Zhijun Li

Lake, river, and sea ice are key components of the Earth’s cryosphere

Ozone (O₃), as a key secondary pollutant, exhibits pronounced spatiotemporal heterogeneity, posing significant challenges to coordinated regional air pollution control. However, systematic understanding of high-resolution O₃ spatial inversion and its driving mechanisms in arid urban agglomerations remains limited. In this study, the Tianshan North Slope Urban Agglomeration (TNSUA) was selected as the study area, and a multi-model comparative framework was established to comprehensively evaluate the O₃ inversion performance of 16 machine learning and deep learning models, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Extremely Randomized Trees (ET), and Gradient Boosting Decision Tree (GBDT). Based on the optimal model performance, high-precision daily O₃ spatial reconstruction for the year 2023 was achieved across the study region. The contributions of individual driving factors and their nonlinear response relationships were quantitatively interpreted using Shapley Additive Explanations (SHAP). Furthermore, a backward trajectory model combined with the Weighted Potential Source Contribution Function (WPSCF) and Weighted Concentration Weighted Trajectory (WCWT) methods was employed to identify potential source regions and transport pathways of O₃. The results indicate that: (1) The XGBoost model exhibited the best performance (R² = 0.93, RPD > 3). The reconstructed results reveal that high O₃ concentrations in 2023 were primarily distributed in southern Urumqi, southern Changji, and southern Tacheng, with southern Urumqi identified as the most prominent hotspot. (2) The spatial variability of O₃ was predominantly driven by downward shortwave radiation (DSR) and air temperature (TEM), both of which showed significant nonlinear responses and threshold effects on O₃ formation. (3) Source apportionment analysis indicates that westerly transport serves as a major exogenous contribution pathway, with potential source regions mainly located in the surrounding areas of the northern Tianshan slope as well as Central Asia, particularly eastern Kazakhstan and northern Kyrgyzstan. This study systematically elucidates the formation mechanisms of O₃ pollution in arid urban agglomerations from three aspects—high-precision inversion, driving mechanism analysis, and cross-regional transport identification—thereby providing a scientific basis for precise air pollution control strategies.

Rapid changes in Arctic summer sea ice exert substantial influences on the polar climate system, maritime navigation, and resource exploitation, while subseasonal-to-seasonal (S2S) prediction of sea ice state remains highly uncertain. Using daily observations and reanalysis data of sea ice concentration (SIC) and thickness (SIT) from 1979 to 2023, together with concurrent atmospheric and oceanic fields, this study develops a multivariate linear Markov model to perform S2S predictions of Arctic summer sea ice. Sensitivity experiments with different variable combinations, weighting strategies, and modal truncation schemes are conducted, and predictive skill is systematically evaluated against persistence and climatological baselines. Results indicate that the model exhibits stable forecast skill without pronounced error accumulation at extended lead times. SIC predictability is primarily governed by its intrinsic spatiotemporal persistence and is significantly modulated by oceanic thermodynamic forcing, particularly sea surface temperature and surface net energy flux, highlighting a pronounced oceanic memory effect. In contrast, local atmospheric dynamic variables provide limited incremental skill. For SIT, predictability is dominated by its own historical state, with SIC contributing marginal short-term improvement and air–sea coupling exerting weak influence. Overall, the proposed framework effectively extracts dominant predictable signals with clear physical interpretability, providing a computationally efficient statistical approach for S2S prediction of Arctic summer sea ice.

Situated at the core of the Asian arid zone, the Tarim River Basin (TRB) serves as a critical indicator of regional hydroclimatic responses to global warming. Utilizing 27 extreme climate indices recommended by the Expert Team on Climate Change Detection and Indices, this study analyzes the spatiotemporal evolution of climate extremes in the TRB from 1960 to 2022 and explores their correlations with primary large-scale atmospheric circulation factors. The results indicate that, at the temporal scale, extreme warm indices (TX90P, TN90P, SU25, TR20) and most extreme precipitation indices (except for CDD) exhibited increasing trends, accompanied by pronounced abrupt changes and periodic characteristics. The changes were characterized by stronger warming at low temperatures than at high temperatures, greater nighttime warming than daytime warming, and larger increases in warm days than cold days. Extreme temperature and precipitation indices underwent abrupt changes in the mid-to-late 1990s and 1980s, respectively. The former exhibits pronounced “cold-warm” oscillations at 10–15-year and 25–35-year scales, while the latter displays distinct “wet-dry” cyclic alternations at 8–9-year and 30–32-year scales. Spatially, extreme temperature indices showed consistent warming across most stations. In contrast, the change trends of extreme precipitation indices displayed obvious spatial heterogeneity, with growth rates generally decreasing from west to east. Further analyses demonstrate that most extreme climate indices exhibit significant linear correlations with the AMO, PDO, NAO, and NOI. Notably, the AMO emerges as the dominant driver of variations in both extreme temperature and precipitation. In the context of accelerated global warming, these insights are pivotal for enhancing regional climate risk management and water resource adaptability.

High-latitude and cold-region environments feature tightly coupled hydrological, cryospheric, and ecological subsystems, where seasonal freeze–thaw cycles, snow cover, permafrost, and river and lake ice fundamentally shape water flows and ecosystem processes

The proportion of granular ice in sea ice layers has markedly increased due to global warming. To investigate the uniaxial compressive behavior of granular sea ice, we conducted a series of experiments using natural piled and level ice samples collected from the Bohai Sea. A total of 311 specimens were tested under controlled temperature conditions ranging from −15 °C to −2 °C and strain rates varying from 10⁻⁵ to 10⁻² s⁻¹. The effects of porosity, strain rate, and failure modes were studied. The results show that both the uniaxial compressive strength and uniaxial compressive elastic modulus were dependent on strain rate and porosity. Granular sea ice exhibited a non-monotonic strength dependence on strain rate, with the strength increasing in the ductile regime and decreasing in the brittle regime. In contrast, the elastic modulus increased monotonically with the strain rate. Both the strength and elastic modulus decreased with increasing porosity. Level ice consistently demonstrated higher strength and an elastic modulus than piled ice at equivalent porosities. Unified parametric models were developed to describe both properties across a wide range of strain rates encompassing the ductile-to-brittle (DBT) regime. The experimental results show that, as porosity decreased, the transition strain rate of granular sea ice shifted from 2.34 × 10⁻³ s⁻¹ at high porosity (45%) to 1.42 × 10⁻⁴ s⁻¹ at low porosity (10%) for level ice and 1.87 × 10⁻³ s⁻¹ to 1.19 × 10⁻³ s⁻¹ for piled ice. These results were compared with classical columnar ice models. These findings are useful for informing the design of vessel and coastal structures intended for use in ice-covered waters.

The cryosphere constitutes a vital component of the Earth’s system

Mathematical models and methods serve as fundamental tools for studying ice-related phenomena in the Yellow River. River ice is driven and constrained by hydrometeorological and geographical conditions, creating a complex system. Regarding the Yellow River, there are some uncertainties that manifest in unique features in this context, including ice–water–sediment mixed transport processes and the distribution of sediment both within the ice and on its surface. These distinctive characteristics are considered to different degrees across different scales. Mathematical models for Yellow River ice developed over the past few decades not only encompass models for the large-scale deterministic evolution of river ice formation and melting, but also uncertainty parameter schemes for deterministic mathematical models reflecting the Yellow River’s particular ice-related characteristics. Moreover, there are modern mathematical results quantitatively describing these characteristics with uncertainty, allowing for a better understanding of the unique ice phenomena in the Yellow River. This review summarizes (a) universal equations established according to thermodynamic and hydrodynamic principles in river ice mathematical models, as well as (b) uncertainty sources caused by the river’s characteristics, ice properties, and hydrometeorological conditions, embedded in parametric schemes reflecting the Yellow River’s ice. The intractable uncertainty-related problems in space–sky–ground telemetric image segmentation and the current status of mathematical processing methods are reviewed. In particular, the current status and difficulties faced by various mathematical models in terms of predicting the freeze-up and break-up times, the formation of ice jams and dams, and the early warning of ice disasters are presented. This review discusses the prospects related to the uncertainties in research results regarding the simulation and prediction of Yellow River ice while also exploring potential future trends in research related to mathematical methods for uncertain problems.

Ice and snow are essential components of the Earth’s cryosphere, contributing significantly to the global climate system and human civilization