Climate variability has distinct spatial patterns with the strongest signal of sea surface temperature (SST) variance residing in the tropical Pacific. This interannual climate phenomenon, the El Niño-Southern Oscillation (ENSO), impacts weather patterns across the globe via atmospheric teleconnections. Pronounced SST variability, albeit of smaller amplitude, also exists in the other tropical basins as well as in the extratropical regions. To improve our physical understanding of internal climate variability across the global oceans, we here make the case for a conceptual model hierarchy that captures the essence of observed SST variability from subseasonal to decadal timescales. The building blocks consist of the classic stochastic climate model formulated by Klaus Hasselmann, a deterministic low-order model for ENSO variability, and the effect of the seasonal cycle on both of these models. This model hierarchy allows us to trace the impacts of seasonal processes on the statistics of observed and simulated climate variability. One of the important outcomes of ENSO’s interaction with the seasonal cycle is the generation of a frequency cascade leading to deterministic climate variability on a wide range of timescales, including the near-annual ENSO Combination Mode. Using the aforementioned building blocks, we arrive at a succinct conceptual model that delineates ENSO’s ubiquitous climate impacts and allows us to revisit ENSO’s observed statistical relationships with other coherent spatio-temporal patterns of climate variability—so called empirical modes of variability. We demonstrate the importance of correctly accounting for different seasonal phasing in the linear growth/damping rates of different climate phenomena, as well as the seasonal phasing of ENSO teleconnections and of atmospheric noise forcings. We discuss how previously some of ENSO’s relationships with other modes of variability have been misinterpreted due to non-intuitive seasonal cycle effects on both power spectra and lead/lag correlations. Furthermore, it is evident that ENSO’s impacts on climate variability outside the tropical Pacific are oftentimes larger than previously recognized and that accurately accounting for them has important implications. For instance, it has been shown that improved seasonal prediction skill can be achieved in the Indian Ocean by fully accounting for ENSO’s seasonally modulated and temporally integrated remote impacts. These results move us to refocus our attention to the tropical Pacific for understanding global patterns of climate variability and their predictability.

中文翻译：

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气候变率三重奏：随机波动、厄尔尼诺现象和季节周期

气候变化具有独特的空间模式，其中最强的海面温度（SST）变化信号位于热带太平洋。这种年际气候现象，即厄尔尼诺南方涛动（ENSO），通过大气遥相关影响全球的天气模式。其他热带盆地和温带地区也存在明显的海温变化，尽管幅度较小。为了提高我们对全球海洋内部气候变化的物理理解，我们在这里提出了一个概念模型层次结构，该模型层次结构捕获了从次季节到十年时间尺度观测到的海表温度变化的本质。这些构建模块包括克劳斯·哈塞尔曼 (Klaus Hasselmann) 制定的经典随机气候模型、ENSO 变化的确定性低阶模型以及季节周期对这两个模型的影响。该模型层次结构使我们能够追踪季节性过程对观测和模拟气候变化统计数据的影响。ENSO 与季节周期相互作用的重要结果之一是产生频率级联，导致大范围时间尺度上的确定性气候变率，包括近年 ENSO 组合模式。使用上述构建模块，我们得出了一个简洁的概念模型，该模型描述了 ENSO 普遍存在的气候影响，并使我们能够重新审视 ENSO 观测到的统计关系与其他连贯的气候变化时空模式（所谓的经验变化模式）。我们证明了正确考虑不同气候现象的线性增长/阻尼率的不同季节阶段的重要性，以及 ENSO 遥相关和大气噪声强迫的季节阶段。我们讨论了以前由于功率谱和超前/滞后相关性上的非直观季节周期效应，ENSO 与其他变率模式的一些关系如何被误解。此外，很明显，ENSO 对热带太平洋以外气候变化的影响往往比之前认识的要大，准确地解释这些影响具有重要意义。例如，事实证明，通过充分考虑 ENSO 的季节性调制和时间综合远程影响，可以提高印度洋的季节性预测技能。这些结果促使我们重新将注意力集中在热带太平洋上，以了解全球气候变化的模式及其可预测性。