International Journal of Climatology最新文献

Drought significantly affects agriculture and ecology in the Horn of Africa (HOA), whereby livelihoods largely depend on rainfed farming. This study aims to analyze drought propagation and its impacts on vegetation and crop productivity, with a specific focus on recovery dynamics. Over the period 2000–2022, we developed and integrated a suite of physically based remote sensing indices, including the Drought Propagation Index (DPI), Crop Stress Index (CSI), Soil Moisture Deficit (SMD), Water Deficit Index (WDI), and Drought Recovery and Rate Index (DRRI), into a novel framework. The performance of this integrated framework was further evaluated against the conventional Standardised Precipitation Index (SPI) to validate its ability for capturing drought propagation and agricultural impacts. The findings identify the eastern and southeastern HOA as major drought hotspots, experiencing severe droughts 70%–100% of the time and exhibiting worsening temporal trends. SPI was strongly correlated with DPI (r = 0.67, p < 0.05), thus proving to be reliable. Vegetation indices showed significant reductions during droughts, while DPI positively correlated with NDVI at 0.56 and EVI at 0.54. Crop production was reduced by 30%–35% in Somalia, especially for maize and sorghum, whereas Ethiopia showed more resistance because of irrigation. The mean recovery time exceeded 2 months during the 2010 and 2016 droughts in southeastern HOA, indicating low resilience, whereas northern areas recovered faster. This framework offers practical recommendations for drought mitigation, drought-resistant crops, and adaptive resource management to deal with vulnerabilities.

The impact of the amplitude characteristic of the intra-seasonal oscillation (ISO) on water vapour transport is examined for the March–May (MAM) and September–November (SON) seasons. The ISO temporal indices are constructed from daily anomalies of the first two principal components of the empirical orthogonal function (EOF) applied on outgoing longwave radiation anomalies, filtered for 25–70 days. A threshold method subsequently applied to the normalised ISO amplitude using the first two EOF components is used to identify extreme events associated with ISO peaks. This allows identifying 129 (119) strong ISO events (sISOs) and 39 (40) weak ISO events (wISOs) during the MAM (SON) season. The findings revealed in MAM that, during sISO events, the southern part of the domain experiences a strengthening of the moisture convergence on the Atlantic coast, to the south-east, associated with a westerly moisture transport. In addition, sISO induces negative anomalies in the horizontal component of moist static energy (MSE) advection. wISO are characterised by a predominance of significant moisture divergence to the east of the domain and in the centre of the Congo Basin, accompanied by moisture transport from the Indian Ocean. This event generates positive moisture advection anomalies, fuelled by a zonal influx of MSE. In contrast, the two examined ISO events exhibit opposing patterns of moisture transport during the SON season. Specifically, a dipole structure emerges with divergence anomalies in the north coinciding with convergence anomalies in the south during the sISO. Analysis of the zonal components of moisture transport indicates a reinforcement during wISO events in both seasons, due to the reinforcement of the African Easterly Jet (AEJ). During wISOs, stronger low-level westerlies facilitate increased moisture import from the Atlantic Ocean. Furthermore, compared to sISO events, wISOs are associated with more intense northern and southern components of the AEJ. Finally, uncentred pattern correlation coefficients between strong and weak ISO events and the different phases of the Madden-Julian Oscillation (MJO) vary depending on the season and the specific MJO phase.

With the emergence of the ‘Arctic amplification’ phenomenon in recent years, vegetation in the circum-Arctic region has exhibited significant greening trends, while summer Arctic cyclones have also shown a notable increase in both frequency and intensity. To explore the potential relationship between Arctic vegetation changes—particularly in the northern margin of the Eurasian continent (NME)—and the intensification of Arctic cyclone activity under the backdrop of accelerated Arctic warming, this study investigates the impact of vegetation greening on the intensity of summer Arctic cyclones in this region. Using GIMSS 3G+ NDVI data from 1982 to 2022, combined with the WRF numerical weather model, the analysis reveals that, compared to the 1980s, tundra regions such as the central and eastern parts of the NME experienced the most pronounced increase in vegetation, corresponding to significant warming in these areas. As temperatures rose markedly, the north–south land-sea temperature gradient in the NME intensified, enhancing atmospheric baroclinicity in coastal regions. Consequently, cyclone intensity increased accordingly. The rise in the Green Vegetation Fraction (GVF) led to an increase in the Leaf Area Index (LAI) while reducing surface albedo, allowing the surface to absorb more shortwave solar radiation. This process plays a critical role in driving the rise in near-surface temperatures and the development of cyclones.

Drought-flood abrupt alternation (DFAA)—characterised by rapid transitions between drought and flood events—is an extreme compound natural hazard that threatens ecosystems, particularly in fragile environments such as karst regions. Soil moisture is a key indicator of surface hydrological processes, directly regulating vegetation growth and recovery. This study utilises the Standardised Soil Moisture Index (SSMI) to identify DFAA events and applies Granger causality analysis to reveal soil moisture-vegetation interactions. The impact of DFAA on vegetation productivity is quantified, with a comparative analysis between karst and non-karst regions in southern China. The results indicate that: (1) From 2000 to 2020, SSMI showed a significant upward trend. Drought-to-flood events were more frequent, intense, and longer in duration than flood-to-drought events. Both types of events also exhibited substantial spatial heterogeneity. (2) SSMI and NDVI were positively correlated, with the strongest correlation reaching 0.32 at a 60-day lag. Bidirectional causality between SSMI and NDVI was predominant, affecting 49.24% of the study area. (3) Flood-to-drought events synergistically intensified drought–flood stress, whilst drought-to-flood events mitigated stress effects. Consequently, vegetation recovery was slower after flood-to-drought events, particularly in hydrologically vulnerable karst regions.

Compound drought and heatwave (CDHW) events are complex climate extremes driven by global climate change, making it challenging to estimate their comprehensive risk using univariate statistics. To address this challenge, we construct a two-dimensional joint function based on the duration and severity of CDHWs using China as a case study, enabling assessment of joint occurrence probability under diverse scenarios. Whilst previous studies have conducted multi-dimensional risk assessments, most have focused on individual variables or examined specific aspects of drought-heatwave relationships. By simultaneously integrating duration and severity through a bivariate joint function, our approach advances this line of research and provides a comprehensive multi-dimensional framework for assessing the compound characteristics of CDHWs. The results revealed that for China as a whole, changes in the severity threshold had a greater impact on extreme CDHWs, and the joint occurrence probability of severe CDHWs (events with a duration exceeding 7 days and a severity surpassing the 80th, 90th, or 95th percentile) was more sensitive to the severity. The conditional probability rose more rapidly for short-term events (3 days) than for long-term events (5–7 days). When the duration exceeded 7 days and the severity exceeded different thresholds, the joint occurrence probability of CDHWs was within the range of 2%–6%. Amongst the different regions, the duration and severity had varying impacts on the joint occurrence probability. The extreme CDHWs in North China, Northeast China, and western Northwest China were more strongly influenced by the duration. Jianghuai, South China, Southwest China, eastern Northwest China, and the Tibetan Plateau were more prone to longer-lasting extreme CDHWs. In these areas, when CDHWs lasting more than 7 days occurred, changing the severity threshold had a greater impact on their extremity. In North China in 1997, the longest CDHW had joint return periods of over 1000 years when both duration and severity exceeded thresholds, and over 500 years when either exceeded thresholds. The findings demonstrate the variations in the impacts of duration and severity on the joint probability of CDHWs in different regions of China.

This study analysed the climate variability of the Rainfall Anomaly Index (RAI) and its relationship with global climate indices in Rio Grande do Norte (RN), Brazil, using monthly precipitation data from 115 weather stations (1963–2023). RN experienced significant changes in precipitation, with both positive and negative anomalies over the decades. Principal Component Analysis (PCA) revealed 12 components explaining 73.15% of the total variance, whereas the K-means algorithm identified three homogeneous regions corresponding to the state's climatology. Pearson's correlation coefficient showed that teleconnections such as the Atlantic Multidecadal Oscillation (AMO), Tropical North Atlantic (TNA), and Niño indices had a direct relationship with rainfall patterns, whereas the Global Mean Land/Ocean Temperature Index (LOTI) and Solar Flux exhibited an inverse relationship, suggesting that higher solar radiation and global temperatures are linked to fewer anomalous rains. The Niño indices showed substantial variation during extreme drought events in 1966, 1982–83, 1987–88, 1991–92, 1997–98, and 2015–16. Wavelet coherence analysis revealed interannual and interdecadal periodicities in rainfall anomalies, particularly in region R3, characterised by low rainfall and frequent droughts. The findings emphasise the importance of understanding extreme climate patterns over time to improve climate prediction and resource management in the region.

The present study investigates the impact of Sea Surface Temperature (SST) extremes on precipitation extremes in India's six homogeneous climate regions from 1981 to 2020. A wavelet-based complex network analysis was used to explore the connections between precipitation extremes and SST extremes. The Maximum Overlap Discrete Wavelet Transform (MODWT) technique was employed for time series decomposition to identify these links across different temporal scales. Spearman correlation was then used to determine the relationships between SST extremes and precipitation extremes at the grid level. Complex networks were then constructed for each of India's climate regions, focusing on both positive and negative correlations with various global sea regions. Results reveal that precipitation extremes at shorter time scales are predominantly driven by proximal oceanic regions such as the Bay of Bengal (BOB), Arabian Sea (ARS), and Eastern Indian Ocean (EIO), reflecting strong monsoon–SST coupling through moisture convergence and synoptic convection processes. As the time scale increases, remote SST influences from the Atlantic (NAO, EAO), Pacific (EPO, NPO, SPO), and Southern Hemisphere oceans (SIO, SAO, SOO) become increasingly dominant, highlighting the role of atmospheric teleconnections, jet stream modulation, and cross-equatorial moisture transport. Interannual scales show the widest and most diverse SST control, whilst decadal-scale influence is generally weak and region-specific. The regional analysis demonstrates distinct propagation pathways of SST influence, with West Central India showing consistent multiscale sensitivity, Central Northeast and Northeast India transitioning from local to global control, and South Peninsular India retaining strong regional dominance. These insights underscore the necessity of integrating both regional and global SST indices into forecasting frameworks for improved prediction and climate adaptation strategies in India.

Recent climate change motivates the creation of high-resolution and high-quality reference datasets of essential environmental variables as the necessary base for adaptation planning. To obtain these maps, a widely used procedure is to interpolate in situ observations, for which several different methods were developed in the past decades. In this study, we calculate gridded daily maps of precipitation and temperature at the regional scale in Abruzzo (central Italy), comparing different interpolation methods: universal kriging, radial basis function and gradient boosting forest. We validated the results against an independent set of stations from the same network used to produce the gridded dataset (1994–2013). The interpolated values were also compared with those obtained from two widely used global datasets (CHELSA and WorldClim). Universal kriging achieved the best performance, with a daily root mean square error of ~0.45 mm/day for precipitation and ~1.2°C for temperature. Seasonality affects the bias values, being larger in winter for precipitation and in summer for temperature, as well as in isolated stations in mountainous areas. Our gridded dataset (ADAMO, ~0.01° and daily resolution) shows decreased bias with respect to the global databases. There was a considerable discrepancy for precipitation (RMSE ≥ 60 mm/month for CHELSA and WorldClim, while ADAMO was 35 mm/month), and a too strong altitude effect for temperature, especially in WorldClim. Temperature increased its RMSE in summer compared to winter error, more pronounced in global datasets, while precipitation had an increase in winter with respect to summer, more evident in the ADAMO dataset. These results show that in regions with large topographic variability, the implementation of datasets with diffuse local observations is expected to be more accurate than global datasets. This is particularly relevant for fields such as climatology, ecology and biology that require climate information with high accuracy.