Scholarlink

<p>The winter North Atlantic Oscillation (NAO) is a dominant mode of climate variability affecting temperature and precipitation across the Northern Hemisphere, yet its prediction at seasonal-to-decadal&#xa0;(S2D) lead times remains challenging. Here, using multi-year hindcasts from a multi-model ensemble initialized on 1 November for 1962–2019, we show that NAO skill one year ahead improves significantly when the El Niño–Southern Oscillation (ENSO) undergoes a phase transition next year. This improvement is linked to the northward propagation of anomalous atmospheric angular momentum, which dynamically organizes the NAO and is captured in reanalysis and models. During ENSO transition years, prediction skill increases with ensemble size, and when more than 10 members are used, the forecasts display the signal-to-noise paradox. These findings highlight the potential for enhanced one-year NAO predictability when ENSO transitions are present and large ensemble sizes are used in S2D prediction systems, given the skillful prediction of ENSO phase transitions at one-year lead times by multi-model ensembles.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

ENSO phase transition enables prediction of winter North Atlantic Oscillation one year ahead

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月25日

查看全文
  • Kiwook Kim,
  • Myong-In Lee,
  • Adam A. Scaife,
  • Doug M. Smith

摘要

The winter North Atlantic Oscillation (NAO) is a dominant mode of climate variability affecting temperature and precipitation across the Northern Hemisphere, yet its prediction at seasonal-to-decadal (S2D) lead times remains challenging. Here, using multi-year hindcasts from a multi-model ensemble initialized on 1 November for 1962–2019, we show that NAO skill one year ahead improves significantly when the El Niño–Southern Oscillation (ENSO) undergoes a phase transition next year. This improvement is linked to the northward propagation of anomalous atmospheric angular momentum, which dynamically organizes the NAO and is captured in reanalysis and models. During ENSO transition years, prediction skill increases with ensemble size, and when more than 10 members are used, the forecasts display the signal-to-noise paradox. These findings highlight the potential for enhanced one-year NAO predictability when ENSO transitions are present and large ensemble sizes are used in S2D prediction systems, given the skillful prediction of ENSO phase transitions at one-year lead times by multi-model ensembles.