Jing-jia Luo - Academia.edu (original) (raw)

Papers by Jing-jia Luo

Research paper thumbnail of Multi-task machine learning improves multi-seasonal prediction of the Indian Ocean Dipole

Nature Communications

As one of the most predominant interannual variabilities, the Indian Ocean Dipole (IOD) exerts gr... more As one of the most predominant interannual variabilities, the Indian Ocean Dipole (IOD) exerts great socio-economic impacts globally, especially on Asia, Africa, and Australia. While enormous efforts have been made since its discovery to improve both climate models and statistical methods for better prediction, current skills in IOD predictions are mostly limited up to three months ahead. Here, we challenge this long-standing problem using a multi-task deep learning model that we name MTL-NET. Hindcasts of the IOD events during the past four decades indicate that the MTL-NET can predict the IOD well up to 7-month ahead, outperforming most of world-class dynamical models used for comparison in this study. Moreover, the MTL-NET can help assess the importance of different predictors and correctly capture the nonlinear relationships between the IOD and predictors. Given its merits, the MTL-NET is demonstrated to be an efficient model for improved IOD prediction.

Research paper thumbnail of Corrigendum to: ACCESS-S1: The new Bureau of Meteorology multi-week to seasonal prediction system

Journal of Southern Hemisphere Earth Systems Science

ACCESS-S1 will be the next version of the Australian Bureau of Meteorology's seasonal predict... more ACCESS-S1 will be the next version of the Australian Bureau of Meteorology's seasonal prediction system, due to become operational in early 2018. The multiweek and seasonal performance of ACCESS-S1 has been evaluated based on a 23-year hindcast set and compared to the current operational system, POAMA. The system has considerable enhancements compared to POAMA, including higher vertical and horizontal resolution of the component models and state-ofthe-art physics parameterisation schemes. ACCESS-S1 is based on the UK Met Office GloSea5-GC2 seasonal prediction system, but has enhancements to the ensemble generation strategy to make it appropriate for multi-week forecasting, and a larger ensemble size. ACCESS-S1 has markedly reduced biases in the mean state of the climate, both globally and over Australia, compared to POAMA. ACCESS-S1 also better predicts the early stages of the development of the El Niño Southern Oscillation (through the predictability barrier) and the Indian Oce...

Research paper thumbnail of Seasonal Predictions of Summer Precipitation in the Middle-lower Reaches of the Yangtze River with Global and Regional Models Based on NUIST-CFS1.0

Advances in Atmospheric Sciences

Research paper thumbnail of Prediction of Arctic Temperature and Sea Ice Using a High-Resolution Coupled Model

Journal of Climate

Under global warming, surface air temperature has risen rapidly and sea ice has decreased markedl... more Under global warming, surface air temperature has risen rapidly and sea ice has decreased markedly in the Arctic. These drastic climate changes have brought about various severe impacts on the vulnerable environment and ecosystem there. Thus, accurate prediction of Arctic climate becomes more important than before. Here we examine the seasonal to interannual predictive skills of 2-m air temperature (2-m T) and sea ice cover (SIC) over the Arctic region (70°–90°N) during 1980–2014 with a high-resolution global coupled model called the Met Office Decadal Prediction System, version 3 (DePreSys3). The model captures well both the climatology and interannual variability of the Arctic 2-m T and SIC. Moreover, the anomaly correlation coefficient of Arctic-averaged 2-m T and SIC shows statistically significant skills at lead times up to 16 months. This is mainly due to the contribution of strong decadal trends. In addition, it is found that the peak warming trend of Arctic 2-m T lags the ma...

Research paper thumbnail of Spatial Modelling of Bacterial Diversity over the Selected Regions in Bangladesh by Next-Generation Sequencing: Role of Water Temperature

Applied Sciences, 2020

In this study, a spatial model has been developed to investigate the role of water temperature to... more In this study, a spatial model has been developed to investigate the role of water temperature to the distribution of bacteria over the selected regions in the Bay of Bengal, located in the southern region of Bangladesh using next-generation sequencing. Bacterial concentration, quantitative polymerase chain reactions, and sequencing were performed on water samples and identified Acidobacteria, Actinobacteria, Bacteroidetes, Chlorobi, Chloroflexi, Cyanobacteria, Firmicutes, Nitrospirae, Planctomycetes, Proteobacteria, and Verrucomicrobia. The spatial model tessellated the parts of the Bay of Bengal with hexagons and analyzed the relationship between the distribution of bacteria and water temperature. A geographically weighted regression was used to observe whether water temperature contributed strongly or weakly to the distribution of bacteria. The residuals were examined to assess the model’s fitness. The spatial model has the potential to predict the bacterial diversity in the sele...

Research paper thumbnail of Atlantic Niño/Niña Prediction Skills in NMME Models

Atmosphere, 2021

The Atlantic Niño/Niña, one of the dominant interannual variability in the equatorial Atlantic, e... more The Atlantic Niño/Niña, one of the dominant interannual variability in the equatorial Atlantic, exerts prominent influence on the Earth’s climate, but its prediction skill shown previously was unsatisfactory and limited to two to three months. By diagnosing the recently released North American Multimodel Ensemble (NMME) models, we find that the Atlantic Niño/Niña prediction skills are improved, with the multi-model ensemble (MME) reaching five months. The prediction skills are season-dependent. Specifically, they show a marked dip in boreal spring, suggesting that the Atlantic Niño/Niña prediction suffers a “spring predictability barrier” like ENSO. The prediction skill is higher for Atlantic Niña than for Atlantic Niño, and better in the developing phase than in the decaying phase. The amplitude bias of the Atlantic Niño/Niña is primarily attributed to the amplitude bias in the annual cycle of the equatorial sea surface temperature (SST). The anomaly correlation coefficient scores ...

Research paper thumbnail of Impacts of Tropical Modes of Climate Variations Impacts of Tropical Modes of Climate Variations

Natural modes of climate variations such as Indian Ocean Dipole (IOD), El Nino/Southern Oscillati... more Natural modes of climate variations such as Indian Ocean Dipole (IOD), El Nino/Southern Oscillation (ENSO) and recently identified ENSO Modoki have huge impacts on many parts of the world. For example, some of the extreme flooding events in East Africa and droughts in Australia are associated with the positive IODs. The impact was severe when in a rare turn of the history three positive dipole events evolved back to back during 2006, 2007 and 2008. In addition, more number of El Nino Modoki (which causes a different teleconnection pattern as compared to that of ENSO) events are observed in recent decades. These climate phenomena also influence high-frequency weather events by either anchoring or destroying the triggering mechanisms. Furthermore, these climate variations influence the coastal securities by modulating coastal sea level variations on interannual to decadal time scales. Therefore, it has become an essential task to understand these changes in the characteristics of the ...

Research paper thumbnail of Recent Changes in Indo-Pacific Climate Fluctuations

Research paper thumbnail of Seasonal Prediction of Summer Precipitation in the Middle and Lower Reaches of the Yangtze River Valley: Comparison of Machine Learning and Climate Model Predictions

Water, 2021

The middle and lower reaches of the Yangtze River valley (YRV), which are among the most densely ... more The middle and lower reaches of the Yangtze River valley (YRV), which are among the most densely populated regions in China, are subject to frequent flooding. In this study, the predictor importance analysis model was used to sort and select predictors, and five methods (multiple linear regression (MLR), decision tree (DT), random forest (RF), backpropagation neural network (BPNN), and convolutional neural network (CNN)) were used to predict the interannual variation of summer precipitation over the middle and lower reaches of the YRV. Predictions from eight climate models were used for comparison. Of the five tested methods, RF demonstrated the best predictive skill. Starting the RF prediction in December, when its prediction skill was highest, the 70-year correlation coefficient from cross validation of average predictions was 0.473. Using the same five predictors in December 2019, the RF model successfully predicted the YRV wet anomaly in summer 2020, although it had weaker ampli...

Research paper thumbnail of Modeling of tropical cyclone activity over the North Indian Ocean using generalised additive model and machine learning techniques: role of Boreal summer intraseasonal oscillation

Natural Hazards, 2021

This study investigates the contribution of Boreal Summer Intraseasonal Oscillation (BSISO) to th... more This study investigates the contribution of Boreal Summer Intraseasonal Oscillation (BSISO) to the tropical cyclone (TC) activity over the North Indian Ocean (NIO) and assesses the prediction skill of a statistical Generalised Additive Model (GAM) and two machine learning techniques—Random Forest (RF) and Support Vector Regression (SVR). Joint Typhoon Warning Centre TC and BSISO1 Index data have been used for a period of 33-year (1981–2013). By considering eight phases of BSISO, prediction models have been developed using a kernel density estimation for the TC genesis, Euler integration step to fit the tracks, and a country mask approach for the landfall across the NIO rim countries. Result shows that GAM has the highest prediction skill compared to the RF and SVR. Westward and Northward moving TCs are controlled by the wind and the TC activities during BSISO phases which modulated by wind matched well against observations over the NIO. Distance calculation validation method is applied to assess the skill of models.

Research paper thumbnail of Future changes in the frequency of extreme droughts over China based on two large ensemble simulations

Journal of Climate, 2021

Future changes in the frequency of extreme drought events are of vital importance for risk assess... more Future changes in the frequency of extreme drought events are of vital importance for risk assessment and relevant policy making. But a reliable estimation of their probability is intrinsically challenging due to limited available observations or simulations. Here, we use two large ensemble simulations, 50 members from CanESM2 and 40 members from CESM1 under the future RCP8.5 scenario, to elaborate a reliable projection of the 100-year drought events (once in a century) under different warming levels. It is however necessary to firstly remove systematic biases for the simulated temperature and precipitation through a bias-correction method based on quantile mapping. Droughts are diagnosed with the Standardized Precipitation Evapotranspiration Index (SPEI), which considers both precipitation and potential evapotranspiration (PET, involving temperature). The results show that the frequency of extreme droughts increases with the continued global warming. Some differences between the tw...

Research paper thumbnail of Effect of circulation variation associated with East Asian jet on spring rainfall over North China and Yangtze-Huaihe River Valley

Atmospheric Research, 2021

Abstract The present study investigates the association of East Asian westerly jet (EAWJ) variati... more Abstract The present study investigates the association of East Asian westerly jet (EAWJ) variations with spring rainfall anomalies in Northern China and Yangtze-Huaihe River Valley (NC-YHV) and the dynamics using reanalysis datasets. Based on the climatology and interannual variation in 200-hPa zonal winds, the index EAWJI is defined as the average 200-hPa zonal wind velocity over a zonal 10-degree-width belt centered around the seasonal-mean jet axis between 105°E and 145°E. Associated with anomalously strengthened EAWJ, significant negative rainfall anomalies are observed over NC-YHV. The dynamics are as follows. When the EAWJ is anomalously intensified, a quasi-barotropic Pacific-Japan-like (PJ) teleconnection along coastal China and an associated anomalous westerly flow over NC-YHV are observed. In middle-lower troposphere, Tibetan Plateau (TP) drastically reduces the anomalous southwesterly momentum transported into NC-YHV, turning the westerly anomalies into northwesterly anomalies. The anomalous northwesterly winds over NC-YHV advect cold and dry air southeastward toward NC-YHV, which induce downward motion (diabatic heating feedback is weak) and negative moisture anomalies, respectively, and thus cause reduced rainfall anomalies over NC-YHV. Anomalous winter SSTAs in western Pacific and tropical Indian Ocean associated with ENSO are sustained until spring, inducing barotropic waves that propagate northwards to cause EAWJ-associated circulation anomalies and thus bring about spring rainfall anomalies in NC-YHV. The quasi-barotropic features of the EAWJ-associated circulation anomalies and their association with the northward propagation of tropical SSTA-induced barotropic waves together suggest that EAWJ-associated circulation variations are at least partly among the external forcings responsible for spring rainfall anomalies in NC-YHV.

Research paper thumbnail of Trends and Variabilities of Thunderstorm Days over Bangladesh on the ENSO and IOD Timescales

Atmosphere, 2020

Thunderstorms (TS) are one of the most devastating atmospheric phenomena, which causes massive da... more Thunderstorms (TS) are one of the most devastating atmospheric phenomena, which causes massive damage and adverse losses in various sectors, including agriculture and infrastructure. This study investigates the spatiotemporal variabilities of TS days over Bangladesh and their connection with El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). The TS, ENSO and IOD years’ data for 42 years (1975–2016) are used. The trend in TS days at the spatiotemporal scale is calculated using Mann Kendall and Spearman’s rho test. Results suggest that the trend in TS days is positive for all months except December and January. The significant trends are found for May and June, particularly in the northern and northeastern regions of Bangladesh. In the decadal scale, most of the regions show a significant upward trend in TS days. Results from the Weibull probability distribution model show the highest TS days in the northeastern region. The connection between TS days and ENSO/IOD indic...

Research paper thumbnail of The Tropics

Bulletin of the American Meteorological Society, 2021

Research paper thumbnail of The Tropics

Bulletin of the American Meteorological Society, 2020

Research paper thumbnail of Statistical Approach to Observe the Atmospheric Density Variations Using Swarm Satellite Data

Atmosphere, 2020

Over time, the initial algorithms to derive atmospheric density from accelerometers have been sig... more Over time, the initial algorithms to derive atmospheric density from accelerometers have been significantly enhanced. In this study, we discussed one of the accurate accelerometers—the Earth’s Magnetic Field and Environment Explorers, more commonly known as the Swarm satellites. Swarm satellite–C level 2 (measurements from the Swam accelerometers) density, solar index (F10.7), and geomagnetic index (Kp) data have been used for a year (mid 2014–2015), and the different types of temporal (the diurnal, multi–day, solar–rotational, semi–annual, and annual) atmospheric density variations have been investigated using the statistical approaches of correlation coefficient and wavelet transform. The result shows the density varies due to the recurrent geomagnetic force at multi–day, solar irradiance during the day, appearance and disappearance of the Sun’s active region, Sun–Earth distance, large scale circulation, and the formation of an aurora. Additionally, a correlation coefficient was u...

Research paper thumbnail of Seasonal movement prediction of tropical cyclone over the North Indian Ocean by using atmospheric climate variables in statistical models

Atmospheric Research, 2020

Research paper thumbnail of Discovery of Chile Niño/Niña

Geophysical Research Letters, 2020

Research paper thumbnail of Ocean Impacts on Australian Interannual to Decadal Precipitation Variability

Research paper thumbnail of Synoptic Features Responsible for Heat Waves in Central Africa, a Region with Strong Multidecadal Trends

Journal of Climate, 2019

Central Africa (CA) is identified as a location of a large positive trend of the occurrence of he... more Central Africa (CA) is identified as a location of a large positive trend of the occurrence of heat waves (HWs) during 1979–2016, appearing to result mostly from a regime shift around the year 2000. Therefore, we study the evolution of synoptic features associated with the occurrence of HW events in CA. It is found that the HW-related circulation is typically characterized by an anomalous convergence in the upper troposphere but there are important differences for HW events occurring in the south region of CA (CA_S) versus the north region (CA_N). For the occurrence of the HW events in CA_S, the anomalous subsidence associated with upper troposphere anomalous convergence is the dominant factor for their occurrence and magnitude: the strong subsidence leads to warming through greater solar insolation. The HW events in CA_S are also accompanied by an anomalous surface anticyclone in the north with anomalous northerly flow transporting heat into the CA_S region. In contrast, although t...

Research paper thumbnail of Multi-task machine learning improves multi-seasonal prediction of the Indian Ocean Dipole

Nature Communications

As one of the most predominant interannual variabilities, the Indian Ocean Dipole (IOD) exerts gr... more As one of the most predominant interannual variabilities, the Indian Ocean Dipole (IOD) exerts great socio-economic impacts globally, especially on Asia, Africa, and Australia. While enormous efforts have been made since its discovery to improve both climate models and statistical methods for better prediction, current skills in IOD predictions are mostly limited up to three months ahead. Here, we challenge this long-standing problem using a multi-task deep learning model that we name MTL-NET. Hindcasts of the IOD events during the past four decades indicate that the MTL-NET can predict the IOD well up to 7-month ahead, outperforming most of world-class dynamical models used for comparison in this study. Moreover, the MTL-NET can help assess the importance of different predictors and correctly capture the nonlinear relationships between the IOD and predictors. Given its merits, the MTL-NET is demonstrated to be an efficient model for improved IOD prediction.

Research paper thumbnail of Corrigendum to: ACCESS-S1: The new Bureau of Meteorology multi-week to seasonal prediction system

Journal of Southern Hemisphere Earth Systems Science

ACCESS-S1 will be the next version of the Australian Bureau of Meteorology's seasonal predict... more ACCESS-S1 will be the next version of the Australian Bureau of Meteorology's seasonal prediction system, due to become operational in early 2018. The multiweek and seasonal performance of ACCESS-S1 has been evaluated based on a 23-year hindcast set and compared to the current operational system, POAMA. The system has considerable enhancements compared to POAMA, including higher vertical and horizontal resolution of the component models and state-ofthe-art physics parameterisation schemes. ACCESS-S1 is based on the UK Met Office GloSea5-GC2 seasonal prediction system, but has enhancements to the ensemble generation strategy to make it appropriate for multi-week forecasting, and a larger ensemble size. ACCESS-S1 has markedly reduced biases in the mean state of the climate, both globally and over Australia, compared to POAMA. ACCESS-S1 also better predicts the early stages of the development of the El Niño Southern Oscillation (through the predictability barrier) and the Indian Oce...

Research paper thumbnail of Seasonal Predictions of Summer Precipitation in the Middle-lower Reaches of the Yangtze River with Global and Regional Models Based on NUIST-CFS1.0

Advances in Atmospheric Sciences

Research paper thumbnail of Prediction of Arctic Temperature and Sea Ice Using a High-Resolution Coupled Model

Journal of Climate

Under global warming, surface air temperature has risen rapidly and sea ice has decreased markedl... more Under global warming, surface air temperature has risen rapidly and sea ice has decreased markedly in the Arctic. These drastic climate changes have brought about various severe impacts on the vulnerable environment and ecosystem there. Thus, accurate prediction of Arctic climate becomes more important than before. Here we examine the seasonal to interannual predictive skills of 2-m air temperature (2-m T) and sea ice cover (SIC) over the Arctic region (70°–90°N) during 1980–2014 with a high-resolution global coupled model called the Met Office Decadal Prediction System, version 3 (DePreSys3). The model captures well both the climatology and interannual variability of the Arctic 2-m T and SIC. Moreover, the anomaly correlation coefficient of Arctic-averaged 2-m T and SIC shows statistically significant skills at lead times up to 16 months. This is mainly due to the contribution of strong decadal trends. In addition, it is found that the peak warming trend of Arctic 2-m T lags the ma...

Research paper thumbnail of Spatial Modelling of Bacterial Diversity over the Selected Regions in Bangladesh by Next-Generation Sequencing: Role of Water Temperature

Applied Sciences, 2020

In this study, a spatial model has been developed to investigate the role of water temperature to... more In this study, a spatial model has been developed to investigate the role of water temperature to the distribution of bacteria over the selected regions in the Bay of Bengal, located in the southern region of Bangladesh using next-generation sequencing. Bacterial concentration, quantitative polymerase chain reactions, and sequencing were performed on water samples and identified Acidobacteria, Actinobacteria, Bacteroidetes, Chlorobi, Chloroflexi, Cyanobacteria, Firmicutes, Nitrospirae, Planctomycetes, Proteobacteria, and Verrucomicrobia. The spatial model tessellated the parts of the Bay of Bengal with hexagons and analyzed the relationship between the distribution of bacteria and water temperature. A geographically weighted regression was used to observe whether water temperature contributed strongly or weakly to the distribution of bacteria. The residuals were examined to assess the model’s fitness. The spatial model has the potential to predict the bacterial diversity in the sele...

Research paper thumbnail of Atlantic Niño/Niña Prediction Skills in NMME Models

Atmosphere, 2021

The Atlantic Niño/Niña, one of the dominant interannual variability in the equatorial Atlantic, e... more The Atlantic Niño/Niña, one of the dominant interannual variability in the equatorial Atlantic, exerts prominent influence on the Earth’s climate, but its prediction skill shown previously was unsatisfactory and limited to two to three months. By diagnosing the recently released North American Multimodel Ensemble (NMME) models, we find that the Atlantic Niño/Niña prediction skills are improved, with the multi-model ensemble (MME) reaching five months. The prediction skills are season-dependent. Specifically, they show a marked dip in boreal spring, suggesting that the Atlantic Niño/Niña prediction suffers a “spring predictability barrier” like ENSO. The prediction skill is higher for Atlantic Niña than for Atlantic Niño, and better in the developing phase than in the decaying phase. The amplitude bias of the Atlantic Niño/Niña is primarily attributed to the amplitude bias in the annual cycle of the equatorial sea surface temperature (SST). The anomaly correlation coefficient scores ...

Research paper thumbnail of Impacts of Tropical Modes of Climate Variations Impacts of Tropical Modes of Climate Variations

Natural modes of climate variations such as Indian Ocean Dipole (IOD), El Nino/Southern Oscillati... more Natural modes of climate variations such as Indian Ocean Dipole (IOD), El Nino/Southern Oscillation (ENSO) and recently identified ENSO Modoki have huge impacts on many parts of the world. For example, some of the extreme flooding events in East Africa and droughts in Australia are associated with the positive IODs. The impact was severe when in a rare turn of the history three positive dipole events evolved back to back during 2006, 2007 and 2008. In addition, more number of El Nino Modoki (which causes a different teleconnection pattern as compared to that of ENSO) events are observed in recent decades. These climate phenomena also influence high-frequency weather events by either anchoring or destroying the triggering mechanisms. Furthermore, these climate variations influence the coastal securities by modulating coastal sea level variations on interannual to decadal time scales. Therefore, it has become an essential task to understand these changes in the characteristics of the ...

Research paper thumbnail of Recent Changes in Indo-Pacific Climate Fluctuations

Research paper thumbnail of Seasonal Prediction of Summer Precipitation in the Middle and Lower Reaches of the Yangtze River Valley: Comparison of Machine Learning and Climate Model Predictions

Water, 2021

The middle and lower reaches of the Yangtze River valley (YRV), which are among the most densely ... more The middle and lower reaches of the Yangtze River valley (YRV), which are among the most densely populated regions in China, are subject to frequent flooding. In this study, the predictor importance analysis model was used to sort and select predictors, and five methods (multiple linear regression (MLR), decision tree (DT), random forest (RF), backpropagation neural network (BPNN), and convolutional neural network (CNN)) were used to predict the interannual variation of summer precipitation over the middle and lower reaches of the YRV. Predictions from eight climate models were used for comparison. Of the five tested methods, RF demonstrated the best predictive skill. Starting the RF prediction in December, when its prediction skill was highest, the 70-year correlation coefficient from cross validation of average predictions was 0.473. Using the same five predictors in December 2019, the RF model successfully predicted the YRV wet anomaly in summer 2020, although it had weaker ampli...

Research paper thumbnail of Modeling of tropical cyclone activity over the North Indian Ocean using generalised additive model and machine learning techniques: role of Boreal summer intraseasonal oscillation

Natural Hazards, 2021

This study investigates the contribution of Boreal Summer Intraseasonal Oscillation (BSISO) to th... more This study investigates the contribution of Boreal Summer Intraseasonal Oscillation (BSISO) to the tropical cyclone (TC) activity over the North Indian Ocean (NIO) and assesses the prediction skill of a statistical Generalised Additive Model (GAM) and two machine learning techniques—Random Forest (RF) and Support Vector Regression (SVR). Joint Typhoon Warning Centre TC and BSISO1 Index data have been used for a period of 33-year (1981–2013). By considering eight phases of BSISO, prediction models have been developed using a kernel density estimation for the TC genesis, Euler integration step to fit the tracks, and a country mask approach for the landfall across the NIO rim countries. Result shows that GAM has the highest prediction skill compared to the RF and SVR. Westward and Northward moving TCs are controlled by the wind and the TC activities during BSISO phases which modulated by wind matched well against observations over the NIO. Distance calculation validation method is applied to assess the skill of models.

Research paper thumbnail of Future changes in the frequency of extreme droughts over China based on two large ensemble simulations

Journal of Climate, 2021

Future changes in the frequency of extreme drought events are of vital importance for risk assess... more Future changes in the frequency of extreme drought events are of vital importance for risk assessment and relevant policy making. But a reliable estimation of their probability is intrinsically challenging due to limited available observations or simulations. Here, we use two large ensemble simulations, 50 members from CanESM2 and 40 members from CESM1 under the future RCP8.5 scenario, to elaborate a reliable projection of the 100-year drought events (once in a century) under different warming levels. It is however necessary to firstly remove systematic biases for the simulated temperature and precipitation through a bias-correction method based on quantile mapping. Droughts are diagnosed with the Standardized Precipitation Evapotranspiration Index (SPEI), which considers both precipitation and potential evapotranspiration (PET, involving temperature). The results show that the frequency of extreme droughts increases with the continued global warming. Some differences between the tw...

Research paper thumbnail of Effect of circulation variation associated with East Asian jet on spring rainfall over North China and Yangtze-Huaihe River Valley

Atmospheric Research, 2021

Abstract The present study investigates the association of East Asian westerly jet (EAWJ) variati... more Abstract The present study investigates the association of East Asian westerly jet (EAWJ) variations with spring rainfall anomalies in Northern China and Yangtze-Huaihe River Valley (NC-YHV) and the dynamics using reanalysis datasets. Based on the climatology and interannual variation in 200-hPa zonal winds, the index EAWJI is defined as the average 200-hPa zonal wind velocity over a zonal 10-degree-width belt centered around the seasonal-mean jet axis between 105°E and 145°E. Associated with anomalously strengthened EAWJ, significant negative rainfall anomalies are observed over NC-YHV. The dynamics are as follows. When the EAWJ is anomalously intensified, a quasi-barotropic Pacific-Japan-like (PJ) teleconnection along coastal China and an associated anomalous westerly flow over NC-YHV are observed. In middle-lower troposphere, Tibetan Plateau (TP) drastically reduces the anomalous southwesterly momentum transported into NC-YHV, turning the westerly anomalies into northwesterly anomalies. The anomalous northwesterly winds over NC-YHV advect cold and dry air southeastward toward NC-YHV, which induce downward motion (diabatic heating feedback is weak) and negative moisture anomalies, respectively, and thus cause reduced rainfall anomalies over NC-YHV. Anomalous winter SSTAs in western Pacific and tropical Indian Ocean associated with ENSO are sustained until spring, inducing barotropic waves that propagate northwards to cause EAWJ-associated circulation anomalies and thus bring about spring rainfall anomalies in NC-YHV. The quasi-barotropic features of the EAWJ-associated circulation anomalies and their association with the northward propagation of tropical SSTA-induced barotropic waves together suggest that EAWJ-associated circulation variations are at least partly among the external forcings responsible for spring rainfall anomalies in NC-YHV.

Research paper thumbnail of Trends and Variabilities of Thunderstorm Days over Bangladesh on the ENSO and IOD Timescales

Atmosphere, 2020

Thunderstorms (TS) are one of the most devastating atmospheric phenomena, which causes massive da... more Thunderstorms (TS) are one of the most devastating atmospheric phenomena, which causes massive damage and adverse losses in various sectors, including agriculture and infrastructure. This study investigates the spatiotemporal variabilities of TS days over Bangladesh and their connection with El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). The TS, ENSO and IOD years’ data for 42 years (1975–2016) are used. The trend in TS days at the spatiotemporal scale is calculated using Mann Kendall and Spearman’s rho test. Results suggest that the trend in TS days is positive for all months except December and January. The significant trends are found for May and June, particularly in the northern and northeastern regions of Bangladesh. In the decadal scale, most of the regions show a significant upward trend in TS days. Results from the Weibull probability distribution model show the highest TS days in the northeastern region. The connection between TS days and ENSO/IOD indic...

Research paper thumbnail of The Tropics

Bulletin of the American Meteorological Society, 2021

Research paper thumbnail of The Tropics

Bulletin of the American Meteorological Society, 2020

Research paper thumbnail of Statistical Approach to Observe the Atmospheric Density Variations Using Swarm Satellite Data

Atmosphere, 2020

Over time, the initial algorithms to derive atmospheric density from accelerometers have been sig... more Over time, the initial algorithms to derive atmospheric density from accelerometers have been significantly enhanced. In this study, we discussed one of the accurate accelerometers—the Earth’s Magnetic Field and Environment Explorers, more commonly known as the Swarm satellites. Swarm satellite–C level 2 (measurements from the Swam accelerometers) density, solar index (F10.7), and geomagnetic index (Kp) data have been used for a year (mid 2014–2015), and the different types of temporal (the diurnal, multi–day, solar–rotational, semi–annual, and annual) atmospheric density variations have been investigated using the statistical approaches of correlation coefficient and wavelet transform. The result shows the density varies due to the recurrent geomagnetic force at multi–day, solar irradiance during the day, appearance and disappearance of the Sun’s active region, Sun–Earth distance, large scale circulation, and the formation of an aurora. Additionally, a correlation coefficient was u...

Research paper thumbnail of Seasonal movement prediction of tropical cyclone over the North Indian Ocean by using atmospheric climate variables in statistical models

Atmospheric Research, 2020

Research paper thumbnail of Discovery of Chile Niño/Niña

Geophysical Research Letters, 2020

Research paper thumbnail of Ocean Impacts on Australian Interannual to Decadal Precipitation Variability

Research paper thumbnail of Synoptic Features Responsible for Heat Waves in Central Africa, a Region with Strong Multidecadal Trends

Journal of Climate, 2019

Central Africa (CA) is identified as a location of a large positive trend of the occurrence of he... more Central Africa (CA) is identified as a location of a large positive trend of the occurrence of heat waves (HWs) during 1979–2016, appearing to result mostly from a regime shift around the year 2000. Therefore, we study the evolution of synoptic features associated with the occurrence of HW events in CA. It is found that the HW-related circulation is typically characterized by an anomalous convergence in the upper troposphere but there are important differences for HW events occurring in the south region of CA (CA_S) versus the north region (CA_N). For the occurrence of the HW events in CA_S, the anomalous subsidence associated with upper troposphere anomalous convergence is the dominant factor for their occurrence and magnitude: the strong subsidence leads to warming through greater solar insolation. The HW events in CA_S are also accompanied by an anomalous surface anticyclone in the north with anomalous northerly flow transporting heat into the CA_S region. In contrast, although t...