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}
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}
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}
A. T. Leite‐Filho, B. Soares-Filho, Ubirajara de Oliveira
Deforestation in the Brazilian Amazon (BA) for cattle and soybean production has significant consequences for the various aspects of the climate system. Land surface modifications due to deforestation directly influence surface energy and moisture availability, hence impacting rainfall patterns, air temperature and the onset of the agricultural rainy season. Here, we assess the forest loss‐related climate risks for the first and second crop seasons of the soy‐maize double cropping system in the BA. We utilized long‐term, daily, remote sensed climate data and annual land‐use maps as input for a machine learning algorithm to isolate the signal of forest loss on the climate. Our findings indicate that forest loss in the BA intensifies the risks of climate change from the local to the regional geographical scale, with the impact being more pronounced at the regional scale. Between 1999 and 2019, largely deforested regions exhibited a delay of approximately 76 days in the onset of the agricultural rainy season. These regions also experienced a 360 mm decrease in rainfall and an increase in maximum air temperature of 2.5°C. In view of these results, there are collective advantages of halting deforestation. Conservation of the Amazon Forest is vital for maintaining the early onset of the agricultural rainy season, favourable temperatures and adequate rainfall volume needed for attaining high yields in the soy‐maize double cropping system.
{"title":"Climate risks to soy‐maize double‐cropping due to Amazon deforestation","authors":"A. T. Leite‐Filho, B. Soares-Filho, Ubirajara de Oliveira","doi":"10.1002/joc.8381","DOIUrl":"https://doi.org/10.1002/joc.8381","url":null,"abstract":"Deforestation in the Brazilian Amazon (BA) for cattle and soybean production has significant consequences for the various aspects of the climate system. Land surface modifications due to deforestation directly influence surface energy and moisture availability, hence impacting rainfall patterns, air temperature and the onset of the agricultural rainy season. Here, we assess the forest loss‐related climate risks for the first and second crop seasons of the soy‐maize double cropping system in the BA. We utilized long‐term, daily, remote sensed climate data and annual land‐use maps as input for a machine learning algorithm to isolate the signal of forest loss on the climate. Our findings indicate that forest loss in the BA intensifies the risks of climate change from the local to the regional geographical scale, with the impact being more pronounced at the regional scale. Between 1999 and 2019, largely deforested regions exhibited a delay of approximately 76 days in the onset of the agricultural rainy season. These regions also experienced a 360 mm decrease in rainfall and an increase in maximum air temperature of 2.5°C. In view of these results, there are collective advantages of halting deforestation. Conservation of the Amazon Forest is vital for maintaining the early onset of the agricultural rainy season, favourable temperatures and adequate rainfall volume needed for attaining high yields in the soy‐maize double cropping system.","PeriodicalId":505763,"journal":{"name":"International Journal of Climatology","volume":"12 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139803945","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}
Kevin Chicaeme‐Ordoñez, Astrid Baquero‐Bernal, John F. Mejía
This study shows vertical profiles and spatial distribution of upper‐air icing frequency over the tropical Americas. We estimated the in‐flight icing (IFI) over Colombia using the Current Icing Product‐sonde‐A algorithm over two data sets: (1) vertical soundings of temperature and relative humidity and surface station data taken at 12 Coordinated Universal Time or UTC (07 Local Time or LT) on five sites and (2) ERA5 at 00, 06, 12 and 18 UTC (19, 01, 07 and 13 LT). In either case, icing was defined for IFI values exceeding 0.01. Results show that icing tends to occur between 550 and 300 hPa (4.5 and 8.6 km altitude), with a maximum at 500–550 hPa and monotonically decreasing to zero until reaching 300 hPa. Aeronautic reports were used to evaluate the total column IFI and a layer‐based IFI detection with a probability of detection of 87% and 71%, respectively. The annual cycle of IFI is modulated by the meridional migration of the Intertropical Convergence Zone (ITCZ) with a bimodal distribution with peaks during the rainiest seasons. Spatially, IFI hotspots are found in the Pacific, the Andes Mountains and the Amazonia regions of Colombia; the northern Colombia Caribbean region show lower IFI frequency with a relative maximum collocated over the Sierra Nevada de Santa Marta mountains. The IFI exhibits a strong diurnal cycle with a high between night‐time to early morning and a low around noon.
{"title":"Climatology of icing conditions over Colombia based on ERA5 reanalysis and in situ observations","authors":"Kevin Chicaeme‐Ordoñez, Astrid Baquero‐Bernal, John F. Mejía","doi":"10.1002/joc.8359","DOIUrl":"https://doi.org/10.1002/joc.8359","url":null,"abstract":"This study shows vertical profiles and spatial distribution of upper‐air icing frequency over the tropical Americas. We estimated the in‐flight icing (IFI) over Colombia using the Current Icing Product‐sonde‐A algorithm over two data sets: (1) vertical soundings of temperature and relative humidity and surface station data taken at 12 Coordinated Universal Time or UTC (07 Local Time or LT) on five sites and (2) ERA5 at 00, 06, 12 and 18 UTC (19, 01, 07 and 13 LT). In either case, icing was defined for IFI values exceeding 0.01. Results show that icing tends to occur between 550 and 300 hPa (4.5 and 8.6 km altitude), with a maximum at 500–550 hPa and monotonically decreasing to zero until reaching 300 hPa. Aeronautic reports were used to evaluate the total column IFI and a layer‐based IFI detection with a probability of detection of 87% and 71%, respectively. The annual cycle of IFI is modulated by the meridional migration of the Intertropical Convergence Zone (ITCZ) with a bimodal distribution with peaks during the rainiest seasons. Spatially, IFI hotspots are found in the Pacific, the Andes Mountains and the Amazonia regions of Colombia; the northern Colombia Caribbean region show lower IFI frequency with a relative maximum collocated over the Sierra Nevada de Santa Marta mountains. The IFI exhibits a strong diurnal cycle with a high between night‐time to early morning and a low around noon.","PeriodicalId":505763,"journal":{"name":"International Journal of Climatology","volume":"12 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139867236","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}
Historical meteorological droughts are analysed over the Coordinated Regional Downscaling Experiment‐Central America, Caribbean and Mexico (CORDEX‐CAM) domain during 1981–2010, with particular emphasis on the North American monsoon (NAM) and the mid‐summer drought (MSD) regions. We analyse droughts based on the standardized precipitation index (SPI‐12) and the standardized precipitation‐evapotranspiration index (SPEI‐12) using observations from CRU, CHIRPS, GPCP and ERA5‐Land reanalysis (ERA5), and assess the skill of the regional climate model RegCM4 (version 7) at 25 km resolution driven by ERA‐Interim (Reg‐ERA) and by three global climate models (Reg‐GCMs: Reg‐Had, Reg‐MPI and Reg‐GFDL). Observational data sets show large spatial variations in drought frequency, and both Reg‐ERA and Reg‐GCMs have difficulties simulating it. RegCM4 shows positive precipitation and water balance biases over mountain regions and negative ones over Central America, possibly due to the complex terrain and poor observational data coverage. Despite differences among observations, the trend in droughts, duration and severity show consistent dry hot spots (regions with long‐duration severe droughts) over the western United States, the United States‐Mexico border region, the NAM, the Yucatan Peninsula and northern Central America, with stronger values of SPEI‐12 than SPI‐12, particularly over the subtropical regions. Reg‐ERA and ERA5 show similar spatial patterns and similar positive and negative spatial biases relative to observations. Reg‐ERA and Reg‐Had adequately simulate the spatial patterns of the trend, duration and severity of droughts, with smaller biases in SPI‐12 than SPEI‐12; in contrast, Reg‐MPI and Reg‐GFDL overestimate the trend biases over northwest CAM. Observations, reanalysis, and RegCM4 capture an inverse drought response between the NAM and the MSD regions linked to climate teleconnections; however, a stronger drought signal is observed in the NAM, which appears to be linked to decadal variations from negative to positive phases of the Atlantic Multidecadal Oscillation combined with La Niña conditions (negative El Niño 1+2 phase).
{"title":"Historical meteorological droughts over the CORDEX‐CAM (Central America, Caribbean and Mexico) domain: Evaluating the simulation of dry hot spots with RegCM4","authors":"Luisa Andrade‐Gómez, Tereza Cavazos","doi":"10.1002/joc.8374","DOIUrl":"https://doi.org/10.1002/joc.8374","url":null,"abstract":"Historical meteorological droughts are analysed over the Coordinated Regional Downscaling Experiment‐Central America, Caribbean and Mexico (CORDEX‐CAM) domain during 1981–2010, with particular emphasis on the North American monsoon (NAM) and the mid‐summer drought (MSD) regions. We analyse droughts based on the standardized precipitation index (SPI‐12) and the standardized precipitation‐evapotranspiration index (SPEI‐12) using observations from CRU, CHIRPS, GPCP and ERA5‐Land reanalysis (ERA5), and assess the skill of the regional climate model RegCM4 (version 7) at 25 km resolution driven by ERA‐Interim (Reg‐ERA) and by three global climate models (Reg‐GCMs: Reg‐Had, Reg‐MPI and Reg‐GFDL). Observational data sets show large spatial variations in drought frequency, and both Reg‐ERA and Reg‐GCMs have difficulties simulating it. RegCM4 shows positive precipitation and water balance biases over mountain regions and negative ones over Central America, possibly due to the complex terrain and poor observational data coverage. Despite differences among observations, the trend in droughts, duration and severity show consistent dry hot spots (regions with long‐duration severe droughts) over the western United States, the United States‐Mexico border region, the NAM, the Yucatan Peninsula and northern Central America, with stronger values of SPEI‐12 than SPI‐12, particularly over the subtropical regions. Reg‐ERA and ERA5 show similar spatial patterns and similar positive and negative spatial biases relative to observations. Reg‐ERA and Reg‐Had adequately simulate the spatial patterns of the trend, duration and severity of droughts, with smaller biases in SPI‐12 than SPEI‐12; in contrast, Reg‐MPI and Reg‐GFDL overestimate the trend biases over northwest CAM. Observations, reanalysis, and RegCM4 capture an inverse drought response between the NAM and the MSD regions linked to climate teleconnections; however, a stronger drought signal is observed in the NAM, which appears to be linked to decadal variations from negative to positive phases of the Atlantic Multidecadal Oscillation combined with La Niña conditions (negative El Niño 1+2 phase).","PeriodicalId":505763,"journal":{"name":"International Journal of Climatology","volume":"29 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139875532","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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