International Journal of Climatology最新文献

{"title":"Diverging Projections of Future Droughts in High-End Climate Scenarios for Different Potential Evapotranspiration Methods: A National-Scale Assessment for Poland","authors":"Paweł Marcinkowski,&nbsp;Mohammad Reza Eini,&nbsp;Nelson Venegas-Cordero,&nbsp;Maciej Jefimow,&nbsp;Mikołaj Piniewski","doi":"10.1002/joc.8674","DOIUrl":"https://doi.org/10.1002/joc.8674","url":null,"abstract":"<div>\u0000 \u0000 <p>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.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 16","pages":"5902-5917"},"PeriodicalIF":3.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Evaluation of Niño1+2 and Niño3.4 Indices in Terms of ENSO Effects Over the Euro-Mediterranean Region","authors":"Ece Yavuzsoy-Keven,&nbsp;Yasemin Ezber,&nbsp;Omer Lutfi Sen","doi":"10.1002/joc.8669","DOIUrl":"https://doi.org/10.1002/joc.8669","url":null,"abstract":"<div>\u0000 \u0000 <p>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 &gt; 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.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 16","pages":"5839-5856"},"PeriodicalIF":3.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regulatory factors and climatic impacts of marine heatwaves over the Arctic Ocean from 1982 to 2020","authors":"Xiaojuan Zhang,&nbsp;Fei Zheng,&nbsp;Zhiqiang Gong","doi":"10.1002/joc.8630","DOIUrl":"https://doi.org/10.1002/joc.8630","url":null,"abstract":"<p>Arctic warming has been substantially greater than that in the rest of the world and has had an important influence on the global climate. This study first explores the temporal and spatial evolutionary characteristics of marine heatwaves (MHWs) over the Arctic Ocean in multiyear ice (MYI), first-year ice (FYI), and open-water (OPW) regions from 1982 to 2020. MHWs in the Arctic Ocean show obvious spatial and seasonal variations, mainly occurring over the FYI region in the JAS (July–August–September, JAS), and their occurrences have a significant increasing trend in recent decades, accompanied by an abrupt increase since 2010. Furthermore, a multivariable network-based method is adopted to delineate the relationship between different climatic factors and MHWs in the Arctic Ocean and the climatic impacts of MHWs. The results show that the correlations between different climatic factors and MHWs in JAS in 2010–2020 are generally stronger than those in 1982–2009, and the main influencing factors of MHWs in different ice covers are different. MHWs in the MYI region are mainly affected by freshwater dilution processes, such as sea-ice concentrations (SIC), precipitation, and mixed-layer salinity. For the FYI region, the 2-m air temperature and total heat flux mainly affect MHWs by thermodynamic processes, and the 500-hPa geopotential height affects MHWs mainly by large-scale atmospheric circulation. The MHWs in the OPW region are mainly related to the SIC, 850-hPa geopotential height, and 10-m <i>v</i>-wind, indicating that they are correlated with atmospheric processes and wind fields. MHWs in JAS are also revealed to reduce or delay the formation of sea ice in OND (October–November–December, OND) by storing more abnormal heat, indicating that unfrozen ocean surfaces may lead to enhanced Arctic amplification in the following seasons.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 15","pages":"5297-5319"},"PeriodicalIF":3.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142762401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Climatological Analysis of Upper-Level Velocity Potential Using Global Weather Reanalysis, 1959–2020","authors":"Tyler J. Stanfield,&nbsp;Craig Allen Ramseyer","doi":"10.1002/joc.8659","DOIUrl":"https://doi.org/10.1002/joc.8659","url":null,"abstract":"<p>Upper-level (200 hPa) velocity potential (VP200) is useful in identifying areas of rising or sinking atmospheric motions on varying temporal scales (e.g., weekly, seasonal, interannual) especially in the global tropics. These areas are associated with enhancement (rising motion) or suppression (sinking motion) of tropical convection and subsequent weather phenomena dependent on these processes (e.g., tropical cyclones). This study employed commonly used global weather reanalysis datasets to calculate and compare VP200 on interannual through multidecadal temporal scales and quantify any differences that existed between them from 1959 to 2020 over four key regions of tropical variability (Equatorial Africa, Amazon Basin, Equatorial Central Pacific, and Equatorial Indonesia). To supplement this analysis, the highly correlated variables to VP200 of outgoing longwave radiation (OLR) and daily precipitation rate were used and directly compared with independent OLR and precipitation datasets to determine the reanalysis' level of agreement with the independent data. The ECMWF ERA5 held the highest agreement to these data over all regions examined and was reasoned to have the highest confidence in accurately capturing the variability of VP200 fields for the study period. Confidence was decreased in the usefulness of the NCEP/NCAR Reanalysis 1 as it consistently performed poorly over much of the study domain. The results of this study also emphasised the usefulness in ensemble-based approaches to assess climate variability and understanding of potential biases and uncertainties that are inherent in these data sources.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 15","pages":"5667-5692"},"PeriodicalIF":3.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joc.8659","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142762402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing Satellite, Reanalysis, Fused and Gridded (In Situ) Precipitation Products Over Türkiye","authors":"Abdullah Akbas,&nbsp;Hasan Ozdemir","doi":"10.1002/joc.8671","DOIUrl":"https://doi.org/10.1002/joc.8671","url":null,"abstract":"<p>Precipitation is the fundamental source for various research areas, including hydrology, climatology, geomorphology, and ecology, serving essential roles in modelling, distribution, and process analysis. However, the accuracy and precision of spatially distributed precipitation estimates is a critical issue, particularly for daily scale and topographically complex areas. Although many datasets have been developed based on different algorithms and sources are developed for this purpose, determining which of these datasets best reflects actual conditions is quite challenging. This study, hence, aims to compare the 25 global distributed precipitation estimates (gridded, satellite, model, and fused) concerning 221 ground-based observations based on the ranking of 18 continuous (evaluation statistics), eight categorical (precipitation indices), and two seasonality metric (high and low precipitation). Upon examining the results, gridded and model precipitation data including APHRODITE (Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation), CPC (Global Unified Gauge-Based Analysis of Daily Precipitation), ERA5-Land (ECMWF Reanalysis 5th Generation for Lands), and CFSR (Climate Forecast System Reanalysis) occupy the top four positions in continuous metrics. In contrast, satellite data such as PERSIANN-PDIR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), CMORPH (Climate Prediction Center morphing method), IMERG (The Integrated Multi-Satellite Retrievals for GPM), and TRMM-TMPA (Tropical Rainfall Measuring Mission/Multi-satellite Precipitation Analysis) dominate in the top four positions in categorical metrics. For seasonality of high and low precipitation, fused, gridded, and reanalyses products such as CPC, MSWEP (Multi-Source Weighted-Ensemble Precipitation, version 2), HydroGFD (Hydrological Global Forcing Data), CFSR rank among top four. Based on the first five rankings of all metrics, fused (multiple sourced) and gridded datasets accurately reflect the actual situations compared to other precipitation products. Reanalysis (model) and satellite-based follow this rank, respectively. The results clearly indicate that fused precipitation derived products from multiple sources offer better accuracy and precision in representing the spatial distribution of precipitation on a daily scale.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 16","pages":"5873-5889"},"PeriodicalIF":3.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joc.8671","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reducing the Uncertainty in the Tropical Precipitation through a Multi-Criteria Decision-Making Approach","authors":"Archana Majhi,&nbsp;C. T. Dhanya,&nbsp;Sonali Pattanayak,&nbsp;Sumedha Chakma","doi":"10.1002/joc.8665","DOIUrl":"https://doi.org/10.1002/joc.8665","url":null,"abstract":"<div>\u0000 \u0000 <p>The inherent model uncertainty in precipitation projections is found to be more dominant over tropical regions thereby reducing the reliability of using them in climate change impact assessment studies. To address such issues, a subset of well performing global climate models (GCMs) can provide narrow range of possible future outcomes, which can be helpful in formulating mitigation and adaptation strategies that are more targeted and efficient. In this study, climate models are selected based on their performance in simulating relative humidity and vertical velocity since these variables play an important role in precipitation simulation and significantly contribute toward the intermodel spread. The models are evaluated by using various statistical performance measures and ranked using multi-criteria decision-making approaches. Finally, based on Jenks natural breaks optimization algorithm, subset of GCMs consisting of ACCESS1.0, ACCESS1.3 and INM-CM4 models, are considered as the best possible subset for precipitation simulation over tropical land regions. Two observational precipitation datasets are further considered to investigate the effectiveness of the proposed framework. The proposed methodology is validated to be effective in identifying the best climate models since the resulting subset is capable of both capturing observed precipitation and minimizing the uncertainty in future projections. Hence, this methodology can be utilized further for performance evaluation of GCMs focusing different geography and climatic drivers.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 16","pages":"5773-5790"},"PeriodicalIF":3.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial Interpolation of Seasonal Precipitations Using Rain Gauge Data and Convection-Permitting Regional Climate Model Simulations in a Complex Topographical Region","authors":"Valentin Dura,&nbsp;Guillaume Evin,&nbsp;Anne-Catherine Favre,&nbsp;David Penot","doi":"10.1002/joc.8662","DOIUrl":"https://doi.org/10.1002/joc.8662","url":null,"abstract":"<p>In mountainous areas, accurately estimating the long-term climatology of seasonal precipitations is challenging due to the lack of high-altitude rain gauges and the complexity of the topography. This study addresses these challenges by interpolating seasonal precipitation data from 3189 rain gauges across France over the 1982–2018 period, using geographical coordinates, and altitude. In this study, an additional predictor is provided from simulations of a Convection-Permitting Regional Climate Model (CP-RCM). The simulations are averaged to obtain seasonal precipitation climatology, which helps capture the relationship between topography and long-term seasonal precipitation. Geostatistical and machine learning models are evaluated within a cross-validation framework to determine the most appropriate approach to generate seasonal precipitation reference fields. Results indicate that the best model uses a machine learning approach to interpolate the ratio between long-term seasonal precipitation from observations and CP-RCM simulations. This method successfully reproduces both the mean and variance of observed data, and slightly outperforms the best geostatistical model. Moreover, incorporating the CP-RCM outputs as an explanatory variable significantly improves interpolation accuracy and altitude extrapolation, especially when the rain gauge density is low. These results imply that the commonly used altitude-precipitation relationship may be insufficient to derive seasonal precipitation fields. The CP-RCM simulations, increasingly available worldwide, present an opportunity for improving precipitation interpolation, especially in sparse and complex topographical regions.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 16","pages":"5745-5760"},"PeriodicalIF":3.5,"publicationDate":"2024-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joc.8662","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implications of CMIP6 Models-Based Climate Biases and Runoff Sensitivity on Runoff Projection Uncertainties Over Central India","authors":"Shoobhangi Tyagi,&nbsp;Sandeep Sahany,&nbsp;Dharmendra Saraswat,&nbsp;Saroj Kanta Mishra,&nbsp;Amlendu Dubey,&nbsp;Dev Niyogi","doi":"10.1002/joc.8661","DOIUrl":"https://doi.org/10.1002/joc.8661","url":null,"abstract":"<p>Accurate runoff projections are vital for developing climate adaptation strategies, yet significant uncertainties persist. The commonly employed approaches to constrain these uncertainties rely on the stationarity of climate biases and runoff sensitivity, which may not hold for climate-sensitive regions (e.g., semi-arid regions). This study investigates the validity of the stationarity assumption across 29 CMIP6 models, encompassing diverse climate biases (Dry Warm, Wet Warm, Dry Cold, and Wet Cold), utilising a semi-arid region in central India as a testbed. The implications of this assumption on runoff projection uncertainties were comprehensively assessed across the runoff modelling chain for three time periods (the 2030s, 2060s and 2090s) based on the Soil and Water Assessment Tool (SWAT) simulations. The results highlight the non-stationary nature of climate biases and runoff sensitivity under future scenarios, challenging the widespread applicability of common uncertainty-constraining approaches. Moreover, the impact of non-stationarity on runoff projection uncertainty was found to be strongly influenced by the choice of GCMs, preprocessing methods and climate change scenarios. In the 2030s, GCMs dominate runoff uncertainty, with dry models exhibiting ~10%–15% higher uncertainty compared to warm models, which is further amplified when interacting with warm biases. However, from the mid-century onwards, the bias-adjustment approaches and climate change scenarios significantly shape runoff projection uncertainties under non-stationary conditions. These findings emphasise the potential of climate bias and runoff sensitivity-based GCM selection for reducing runoff uncertainty in near-future assessment (2030s). For mid-term and long-term runoff projections, addressing diverse climate biases through bias-adjustment approaches is more viable. This study offers critical insights to prioritise the development of a non-stationarity-based approach for reliable runoff projections in climate-sensitive regions.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 16","pages":"5727-5744"},"PeriodicalIF":3.5,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joc.8661","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the Limitations of Multi-Model Ensembling of Climate Model Outputs in Capturing Climate Extremes","authors":"Velpuri Manikanta,&nbsp;V. Manohar Reddy,&nbsp;Jew Das","doi":"10.1002/joc.8660","DOIUrl":"https://doi.org/10.1002/joc.8660","url":null,"abstract":"<div>\u0000 \u0000 <p>In the context of climate change, the widespread practice of directly employing Multi-Model Ensembles (MMEs) for projecting future climate extremes, without prior evaluation of MME performance in historical periods, remains underexplored. This research addresses this gap through a comprehensive analysis of ensemble means derived from CMIP6-based models, including both simple and weighted averages of precipitation (SEMP and WEMP) and temperature (SEMT and WEMT) time series, as well as simple (SEME) and weighted (WEME) averages of extremes based on model-by-model analysis. The study evaluates the efficacy of MMEs in capturing mean annual values of ETCCDI indices over India for the period 1951–2014, utilising the IMD gridded data set as a reference. The results reveal that SEME and WEME consistently align closely with IMD data across various precipitation indices. At the same time, SEMP and WEMP consistently display underestimation biases ranging from 20% to 80% across all precipitation indices, except for CWD, where there is an overestimation bias. Moreover, SEMP and WEMP consistently underestimate CDD and overestimate CWD, indicating a systematic bias in these ensemble means, while WEME and SEME demonstrate satisfactory performance. SEMT and WEMT exhibit notable underestimation in temperature indices. In summary, adopting SEME and SEMT leads to a more robust assessment of precipitation and temperature extremes, respectively. These findings highlight the limitations of traditional MME methodologies in reproducing observed extreme precipitation events across various climatic zones in India, offering essential insights for refining climate models and improving the reliability of climate projections specific to the Indian subcontinent.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 16","pages":"5711-5726"},"PeriodicalIF":3.5,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multiscalar Standardized Vapor Pressure Deficit Index for Drought Monitoring and Impacts","authors":"Isioma Jessica Nwayor,&nbsp;Scott M. Robeson,&nbsp;Darren L. Ficklin,&nbsp;Justin T. Maxwell","doi":"10.1002/joc.8668","DOIUrl":"https://doi.org/10.1002/joc.8668","url":null,"abstract":"<p>Vapour pressure deficit (VPD) is a critical measure of the atmospheric demand for water and can be used to assess short-term and seasonal drought. To provide for probabilistic comparisons of VPD across space and time, we develop a Standardized Vapor Pressure Deficit Index (SVPDI). Similar to the way that other standardised drought indices are used, SVPDI allows for the analysis and comparison of changes in VPD across regions with different base level VPD values. It also should be useful for analysing impacts on vegetation that has varying levels of adaptation to high VPD. We use 1-, 3-, 6- and 12-month timescales for the development of SVPDI and show that the gamma distribution is superior to other zero-limited probability distributions for analysing VPD and, therefore, for calculating SVPDI. Then, focusing on the short-term variations at the 1- and 3-month timescales, we show how SVPDI has changed globally from 1958 to 2023 and how those changes differ from those of the commonly used Standardized Precipitation Evaporation Index (SPEI). We find that SVPDI shows more widespread drying conditions that also are larger in magnitude compared to those of SPEI. Although the two indices are moderately well correlated across the terrestrial surface, we discover that they are more decoupled in humid and arid regions compared to dry sub-humid and semi-arid regions. Using four locations that have recently experienced severe drought, we find that SVPDI generally showed longer drought duration and more severe drought events in the last decade when compared to SPEI.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 16","pages":"5825-5838"},"PeriodicalIF":3.5,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joc.8668","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}