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<p>The impacts of El Niño-Southern Oscillation (ENSO) on the North Atlantic and European sector (NAE) climate are season-dependent and, in some cases, not linear and/or not stationary. Previous studies have found multidecadal variability in ENSO teleconnections to NAE in certain seasons, relating it to changes in the background state. However, the stationarity of the teleconnection and its surface impacts in Europe during early winter remain largely unexplored, a gap intended to be addressed in this study. The observational analysis reveals changes in the teleconnection impacts and mechanisms over recent decades. These changes have strong implications for the assessment of seasonal predictability, hence the performance of the SEAS5 seasonal prediction model is analysed. While SEAS5 does not accurately capture the observed non-stationarity, it displays pronounced multidecadal changes in forecast skill. This implies the emergence of windows of opportunity for seasonal forecasting, where predictability may be higher than initially expected.</p>

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Multidecadal variability of the ENSO early-winter teleconnection to Europe and implications for seasonal forecasting

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月19日

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  • Pablo Fernández-Castillo,
  • Teresa Losada,
  • Belén Rodríguez-Fonseca,
  • Diego García-Maroto,
  • Elsa Mohino,
  • Luis Durán

摘要

The impacts of El Niño-Southern Oscillation (ENSO) on the North Atlantic and European sector (NAE) climate are season-dependent and, in some cases, not linear and/or not stationary. Previous studies have found multidecadal variability in ENSO teleconnections to NAE in certain seasons, relating it to changes in the background state. However, the stationarity of the teleconnection and its surface impacts in Europe during early winter remain largely unexplored, a gap intended to be addressed in this study. The observational analysis reveals changes in the teleconnection impacts and mechanisms over recent decades. These changes have strong implications for the assessment of seasonal predictability, hence the performance of the SEAS5 seasonal prediction model is analysed. While SEAS5 does not accurately capture the observed non-stationarity, it displays pronounced multidecadal changes in forecast skill. This implies the emergence of windows of opportunity for seasonal forecasting, where predictability may be higher than initially expected.