Advances in Climate Change Research最新文献

Environmental factors are essential input variables for susceptibility assessment models of mountain geohazards. However, the existing literature provides a limited understanding of the relative contribution of these factors to the occurrence of geohazards with a warming climate, posing tremendous challenges for risk management in mountainous areas. Ya'an city is susceptible to hazards because of its steep terrain, abundant precipitation and active seismic activity. In this regard, we utilise the GeoDetector model to extract critical environmental factors affecting the spatial patterns of mountain geohazards (i.e., landslide, debris flow and rockfall) in Southwest China. The analysis indicates that the factors with the highest explanatory power for the spatial distribution of landslides, debris flows, and rockfalls are soil property, extreme precipitation and extreme temperature, respectively. Notably, we revealed the synergistic effects among factors given their larger q-value than individual ones. We further explored the responses of mountain geohazards to climate change, including the rising temperature and precipitation, because the frequent occurrence of mountain geohazards is closely related to a warming climate. The variation in snow water equivalent caused by antecedent snowfall and snowdrifts acts as a crucial indicator for geohazards, highlighting the significance of snow and wind observations in meteorological nowcasting and disaster prewarning. We disclose the phenomenon of the geohazard hysteresis to the precipitation peak resulting from the top–down (i.e., precipitation-runoff and surface-deep soil moisture) peak shifts. Our work is expected to enhance the precision of susceptibility assessment models and the reliability of short-term forecasts for mountain geohazards.

Urolithiasis is a heat-specific disease. Exploring heat-related urolithiasis susceptibility subtypes, economic burden, and modifying factors could assist governments in targeting interventions to reduce the heat-related health risks of urolithiasis morbidity. We collected data on 23,492 patients with upper urinary tract stones (main subtypes of urolithiasis) from 2013 to 2017 in Nanjing, China. We adopted generalized additive quasi-Poisson models to examine the associations between daily mean temperatures and morbidity of upper urinary tract stones, while generalized additive Gaussian models were used to explore the relationships between temperatures and log-transformed medical costs. We examined the modification effects of disease subtypes (kidney and ureteral calculus), sex, and age through stratified analyses and the modification effects of other meteorological factors by introducing interaction terms in the models. We found that short-term summer heat exposure has a statistically significant effect on ureteral calculus morbidity but not on kidney calculus morbidity. For ureter calculus, a 1 °C temperature increase was associated with a 4.36% (95% confidence interval [CI]: 1.94%, 6.83%) increase in daily hospitalization and a 5.44% (95% CI: 2.71%, 8.25%) increase in daily medical costs. The attributable fraction associated with heat (greater than the median value of daily mean temperature, 26.8 °C) was 7.85% (95% empirical confidence interval [eCI]: 3.64%, 11.44%) for hospitalization and 9.36% (95% eCI: 4.91%, 13.14%) for medical costs. The effects of heat on ureter calculus morbidity were significantly higher among the males and those with high sunshine duration than females and those with low sunshine duration. Short-term summer heat exposure was associated with increased morbidity and medical costs of ureteral calculus. Relevant government organizations should take effective intervention measures, including community health education, to reduce the health hazards and economic losses caused by heat.

Wind energy development in Central Asia can help alleviate drought and fragile ecosystems. Nevertheless, current studies mainly used the global climate models (GCMs) to project wind speed and energy. The simulated biases in GCMs remain prominent, which induce a large uncertainty in the projected results. To reduce the uncertainties of projected near-surface wind speed (NSW) and better serve the wind energy development in Central Asia, the Weather Research and Forecasting (WRF) model with bias-corrected GCMs was employed. Compared with the outputs of GCMs, dynamical downscaling acquired using the WRF model can better capture the high- and low-value centres of NSWS, especially those of Central Asia's mountains. Meanwhile, the simulated NSWS bias was also reduced. For future changes in wind speed and wind energy, under the Representative Concentration Pathway 4.5 (RCP4.5) scenario, NSWS during 2031–2050 is projected to decrease compared with that in 1986–2005. The magnitude of NSWS reduction during 2031–2050 will reach 0.1 m s⁻¹, and the maximum reduction is projected to occur over the central and western regions (>0.2 m s⁻¹). Furthermore, future wind power density (WPD) can reveal nonstationarity and strong volatility, although a downward trend is expected during 2031–2050. In addition, the higher frequency of wind speeds at the turbine hub height exceeding 3.0 m s⁻¹ can render the plain regions more suitable for wind energy development than the mountains from 2031 to 2050. This study can serve as a guide in gaining insights into future changes in wind energy across Central Asia and provide a scientific basis for decision makers in the formulation of policies for addressing climate change.

Numerical models serve as an essential tool to investigate the causes and effects of Arctic sea ice changes. Evaluating the simulation capabilities of the most recent CMIP6 models in sea ice volume flux provides references for model applications and improvements. Meanwhile, reliable long-term simulation results of the ice volume flux contribute to a deeper understanding of the sea ice response to global climate change. In this study, the sea ice volume flux through six Arctic gateways over the past four decades (1979–2014) were estimated in combination of satellite observations of sea ice concentration (SIC) and sea ice motion (SIM) as well as the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) reanalysis sea ice thickness (SIT) data. The simulation capability of 17 CMIP6 historical models for the volume flux through Fram Strait were quantitatively assessed. Sea ice volume flux simulated from the ensemble mean of 17 CMIP6 models demonstrates better performance than that from the individual model, yet IPSL-CM6A-LR and EC-Earth3-Veg-LR outperform the ensemble mean in the annual volume flux, with Taylor scores of 0.86 and 0.50, respectively. CMIP6 models display relatively robust capability in simulating the seasonal variations of volume flux. Among them, CESM2-WACCM performs the best, with a correlation coefficient of 0.96 and a Taylor score of 0.88. Conversely, NESM3 demonstrates the largest deviation from the observation/reanalysis data, with the lowest Taylor score of 0.16. The variability of sea ice volume flux is primarily influenced by SIM and SIT, followed by SIC. The extreme large sea ice export through Fram Strait is linked to the occurrence of anomalously low air temperatures, which in turn promote increased SIC and SIT in the corresponding region. Moreover, the intensified activity of Arctic cyclones and Arctic dipole anomaly could boost the southward sea ice velocity through Fram Strait, which further enhance the sea ice outflow.