Crowdsourced observation networks are typically much more dense than those maintained by National Meteorological Services, and sample a much wider range of local climates. This offers an opportunity to build observed climatologies that are more representative of lived experience, particularly in cities. This study provides a worked example to show their potential for improving operational climate services, and to identify the challenges to realizing that potential. To demonstrate the concept, data from personal weather stations, obtained through citizen science, are used to build an observed record of daily maximum temperatures in 2020 in Manchester (UK). This record is compared to the standard baseline used in a current climate service, showing a substantial increase in the estimated heat hazard. If such potential benefits are to be realized in a climate service, it will be necessary to first build an alternative observed baseline of decadal length and at national or international scale. This requires further work to acquire, quality‐control, exposure‐control and map the crowdsourced observations.
{"title":"The importance of crowdsourced observations for urban climate services","authors":"Timothy D. Mitchell, Matthew J. Fry","doi":"10.1002/joc.8390","DOIUrl":"https://doi.org/10.1002/joc.8390","url":null,"abstract":"Crowdsourced observation networks are typically much more dense than those maintained by National Meteorological Services, and sample a much wider range of local climates. This offers an opportunity to build observed climatologies that are more representative of lived experience, particularly in cities. This study provides a worked example to show their potential for improving operational climate services, and to identify the challenges to realizing that potential. To demonstrate the concept, data from personal weather stations, obtained through citizen science, are used to build an observed record of daily maximum temperatures in 2020 in Manchester (UK). This record is compared to the standard baseline used in a current climate service, showing a substantial increase in the estimated heat hazard. If such potential benefits are to be realized in a climate service, it will be necessary to first build an alternative observed baseline of decadal length and at national or international scale. This requires further work to acquire, quality‐control, exposure‐control and map the crowdsourced observations.","PeriodicalId":505763,"journal":{"name":"International Journal of Climatology","volume":"445 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139835499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Véronique Michot, Thomas Corpetti, J. Ronchail, J. Espinoza, D. Arvor, B. Funatsu, Vincent Dubreuil
Due to its size and geographical features, different average annual rainfall regimes co‐exist in the Amazon basin, with distinct year‐to‐year variability dependent on regions within the basin. In this study, we define and explain the seasonal regional types of annual regimes, that is, years with similar seasonal anomalies. Our work is based on a 205 rain gauge network distributed over five Amazonian countries, spanning a period over 30 years. Using a spectral clustering method, we identified seven sub‐regions within the basin in which annual rainfall regimes are spatially homogenous. For each sub‐domain, we estimated specific parameters that characterize the rainy season (onset and demise dates, sign and duration of rainfall anomalies). Finally, using spectral analysis we identified between two and four ‘seasonal type’ of precipitation in these seven sub‐domains. Most of these seasonal types are in phase with the large‐scale atmospheric circulation, which explains the temporal link with rainfall anomalies. The seasonal types result of the superposition of inter‐annual and intra‐seasonal variability whose factors are then difficult to identify and attribute. Part of the rainfall anomalies characterizing seasonal types is related to the inter‐annual variability of the sea surface temperature in the Atlantic or the Pacific oceans, especially in the northeast and southeast part of the Amazon basin, whereas in other parts, strong intra‐seasonal and local factors have a larger impact. The same sign and duration of anomalies do not concomitantly affect the various regions of the Amazon basin, confirming that one mode of variability does not homogeneously affect precipitation in different parts of the basin.
{"title":"Seasonal types in homogeneous rainfall regions of the Amazon basin","authors":"Véronique Michot, Thomas Corpetti, J. Ronchail, J. Espinoza, D. Arvor, B. Funatsu, Vincent Dubreuil","doi":"10.1002/joc.8380","DOIUrl":"https://doi.org/10.1002/joc.8380","url":null,"abstract":"Due to its size and geographical features, different average annual rainfall regimes co‐exist in the Amazon basin, with distinct year‐to‐year variability dependent on regions within the basin. In this study, we define and explain the seasonal regional types of annual regimes, that is, years with similar seasonal anomalies. Our work is based on a 205 rain gauge network distributed over five Amazonian countries, spanning a period over 30 years. Using a spectral clustering method, we identified seven sub‐regions within the basin in which annual rainfall regimes are spatially homogenous. For each sub‐domain, we estimated specific parameters that characterize the rainy season (onset and demise dates, sign and duration of rainfall anomalies). Finally, using spectral analysis we identified between two and four ‘seasonal type’ of precipitation in these seven sub‐domains. Most of these seasonal types are in phase with the large‐scale atmospheric circulation, which explains the temporal link with rainfall anomalies. The seasonal types result of the superposition of inter‐annual and intra‐seasonal variability whose factors are then difficult to identify and attribute. Part of the rainfall anomalies characterizing seasonal types is related to the inter‐annual variability of the sea surface temperature in the Atlantic or the Pacific oceans, especially in the northeast and southeast part of the Amazon basin, whereas in other parts, strong intra‐seasonal and local factors have a larger impact. The same sign and duration of anomalies do not concomitantly affect the various regions of the Amazon basin, confirming that one mode of variability does not homogeneously affect precipitation in different parts of the basin.","PeriodicalId":505763,"journal":{"name":"International Journal of Climatology","volume":"386 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139837066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lewis G. Ireland, J. Robbins, Robert Neal, Rosa Barciela, Rebecca Gilbert
This work aims to define a set of representative weather patterns for South Africa that can be utilized to support impact‐based forecasting of heatwave events. Sets of weather patterns have been generated using k‐means clustering on daily ERA5 reanalysis data between 1979 and 2020. Different pattern sets were generated by varying the clustering atmospheric variable, the spatial domain and the number of weather patterns. These weather patterns are evaluated using the explained variation score to assess their ability to represent the variability of the maximum daily 2m temperature (Tmax,2m). The results indicate that a set of 30 weather patterns generated using mean sea‐level pressure, with a clustering domain in the range 15°–34°E and 21°–36°S, provides a reasonable representation of Tmax,2m variability across South Africa. The implementation of an appropriate weather pattern set into a medium‐range forecasting tool has the potential to extend the prediction of high‐impact weather events in South Africa, such as heatwaves, and also highlight specific impacts on the population, for example, food and water insecurity, heat exhaustion or energy and transport impacts.
{"title":"Generating weather pattern definitions over South Africa suitable for future use in impact‐orientated medium‐range forecasting","authors":"Lewis G. Ireland, J. Robbins, Robert Neal, Rosa Barciela, Rebecca Gilbert","doi":"10.1002/joc.8396","DOIUrl":"https://doi.org/10.1002/joc.8396","url":null,"abstract":"This work aims to define a set of representative weather patterns for South Africa that can be utilized to support impact‐based forecasting of heatwave events. Sets of weather patterns have been generated using k‐means clustering on daily ERA5 reanalysis data between 1979 and 2020. Different pattern sets were generated by varying the clustering atmospheric variable, the spatial domain and the number of weather patterns. These weather patterns are evaluated using the explained variation score to assess their ability to represent the variability of the maximum daily 2m temperature (Tmax,2m). The results indicate that a set of 30 weather patterns generated using mean sea‐level pressure, with a clustering domain in the range 15°–34°E and 21°–36°S, provides a reasonable representation of Tmax,2m variability across South Africa. The implementation of an appropriate weather pattern set into a medium‐range forecasting tool has the potential to extend the prediction of high‐impact weather events in South Africa, such as heatwaves, and also highlight specific impacts on the population, for example, food and water insecurity, heat exhaustion or energy and transport impacts.","PeriodicalId":505763,"journal":{"name":"International Journal of Climatology","volume":"186 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139840538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
On August 8th, 2022, an extreme rainfall event (the 88ER) occurred over South Korea's metropolitan area and resulted in immense losses of human lives and properties. Previous study has attributed the rainfall event to the intersection of warm and cold air induced by a Northeast China Cold Vortex (NCCV) and the persistently northward displacement of the West Pacific Subtropical High (WPSH). However, in addition to dynamic drivers, understanding the moisture transport of the 88ER is likewise crucial for developing effective strategies to prevent rainstorm disasters. In this study, based on the output from a WRF model, the primary moisture sources and transport pathways of the 88ER are investigated in a Lagrangian view. The Yellow Sea and East China Sea (YSECS) are identified as the most significant moisture source region (84.42%), followed by South Korea (KR), the eastern China (EC) and Democratic People's Republic of Korea (DPRK), which contribute 12.52%, 1.52% and 1.43% of the released moisture, respectively. Furthermore, to assess the sensitivity of moisture fluxes and heavy rainfall to the sea surface temperature (SST) anomalies in the YSECS, an additional WRF model experiment is conducted in which the SST anomalies are replaced by the average SST over the past 30 years. It is found that the SST anomalies in the YSECS cause differences in atmospheric circulation, and therefore exert a strong influence on moisture transport. The SST anomalies finally enhance the moisture contribution of the YSECS by 1.72%, but decrease that over KR, EC and DPRK by 1.03%, 0.35% and 0.33%, respectively.
{"title":"Moisture sources and pathways during an extreme rainfall event over South Korea and the role of sea surface temperature anomalies in the Yellow Sea and East China Sea","authors":"Yuan Cao, Zeyu Qiao, Weidong Li, Guangheng Ni, Yinglin Tian, Jiahui Liu, Deyu Zhong, Yu Zhang, Guangqian Wang, Xilin Hu, Jiajia Liu","doi":"10.1002/joc.8391","DOIUrl":"https://doi.org/10.1002/joc.8391","url":null,"abstract":"On August 8th, 2022, an extreme rainfall event (the 88ER) occurred over South Korea's metropolitan area and resulted in immense losses of human lives and properties. Previous study has attributed the rainfall event to the intersection of warm and cold air induced by a Northeast China Cold Vortex (NCCV) and the persistently northward displacement of the West Pacific Subtropical High (WPSH). However, in addition to dynamic drivers, understanding the moisture transport of the 88ER is likewise crucial for developing effective strategies to prevent rainstorm disasters. In this study, based on the output from a WRF model, the primary moisture sources and transport pathways of the 88ER are investigated in a Lagrangian view. The Yellow Sea and East China Sea (YSECS) are identified as the most significant moisture source region (84.42%), followed by South Korea (KR), the eastern China (EC) and Democratic People's Republic of Korea (DPRK), which contribute 12.52%, 1.52% and 1.43% of the released moisture, respectively. Furthermore, to assess the sensitivity of moisture fluxes and heavy rainfall to the sea surface temperature (SST) anomalies in the YSECS, an additional WRF model experiment is conducted in which the SST anomalies are replaced by the average SST over the past 30 years. It is found that the SST anomalies in the YSECS cause differences in atmospheric circulation, and therefore exert a strong influence on moisture transport. The SST anomalies finally enhance the moisture contribution of the YSECS by 1.72%, but decrease that over KR, EC and DPRK by 1.03%, 0.35% and 0.33%, respectively.","PeriodicalId":505763,"journal":{"name":"International Journal of Climatology","volume":"30 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139845628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Yangtze River basin (YRB) and its southern region in China (20°–34°N, 104°–123°E, YRBSC) are highly susceptible to climate change and experience extreme hydrological events. To understand the spatial and temporal distribution of summer runoff in these regions, a statistical diagnosis method was applied using monthly mean runoff grid data, global Sea Surface Temperature (SST) data and meteorological reanalysis data from 1980 to 2022. The analysis revealed that variations in the isotropic phase within the YRBSC and the north–south inverse phase with the Yangtze River as the boundary are the main modes of summer runoff. Furthermore, a strong correlation was observed between winter SST anomalies (SSTAs) and late summer runoff in the YRBSC, as determined through singular value decomposition (SVD). In the first type of positive SSTA years, the eastward advance of the South Asian high pressure (SAH) and westward shift of the subtropical high pressure (SH) result in sufficient water vapour, strong upward movement and increased summer runoff. The second type of positive SSTA years exhibits a westward retreat of the SAH, upward movement north of 28°N, and downward movement between 20°N and 28°N. These conditions, combined with water vapour intermixing and dispersion, lead to a northward increase and southward decrease of summer runoff in the YRBSC, with the boundary at 28°N. Additionally, the study analysed the extreme drought situation observed in the YRB during the summer of 2022. The findings of this research provide valuable insights for ecological environmental protection, water resource planning and management in the region.
{"title":"The impact and mechanism analysis of preceding sea surface temperature anomalies on summer runoff in the Yangtze River basin and its southern region","authors":"Siyu Zhang, Jun Qin, Hong‐Li Ren","doi":"10.1002/joc.8392","DOIUrl":"https://doi.org/10.1002/joc.8392","url":null,"abstract":"The Yangtze River basin (YRB) and its southern region in China (20°–34°N, 104°–123°E, YRBSC) are highly susceptible to climate change and experience extreme hydrological events. To understand the spatial and temporal distribution of summer runoff in these regions, a statistical diagnosis method was applied using monthly mean runoff grid data, global Sea Surface Temperature (SST) data and meteorological reanalysis data from 1980 to 2022. The analysis revealed that variations in the isotropic phase within the YRBSC and the north–south inverse phase with the Yangtze River as the boundary are the main modes of summer runoff. Furthermore, a strong correlation was observed between winter SST anomalies (SSTAs) and late summer runoff in the YRBSC, as determined through singular value decomposition (SVD). In the first type of positive SSTA years, the eastward advance of the South Asian high pressure (SAH) and westward shift of the subtropical high pressure (SH) result in sufficient water vapour, strong upward movement and increased summer runoff. The second type of positive SSTA years exhibits a westward retreat of the SAH, upward movement north of 28°N, and downward movement between 20°N and 28°N. These conditions, combined with water vapour intermixing and dispersion, lead to a northward increase and southward decrease of summer runoff in the YRBSC, with the boundary at 28°N. Additionally, the study analysed the extreme drought situation observed in the YRB during the summer of 2022. The findings of this research provide valuable insights for ecological environmental protection, water resource planning and management in the region.","PeriodicalId":505763,"journal":{"name":"International Journal of Climatology","volume":"110 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139785442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Di Bernardino, A. Iannarelli, Stefano Casadio, A. Siani
This article analyses the winter warm spells (WWS) that occurred in central Mediterranean over the period 1993–2022, examining the daily maximum temperatures collected at eight airport sites located in the Italian Peninsula, belonging to different climate zones. According to the definition proposed in 1999 by the Expert Team on Climate Change Detection and Indices (ETCCDI), a WWS is a sequence of at least six consecutive days when daily maximum air temperature exceeds the calendar day 90th percentile centred on a 5‐day window for a base period. WWS occurring over the entire Italian territory or only over northern/central/southern Italy have been identified and related to the peculiar synoptic conditions. It was found that December is the month most prone to WWS and, on average, WWS last 9.4 days in northern Italy, 6.6 days in central Italy, and 8.5 days in southern Italy. Over the period under investigation, the Italian Peninsula experienced only one common event characterized by persistent high‐pressure systems associated with air subsidence over western Mediterranean and, therefore, with exceptional warming. Finally, it has been proven that the definition of WWS proposed by ETCCDI allows to capture synoptic scale events but, in orographically complex areas such as Italy, underestimates moderate spells, which generally might have a duration of at least 3 days. Consequently, it is important to consider the possibility of reducing the period length threshold used for the detection of WWS when orographically heterogeneous regions are studied.
{"title":"Winter warm spells over Italy: Spatial–temporal variation and large‐scale atmospheric circulation","authors":"A. Di Bernardino, A. Iannarelli, Stefano Casadio, A. Siani","doi":"10.1002/joc.8388","DOIUrl":"https://doi.org/10.1002/joc.8388","url":null,"abstract":"This article analyses the winter warm spells (WWS) that occurred in central Mediterranean over the period 1993–2022, examining the daily maximum temperatures collected at eight airport sites located in the Italian Peninsula, belonging to different climate zones. According to the definition proposed in 1999 by the Expert Team on Climate Change Detection and Indices (ETCCDI), a WWS is a sequence of at least six consecutive days when daily maximum air temperature exceeds the calendar day 90th percentile centred on a 5‐day window for a base period. WWS occurring over the entire Italian territory or only over northern/central/southern Italy have been identified and related to the peculiar synoptic conditions. It was found that December is the month most prone to WWS and, on average, WWS last 9.4 days in northern Italy, 6.6 days in central Italy, and 8.5 days in southern Italy. Over the period under investigation, the Italian Peninsula experienced only one common event characterized by persistent high‐pressure systems associated with air subsidence over western Mediterranean and, therefore, with exceptional warming. Finally, it has been proven that the definition of WWS proposed by ETCCDI allows to capture synoptic scale events but, in orographically complex areas such as Italy, underestimates moderate spells, which generally might have a duration of at least 3 days. Consequently, it is important to consider the possibility of reducing the period length threshold used for the detection of WWS when orographically heterogeneous regions are studied.","PeriodicalId":505763,"journal":{"name":"International Journal of Climatology","volume":"6 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139850571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Mediterranean region, noted for its climatic uniqueness and rapid urban expansion, is a critical area for climate change studies. This research investigates the increase in extreme temperatures, particularly focusing on tropical nights and their socio‐economic implications. Our aim was to analyse the spatiotemporal changes, including long‐term variation and trends in the tropical night indices in the Mediterranean region over 73 years (1950–2022). To achieve this, we utilized ERA5‐Land reanalysis data, conducting a comparative analysis to highlight the differential impacts of urbanization on tropical nights in urban and non‐urban areas. The study reveals a significant rise in the frequency of tropical nights region‐wide. Specifically, the onset of the tropical night season is occurring earlier, with an advancement of approximately 17.3 days per decade, while the season's end is delayed by about 17.1 days per decade, effectively prolonging the duration of tropical nights. This change is most pronounced in urban areas, where tropical nights have increased more significantly compared to non‐urban regions, highlighting the exacerbating effect of urbanization on nocturnal temperature trends. Overall, our findings underline the combined effects of anthropogenic climate change and urban development on the increased occurrence and intensity of tropical nights in the Mediterranean region.
{"title":"Tropical nights in the Mediterranean: A spatiotemporal analysis of trends from 1950 to 2022","authors":"Doğukan Doğu Yavaşlı, E. Erlat","doi":"10.1002/joc.8394","DOIUrl":"https://doi.org/10.1002/joc.8394","url":null,"abstract":"The Mediterranean region, noted for its climatic uniqueness and rapid urban expansion, is a critical area for climate change studies. This research investigates the increase in extreme temperatures, particularly focusing on tropical nights and their socio‐economic implications. Our aim was to analyse the spatiotemporal changes, including long‐term variation and trends in the tropical night indices in the Mediterranean region over 73 years (1950–2022). To achieve this, we utilized ERA5‐Land reanalysis data, conducting a comparative analysis to highlight the differential impacts of urbanization on tropical nights in urban and non‐urban areas. The study reveals a significant rise in the frequency of tropical nights region‐wide. Specifically, the onset of the tropical night season is occurring earlier, with an advancement of approximately 17.3 days per decade, while the season's end is delayed by about 17.1 days per decade, effectively prolonging the duration of tropical nights. This change is most pronounced in urban areas, where tropical nights have increased more significantly compared to non‐urban regions, highlighting the exacerbating effect of urbanization on nocturnal temperature trends. Overall, our findings underline the combined effects of anthropogenic climate change and urban development on the increased occurrence and intensity of tropical nights in the Mediterranean region.","PeriodicalId":505763,"journal":{"name":"International Journal of Climatology","volume":"101 399","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139794348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The climatology and variability of the January to February (JF) season in eastern Africa's (EA) precipitation are examined during the 1960–2020 period, as off‐season climate could have dire consequences, considering agricultural practices tie to the seasonal cycle of precipitation. The analysis in this study is divided into four parts. The first is the climatological background of variability during the JF season. Second, the spatiotemporal variability of the leading mode of the JF precipitation is described using an empirical orthogonal function (EOF) method. Third, anomalous atmospheric circulations linked to the variability of the JF precipitation were examined through composite analysis. Fourth, the link between JF precipitation and sea surface temperature (SST) is explored using composite and correlation analyses. The leading mode (EOF1) shows a monopole variation, with a positive anomaly in the entire region accounting for 55.1% of the total variance. EOF1 is linked to the SST anomaly (SSTA) over the tropical Indian Ocean (TIO). A warm (cool) SSTA in the TIO induces diabatic warming/adiabatic cooling (diabatic cooling/adiabatic warming). This leads to the rising (sinking) of warm and moist air (cold and dry air) from the lower to higher (higher to lower) troposphere via the ascending (descending) branch of the Walker circulation and contributes to the upper warm (cold) temperature anomaly centred at ~300 hPa. The warm (cold) anomaly is closely associated with the upper‐level westerly (easterly) and divergence (convergence) anomalies at the upper side of the warm (cold) core, coupled with ascending (descending) and deep wet (dry) anomalies below the warm (cold) core. This induces moisture convergence (divergence) and unstable (stable) conditions that favour (suppresses) precipitation over EA. Consequently, this study may facilitate the prediction of the JF precipitation and decrease in socio‐economic losses in EA.
{"title":"Climatological characteristics and interannual variability of the leading mode of eastern African precipitation in January and February","authors":"Laban Lameck Kebacho","doi":"10.1002/joc.8387","DOIUrl":"https://doi.org/10.1002/joc.8387","url":null,"abstract":"The climatology and variability of the January to February (JF) season in eastern Africa's (EA) precipitation are examined during the 1960–2020 period, as off‐season climate could have dire consequences, considering agricultural practices tie to the seasonal cycle of precipitation. The analysis in this study is divided into four parts. The first is the climatological background of variability during the JF season. Second, the spatiotemporal variability of the leading mode of the JF precipitation is described using an empirical orthogonal function (EOF) method. Third, anomalous atmospheric circulations linked to the variability of the JF precipitation were examined through composite analysis. Fourth, the link between JF precipitation and sea surface temperature (SST) is explored using composite and correlation analyses. The leading mode (EOF1) shows a monopole variation, with a positive anomaly in the entire region accounting for 55.1% of the total variance. EOF1 is linked to the SST anomaly (SSTA) over the tropical Indian Ocean (TIO). A warm (cool) SSTA in the TIO induces diabatic warming/adiabatic cooling (diabatic cooling/adiabatic warming). This leads to the rising (sinking) of warm and moist air (cold and dry air) from the lower to higher (higher to lower) troposphere via the ascending (descending) branch of the Walker circulation and contributes to the upper warm (cold) temperature anomaly centred at ~300 hPa. The warm (cold) anomaly is closely associated with the upper‐level westerly (easterly) and divergence (convergence) anomalies at the upper side of the warm (cold) core, coupled with ascending (descending) and deep wet (dry) anomalies below the warm (cold) core. This induces moisture convergence (divergence) and unstable (stable) conditions that favour (suppresses) precipitation over EA. Consequently, this study may facilitate the prediction of the JF precipitation and decrease in socio‐economic losses in EA.","PeriodicalId":505763,"journal":{"name":"International Journal of Climatology","volume":"56 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139857184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ruochen Huang, Bin Yong, Fan Huang, Hao Wu, Z. Shen, Da Qian
The fifth generation European Centre for Medium‐Range Weather Forecasts Reanalysis on global land surface (ERA5‐Land), the Multi‐Source Weighted‐Ensemble Precipitation (MSWEP), and the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) are three representative precipitation estimates with quasi‐global coverage, high‐resolution and long‐term record. This study concentrates on investigating, for the first time, the long‐term spatiotemporal accuracy and regional applicability of these precipitation estimates at a daily scale in the Yellow River basin (YRB) using 39 complete years of data record (1981–2019), with a special focus on their capability on monitoring the extreme precipitation events with short duration and the continuous heavy precipitation events. Results indicate that MSWEP generally performs better than ERA5‐Land and CHIRPS in almost all seasons and subregions, with the highest Pearson correlation coefficient and critical success index, lowest root mean square error and false alarm ratio. ERA5‐Land presents a severe overestimation of precipitation amount, particularly in the plateau climate region (BIAS = 52.27%), but well reflects its spatial–temporal patterns in the YRB. As for the detecting capability, MSWEP shows the best accuracy in detecting extreme precipitation, particularly in maximum consecutive 5‐day precipitation (RX5day). The MSWEP better represents the spatial distribution of maximum 1‐day precipitation and maximum consecutive 5‐day precipitation in the YRB, but it shows a significant overestimation in zone Southern Qinghai. MSWEP and CHIRPS have better performance of temporal variation consistency in annual precipitation with ground reference than ERA5‐Land, while ERA5‐Land performs well in capturing extreme precipitation temporal variation, especially for continuous heavy precipitation events. This study can provide useful guidance when choosing long‐term precipitation products for hydrometeorological applications and climate‐related studies in the YRB.
{"title":"A comprehensive investigation of three long‐term precipitation datasets: Which performs better in the Yellow River basin?","authors":"Ruochen Huang, Bin Yong, Fan Huang, Hao Wu, Z. Shen, Da Qian","doi":"10.1002/joc.8383","DOIUrl":"https://doi.org/10.1002/joc.8383","url":null,"abstract":"The fifth generation European Centre for Medium‐Range Weather Forecasts Reanalysis on global land surface (ERA5‐Land), the Multi‐Source Weighted‐Ensemble Precipitation (MSWEP), and the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) are three representative precipitation estimates with quasi‐global coverage, high‐resolution and long‐term record. This study concentrates on investigating, for the first time, the long‐term spatiotemporal accuracy and regional applicability of these precipitation estimates at a daily scale in the Yellow River basin (YRB) using 39 complete years of data record (1981–2019), with a special focus on their capability on monitoring the extreme precipitation events with short duration and the continuous heavy precipitation events. Results indicate that MSWEP generally performs better than ERA5‐Land and CHIRPS in almost all seasons and subregions, with the highest Pearson correlation coefficient and critical success index, lowest root mean square error and false alarm ratio. ERA5‐Land presents a severe overestimation of precipitation amount, particularly in the plateau climate region (BIAS = 52.27%), but well reflects its spatial–temporal patterns in the YRB. As for the detecting capability, MSWEP shows the best accuracy in detecting extreme precipitation, particularly in maximum consecutive 5‐day precipitation (RX5day). The MSWEP better represents the spatial distribution of maximum 1‐day precipitation and maximum consecutive 5‐day precipitation in the YRB, but it shows a significant overestimation in zone Southern Qinghai. MSWEP and CHIRPS have better performance of temporal variation consistency in annual precipitation with ground reference than ERA5‐Land, while ERA5‐Land performs well in capturing extreme precipitation temporal variation, especially for continuous heavy precipitation events. This study can provide useful guidance when choosing long‐term precipitation products for hydrometeorological applications and climate‐related studies in the YRB.","PeriodicalId":505763,"journal":{"name":"International Journal of Climatology","volume":"80 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139855435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The sea surface temperature (SST) is not only a crucial external factor in the evolution of the atmosphere, but also a primary factor and premonition signal used in climate prediction. It is challenging to obtain a precise SST for generating accurate initial and boundary conditions in numerical models. This study employs a machine learning approach, that is, a convolutional neural network (CNN) algorithm, to predict SST on a seasonal scale. In particular, the subsurface ocean temperature (OT) and ocean salinity (OS) at depths of 5.02, 15.08, 25.16, 35.28, 45.45 and 76.55 m were used as training factors in developing a CNN prediction model. The results indicate that subsurface OT and OS can persist for 6 months or longer, with a maximum persistence of up to 12 months. Using the CNN prediction model, the SST can be reliably predicted 6 months in advance in most cases. The predicted SST has a mean bias of approximately 0–0.8 K on the globe. The bias is small (below 0.5 K) in the open ocean. The root mean square errors (RMSEs) of hindcasting for Interdecadal Pacific Oscillation, North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation indices are all less than 1.0 K. Specifically, the RMSE for El Niño prediction is less than 0.5 K. This study provides a viable method for establishing initial and boundary conditions for climate prediction.
{"title":"Prediction of seasonal sea surface temperature based on temperature and salinity of subsurface ocean using machine learning","authors":"Sentao Wei, Chenghai Wang, Feimin Zhang, Kai Yang","doi":"10.1002/joc.8384","DOIUrl":"https://doi.org/10.1002/joc.8384","url":null,"abstract":"The sea surface temperature (SST) is not only a crucial external factor in the evolution of the atmosphere, but also a primary factor and premonition signal used in climate prediction. It is challenging to obtain a precise SST for generating accurate initial and boundary conditions in numerical models. This study employs a machine learning approach, that is, a convolutional neural network (CNN) algorithm, to predict SST on a seasonal scale. In particular, the subsurface ocean temperature (OT) and ocean salinity (OS) at depths of 5.02, 15.08, 25.16, 35.28, 45.45 and 76.55 m were used as training factors in developing a CNN prediction model. The results indicate that subsurface OT and OS can persist for 6 months or longer, with a maximum persistence of up to 12 months. Using the CNN prediction model, the SST can be reliably predicted 6 months in advance in most cases. The predicted SST has a mean bias of approximately 0–0.8 K on the globe. The bias is small (below 0.5 K) in the open ocean. The root mean square errors (RMSEs) of hindcasting for Interdecadal Pacific Oscillation, North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation indices are all less than 1.0 K. Specifically, the RMSE for El Niño prediction is less than 0.5 K. This study provides a viable method for establishing initial and boundary conditions for climate prediction.","PeriodicalId":505763,"journal":{"name":"International Journal of Climatology","volume":"176 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139858665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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