International Journal of Climatology最新文献

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.

For the period 1979–2023, tropical cyclone (TC) activity in the South China Sea (SCS) during fall (September–November) exhibited an interdecadal decrease around 2000. The decrease occurred in the observed number of total TCs, migratory TCs moving from the western North Pacific (WNP) into the SCS, and local TCs forming within the SCS. Interdecadal decreases in TC activity from 1979–1999 to 2001–2023 are affected by interdecadal components of lower-level anticyclonic anomalies extending from the Bay of Bengal across the SCS into the tropical WNP. These components effectively hinder TC genesis and subsequent passage frequency over the SCS and the tropical WNP, resulting in interdecadal decreases in TC activity over the SCS. These anomalous anticyclones owe their existence to significant interdecadal variations in sea surface temperatures (SSTs) over the southeastern Indian Ocean (74°–92° E, 16° S-EQ; zone A), mid-latitude North Pacific (170° E–160° W, 26°–40° N; zone B), and off-equatorial eastern North Pacific (150°–126° W, 6°–24° N; zone C). Given these modulating processes, an interdecadal index based on areal-mean values of interdecadal SST components over the above three zones is computed as C−(A + B)/2. This interdecadal index exhibits a relatively coherent phase with interdecadal variability in total TCs over the SCS. For these two variables, their interdecadal time series have a significant correlation coefficient of 0.89. Interdecadal SST variations over the above three zones reflect salient variability features of the Pacific Decadal Oscillation (PDO). On the other hand, the linear trend feature in the global warming time series is obviously different from the interdecadal oscillation feature in the total TC time series over the SCS. The PDO plays a more important role than global warming in modulating interdecadal variability of TC activity over the SCS during fall.

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.

An increase in drought frequency and intensity in most parts of the world is a threat to lives and property. Understanding the underlying climatic drivers of drought occurrence and variability is therefore vital for developing effective early warning systems. Large-scale climate variability patterns, commonly referred to as teleconnections, exert significant influence on regional precipitation and drought dynamics. This study explores the relationship between major teleconnections: Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), and Multivariate El Niño–Southern Oscillation Index (MEI), and drought in Iran by applying the dynamical conditional correlation (DCC) approach together with cluster analysis to capture regional differences. Monthly precipitation data from 1993 to 2016, sourced from 106 meteorological stations, are used to calculate the standardised precipitation index (SPI) with 1-, 3-, 6-, 9-, and 12-month moving averages. The DCC between SPIs and three teleconnections is then analysed and clustered using the Ward's method. Results demonstrate that the MEI and SOI exhibit a strong correlation with the SPIs, while NAO shows an insignificant association with drought patterns in the region. The influence of teleconnections on SPIs exhibited correlations reaching up to ±0.6, reflecting the coherence and density of SPI patterns and the distinct spatial clustering of meteorological stations, with this range varying notably based on terrain complexity and elevation. The strength of teleconnection–SPI relationships appears to be modulated by topographic features, time lag, and shocks, which likely play a crucial role in shaping precipitation dynamics and the spatial distribution of droughts in Iran. The findings underscore the importance of incorporating terrain and elevation when analysing large-scale climatic patterns and their influence on regional climate variability for improved accuracy of weather forecasts of extreme events.

Climate change has increased heatwave frequency, duration, and intensity globally, which is expected to worsen under warmer conditions. Understanding heatwave variabilities is crucial for the ecosystem and human health they impact, yet comprehensive assessments over China at different spatiotemporal scales remain limited. This study evaluates 12 NASA Earth Exchange Global Daily Downscaled Projections' CMIP6 (NEX-GDDP-CMIP6) downscaled data in simulating historical heatwaves (1981–2010) and their projections under SSP2-4.5 and SSP5-8.5 scenarios for near-term (2031–2060) and late-term (2071–2100) periods, using CN05.1 observations and key heatwave metrics. The NEX-GDDP-CMIP6 ensemble mean (MME) outperforms individual models in simulating heatwave frequency (HWF) and duration (HWD), with 65%–87% of China showing biases within ±10%. While the MME effectively captures HWF trend patterns across China, it fails to simulate decreasing HWD trends over southern China and intensity metrics trends over the Tibetan Plateau. Among the individual models, CMCC-ESM2, GFDL-ESM4, and GFDL-CM4 emerge as the three best-performing models. Under SSP5-8.5, about 87% (74%) of regions will experience increases in HWF (HWD). Notably, heatwave frequency over China will exceed 15 events annually during 2071–2100. The entire country will experience intensification of heatwave intensity with larger increments in high-latitude regions. Furthermore, the likelihood of extreme heatwave events under SSP5-8.5 is projected to be double under SSP2-4.5. Northern China faces the highest risk of extreme heatwaves with over 50% probability under late-term SSP5-8.5, followed by Southeastern China. Our results offer comprehensive insights into NEX-GDDP-CMIP6 simulated heatwave variations, necessary for refining global climate models and understanding future risks.

The Huang-Huai River Basin (HHRB) in East China is highly susceptible to frequent floods and droughts, making it one of the most disaster-prone regions in China. Extratropical cyclones (ETCs) are the primary weather systems generating precipitation over the HHRB. This study investigates the spatiotemporal characteristics and precipitation patterns of ETCs affecting the HHRB, based on a long-term dataset (1979–2022) derived from ERA5 reanalysis. On average, 33.2 ETCs traverse the HHRB annually, exhibiting a statistically insignificant increasing trend over the study period. Most ETC activity occurs during summer months and nighttime hours. ETCs contribute to approximately 60% of the HHRB's total precipitation, with their contribution increasing for more intense precipitation events. Based on source regions and tracks, the ETCs are classified into five distinct types: locally generated systems and those originating from northwestern, northeastern, southwestern, and southern China. Locally generated ETCs produce the largest total precipitation, whereas those originating from southwestern China trigger the most intense rainfall events. ETC-induced precipitation plays a crucial role in driving summer floods and droughts over the HHRB. Consequently, a deeper understanding of the characteristics, formation mechanisms, and precipitation-generating processes of these systems is essential for effective disaster prevention and mitigation strategies.