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<p>Sea-level change, one of the essential climate variables (ECVs), primarily driven by ice sheets, mountain and peripheral glacier ablations, ocean steric changes,&#xa0;and retention of water on Earth, is a critical index of climate change. It is important to quantify this ECV accurately to enhance resilience plans and formulate policies towards improved&#xa0;sea-level forecasting. Here, we assess regional sea-level change using data from tide gauge (TG)&#xa0;records, satellite radar altimetry (SA) virtual stations, and collocated GNSS with temporally correlated noise in the west (A1) and east (A2) coastal oceans&#xa0;of North America. We model the noise characteristics using the&#xa0;information criterion for sea-level&#xa0;time series to&#xa0;improve uncertainties that are crucial in the ECV definition. Next, we apply a temporal correlated noise reduction method,&#xa0;the Principal Component Analysis (PCA), to improve signal-to-noise ratio of&#xa0;the sea-level time series. Results indicate that all of the TG and SA sites exhibit reduced uncertainties within both areas after PCA spatial filtering, preserving the inherent trends while improving rate uncertainty accuracy. The rate uncertaintes of TG time series&#xa0;are reduced by 30.6%&#xa0;in A1&#xa0;and 62.31% in A2,&#xa0;while those of SA time series are reduced by 29.2%&#xa0;and 6.58%, respectively. Finally, our findings confirm that absolute sea level rate derived from vertical land motion corrected TG and SA products are generally comparable. Station-specific rates derived from SA and collocated VLM-corrected TG records range from 0.5–4.0 mm/yr. Across stations, the mean difference in estimated rates between the two types of measurements is −0.08 ± 1.14 mm/yr (1 σ), indicating no statistically significant bias.</p>

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A Noise Reduction Approach for Improve North American Regional Sea Level Change from Satellite and In Situ Observations

  • 期刊论文

  • 发表日期：

    2025年7月28日

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  • Xiaoxing He,
  • Jiahui Huang,
  • Jean-Philippe Montillet,
  • Shengdao Wang,
  • Gaël Kermarrec,
  • C. K. Shum,
  • Shunqiang Hu,
  • Fengwei Wang

摘要

Sea-level change, one of the essential climate variables (ECVs), primarily driven by ice sheets, mountain and peripheral glacier ablations, ocean steric changes, and retention of water on Earth, is a critical index of climate change. It is important to quantify this ECV accurately to enhance resilience plans and formulate policies towards improved sea-level forecasting. Here, we assess regional sea-level change using data from tide gauge (TG) records, satellite radar altimetry (SA) virtual stations, and collocated GNSS with temporally correlated noise in the west (A1) and east (A2) coastal oceans of North America. We model the noise characteristics using the information criterion for sea-level time series to improve uncertainties that are crucial in the ECV definition. Next, we apply a temporal correlated noise reduction method, the Principal Component Analysis (PCA), to improve signal-to-noise ratio of the sea-level time series. Results indicate that all of the TG and SA sites exhibit reduced uncertainties within both areas after PCA spatial filtering, preserving the inherent trends while improving rate uncertainty accuracy. The rate uncertaintes of TG time series are reduced by 30.6% in A1 and 62.31% in A2, while those of SA time series are reduced by 29.2% and 6.58%, respectively. Finally, our findings confirm that absolute sea level rate derived from vertical land motion corrected TG and SA products are generally comparable. Station-specific rates derived from SA and collocated VLM-corrected TG records range from 0.5–4.0 mm/yr. Across stations, the mean difference in estimated rates between the two types of measurements is −0.08 ± 1.14 mm/yr (1 σ), indicating no statistically significant bias.