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<p>Ocean bottom pressure (OBP) reflects ocean dynamics, thermodynamics, and Earth’s gravity field, playing a key role in physical oceanography and in reducing aliasing errors in satellite gravimetry. Due to limited observations, high-frequency global OBP studies rely on numerical models, which inherently contain uncertainties. This study employs the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics/Institute of Atmospheric Physics (LASG/IAP) Climate System Ocean Model version 3.0 (LICOM3.0) to simulate global OBP from 2002 to 2018, driven by two different atmospheric reanalysis datasets. Validation against <i>in situ</i> observations shows that LICOM3.0 effectively captures sub-seasonal OBP variability (1–30 d) at the available stations, which are located in the Pacific and along the Atlantic coast. Compared to another ocean model, LICOM3.0 reproduces similar OBP patterns for periods longer than 1 d and spatial scales greater than 500 km, demonstrating its capability for large-scale OBP analysis and assessing inter-model uncertainty. However, the model underestimates OBP amplitudes, and exhibits marked discrepancies at sub-daily periods, in marginal seas, and on spatial scales below 500 km with reference data. These issues are consistent across both experiments, indicating that model configuration contributes to the limitations. Potential sources of error are discussed to support future model improvements. Overall, LICOM3.0 can serve as an effective tool for oceanic scientific applications and for de-aliasing in satellite gravimetry applications.</p>

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Evaluation of the multi-scale variability of ocean bottom pressure in a global ocean general circulation model

  • 期刊论文

  • 发表日期：

    2026年2月1日

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  • Jiahui Bai,
  • Jingwei Xie,
  • Zipeng Yu,
  • Jiangfeng Yu,
  • Hailong Liu,
  • Fan Yang,
  • Pengfei Lin,
  • Tao Zhang,
  • Chunxiang Shi,
  • Yun Xiao

摘要

Ocean bottom pressure (OBP) reflects ocean dynamics, thermodynamics, and Earth’s gravity field, playing a key role in physical oceanography and in reducing aliasing errors in satellite gravimetry. Due to limited observations, high-frequency global OBP studies rely on numerical models, which inherently contain uncertainties. This study employs the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics/Institute of Atmospheric Physics (LASG/IAP) Climate System Ocean Model version 3.0 (LICOM3.0) to simulate global OBP from 2002 to 2018, driven by two different atmospheric reanalysis datasets. Validation against in situ observations shows that LICOM3.0 effectively captures sub-seasonal OBP variability (1–30 d) at the available stations, which are located in the Pacific and along the Atlantic coast. Compared to another ocean model, LICOM3.0 reproduces similar OBP patterns for periods longer than 1 d and spatial scales greater than 500 km, demonstrating its capability for large-scale OBP analysis and assessing inter-model uncertainty. However, the model underestimates OBP amplitudes, and exhibits marked discrepancies at sub-daily periods, in marginal seas, and on spatial scales below 500 km with reference data. These issues are consistent across both experiments, indicating that model configuration contributes to the limitations. Potential sources of error are discussed to support future model improvements. Overall, LICOM3.0 can serve as an effective tool for oceanic scientific applications and for de-aliasing in satellite gravimetry applications.