International Journal of Climatology最新文献

Thunderstorms and lightning are the deadliest natural disasters in the Indian subcontinent. A better understanding of the regional and seasonal features of thunderstorm occurrences can provide valuable information on mitigating hazards in the region. The study demonstrated an improved method of understanding thunderstorm occurrences and thunder day (TD), an essential climate variable over the Indian region, using the total lightning data sets from the last 5 years. Total lightning data sets are used to find the Lightning flash rate density (FRD) in 0.05° grid resolution. Total lightning data are used to calculate TD, with the condition that a grid is considered to have a TD if it records at least two lightning flashes ensuring that TD corresponds to grids covering an area of approximately 15 km radius. A simultaneous comparison of lightning FRD and TD distribution is done in annual, interannual, seasonal, and monthly time scales spatially and statistically. The results reveal that the maximum TD observes 130–150 days in a year over a few places, including North-west Himalayan Jammu (Rajori), Southern peninsular Kerala (Kottayam), and north Odisha (Mayurbhanj). The north-western Himalayan foothills show the maximum TD but not the maximum FRD. The maximum number of TD in the study surpassed all the previous studies made by the station data sets where maximum TD remains in the range of 120–130 days per year. The minimum occurrences of the TD (< 15 days) show over Ladakh, a few parts of Arunachal, and the Kutch Gujarat region. Eastern part of the Indian region observes the most intense and frequent thunderstorms in terms of the number of lightning flash discharges per thunder day.

This study explores the climate zones' shift in Türkiye over time as a result of climate change, focusing on the period from 1954 to 2023. The analysis is based on high-resolution ERA5-Land reanalysis data and includes the record-breaking temperature extremes of the past decade. Climate zones were identified using the Ward linkage method, a hierarchical clustering algorithm approach, applied across 10-year moving windows within 30-year periods. This approach revealed eight distinct homogeneous climate regions and allowed for tracking their changes in location and extent over time. To examine these clusters, the De Martonne aridity index was used to assess temporal and spatial variability. The results indicate a notable contraction of very humid and extremely humid regions, alongside the emergence of a new arid zone in Southeastern Anatolia. The proportion of semi-arid areas increased significantly, from 9.03% in 1954–1983 to 14.62% in 1994–2023, highlighting a clear trend toward aridification. The changes in the zones were not uniform across the country. The Southeastern Anatolia, Mediterranean, Aegean, Marmara, and Central Anatolia geographical regions exhibit the most pronounced shifts, indicating region-specific impacts of climate change. The findings demonstrate the growing influence of climate change on Türkiye's hydroclimatic regime, indicating a clear tendency toward aridification and supporting broader global projections of a northward expansion of the subtropical climate zone.

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.

Jet streams are key components of mid- to high-latitude atmospheric circulation, strongly influencing global weather and climate through heat and momentum transport. Based on our previous work on jet stream shape variability and its climatic impacts, this study assesses the ability of CMIP6 models to simulate the relationship between jet stream shapes and surface air temperature (SAT), using both AMIP and historical runs. Based on weighted Taylor scores, interannual variability skill (IVS) scores and jet stream indices, AMIP simulations generally outperform historical runs in representing both jet streams and SAT. Most models capture SAT anomalies over East Asia and North America, underscoring a robust link between regional temperature extremes and distinct jet patterns. They also reproduce sea level pressure anomalies resembling the Arctic Oscillation (AO), suggesting that AO-like circulation is a key driver of temperature extremes across jet groups. In addition, horizontal temperature advection anomalies closely match SAT anomalies, highlighting their role as a primary term in the thermodynamic equation shaping temperature distributions in critical regions.

Ocean surface waves, which are gravity waves primarily driven by surface winds, significantly influence air-sea interactions, coastal ecology, and marine operations, among others. Tropical climate variability is shaped by ocean–atmosphere coupled modes such as the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), which also influence ocean surface waves through changes in surface winds. This study examines the impact of the Atlantic Zonal Mode (AZM), a less-studied climate driver, on the interannual variability of ocean surface waves in the tropical Indo-West Pacific. A warm AZM induces surface wind anomalies that oppose the climatological winds. As a result, it reduces the wind sea by 0.2–0.3 m in the South China Sea (SCS) and by 0.1–0.2 m each in the Philippine Sea (PS) and the Bay of Bengal (BoB), and causes Southern Ocean swells to dominate by 0.1–0.2 m in the Arabian Sea. It is substantiated by consistent changes in the mean wave period and energy flux into waves. A warm AZM can favour marine operations in the SCS, PS, and BoB. On the contrary, a cold AZM is not observed to cause any statistically significant change in wave activity during the analysis period, highlighting an asymmetric impact of the AZM, the reasons for which require further investigation using wave model sensitivity experiments. Given the future projections of a weakening Atlantic cold tongue under global warming, the effects of warm AZM events may intensify, potentially leading to further reductions in tropical Indo-West Pacific wave activity. These findings highlight the importance of incorporating AZM impacts in wave climate assessments.

A comprehensive strategy that incorporates trend analysis, machine learning (ML), and climate model review is needed to improve water resource forecasts and evaluate hydroclimatic variability. The present study effectively combined various forms of categorical and continuous performance metrics for the CMIP6 and the reanalysis datasets in the Upper Godavari Sub-basin area (UGSB) (India). MERRA2 reanalysis datasets demonstrated the highest accuracy for precipitation forecasting, achieving a POD of 0.82 and CSI of 0.71, while JRA-55 closely followed with a CSI of 0.69. CMIP6 models exhibited overestimation tendencies, with a mean FAR of 0.34, highlighting their limitations in capturing precipitation extremes. Thereafter, to understand the long-term variability of the best reanalysis product, trend analysis was also performed using the Mann-Kendall test, Pettitt's test, Van Neumann ratio (VNR), and Innovative Trend Analysis (ITA). This analysis revealed properly the spatial variability of the precipitation, showing increasing (1.5–2.3 mm/year) and decreasing rates for various stations inside the UGSB. Thereafter, the temporal frequency and the intensity were captured by the Continuous Wavelet Transform (CWT) analysis, which further identified shifts in hydroclimatic variability towards higher frequencies after 2000. Thereafter, the prediction accuracy of prediction datasets of various ML models, which included Random Forest (RF), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and XGBoost models, were optimised by The Harris Hawks Optimization (HHO) algorithm, and the best optimised model, RF-HHO, showed reducing RMSE to 4.92 at Ambajogai, 4.81 at Bodhegaon, and 5.21 at Ranjni. The study highlights the importance of combining reanalysis products, trend analysis, and optimised ML models to improve future precipitation predictions and support effective water resource management.

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.

The Indian Ocean has been warming rapidly over the last few decades. However, this warming has not been uniform, with the southern subtropical Indian Ocean (STIO, 15°S–35°S) cooling until the late 20th century and then warming abruptly. We show that the Indonesian Throughflow (ITF) into the Indian Ocean exhibits strong decadal variability with a weaker negative trend over the recent decades, and hence cannot explain the continued heat gain by the upper ocean of the STIO at a rate of 0.41 (± 0.02) × 10²² J/decade. An increased ITF during the hiatus in global surface warming initiated the STIO warming, resulting in a weaker Mascarene high and its decoupling from the Southern Ocean atmospheric variability. This enhanced Tasman inflow into the Indian Ocean and weakened Agulhas outflow offsetting the recently weakened ITF, caused a positive feedback that continued to warm the upper ocean of the STIO.