International Journal of Climatology最新文献

This study assessed the capability of the historical simulations of phase 5 and 6 of the Coupled Model Intercomparison Project (CMIP5/6) in reproducing the temporal and spatial characteristics of the Interdecadal Pacific Oscillation (IPO) and its impact on global surface air temperature (SAT), surface equivalent potential temperature (Thetae_sfc) and precipitation. The IPO index time series simulated by CMIP5/6 models deviated from observations and struggled to capture the phase evolution characteristics of the IPO. Nevertheless, CMIP5/6 models successfully captured the horseshoe-shaped sea surface temperature anomaly in the Pacific. Additionally, the CMIP5/6 models were able to simulate the IPO's 10–30-year period. Notably, the simulated IPO index exhibited a statistically significant upward trend, which was absent in observations. Additionally, the IPO-related global land SAT, Thetae_sfc and precipitation simulated by CMIP5/6 models performed differently in boreal winter and boreal summer. Furthermore, the IPO-related global land SAT performed better in CMIP5/6 models during boreal winter than that in boreal summer. In CMIP6 models, it improved during both boreal winter and summer compared to CMIP5 models. In terms of the IPO-related global land Thetae_sfc, CMIP5/6 models also performed better during boreal winter than in boreal summer. However, CMIP5 models outperformed CMIP6 models during the boreal summer. In terms of the IPO-related global land precipitation, CMIP5/6 models performed better during boreal summer compared to boreal winter. Moreover, the IPO-related global land precipitation in CMIP6 models improved significantly in boreal winter, but almost the same in boreal summer, compared to CMIP5 models. Further studies showed that the enhancements in simulating IPO's spatial pattern did not correspond to improvements in the model's ability to simulate IPO's global teleconnections.

Drought occurs when there is a sustained decrease in rainfall over an extended period, impacting the socio-cultural and environmental aspects of humans and other living beings. The geographic distribution and timing of droughts play a crucial role in drought management and mitigation strategies. Identifying and predicting the onset of droughts in specific regions, especially in watershed areas, is a primary concern in the field of hydrology. This study focuses on how the spatiotemporal patterns of drought are developing in Turkish Basins using detailed data on Terrestrial Water Storage (TWS), precipitation, and temperature at the pixel level. GRACE (Gravity Recovery and Climate Experiment), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and WorldClim (World Climate) data sets are employed to assess long-term changes of drought on a basin-scale. Spatial analyses are conducted in a Geographic Information System (GIS) environment for the derivation of basinal monthly mean, minimum, and maximum statistics of TWS, precipitation, and temperature anomalies within Turkish Basins. Time series analyses are implemented to investigate the temporal evolution of droughts in these basins, for the basinal monthly mean, minimum, and maximum statistics obtained. The Mann–Kendall trend test and Pettitt change point detection tests are used to assess the statistical significance of the calculated trends and to expose the existence of any change point therein, respectively. The findings of the study indicate that Turkiye faces a significant risk of drought development in nearly all its basins, particularly after 2016. The GRACE dataset provides realistic insights into the temporal behaviour of hydrological droughts. PERSIANN is effective in identifying years with extreme meteorological conditions, and the standardized precipitation index (SPI) shows similar effectiveness, while they are ineffective in exposing significant trends due to the nature of the precipitation data. WorldClim data proves insufficient for modelling the temporal behaviour of droughts in these basins.

A strong cooling event refers to a sharp change in the average temperature over a short period. The rapid change of temperature has important effect on human health and is highly concerned recent years. Based on the observed temperature data set from stations in Southwest China (SWC) from 1979 to 2017, this paper analyses the characteristics of the strong cooling event (SCE). The result shows that SCE occurs with the highest frequency during the time from February to May. Among them, the frequency of SCE in February exhibits an abrupt change before and after 2005 with a significant increase. Further study reveals that the change of SCE frequency in February is associated with the large-scale background circulation patterns. After 2005, there is a cyclonic circulation anomaly in Northeast Asia and an anticyclonic circulation anomaly in the Tibet Plateau (TP). This pattern provides a favourable condition for the southward movement of cold air mass, thereby increasing the frequency of SCE in SWC. Furthermore, it is revealed that there is a strong correlation between the variation of SCE frequency in February and sea surface temperatures (SST) in the Northwest Pacific in January before 2005. The cold SST anomaly could favour the occurrence of extreme TD events in SWC through vertical circulation. After 2005, the correlation between SCE and Northwest Pacific SST is not significant. The sea ice in the northern Barents Sea and Kara Sea becomes the dominant impact factor. The abnormally low sea ice concentration is conducive to strengthen the meridional circulation over East Asia, leading to an increasing frequency of SCE in SWC.

The objective of this work is to provide projections of mean annual and monthly precipitation for the Brazilian Amazon and Cerrado biomes, in the near-term (2021–2040), medium-term (2041–2060) and long-term (2081–2100). The intermediate and most pessimistic Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emissions scenarios were considered. Thus, 34 high-resolution global climate models (GCMs) from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) Phase 6 of the Coupled Model Intercomparison Project (CMIP6) were evaluated. The base period evaluated was from 1981 to 2010. The NEX-GDDP simulations are bias-corrected and spatially disaggregated. The Climate Hazards Group InfraRed Precipitation with Station v2.0 was chosen as the source of observed data due to low availability in situ data. The Kling-Gupta efficiency (KGE) and the global performance indicator were implemented in Google Earth Engine to evaluate the GCMs. The results show that the GCMs perform satisfactorily, except for KACE-1-0-G and IITM-ESM. The median KGE is 0.86 for the biomes. Thus, the Ensemble Model of 32 GCMs (EM-32) indicates a reduction in precipitation in the biomes, except the northern Cerrado. In the most pessimistic scenario, changes in annual precipitation range from 3% to −33% until the end of the century. The north-central Amazon and the northwestern Cerrado are the most affected regions. In general, the monthly precipitations between September and November show the most intense decreasing rates. It is estimated that 91% and 23% of areas in the Amazon and Cerrado biomes, respectively, show robust signs of reduction in mean annual precipitation. Thus, EM-32 shows more intense and robust climate projections, in comparison to the total annual precipitation of the subset of 33 raw CMIP6 models from Working Group I of the IPCC Sixth Assessment Report. Therefore, the EM-32 precipitation projections can be applied to future hydrological and hydrosedimentological investigations.

Heatwaves and droughts increasingly impact public health and societal system in a world subject to global warming. Several studies reported these phenomena all around the world, but there is a dearth of research specifically in West Africa. This study fills that gap by comparing heatwave/heat stress and drought occurrence in three climate zones (Guinea, Sudan and Sahel) of West Africa from 1981 to 2020. The analysis focuses on the comparison of station and gridded datasets. The Cumulative Excess Heat (CumHeat) and the Universal Thermal Climate Index (UTCI) are considered for heatwaves. For drought, the Standardized Precipitation (Evapotranspiration) Index SPI (SPEI) are used at 3-, 6- and 12-month scales. Both heatwave and drought characteristics are investigated as well as their co-occurrence (D-HW). The investigation reveals a good correlation between station and gridded datasets for drought indices. While station data records fewer and less intense heatwave, gridded data indicates longer-lasting heat extremes. The study also demonstrates a strong agreement between the UTCI computed from the Rayman model and ERA5-HEAT dataset, despite timing discrepancies, especially along the Guinea coast. The Sahel region is found to endure higher heat stress levels, with increasing intensity of heatwaves over time. Notably, the study uncovers an increasing frequency of compound D-HW in all zones, especially the Sudan and Sahel zones, offering new insights into the climatic challenges faced by West Africa. These findings emphasize the critical need for improved planning and early warning systems (EWS) to mitigate the impacts of these climate extremes ecosystems and human health.

The sea breeze characteristics for 30 years of summer (1981–2010) in the coastal region of Jiangsu Province were analysed in this study, using the four-step filter method based on ECMWF Fifth-Generation Reanalysis (ERA5) data. The results showed that the filtering method reasonably identified three types of sea breeze. Additionally, approximately 23% of the total summer days remained; the corkscrew type dominates the three types of sea breeze; and the backdoor sea breeze was the least common. Except for the type of backdoor sea breeze, the other two types had relatively fixed dominant weather types on three coastlines. The synoptic weather patterns in the 500-hPa level corresponding to each sea breeze type were different. Further, the details of the diurnal cycle of lower tropospheric circulation indicated the strength of the onshore wind and the location of the large potential temperature gradient at noon being influenced by sea breeze type and coastal location. The wind strength for the three types of sea breeze on the middle coastline was stronger than that on the other two coastlines. On the three coastlines, strong onshore winds were concentrated in areas near the coastline, where they reached their maximum strengths around noon. On the southern coastline, strong onshore winds moved further inland.

This study investigates the connection between significant sea surface temperature (SST) anomalies in the North Pacific during boreal spring (February–April, FMA) and the subsequent South China Sea (SCS) summer monsoon (SCSSM) onset. The SST anomalies, similar to the Pacific meridional mode (PMM), referred to as the PMM+ mode, are defined to examine the new influencing factor on the SCSSM onset. Our findings reveal that the (February–March–April, FMA) PMM+ has a noteworthy positive correlation with the subsequent May SCSSM onset date, with this correlation being minimally affected by the El Niño–Southern Oscillation (ENSO) during preceding winter. A robust positive PMM+ in boreal spring can be persist until May via atmosphere–ocean interaction. The cooling area over Western North Pacific would reduce precipitation heating, thereby generating Rossby waves that reinforce the formation of the anomalous anticyclone over the SCS. As a result, easterly winds and suppressed convection prevail over the SCS, making the SCSSM break out later than normal. Furthermore, the amplification of anticyclonic vorticity anomalies also strengthens the western North Pacific subtropical high (WNPSH) stronger and shifts its position further westward compared to normal years, thereby blocking active convection to the west of the SCS. Given the weakened relationship between El Niño–Southern Oscillation (ENSO) and the SCSSM onset in recent years, the PMM+ could be considered as a promising preceding signal for the SCSSM onset, thus holding significant implications for the SCSSM prediction efforts.

With future global warming projections, how lightning activity changes in the warmer world is still a debated and challenging question. During the Indian pre-monsoon season (March–May), land surface heating and moisture availability due to prevailing winds from the neighbouring oceans provide favourable conditions for thunderstorm formation. Based on 24 years of lightning data from 2000 to 2023 detected by Lightning Imaging Sensor/Optical Transient Detector (LIS/OTD) and Indian Lightning Location Network (ILLN), the trend of lightning flashes over western India (15°–22°N, 72.5°–81°E) has been investigated. Our results demonstrate a steady decline in lightning activity during the pre-monsoon season over western India, which contradicts the previous studies suggesting an increasing lightning trend over the Indian Subcontinent and other parts of the world. Our analysis has shown a falling trend of lightning activity at a rate of −0.066 flashes·km⁻² year⁻¹ from 2000 to 2013 (LIS/OTD) and −0.14 M flashes·year⁻¹ from 2014 to 2023 (ILLN). Our observation and previous research strongly suggested that the pressure difference between the land and the neighbouring oceans during pre-monsoon and monsoon has been weakening for a long time over the Indian region, and we have found a consistent reduction in wind speed over the study region. Here, we propose that the enhanced Indian Ocean warming potentially weakens the land–sea thermal contrast and, thereby, reduces the horizontal pressure gradient. Further, the decreasing trend in the land–sea horizontal pressure gradient resulted in a declining rate of wind speed over western India, affecting moisture transport over land. Thus, the study emphasizes the impact of the decreased land–sea horizontal pressure gradient on declining lighting activity in western India.

This study introduces new insights into the climatology of South Atlantic (SAt) cyclones by employing a novel cyclone life cycle detection method, the CycloPhaser. Utilizing the minimum relative vorticity series and its derivative at the cyclone centre, the program effectively identifies distinct phases in the cyclone life cycle. Cyclone tracks are obtained through the analysis of relative vorticity at 850 hPa, using the ERA5 dataset. The study identified six main cyclone life cycle patterns from the analysis of 28,458 systems. The predominant cyclone type, accounting for approximately 60% of the analysed systems, exhibited a four-phase configuration: incipient, intensification, mature and decay. Detailed statistics for each developmental phase and the overall life cycle are presented, offering valuable comparisons and new insights while corroborating previous research findings. Key genesis regions in the SAt are identified, along with track density maps that reveal distinct preferences in cyclone developmental cycle. The main outcome of this study is the demonstration that the automated classification procedure enables the analysis of cyclones' life cycles to be conducted promptly and with low computing costs, facilitating the comprehensive study of cyclone behaviour with high efficiency.

The Huaihe River basin (HRB) of China located in the climate transition zone between warm temperate and subtropical areas is highly sensitive to climatic change. However, the changes in future climate extreme events under anthropogenic warming and the population exposure to these climate extremes in HRB remain unexplored. Here, using the eight commonly used extreme climate indices and based on the bias-corrections of 16 global climate models (GCMs) in CMIP6, we present a projection and uncertainty analysis of extreme events and investigate the corresponding population exposure risk in HRB under three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, SSP5-8.5). The 16-GCM ensemble mean projects an evident warming trend under all three scenarios with a total increase of 25.6–68.0 days in summer days (>25°C) by the end of the century in HRB. Larger increases (decreases) in maximum and minimum temperatures (frost days) are projected in the western HRB. Very heavy rain days (R20mm), maximum 5-day precipitation (RX5day) and simple daily intensity index (SDII) will experience intensification across most of HRB (especially in southern and western HRB). The consecutive dry days is projected to decrease in northwestern HRB and increase in southern HRB. However, there is a large spatial variability in GCM uncertainty with a higher SSP scenario generally having higher uncertainty. Increases in summer days and R20mm exacerbate population exposure in HRB in near future (2030–2059), but in far future (2070–2099) although summer days (R20mm) continues to rise, population exposure is expected to decrease due to the rapid decline in population density.