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- Author or Editor: Meng Gao x
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Abstract
In this study, temporal trends and spatial patterns of extreme temperature change are investigated at 352 meteorological stations in China over the period 1956–2013. The temperature series are first examined for evidence of long-range dependence at daily and monthly time scales. At most stations there is evidence of significant long-range dependence. Noncrossing quantile regression has been used for trend analysis of temperature series. For low quantiles of daily mean temperature and monthly minimum value of daily minimum temperature (TNn) in January, there is an increasing trend at most stations. A decrease is also observed in a zone ranging from northeastern China to central China for higher quantiles of daily mean temperature and monthly maximum value of daily maximum temperature (TXx) in July. Changes of the large-scale atmospheric circulation partly explain the trends of temperature extremes. To reveal the spatial pattern of temperature changes, a density-based spatial clustering algorithm is used to cluster the quantile trends of daily temperature series for 19 quantile levels (0.05, 0.1, …, 0.95). Spatial cluster analysis identifies a few large clusters showing different warming patterns in different parts of China. Finally, quantile regression reveals the connections between temperature extremes and two large-scale climate patterns: El Niño–Southern Oscillation (ENSO) and the Arctic Oscillation (AO). The influence of ENSO on cold extremes is significant at most stations, but its influence on warm extremes is only weakly significant. The AO not only affects the cold extremes in northern and eastern China, but also affects warm extremes in northeastern and southern China.
Abstract
In this study, temporal trends and spatial patterns of extreme temperature change are investigated at 352 meteorological stations in China over the period 1956–2013. The temperature series are first examined for evidence of long-range dependence at daily and monthly time scales. At most stations there is evidence of significant long-range dependence. Noncrossing quantile regression has been used for trend analysis of temperature series. For low quantiles of daily mean temperature and monthly minimum value of daily minimum temperature (TNn) in January, there is an increasing trend at most stations. A decrease is also observed in a zone ranging from northeastern China to central China for higher quantiles of daily mean temperature and monthly maximum value of daily maximum temperature (TXx) in July. Changes of the large-scale atmospheric circulation partly explain the trends of temperature extremes. To reveal the spatial pattern of temperature changes, a density-based spatial clustering algorithm is used to cluster the quantile trends of daily temperature series for 19 quantile levels (0.05, 0.1, …, 0.95). Spatial cluster analysis identifies a few large clusters showing different warming patterns in different parts of China. Finally, quantile regression reveals the connections between temperature extremes and two large-scale climate patterns: El Niño–Southern Oscillation (ENSO) and the Arctic Oscillation (AO). The influence of ENSO on cold extremes is significant at most stations, but its influence on warm extremes is only weakly significant. The AO not only affects the cold extremes in northern and eastern China, but also affects warm extremes in northeastern and southern China.
Abstract
This observational study attempts to determine factors responsible for the distribution of precipitation over large areas of southern China induced by Bilis, a western North Pacific Ocean severe tropical storm that made landfall on the southeastern coast of mainland China on 14 July 2006 with a remnant circulation that persisted over land until after 17 July 2006. The heavy rainfalls associated with Bilis during and after its landfall can be divided into three stages. The first stage of the rainfall, which occurred in Fujian and Zhejiang Provinces, could be directly induced by the inner-core storm circulation during its landfall. The third stage of rainfall, which occurred along the coastal areas of Guangdong and Fujian Provinces, likely resulted from the interaction between Bilis and the South China Sea monsoon enhanced by topographical lifting along the coast. The second stage of the rainfall, which appeared inland around the border regions between Jiangxi, Hunan, and Guangdong Provinces, caused the most catastrophic flooding and is the primary focus of the current study. It is found that during the second stage of the rainfall all three ingredients of deep moist convection (moisture, instability, and lifting) are in place. Several mechanisms, including vertical wind shear, warm-air advection, frontogenesis, and topography, may have contributed simultaneously to the lifting necessary for the generation of the heavy rainfall at this stage.
Abstract
This observational study attempts to determine factors responsible for the distribution of precipitation over large areas of southern China induced by Bilis, a western North Pacific Ocean severe tropical storm that made landfall on the southeastern coast of mainland China on 14 July 2006 with a remnant circulation that persisted over land until after 17 July 2006. The heavy rainfalls associated with Bilis during and after its landfall can be divided into three stages. The first stage of the rainfall, which occurred in Fujian and Zhejiang Provinces, could be directly induced by the inner-core storm circulation during its landfall. The third stage of rainfall, which occurred along the coastal areas of Guangdong and Fujian Provinces, likely resulted from the interaction between Bilis and the South China Sea monsoon enhanced by topographical lifting along the coast. The second stage of the rainfall, which appeared inland around the border regions between Jiangxi, Hunan, and Guangdong Provinces, caused the most catastrophic flooding and is the primary focus of the current study. It is found that during the second stage of the rainfall all three ingredients of deep moist convection (moisture, instability, and lifting) are in place. Several mechanisms, including vertical wind shear, warm-air advection, frontogenesis, and topography, may have contributed simultaneously to the lifting necessary for the generation of the heavy rainfall at this stage.
Abstract
Haze days induced by aerosol pollution in North and East China have posed a persistent and growing problem over the past few decades. These events are particularly threatening to densely populated cities such as Beijing. While the sources of this pollution are predominantly anthropogenic, natural climate variations may also play a role in allowing for atmospheric conditions conducive to formation of severe haze episodes over populated areas. Here, an investigation is conducted into the effects of changes in global dynamics and emissions on air quality in China’s polluted regions using 35 simulations developed from the Community Earth Systems Model Large Ensemble (CESM LENS) run over the period 1920–2100. It is shown that internal variability significantly modulates aerosol optical depth (AOD) over China; it takes roughly a decade for the forced response to balance the effects from internal variability even in China’s most polluted regions. Random forest regressions are used to accurately model (R 2 > 0.9) wintertime AOD using just climate oscillations, the month of the year, and emissions. How different phases of each oscillation affect aerosol loading is projected using these regressions. AOD responses are identified for each oscillation, with particularly strong responses from El Niño–Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO). As ENSO can be projected a few months in advance and improvements in linear inverse modeling (LIM) may yield a similar predictability for the PDO, results of this study offer opportunities to improve the predictability of China’s severe wintertime haze events and to inform policy options that could mitigate subsequent health impacts.
Abstract
Haze days induced by aerosol pollution in North and East China have posed a persistent and growing problem over the past few decades. These events are particularly threatening to densely populated cities such as Beijing. While the sources of this pollution are predominantly anthropogenic, natural climate variations may also play a role in allowing for atmospheric conditions conducive to formation of severe haze episodes over populated areas. Here, an investigation is conducted into the effects of changes in global dynamics and emissions on air quality in China’s polluted regions using 35 simulations developed from the Community Earth Systems Model Large Ensemble (CESM LENS) run over the period 1920–2100. It is shown that internal variability significantly modulates aerosol optical depth (AOD) over China; it takes roughly a decade for the forced response to balance the effects from internal variability even in China’s most polluted regions. Random forest regressions are used to accurately model (R 2 > 0.9) wintertime AOD using just climate oscillations, the month of the year, and emissions. How different phases of each oscillation affect aerosol loading is projected using these regressions. AOD responses are identified for each oscillation, with particularly strong responses from El Niño–Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO). As ENSO can be projected a few months in advance and improvements in linear inverse modeling (LIM) may yield a similar predictability for the PDO, results of this study offer opportunities to improve the predictability of China’s severe wintertime haze events and to inform policy options that could mitigate subsequent health impacts.
Abstract
Climate change and air pollution are two intimately interlinked global concerns. The frequency, intensity, and duration of heat waves are projected to increase globally under future climate change. A growing body of evidence indicates that health risks associated with the joint exposure to heat waves and air pollution can be greater than that due to individual factors. However, the cooccurrences of heat and air pollution extremes in China remain less explored in the observational records. Here we investigate the spatial pattern and temporal trend of frequency, intensity, and duration of cooccurrences of heat and air pollution extremes using China’s nationwide observations of hourly PM2.5 and O3, and the ERA5 reanalysis dataset over 2013–20. We identify a significant increase in the frequency of cooccurrence of wet-bulb temperature (Tw ) and O3 exceedances (beyond a certain predefined threshold), mainly in the Beijing–Tianjin–Hebei (BTH) region (up by 4.7 days decade−1) and the Yangtze River delta (YRD). In addition, we find that the increasing rate (compared to the average levels during the study period) of joint exceedance is larger than the rate of Tw and O3 itself. For example, Tw and O3 coextremes increased by 7.0% in BTH, higher than the percentage increase of each at 0.9% and 5.5%, respectively. We identify same amplification for YRD. This ongoing upward trend in the joint occurrence of heat and O3 extremes should be recognized as an emerging environmental issue in China, given the potentially larger compounding impact to public health.
Abstract
Climate change and air pollution are two intimately interlinked global concerns. The frequency, intensity, and duration of heat waves are projected to increase globally under future climate change. A growing body of evidence indicates that health risks associated with the joint exposure to heat waves and air pollution can be greater than that due to individual factors. However, the cooccurrences of heat and air pollution extremes in China remain less explored in the observational records. Here we investigate the spatial pattern and temporal trend of frequency, intensity, and duration of cooccurrences of heat and air pollution extremes using China’s nationwide observations of hourly PM2.5 and O3, and the ERA5 reanalysis dataset over 2013–20. We identify a significant increase in the frequency of cooccurrence of wet-bulb temperature (Tw ) and O3 exceedances (beyond a certain predefined threshold), mainly in the Beijing–Tianjin–Hebei (BTH) region (up by 4.7 days decade−1) and the Yangtze River delta (YRD). In addition, we find that the increasing rate (compared to the average levels during the study period) of joint exceedance is larger than the rate of Tw and O3 itself. For example, Tw and O3 coextremes increased by 7.0% in BTH, higher than the percentage increase of each at 0.9% and 5.5%, respectively. We identify same amplification for YRD. This ongoing upward trend in the joint occurrence of heat and O3 extremes should be recognized as an emerging environmental issue in China, given the potentially larger compounding impact to public health.
Abstract
Monitoring and modeling/predicting air pollution are crucial to understanding the links between emissions and air pollution levels, to supporting air quality management, and to reducing human exposure. Yet, current monitoring networks and modeling capabilities are unfortunately inadequate to understand the physical and chemical processes above ground and to support attribution of sources. We highlight the need for the development of an international stereoscopic monitoring strategy that can depict three-dimensional (3D) distribution of atmospheric composition to reduce the uncertainties and to advance diagnostic understanding and prediction of air pollution. There are three reasons for the implementation of stereoscopic monitoring: 1) current observation networks provide only partial view of air pollution, and this can lead to misleading air quality management actions; 2) satellite retrievals of air pollutants are widely used in air pollution studies, but too often users do not acknowledge that they have large uncertainties, which can be reduced with measurements of vertical profiles; and 3) air quality modeling and forecasting require 3D observational constraints. We call on researchers and policymakers to establish stereoscopic monitoring networks and share monitoring data to better characterize the formation of air pollution, optimize air quality management, and protect human health. Future directions for advancing monitoring and modeling/predicting air pollution are also discussed.
Abstract
Monitoring and modeling/predicting air pollution are crucial to understanding the links between emissions and air pollution levels, to supporting air quality management, and to reducing human exposure. Yet, current monitoring networks and modeling capabilities are unfortunately inadequate to understand the physical and chemical processes above ground and to support attribution of sources. We highlight the need for the development of an international stereoscopic monitoring strategy that can depict three-dimensional (3D) distribution of atmospheric composition to reduce the uncertainties and to advance diagnostic understanding and prediction of air pollution. There are three reasons for the implementation of stereoscopic monitoring: 1) current observation networks provide only partial view of air pollution, and this can lead to misleading air quality management actions; 2) satellite retrievals of air pollutants are widely used in air pollution studies, but too often users do not acknowledge that they have large uncertainties, which can be reduced with measurements of vertical profiles; and 3) air quality modeling and forecasting require 3D observational constraints. We call on researchers and policymakers to establish stereoscopic monitoring networks and share monitoring data to better characterize the formation of air pollution, optimize air quality management, and protect human health. Future directions for advancing monitoring and modeling/predicting air pollution are also discussed.
Abstract
Significant uncertainty lies in representing the rain droplet size distribution (DSD) in bulk cloud microphysics schemes and in the derivation of parameters of the function fit to the spectrum from the varying moments of a DSD. Here we evaluate the suitability of gamma distribution functions (GDFs) for fitting rain DSDs against observed disdrometer data. Results illustrate that double-parameter GDFs with prescribed or diagnosed positive spectral shape parameters μ fit rain DSDs better than the Marshall–Palmer distribution function (with μ = 0). The relative errors of fitting the spectrum moments (especially high-order moments) decrease by an order of magnitude [from O(102) to O(101)]. Moreover, introduction of a triple-parameter GDF with mathematically solved μ decreases the relative errors to O(100). Based on further investigation of potential combinations of the three prognostic moments for triple-moment cloud microphysical schemes, it is found that the GDF with parameters determined from predictions of the zeroth, third, and fourth moments (the 034 GDF) exhibits the best fit to rain DSDs compared to other moment combinations. Therefore, we suggest that the 034 prognostic moment group should replace the widely accepted 036 group to represent rain DSDs in triple-moment cloud microphysics schemes. An evaluation of the capability of GDFs to represent rain DSDs demonstrates that 034 GDF exhibits accurate fits to all observed DSDs except for rarely occurring extremely wide spectra from heavy precipitation and extremely narrow spectra from drizzle. The knowledge gained from this assessment can also be used to improve cloud microphysics retrieval schemes and data assimilation.
Abstract
Significant uncertainty lies in representing the rain droplet size distribution (DSD) in bulk cloud microphysics schemes and in the derivation of parameters of the function fit to the spectrum from the varying moments of a DSD. Here we evaluate the suitability of gamma distribution functions (GDFs) for fitting rain DSDs against observed disdrometer data. Results illustrate that double-parameter GDFs with prescribed or diagnosed positive spectral shape parameters μ fit rain DSDs better than the Marshall–Palmer distribution function (with μ = 0). The relative errors of fitting the spectrum moments (especially high-order moments) decrease by an order of magnitude [from O(102) to O(101)]. Moreover, introduction of a triple-parameter GDF with mathematically solved μ decreases the relative errors to O(100). Based on further investigation of potential combinations of the three prognostic moments for triple-moment cloud microphysical schemes, it is found that the GDF with parameters determined from predictions of the zeroth, third, and fourth moments (the 034 GDF) exhibits the best fit to rain DSDs compared to other moment combinations. Therefore, we suggest that the 034 prognostic moment group should replace the widely accepted 036 group to represent rain DSDs in triple-moment cloud microphysics schemes. An evaluation of the capability of GDFs to represent rain DSDs demonstrates that 034 GDF exhibits accurate fits to all observed DSDs except for rarely occurring extremely wide spectra from heavy precipitation and extremely narrow spectra from drizzle. The knowledge gained from this assessment can also be used to improve cloud microphysics retrieval schemes and data assimilation.
Abstract
The current study explores the use of an ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting (WRF) Model to continuously assimilate high-resolution Doppler radar data during the peak-intensity stage of Tropical Cyclone (TC) Vicente (2012) before landfall. The WRF-EnKF analyses and forecasts along with the ensembles initialized from the EnKF analyses at different times were used to examine the subsequent evolution, three-dimensional (3D) structure, predictability, and dynamics of the storm. Vicente was an intense western North Pacific tropical cyclone that made landfall around 2000 UTC 23 July 2012 near the Pearl River Delta region of Guangdong Province, China, with a peak 10-m wind speed around 44 m s−1 along with considerable inland flooding after a rapid intensification process. With vortex- and dynamics-dependent background error covariance estimated by the short-term ensemble forecasts, it was found that the WRF-EnKF could efficiently assimilate the high temporal and spatial resolution 3D radar radial velocity to improve the depiction of the TC inner-core structure of Vicente, which in turn improved the forecasts of the track and intensity along with the associated heavy precipitation inland. The ensemble forecasts and sensitivity analyses were further used to explore the leading dynamics that controlled the prediction and predictability of track, intensity, and rainfall during and after its landfall. Results showed that TC Vicente’s intensity and precipitation forecasts were largely dependent on the initial relationship between TC intensity and location and the initial steering flow.
Abstract
The current study explores the use of an ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting (WRF) Model to continuously assimilate high-resolution Doppler radar data during the peak-intensity stage of Tropical Cyclone (TC) Vicente (2012) before landfall. The WRF-EnKF analyses and forecasts along with the ensembles initialized from the EnKF analyses at different times were used to examine the subsequent evolution, three-dimensional (3D) structure, predictability, and dynamics of the storm. Vicente was an intense western North Pacific tropical cyclone that made landfall around 2000 UTC 23 July 2012 near the Pearl River Delta region of Guangdong Province, China, with a peak 10-m wind speed around 44 m s−1 along with considerable inland flooding after a rapid intensification process. With vortex- and dynamics-dependent background error covariance estimated by the short-term ensemble forecasts, it was found that the WRF-EnKF could efficiently assimilate the high temporal and spatial resolution 3D radar radial velocity to improve the depiction of the TC inner-core structure of Vicente, which in turn improved the forecasts of the track and intensity along with the associated heavy precipitation inland. The ensemble forecasts and sensitivity analyses were further used to explore the leading dynamics that controlled the prediction and predictability of track, intensity, and rainfall during and after its landfall. Results showed that TC Vicente’s intensity and precipitation forecasts were largely dependent on the initial relationship between TC intensity and location and the initial steering flow.
