Uncovering the Substantial Predictability of the 2003 European Summer Heatwave Linked to the Tibetan Plateau
The big challenge in climate science is predicting extreme events, like heat waves, well in advance. Our research focuses on the 2003 European heatwave, one of the most severe and deadliest heat waves in Europe’s recorded history. We find that by assimilating the Tibetan Plateau's soil conditions that influence snow cover, we can predict the 2003 European summer heatwave two years ahead. The Plateau influences weather patterns by affecting atmospheric circulation patterns, known as Rossby waves, which can travel far and impact distant regions like Europe and the Atlantic and Pacific Oceans. This discovery shows the Tibetan Plateau's potential to improve the prediction of extreme events worldwide.
This research uncovers how the Tibetan Plateau (TP) can help predict extreme events, like the 2003 European heatwave, years in advance. This is important because it can improve our ability to forecast and prepare for such events, potentially saving lives and resources. This study is among the first to show the TP's role in subseasonal-to-interannual prediction. By using innovative data assimilation in Earth system models and land surface data from the TP, we can now better understand and predict global weather patterns. This research not only aids climate scientists but also impacts fields like agriculture and disaster management that are impacted by extreme events.
Our research delves into the underexplored potential of the Tibetan Plateau (TP) as a significant source of predictability for extreme events, focusing on the 2003 European summer heatwave. Using coupled climate simulations and hindcast experiments produced by the Energy Exascale Earth System Model (E3SM) and the Flexible Global Ocean-Atmosphere-Land System model (FGOALS), we demonstrate that the TP's spring snow cover anomalies play a crucial role in influencing atmospheric circulation patterns, such as Rossby waves, which contribute to extreme temperature events in Europe. Our findings reveal that these anomalies can be predicted up to two years in advance, highlighting the TP's remote influence on global climate systems, including the Atlantic and Pacific Oceans.
By employing a weakly coupled data assimilation (WCDA) system in E3SM and FGOALS-g2, we incorporate soil moisture and temperature data from the Global Land Data Assimilation System (GLDAS) into the climate models, significantly enhancing the prediction skill for the 2003 heatwave. This approach underscores the TP's pivotal role in modulating interannual climate variability and emphasizes the importance of realistic land state initialization for improving the predictability of extreme events. Our study not only advances understanding of the TP's influence but also sets a foundation for leveraging its climatic impact to enhance global weather and climate prediction models.