Abstract
Climate change is an essential topic in climate science, and the accessibility of accurate, high-resolution datasets in recent years has facilitated the extraction of more insights from big-data resources. Nonetheless, current research predominantly focuses on mean-value changes and largely overlooks changes in the probability distribution. In this study, a novel method called Wasserstein Stability Analysis (WSA) is developed to identify probability density function (PDF) changes, especially the extreme event shift and nonlinear physical value constraint variation in climate change. WSA is applied to the early 21st century and compared with traditional mean-value trend analysis. The results indicate that despite no significant trend, the equatorial eastern Pacific experienced a decline in hot extremes and an increase in cold extremes, indicating a La Niña-like temperature shift. Further analysis at two Arctic locations suggests sea ice severely restricts the hot extremes of surface air temperature. This impact is diminishing as the sea ice melts. By revealing PDF shifts, WSA emerges as a powerful tool to re-examine climate change dynamics, providing enhanced data-driven insights for understanding climate evolution.
Published Version
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