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Understanding the interplay between ENSO and related tropical SST variability using linear inverse models
Climate Dynamics ( IF 3.8 ) Pub Date : 2022-09-15 , DOI: 10.1007/s00382-022-06484-x
Shoichiro Kido , Ingo Richter , Tomoki Tozuka , Ping Chang

The impacts of tropical interbasin interaction (TBI) on the characteristics and predictability of sea surface temperature (SST) in the tropics are assessed with a linear inverse modelling (LIM) framework that uses SST and sea surface height anomalies in the tropical Pacific (PO), Atlantic (AO), and Indian Ocean (IO). The TBI pathways are shown to be successfully isolated in stochastically-forced simulations that modify off-diagonal elements of the linear operators. The removal of TBI leads to a substantial increase in the amplitude of El Niño-Southern Oscillation (ENSO) and related variability. Partial decoupling experiments that eliminate specific coupling components reveal that PO-IO interaction is the dominant contributor, whereas PO-AO and AO-IO interactions play a minor role. A series of retrospective forecast experiments with different operators shows that decoupling leads to a substantial decrease in ENSO prediction skill especially at longer lead times. The relative contributions of individual pathways to forecast skill are generally consistent with the results from the stochastically-forced experiments. Qualitatively similar results are obtained from an additional set of forecast experiments that partially apply initial conditions over specific basins, but several important differences were also found due to differences in the representations of each TBI pathway. Finally, the cause of contrasting SST anomalies over the AO after the extreme 1982/83 and 1997/98 El Niño events is explored using LIM forecast experiments to demonstrate the strength and flexibility of our LIM-based approach.



中文翻译:

使用线性反演模型了解 ENSO 与相关热带海温变化之间的相互作用

热带流域间相互作用 (TBI) 对热带海面温度 (SST) 特征和可预测性的影响通过线性反演模型 (LIM) 框架进行评估,该框架使用热带太平洋 (PO) 的海表温度和海面高度异常、大西洋 (AO) 和印度洋 (IO)。TBI 路径在随机强制模拟中被成功隔离,该模拟修改了线性算子的非对角线元素。TBI 的消除导致厄尔尼诺南方涛动 (ENSO) 的振幅和相关变异性大幅增加。消除特定耦合成分的部分解耦实验表明,PO-IO 相互作用是主要贡献者,而 PO-AO 和 AO-IO 相互作用发挥次要作用。与不同操作员进行的一系列回顾性预报实验表明,脱钩会导致 ENSO 预报技能大幅下降,尤其是在较长的交付周期内。各个路径对预测技能的相对贡献通常与随机强制实验的结果一致。从另外一组预测实验中获得了定性相似的结果,这些实验部分地将初始条件应用于特定盆地,但由于每个 TBI 路径的表示差异,也发现了一些重要的差异。最后,使用 LIM 预报实验探讨了 1982/83 和 1997/98 极端厄尔尼诺事件后 AO 上海温异常对比的原因,以证明我们基于 LIM 的方法的强度和灵活性。

更新日期:2022-09-15
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