International Journal of Climatology最新文献

In the context of global warming, the frequency, intensity, and duration of heatwave events have markedly increased, bearing profound implications for both natural ecosystems and human societies. To effectively cope with this challenge, it is imperative to accurately identify and comprehensively assess the risks posed by heatwaves. This study undertakes a systematic approach and robust methodology to assess heatwave risks by leveraging a diverse array of data sources—encompassing remote sensing, statistical analyses. The methodology integrates the risk triangle theory alongside established risk assessment frameworks laid out by the Intergovernmental Panel on Climate Change (IPCC). Employing a three-dimensional evaluation framework encompassing hazard, exposure, and vulnerability, we unravel spatial–temporal patterns, high-risk zones, and dominant dimensions of heatwave risks contributing to potential disasters. Results indicated that during 1999–2008, roughly 27% of the study areas were affected by high and above risk levels of heatwaves, and the areas with high and very high hazard, exposure, and vulnerability accounted for approximately 19.5%, 10%, and 32.5%, respectively. During 2009–2018, the proportion of areas with high and very high risk, hazard, and exposure increased to about 31%, 26%, and 14%, respectively, while the percentage of areas with high and very high vulnerability decreased to about 24.43%. Notably, Xinjiang and the western part of Northwestern China are characterized by hazard-dominant conditions, while Southern China's risk profile has shifted from 1999–2008 to 2009–2018 from high hazard and vulnerability conditions to a more complex interaction involving hazard, exposure, and vulnerability. Moreover, Northern China and the northern segment of Southwestern China exhibit simultaneous high-risk rankings across hazard, exposure, and vulnerability dimensions, forming a comprehensive high-risk zone. These findings characterize heatwave risk patterns and offer critical insights for risk management decisions, guiding effective disaster prevention and relief measures to ensure socio-economic stability and public health.

Near-surface wind fields in the Prydz Bay and Amery Ice Shelf region of East Antarctica play a crucial role in the formation and variability of Antarctic Bottom Water, a cold, dense water mass that sinks and spreads across the deep ocean basins influencing ocean circulation and modulating earth's climate system. This study investigates the primary modes of variability of these wind fields using the self-organizing map (SOM) method and data from the latest version of the European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalyses (ERA5), spanning four decades from 1979 to 2020. While the wind field climatology, characterized by small seasonal variation, is dominated by katabatic and large-scale forcing, the spatial patterns of the primary variability modes are mainly influenced by synoptic system activities. The overall trend in annual wind speed anomalies is positive across the study region, with the exception of the southwestern part and central Prydz Bay. However, significant trends are observed in only two out of nine SOM nodes (nodes 4 and 9), which collectively explain less than 30% of the averaged trends over the region. The interannual variability in the seasonal occurrences of certain nodes is linked to several well-known climate modes, including the El Niño–Southern Oscillation, the Southern Annular Mode and Zonal Wavenumber 3. Our results provide a reference for forecasting the occurrence frequency of specific patterns, which could help mitigate the impact of extreme wind events through improved forecasting.

Compared to independent drought or extreme heat events, compound drought and heat events (CDHEs) can pose more adverse impacts on ecosystems and human society. As the anthropogenic influence on CDHEs in China remains largely unquantified, we analyse both observed and simulated changes in the occurrence of CDHEs over China from 1961 to 2014, and perform detection and attribution analyses utilizing an optimal fingerprinting method, based on observations, reanalysis data and numerical experiments from seven Coupled Model Intercomparison Project Phase 6 (CMIP6) models. Upward trends are found in observed CDHEs during 1961–2014 over the majority of China, especially in the last two decades. The increase in CDHEs is largely attributed to human influence. Anthropogenic forcing is detectable in the observed CDHEs changes in the whole China and its eastern region. Furthermore, a three-signal analysis indicates that greenhouse gases play a key role in increasing CDHEs over China, while natural forcing has a negligible effect. Anthropogenic aerosol emissions also play a detectable role in offsetting the greenhouse gas-induced increase of CDHEs over China.

The theory of radiative transfer in the atmosphere is crucial in the study of climate, because radiative exchanges are at the origin of the atmospheric dynamics. It is therefore important to evaluate this phenomenon in order to be able to take effective measures to tackle climate change. The objective of this work is to evaluate the capability of the RegCM5 climate model to reproduce radiative transfer over Central Africa. The analysis is carried out over a 10-year period, from January 2002 to December 2011 preceded by 1 year as spin-up. RegCM5 model were evaluated using the ERA5 dataset for the radiative transfer parameters (the shortwave radiation [SWR], longwave radiation [LWR], cloud cover [CLT], surface albedo [ALB] and surface temperature), as well as CHIRPS dataset for precipitation. Three subregions were identified for more specific analysis of the model, namely the Sahel, Congo basin and Cameroon highlands. Two radiative schemes were used: the radiative scheme of the community climate model (CCM) and Rapid Radiative Transfer Model (RRTM). The assessment of radiative transfer parameters was carried out by examining their seasonal variability and annual cycles using data from two RegCM5 experiments, RegCM5-CCM3 and RegCM5-RRTM. Before this assessment, a sensibility analysis to convective schemes carried out with the default RegCM5 radiative scheme (CCM3) shows that Grell scheme with Arakawa and Shulber closure is the best scheme to represent key radiation parameters (LWR and SWR). This convective scheme is therefore used for assessing the two Radiative transfer schemes. Results show that both RegCM5 experiments simulate relatively well the variables linked to radiative transfer for the four seasons of the year. However, RegCM5 with RRTM as radiative scheme depicts better performance over all subregions and seasons, suggesting that the choice of this scheme does not depend on land cover, topography and rainfall regimes in a complex region such as Central Africa.

Although previous research on climate change in the Arabian Peninsula (AP) has studied changes in individual meteorological variables, an analysis of changes in the overall weather conditions based on multiple meteorological variables is limited. Based on an air mass-based classification system, this research explored the local climate (1979–2023) of daily surface weather conditions (i.e., air masses) and associated changes over the Peninsula. For this purpose, the gridded weather typing classification (GWTC-2), an approach to classify multivariate surface weather situations relative to the average local climate, was utilized as it demonstrated outstanding performance in capturing daily weather characteristics in the Peninsula. Cold air mass (C), days with low temperature and near average humidity, was the most common cool weather type, with notable occurrences from Nov to Feb. Warm air mass (W), hot days with near average humidity, was the most common warm weather condition and maintained a marked presence throughout the year, with notable occurrences in summer. Coastal regions showed higher presences of humid warm (HW) and humid (H) days, whereas the central parts demonstrated higher occurrences of air masses of below-average humidity (dry [D], dry cold [DC], and dry warm [DW]). Much of the Peninsula showed high intra-annual variability in cool, warm, humid, and dry air masses. Findings from trend analysis reinforce findings from previous studies related to ongoing warming over the Peninsula, as cool weather types (DC, C, and HC) are becoming less frequent, while warm weather types (W, DW, and HW) are becoming more frequent. This analysis further detected decreases in the average weather conditions along with reduced duration of C-type and increases in the length of warm weather types, further aggravating thermal stress across the AP.

The synergistic interactions between urban heat islands (UHI) and heat waves (HW) continue to be debated. Despite the expectations of UHI intensification during HW, several studies have demonstrated variations. Notably, there is a dearth of investigations concerning the UHI–HW synergy in tropical climate cities amidst the escalating trend of more frequent and severe HW in Southeast Asia. To address this gap, our study aimed to investigate the synergies between the UHI and HW phenomena in Greater Kuala Lumpur (GKL) and its surrounding areas. We employed the advanced research version 4.2.2 of the Weather Research and Forecasting (WRF) model, coupled with a single-layer Urban Canopy Model (UCM), to examine the impact of UHI during two heat wave events in 2016 (Case 1) and 2020 (Case 2), against the periods immediately before and after these events, which we refer to as Pre-Post HW (PPHW), in GKL. An elevated UHI intensity (UHII) was evident during the HW in both observations and simulations, with a noticeable distinction particularly observed in Case 1. During HW, observed data indicates average UHII peaks at 1.8°C (0100 LST (UTC+8)) and 1.7°C (1500 LST) in Cases 1 and 2, respectively. In contrast, those for PPHW days for Cases 1 and 2 are 1.5°C (0000 LST) and 1.2°C (0100 LST), respectively. The maximum observed heat loads are likely to occur at noon, reaching 2.3°C at 1600 LST in Case 1 and 3.7°C at 1500 LST in Case 2. LST stands for local standard time. Heat flux component analysis from the surface energy balance model confirmed the UHI–HW synergy. A notable difference in the Bowen Ratio between urban and rural areas highlights the effect of urbanisation on heat fluxes, potentially exacerbating urban discomfort during HW. Consistent across all measurement methods, the evidence indicates a clear and positive synergy between the UHI and HW in the GKL. This study can potentially deliver valuable insights, especially in urban planning, where the implications of weather events are substantial.

Based on the daily temperature, wind speed, relative humidity, sunshine hours and precipitation in Sichuan Province from 1961 to 2022, the spatiotemporal variation characteristics of tourism climate comfort in Sichuan Province were analysed using the temperature–humidity index (THI), precipitation index (P), sunshine duration index (SSD), wind chill index (WCI) and tourism climate index (TCI). The results show that the average annual TCI in Sichuan Province over the past 62 years is “acceptable” or above and shows a significant increasing trend at a rate of 0.018 per decade. On average, the best travel comfort period in Sichuan Province is from March to May and from October to November. The TCI increased significantly in cold seasons such as February and November with rates of 0.064 and 0.053 per decade, respectively. The increase in TCI is mainly controlled by the increase in THI and WCI, both of which are related to the increase in temperature, with a greater effect in cold seasons and at higher elevations. Most of China's 5A-level tourist attractions in Sichuan Province (14 of 17) have an annual TCI of “acceptable” or better. Similarly, the top 3 months for climate comfort at these attractions tend to be March to May and October, with ratings reaching “good” or even “very good.” For the other three destinations, all characterized by glaciers and mountains, the top 3 months for climate comfort are concentrated in the warm months. The overall tourism climate comfort of Sichuan Province is increasing, and the climate is changing in a positive way for the tourism industry.

Different from the general heatwaves that mainly occur in southeastern China, the super heatwaves in China are concentrated mostly in the mid–lower reaches of the Yangtze River basin. Daily maximum temperature data over land from CPC/NOAA revealed that the basin has become the centre with the highest frequency increase of super heatwaves in the East Asian monsoon regions in the 21st century. Further analyses also indicted that the extent of super heatwaves in the basin has a much higher increasing rate than that of general heatwaves by using running thresholds. The westward extension of the western Pacific subtropical high (WPSH) plays the most dominant role. Statistical results suggested that a 10-gpm increment of the geopotential height (GPH) over the basin leads to a 0.43°C increase in the regionally averaged maximum temperature. Additionally, spatial extent of the super heatwave may expand by approximately 4%. In contrast to the stable eastern boundary of the North Africa high, the western boundary of the WPSH has significantly expanded westward in the 21st century. This expansion has led to the enhancement of the GPH over the Yangtze River basin, resulting in the super heatwave centre due to the heat-dome effect. Projections from 29 Coupled Model Intercomparison Project Phase 6 (CMIP6) models under moderate greenhouse gas emission scenario (SSP2-4.5) suggest that the GPH over the Yangtze River basin will continue to strengthen throughout the 21st century. This implies that the Yangtze River basin will continue to be the centre of super heatwaves in East Asia monsoon region.

The atmospheric activity on Kalimantan Island (KI) is important for regulating regional weather and climate. This study investigates the effect of autumn soil moisture over KI on following winter precipitation over southern China (SC) during 1968–2014. The results show that the autumn soil moisture over the KI has a significant negative correlation with subsequent winter precipitation over SC. The correlation remains statistically significant when using partial correlation to filter out the concurrent influences of El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) signals. The soil moisture anomalies over KI, which initiate in the autumn and persist into the winter, lead to changes in local thermal conditions and atmospheric temperature. Negative soil moisture anomalies over KI will result in positive heating anomalies of the atmosphere above the land surface. This atmospheric heating causes ascending motion, which creates a semi-closed vertical circulation from KI to the tropical northwest Pacific. This vertical circulation would strengthen the northwest Pacific anticyclone and weaken the East Asian winter monsoon (EAWM). Consequently, southwesterly water vapour flux prevails in the SC as well as the South China Sea (SCS), facilitating the transportation of more water vapour into the SC. Simultaneously, water vapour convergence in the SC. Collectively, these contribute to an addition of precipitation over SC.

We describe the bias adjustment and downscaling to a resolution of 1 × 1 km of daily temperature, precipitation and solar radiation of a multimodel ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs with the local observational data set BhutanClim for Bhutan and an analysis of the results for 1996–2100. The GCMs were selected based on their ability to reproduce local temporal and spatial precipitation patterns, resulting in a total of 14 models for each shared socioeconomic pathway (SSP). The results are shown for five global warming levels (GWLs) 1.5, 2, 3, 4 and 5°C, providing insight into the projected impacts of climate change at different warming levels. The daily temperature and precipitation data are publicly available, which is of great benefit for climate impact studies, such as ecological, hydrological or infrastructure-related studies. In this paper, we discuss showcase climate indicators. Peak-over-threshold climate indicators such as hot days above 30°C are relevant for human health. In the capital city of Thimphu, these are currently rare, at 4 days per year, but might increase to more than 80 for GWL5.0. Precipitation increases in the summer months, but slightly decreases in the drier winter months. Accordingly, the 3-month SPEI drought index is projected to increase in summer, but strongly decreases in the winter months, mostly pronounced in February. The increasing severity of late-winter and spring droughts might have a negative impact on vegetation, wildfires and water supply.