International Journal of Climatology最新文献

The impact of extreme events in risk analysis depends on factors such as magnitude, duration, timing and whether the system recovers fully before the next event occurs. While previous studies have primarily examined the drivers and characteristics of individual extremes, less focus has been given to the concurrent or compounding nature of extremes across adjacent seasons. Thus, understanding the dynamics of such compound extremes, particularly dryness and wetness, is crucial. To address these concerns, a Multi Scalar Drought Index (MSDI) is formulated using precipitation and temperature data from three river basins (Brahmani, Baitarani and Cauvery) of eastern and southern India. The combinations of dryness and wetness, such as Dry-Dry, Dry-Wet, Wet-Dry and Wet-Wet, between consecutive seasons are analysed across four seasons (summer, rainy, autumn and winter). The prolonged dryness/wetness along with dry/wet year are evaluated from baseline (1979–2018) to projected COrdinated Regional Climate Downscaling Experiment (CORDEX) future period (2020–2099). The spatio-temporal variations in intra-annual dry-wet extremes are identified using the Mann–Kendall test. The results suggest that the eastern Indian river basins, particularly the Brahmani and Baitarani basins, experience more frequent occurrences of compounding dryness-wetness compared to Cauvery river which is a southern Indian basin. Future scenarios indicate a trend towards dryness during the monsoon season in Brahmani and Baitarani basins, with frequent wet extremes in late autumn and winter. Abrupt transitions between dryness and wetness are prevalent during the Rainy-Autumn and Autumn-Winter seasons in Brahmani and Baitarani basins. The increased frequency of compound dry-wet extremes poses significant socio-economic risks, including reduced agricultural productivity, water management challenges and heightened vulnerability of local livelihoods dependent on consistent water availability. The results of this study provide a scientific reference for sustainable agriculture and water resource management to predict future seasonal dry and wet alternations and develop effective mitigation strategies.

The anomalous variability of extreme cases of the tropical tropopause provides insight into the stratosphere-troposphere exchange process crucial for understanding climate change. The present study analyses the extreme variability of the tropopause and its thermal structure over the tropics using GPS radio occultation data over the period 2006–2019. The extremely cold and warm tropopauses and extremely high and low tropopauses are identified based on the cold point tropopause temperature and height, respectively, when their values exceed two standard deviations with respect to their climatological means. The analyses revealed frequent occurrence of extreme cases of tropopause over the Atlantic Ocean compared to the Western Pacific Ocean. Individually, extremely warm and low cases occur more frequently over the subtropics, while extremely cold and high cases occur frequently over the deep tropics. These extreme cases pose different thermal structures bounded within the extremely low and high tropopauses throughout the tropics. The height difference between the extremely high and low tropopause cases is wider over the Atlantic Ocean and adjoining areas compared to the western Pacific Ocean. The temperature difference between the extremely warm and cold tropopause cases is higher in the Atlantic, Central Pacific, and Indonesian regions compared to the American, Indian Ocean, and western Pacific Ocean regions. The relationship between the El Niño Southern Oscillation (ENSO) phases and extreme tropopause cases is also investigated which reveals a higher occurrence of extremely high tropopause cases during the El Niño phase while low tropopause cases during the La Niña phase. Our analysis also revealed the thermal patterns of the extreme cases characterising colder and sharper tropopause over the convective regions compared to subsidence regions.

The reliability of seasonal snow cover information is constrained by limitation of in situ observations and uncertainties in remote sensing data and model simulations in alpine region, thus posing important challenges to understanding the climate system and water resource management in alpine region. Here, the assimilation of daily cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) normalised difference snow index (NDSI) product into an intermediate complexity snow mass and energy balance model—Flexible Snow Model (version FSM2_MO)—was implemented. The aim is to improve the model simulations of seasonal snow cover (snow-covered extent; SCE, snow depth; SD, snow water equivalent; SWE, and snowmelt runoff; SMR) in the alpine region (a case of the upper-middle reaches of the Heihe River basin, Northwest China). The results indicate comprehensive improvement in the simulation of SCE, SD, and SMR in the study area through data assimilation, with the ability to significantly reduce prior biases of the FSM2_MO. Based on the independent daily cloud-free Advanced Very High Resolution Radiometer (AVHRR) SCE product, the updated SCE simulation (i.e., data assimilation) showed a reduction in mean absolute error (MAE) from 10.46% to 7.16%, root mean square error (RMSE) from 16.14% to 12.26%, and an increase in Pearson's correlation coefficient (CC) from 0.18 to 0.67 compared with the open loop simulation (i.e., without assimilation). The evaluation results of SD observation data showed that data assimilation improved SD simulation compared with the open loop run (OL). And utilising the monthly discharge observations at the Yingluoxia hydrological station, data assimilation slightly improved the SMR simulation. The updated SMR simulation achieved a CC of 0.91, Nash-Sutcliffe efficiency coefficient (NSE) of 0.73, and Kling-Gupta efficiency coefficient (KGE) of 0.76. Moreover, the Landsat 8-derived snow cover map and Sentinel-1-derived SD also indicated that the updated simulation effectively filled in the missing snow cover and removed the superfluous snow cover predicted by the OL simulation in terms of spatial distribution.

We use a multivariate analysis to study droughts in Finland using the Joint Deficit Index (JDI). Subsequently, the joint probability of occurrence of drought characteristics was analysed using Vine copulas. For this purpose, we used monthly precipitation from 22 meteorological stations across Finland in the period 1980–2021. The JDI time series showed that Finland had a normal wetness condition most of the time during the studied period. Trend analysis of the JDI time series using a modified Mann–Kendall test showed that there was no significant trend in the values of this index during the studied period. The drought characteristics, including severity, duration and inter-arrival time (IAT), were extracted from the JDI time series for each station. The trend analysis of drought characteristics showed that only the Tohmajärvi Kemie station in eastern Finland had a significant negative trend in drought duration and severity. Furthermore, of the 22 stations studied, only two stations showed a significant increasing trend in the duration and severity of drought at the 10% level. The drought characteristics at the remaining stations showed no significant trend at the 10% level of significance. For stations with non-stationary drought characteristics, generalised additive models for location, scale, and shape (GAMLSS) were used for frequency analysis. The correlation between the three characteristics of severity, duration and IAT was investigated using Kendall's Tau statistic. The results showed a high correlation between the two variables duration and severity and a moderate and acceptable correlation between drought severity and IAT as well as the pair of duration and IAT. In the following, copula functions were used to construct a trivariate distribution of the drought characteristics. Among the copulas tested, the R-vine copula and its independent mode have the best fit for the variables under study and provide a suitable tree sequence. Finally, using the aforementioned copulas and their conditional density, the frequency analysis of the three drought variables was performed. The results of this study were presented in the form of four-dimensional graphs to estimate the joint probability of occurrence of drought characteristics based on the JDI. These graphs are presented according to the precipitation conditions of each station, and by having a drought characteristic, other characteristics can be estimated with different probabilities. The proposed method is very efficient in analysing the joint frequency of drought characteristics due to the consideration of the effective parameters and the use of conditional density.

It has been broadly reported that future climate change will most likely affect the spatio-temporal distribution of water resources and consequently droughts. There is a prevailing notion that an increase in temperature and frequency of heat waves are expected to result in more intense droughts in the coming years. In this study, we aimed to evaluate the effect of the potential evapotranspiration (PET) method selection on future drought projections over Poland. In our study, simulations of the Soil and Water Assessment Tool (SWAT) model were conducted, utilising an ensemble of six EURO-CORDEX projections, spanning the period from 2006 to 2100 under the RCP8.5 scenario. Two model setups with two different PET methods (Penman-Monteith—PM and Hargreaves—HAR) were used. For drought conditions evaluation we selected the Standardized Precipitation Index (SPI) and Standardized Precipitation-Evapotranspiration Index (SPEI) for meteorological drought, Standardized Streamflow Index (SSI) for hydrological drought, and Standardized Soil Moisture Index (SMI) for agricultural drought. The meteorological and hydrological droughts were calculated using a 12-month time aggregation window, while agricultural drought was calculated using a 3-month window. Climate projections revealed that by 2080s annual mean temperature and precipitation increase is expected by up to +3.4°C and +10.3% respectively. Under future climate conditions duration and severity of meteorological droughts are projected to decrease. PM method leads to a higher PET increases (1.35 mm year⁻¹) than the HAR method (1.1 mm year⁻¹) throughout the century which entail diverging signal of change for agricultural and hydrological droughts. PM- and HAR-based simulations indicate increase in the total duration and cumulative severity of agricultural droughts, buthowever, for HAR-based projections, the increase is much less. For hydrological droughts the signal of change is similar for both PET methods, but considerably distinct in magnitude. Considering the entire simulation period, by the end of the century cumulative severity of hydrological droughts is projected to decrease, with a much more pronounced decline for HAR (70% reduction) than for the PM method (35% reduction). Our study demonstrated that methodological choices are crucial to the assessment of future drought risk under climate change.

Global or regional impacts of El Niño Southern Oscillation (ENSO) have predominantly been investigated through the Niño3.4 index, representing the sea surface temperature (SST) variations in the central Tropical Pacific. In this study, we comparatively evaluated the usefulness of Niño1+2, a relatively less utilised index that represents SST variability in the Eastern Tropical Pacific. In our analyses, we focused on ENSO impacts on Euro-Mediterranean (EM) climate variability during boreal winter, using data from the NCEP/NCAR Reanalysis. The correlation analysis involving Niño1+2 depicts more distinct temperature and precipitation patterns over the EM region. Amongst the SST-based Niño indices, it has the highest correlation with the East Atlantic index (0.47, statistically significant at > 99% confidence level), a prominent regional teleconnection associated primarily with the strength of the East Atlantic ridge, which produces dipole-type climate patterns between East Atlantic/Western Europe and Central/Eastern Mediterranean. Moreover, its lagged correlations with the following spring (0.39), summer (0.31), and autumn (0.36) are all statistically significant at ≥ 99% confidence levels. The composite analysis shows that different Niño regions have distinct effects on atmospheric circulation and climate in the EM region. The Niño1+2 index is particularly helpful in identifying the years when warm SST anomalies of El Niño extend to the Eastern Equatorial Pacific, which results in a reversal of temperatures across the EM region. Thus, this study suggests that Niño1+2 is a useful index for studying climate variability and predictability in the EM region, especially when used in conjunction with other Niño indices, as it captures some ENSO features that they may not encompass.