Evaluation of ENSO in CMIP5 and CMIP6 models and its significance in the rainfall in Northeast Thailand
Evaluation of ENSO in CMIP5 and CMIP6 models and its significance in the rainfall in Northeast Thailand
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DOI：
10.1007/s00704-023-04585-z
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发表时间：
2023-08
影响因子：
3.4
通讯作者：
Yenushi K. De Silva;M. Babel;A. A. Abatan-A.;Dibesh Khadka;Jothiganesh Shanmugasundaram
中科院分区：
文献类型：
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作者：
Yenushi K. De Silva;M. Babel;A. A. Abatan-A.;Dibesh Khadka;Jothiganesh Shanmugasundaram
厄尔尼诺-南方涛动（ENSO）是热带太平洋大气和海洋相互作用引起的一种重要的内部气候变率。它是泰国东北部年际降雨变化的主要驱动力，其中农业是最大的经济部门之一。因此，必须了解气候模型模拟厄尔尼诺/南方涛动现象的基本特征和预测其在该区域的影响的能力。我们评估了12个气候模型的能力，从第六阶段的耦合模式相互比较项目（CMIP 6）和他们的12个前辈模型从第五阶段（CMIP 5）模拟ENSO及其对泰国东北部降雨的影响，考虑到模拟观测指数，模式，变率，峰值和季节性相位锁定ENSO的7个标准下使用13个性能指标。对近期（2021-2050年）厄尔尼诺/南方涛动事件的强度和频率作了预测。虽然6个CMIP 5和8个CMIP 6模式在模拟ENSO评估指标的一半（性能得分> 40%），如ENSO变率，季节相位锁定，ENSO变率的位置，以及SST变率的主导次峰方面表现良好，但我们没有发现CMIP 6与CMIP 5模式相比改进ENSO模拟的非常令人信服的证据。然而，由于太平洋的东风与印度洋的西南气流相互作用，在泰国东北部观测到极端拉尼娜事件的高降雨量和极端厄尔尼诺事件的低降雨量，降雨量异常分别为0.4毫米/天和-0.3毫米/天。相应的海平面气压图进一步证实了与该区域这一现象有关的机制。两个阶段模型的集合平均值预测，在不久的将来（2021-2050年），强度（32-47%）和频率（10-50%）将增加，在中等和高排放情景下，CMIP 6模型的增幅略高于CMIP 5模型。
El Niño Southern Oscillation (ENSO) is a significant form of internal climate variability resulting from the interactions between the atmosphere and ocean in the tropical Pacific. It is a main driver of interannual rainfall variability in Northeast Thailand, where rainfed agriculture is one of the largest economic sectors. Therefore, it is essential to understand the ability of climate models to simulate the basic characteristics of ENSO phenomena and project its impacts in the region. We evaluated the ability of 12 climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and their 12 predecessor models from the fifth phase (CMIP5) to simulate ENSO and its impact on rainfall over Northeast Thailand using thirteen performance metrics under seven criteria considering the simulation of observed indices, pattern, variability, peaks and seasonal phase locking of ENSO. The intensity and frequency of the ENSO events were projected for the near future (2021–2050). Although six CMIP5 and eight CMIP6 models performed well in simulating half of the ENSO evaluation metrics (performance score > 40%) such as ENSO variability, seasonal phase locking, location of ENSO variability, and dominant secondary peak in SST variability, we did not find very compelling evidence of improved ENSO simulation in CMIP6 compared to CMIP5 models. However, high rainfall in extreme La Niña events and low rainfall in extreme El Niño events with rainfall anomalies of 0.4 mm/day and − 0.3 mm/day, respectively were observed over Northeast Thailand due to the interaction of the easterly winds from the Pacific Ocean with the south-westerly flow from the Indian Ocean. The corresponding sea level pressure maps further confirmed the mechanisms associated with this phenomenon over the region. The ensemble averages of models from both phases predicted an increase in intensity (by 32–47%) and frequency (by 10–50%) in the near-future (2021–2050) with a slightly higher increase by CMIP6 models compared to CMIP5 models under medium and high emission scenarios.