arXiv subjects

Banglin Zhang

Publications and source records attributed to Banglin Zhang.

5 recordsLinked to original sources

arXiv2026

Characterizing the Evolution of Tropical Cyclone Thermal Structure: A Tropical Cyclone Thermal Phase Space

Current understandings of the tropical cyclone (TC) warm core primarily relies on statistical averages from soundings and satellite products, which provide a climatological thermal state but cannot adequately characterize the continuous three-dimensional evolution of the warm core throughout the TC life cycle. In this study, Empirical Orthogonal Function (EOF) analysis was applied to three-dimensional temperature anomalies of Western North Pacific (WNP) TCs derived from the ERA5 reanalysis dataset for the period 1979-2024. We found that the three leading EOF modes effectively capture the primary characteristics of the TC thermal structure: the first mode represents the typical warm core structure; the second mode characterizes vertical baroclinicity; and the third pattern captures the horizontal asymmetry of the temperature anomalies. Using the first three principal components (PCs), we established a three-dimensional Cartesian coordinate system. Within this framework, two phase parameters define a thermal phase space to visualize the TC's three-dimensional thermal structure: one diagnoses the vertical barotropic or baroclinic structure, and the other quantifies the horizontal asymmetry of the thermal field. The results demonstrate that the trajectory in the phase diagram effectively captures the observed intensity changes and thermal structural evolution. The EOF-based phase diagrams offer promising insights and provide a novel, objective tool for analyzing and diagnosing structural thermodynamic characteristics and intensity evolution by quantifying key thermal features and their dynamic relationships with TC intensity. This capability thereby holds substantial potential for advancing both theoretical understanding and operational forecasting of TCs.

physics.ao-ph↗

arXiv2026

Recent Sharp Rise in Inhomogeneous Hydrological Extremes Stress Vegetation Growth in China

The intensifying spatial inhomogeneity of rainfall under greenhouse warming implies that more inhomogeneous hydrological extremes (IHEs), i.e., coexistence of extreme rainfall or drought, may be triggered. While vegetation growth in China is sensitive to hydrological hazards, the variability of IHEs and their ecological impacts remain underexplored. Here, we find a significant increase in IHEs during China's growing season since 2000 (+2.1 events or +14.52 days per decade), with a rapid sharp rise to an annual average of 6.4 events or 42.0 days in the past five years. The primary driver is the enhanced inhomogeneity of moisture-dynamic coupled weather conditions, overlapping with a northward shift of climatological precipitation distribution. This results in a "Wet-North and Dry-South" pattern of IHE impacts, which poses severe and asymmetric threats to vegetation growth in China, with the expansion of drought areas exerts stronger stress on vegetation than the compensatory effects of rainfall. Our findings suggest that the sharp rises of IHEs tend to yield net negative impacts on vegetation growth, highlighting the need for stronger hydrological management to reduce future risks.

physics.ao-ph↗

arXiv2026

Global-mean surface air temperature change overestimates global warming rate

Current climate policies are targeted at slowing down the global warming rate, or the global mean surface air temperature (SAT) change ({\Delta}Tmean), which is measured by the arithmetic mean approach under the assumption that SAT changes are symmetrically distributed. However, in reality, the SAT change is asymmetric in nature and its influence on the {\Delta}Tmean interpretation seldom received attention in previous research about climate change. This study theorizes, based on the image histogram approach, that while {\Delta}Tmean measures the Earth's overall SAT change, it yields a value larger than the global-scale SAT change ({\Delta}Tgs) because of the asymmetrical distribution of SAT change. Results show that {\Delta}Tmean is greater than that based on {\Delta}Tgs by 0.19-0.23{\deg}C from 2000-2019, relative to the global SAT in 1979. In future climate projections, where more significant inhomogeneous warming is expected, the disagreement between {\Delta}Tmean and {\Delta}Tgs reach 0.27-0.54{\deg}C by the end of the 21st century (2080-2099) under different emission scenarios. The large difference between {\Delta}Tmean and {\Delta}Tgs implies that there is a net positive regional-scale warming effect over the globe which is mainly contributed by Arctic Amplification. This paper constitutes a warning that extreme regional warming effects could have large impacts on the interpretation of {\Delta}Tmean, which is often considered in climate assessments and policies making, and illustrates the limitations and cautions inherent in using {\Delta}Tmean as the only indicator of the global warming rate.

physics.ao-ph↗

arXiv2025

Questioning whether seasonal advance of intense tropical cyclones since the 1980s truly exists

Shan et al. (2023) recently reported significant seasonal advances of intense tropical cyclones (TCs) in both the Northern Hemisphere (NH) and Southern Hemisphere (SH) since the 1980s, and emphasized the data insensitivity of this conclusion, based on the Advanced Dvorak Technique-Hurricane Satellite (ADT-HURSAT) and the International Best Track Archive for Climate Stewardship (IBTrACS) datasets. However, this conclusion contradicts our recent findings. Following the procedures outlined in Shan et al., our analysis reveals that the seasonal advancing trend of intense TCs does not pass the significance test in the SH. Meanwhile, for the NH, the trend is statistically significant only when using the ADT-HURSAT, but not when using the IBTrACS. These discrepancies may be due to flaws in the calculations performed by Shan et al. The above findings raise doubts about the reproducibility and validity of Shan et al.'s conclusions regarding the global seasonal advance of intense TCs. We argue that the reported seasonal advance of intense TCs since the 1980s is inconclusive, and further investigations are needed.

physics.ao-ph↗

arXiv2025

AI Models Still Lag Behind Traditional Numerical Models in Predicting Sudden-Turning Typhoons

Given the interpretability, accuracy, and stability of numerical weather prediction (NWP) models, current operational weather forecasting relies heavily on the NWP approach. In the past two years, the rapid development of Artificial Intelligence (AI) has provided an alternative solution for medium-range (1-10 days) weather forecasting. Bi et al. (2023) (hereafter Bi23) introduced the first AI-based weather prediction (AIWP) model in China, named Pangu-Weather, which offers fast prediction without compromising accuracy. In their work, Bi23 made notable claims regarding its effectiveness in extreme weather predictions. However, this claim lacks persuasiveness because the extreme nature of the two tropical cyclones (TCs) examples presented in Bi23, namely Typhoon Kong-rey and Typhoon Yutu, stems primarily from their intensities rather than their moving paths. Their claim may mislead into another meaning which is that Pangu-Weather works well in predicting unusual typhoon paths, which was not explicitly analyzed. Here, we reassess Pangu-Weather's ability to predict extreme TC trajectories from 2020-2024. Results reveal that while Pangu-Weather overall outperforms NWP models in predicting tropical cyclone (TC) tracks, it falls short in accurately predicting the rarely observed sudden-turning tracks, such as Typhoon Khanun in 2023. We argue that current AIWP models still lag behind traditional NWP models in predicting such rare extreme events in medium-range forecasts.

physics.ao-ph↗