Yongwen Zhang

The Tibetan Plateau (TP), known as the Asian Water Tower, supplies water to billions of people but is undergoing rapid warming and wetting. Whether this increased moisture stabilizes the regional hydrological system or merely provides a transient buffer remains unclear. Here we apply an entropy-based framework to quantify the structural organization of the TP’s soil moisture system. During 2000–2024, regional wetting has driven a long-term decline in entropy, reflecting an increase in system order and enhanced hydrological buffering capacity. This ordering is modulated by ENSO, which regulates regional heterogeneity via a spatial dipole. However, CMIP6 projections reveal a reversal: entropy increases under continued warming and regional contrasts intensify, with some models exhibiting transition-like behavior. Our findings suggest that while current wetting provides a stabilizing buffer, continued warming is projected to amplify spatial heterogeneity, thereby elevating instability risk in the Asian Water Tower and threatening downstream water security.

Sudden stratospheric warmings (SSWs) are among the most dramatic regime transitions in the winter stratosphere, yet their onset remains difficult to diagnose and predict. We explore SSWs from a critical-transition perspective using Eigen Microstate Theory (EMT), which provides an entropy-based measure of how the circulation reorganizes during the event life cycle. In reanalysis composites of major SSWs, we identify a robust, non-monotonic entropy evolution: it rises during vortex deceleration, reaches a maximum prior to onset, and then collapses sharply as the vortex breaks. This “order–disorder–order” sequence provides direct empirical evidence that SSWs exhibit signatures of phase transitions and criticality in the real atmosphere. To connect this statistical signature to dynamics, we analyze a one-dimensional wave–mean-flow interaction model that captures the nonlinear feedbacks underpinning vortex destabilization. The model reproduces the same entropy peak and collapse when the system is driven toward instability, supporting the interpretation of eigen-microstate entropy as an order parameter for an intrinsically nonequilibrium transition. Across both reanalysis and model experiments, supported by analytical considerations, the entropy shows a pronounced response as the system approaches loss of stability and provides a clearer precursor than conventional single-series early-warning indicators such as lag-1 autocorrelation (AR1). These results suggest a physically interpretable, entropy-based diagnostic of SSW criticality with potential value for subseasonal prediction.

Understanding the spatiotemporal interplay between the duration of extreme precipitation events and wet-day frequency (i.e., the relative number of days that exceed a minimal daily precipitation threshold) is critical for predicting hydrological risks in a changing climate. Here, we analyze ERA5 reanalysis and employ percentile thresholds to define extreme precipitation events in daily scale, investigating global patterns of the duration of extreme precipitation events across areas classified by wet-day frequency. Our analysis reveals that increased wet‐day frequencies are generally associated with a steeper decay of duration distribution and lower mean durations, except in areas with a frequency exceeding 0.8, where the mean duration slightly increases. This overall negative correlation mainly stems from limitations in atmospheric moisture availability, while the anomalous increase in high wet-day frequency areas is attributed to their concentration in low-latitude regions, where extreme precipitation events have longer durations. Further analysis reveals a distinct latitudinal dichotomy under this phenomenon: mid-latitude regions (30°–60°) show a strong linear negative correlation (r = − 0.77), governed by synoptic-scale dynamics and wind-modulated precipitation systems. In contrast, low-latitude regions (0°–30°) demonstrate a nonlinear (initially increasing then decreasing) relationship with a significant correlation (r = 0.63) between these durations and amplified annual precipitation cycles, typically linked to seasonal rainfall clustering mechanisms. Threshold sensitivity tests confirm the robustness of these results, except in low latitudes and at the 99th percentile threshold, due to limited data sample. The MSWEP and PERSIANN datasets are also tested with similar results found. These finding can enhance prediction accuracy for extreme precipitation events and inform regional flood risk management.

Earthquake activity poses significant risks to both human survival and economic development. However, earthquake forecasting remains a challenge due to the complex, poorly understood interactions that drive seismic events. In this study, we construct an earthquake percolation model to examine the relationships between earthquakes and the underlying patterns and processes in Southern California. Our results demonstrate that the model can capture the spatiotemporal and magnitude characteristics of seismic activity. Through clustering analysis, we identify two distinct regimes: a continuous increase driven by earthquake clustering, and a discontinuous increase resulting from the merging of clusters dominated by large, distinct mega-earthquakes. Notably, in the continuous increase regime, we observe that clusters exhibit a broader spatiotemporal distribution, suggesting long-range and long-term correlations. Additionally, by varying the magnitude threshold, we explore the scaling behavior of earthquake percolation. The robustness of our findings is confirmed through comparison with multiple shuffling tests.

SUMMARY: The scientific process of earthquake forecasting involves estimating the probability and intensity of earthquakes in a specific area within a certain timeframe, based on seismic activity features and observational data. Among the various methodologies, epidemic-type aftershock sequence (ETAS) models, rooted in seismic empirical laws, stand as widely used tools for earthquake forecasting. In this study, we introduce the CL-ETAS model, a novel approach that integrates convolutional long short-term memory (ConvLSTM), a deep learning model, with the ETAS model. Specifically, we leverage the forecasting outputs of ETAS to enhance both the training and forecasting processes within the ConvLSTM framework. Through forecasting tests, our findings illustrate the effectiveness of the CL-ETAS model in capturing the trends observed in earthquake numbers ($M \ge 3$) in Southern California following three main shocks. Overall, our model outperforms both a simple ETAS model and ConvLSTM in this context.

Our study utilizes a global reanalysis of near-surface daily air temperature data spanning the years from 1949 to 2019 to construct climate networks. By employing community detection for each year, we reveal the evolving community structure of the climate network within the context of global warming. Our findings indicate significant changes in measures such as network modularity and the number of communities over the past 30 years. Notably, the community structure of the climate network has undergone a discernible transition since the early 1980s. We attribute this transition to the substantial increase in isolated nodes since the 1980s, primarily concentrated in equatorial ocean regions. Additionally, we demonstrate that nodes experiencing amplified isolation tend to diminish connectivity with other nodes globally, particularly those within the same oceanic basin, while showing a significant strengthening of connections with the Eurasian and North African continents. We deduce that the mechanism driving amplified isolation in the climate network may be comprehended through the weakening of tropical circulations, such as the Hadley cell and Walker circulation, in response to increasing greenhouse gases.

Mainshocks are often followed by increased earthquake activity (aftershocks). According to the Omori-Utsu law, the rate of aftershocks decays as a power law over time. While aftershocks typically occur in the vicinity of the mainshock, previous studies have suggested that mainshocks can also trigger earthquakes in remote locations, beyond the range of aftershocks. Here we analyze the rate of earthquakes that occurred after mega-earthquakes (with a magnitude of 7.5 or higher) and show that there is a significantly higher occurrence of mega-earthquakes that are followed by reduced activity beyond a certain distance from the epicenter compared to the expected frequency; the results are based on statistical tests we developed. However, the remote earthquake rate after the strongest earthquakes (magnitude ≥8) can also be significantly higher than the expected rate. Comparing our findings to the global Epidemic-Type Aftershock Sequence model, we find that the model does not capture the above findings, hinting at a potential missing mechanism. We suggest that the reduced earthquake rate is due to the release of global energy/tension after substantial mainshock events. This conjecture holds the potential to enhance our comprehension of the intricacies governing post-seismic activity.

Our study utilizes global reanalysis of near-surface daily air temperature data, spanning from 1949 to 2019, to construct climate networks. By employing community detection for each year, we reveal the evolving community structure of the climate network within the context of global warming. Our findings indicate significant changes in measures such as the network modularity, the number of communities, and the average community size over the past 30 years. Notably, the community structure of the climate network undergoes a discernible transition around 1982. We attribute this transition to the substantial increase in isolated nodes after 1982, primarily concentrated in equatorial ocean regions. Additionally, we demonstrate that nodes experiencing amplified isolation tend to diminish connectivity with other nodes globally, particularly those within the same oceanic basin, while showing a significant strengthening of connections with the Eurasian and North African continents. We propose that the mechanism behind the amplified isolation in the climate network can be understood through weakened ocean current interactions under global warming.