The future of the El Niño–Southern Oscillation: using large ensembles to illuminate time-varying responses and inter-model differences
The future of the El Niño–Southern Oscillation: using large ensembles to illuminate time-varying responses and inter-model differences
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DOI：
10.5194/esd-14-413-2023
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发表时间：
2023-04
影响因子：
7.3
通讯作者：
N. Maher;Robert C.J. Wills;P. DiNezio;J. Klavans;Sebastian Milinski;S. Sanchez;S. Stevenson;M. Stuecker;X. Wu
中科院分区：
文献类型：
--
作者：
N. Maher;Robert C.J. Wills;P. DiNezio;J. Klavans;Sebastian Milinski;S. Sanchez;S. Stevenson;M. Stuecker;X. Wu
抽象的。厄尔尼诺-南方涛动 (ENSO) 的未来变化是不确定的，一方面是因为气候模型之间的未来预测不同，另一方面是因为 ENSO 巨大的内部变异性使观测和个别气候模型模拟中被迫变化的诊断变得模糊。通过利用 14 个单一模型初始条件大系综 (SMILE)，我们可以将 ENSO 海面温度 (SST) 变化对人为强迫的时间演化响应与每个 SMILE 的内部变化分开。我们发现许多模型随时间发生非线性变化，并且 ENSO 和平均状态热带太平洋纬向海温梯度的预测变化存在相当大的模型间差异。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的线性关系，尽管热带太平洋海温梯度的强制变化通常发生在21世纪晚于ENSO海温变率的变化，这可能导致线性关系的偏离。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加强位于单强迫 SMILE 可用的模型分布的边缘。我们的结果强调了 SMILE 在研究 ENSO 预测中的时间相关强迫响应和模型间差异方面的价值。许多模型中发现的 ENSO 海温变化的非线性变化表明了描述这种时间相关行为的重要性，因为这意味着 ENSO 的影响在整个 21 世纪可能会发生巨大变化。
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.