions to Chandler Wobble with the amplitude attenuation from 2012 to 2022

“…1, we conclude that the oceanic AAM-mass excitation (light blue lines in panels (a)&(b)) is one order of magnitude smaller than the continental AAM-mass excitation (orange lines in panels (a)&(b)). Prior studies have also demonstrated a comparable outcome (Nastula et al 2009;Zotov and Bizouard 2015), which can be attributed in part to the equilibrium between atmospheric mass transfer over the ocean and evaporation precipitation cycles. Moreover, the magnitudes of the land and ocean AAM-motion excitation are about 2-3 times larger than those of the continental AAM-mass excitation.…”

“…Numerous studies have been conducted to examine the impact of regional AAM on the excitations of polar motion (Nastula and Salstein 1999;Nastula et al 2009Nastula et al , 2014, wherein notable investigations have revealed a general resemblance between the regional contributions and the global excitations of polar motion. Zotov and Bizouard (2015) investigated the impact of regional atmospheric factors on the CW. They found a pattern of the CW that exhibited an inverse relationship with the AAM, with this pattern emerging over a period of approximately 20 years in both the Northern and Southern hemispheres.…”

“…They found a pattern of the CW that exhibited an inverse relationship with the AAM, with this pattern emerging over a period of approximately 20 years in both the Northern and Southern hemispheres. In contrast to the approach taken by Nastula et al (2009) as well as Zotov and Bizouard (2015), who estimated the contributions of highresolution regional sectors to the AAM, our study focuses solely on differentiating the AAM between continental and oceanic regions (in this paper, the continental and oceanic AAM refer to the AAM over the land and ocean, respectively). In addition, the AAM is further divided into mass and motion terms.…”

Variable Chandler and Annual Wobbles in Earth’s Polar Motion During 1900–2015

“…Although CW has been under investigation for more than a century, its excitation mechanism has remained elusive (Lenhardt and Groten 1985;King and Agnew 1991;Gross 2000;Höpfner 2004;Malkin and Miller 2010). Up to now, various hypotheses on CW excitation have been proposed, such as atmospheric and oceanic processes (Brzeziński et al , 2012Bizouard et al 2011;Brzeziński and Nastula 2002;Salstein 2000;Gross et al 2003;Zotov and Bizouard 2015), wind and surface pressure variations (Wahr 1982;Gross et al 2003), groundwater impulses, changes in snow cover, interaction at the boundary between the core and mantle, and earthquakes (Dahlen 1971(Dahlen , 1973Höpfner 2004;Smylie et al 2015).…”

“…Zero-phase digital filters were applied to filter out the CW and AW from several homogeneous PM time series, distinguished by a variety of sampling intervals the polar motion time series, such as POLE2001 (JPL), EOP (IERS) C01, OA00 (AICAS), EOP (IERS) C04, and SPACE2001 (JPL) (Höpfner 2004). Furthermore, in resent years, Malkin and Miller (2010) applied the singular spectrum analysis (SSA) and Fourier transform to extract the CW and then examined the CW amplitude and phase variations by means of the wavelet transform and Hilbert transform, their analysis had shown that besides the well-known CW phase jump in the 1920s, two other large phase jumps have been found in the 1850s and 2000s, and as in the 1920s, these phase jumps occurred with a sharp decrease in the CW in the 2000s; Zotov and Bizouard (2015) utilized the time domain excitation in Chandler frequency band which was extracted by Panteleev's filtering method to investigate the evolution of the regional atmospheric influence on CW; Smylie et al (2015) applied the maximum entropy method (MEM) to remove the AW, and leaving a pure CW and secular polar shift; hence, it overcomes the usual problem of how to avoid mixing the AW and CW which is beneficial to reveal details of the effect of earthquakes on the polar motion. However, because of the edge effect present in all of the above methods, a few of the estimates at the beginning and end of the time series may be less accurate, and the closer to the edge point, the less accurate the estimates would appear to be.…”