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.

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.

Cold surges entering the Gulf of Mexico (GoM) generate northerly winds (Norte events), preceded by warm southerly winds (Surada events). These events were characterised in the Eastern Bay of Campeche (EBC) using ERA5 reanalysis data for 2002–2024. Both Norte and Surada events exhibited distinct intensity and predominant direction patterns in the EBC, compared with the broader GoM. Therefore, the separate identification of both events specific to the EBC was proposed. A minimum wind magnitude threshold (E) was determined for the EBC, above which events were considered sufficiently intense to be classified as Norte (2.4 ms⁻¹) or Surada (1.3 ms⁻¹) events in this region. It was shown that Norte events lasting < 24 h were associated with more intense Surada events; overall, 70.1% of Surada events exceeded the intensity of the corresponding Norte events, with a mean difference of 1.8 m s⁻¹. Empirical Orthogonal Functions analysis for the southern GoM revealed that, in ~30% of the cases (Modes 2 and 3), cold fronts moved with a significant zonal component in the southern GoM. This pattern was associated with intense Surada events and weak or even absent Norte events in the EBC. Mode 1 accounted for more than 63% of the variability and corresponded to Surada events followed by Norte events with similar behaviour in the entire region. This study demonstrates a distinct wind behaviour associated with cold surges on the eastern and western sides of the Bay of Campeche in the southern GoM. Norte and Surada events can negatively impact local marine and fisheries sectors, as wind intensity forecasts are often generalised for the entire GoM and can be unreliable in the EBC. These events are critical for coastal circulation and influence ecosystem dynamics, fisheries, and key oceanographic processes such as coastal-trapped waves, upwellings, and wave fields.

Over the last 75 years, the Ohio River Valley has experienced significant changes in snowfall frequency, timing and magnitude that resulted in meaningful societal impacts. Through the application of a synoptic climatology, this study furthers the understanding of the synoptic-scale atmospheric forcings responsible for snowfall and explains how their variations influenced observed snowfall trends from 1948 to 2021. Three dominant atmospheric environments were identified as being responsible for snowfall in this region: (1) types resulting in northwesterly flow conductive to lake-effect snowfall primarily affecting the northeast region, (2) types with mid-latitude cyclones generating wrap-around or frontal snowfall in the central and southern regions and (3) types with high-pressure centers over or adjacent to the region producing lighter snowfall due likely to smaller-scale processes. Analysis revealed significant temporal trends in the seasonal frequencies of specific snowfall types that, in combination with changes to daily snowfall magnitudes over time, result in significant trends to seasonal snowfall totals per type and align with previously reported domain-wide snowfall trends. Further, many of the snowfall-producing weather types' inter-annual frequencies were significantly correlated to the phase of teleconnection indices. When the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) were negatively phased, more than half of the weather types were significantly more frequent due to the larger-scale atmosphere providing suitable conditions for the development of the snowfall-producing types. These findings underscore the importance of understanding the integration of weather systems across scales and how changes manifest across a cascade of physical forcing mechanisms.

In the tropical Pacific, El Niño (EN) typically causes positive precipitation anomalies further east than La Niña (LN)'s negative anomalies. This study constructs an idealised scenario of symmetrical CO₂ ramp-up (RU) and ramp-down (RD) phases to analyse the changes in asymmetric precipitation response to EN and LN. The results reveal that, during the CO₂ RD phase, tropical Pacific precipitation anomalies in EN and LN both shift more eastward and southward compared to the RU phase, but with more pronounced shifts in EN than LN. These structural changes in precipitation anomalies are primarily driven by the dynamic component associated with changes in circulation anomalies, which cannot be explained by variations in sea surface temperature (SST) anomalies. The spatial pattern of climatological SST changes between the CO₂ RD and RU phases exhibits an EN-like warming structure, particularly extending southward, which enhances low-level moisture and weakens atmospheric stability. Consequently, precipitation during EN responds more strongly to SST anomalies in this region, accompanied by eastward and southward shifts. While LN partially offsets the impact of the climatological warming pattern and results in less pronounced shifts, thereby contributing to the structural changes in the EN–LN asymmetry of precipitation anomalies.