International Journal of Climatology最新文献

A methodology is presented for downscaling the Euro-CORDEX climatic projections in order to derive spatially and temporally correlated weather fields that can be used for risk and resilience assessment of large-scale asset portfolios or interconnected infrastructure. The temporal resolution of the Euro-CORDEX data is downscaled to a 10 min basis by employing a modified analogue-type approach that utilizes the k-NN algorithm along with measurements from weather stations. The aim is to generate composite “Frankenstein” days comprising 144 jigsaw pieces of observed 10 min timeseries that are scaled and/or shifted, and matched together to form a continuous daily record. These point-estimates, valid only at the locations of the weather stations, are expanded spatially by employing high-fidelity weather intensity measure fields that provide variable yet synchronous patterns of weather parameters at all locations of interest. As a case study, the Euro-CORDEX projections for wind, temperature, and precipitation are downscaled for the Metsovo-Panagia segment of Egnatia Odos highway in Greece, by employing high-fidelity Computational Fluid Dynamic simulations that account for the topography of the site to simulate turbulent wind flows. These are combined with measurements of two local weather stations to generate the Frankenstein timeseries and corresponding weather fields that can be used for estimating operability, recovery and direct/indirect loss statistics on an event-by-event basis for an ensemble of interconnected highway assets.

Understanding drought occurrence and evolution is important in minimizing the impacts associated with it. This work assesses the performance of 10 commonly used meteorological indices to measure drought in Morocco. The studied indices are Deciles Index (DI), Percent of Normal Index (PNI), Z-Score Index (ZSI), China-Z Index (CZI), Rainfall Anomaly Index (RAI), Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), its self-calibrated variant (scPDSI) and Palmer Z Index (PZI). Rainfall and temperature gridded data is sourced from PERSIANN-CDR and ERA5, respectively, for the period 1983–2021. The study area exhibits three main climatic regimes; subhumid, semi-arid and arid, with a drying and warming climate, as depicted by the rainfall and temperature trends analysis. Results show that most rainfall-based indices perform relatively poorly in drought monitoring in the study area. DI and PNI appear to be inconsistent and abnormally responsive to rainfall. RAI reports droughts 56.5% more frequently and slightly underestimate drought intensity compared to other indices. Similarly, ZSI and CZI largely underestimate drought intensity. PDSI and scPDSI are computationally demanding, often underestimate drought intensity and overestimate drought duration by at least 115% compared to SPI and SPEI. Conversely, PZI can be used for drought onset detection as it reported droughts early compared to the other indices. SPI and SPEI perform overall better regarding their consistent drought identification and severity assessment. However, SPEI is found to be more suitable than SPI in the arid and semi-arid regions and performed better considering the warming climate of the country.

This paper presents an analysis of the relationships between soil moisture, cloud cover, solar radiation, air temperature and humidity, and precipitation during the warm half of the year in East-Central Europe over the years 1971–2020. The temporal and spatial variability of these meteorological elements is presented in association with the occurrence of anticyclonic blocking events over the study area. It demonstrates that changes in soil moisture in East-Central Europe point to the combined influence of many meteorological factors resulting from the atmospheric circulation, and are an indicator of the comprehensive relationships among those factors. The main factors affecting soil water content are precipitation and evapotranspiration, which in turn depend on air humidity, cloudiness, intensity of solar radiation and air temperature. The increase in the frequency and duration of sequences of days with blocking events in East-Central Europe has contributed to an increased probability of longer periods with soil moisture negative anomalies.

Climatic change over the globe due to global warming affects the characteristics of climate variables that have critical implications on large fraction of population that depends on agriculture for livelihood like Pakistan. Consequently, this study examined how high horizontal grid resolution CMIP6 models simulate the observed precipitation variability during 1981–2014 and further explored the future changes during 2017–2050 under high emission scenario SSP5-8.5 over Pakistan region. The performances of 12 (CMIP6) High Resolution Model Inter-comparison Project version 1.0 (hereafter; HighResMIP) GCMs and their ensemble means in reproducing the observed climate were calculated at each station in the study domain and formed the basis for deriving HighResMIP ranking. Further, the study employed Shannon's Entropy and a modified version of Criteria Importance through Inter-Criteria Correlation (D-CRITIC) method to build an ensemble mean from the best performing models. Evaluation of HighResMIP GCMs performance revealed that most models showed mixed signals in the region, with fewer models such as HadGEM3-GC31-HH, HadGEM3-GC31-HM and HadGEM3-GC31-MM showing good agreement with the observed precipitation. Overall, HighResMIP multi-model ensemble outperforms precipitation distribution over individual models. D-CRITIC based ensemble mean implies higher increase in precipitation than entropy approach. Future changes depict an increase in mean annual in the northern region relative to the historical period. A pronounced increase of about 16%–18% in precipitation was noted in HadGEM3-GC31-HH and HiRAM-SIT-HR. Conversely, FGOAL-f3-H project noteworthy reduction (21%) in precipitation in the near future (2017–2050). The projected seasonal precipitation shows upsurge pattern of 5%–28% in pre-monsoon season, whereas the reduction in monsoon precipitation is projected to be 29%–40%. The findings of this study can help in building future climate resilience and developing strategic policies in Pakistan.

The Mediterranean region contains some of the areas with the highest urban density in the world, and these areas keep expanding, making this region a “hotspot” of climate change. Life in the Mediterranean unfolds predominantly outdoors throughout the year, exposing its inhabitants to an evolving climate that is progressively harsher and punctuated by increasingly frequent extreme weather events. More and more people are going to be exposed to severe heat waves, droughts, water shortage, dust storms, forest fires and poor air quality on the one hand, and torrential rains and floods on the other hand. The urban heat island further increases thermal stress of city dwellers and plays a key role in citizens' health and well-being. In this exhaustive review, based on state-of-the-art studies we delve into the realm of climate change and extreme weather phenomena as they intersect with urban populations in Mediterranean cities, both in the present and in the foreseeable future. Our focus lies on identifying knowledge gaps, inconsistencies in observed climatic hazards and shortcomings in assessing the associated risks and their societal and environmental ramifications. Moreover, we undertake a comprehensive survey of future predictions exploring the variables thermal stress, air pollution, air quality and characteristics of the hydro-climatic systems, that is, droughts, fires and floods. Yet, critical knowledge gaps persist in understanding the science, the coping mechanisms, the strategies for preparedness and adaptation and the intricate interplay between these facets and societal dynamics. The developing countries in the Mediterranean region stand exceptionally vulnerable. It is imperative for more affluent nations to share their expertise and extend assistance to less developed counterparts, aiding them in navigating climate-related challenges, devising adaptive strategies and facilitating their implementation.

Change over recent decades in the world's five Mediterranean Climate Regions (MCRs) of quantities of relevance to water resources, ecosystems and fire are examined for all seasons and placed in the context of changes in large-scale circulation. Near-term future projections are also presented. It is concluded that, based upon agreement between observational data sets and modelling frameworks, there is strong evidence of radiatively-driven drying of the Chilean MCR in all seasons and southwest Australia in winter. Observed drying trends in California in fall, southwest southern Africa in fall, the Pacific Northwest in summer and the Mediterranean in summer agree with radiatively-forced models but are not reproduced in a model that also includes historical sea surface temperature (SST) forcing, raising doubt about the human-origin of these trends. Observed drying in the Mediterranean in winter is stronger than can be accounted for by radiative forcing alone and is also outside the range of the SST-forced ensemble. It is shown that near surface vapour pressure deficit (VPD) is increasing almost everywhere but that, surprisingly, this is contributed to in the Southern Hemisphere subtropics to mid-latitudes by a decline in low-level specific humidity. The Southern Hemisphere drying, in terms of precipitation and specific humidity, is related to a poleward shift and strengthening of the westerlies with eddy-driven subsidence on the equatorward side. Model projections indicate continued drying of Southern Hemisphere MCRs in winter and spring, despite ozone recovery and year-round drying in the Mediterranean. Projections for the North American MCR are uncertain, with a large contribution from internal variability, with the exception of drying in the Pacific Northwest in summer. Overall the results indicate continued aridification of MCRs other than in North America with important implications for water resources, agriculture and ecosystems.

Anthropogenic climate change induced weather and climate extremes have led to frequent heat waves, droughts and floods threatening water resources and food security for an agricultural country like Pakistan. Despite their significance, the trends and variability of extreme temperature and precipitation indices and associated large-scale drivers in the agro-ecological zones (AEZs) of Pakistan remain unknown and need urgent attention because of abrupt climate change. The present study documents the spatiotemporal variations of climate change indices together with the elevation-dependent variability trends over various AEZs in Pakistan for the period of 42 years (1979–2020). Nonparametric Mann–Kendall (MK) and Sen's slope (SS) estimator tests have been employed for trend estimation. Results indicated linearly increasing (warming) and statistically significant trends in Tmean, TNx, WSDI and TR20 whereas significant decreasing (cooling) trends in cool nights (−1.73 days·decade⁻¹) and cold spells (−1.28 days·decade⁻¹). The spatial distribution of temperature indices trends depicts robust warming over southwestern and central zones while cooling trends over northern zones. Regarding precipitation extremes, all indices have shown increasing (wetter) trends with a significant increase in PRCPTOT and RX5day. The stations in northern and subhumid AEZs received more precipitation compared to other zones. Elevation-dependent trends in temperature indices exhibited a statistically significant positive (negative) relationship with cold (warm) tails. Most of the extreme precipitation indices have a weak, but positive association with elevation except SDII. The weakening of South Asian subtropical upper-level jet by a high-pressure system over northeast Pakistan resulted in amplified land surface temperatures. However, the spatial patterns of zonal winds indicate a trough over Pakistan's southern and central parts, with warmer sea-surface temperature, low sea-level pressure and easterly anomalies, favour moisture transport and precipitation in Pakistan. The outcomes of present study will be useful in addressing various climate-induced disasters occurring in various AEZs of Pakistan.

Cold fronts often bring catastrophic weather events, which are exacerbated under global warming. Thus, the automatic and objective identification of cold fronts will be helpful for accurate forecasting and comprehensive analysis of cold fronts. Recently, machine learning methods have been applied to meteorological study. In this study, a cold front identification method based on the deep learning model Mask R-CNN is proposed to automatically identify cold fronts from massive data. The Mask R-CNN method shows high accuracy after the comparison with traditional methods and is effective for identifying the cold fronts in both continuous time and extreme precipitation events. Based on the obtained cold-front samples, we conduct some statistical analysis. The results show that the frequency of cold front is unevenly distributed over Eurasia, with the highest in the Daxing'anling region and the mid-latitude storm axis, especially in winter. The method and results presented in this study may have some implications for the application of deep learning models in weather system identification.

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.