International Journal of Climatology最新文献

Flash drought events can be characterised by the quick depletion of crop root zone soil moisture (rapid intensification) and hence can be termed as agricultural flash droughts. These events can have devastating impacts, such as increasing the risk of agricultural yield loss, heatwaves and increased wildfire risk, which further have cascading impacts on the socio-economic conditions. The regional hotspots of flash droughts are analysed for winter, pre-monsoon, monsoon and post-monsoon seasons over India from 1981 to 2020. We assess the impact of the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on the flash drought frequency (FDF: number of flash drought events). The causal connection of the FDF with the ENSO and IOD is analysed using the PCMCI (Peter and Clark's algorithm combined with the Momentary Conditional Independence) algorithm. The monsoon season (June–September) is found to be more prone to flash droughts with higher spatial/regional average values of total per pixel FDF during the 40-year period over the Central Northeast (~54) and West Central (~41) regions. It is observed that the fraction of the total number of flash droughts during the El Niño years (38.8%) is higher as compared with that in La Niña years (25.7%). It is also found that the co-occurrence of positive/negative IOD with the El Niño phase can alter the seasonal fraction of FDF over India, highlighting the high complexity in the ENSO–IOD interactions. The causal analysis shows that only the Southern Peninsula and West Central regions have significant direct and lagged causal links of average per pixel FDF with IOD. Whereas, similar (direct and lagged) causal connections are observed between the ENSO and IOD. This study reveals that flash droughts and their teleconnections vary greatly among the seasons and regions in India, limiting its accurate predictions and increasing the risk to agricultural communities.

The unique topography and location of the Tibetan Plateau (TP) often result in extreme weather events, which have led to disastrous consequences for the TP and its downstream regions. The TP summer monsoon (TPSM) and the TP vortex (TPV) play key roles in the transfer and redistribution of water vapour during the summer months on the TP and are increasingly active in summer and disappear in winter. However, it remains uncertain if a relationship between these systems. Understanding the relationship between these two systems is crucial for uncovering precipitation patterns on the TP, improving weather forecasting accuracy and reducing socioeconomic losses resulting from weather-related disasters. On the basis of GLDAS and ERA5 reanalysis data from 1996 to 2022, the relationships between TPVs and the TPSM were explored in terms of their intensity and spatial characteristics, and their impacts on the spatial distributions of precipitation across the TP were examined in this study. The results indicated that the monthly mean TPSM index agreed very well with the TPV in terms of the annual number formed, duration and intensity, especially in July and August. The investigation of the movement of the center of the TPSM from May to October revealed that the center of the TPSM moves westward when the TPV is active and moves eastward when the TPV is less active. In years with a strong TPSM, the precipitation location generated by TPVs was biased toward the east. This finding could be attributed to the greater number of TPV events and the fact that the TPVs in years with a stronger TPSM moved eastward across a greater distance than those in years with a weaker TPSM. These findings highlightthe contribution of the joint relationship between the TPSM and TPV to seasonal circulation changes and could provide a new perspective for the study and prediction of precipitation distributions on the TP.

This study assesses the performance of 28 NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP-CMIP6) models and their multi-model ensemble (MME) in simulating mean and extreme precipitation across sub-Saharan Africa from 1985 to 2014. The Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) are used as reference datasets. Various statistical metrics such as the mean bias (MB), spatial correlation coefficients (SCCs), Taylor skill scores (TSS) and comprehensive ranking index (CRI) are employed to evaluate the performance of NEX-GDDP-CMIP6 models at both annual and seasonal scales. Results show that the NEX-GDDP-CMIP6 can reproduce the observed annual precipitation cycle in all the subregions, with the model spread within observational uncertainties. The MME also successfully reproduces the spatial distribution of mean precipitation, achieving SCCs and TSSs greater than 0.8 across all subregions. The biases in mean precipitation are consistent across different reference datasets. However, most of the NEX-GDDP-CMIP6 models show trends of mean precipitation opposite to observations. While the MME can generally reproduce the spatial distribution of extreme precipitation, its performance varies with the reference dataset, particularly for the number of rainy days (RR1) and maximum consecutive dry days (CDD). TSS values for extreme precipitation indices differ significantly by region, reference data and index, with the lowest values over South Central Africa and the highest over West Southern Africa. The CRI indicates that no single model consistently outperforms others across all subregions, even within the same region, when compared to both MSWEP and CHIRPS. These results may be helpful when using NEX-GDDP-CMIP6 models for future projections and impact assessment studies in sub-Saharan Africa.

The main purpose of this paper was to identify significant changes in air moisture conditions in Poland, which accompany climate warming. Meteorological data used in the research included the relative humidity (RH) values from 48 stations obtained from the Institute of Meteorology and Water Management—National Research Institute from 1966 to 2020. The monthly mean, standard deviation (SD) and coefficient of variation of RH from 12 PM for all months (with particular reference to the middle months of seasons—January, April, July and October) were used. Additionally, the dry weather (RH < 30%) frequency in the warm half of the year was observed. Long-term changes were found by comparing relative humidity values in three 15-year subperiods (1966–1980, 1986–2000 and 2006–2020) and the statistical significance was estimated using the Mann-Kendall test. The most considerable long-term changes were noticed in April and July, especially in the last 15-year subperiod. The statistical significance was higher, mostly in warmer months. The SD was also higher in April and July than in January and October. Hence the humidity conditions in the warmer half of the year fluctuated more and more widely. A significant decrease in the RH mean values and an increase in SDs in spring and summer impact the increasing frequency of dry weather. Relations between meteorological characteristics suggest the warming climate contributes to drying the near-surface atmosphere but also impacts intensive precipitation events or snow cover parameters. The decreasing trend of long-term relative humidity may negatively impact the environment, human health and well-being and cause serious economic losses.

The uneven global distribution of rainfall significantly impacts water resources and environmental sustainability, emphasising the need for reliable climate prediction models. Accurate predictions are vital for sectors such as food security, urban planning and disaster management. Data from ground stations, radars and satellites are essential, despite challenges like instrumental errors. Satellites, with their comprehensive sensors, are crucial for atmospheric observations, aiding in the prediction of large-scale climatic events. Climate models such as CHIRPS, GLDAS, TerraClimate, and PERSIANN use different approaches to analyse precipitation data, which is key to understanding its spatial and temporal variability. This study evaluated (rainfall data) from these four climate models over 20 years (within the Brazilian territory), focusing on the spatiotemporal behaviour of rainfall using statistical metrics such as R ², RMSE, and MAPE. The findings showed that CHIRPS had the best performance (R ² = 0.843; RMSE = 42.83; MAPE = 0.09%), excelling in both overall database and extreme event analyses. TerraClimate, initially the lowest-performing model (R ² = 0.413; RMSE = 91.56; MAPE = 0.23%), improved significantly when combined with elevation through multiple linear regression (MLR), achieving R ² of 0.718, RMSE of 31.14, and MAPE of 9.56%. This made TerraClimate a viable model for studying the Flying Rivers. The study highlights that model selection should align with the specific characteristics of the area under consideration, with CHIRPS being particularly suitable for the studied region. This research enhances the understanding of the effectiveness of these models in estimating rainfall compared to in situ measurements, which is crucial for various applications. The authors advocate for further studies to advance research on the Flying Rivers, their significance, and the impacts of climate change on them.

The mechanisms associated with the transitions of strong El Niño (EN) events and their implications for the South American precipitation were investigated for the 1950–2023 period. Strong EN events exhibit cyclic or episodic characteristics in their transitions. Cyclic EN events are both preceded and followed by La Niña (LN) conditions, whereas episodic EN events are preceded by neutral conditions, with a more uncertain transition following. For cyclic EN, tropical Pacific mechanisms initiates and peak warming in the eastern tropical Pacific from austral winter to early summer. In contrast, for episodic EN, coupled subtropical and tropical Pacific mechanisms, respectively, initiate and peak warming in the central tropical Pacific from autumn to late summer. The Pacific Decadal Oscillation (PDO) mean state modulates EN's decay stage. During the +PDO mean state, cyclones in the eastern subtropical Pacific of both hemispheres sustain the warming of episodic EN, whereas during the −PDO mean state, anticyclones in the eastern subtropical Pacific accelerate the decay of cyclic EN, favouring its transition to an LN. These mechanisms explain why episodic EN initiates earlier, peaks later, is more intense and decays more slowly than cyclic EN. During an episodic EN summer, the strengthened atmospheric circulation maintains the Atlantic Intertropical Convergence Zone (ITCZ) north of the equator, causing persistent negative precipitation anomalies in north–northeastern South America (SA) until the following winter, while positive precipitation anomalies in southeastern SA are driven by south–southeastward moisture transport from equatorial Atlantic. Conversely, during a cyclic EN summer, negative (positive) precipitation anomalies impact north–northwestern (southeastern) SA; however, the anomalous atmospheric circulation and precipitation in SA quickly return to normal conditions in the autumn, and positive precipitation anomalies appear in northern SA in the following winter. Understanding these mechanisms is crucial for predicting EN's future changes and, consequently, their potential socio-economic impacts globally.

The study investigated precipitable water vapour (PWV) variation over Northeast Japan by using more than two decades (2000 to 2022) of measurements. These data have been extracted from 36 Global Navigation Satellite System (GNSS) networks and interpolated with thin-plate spline interpolation to understand its spatial variation over the region. PWV annual and seasonal variation is studied and correlated with ERA5 reanalysis observations. These PWV variation shows that the northern part of Northeast Japan which is located at higher latitudes showed a smaller magnitude compared to PWV magnitude in the southern part of low latitudes. This is possible because the cold air is drier and the warm air holds more moisture in the lower southern part. The correlation coefficient variation based on geographical area is visible which quantifies the consistency between GNSS PWV and ERA5 and visualises the effects in sampling. The southern part, where the correlation coefficients are very high (more than 0.80), indicates good agreements between GNSS PWV and ERA5 values and can be considered well-sampled observations. Moreover, the correlation coefficients drop the values to approximately 0.40–0.60 for poorly sampled observations on the northern coast, pointing out the interannual inconsistency of PWV measurements, which are loosely related to ERA5 observations. Finally, the correlation coefficient between PWV with surface temperature and atmospheric pressure over Northeast Japan is investigated. This is clear from the results that the correlation coefficient between PWV and temperature is around 0.98 for each site, indicating that PWV has a very high dependency on surface temperature. The correlation coefficient between PWV and pressure is highly negative around −0.80, indicating their inverse relationship. We believe the outcomes from this study will contribute to a better understanding of PWV over Japan and the future refinements of atmospheric models over the East Asian region.