International Journal of Climatology最新文献

Droughts still is a growing challenge, particularly in Poland's temperate climate zone, impacting agriculture, water management, and ecosystems. This study investigates the spatiotemporal variability of summer hydrological droughts (May–October) from 1993 to 2022 across Poland's seven physiographic regions. The motivation stems from the need to understand drought patterns amidst climate change and to inform strategies for mitigating water shortages. Despite extensive research on droughts in Europe, limited studies have addressed the spatial and temporal variability of hydrological droughts across Poland's diverse physiographic regions, particularly concerning the interplay of precipitation, potential evapotranspiration (PET), standardised runoff index (SRI), and temperature. Data from over 52 synoptic stations and 125 gauging stations were analysed, focusing on regional monthly averages of air temperature, precipitation, PET, SRI and the annual number of drought days, defined using the Q_90% threshold flow rate. Results revealed marked regional variability in drought conditions, with the northern and central regions experiencing the highest number of drought days, especially after 2015. Mountainous areas, including the Carpathians and Sudetes, recorded fewer drought days due to higher precipitation and lower PET compared to lowland regions. Trend analysis indicated rising temperatures across all regions, driving increased PET, particularly in northern areas. Precipitation trends were inconsistent, with some regions observing limited changes. Notably, a strong correlation between PET and precipitation was observed in lowland regions, while mountainous areas exhibited a negative correlation, where higher precipitation curtailed evaporation. The SRI highlighted a clear intensification of hydrological drought across Poland, especially in lowlands, driven by rising temperatures, limited precipitation recovery, and human activities that reduce water retention. This research addresses the gap in understanding regional drought dynamics and confirms the critical role of temperature and evapotranspiration in intensifying droughts. Findings provide valuable insights into hydrological drought patterns in Poland, contributing to more informed water resource management amidst ongoing climate changes.

This study investigates the spatiotemporal evolution, climatic drivers, and predictive modelling of heatwaves (HWs) over Tamil Nadu (TN), India, during 1981–2030, using India Meteorological Department (IMD) gridded temperature and reanalysis datasets. The analysis reveals a progressive intensification and inland expansion of HW frequency and duration, with hotspots in Chennai, Cuddalore, Trichy, Madurai and Vellore during 2011–2020. El Niño–Southern Oscillation (ENSO)– Indian Ocean Dipole (IOD) coupling emerged as the dominant large-scale driver of HW variability. The concurrence of El Niño and nIOD (Negative IOD) phases significantly enhanced HW frequency and duration through large-scale subsidence, reduced convective activity and suppressed cloud cover. Conversely, La Niña years exhibited enhanced moisture advection, increased cloudiness and weaker heat extremes. ENSO-driven anomalies in relative humidity (RH), total cloud cover (TCC) and wind circulation clearly demonstrated that El Niño-induced dryness and weakened low-level winds favor heatwave amplification, while La Niña fosters convective moderation. In addition, positive Atlantic Multidecadal Oscillation (AMO) and negative Arctic Oscillation (AO) phases during the recent decade strengthened subsidence and stagnant warm-air conditions, jointly contributing to intensified HWs, suggesting an increasing influence of extra-tropical oscillations under a warming climate. A hybrid Artificial Neural Network–Long Short-term Bi-directional Memory (ANN–LSBM) model integrating dynamic predictors (T_max, RH, TCC) and climate indices (ENSO, IOD, AO, AMO) achieved a Root Mean Square Error (RMSE) of 19.97 and R² of 0.48, demonstrating stable convergence and moderate predictive skill. The findings emphasize the growing importance of both tropical and extra-tropical climate drivers in modulating regional heat extremes and underline the urgent need for state-level planner awareness, adaptive heat action plans, and integration of Artificial Intelligence (AI)-based predictive tools to enhance TN's heatwave preparedness and resilience. In addition, land use and land cover (LULC) change detection between 2011 and 2020 revealed significant land transformation across TN, characterized by an increase in built-up area and a decline in barren and floodplain regions. These transitions indicate rapid urbanization, surface modification and reduced vegetative cover, which have collectively contributed to enhanced surface heating and intensified local heatwave conditions. The LULC findings reinforce that regional warming and urban expansion are critical non-climatic drivers influencing the observed escalation in heatwave frequency and intensity.

The far-reaching and devastating impacts of drought underscored the need for effective drought forecasting. This study addressed the critical gap of the often-overlooked impacts of input data uncertainty on the forecasting accuracy. The study incorporated the input data uncertainty as a novel approach into the hybrid Extreme Gradient Boosting-Recurrent Neural Network (XGBoost-RNN) to forecast Standardised Precipitation Index (SPI) and Standardised Precipitation Evapotranspiration Index (SPEI) across different time scales in Malaysia. The results were assessed using coefficient of correlation (R), root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), Nash Sutcliffe Efficiency (NSE) and Wilmott's Index (WI). The XGBoost-RNN incorporating input data uncertainty (XGBoost-RNN-ID) outperformed its counterpart, achieving R values up to 0.9988, WI up to 0.9994, and RSME, MAE, and MSE as low as 0.0466, 0.0367, and 0.0022. The NSE values categorised XGBoost-RNN-ID as “very good” across all time scales, demonstrating its reliability in drought forecasting.

Air temperature is a key variable influencing numerous chemical, physical, hydrological and biological processes; however, long-term observations are lacking, particularly at high elevations. This study presents a statistical methodology for reconstructing daily air temperature time series at the highest permanently manned meteorological station in Switzerland, the Jungfraujoch (3571 a.s.l.), based on observations from 30 lower-altitude stations (485–2691 m a.s.l.) dating back to 1900. The reconstructed time series were then compared with observations from 1933 to 2023, as well as with two high-resolution gridded datasets (HISTALP and Imfeld et al. 2023) to validate the period from 1900 to 1933. We found that (i) the selection of stations with temporally consistent long-term observations is a critical issue, (ii) model performance, efficiency and errors are primarily influenced by altitude, (iii) the Kling–Gupta Efficiency (KGE) is an appropriate metric for defining the ensemble simulation, considering correlation and bias on the mean and standard deviation values, (iv) the ensemble simulation increases the temporal consistency of the estimated time series and mediates the latitudinal and longitudinal gradients, (v) comparable performance to existing datasets is achieved, despite the low-data requirements, but with greater computational efficiency, and (vi) the estimated time series can provide a benchmark for evaluating time series anomalies and for a deeper analysis of the elevation-dependent warming issue.

The marine heat wave (MHW) events are characterised in terms of duration, maximum intensity, and frequency of events. This study uses the High Resolution Model Intercomparison Project (HighResMIP) simulations to investigate how the horizontal resolution and percentile thresholds used to define the MHWs influence the characteristics of these events over the northern Indian Ocean region around the Arabian Sea (AS) and Bay of Bengal (BoB). It is shown that the simulation of the MHW characteristics improves with an increase in the model's horizontal resolution. The MHW events in the AS region tend to be less frequent but of longer duration (10%–25%) as compared to the BoB region. Further, the MHW events obtained using the 99th percentile threshold will be of shorter duration (30%–50%) and higher intensity (70%–75%) as compared to the 90th percentile threshold event. It is demonstrated that the duration, maximum intensity, and frequency of the MHW events will increase up to 75%, 35%, and 98%, respectively in the future projections under the SSP5-8.5 scenario depending on the model and percentile threshold. It is also shown that the severe and extreme MHW events that were absent during the historical periods are projected to significantly increase in the near future. Such events will be more frequent and of longer duration under the global warming scenario. However, there exists a considerable inter-model variability in the depiction of these projected changes, which introduces uncertainty in the estimation of the future risks and impacts of MHW events, thereby posing greater challenges for the adaptation and mitigation strategies.