When unprecedented rainfall struck southern China in early June 2024, the scientific community scrambled to make sense of the surrounding climatic events. The torrential rains bore witness to extreme weather phenomena, leading to far-reaching consequences. Until then, a coherent explanation for this natural disaster remained elusive. However, insights gleaned from the Global Energy and Water Cycle Exchanges/Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal-to-Seasonal Prediction (GEWEX/LS4P) project unveiled potential links between land temperatures over the Tibetan Plateau (TP) and summer precipitation patterns in East Asia. Researchers observed a historical correlation: warmer springs in the TP often led to wetter summers in southern China and conversely, colder springs tended to precede drier summers.
The pivotal moment in understanding the June 2024 weather event came as scientists analyzed spring land temperatures over the TP, which recorded the most significant warming since 1980, occurring just prior to the flooding. This finding triggered a hypothesis: could the abnormally high temperatures in the TP be a catalyst for the unprecedented precipitation in southern China? This inquiry gained momentum during the GEWEX Open Science Conference held in Japan from July 7-12. During this event, a consortium of renowned scientists—including those from the University of California, Los Angeles (UCLA), the Institute of Atmospheric Physics under the Chinese Academy of Sciences, and other respected institutions—came together to apply their expertise towards this pressing issue.
The urgency of the situation necessitated the use of advanced Earth System Models (ESMs), specifically those developed by the National Center for Environmental Research and UCLA. The research team, spearheaded by various climatologists and meteorologists, worked diligently to derive initial condition data, which would enhance the model simulations in light of the ongoing rainfall disaster. This cooperation proved indispensable, considering the real-time nature of their endeavors and the desperate need for scientifically rooted explanations for the catastrophic weather patterns unfolding.
Nevertheless, the project faced significant initial hurdles. The ESM, tasked with simulating the extreme temperatures across the TP, initially struggled to depict the observed anomalies accurately. Shortly after its deployment, the model showed a notable dry bias, failing to account for the heavy June precipitation that affected southern China. The turning point arrived through improved land temperature initialization in the ESM, which allowed it to replicate the observed anomalies more effectively. The corrected model ultimately demonstrated success by capturing approximately 55% of the observed June rainfall anomaly, thus confirming the hypothesis that anomalies were not merely coincidental.
Observations further revealed that the ESM model had replicable patterns that extended beyond the TP and southern China. It accurately simulated the heavy rainfall in Bangladesh, which received significant media coverage due to severe flooding, as well as indicating abnormally wet conditions over the eastern TP and southern Japan. Notably, the model also picked up dry spells in northern China, showcasing its broad applicability in understanding global weather patterns. Such simulations serve as a testament to the evolving skill of climate models in capturing complex interplays between different geographic regions.
Additionally, in scrutinizing the role of sea surface temperatures (SST) traditionally regarded as pivotal in climate forecasting, results from the study suggested a surprisingly minimal influence. The moderate anomalies observed in global SSTs during May and June 2024 were only responsible for approximately 17% of the precipitation anomaly. This highlighted the necessity of examining land-based temperature influences for accurate weather predictions, particularly when striving to predict extreme hydroclimatic events.
Forecasters have grappled with low predictive accuracy concerning subseasonal-to-seasonal (S2S) precipitation events for several years. In a bid to alleviate this challenge, the World Meteorological Organization launched the S2S Prediction Project. This initiative aims to enhance predictions for climatic and weather events spanning from two weeks to three months. Identifying land initialization and configuration as crucial elements in improving predictive abilities, the project aims to integrate multiple factors, recognizing the predictive power embedded within land surface temperature anomalies.
Despite advancements in understanding how land temperatures can influence weather patterns, this concept remains underappreciated in mainstream climate science. The LS4P initiative, comprised of leading research centers focused on climate and weather prediction, has made considerable strides in demonstrating the role of temperature anomalies arising from high-altitude terrains. Their commitment has led to numerous peer-reviewed publications affirming the significance of these findings, which is both a remarkable scientific achievement and a consequential societal contribution.
In gathering support for the project and its findings, Professor Yongkang Xue of UCLA remarked on the promising potential of implementing these insights to advance methodologies for operational predictions of extreme weather events. Given the increasing frequency and intensity of anomalous meteorological incidents worldwide, further inquiry into land temperature initialization could soon become pivotal for effective forecasting strategies. The research underscores the need for collaboration among global scientific communities to explore effective predictive frameworks within S2S forecasting.
The results derived from this research address not only the present maladaptation in predicting extreme events but also highlight the long-standing challenge of identifying the triggers behind climate-induced disasters. Traditionally, unraveling the threads linking climate anomalies with disastrous weather has taken significant time. This study, which drew on rapid data processing techniques, has urgently brought to light the Turkish Plateau’s excessive land heating as a principal factor influencing the unusual rainfall and subsequent flooding in southern China. Researchers remain keen on publishing their findings, hoping to inspire further interdisciplinary studies that will broaden the understanding of such climatic phenomena.
The initiative taken by the LS4P group signifies not just a scientific endeavor but also one with substantial implications for disaster preparedness and public awareness. By disseminating information that bridges science and community understanding, they aim to inform strategies that mitigate the impacts of future extreme weather events. As the academic community gears up for more extensive inquiries into hydroclimatic events, the preliminary findings serve to underscore the potent significance that land temperature anomalies hold in climate science.
The researchers express hope that their forthcoming paper published in the prestigious journal Science Bulletin will catalyze additional research and spark a larger discussion on the mechanisms driving extreme precipitation events. As the story of the June 2024 rainfall unfolds, the scientific narrative surrounding these phenomena provides a vital look at how interconnected climatic factors can lead to severe natural disasters, underscoring the importance of proactive research and methodologies that acknowledge the complexities of our global climate system.
Subject of Research: The influence of Tibetan Plateau land temperatures on summer precipitation patterns in East Asia.
Article Title: Spring Warming on Tibetan Plateau Linked to June 2024 Catastrophic Rainfall in Southern China.
News Publication Date: October 2023.
Web References: DOI.
References: Science Bulletin.
Image Credits: ©Science China Press.
Keywords: Extreme Weather, Tibetan Plateau, Precipitation Prediction, Climate Change, Hydroclimate Events, Earth System Models, S2S Prediction, Climate Science, Disaster Preparedness, Global Weather Patterns.
