Mohammed Magdy Hamed, Ali Salem Al-Sakkaf, Mohammed Rady, Shamsuddin Shahid
Global warming has significantly increased the frequency and intensity of extreme events, which have catastrophic consequences for ecosystems and humans. Despite efforts to assess the impact of climate change on the potential risk of Borneo, most research has focused on partial regions, considering short timescales and a limited number of temperature and precipitation extremes indices to quantify the expected climate risks. This study employed a new method of climate risk assessment of Borneo based on the combined changes in various climate parameters. It estimated 23 climate indices at all grid points covering Borneo for three overlapping sub-periods (1951–1980, 1961–1990, 1991–2020). The modified Mann-Kendall test was employed to identify grid points exhibiting significant increasing or decreasing trends of each index for each sub-period. Finally, significant trends of 23 indices were integrated to estimate the potential climate risk indicator (RI) based on the combined changes in various climate parameters for each grid point and sub-period. Temperature indices showed a clear warming trend across Borneo Island, particularly in the eastern regions, with absolute temperature indices showing an increase of 0.5°C–2.5°C in 1991–2020 compared to the reference period (1951–1980). However, extreme cold temperatures have become less prevalent over the study period. There is a shift from light consecutive rainfall days towards more heavy and short-duration rainfall events. Therefore, there are indications of intensifying rainfall events over the island's southern half, counterbalanced by drying trends in the northern regions, especially Brunei. The spatial distribution of RI revealed an overall 184% increase in climate risk on the island in recent years (1991–2020) compared to the reference period. The highest rise in RI was in the central east of the island, mostly due to significant increases in rainfall and temperature indices. The findings can inform adaptation initiatives to manage escalating heat and flood risks while guiding additional research to explain further the complex climatic changes occurring in this ecologically and socially vital region.
{"title":"Temperature and Precipitation Extremes Over Borneo Island: An Integrated Climate Risk Assessment","authors":"Mohammed Magdy Hamed, Ali Salem Al-Sakkaf, Mohammed Rady, Shamsuddin Shahid","doi":"10.1002/joc.8682","DOIUrl":"https://doi.org/10.1002/joc.8682","url":null,"abstract":"<div>\u0000 \u0000 <p>Global warming has significantly increased the frequency and intensity of extreme events, which have catastrophic consequences for ecosystems and humans. Despite efforts to assess the impact of climate change on the potential risk of Borneo, most research has focused on partial regions, considering short timescales and a limited number of temperature and precipitation extremes indices to quantify the expected climate risks. This study employed a new method of climate risk assessment of Borneo based on the combined changes in various climate parameters. It estimated 23 climate indices at all grid points covering Borneo for three overlapping sub-periods (1951–1980, 1961–1990, 1991–2020). The modified Mann-Kendall test was employed to identify grid points exhibiting significant increasing or decreasing trends of each index for each sub-period. Finally, significant trends of 23 indices were integrated to estimate the potential climate risk indicator (RI) based on the combined changes in various climate parameters for each grid point and sub-period. Temperature indices showed a clear warming trend across Borneo Island, particularly in the eastern regions, with absolute temperature indices showing an increase of 0.5°C–2.5°C in 1991–2020 compared to the reference period (1951–1980). However, extreme cold temperatures have become less prevalent over the study period. There is a shift from light consecutive rainfall days towards more heavy and short-duration rainfall events. Therefore, there are indications of intensifying rainfall events over the island's southern half, counterbalanced by drying trends in the northern regions, especially Brunei. The spatial distribution of RI revealed an overall 184% increase in climate risk on the island in recent years (1991–2020) compared to the reference period. The highest rise in RI was in the central east of the island, mostly due to significant increases in rainfall and temperature indices. The findings can inform adaptation initiatives to manage escalating heat and flood risks while guiding additional research to explain further the complex climatic changes occurring in this ecologically and socially vital region.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 16","pages":"6040-6064"},"PeriodicalIF":3.5,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860314","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}
Shakirudeen Lawal, Bruce Hewitson, Christopher Lennard
Prediction of seasonal onset is crucial to agriculture in southern and eastern Africa. Here, we applied two definitions of onset, namely meteorological and agricultural (crop-germination), to evaluate CMIP6 models through the lens of rainfall onset over representative maize agricultural regions of South Africa, Tanzania, Malawi and Zambia. We use the ERA5 reanalysis as a proxy for observations, and robust regression to calculate a statistical comparison of the onset definitions for the period 1979–2021. Evaluation of ERA5 reanalysis shows similar magnitude and pattern as gauge based MSWEP. Our results show that, for meteorological onset, Johannesburg, with a subtropical highland climate, experienced earliest onset after 23 December; and an increasing trend (later onset) but not statistically significant (p = 0.2). Over Bethlehem, which has continental climate, the earliest onset date was after October 9 and an increasing interannual variability since 2000 is noted. The standard deviation of onset dates across the regions shows an East-Central-South gradient. We also found that the crop-germination onset definition shows earlier onset of seasonal rains, it differs considerably across regions, and has higher interannual variability, in comparison with the meteorological definition. Over Lilongwe, Mbeya and Lusaka, late meteorological onset with a weak positive and insignificant trend is observed. The CMIP6 model's representation of onset trend differs from reanalysis data, with inter-model differences. Late meteorological onset is underestimated by GFDL-CM4 and MPI while INM5, MPI and NorESM overestimate the observed earliest onset. The largest bias is shown by INM and MPI which simulate earliest and latest onset as 190 (07 January) and 206 (23 January) respectively. In addition, models often fail to simulate sufficient precipitation to produce onset for seed germination and crop development. The ACCESS model showed an insignificant trend (p value = 2) and later onset over Lilongwe, an insignificant trend (p value = 0.9) over Lusaka, and an earlier onset over Mbeya. Using the agricultural onset definition, over Bethlehem, all the models and the ERA5 reanalysis did not produce enough precipitation to meet onset conditions. We suggest that rainfall onset studies use several definitions or metrics of onset and that the choice of metric be informed by the research question. Using such an ensemble of onset metrics contributes to a better understanding of variability and uncertainties in agricultural productivity.
{"title":"Assessing the Climatological Characteristics of Observed and Simulated Seasonal Onset of Precipitation Over Southern and Eastern Africa","authors":"Shakirudeen Lawal, Bruce Hewitson, Christopher Lennard","doi":"10.1002/joc.8666","DOIUrl":"https://doi.org/10.1002/joc.8666","url":null,"abstract":"<p>Prediction of seasonal onset is crucial to agriculture in southern and eastern Africa. Here, we applied two definitions of onset, namely meteorological and agricultural (crop-germination), to evaluate CMIP6 models through the lens of rainfall onset over representative maize agricultural regions of South Africa, Tanzania, Malawi and Zambia. We use the ERA5 reanalysis as a proxy for observations, and robust regression to calculate a statistical comparison of the onset definitions for the period 1979–2021. Evaluation of ERA5 reanalysis shows similar magnitude and pattern as gauge based MSWEP. Our results show that, for meteorological onset, Johannesburg, with a subtropical highland climate, experienced earliest onset after 23 December; and an increasing trend (later onset) but not statistically significant (<i>p</i> = 0.2). Over Bethlehem, which has continental climate, the earliest onset date was after October 9 and an increasing interannual variability since 2000 is noted. The standard deviation of onset dates across the regions shows an East-Central-South gradient. We also found that the crop-germination onset definition shows earlier onset of seasonal rains, it differs considerably across regions, and has higher interannual variability, in comparison with the meteorological definition. Over Lilongwe, Mbeya and Lusaka, late meteorological onset with a weak positive and insignificant trend is observed. The CMIP6 model's representation of onset trend differs from reanalysis data, with inter-model differences. Late meteorological onset is underestimated by GFDL-CM4 and MPI while INM5, MPI and NorESM overestimate the observed earliest onset. The largest bias is shown by INM and MPI which simulate earliest and latest onset as 190 (07 January) and 206 (23 January) respectively. In addition, models often fail to simulate sufficient precipitation to produce onset for seed germination and crop development. The ACCESS model showed an insignificant trend (<i>p</i> value = 2) and later onset over Lilongwe, an insignificant trend (<i>p</i> value = 0.9) over Lusaka, and an earlier onset over Mbeya. Using the agricultural onset definition, over Bethlehem, all the models and the ERA5 reanalysis did not produce enough precipitation to meet onset conditions. We suggest that rainfall onset studies use several definitions or metrics of onset and that the choice of metric be informed by the research question. Using such an ensemble of onset metrics contributes to a better understanding of variability and uncertainties in agricultural productivity.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 16","pages":"5791-5808"},"PeriodicalIF":3.5,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joc.8666","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860506","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}
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.
{"title":"Assessment of Basin-Scale Concurrent Dry and Wet Extreme Dynamics Under Multimodel CORDEX Climate Scenarios","authors":"Sushree Swagatika Swain, Ashok Mishra, Chandranath Chatterjee","doi":"10.1002/joc.8677","DOIUrl":"https://doi.org/10.1002/joc.8677","url":null,"abstract":"<div>\u0000 \u0000 <p>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.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 16","pages":"5950-5968"},"PeriodicalIF":3.5,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860505","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}
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.
{"title":"Thermal Characteristics of the Extreme Cases of Tropopause Over the Tropics","authors":"Pooja Purushotham, Sanjay Kumar Mehta, Saleem Ali, Masatomo Fujiwara, Susann Tegtmeier","doi":"10.1002/joc.8676","DOIUrl":"https://doi.org/10.1002/joc.8676","url":null,"abstract":"<div>\u0000 \u0000 <p>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.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 16","pages":"5934-5949"},"PeriodicalIF":3.5,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860490","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}
Precipitation changes dynamically in the Mediterranean region. Therefore, the projection of future precipitation and its historical distribution mechanism is essential for climate mitigation and adaptation. In this study, a stepwise clustered precipitation downscaling method (SCPD) was developed and adopted in the Mediterranean region to reveal the inherent variation rules and trends over the future 100 years under two SSP scenarios. A cutting and merging multivariate process is introduced to build a cluster tree for supporting further downscaling and projecting steps. The ensemble average from the global climate model (GCM) dataset is used for precipitation projections. The precipitation performance of SCPD, evaluated by R2, is fairly decent. The precipitation projections vary with the original rainfall patterns over the gauge stations. Dry places tend to become comparably drier in the future. Precipitation in the northern Mediterranean region shows a drier winter–spring and wetter summer–autumn. Opposite trends emerged in the southern part, with increasing winter precipitation and decreasing summer rainfall. The rising carbon dioxide concentration will further intensify the decrease in rainfall. However, the centres of these two EOFs are not identical. The contributions of NAO (positive) and Niño 3.4 (negative) to PC1 are relatively high. Accordingly, the strongest positive correlation with PC2 is SCAND, as well as negative correlations with AO, NAO and EAWR. Positive anomaly precipitation is attributed to PC1, whereas PC2 is responsible for most of the negative variance precipitation.
{"title":"A Stepwise-Clustered Precipitation Downscaling Method for Ensemble Climatic Projections in the Mediterranean Region","authors":"Siyu Wang, Guohe Huang, Chong Zhang, Chen Lu","doi":"10.1002/joc.8651","DOIUrl":"https://doi.org/10.1002/joc.8651","url":null,"abstract":"<p>Precipitation changes dynamically in the Mediterranean region. Therefore, the projection of future precipitation and its historical distribution mechanism is essential for climate mitigation and adaptation. In this study, a stepwise clustered precipitation downscaling method (SCPD) was developed and adopted in the Mediterranean region to reveal the inherent variation rules and trends over the future 100 years under two SSP scenarios. A cutting and merging multivariate process is introduced to build a cluster tree for supporting further downscaling and projecting steps. The ensemble average from the global climate model (GCM) dataset is used for precipitation projections. The precipitation performance of SCPD, evaluated by <i>R</i>\u0000 <sup>2</sup>, is fairly decent. The precipitation projections vary with the original rainfall patterns over the gauge stations. Dry places tend to become comparably drier in the future. Precipitation in the northern Mediterranean region shows a drier winter–spring and wetter summer–autumn. Opposite trends emerged in the southern part, with increasing winter precipitation and decreasing summer rainfall. The rising carbon dioxide concentration will further intensify the decrease in rainfall. However, the centres of these two EOFs are not identical. The contributions of NAO (positive) and Niño 3.4 (negative) to PC1 are relatively high. Accordingly, the strongest positive correlation with PC2 is SCAND, as well as negative correlations with AO, NAO and EAWR. Positive anomaly precipitation is attributed to PC1, whereas PC2 is responsible for most of the negative variance precipitation.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 15","pages":"5537-5559"},"PeriodicalIF":3.5,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joc.8651","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142762493","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}
Annalisa Di Bernardino, Stefano Casadio, Anna Maria Iannarelli, Anna Maria Siani
In this study, the daily maximum and minimum temperatures measured over the period 1961–2020 by 18 stations located near the Mediterranean coast are analysed to evaluate temperature trends and to compute 10 climate indices, selected among those proposed by the Expert Team on Climate Change Detection and Indices to monitor the occurrence of climate extremes of temperature. The trend analysis is performed using the Seasonal-Kendall test. The results show statistically significant positive trends in both above-mentioned variables throughout the Mediterranean, although the rate of warming is more marked in the minimum than in the maximum temperature and is more evident in the western portion of the Mediterranean Basin from 1990 onward. The climate extremes indices are evaluated assuming two different base periods (1961–1990 and 1991–2020). The shift forward of the base period involves a general rise in the percentile-based thresholds used for identifying temperature extremes, as a natural consequence of the current atmospheric warming, resulting in fewer warm events and more cold events since the 1990s using a warmer/later base period. The application of the Seasonal-Kendall test to the occurrence of extreme warm and cold events reveals that the western portion of the Mediterranean and the Adriatic Sea are the subregions most influenced by the base period update. This study demonstrates that the selection of the base period for the identification of extreme temperature events significantly impacts the results, and that the choice of a recent base period partially masks the ongoing atmospheric warming. The results suggest that the base period 1961–1990 might be more appropriate for climatological studies, as it provides a solid and stable baseline, and that attention must be paid when scientific results are shared with stakeholders, so as not to alter the communication of warnings and specific risks for the population.
{"title":"Temperature Trends and Influence of the Base Period Selection on Climate Indices in the Mediterranean Region Over the Period 1961–2020","authors":"Annalisa Di Bernardino, Stefano Casadio, Anna Maria Iannarelli, Anna Maria Siani","doi":"10.1002/joc.8678","DOIUrl":"https://doi.org/10.1002/joc.8678","url":null,"abstract":"<p>In this study, the daily maximum and minimum temperatures measured over the period 1961–2020 by 18 stations located near the Mediterranean coast are analysed to evaluate temperature trends and to compute 10 climate indices, selected among those proposed by the Expert Team on Climate Change Detection and Indices to monitor the occurrence of climate extremes of temperature. The trend analysis is performed using the Seasonal-Kendall test. The results show statistically significant positive trends in both above-mentioned variables throughout the Mediterranean, although the rate of warming is more marked in the minimum than in the maximum temperature and is more evident in the western portion of the Mediterranean Basin from 1990 onward. The climate extremes indices are evaluated assuming two different base periods (1961–1990 and 1991–2020). The shift forward of the base period involves a general rise in the percentile-based thresholds used for identifying temperature extremes, as a natural consequence of the current atmospheric warming, resulting in fewer warm events and more cold events since the 1990s using a warmer/later base period. The application of the Seasonal-Kendall test to the occurrence of extreme warm and cold events reveals that the western portion of the Mediterranean and the Adriatic Sea are the subregions most influenced by the base period update. This study demonstrates that the selection of the base period for the identification of extreme temperature events significantly impacts the results, and that the choice of a recent base period partially masks the ongoing atmospheric warming. The results suggest that the base period 1961–1990 might be more appropriate for climatological studies, as it provides a solid and stable baseline, and that attention must be paid when scientific results are shared with stakeholders, so as not to alter the communication of warnings and specific risks for the population.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 16","pages":"5969-5985"},"PeriodicalIF":3.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joc.8678","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860223","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}
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.
{"title":"Remote Sensing Data Assimilation to Improve the Seasonal Snow Cover Simulations Over the Heihe River Basin, Northwest China","authors":"Gang Deng, Xiuguo Liu, Qikai Shen, Tongchang Zhang, Qihao Chen, Zhiguang Tang","doi":"10.1002/joc.8656","DOIUrl":"https://doi.org/10.1002/joc.8656","url":null,"abstract":"<div>\u0000 \u0000 <p>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.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 15","pages":"5621-5640"},"PeriodicalIF":3.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142762336","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}
Foehn winds, characterised by abrupt temperature increases and wind speed changes, significantly impact regions on the leeward side of mountain ranges, e.g., by spreading wildfires. Understanding how foehn occurrences change under climate change is crucial. As foehn is a meteorological phenomenon, its prevalence has to be inferred from meteorological measurements employing suitable classification schemes. Hence, this approach is typically limited to specific periods for which the necessary data are available. We present a novel approach for reconstructing historical foehn occurrences using a combination of unsupervised and supervised probabilistic statistical learning methods. We utilise in situ measurements (available for recent decades) to train an unsupervised learner (finite mixture model) for automatic foehn classification. These labelled data are then linked to reanalysis data (covering longer periods) using a supervised learner (lasso or boosting). This allows us to reconstruct past foehn probabilities based solely on reanalysis data. Applying this method to ERA5 reanalysis data for six stations across Switzerland and Austria achieves accurate hourly reconstructions of north and south foehn occurrence, respectively, dating back to 1940. This paves the way for investigating how seasonal foehn patterns have evolved over the past 83 years, providing valuable insights into climate change impacts on these critical wind events.
{"title":"Long-Term Foehn Reconstruction Combining Unsupervised and Supervised Learning","authors":"Reto Stauffer, Achim Zeileis, Georg J. Mayr","doi":"10.1002/joc.8673","DOIUrl":"https://doi.org/10.1002/joc.8673","url":null,"abstract":"<p>Foehn winds, characterised by abrupt temperature increases and wind speed changes, significantly impact regions on the leeward side of mountain ranges, e.g., by spreading wildfires. Understanding how foehn occurrences change under climate change is crucial. As foehn is a meteorological phenomenon, its prevalence has to be inferred from meteorological measurements employing suitable classification schemes. Hence, this approach is typically limited to specific periods for which the necessary data are available. We present a novel approach for reconstructing historical foehn occurrences using a combination of unsupervised and supervised probabilistic statistical learning methods. We utilise in situ measurements (available for recent decades) to train an unsupervised learner (finite mixture model) for automatic foehn classification. These labelled data are then linked to reanalysis data (covering longer periods) using a supervised learner (lasso or boosting). This allows us to reconstruct past foehn probabilities based solely on reanalysis data. Applying this method to ERA5 reanalysis data for six stations across Switzerland and Austria achieves accurate hourly reconstructions of north and south foehn occurrence, respectively, dating back to 1940. This paves the way for investigating how seasonal foehn patterns have evolved over the past 83 years, providing valuable insights into climate change impacts on these critical wind events.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 16","pages":"5890-5901"},"PeriodicalIF":3.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joc.8673","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860221","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}
Rasoul Mirabbasi, Björn Klöve, Ali Torabi Haghighi
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.
{"title":"Multivariate Frequency Analysis of Drought Characteristics in Finland Using Vine Copulas","authors":"Rasoul Mirabbasi, Björn Klöve, Ali Torabi Haghighi","doi":"10.1002/joc.8679","DOIUrl":"https://doi.org/10.1002/joc.8679","url":null,"abstract":"<div>\u0000 \u0000 <p>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.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 16","pages":"5986-6013"},"PeriodicalIF":3.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860199","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}
Luís Gustavo Cattelan, Caio R. C. Mattos, Matheus Bonifácio Pamplona, Marina Hirota
The Cerrado biome, renowned for its biodiversity and threatened by rapid land transformation, encompasses a vast savanna ecosystem in Brazil. The region is characterised by a seasonal climate, influenced by a myriad of meteorological systems creating diverse and non-homogeneous rainfall regimes across the region. To account for this heterogeneity, we propose a novel classification of the Cerrado using rainfall data to delineate three distinct climatic regions: Eastern, Southern and Central-West Cerrado. The Eastern region exhibited the driest and most seasonal climate, marked by high vapour pressure deficit (VPD) and predominantly open-canopy vegetation. Conversely, the Southern region, characterised by lower seasonality, boasts a higher proportion of forest cover and lower mean VPD. The Central-West region, encompassing diverse landscapes, featured areas with higher precipitation levels, particularly along the Amazônia border. Furthermore, we conducted trend analyses on observed station data and used CMIP6 models to evaluate future scenarios under differing emissions trajectories. While observed trends in mean rainfall were marginal, VPD demonstrated a notable upward trend of approximately 1% annually throughout the biome. Climate models indicated a substantial drying close to the Amazônia border, and wetter conditions in the southeast. All Cerrado regions are anticipated to experience amplified seasonality and VPD, with VPD projected to surge by approximately 30% (60%) under low (high) emissions scenarios by the end of the century. Notably, the transition from the dry to wet season was the most affected. Our study provides critical insights on how the climatic heterogeneity of the Cerrado shapes vegetation structure distribution and how future changes will exacerbate water stress throughout the biome. These findings underscore the importance of understanding climate variability for effective conservation and management strategies in the region.
{"title":"Mapping Climatic Regions of the Cerrado: General Patterns and Future Change","authors":"Luís Gustavo Cattelan, Caio R. C. Mattos, Matheus Bonifácio Pamplona, Marina Hirota","doi":"10.1002/joc.8670","DOIUrl":"https://doi.org/10.1002/joc.8670","url":null,"abstract":"<p>The Cerrado biome, renowned for its biodiversity and threatened by rapid land transformation, encompasses a vast savanna ecosystem in Brazil. The region is characterised by a seasonal climate, influenced by a myriad of meteorological systems creating diverse and non-homogeneous rainfall regimes across the region. To account for this heterogeneity, we propose a novel classification of the Cerrado using rainfall data to delineate three distinct climatic regions: Eastern, Southern and Central-West Cerrado. The Eastern region exhibited the driest and most seasonal climate, marked by high vapour pressure deficit (VPD) and predominantly open-canopy vegetation. Conversely, the Southern region, characterised by lower seasonality, boasts a higher proportion of forest cover and lower mean VPD. The Central-West region, encompassing diverse landscapes, featured areas with higher precipitation levels, particularly along the Amazônia border. Furthermore, we conducted trend analyses on observed station data and used CMIP6 models to evaluate future scenarios under differing emissions trajectories. While observed trends in mean rainfall were marginal, VPD demonstrated a notable upward trend of approximately 1% annually throughout the biome. Climate models indicated a substantial drying close to the Amazônia border, and wetter conditions in the southeast. All Cerrado regions are anticipated to experience amplified seasonality and VPD, with VPD projected to surge by approximately 30% (60%) under low (high) emissions scenarios by the end of the century. Notably, the transition from the dry to wet season was the most affected. Our study provides critical insights on how the climatic heterogeneity of the Cerrado shapes vegetation structure distribution and how future changes will exacerbate water stress throughout the biome. These findings underscore the importance of understanding climate variability for effective conservation and management strategies in the region.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 16","pages":"5857-5872"},"PeriodicalIF":3.5,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joc.8670","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860210","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}
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