T. Luong, H. Dasari, Quang-Van Doan, A. K. Alduwais, I. Hoteit
The Kingdom of Saudi Arabia (KSA) is characterized by a desert climate, with rainfall mainly occurring during the cooler months (November–April) and sometimes in conjunction with intense extratropical systems that can cause serious damage and casualties. Given the vast size of KSA, there are gaps in understanding the association between large‐scale atmospheric circulations and local organized rainfall events, and in characterizing the diversity of this association. To address these gaps, we analyse an in‐house 5‐km horizontal grid spacing regional atmospheric reanalysis that has been specifically generated for the Arabian Peninsula to explore the mechanisms behind the organized rainfall events over KSA. Nine major regions with distinct climate regimes were objectively selected to represent KSA rainfall climatology. The results demonstrate that organized thunderstorms over KSA only occur under sufficient moisture and environmental instabilities. Mesoscale convective systems responsible for organized rainfall generally develop and propagate with low‐level moisture flow from the nearby seas (the Red Sea to the west and Arabian Gulf to the east) toward the desert. In the central part of KSA, the most frequent physical mechanism responsible for rainfall is winter extratropical influence, followed by spring extratropical–tropical interactions, and spring tropical influence. The east coast is characterized by two rainfall modes: a continuous southwest–northeast rain corridor and concentrated southwestern rain. Large‐scale organized convection following three physically distinct mechanisms (extratropical, transition and tropical) is revealed along the west coast.
{"title":"Organized precipitation and associated large‐scale circulation patterns over the Kingdom of Saudi Arabia","authors":"T. Luong, H. Dasari, Quang-Van Doan, A. K. Alduwais, I. Hoteit","doi":"10.1002/joc.8524","DOIUrl":"https://doi.org/10.1002/joc.8524","url":null,"abstract":"The Kingdom of Saudi Arabia (KSA) is characterized by a desert climate, with rainfall mainly occurring during the cooler months (November–April) and sometimes in conjunction with intense extratropical systems that can cause serious damage and casualties. Given the vast size of KSA, there are gaps in understanding the association between large‐scale atmospheric circulations and local organized rainfall events, and in characterizing the diversity of this association. To address these gaps, we analyse an in‐house 5‐km horizontal grid spacing regional atmospheric reanalysis that has been specifically generated for the Arabian Peninsula to explore the mechanisms behind the organized rainfall events over KSA. Nine major regions with distinct climate regimes were objectively selected to represent KSA rainfall climatology. The results demonstrate that organized thunderstorms over KSA only occur under sufficient moisture and environmental instabilities. Mesoscale convective systems responsible for organized rainfall generally develop and propagate with low‐level moisture flow from the nearby seas (the Red Sea to the west and Arabian Gulf to the east) toward the desert. In the central part of KSA, the most frequent physical mechanism responsible for rainfall is winter extratropical influence, followed by spring extratropical–tropical interactions, and spring tropical influence. The east coast is characterized by two rainfall modes: a continuous southwest–northeast rain corridor and concentrated southwestern rain. Large‐scale organized convection following three physically distinct mechanisms (extratropical, transition and tropical) is revealed along the west coast.","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141273670","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}
In this work, historical simulations of CMIP6 GCMs are evaluated with respect to the ERA5 reanalysis dataset, in order to examine their ability to assess human thermal load in Europe in the winter season. The period of 1981–2010 is chosen for the analysis, and thermal load is expressed via the clothing resistance index (rcl index; expressed in clo). It is found that the GCMs are able to reproduce the areal differences of thermal load satisfactorily, the spatial correlation with the reanalysis is greater than 0.95 in all cases. The effects of the main geographical constraints (latitude, continentality and elevation) are shown by all GCM simulations, as rcl index values are greater at higher latitudes, away from the ocean and in mountainous areas, although GCMs only capture major mountains (the Caucasus, the Armenian Highlands, the Scandinavian Mountains, the Alps). The root‐mean‐square error (RMSE) is around 0.2 clo in all cases, GCMs generally perform better in homogenous lowland areas, while results are less accurate in highlands and mountains owing to the coarse horizontal resolution of GCMs (~1°). The smallest errors occur over central and western Europe and the Mediterranean region, while results tend to be less accurate over the northeastern part of Europe. Biases in the estimation of heat deficit can mainly be attributed to biases in temperature, but biases in wind speed and atmospheric downward radiation seem to be important factors as well.
{"title":"Comparison of CMIP6 model performance in estimating human thermal load in Europe in the winter season","authors":"Zsófia Szalkai, E. Kristóf, A. Zsákai, F. Ács","doi":"10.1002/joc.8526","DOIUrl":"https://doi.org/10.1002/joc.8526","url":null,"abstract":"In this work, historical simulations of CMIP6 GCMs are evaluated with respect to the ERA5 reanalysis dataset, in order to examine their ability to assess human thermal load in Europe in the winter season. The period of 1981–2010 is chosen for the analysis, and thermal load is expressed via the clothing resistance index (rcl index; expressed in clo). It is found that the GCMs are able to reproduce the areal differences of thermal load satisfactorily, the spatial correlation with the reanalysis is greater than 0.95 in all cases. The effects of the main geographical constraints (latitude, continentality and elevation) are shown by all GCM simulations, as rcl index values are greater at higher latitudes, away from the ocean and in mountainous areas, although GCMs only capture major mountains (the Caucasus, the Armenian Highlands, the Scandinavian Mountains, the Alps). The root‐mean‐square error (RMSE) is around 0.2 clo in all cases, GCMs generally perform better in homogenous lowland areas, while results are less accurate in highlands and mountains owing to the coarse horizontal resolution of GCMs (~1°). The smallest errors occur over central and western Europe and the Mediterranean region, while results tend to be less accurate over the northeastern part of Europe. Biases in the estimation of heat deficit can mainly be attributed to biases in temperature, but biases in wind speed and atmospheric downward radiation seem to be important factors as well.","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141272795","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}
Obaidullah Salehie, Mohamad Hidayat bin Jamal, T. Ismail, Sobri Bin Harun, Shamsuddin Shahid
Water scarcity is a major challenge facing many regions worldwide, especially arid and semi‐arid areas that are increasingly vulnerable to climate change. This study aimed to project water availability in the Amu Darya Basin (ADB) of Central Asia under four Shared Socioeconomic Pathways (SSPs) from the Coupled Model Intercomparison Project Phase Six (CMIP6) during two upcoming periods (2020–2059 and 2060–2099). The study used a robust machine learning approach, namely a Random Forest (RF) model, to simulate Gravity Recovery and Climate Experiment (GRACE) Terrestrial Water Storage (TWS) data from precipitation and maximum and minimum temperatures (Tmax and Tmin). It then incorporated precipitation, Tmax and Tmin from four selected CMIP6 GCMs, into a water storage model to project spatiotemporal changes in water availability across the basin. The study also evaluated the relative impacts of land use and population on TWS. Results indicate an increase in TWS by approximately 4 cm in the basin's eastern, northwestern and southwestern regions in both future periods, while a decrease by approximately −4 cm in the remaining areas. These projections suggest that TWS will decline in densely populated regions and increase in certain intensively cultivated areas. The most pronounced increase in TWS is anticipated in the snow‐covered Tundra climate zone of the basin. This is attributed to the melting of glaciers, which contributes to runoff in the tributaries of the Amu River. The findings highlight the importance of considering climate change and socioeconomic factors when projecting water availability in arid and semi‐arid regions. The projected changes in TWS have important implications for water resources management in the ADB, particularly in densely populated and intensively cultivated areas.
{"title":"Projection of future water availability in the Amu Darya Basin","authors":"Obaidullah Salehie, Mohamad Hidayat bin Jamal, T. Ismail, Sobri Bin Harun, Shamsuddin Shahid","doi":"10.1002/joc.8490","DOIUrl":"https://doi.org/10.1002/joc.8490","url":null,"abstract":"Water scarcity is a major challenge facing many regions worldwide, especially arid and semi‐arid areas that are increasingly vulnerable to climate change. This study aimed to project water availability in the Amu Darya Basin (ADB) of Central Asia under four Shared Socioeconomic Pathways (SSPs) from the Coupled Model Intercomparison Project Phase Six (CMIP6) during two upcoming periods (2020–2059 and 2060–2099). The study used a robust machine learning approach, namely a Random Forest (RF) model, to simulate Gravity Recovery and Climate Experiment (GRACE) Terrestrial Water Storage (TWS) data from precipitation and maximum and minimum temperatures (Tmax and Tmin). It then incorporated precipitation, Tmax and Tmin from four selected CMIP6 GCMs, into a water storage model to project spatiotemporal changes in water availability across the basin. The study also evaluated the relative impacts of land use and population on TWS. Results indicate an increase in TWS by approximately 4 cm in the basin's eastern, northwestern and southwestern regions in both future periods, while a decrease by approximately −4 cm in the remaining areas. These projections suggest that TWS will decline in densely populated regions and increase in certain intensively cultivated areas. The most pronounced increase in TWS is anticipated in the snow‐covered Tundra climate zone of the basin. This is attributed to the melting of glaciers, which contributes to runoff in the tributaries of the Amu River. The findings highlight the importance of considering climate change and socioeconomic factors when projecting water availability in arid and semi‐arid regions. The projected changes in TWS have important implications for water resources management in the ADB, particularly in densely populated and intensively cultivated areas.","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141273612","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}
Raghav Srinivasan, Trevor Carey‐Smith, Linda Wang, Andrew Harper, Sam M. Dean, Gregor Macara, Ruotong Wang, Stephen Stuart
New Zealand's National Institute of Water and Atmospheric Research publishes climate normals for New Zealand that are used for reporting the regional state of the climate, climate extremes and variability. Temperature and precipitation patterns are affected by both anthropogenic climate change and natural climate variability which in turn affect the climatological normal values calculated every decade. This study investigates how New Zealand's normals for temperature and precipitation have shifted over time at the national, regional and seasonal scales from the 1941–1970 period to the 1991–2020 period. Contrary to WMO recommendations, but aligned with many other countries, New Zealand's climate normals have traditionally not undergone homogenisation. The impact of introducing some homogenisation in the latest 1991–2020 normals, in contrast to historical non‐homogenized station normals, has been assessed using a new homogenized “Seventeen‐Station” temperature series from 1941 to 2020. We demonstrate that interpolating sparse non‐homogenized normals spatially to a grid can produce significant erroneous patterns and therefore undermine the accuracy of the conclusions drawn when using such normals. We find that the historical non‐homogenized temperature normals have a consistent negative bias in the long‐term trend at national, regional and seasonal scales. Our analysis of homogeneity tested precipitation showed consistent decreases at a national scale across all normal periods relative to the 1951–1980 precipitation normal. We also highlight how fixed period temperature and precipitation normals do not fully reflect the current state of a climate that is influenced by decadal variability and global warming. To derive normals fit for use in a changing climate it is suggested that automated methods for broad data homogenisation be developed along with alternative methods to derive normals that account for a non‐stationary climate.
{"title":"Moving to a new normal: Analysis of shifting climate normals in New Zealand","authors":"Raghav Srinivasan, Trevor Carey‐Smith, Linda Wang, Andrew Harper, Sam M. Dean, Gregor Macara, Ruotong Wang, Stephen Stuart","doi":"10.1002/joc.8521","DOIUrl":"https://doi.org/10.1002/joc.8521","url":null,"abstract":"New Zealand's National Institute of Water and Atmospheric Research publishes climate normals for New Zealand that are used for reporting the regional state of the climate, climate extremes and variability. Temperature and precipitation patterns are affected by both anthropogenic climate change and natural climate variability which in turn affect the climatological normal values calculated every decade. This study investigates how New Zealand's normals for temperature and precipitation have shifted over time at the national, regional and seasonal scales from the 1941–1970 period to the 1991–2020 period. Contrary to WMO recommendations, but aligned with many other countries, New Zealand's climate normals have traditionally not undergone homogenisation. The impact of introducing some homogenisation in the latest 1991–2020 normals, in contrast to historical non‐homogenized station normals, has been assessed using a new homogenized “Seventeen‐Station” temperature series from 1941 to 2020. We demonstrate that interpolating sparse non‐homogenized normals spatially to a grid can produce significant erroneous patterns and therefore undermine the accuracy of the conclusions drawn when using such normals. We find that the historical non‐homogenized temperature normals have a consistent negative bias in the long‐term trend at national, regional and seasonal scales. Our analysis of homogeneity tested precipitation showed consistent decreases at a national scale across all normal periods relative to the 1951–1980 precipitation normal. We also highlight how fixed period temperature and precipitation normals do not fully reflect the current state of a climate that is influenced by decadal variability and global warming. To derive normals fit for use in a changing climate it is suggested that automated methods for broad data homogenisation be developed along with alternative methods to derive normals that account for a non‐stationary climate.","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141275634","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}
This study investigated relationships between year‐to‐year variability in precipitation in the rainy season in Mozambique and major modes of climate variability in the Tropics and subtropics. The Niño3.4 index was strongly negatively correlated with precipitation in Mozambique's southern and central regions. We suggest that Rossby wave propagation reaching Southern Africa from the tropical Pacific is key to the relationship between precipitation in Mozambique and El Niño–Southern Oscillation. Subtropical Indian Ocean Dipole did not lead rainy‐season precipitation, but showed a simultaneous correlation with precipitation in southern, central and northeastern regions. Benguela Niño was found to have a significant positive lead correlation by 6 months with precipitation in the southern, central and northwestern regions. In contrast, Indian Ocean Dipole led precipitation in the southern, central and northeastern regions by 3 months. Overall, the modes of climate variability exerted stronger control over precipitation variability in southern and central Mozambique, and weaker control in northern Mozambique, particularly in the northwestern region.
{"title":"The influence of tropical and subtropical modes of climate variability on precipitation in Mozambique","authors":"Luis Adriano Chongue, K. Nishii","doi":"10.1002/joc.8509","DOIUrl":"https://doi.org/10.1002/joc.8509","url":null,"abstract":"This study investigated relationships between year‐to‐year variability in precipitation in the rainy season in Mozambique and major modes of climate variability in the Tropics and subtropics. The Niño3.4 index was strongly negatively correlated with precipitation in Mozambique's southern and central regions. We suggest that Rossby wave propagation reaching Southern Africa from the tropical Pacific is key to the relationship between precipitation in Mozambique and El Niño–Southern Oscillation. Subtropical Indian Ocean Dipole did not lead rainy‐season precipitation, but showed a simultaneous correlation with precipitation in southern, central and northeastern regions. Benguela Niño was found to have a significant positive lead correlation by 6 months with precipitation in the southern, central and northwestern regions. In contrast, Indian Ocean Dipole led precipitation in the southern, central and northeastern regions by 3 months. Overall, the modes of climate variability exerted stronger control over precipitation variability in southern and central Mozambique, and weaker control in northern Mozambique, particularly in the northwestern region.","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141276139","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}
Northeast India (NEI) receives most of its rainfall in the southwest monsoon (SWM) season. The region is known to have frequent and persistent rainfall events of high intensities and the region is vulnerable to potential high‐impact meteorological events. This study analyses observed daily rainfall data for the SWM months (JJAS) during 1991–2020 to better understand the climatology of high‐intensity rainfall (HIR) occurrences and their persistence. The agglomerative hierarchical cluster analysis has delineated four distinct clusters and Cramer's t test indicates no significant fluctuations at 5% significant level in the HIR events in these clusters. ERA‐5 reanalysis data have been used to find the moisture transport for synoptic‐scale (3–7 days), quasi‐bi‐weekly oscillations (10–20 days) and intraseasonal oscillations (30–60 days) for each cluster. Under positive synoptic‐scale phases, HIR in the clusters on southern latitudes of NEI occurs due to moisture incursion from the Bay of Bengal from southwesterlies at 850 hPa, and in the clusters on the northern latitudes, it is primarily due to westerlies. For, quasi‐bi‐weekly oscillations, westerlies at 850 hPa are favourable for moisture transport in most of the clusters during its positive phase. On the other hand, for positive phase of intraseasonal oscillations, westerlies at 850 hPa dominate the moisture transport in all the clusters. Also, most of the HIR events occur whenever Madden–Julian Oscillation (MJO) is in phase 1 and 2 with higher amplitude (RMM ≥1).
印度东北部（NEI）的大部分降雨量来自西南季风季节。众所周知，该地区降雨频繁且持续时间长，强度高，容易受到潜在的高影响气象事件的影响。本研究分析了 1991-2020 年间西南季风月份（JJAS）的日降雨量观测数据，以更好地了解高强度降雨（HIR）的气候学特征及其持续性。聚类分层聚类分析划分出四个不同的聚类，Cramer's t 检验表明，在 5%的显著水平上，这些聚类中的高强度降雨事件没有显著波动。ERA-5再分析数据被用于发现每个聚类的同步尺度（3-7天）、准双周振荡（10-20天）和季节内振荡（30-60天）的水汽输送。在正会合尺度相位下，东北地区南部纬度各岛群的 HIR 是由于 850 hPa 西南风从孟加拉湾侵入造成的，而北部纬度各岛群的 HIR 则主要是由西风造成的。就准双周振荡而言，在其正相位期间，850 百帕高度的西风有利于大部分气团的水汽输送。另一方面，在季内振荡的正相位，850 hPa 的西风在所有集群的水汽输送中占主导地位。此外，每当马登-朱利安涛动（MJO）处于振幅较大（RMM ≥1）的第 1 和第 2 阶段时，大多数 HIR 事件都会发生。
{"title":"High‐intensity rainfall over northeast India: Spatial pattern, short‐term fluctuations and associated multiscale oscillations","authors":"Sunit Das, M. Mohapatra, U. K. Sahoo, H. Baisya","doi":"10.1002/joc.8525","DOIUrl":"https://doi.org/10.1002/joc.8525","url":null,"abstract":"Northeast India (NEI) receives most of its rainfall in the southwest monsoon (SWM) season. The region is known to have frequent and persistent rainfall events of high intensities and the region is vulnerable to potential high‐impact meteorological events. This study analyses observed daily rainfall data for the SWM months (JJAS) during 1991–2020 to better understand the climatology of high‐intensity rainfall (HIR) occurrences and their persistence. The agglomerative hierarchical cluster analysis has delineated four distinct clusters and Cramer's t test indicates no significant fluctuations at 5% significant level in the HIR events in these clusters. ERA‐5 reanalysis data have been used to find the moisture transport for synoptic‐scale (3–7 days), quasi‐bi‐weekly oscillations (10–20 days) and intraseasonal oscillations (30–60 days) for each cluster. Under positive synoptic‐scale phases, HIR in the clusters on southern latitudes of NEI occurs due to moisture incursion from the Bay of Bengal from southwesterlies at 850 hPa, and in the clusters on the northern latitudes, it is primarily due to westerlies. For, quasi‐bi‐weekly oscillations, westerlies at 850 hPa are favourable for moisture transport in most of the clusters during its positive phase. On the other hand, for positive phase of intraseasonal oscillations, westerlies at 850 hPa dominate the moisture transport in all the clusters. Also, most of the HIR events occur whenever Madden–Julian Oscillation (MJO) is in phase 1 and 2 with higher amplitude (RMM ≥1).","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141279787","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}
Trends in temperature variability are often referred to have higher effect on temperature extremes than trends in the mean. We investigate trends in three complementary measures of intraseasonal temperature variability: (a) standard deviation of mean daily temperature (SD), (b) mean absolute value of day‐to‐day temperature change (DTD) and (c) 1‐day lagged temporal autocorrelation of temperature (LAG). It is a well‐established fact that different types of data (station, gridded, reanalyses) provide different temperature characteristics and particularly their trends. Moreover, we have uncovered that trends in measures of variability are considerably sensitive to data inhomogeneities. Therefore, we use five different datasets, one station based (ECA&D), one gridded (EOBS) and three reanalyses (JRA‐55, NCEP/NCAR, 20CR), and compare them. The period from 1961 to 2014 where all datasets overlap is examined, and the linear regression method is utilized to calculate trends of investigated measures in summer and winter. Intraseasonal temperature variability tends to decrease in winter, especially in eastern and northern Europe, where trends below −7%·decade−1 are detected for all measures. Decreases in DTD and LAG (indicating increase in persistence) prevail also in summer while summer SD tends to increase. The increase in the width of temperature distribution and the simultaneous increase in persistence indicate a tendency towards the rise in the frequency of extended extreme events in summer. Unlike previous studies, our results imply that reanalyses are not the least accurate in determining trends. JRA‐55 appears to be the least diverging from other datasets, while the largest discrepancies are detected for DTD at station data.
{"title":"Trends in intraseasonal temperature variability in Europe: Comparison of station data with gridded data and reanalyses","authors":"Tomáš Krauskopf, Radan Huth","doi":"10.1002/joc.8512","DOIUrl":"https://doi.org/10.1002/joc.8512","url":null,"abstract":"Trends in temperature variability are often referred to have higher effect on temperature extremes than trends in the mean. We investigate trends in three complementary measures of intraseasonal temperature variability: (a) standard deviation of mean daily temperature (SD), (b) mean absolute value of day‐to‐day temperature change (DTD) and (c) 1‐day lagged temporal autocorrelation of temperature (LAG). It is a well‐established fact that different types of data (station, gridded, reanalyses) provide different temperature characteristics and particularly their trends. Moreover, we have uncovered that trends in measures of variability are considerably sensitive to data inhomogeneities. Therefore, we use five different datasets, one station based (ECA&D), one gridded (EOBS) and three reanalyses (JRA‐55, NCEP/NCAR, 20CR), and compare them. The period from 1961 to 2014 where all datasets overlap is examined, and the linear regression method is utilized to calculate trends of investigated measures in summer and winter. Intraseasonal temperature variability tends to decrease in winter, especially in eastern and northern Europe, where trends below −7%·decade−1 are detected for all measures. Decreases in DTD and LAG (indicating increase in persistence) prevail also in summer while summer SD tends to increase. The increase in the width of temperature distribution and the simultaneous increase in persistence indicate a tendency towards the rise in the frequency of extended extreme events in summer. Unlike previous studies, our results imply that reanalyses are not the least accurate in determining trends. JRA‐55 appears to be the least diverging from other datasets, while the largest discrepancies are detected for DTD at station data.","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141098743","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}
Derek T. Thompson, Barry D. Keim, Vincent M. Brown
This paper details the creation of a tropical cyclone (TC) size dataset using the NCEP/NCAR Reanalysis I dataset for landfalling TCs along the United States coastline from 1948 to 2022. The radius of the outermost closed isobar (ROCI) is used as the size parameter. The dataset comprises landfall ROCI for 220 TCs. Storms are split into three zones (Texas–Alabama, Florida and Georgia–Maine) to determine if TC size varies geographically. Results showed a significant difference in landfall size, with Florida storms larger on average than the Texas–Alabama storms. Additionally, TC size increased with increasing intensity from tropical storm to Category 3, and storms tended to be larger later in the hurricane season, but there was no significant trend in landfall size over the 75‐year period. ROCI exhibited statistically significant positive correlations with longitude and wind speed and a negative correlation with the outermost closed isobar's pressure. The dataset's creation is an example of how reanalysis datasets can be used to develop a TC size climatology.
{"title":"Construction of a tropical cyclone size dataset using reanalysis data","authors":"Derek T. Thompson, Barry D. Keim, Vincent M. Brown","doi":"10.1002/joc.8511","DOIUrl":"https://doi.org/10.1002/joc.8511","url":null,"abstract":"This paper details the creation of a tropical cyclone (TC) size dataset using the NCEP/NCAR Reanalysis I dataset for landfalling TCs along the United States coastline from 1948 to 2022. The radius of the outermost closed isobar (ROCI) is used as the size parameter. The dataset comprises landfall ROCI for 220 TCs. Storms are split into three zones (Texas–Alabama, Florida and Georgia–Maine) to determine if TC size varies geographically. Results showed a significant difference in landfall size, with Florida storms larger on average than the Texas–Alabama storms. Additionally, TC size increased with increasing intensity from tropical storm to Category 3, and storms tended to be larger later in the hurricane season, but there was no significant trend in landfall size over the 75‐year period. ROCI exhibited statistically significant positive correlations with longitude and wind speed and a negative correlation with the outermost closed isobar's pressure. The dataset's creation is an example of how reanalysis datasets can be used to develop a TC size climatology.","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101953","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}
This study demonstrates asymmetric relationships between El Niño–Southern Oscillation (ENSO) and tropical cyclones (TCs) affecting the Philippines during October–December. In El Niño or La Niña years, the number of TCs impacting the Philippines may increase or decrease. These variations result in four ENSO–TC variability types all of which exhibit strong sea surface temperature (SST) anomalies across the equatorial eastern Pacific. The major difference between the active and inactive types in terms of El Niño or La Niña years is related to the magnitude of SST anomalies in the tropical western Pacific (TWP) over the 120°–150°E region. During El Niño years, moderate cold SST anomalies in this TWP region cause an anomalous divergent centre around the 120°–130°E zone to evoke an anomalous cyclone east of the Philippines. In the western North Pacific (WNP), this anomalous cyclone causes more TCs to form and move toward the Philippines, resulting in active TC activity. For the inactive TC type during El Niño years, very weak cold SST anomalies in the aforementioned TWP region correspond with a northeastward‐extended anomalous divergent centre over the 120°–140°E, 10°S–20°N zone and an anomalous anticyclone across the Philippines and its eastern side. Decreases in the formation of the WNP TC and movement toward the Philippines lead to inactive TC activity. The large‐scale anomalies and regulating processes are mainly opposite between the active TC type during El Niño years and the inactive TC type during La Niña years. These two types are influenced by interdecadal variability of the Pacific decadal oscillation. Opposite anomalies and regulating processes also occur between the inactive TC type during El Niño years and the active TC type during La Niña years. The former type is jointly modulated by the positive Indian Ocean Dipole mode and central‐Pacific El Niño.
{"title":"Asymmetric El Niño–Southern Oscillation and tropical cyclone relationships in the Philippines during October–December","authors":"T. Lai, Jau‐Ming Chen","doi":"10.1002/joc.8516","DOIUrl":"https://doi.org/10.1002/joc.8516","url":null,"abstract":"This study demonstrates asymmetric relationships between El Niño–Southern Oscillation (ENSO) and tropical cyclones (TCs) affecting the Philippines during October–December. In El Niño or La Niña years, the number of TCs impacting the Philippines may increase or decrease. These variations result in four ENSO–TC variability types all of which exhibit strong sea surface temperature (SST) anomalies across the equatorial eastern Pacific. The major difference between the active and inactive types in terms of El Niño or La Niña years is related to the magnitude of SST anomalies in the tropical western Pacific (TWP) over the 120°–150°E region. During El Niño years, moderate cold SST anomalies in this TWP region cause an anomalous divergent centre around the 120°–130°E zone to evoke an anomalous cyclone east of the Philippines. In the western North Pacific (WNP), this anomalous cyclone causes more TCs to form and move toward the Philippines, resulting in active TC activity. For the inactive TC type during El Niño years, very weak cold SST anomalies in the aforementioned TWP region correspond with a northeastward‐extended anomalous divergent centre over the 120°–140°E, 10°S–20°N zone and an anomalous anticyclone across the Philippines and its eastern side. Decreases in the formation of the WNP TC and movement toward the Philippines lead to inactive TC activity. The large‐scale anomalies and regulating processes are mainly opposite between the active TC type during El Niño years and the inactive TC type during La Niña years. These two types are influenced by interdecadal variability of the Pacific decadal oscillation. Opposite anomalies and regulating processes also occur between the inactive TC type during El Niño years and the active TC type during La Niña years. The former type is jointly modulated by the positive Indian Ocean Dipole mode and central‐Pacific El Niño.","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101814","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}
In this study, a new skill score (SS) is proposed to evaluate the performance of climatological East Asian summer precipitation (EASP) in the Coupled Model Intercomparison Project Phase 6 (CMIP6) over the historical period. By applying the empirical orthogonal function (EOF) to the EASP bias of CMIP6 models, the intermodel spread of EASP bias is revealed to be dominated by the first two modes: the uniform precipitation bias pattern and the north–south dipole precipitation bias pattern. Then the SS is constructed by the weighted‐average model‐observation distances regarding different EOF modes, where the model‐observation distance in a certain EOF mode is defined as the difference between their principal components, and the weight is the corresponding percentage variance. The perfect‐models ensemble based on the SS shows a spatial magnitude close to the observation, indicating that the SS effectively depicts the models' historical performance. However, no robust relationship is found between the model's historical performance and future projection regarding the EASP. This is because they are governed by different physical factors. The historical EASM is determined by the thermal responses to a specific radiative forcing, while the future change in EASP is associated with the warming rate along with the increased radiative forcing.
本研究提出了一种新的技能评分（SS）来评估历史时期东亚夏季降水（EASP）在耦合模式相互比较项目第六阶段（CMIP6）中的气候学表现。通过对 CMIP6 模式的东亚夏季降水偏差应用经验正交函数（EOF），发现东亚夏季降水偏差的模式间传播主要由前两种模式主导：均匀降水偏差模式和南北偶极降水偏差模式。然后，根据不同 EOF 模式的加权平均模式-观测距离构建 SS，其中，某一 EOF 模式下的模式-观测距离定义为其主分量之差，权重为相应的方差百分比。基于 SS 的完美模型集合显示出与观测值接近的空间幅度，表明 SS 有效地描述了模型的历史表现。然而，在 EASP 方面，模型的历史表现与未来预测之间没有发现稳健的关系。这是因为它们受不同物理因素的制约。历史上的 EASM 是由对特定辐射强迫的热响应决定的，而未来 EASP 的变化则与辐射强迫增加时的升温速率有关。
{"title":"Evaluating the East Asian summer precipitation from the perspective of dominant intermodel spread modes and its implication for future projection","authors":"Jian Shi","doi":"10.1002/joc.8491","DOIUrl":"https://doi.org/10.1002/joc.8491","url":null,"abstract":"In this study, a new skill score (SS) is proposed to evaluate the performance of climatological East Asian summer precipitation (EASP) in the Coupled Model Intercomparison Project Phase 6 (CMIP6) over the historical period. By applying the empirical orthogonal function (EOF) to the EASP bias of CMIP6 models, the intermodel spread of EASP bias is revealed to be dominated by the first two modes: the uniform precipitation bias pattern and the north–south dipole precipitation bias pattern. Then the SS is constructed by the weighted‐average model‐observation distances regarding different EOF modes, where the model‐observation distance in a certain EOF mode is defined as the difference between their principal components, and the weight is the corresponding percentage variance. The perfect‐models ensemble based on the SS shows a spatial magnitude close to the observation, indicating that the SS effectively depicts the models' historical performance. However, no robust relationship is found between the model's historical performance and future projection regarding the EASP. This is because they are governed by different physical factors. The historical EASM is determined by the thermal responses to a specific radiative forcing, while the future change in EASP is associated with the warming rate along with the increased radiative forcing.","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141100349","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}
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