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<p>Trends of essential climate variables are often estimated from climate data records to quantify changes in the Earth system. An understanding of the uncertainty in a trend is essential for accurately determining the significance of a trend and attributing its causes. Despite this importance, trend-uncertainty estimates rarely account for all known sources of uncertainty. Common approaches neglect measurement-system instability or neglect the impact of natural variability on trend uncertainty. Such neglect can result in over-confidence in trend estimates. This study addresses trend-uncertainty assessment, particularly the need to account for the combined effects of measurement instability and natural variability on the trend uncertainty. The study presents a novel, unified framework for trend estimation that combines available measurement uncertainty information with empirical modelling of natural climate variability to achieve a more accurate uncertainty estimate. The framework is demonstrated for a time series of global mean sea level observations, obtaining more realistic trend-uncertainty values. The framework is applicable to most other climate data records. Adopting this approach will enhance confidence in climate change analysis through more accurate trend-uncertainty assessment in climate studies.</p>

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A Unified Framework for Trend Uncertainty Assessment in Climate Data Records: Demonstration on Global Mean Sea Level

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月12日

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  • Kevin Gobron,
  • Roland Hohensinn,
  • Xavier Loizeau,
  • Claire E. Bulgin,
  • Christopher J. Merchant,
  • Emma R. Woolliams,
  • Maurice G. Cox,
  • Wouter Dorigo,
  • Thomas Howard,
  • Mary Langsdale,
  • Adam C. Povey,
  • Michaël Ablain,
  • Janusz Bogusz,
  • Alexander Gruber,
  • Anna Klos,
  • Jonathan Mittaz

摘要

Trends of essential climate variables are often estimated from climate data records to quantify changes in the Earth system. An understanding of the uncertainty in a trend is essential for accurately determining the significance of a trend and attributing its causes. Despite this importance, trend-uncertainty estimates rarely account for all known sources of uncertainty. Common approaches neglect measurement-system instability or neglect the impact of natural variability on trend uncertainty. Such neglect can result in over-confidence in trend estimates. This study addresses trend-uncertainty assessment, particularly the need to account for the combined effects of measurement instability and natural variability on the trend uncertainty. The study presents a novel, unified framework for trend estimation that combines available measurement uncertainty information with empirical modelling of natural climate variability to achieve a more accurate uncertainty estimate. The framework is demonstrated for a time series of global mean sea level observations, obtaining more realistic trend-uncertainty values. The framework is applicable to most other climate data records. Adopting this approach will enhance confidence in climate change analysis through more accurate trend-uncertainty assessment in climate studies.