Decreasing predictability as a precursor indicator for abrupt climate change

He, Wenping; Xie, Xiaoqiang; Mei, Ying; Wan, Shiquan; Zhao, Shanshan

doi:10.1007/s00382-021-05676-1

Cited by 14 publications

(4 citation statements)

References 41 publications

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“…In this study, we applied the improved‐LDA method to identify the analogs. Numerous studies have successfully employed the NLLE method to investigate the predictability of atmospheric and oceanic systems (Ding et al., 2008; He et al., 2021; Hou et al., 2018; Hou, Li, Ding, et al, 2022; Li et al., 2018; Mengist & Seo, 2022).…”

Section: Methodsmentioning

confidence: 99%

Asymmetric Influences of ENSO Phases on the Predictability of North Pacific Sea Surface Temperature

Hou,

Li,

Diao

et al. 2024

Geophysical Research Letters

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The North Pacific sea surface temperature (SST) has a profound climatic influence. The El Niño‐Southern Oscillation (ENSO) significantly impacts the North Pacific SST; however, the influence of the distinct phases of ENSO on SST predictability remains unclear. To overcome the model limitations, this study assessed SST predictability under diverse ENSO phases using reanalysis. The predictability limit of the North Pacific SST under La Niña (8.4 months) is longer than that under Neutral (5.9 months) and El Niño (5.5 months) conditions, which unveils asymmetry. This asymmetry mirrors contemporary multimodal prediction skills. Error growth dynamics reveal La Niña's robust signal strength with a slow error growth rate, in contrast to El Niño's weaker signal and faster error growth. There exhibits intermediate signal strength and elevated error growth in Neutral condition. Physically, predictability signal strength aligns with SST variability, whereas the error growth rate correlates with atmospheric‐ocean heating anomalies. La Niña, which induces positive heating anomalies, minimizes the impact of atmospheric noise, resulting in lower error growth. The result is beneficial for improving North Pacific SST predictions.

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Section: Methodsmentioning

confidence: 99%

Asymmetric Influences of ENSO Phases on the Predictability of North Pacific Sea Surface Temperature

Hou,

Li,

Diao

et al. 2024

Geophysical Research Letters

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“…The climate system is one of the intricate, nonlinear dissipative mega-systems [19][20][21]. In the context of global warming, the increasing frequency of extreme events has objectively heightened the difficulty of seasonal climate prediction.…”

Section: Introductionmentioning

confidence: 99%

Predicting Summer Precipitation Anomalies in the Yunnan–Guizhou Plateau Using Spring Sea-Surface Temperature Anomalies

Tuo,

Qiao,

Liu

et al. 2024

Atmosphere

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By constructing a correlation network between global sea surface temperature anomalies (SSTAs) and summer precipitation anomalies in the Yunnan–Guizhou Plateau, key SST regions influencing summer precipitation anomalies in the Yunnan–Guizhou Plateau were selected. It was found that spring SSTAs in the Bay of Bengal, southwestern Atlantic, and eastern Pacific are crucial for influencing summer precipitation anomalies in the Yunnan–Guizhou Plateau. Setting SSTAs from these three regions as predictor variables 3 months in advance, we constructed multiple linear regression (MLR), ridge regression (RR), and lasso regression (LR) models to predict summer precipitation anomalies over the Yunnan–Guizhou region. The training phase involved data spanning from 1961 to 2005, which aimed to predict precipitation anomalies in the Yunnan–Guizhou Plateau for the period extending from 2006 to 2022. Based on MLR, RR, and LR models, the correlations between predicted values and observed summer precipitation anomalies in Yunnan–Guizhou were 0.48, 0.46, and 0.46, respectively. These values were all higher than the correlation coefficients of the NCC_CSM model’s predicted and observed values. Additionally, its performance in predicting summer precipitation anomalies over the Yunnan–Guizhou region, based on key SST regions, was assessed using performance metrics such as anomaly correlation coefficient (ACC), anomaly sign consistency rate (PC), and trend anomaly comprehensive score (PS score). The average ACC of MLR, RR, and LR models was higher than that of the NCC_CSM model’s predictions. For MLR, RR, LR, and NCC_CSM models, the PCs exceeding 50% of the year were 14, 14, 11, and 10, respectively. Furthermore, the average PS score for predicting summer precipitation anomalies over the Yunnan–Guizhou region using MLR, RR, and LR was approximately 73 points; 8 higher than the average PS score of the NCC_CSM model. Therefore, predicting summer precipitation anomalies over the Yunnan–Guizhou region based on key SST regions is of great significance for improving the prediction skills of precipitation anomalies in this region.

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“…Scheffer et al proposed a novel method to study the tipping point by using critical slowing down phenomenon [17,18]. This phenomenon can be found in a wide range of fields in nature and human society [19,20], often leading to catastrophic consequences, such as the decline of civilizations or the fall of dynasties caused by abrupt climate changes [21,22]. Hence, it is quite necessary to research the EWSs for the critical transition of nonlinear systems and the possible mechanisms involved.…”

Section: Introductionmentioning

confidence: 99%

Early Warning Signals of Dry-Wet Transition Based on the Critical Slowing Down Theory: An Application in the Two-Lake Region of China

Yan

Hou

et al. 2023

Atmosphere

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In recent years, the dry-wet transition (DWT), which often leads to regional floods and droughts, has become increasingly frequent in the Poyang Lake basin and the Dongting Lake basin (hereinafter referred to as the two-lake region). This study aims to investigate the early warning signals (EWSs) for DWT events. Firstly, based on the standardized precipitation index (SPI) at 161 meteorological stations in the two-lake region from 1961 to 2020, the two-lake region is divided into four sub-regions by the Rotational Empirical Orthogonal Function (REOF) analysis method. Then, the occurrence time of the DWT events in each sub-region is determined by the moving t-test (MTT) technique. Finally, by using two indicators (variance and the auto-correlation coefficient) to describe the critical slowing down (CSD) phenomenon, the EWSs denoting the DWT events in all sub-regions are investigated. The results reveal that there was a significant dry-to-wet (wet-to-dry) event around 1993 (2003) in the two-lake region during the last 60 years. The phenomenon of CSD, where the auto-correlation coefficient and variance increases are found in all sub-regions around 10 years before the DWT, suggests that it can be taken as an EWS for the DWT events. This study confirms the effectiveness of applying the slowing down theory in investigating the EWSs for abrupt changes in the two-lake region, aiming to provide a theoretical basis for effective prevention and mitigation against disasters in this region. Moreover, it is expected to be well-applied to the middle and lower reaches of the Yangtze River.

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Decreasing predictability as a precursor indicator for abrupt climate change

Cited by 14 publications

References 41 publications

Asymmetric Influences of ENSO Phases on the Predictability of North Pacific Sea Surface Temperature

Asymmetric Influences of ENSO Phases on the Predictability of North Pacific Sea Surface Temperature

Predicting Summer Precipitation Anomalies in the Yunnan–Guizhou Plateau Using Spring Sea-Surface Temperature Anomalies

Early Warning Signals of Dry-Wet Transition Based on the Critical Slowing Down Theory: An Application in the Two-Lake Region of China

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