International Journal of Climatology最新文献

The apparent temperature (APT) has significant impacts on human health; however, its variability across China, in particular, based on in situ observations, remains unclear. Based on daily meteorological station observation datasets, this study analysed the variability and spatial distribution of summer APT in China during 1960–2019, which is a compound variable combining surface air temperature (SAT), relative humidity, and wind speed. The contribution of SAT, relative humidity, and wind speed to the variation of APT is examined. It is shown that the variation of SAT plays a determining role in the variation of APT, whereas it displays strong regional differences from the impacts of relative humidity and wind speed. The leading mode of APT displays a mono-sign pattern throughout China, explaining 31.6% of the variance, which is significantly related to the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO). The combination of the negative phase of PDO and the positive phase of AMO is associated with higher APT in China, whereas the negative AMO and positive PDO phases tend to accompany with lower APT. Results show that the combination of the opposite phases of AMO and PDO is associated with anomalous teleconnections and surface conditions. Anomalous cyclonic circulation is observed over China during the PDO–AMO+ phase, and abnormal thermal low pressure exists in the lower troposphere. Meanwhile, the soil moisture and cloud cover decreased, allowing more solar radiation to reach the ground. This causes an increase in SAT and water vapour convergence, resulting in an increase in the humidity and leading to an increase in APT. An opposite situation is found in the associated circulation pattern and surface conditions during the PDO + AMO-phase, contributing to decreased APT. The above results provide scientific insights into the long-term variability of summer APT in China, highlighting the modulation of natural variability in impacting the variation of summer APT over China.

The air pollution transmission channel in North China, also termed the ‘2 + 26’ cities, has the most severe pollution problem in China. Based on the meteorological and environmental observation data from 2014 to 2022, the spatial–temporal features of the air quality index (AQI) in the ‘2 + 26’ cities are first compared to reveal the different changes in winter and summer. Since the issue of the Air Pollution Prevention and Control Action Plan in China in 2013, the winter AQI averaged at the ‘2 + 26’ cities shows a significant decreasing trend of −9.725 μg m⁻³ per year from 2014 to 2022, but it shows much slight variation in summer. In summer, the ozone (O₃) pollutant is the major contributor to the AQI. Results indicate that higher T_max and lower relative humidity (RH) are conducive to higher O₃ concentration and more severe air pollution. The averaged T_max at these cities has a significant increasing trend of 0.28°C per decade, while RH decreases also significantly at a rate of −1.08% per decade. This may be the main reason why the summer O₃ concentration maintains stability at a high level in the recent 9 years. Results also indicate that the ‘heat dome’ effect, characterised by a persistent high pressure and warming air subsidence from the upper to lower troposphere, is reinforcing in recent years, and this is favourable for the occurrence of long-lasting dry-type high temperatures and the enhancement of O₃ pollution. Projections from 19 CMIP6 models show that the high pressure system will remarkably strengthen in the near, middle, and long terms under the moderate emission scenario (SSP2-4.5). Therefore, the ‘heat dome’ effect will exacerbate the ozone pollution in the future.

The Central Asian region, characterised by its arid climate and fragile ecological environment, is highly sensitive to climate change, necessitating focused research on its future climate. This study utilises one global climate model (MPI-ESM1.2-HR) simulation from the sixth Coupled Model Intercomparison Project (CMIP6) to drive the regional climate model WRF for high-resolution (25 km) simulations within the Coordinated Regional Downscaling Experiment (CORDEX) program. These simulations target future climate changes under both low and high emission scenarios, SSP1-2.6 and SSP5-8.5. The historical simulation (1995–2014) was evaluated, indicating that the WRF model can reproduce better spatial and temporal patterns of temperature and precipitation in Central Asia compared to the global model, with reduced mean biases and more detailed topography insights, especially in mountainous regions. Future climate projections (2021–2060) indicate a significant temperature increase across Central Asia, correlating with rising greenhouse gas concentrations. Under SSP1-2.6, the average annual temperature rise for the mid-term future (2041–2060) is projected at 1.47°C, and under SSP5-8.5, it could reach 1.99°C. Winter warming is most rapid, especially in the central regions (approximately 43°N–47°N), while the southeastern high-altitude areas experience the biggest warming in summer. The spatial distribution of seasonal warming is very consistent with the reduction of surface albedo. The study also predicts an overall increase in average annual precipitation, with the most significant rise in the southwestern region and a decrease in the northeast. Both SSP1-2.6 and SSP5-8.5 scenarios project a precipitation increase, which is more pronounced in the mid-term than the near-term future (2021–2040). Precipitation is expected to rise in winter across Central Asia, while in summer it shows a varied pattern of increase in the west and decrease in the east, which is probably contributed to the changes of moisture flux.

Rainfall data plays a pivotal role in modelling complex and nonlinear hydro-meteorological systems. In this process, the availability and fidelity of rainfall data at different spatial and temporal scales are of utmost importance. Gaps in the rainfall data are inevitable, resulting from human errors and instrumental/sensor malfunctions. The sparse availability of station data in the complex terrains of the Himalayas compounds the problem. The imputation of these gaps requires robust methods with higher accuracy and precision to capture the observed rainfall skewness in frequency and magnitude. This study has considered gap-filling of missing data at different elevations under three agro-climatic zones in Himachal Pradesh, India. For that, seven machine learning methods were used to estimate the missing daily rainfall data, including multiple linear regression (MLR), support vector regression with radial basis function kernel (SVR-RBF), K-nearest neighbours (KNN), random forest (RF), multilayer perceptron (MLP), extreme gradient boosting (XGBoost) and Lasso regression. For a comprehensive evaluation of the estimations, the analysis was structured into two tiers: overall (the entire time series) and RIC (rainfall-intensity-classes) assessments, ensuring a more robust comparison of the imputation methods. The methods were evaluated using the coefficient of determination (R²), correlation coefficient (r), root mean square error (RMSE) and mean absolute error (MAE). The results showed that, in both overall and RIC analyses, MLP consistently demonstrated higher accuracy and lower estimation errors, making it the most suitable method for imputing missing daily rainfall data across different elevation zones in the complex Himalayan terrain considered in this study.

Extreme precipitation aggregated in time and space will lead to the superposition and amplification of disaster risks, causing significant impacts on social economy and ecological environment. Incorporating the area factor into the extreme precipitation risk assessment framework using intensity-duration-area-frequency (IDAF) curves can effectively evaluate the superposition and amplification effects of extreme precipitation events. Therefore, this study utilised high spatiotemporal resolution precipitation data (i.e., 0.1° × 0.1° and 3 h) from the Yangtze River basin (YRB) in the period of 1979 to 2020 to establish seasonal and annual IDAF curves at multiple spatiotemporal scales (i.e., 3–96 h and 144–961 km²) for the first time in the YRB. The extreme precipitation intensity at different spatiotemporal scales and return periods was estimated, and the patterns of its variation with spatiotemporal scales were also investigated. The results indicated that: (1) The goodness-of-fit of the Extended Generalised Pareto Distribution –IDAF (EGPD-IDAF) model exhibited significant seasonality and scale dependency, with the best fitting effect in summer and at the mesoscale (i.e., 24 h and 144 km²); (2) At the 3–6 h scale, extreme precipitation return levels in the YRB exhibited higher sensitivity to variations in area, showing greater fluctuations, whereas as the temporal scale increased, the impact of area variation gradually weakened; (3) The maximum return levels of extreme precipitation in the eastern sub-basins of the YRB occurred at the spatiotemporal scale of 3 h and 144 km², representing local short-duration heavy precipitation, while those in the inland sub-basins occurred at the large scale of 961 km² with a temporal scale of 24 or 48 h. This study elucidates the area effect of extreme precipitation events in the YRB and establishes the relationship between extreme precipitation return levels and duration and area, offering significant value for regional climate services and disaster risk management.

In spring of 2022, northern China experienced much warmer temperatures and a severe precipitation deficit, which led to an extreme drought over the region. Our analysis indicates that this extreme spring drought is associated with the extreme La Niña event and unprecedented warming sea surface temperature (SST) anomalies in the mid-latitude North Pacific and North Atlantic. On the interannual time scale, the La Niña could lead to an anomalous cyclone over the western North Pacific and strengthen the southern part of the East Asian trough. The resultant downward motion and water vapour divergence anomalies dominate northern China, which cause an increase in air temperature and potential evapotranspiration (PET) and a decrease in precipitation, consequently intensifying the spring drought over northern China. In contrast, the warm SST anomalies over the mid-latitude North Pacific and North Atlantic influence the northern China spring drought on the interdecadal time scale. Both the warm SST anomalies over the two oceans are related to a Rossby wave train that causes an anomalous anticyclone over the upstream of northern China. As a result, there are anomalous downward motions over northern China, favouring the increase in regional air temperature and PET. Accordingly, the warm SST anomalies over the mid-latitude North Pacific and North Atlantic could aggravate the spring drought over northern China. Leave-one-out validation analysis indicates that the aforementioned SST anomalies are potential predictors for the spring drought over northern China.