Complex systems are composed of many interacting components whose collective behavior cannot be easily predicted from the properties of individual parts. My research focuses on uncovering universal behaviors in complex systems and predicting how these systems respond to changing control parameters.
This includes studying phase transitions, self-organization, and emergent phenomena across different domains — from physical systems to the Earth’s climate and ecological networks.
Key topics
Resilience and early warning signals
Developing and refining statistical indicators, such as critical slowing down (CSD), to anticipate abrupt state transitions. A key focus is systematically addressing empirical data challenges, such as missing values and outliers, to improve the reliability of resilience assessments.
Complexity metrics and entropy frameworks
Utilizing novel approaches, including Eigen Microstates Theory (EMT) and entropy-based frameworks, to quantify system disorder, detect phase transitions, and disentangle the overlapping effects of anthropogenic and climate-induced drivers in multivariate systems.
Self-organized criticality and scaling
Applying statistical physics to identify universal signatures of self-organized criticality (SOC), power-law distributions, and finite-size scaling in macroscopic phenomena, explaining how emergent, self-regulating systems operate near critical states.
Research objects
Ecology
Ecological resilience
Assessing the stability and transition risks of forest ecosystems and vegetation networks under climate change. This includes quantifying ecosystem productivity resilience and identifying abrupt state transitions driven by warming and water availability.
Earth system science
Hydrological and climate systems
Investigating the spatiotemporal organization and destabilization of critical water resources, such as the thermodynamic amplification of atmospheric rivers and the warming-driven entropy rise in the Asian Water Tower.
Marine science
Coastal marine systems
Uncovering systemic state transitions and identifying dominant drivers in complex coastal environments, such as the Bohai Sea, to evaluate environmental policies and the dynamic interactions between human activities and natural variability.
The resilience of natural systems, such as climate or ecosystems, is increasingly threatened by anthropogenic pressures, making it essential to quantify resilience changes before abrupt and irreversible regime shifts occur. Widely used data-driven resilience indicators based on variance and autocorrelation detect “critical slowing down,” a signature of decreasing stability and possible impending critical transitions in dynamical systems with alternative equilibria. However, the interpretation of these indicators is complicated by common data issues such as missing values and outliers, whose effects remain poorly understood. Here, we develop a general mathematical framework that rigorously characterizes the statistical dependency between variance- and autocorrelation-based resilience indicators, revealing that their agreement is fundamentally driven by the time series’ initial data point. Using synthetic and empirical data, we demonstrate that missing values substantially weaken the agreement of resilience indicators, while outliers introduce systematic biases that lead to overestimation of resilience based on temporal autocorrelation. Our results provide a necessary and rigorous foundation for preprocessing strategies and accuracy assessments across the growing number of disciplines that use empirical data to infer changes in system resilience. Data gaps and outliers distort the statistical indicators used to detect loss of resilience in natural systems.
@article{liu2026SA,author={Liu, Teng and Morr, Andreas and Bathiany, Sebastian and Blaschke, Lana L and Qian, Zhen and Diao, Chan and Smith, Taylor and Boers, Niklas},title={Data gaps and outliers distort critical-slowing-down-based resilience indicators},journal={Science Advances},volume={12},number={11},dimension={true},pages={eaee1916},year={2026},month=mar,doi={10.1126/sciadv.aee1916},url={https://www.science.org/doi/abs/10.1126/sciadv.aee1916},}
Atmospheric rivers (ARs) are essential components of the global hydrological cycle, with profound implications for water resources, extreme weather events, and climate dynamics. Yet, the statistical organization and underlying physical mechanisms of AR intensity and evolution remain poorly understood. Here we apply methods from statistical physics to analyze the full life cycle of ARs and identify universal signatures of self-organized criticality. We demonstrate that AR morphology exhibits nontrivial fractal geometry, while AR event sizes—quantified via integrated water vapor transport—follow robust power-law distributions, displaying finite-size scaling. To interpret these emergent behaviors, we develop a moisture avalanche model that reproduces the observed scaling laws and links them to threshold-driven moisture transport and precipitation dissipation. These scaling properties persist under warming scenarios, suggesting that ARs operate near a critical state as emergent, self-regulating systems. Concurrently, we observe a systematic poleward migration and intensification of ARs, driven by thermodynamic amplification and dynamical reorganization. Our findings establish a statistical physics framework for ARs, connecting critical phenomena to the spatiotemporal structure of extreme events in a warming climate.
@article{wang2026prl,title={Self-Organized Criticality in Atmospheric Rivers},author={Wang, Shang and Meng, Jun and Fang, Sheng and Liu, Teng and Christensen, Kim and Kurths, Jürgen and Fan, Jingfang},journal={Physical Review Letters},volume={136},issue={9},pages={094201},numpages={8},year={2026},month=mar,dimension={true},doi={10.1103/7l2l-g5vn},url={https://link.aps.org/doi/10.1103/7l2l-g5vn},}
Coastal systems are shaped by complex interactions among physical, chemical, and anthropogenic factors, yet diagnosing system-level transitions remains a major challenge due to their multivariate and dynamic nature. Here, we introduce a novel framework based on Eigen Microstates Theory (EMT) to capture the evolving dynamics of multivariable coastal systems through emergent patterns in variable interactions. Applying this theory to the Bohai Sea, a representative coastal complex system, we quantify system disorder using the entropy of eigen microstates and identify state transitions in Bohai Sea corresponding to ecological events such as red tides. These emergent eigen microstates reflect coupling among variables, allowing the identification of dominant drivers and attribution of variable behavior to anthropogenic versus climate-induced influences. Human activity is identified as the primary long-term driver of eutrophication, while natural variability modulates its intensity and timing. Furthermore, the EMT framework allows for the disentanglement of overlapping effect from different drivers, providing a robust basis for evaluating the effectiveness of environmental policies. Our analysis reveals that recent regulatory interventions, though successful in curbing nutrient inputs, were not fully reflected in nutrient dynamics because of the concurrent increases in climate-driven nutrient transport. These results underscore the importance of coordinated monitoring and management strategies that account for both human and natural contributions to coastal change, aligning with the United Nations Sustainable Development Goal (SDG) 14.1 and providing support for its implementation. This framework offers a transferable tool for uncovering state transitions and disentangling interacting drivers in complex coastal systems under global change.
@article{huang2026holistic,title={Holistic Evolution of the Bohai Sea Complex System: Insights from Interacting Drivers},author={Huang, Han and Zou, Tao and Liu, Teng and Wang, Hongyu and Wang, Zhixuan and Tao, Ningning and Li, Yanfang and Fan, Hao and Xie, Fei and Zhai, Weidong and Wang, Guizhi and Zhang, Yongwen and Fan, Jingfang and Qin, Song and Dai, Minhan and Chen, Xiaosong},journal={Marine Environmental Research},doi={10.1016/j.marenvres.2026.107905},pages={107905},dimension={true},month=feb,year={2026},publisher={Elsevier}}
The Tibetan Plateau (TP), known as the "Asian Water Tower," is currently undergoing a rapid wetting trend. While this moisture increase is often viewed as beneficial for water availability, it remains unclear whether the hydrological system itself is becoming more resilient or drifting toward instability. Here, we apply an entropy-based framework to quantify the changing structural organization of the TP’s soil moisture system. We show that from 2000 to 2024, regional wetting has driven a long-term decline in entropy, reflecting an increase in system order and stability due to enhanced hydrological buffering capacity. This stability is modulated by the El Niño-Southern Oscillation (ENSO), which regulates regional heterogeneity via a distinct spatial dipole. Crucially, however, CMIP6 climate projections reveal an alarming reversal: future warming triggers a rise in entropy. This transition signals a loss of systemic resilience, characterized by intensified spatial disorder and potential abrupt regime shifts by the mid-century. Our findings suggest that while current wetting provides a stabilizing buffer, continued warming is projected to amplify spatial heterogeneity, thereby destabilizing the Asian Water Tower, with significant risks for downstream water security.
@article{xie2026warming,title={Warming-driven rise in soil moisture entropy signals destabilization of the Asian Water Tower},author={Xie, Yiran and Liu, Teng and Ma, Xuan and Lyu, Yingshuo and Wang, Xu and Qian, Yatong and Wang, Ming and Chen, Xiaosong},journal={arXiv preprint arXiv:2601.01534},doi={10.48550/arXiv.2601.01534},year={2026}}
Climate variability is increasingly threatening forest ecosystem functioning and carbon sink stability, reducing resilience and triggering abrupt state transitions. Ecosystem productivity resilience (EPR)—the ability to maintain and recover carbon sequestration function under disturbance—is a key dimension of functional resilience. Declining EPR often precedes functional anomalies and signals increased risk of critical transitions. However, previous studies have largely focused on temporal trends and lack spatially explicit methods for assessing resilience states and transition risks. We develop a composite indicator framework to quantify EPR variation using critical slowing down (CSD) metrics derived from gross primary production (GPP) time series. By integrating structure and process characteristics, we identify EPR states and potential transitions under climate change. Applied across China from 2000 to 2018, the framework reveals that 57.47% of forests experienced an EPR decline, primarily driven by climatic water availability. Coniferous-broadleaf forests in the temperate zone experienced the most severe abrupt declines under both water surplus and deficit. Reductions in EPR and productivity are statistically linked and emerge as ecosystems approach critical thresholds. Notably, 24.58% of forests have transitioned from a state of uniform stability to unstable multistability. As projected EPR degradation escalates under persistent warming, high-risk transitions increasingly cluster in boundary areas and high-vulnerability shifts toward productive lower latitudes. Our findings highlight the urgency of incorporating the EPR indicator system into resilience assessments and informing ecological adaptation management.
@article{LYU2025114388,title={An indicator framework for assessing forest ecosystem productivity resilience and transition risks under climate change},journal={Ecological Indicators},volume={181},pages={114388},year={2025},issn={1470-160X},dimensions={true},month=dec,doi={10.1016/j.ecolind.2025.114388},author={Lyu, Yingshuo and Zheng, Xi and Wang, Han and Liu, Teng and Chao, Chutong and Ou, Xiaoyang},keywords={Ecosystem productivity resilience (EPR), Critical slowing down (CSD) metrics, Climatic water availability, State transitions, Forest ecosystem, Adaptive forest management, Spatial resilience assessment}}
The Tibetan Plateau (TP) and surrounding regions, vital to global energy and water cycles, are profoundly influenced by climate change and anthropogenic activities. Despite widespread attention to vegetation greening across the region since the 1980s, its underlying mechanisms remain poorly understood. This study employs the eigen microstates method to quantify vegetation greening dynamics using long-term remote sensing and reanalysis data. We identify two dominant modes that collectively explain more than 61% of the vegetation dynamics. The strong seasonal heterogeneity in the southern TP, primarily driven by radiation and agricultural activities, is reflected in the first mode, which accounts for 46.34% of the variance. The second mode, which explains 15% of the variance, is closely linked to deep soil moisture (SM3, 28 cm to 1 m). Compared to precipitation and surface soil moisture (SM1 and SM2, 0–28 cm), our results show that deep soil moisture exerts a stronger and more immediate influence on vegetation growth, with a one-month response time. This study provides a complexity theory-based framework to quantify vegetation dynamics and underscores the critical influence of deep soil moisture on greening patterns in the TP.
@article{xie2025ecosystem,title={Ecosystem evolution and drivers across the Tibetan Plateau and surrounding regions},author={Xie, Yiran and Wang, Xu and Qian, Yatong and Liu, Teng and Fan, Hao and Chen, Xiaosong},journal={Journal of Environmental Management},volume={380},pages={124885},month=apr,year={2025},publisher={Elsevier},doi={10.1016/j.jenvman.2025.124885},dimensions={true},}
Many biological processes employ mechanisms involving the locations and interactions of multiple components. Given that most biological processes occur in three dimensions, the simultaneous measurement of three-dimensional locations and interactions is necessary. However, the simultaneous three-dimensional precise localization and measurement of interactions in real time remains challenging. Here, we report a new microscopy technique to localize two spectrally distinct particles in three dimensions with an accuracy (2.35σ) of tens of nanometers with an exposure time of 100 ms and to measure their real-time interactions using fluorescence resonance energy transfer (FRET) simultaneously. Using this microscope, we tracked two distinct vesicles containing t-SNAREs or v-SNARE in three dimensions and observed FRET simultaneously during single-vesicle fusion in real time, revealing the nanoscale motion and interactions of single vesicles in vesicle fusion. Thus, this study demonstrates that our microscope can provide detailed information about real-time three-dimensional nanoscale locations, motion, and interactions in biological processes.
@article{chen2021simultaneous,title={Simultaneous real-time three-dimensional localization and FRET measurement of two distinct particles},author={Chen, Xingxiang and Liu, Teng and Qin, Xianan and Nguyen, Quang Quan and Lee, Sang Kwon and Lee, Chanwoo and Ren, Yaguang and Chu, Jun and Zhu, Guang and Yoon, Tae-Young and others},journal={Nano Letters},volume={21},number={18},pages={7479--7485},year={2021},month=sep,publisher={ACS Publications},doi={10.1021/acs.nanolett.1c01328},dimensions={true}}
@article{qin2020simultaneous,title={Simultaneous tracking of two motor domains reveals near simultaneous steps and stutter steps of myosin 10 on actin filament bundles},author={Qin, Xianan and Yoo, Hanna and Cheng, Harry Chun Man and Nguyen, Quang Quan and Li, Jing and Liu, Xiaoyan and Prunetti, Laurence and Chen, Xingxiang and Liu, Teng and Sweeney, H Lee and others},journal={Biochemical and biophysical research communications},volume={525},number={1},pages={94--99},year={2020},publisher={Elsevier},dimensions={true},doi={10.1016/j.bbrc.2020.02.039},}
@article{qin2020increased,title={{Increased confinement and polydispersity of STIM1 and Orai1 after Ca2+ store depletion}},author={Qin, Xianan and Liu, Lei and Lee, Sang Kwon and Alsina, Adolfo and Liu, Teng and Wu, Chao and Park, Hojeong and Yu, Chenglong and Kim, Hajin and Chu, Jun and others},journal={Biophysical Journal},volume={118},number={1},pages={70--84},year={2020},publisher={Elsevier},dimensions={true},doi={10.1016/j.bpj.2019.11.019},}
Marine Environmental Research, Feb 2026
arXiv preprint arXiv:2601.01534, 2026
Ecological Indicators, Dec 2025
Journal of Environmental Management, Apr 2025
Biochemical and biophysical research communications, 2020