International Journal of Climatology最新文献

This study derives the climatology of surface currents in the Arabian Gulf using the current velocities obtained from the Copernicus Marine Service (CMEMS) for the period 1993–2019. It reveals distinct temporal and spatial variability in the surface current speeds induced by the variability in surface winds, bathymetry and the changes in the lateral gradients in density. The mean speed of the Iranian Coastal Current (ICC) during summer reaches up to 0.33 m·s⁻¹ along the coast of Iran, while the mean speed of Arabian Coastal Current (ACC) reaches up to 0.26 m·s⁻¹ along the coast of Saudi Arabia. We found the occurrence of 2 major and 1 minor cyclonic eddies in the annual, seasonal and monthly climatology, while these eddies are more prevalent during summer. The major cyclonic eddy in the central Gulf develops in May and persists till November with varying patterns, and decays in December. The climatological mean current speeds are higher during summer compared to winter, due to the seasonal changes in thickness of the surface layer by the stratification/destratification processes. The highest mean current speeds along the coast of Qatar are found in June and the lowest in winter months. The highest annual, monthly and seasonal mean current speeds are observed along the north and northeast coast of Qatar, while the lowest are observed along the west coast and southeast coast of Qatar. Interannual variability in surface current speeds is evident, with notable links with the El Niño–Southern Oscillations (ENSO) and Indian Ocean Dipole (IOD). The annual mean current speeds show positive trends, of the order of 0.06–0.14 cm·s⁻¹·year⁻¹ in the offshore regions and 0.05–0.24 cm·s⁻¹·year⁻¹ in the nearshore regions, wherein the highest positive trend is observed off Ras Laffan and the lowest off Dukhan.

This study analyses the spatio-temporal variability of heatwave characteristics and their association with large-scale climate drivers across seven climatic sub-regions in Vietnam, including the Northwest (R1), Northeast (R2), Red River Delta (R3), North Central (R4), South Central (R5), Central Highlands (R6) and the South (R7). The analysis is based on observed daily maximum temperatures from 102 meteorological stations, spanning the period 1980–2020. The obtained results reveal diverse heatwave patterns across the country. Amongst the seven climatic sub-regions of Vietnam, the R3 and R4 sub-regions experienced more frequent heatwaves and a higher number of heatwave days, but shorter durations. In contrast, other sub-regions had fewer heatwave events and heatwave days but experienced longer-lasting heatwaves. The intensity of heatwave events varies amongst sub-regions, with the highest value in the R4 sub-region, and the lowest in R7. Notably, the R1–R5 sub-regions are affected by heatwaves over larger areas, compared to others. Additionally, the findings confirm that the lagged influence of El Niño–Southern Oscillation (ENSO) is the primary climatic driver of heatwave characteristics in Vietnam. Generally, heatwaves tend to occur more frequently in the years following El Niño events than after La Niña events. This observation provides opportunities for developing a system of seasonal predictions of heatwaves in Vietnam. The impact of ENSO on the number of heatwave events and heatwave days is evident in five out of seven sub-regions, with less impact in the R2 and R7 sub-regions. However, it does not significantly affect heatwave intensity.

Based on the observed maximum temperature (T_max), relative humidity (RH) and NCEP/NCAR reanalysis data during 1981–2021, basic temporal features and dominant atmospheric circulation patterns of dry-type and humid-type heatwaves in North China are investigated and compared. Statistical results indicate the dry heatwaves occur mainly in early summer (from early June to early July), that is, before the rainy season of North China, while the humid heatwaves have a high frequency in mid-July to mid-August. During the research period, the increasing trend of dry heatwaves is 0.67 days·decade⁻¹, while the humid heatwaves increase at a greatly higher rate of 1.85 days·decade⁻¹. For the dry heatwave, a high ridge in the subtropical westerlies plays the main role, and the northerly wind in the east of the ridge reduces the air moisture convergence over the region. However, for the humid heatwave, the westward and northward propagations of the western Pacific subtropical high (WPSH) may make the major contribution, and the southerly wind anomalies in the west of the WPSH enlarge the water vapour to the region. The adiabatic heating in subsiding air at all levels and horizontal temperature advection at lower troposphere are stronger for dry heatwaves than for humid heatwaves, which cause a higher T_max for the former type. These results highlight the diversity of the heatwaves in North China, which suggests that multiple local and large-scale subseasonal circulations should be considered to improve the subseasonal to seasonal forecast skills for heat extremes.

Global warming has become a critical environmental, social, and economic threat, with increasing frequency and intensity of extreme weather events. This study aims to analyse temperature trends and climate indices in the Po Valley, a significant economic and agricultural region in Italy, by examining data from two historical stations: the urban Modena Observatory and the rural Mount Cimone Observatory. The analysis extends previous studies to 2018, assessing the magnitude of climate changes since the 1950s and isolating the Urban Heat Island (UHI) effect in Modena. Significant warming trends were confirmed at both sites, with in maximum (TX) and minimum (TN) temperatures trends nearly doubling from 1981 to 2018 compared to 1951–2018. For example, TX trends reached 0.84°C·decade⁻¹ in Modena and 0.62°C·decade⁻¹ at Mount Cimone, while TN trends were 0.77 and 0.80°C·decade⁻¹, respectively. Extreme climate indices showed a substantial increase in warm days and nights (TX90p and TN90p, respectively). Particularly we found TX90p of 27.5 days·decade⁻¹ in Modena and 15 days·decade⁻¹ at Mount Cimone while TN90p of 29.5 days·decade⁻¹ in Modena, 22 days·decade⁻¹ at Mount Cimone. The UHI effect significantly impacts Modena's temperature trends. Urbanization contributes up to 65% of the rise in warm nights. Specifically, frost days decreased by 1.88 days·decade⁻¹ (37% of Urban Contribute, UC), tropical nights increased by 5.16 days·decade⁻¹ (57% UC), warm nights increased by 12.7 days·decade⁻¹ (65% UC), and cool nights decreased by 3.19 days·decade⁻¹ (39% UC). Overall, the study underscores the importance of considering both global and local factors in regional climate trend analysis.

Ecological simulations including forest and vegetation growth models require climate inputs that match the resolution and extent of the process being modelled. Climate inputs are often derived at resolutions coarser than the scale of many ecosystem processes. Machine learning models can be trained to spatially downscale climate data to fine (30 m) resolution using topographic variables such as elevation, aspect and other site-specific factors. Statistically downscaled climate models will have spatially varying uncertainty that is not usually incorporated into downscaling techniques for error propagation into later models, are often applied on smaller areas, are not fine enough resolutions for many modelling techniques, or are not always scalable to large spatial extents. There remains opportunity to leverage machine learning advancements to downscale climate to very fine (30 m) resolutions with associated spatially explicit uncertainty to represent microclimatic variation in ecological models. In this study, we used quantile machine learning to produce 30 m downscaled temperature and precipitation data and associated model prediction uncertainty for the state of California. Temperature models were accurate at downscaling 4 km climate data to 30 m, performing better than the 4 km data at high and low slope positions and at high elevations, especially where there were fewer weather observations. Precipitation model predictions did not show global improvement over the 4 km scale, but were more accurate at high elevations, slopes with higher solar radiation and in valleys. For all climate variables, the added detail of spatial explicit uncertainty via 90% prediction intervals provides critical insight into the utility of empirically downscaled climate. The resulting 30 m spatially contiguous outputs can be used as ecological model inputs with uncertainty propagation, to illuminate climate trends over time as a function of fine-scale spatial factors, and to highlight areas of spatially explicit uncertainty.

Monsoon precipitation variability over the Indo-China Peninsula (ICP) has become more complicated affected by global warming. In this study, the modulation of Pacific decadal oscillation (PDO) on the relationship between El Niño–Southern Oscillation (ENSO) and the rainy season onset over the ICP were investigated. The results showed that the ICP rainy season onset were predominantly correlated with winter sea surface temperature anomalies (SSTAs) in the East Pacific Ocean, with late and early onsets following El Niño and La Niña events, respectively. During the warm and cold PDO phase, the correlations tended to be substantially strengthened and weakened, respectively. Further analysis indicates that PDO significantly influenced the effects of ENSO on the ICP rainy season onset by modulating SSTAs and low-level wind fields. During the El Niño events, abnormal easterlies over the Bay of Bengal (BoB) and southern ICP suppressed water vapour transporting to the ICP, which may be related to the zonal SST anomaly gradient between the Indian Ocean and the Northwest Pacific Ocean. When the El Niño occurred during the warm PDO phase, the rainy season onsets were later. The anomalous easterlies became stronger corresponds to the increasing zonal sea surface temperature anomaly (SSTA) gradient between the Indian Ocean and the Northwest Pacific Ocean. There was no significant anomaly on the rainy season onset during the cold PDO phase. During the La Niña events, the abnormal westerlies in BOB accelerated water vapour transport, and the rainy season onset were earlier during the warm and cold PDO phase. The modulating effects of PDO on La Niña were less than those on El Niño. These results suggest that the predictability of rainy season onset over the ICP can be improved through PDO and thus help agricultural planning and water resources management.

Flash droughts (FDs) are distinguished by a rapid development associated with strong precipitation deficits and/or increases in atmospheric evaporative demand in the short-term, but little is known about the atmospheric conditions underlying these events. In this study, we analyse for the first time the atmospheric dynamics involved in the development of FDs in Spain over the period 1961–2018. FDs are related to large-scale atmospheric circulation patterns affecting the region, in particular with the positive phase of the North Atlantic Oscillation (NAO). The NAO is the main atmospheric driver of FDs in winter and autumn, and it is essential in explaining FD development in spring. We also found that FDs are typically linked to strong positive anomalies in 500 hPa geopotential height and sea level pressure over the region during the weeks prior to the onset. At the synoptic scale, the most common weather types (WTs) recorded during the development of FDs are Anticyclonic Western (ANT_W_AD), East (E_AD) and Northeast (NE_AD) advection, and Anticyclonic (ANTICYC). In particular, ANTICYC WT is the main atmospheric driver of FDs in summer. Ridging conditions occur frequently during FDs in all seasons, being the most important factor controlling FD development in spring. Likewise, we noted that some of the FDs recorded in summer are related to and/or exacerbated by Saharan air intrusions associated with pronounced ridges. The results of this research have important implications for the understanding, monitoring and prediction of FDs in Spain, providing a detailed assessment of the main atmospheric dynamics involved in FD triggering at different spatial scales.

Monthly and seasonal precipitation forecasts can potentially assist disaster risk reduction and water resource management. The aim of this study is to assess the skill of an ensemble framework for monthly and seasonal precipitation forecasts over Iran by focusing on system design and model performance evaluation. The ensemble framework presented in this paper is based on a one-way double-nested model that uses Weather Research and Forecasting (WRF) modelling system to downscale the second version of the NCEP Climate Forecast System (CFSv2). The performance is evaluated for October–April period at 1-, 2- and 3-month lead time. Multiple initial conditions, model parameters and physics are used to construct ensemble members. Using quantile mapping (QM) method, the outputs of the model are bias corrected. This methodology is applied for two periods: (i) climatology from 2000 to 2019 to evaluate the model's ability to precipitation forecast on a monthly and seasonal time scale; (ii) the forecast for 2020 to evaluate the model's performance operationally. The model evaluation is performed using the continuous (e.g., RMSE, r, MBE, NSE) and categorical (e.g., POD, FAR, PC, Heidke skill score) assessment metrics. We conclude that model outputs were improved by the QM bias correction method. According to results, the proposed ensemble framework can accurately predict amount of monthly and seasonal precipitation in Iran with an accuracy of 58 to 45% for lead-1 to 3. For all three lead times, the averaged NSE, CC, MBE, and RMSE were 0.4, 0.56, −15.5, and 41.6, indicating that the framework has reasonable performance. Our results suggest that precipitation forecast accuracy varies with lead time, so the accuracy for lead-1 is higher than lead-2 and lead-3. Additionally, the model's accuracy differs in various regions of the country and decreases in the spring. Using the approach for an operational case, it was found that the spatial features of precipitation predicted by the framework were close to those observed.

Rainfall in East Asia is affected by two rain-bearing systems: tropical cyclones and monsoon-related frontal systems. Distinguishing typhoon rainfall (TR) and non-typhoon rainfall (non-TR) helps to understand the evolution process of regional rainfall at different timescales. Taking Fujian Province in the southeast coast of China as an example, based on the fixed box approach of separating TR, the method of determining the size of fixed box is explored. TR and non-TR are separated in Fujian Province, and the spatial variations of TR and non-TR at different timescales (annual, monthly, day of annual-maximum-rain) are analysed. The results showed that (1) according to the relationship between the sizes of fixed box and the rate of change of TR, the size of fixed box could be reasonably determined; thus, the expended size of fixed box suitable for separating TR in Fujian was 3.5°, namely the range of 20°–31.8°N and 112.3°–124.2°E. (2) The spatial variations of TR at different timescales in Fujian were similar: TR decreased from the coast to the inland, and the northeast coast of Fujian was the high-value region. Due to the difference of water vapour sources, non-TR in March–June increased from the coast to the inland, but the high value of non-TR in July–September was distributed in the eastern coast. (3) The contribution rates of average TR to the total rainfall of the year, July–September and 1 day were 12.8%, 34.6% and 35.7%, respectively. In eastern coast, TR in July–September accounted for 1/3–1/2 of total rainfall, and the rainfall in a day was mainly affected by TR; while in western inland, rainfall was mainly non-TR and the influence of TR was less than 1/4.