El Niño-Southern振荡的未来:使用大集合来阐明时变响应和模式间差异

Nicola Maher, Robert C. Jnglin Wills, Pedro DiNezio, Jeremy Klavans, Sebastian Milinski, Sara C. Sanchez, Samantha Stevenson, Malte F. Stuecker, Xian Wu
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引用次数: 6

摘要

摘要厄尔尼诺Niño-Southern涛动(ENSO)的未来变化是不确定的,因为未来的预测在气候模式之间存在差异,而且ENSO的巨大内部变率使观测结果和个别气候模式模拟中对强迫变化的诊断变得困难。通过利用14个单一模式初始条件大集合(SMILEs),我们将ENSO海表温度(SST)变率对人为强迫的时间演化响应从每个SMILE的内部变率中分离出来。我们在许多模式中发现了非线性的时间变化,并且在ENSO和平均状态热带太平洋纬向海温梯度的预估变化中存在相当大的模式间差异。我们证明了ENSO海温变率的变化与热带太平洋纬向海温梯度之间存在线性关系,尽管热带太平洋海温梯度的强迫变化通常发生在21世纪晚于ENSO海温变率的变化,这可能导致偏离线性关系。单强迫smile显示了人为强迫(气溶胶和温室气体)对ENSO海温变率历史变化的潜在贡献,而观测到的热带太平洋海温梯度的历史增强位于那些可获得单强迫smile的模式扩展的边缘。我们的研究结果强调了SMILEs在研究ENSO预测中随时间变化的强迫响应和模式间差异方面的价值。在许多模式中发现的ENSO海温变率的非线性变化表明了表征这种随时间变化的行为的重要性,因为这意味着ENSO影响在整个21世纪可能会发生巨大变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The future of the El Niño–Southern Oscillation: using large ensembles to illuminate time-varying responses and inter-model differences
Abstract. Future changes in the El Niño–Southern Oscillation (ENSO) are uncertain, both because future projections differ between climate models and because the large internal variability of ENSO clouds the diagnosis of forced changes in observations and individual climate model simulations. By leveraging 14 single model initial-condition large ensembles (SMILEs), we robustly isolate the time-evolving response of ENSO sea surface temperature (SST) variability to anthropogenic forcing from internal variability in each SMILE. We find nonlinear changes in time in many models and considerable inter-model differences in projected changes in ENSO and the mean-state tropical Pacific zonal SST gradient. We demonstrate a linear relationship between the change in ENSO SST variability and the tropical Pacific zonal SST gradient, although forced changes in the tropical Pacific SST gradient often occur later in the 21st century than changes in ENSO SST variability, which can lead to departures from the linear relationship. Single-forcing SMILEs show a potential contribution of anthropogenic forcing (aerosols and greenhouse gases) to historical changes in ENSO SST variability, while the observed historical strengthening of the tropical Pacific SST gradient sits on the edge of the model spread for those models for which single-forcing SMILEs are available. Our results highlight the value of SMILEs for investigating time-dependent forced responses and inter-model differences in ENSO projections. The nonlinear changes in ENSO SST variability found in many models demonstrate the importance of characterizing this time-dependent behavior, as it implies that ENSO impacts may vary dramatically throughout the 21st century.
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