Air and Water Contributions to Polar Motion Excitation

Abstract

Modern space-geodetic observations show that the Earth’s polar motion occurs over a broad range of frequencies from below the Chandler frequency at fractions of a cycle per year (cpy), up to tens of cycles per year. Across this entire frequency band, the excitation sources for polar motion are only partially understood. Several studies have confirmed that meteorological effects, especially air mass redistribution, are correlated with observed polar motion within various frequency bands,(Wilson and Haubrich, 1976a, Wahr, 1982a, Eubanks et al, 1989), but the correlation has been imperfect, and the variance of observed air and water motion insufficient, suggesting that additional unknown sources must exist This paper reviews the studies of the contributions of air and water redistribution to polar motion excitation, with particular attention to estimates of the effects of continental water storage variation. Additional details are presented by Kuehne (1989). Because water can be stored on land in many forms, including ground water, ice, vegetation, and soil moisture, the estimates of water storage effects are difficult to obtain, but the general order of magnitude can be inferred from the widely available records of precipitation, in combination with other data and reasonable assumptions about climatological variation. The magnitude of the estimates suggests that continental water storage does not account for the unexplained portion of polar motion. A discrepancy in polar motion excitation persists over a broad band of frequencies including the annual frequency, where air and water are the certain cause. This implies that there are additional polar motion excitation sources in the atmosphere and oceans which remain unidentified.KeywordsWater StorageVery Long Baseline InterferometryPolar MotionAnnual FrequencyFourier Power SpectrumThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.