2020 Vol. 37, No. 12
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2020, 37(12): 1297-1309.
doi: 10.1007/s00376-020-0054-2
Abstract:
Surface ozone (O3) and fine particulate matter (PM2.5) are dominant air pollutants in China. Concentrations of these pollutants can show significant differences between urban and nonurban areas. However, such contrast has never been explored on the country level. This study investigates the spatiotemporal characteristics of urban-to-suburban and urban-to-background difference for O3 (Δ[O3]) and PM2.5 (Δ[PM2.5]) concentrations in China using monitoring data from 1171 urban, 110 suburban, and 15 background sites built by the China National Environmental Monitoring Center (CNEMC). On the annual mean basis, the urban-to-suburban Δ[O3] is −3.7 ppbv in Beijing–Tianjin–Hebei, 1.0 ppbv in the Yangtze River Delta, −3.5 ppbv in the Pearl River Delta, and −3.8 ppbv in the Sichuan Basin. On the contrary, the urban-to-suburban Δ[PM2.5] is 15.8, −0.3, 3.5 and 2.4 μg m−3 in those areas, respectively. The urban-to-suburban contrast is more significant in winter for both Δ[O3] and Δ[PM2.5]. In eastern China, urban-to-background differences are also moderate during summer, with −5.1 to 6.8 ppbv for Δ[O3] and −0.1 to 22.5 μg m−3 for Δ[PM2.5]. However, such contrasts are much larger in winter, with −22.2 to 5.5 ppbv for Δ[O3] and 3.1 to 82.3 μg m−3 for Δ[PM2.5]. Since the urban region accounts for only 2% of the whole country’s area, the urban-dominant air quality data from the CNEMC network may overestimate winter [PM2.5] but underestimate winter [O3] over the vast domain of China. The study suggests that the CNEMC monitoring data should be used with caution for evaluating chemical models and assessing ecosystem health, which require more data outside urban areas.
Surface ozone (O3) and fine particulate matter (PM2.5) are dominant air pollutants in China. Concentrations of these pollutants can show significant differences between urban and nonurban areas. However, such contrast has never been explored on the country level. This study investigates the spatiotemporal characteristics of urban-to-suburban and urban-to-background difference for O3 (Δ[O3]) and PM2.5 (Δ[PM2.5]) concentrations in China using monitoring data from 1171 urban, 110 suburban, and 15 background sites built by the China National Environmental Monitoring Center (CNEMC). On the annual mean basis, the urban-to-suburban Δ[O3] is −3.7 ppbv in Beijing–Tianjin–Hebei, 1.0 ppbv in the Yangtze River Delta, −3.5 ppbv in the Pearl River Delta, and −3.8 ppbv in the Sichuan Basin. On the contrary, the urban-to-suburban Δ[PM2.5] is 15.8, −0.3, 3.5 and 2.4 μg m−3 in those areas, respectively. The urban-to-suburban contrast is more significant in winter for both Δ[O3] and Δ[PM2.5]. In eastern China, urban-to-background differences are also moderate during summer, with −5.1 to 6.8 ppbv for Δ[O3] and −0.1 to 22.5 μg m−3 for Δ[PM2.5]. However, such contrasts are much larger in winter, with −22.2 to 5.5 ppbv for Δ[O3] and 3.1 to 82.3 μg m−3 for Δ[PM2.5]. Since the urban region accounts for only 2% of the whole country’s area, the urban-dominant air quality data from the CNEMC network may overestimate winter [PM2.5] but underestimate winter [O3] over the vast domain of China. The study suggests that the CNEMC monitoring data should be used with caution for evaluating chemical models and assessing ecosystem health, which require more data outside urban areas.
2020, 37(12): 1310-1322.
doi: 10.1007/s00376-020-0076-9
Abstract:
Industrial pollution has a significant effect on aerosol properties in Changsha City, a typical city of central China. Therefore, year-round measurements of aerosol optical, radiative and chemical properties from 2012 to 2014 at an urban site in Changsha were analyzed. During the observation period, the energy structure was continuously optimized, which was characterized by the reduction of coal combustion. The aerosol properties have obvious seasonal variations. The seasonal average aerosol optical depth (AOD) at 500 nm ranged from 0.49 to 1.00, single scattering albedo (SSA) ranged from 0.93 to 0.97, and aerosol radiative forcing at the top of the atmosphere (TOA) ranged from −24.0 to 3.8 W m−2. The chemical components also showed seasonal variations. Meanwhile, the scattering aerosol, such as organic carbon, SO42−, NO3−, and NH4+ showed a decrease, and elemental carbon increased. Compared with observation in winter 2012, AOD and TOA decreased by 0.14 and −1.49 W m−2 in winter 2014. The scattering components, SO42−, NO3− and NH4+, decreased by 12.8 μg m−3 (56.8%), 9.2 μg m−3 (48.8%) and 6.4 μg m−3 (45.2%), respectively. The atmospheric visibility and pollution diffusion conditions improved. The extinction and radiative forcing of aerosol were significantly controlled by the scattering aerosol. The results indicate that Changsha is an industrial city with strong scattering aerosol. The energy structure optimization had a marked effect on controlling pollution, especially in winter (strong scattering aerosol).
Industrial pollution has a significant effect on aerosol properties in Changsha City, a typical city of central China. Therefore, year-round measurements of aerosol optical, radiative and chemical properties from 2012 to 2014 at an urban site in Changsha were analyzed. During the observation period, the energy structure was continuously optimized, which was characterized by the reduction of coal combustion. The aerosol properties have obvious seasonal variations. The seasonal average aerosol optical depth (AOD) at 500 nm ranged from 0.49 to 1.00, single scattering albedo (SSA) ranged from 0.93 to 0.97, and aerosol radiative forcing at the top of the atmosphere (TOA) ranged from −24.0 to 3.8 W m−2. The chemical components also showed seasonal variations. Meanwhile, the scattering aerosol, such as organic carbon, SO42−, NO3−, and NH4+ showed a decrease, and elemental carbon increased. Compared with observation in winter 2012, AOD and TOA decreased by 0.14 and −1.49 W m−2 in winter 2014. The scattering components, SO42−, NO3− and NH4+, decreased by 12.8 μg m−3 (56.8%), 9.2 μg m−3 (48.8%) and 6.4 μg m−3 (45.2%), respectively. The atmospheric visibility and pollution diffusion conditions improved. The extinction and radiative forcing of aerosol were significantly controlled by the scattering aerosol. The results indicate that Changsha is an industrial city with strong scattering aerosol. The energy structure optimization had a marked effect on controlling pollution, especially in winter (strong scattering aerosol).
2020, 37(12): 1323-1337.
doi: 10.1007/s00376-020-0123-6
Abstract:
East China (23.6°–38.4°N, 113.6°–122.9°E) is the largest developed region in China. Based on CO2 products retrieved from the Greenhouse Gases Observing Satellite (GOSAT), the spatial and temporal distributions of CO2 mixing ratios in East China during 2014–17 are discussed, and the retrieved CO2 from AIRS (Atmospheric Infrared Sounder) and OCO-2 (Orbiting Carbon Observatory-2), as well as WLG (Waliguan) background station observations, are compared with those of GOSAT. The annual CO2 retrieved from GOSAT in East China ranged from 398.96 ± 0.24 ppm in 2014 to 407.39 ± 0.20 ppm in 2017, with a growth rate of 2.82 ± 0.15 ppm yr−1, which were higher than in other regions of China. The seasonal cycle presented a maximum in spring and a minimum in summer or autumn. Higher values were mainly concentrated in the coastal areas of Zhejiang Province, and lower values were concentrated in Jiangxi and the north of Fujian Province. CO2 observed in Fujian and parts of Jiangxi increased by less than 1.0 ppm during 2014–15, but enhanced significantly by more than 5.0 ppm during 2015–16, perhaps influenced by local emissions and global impacts. We calculated year-to-year CO2 enhancements in the Yangtze River Delta region during 2014–17 that were relatively low and stable, due to the region’s carbon emissions control and reduction policies. The annual and seasonal amplitudes of CO2 retrieved from AIRS were lower than those from GOSAT in East China, probably owing to the CO2 retrieved from AIRS better reflecting the characteristics of the mid-troposphere, while GOSAT is more representative of near-surface CO2. The spatial and temporal distribution characteristics of CO2 retrieved from OCO-2 were close to those from GOSAT in East China.
East China (23.6°–38.4°N, 113.6°–122.9°E) is the largest developed region in China. Based on CO2 products retrieved from the Greenhouse Gases Observing Satellite (GOSAT), the spatial and temporal distributions of CO2 mixing ratios in East China during 2014–17 are discussed, and the retrieved CO2 from AIRS (Atmospheric Infrared Sounder) and OCO-2 (Orbiting Carbon Observatory-2), as well as WLG (Waliguan) background station observations, are compared with those of GOSAT. The annual CO2 retrieved from GOSAT in East China ranged from 398.96 ± 0.24 ppm in 2014 to 407.39 ± 0.20 ppm in 2017, with a growth rate of 2.82 ± 0.15 ppm yr−1, which were higher than in other regions of China. The seasonal cycle presented a maximum in spring and a minimum in summer or autumn. Higher values were mainly concentrated in the coastal areas of Zhejiang Province, and lower values were concentrated in Jiangxi and the north of Fujian Province. CO2 observed in Fujian and parts of Jiangxi increased by less than 1.0 ppm during 2014–15, but enhanced significantly by more than 5.0 ppm during 2015–16, perhaps influenced by local emissions and global impacts. We calculated year-to-year CO2 enhancements in the Yangtze River Delta region during 2014–17 that were relatively low and stable, due to the region’s carbon emissions control and reduction policies. The annual and seasonal amplitudes of CO2 retrieved from AIRS were lower than those from GOSAT in East China, probably owing to the CO2 retrieved from AIRS better reflecting the characteristics of the mid-troposphere, while GOSAT is more representative of near-surface CO2. The spatial and temporal distribution characteristics of CO2 retrieved from OCO-2 were close to those from GOSAT in East China.
2020, 37(12): 1338-1358.
doi: 10.1007/s00376-020-0021-y
Abstract:
Utilizing three different sets of reanalysis data, this study examines the long- and short-lived observed positive North Atlantic Oscillation (NAO) events (referred to as NAO+_LE and NAO+_SE) and long- and short-lived observed negative NAO events (referred to as NAO−_LE and NAO−_SE). Composite results indicate that the NAO-like circulation anomalies associated with the long-lived NAO events can reach the stratosphere, while they are primarily confined to the troposphere in the short-lived NAO events. Thus, the coupling/connection of stratospheric and tropospheric circulation anomalies is much better (worse) in the long-lived (short-lived) NAO events. A series of modified stratospheric initial-value experiments conducted with a simplified model indicate that a better (worse) connection between stratospheric and tropospheric circulation anomalies in the initial-value fields tend to gradually induce the NAO-like tropospheric circulation anomalies in the troposphere on the subsequent days, and thus naturally elongate (reduce) the lifetimes of the original NAO events by altering the tropospheric synoptic eddy vorticity flux over the North Atlantic region.
Utilizing three different sets of reanalysis data, this study examines the long- and short-lived observed positive North Atlantic Oscillation (NAO) events (referred to as NAO+_LE and NAO+_SE) and long- and short-lived observed negative NAO events (referred to as NAO−_LE and NAO−_SE). Composite results indicate that the NAO-like circulation anomalies associated with the long-lived NAO events can reach the stratosphere, while they are primarily confined to the troposphere in the short-lived NAO events. Thus, the coupling/connection of stratospheric and tropospheric circulation anomalies is much better (worse) in the long-lived (short-lived) NAO events. A series of modified stratospheric initial-value experiments conducted with a simplified model indicate that a better (worse) connection between stratospheric and tropospheric circulation anomalies in the initial-value fields tend to gradually induce the NAO-like tropospheric circulation anomalies in the troposphere on the subsequent days, and thus naturally elongate (reduce) the lifetimes of the original NAO events by altering the tropospheric synoptic eddy vorticity flux over the North Atlantic region.
2020, 37(12): 1359-1374.
doi: 10.1007/s00376-020-0035-5
Abstract:
With the increasing availability of precipitation radar data from space, enhancement of the resolution of spaceborne precipitation observations is important, particularly for hazard prediction and climate modeling at local scales relevant to extreme precipitation intensities and gradients. In this paper, the statistical characteristics of radar precipitation reflectivity data are studied and modeled using a hidden Markov tree (HMT) in the wavelet domain. Then, a high-resolution interpolation algorithm is proposed for spaceborne radar reflectivity using the HMT model as prior information. Owing to the small and transient storm elements embedded in the larger and slowly varying elements, the radar precipitation data exhibit distinct multiscale statistical properties, including a non-Gaussian structure and scale-to-scale dependency. An HMT model can capture well the statistical properties of radar precipitation, where the wavelet coefficients in each sub-band are characterized as a Gaussian mixture model (GMM), and the wavelet coefficients from the coarse scale to fine scale are described using a multiscale Markov process. The state probabilities of the GMM are determined using the expectation maximization method, and other parameters, for instance, the variance decay parameters in the HMT model are learned and estimated from high-resolution ground radar reflectivity images. Using the prior model, the wavelet coefficients at finer scales are estimated using local Wiener filtering. The interpolation algorithm is validated using data from the precipitation radar onboard the Tropical Rainfall Measurement Mission satellite, and the reconstructed results are found to be able to enhance the spatial resolution while optimally reproducing the local extremes and gradients.
With the increasing availability of precipitation radar data from space, enhancement of the resolution of spaceborne precipitation observations is important, particularly for hazard prediction and climate modeling at local scales relevant to extreme precipitation intensities and gradients. In this paper, the statistical characteristics of radar precipitation reflectivity data are studied and modeled using a hidden Markov tree (HMT) in the wavelet domain. Then, a high-resolution interpolation algorithm is proposed for spaceborne radar reflectivity using the HMT model as prior information. Owing to the small and transient storm elements embedded in the larger and slowly varying elements, the radar precipitation data exhibit distinct multiscale statistical properties, including a non-Gaussian structure and scale-to-scale dependency. An HMT model can capture well the statistical properties of radar precipitation, where the wavelet coefficients in each sub-band are characterized as a Gaussian mixture model (GMM), and the wavelet coefficients from the coarse scale to fine scale are described using a multiscale Markov process. The state probabilities of the GMM are determined using the expectation maximization method, and other parameters, for instance, the variance decay parameters in the HMT model are learned and estimated from high-resolution ground radar reflectivity images. Using the prior model, the wavelet coefficients at finer scales are estimated using local Wiener filtering. The interpolation algorithm is validated using data from the precipitation radar onboard the Tropical Rainfall Measurement Mission satellite, and the reconstructed results are found to be able to enhance the spatial resolution while optimally reproducing the local extremes and gradients.
2020, 37(12): 1375-1388.
doi: 10.1007/s00376-020-0002-1
Abstract:
In this study, the impacts of the tropical Pacific–Indian Ocean associated mode (PIOAM) on Madden–Julian Oscillation (MJO) activity were investigated using reanalysis data. In the positive (negative) phase of the PIOAM, the amplitudes of MJO zonal wind and outgoing longwave radiation are significantly weakened (enhanced) over the Indian Ocean, while they are enhanced (weakened) over the central and eastern Pacific. The eastward propagation of the MJO can extend to the central Pacific in the positive phase of the PIOAM, whereas it is mainly confined to west of 160°E in the negative phase. The PIOAM impacts MJO activity by modifying the atmospheric circulation and moisture budget. Anomalous ascending (descending) motion and positive (negative) moisture anomalies occur over the western Indian Ocean and central-eastern Pacific (Maritime Continent and western Pacific) during the positive phase of the PIOAM. The anomalous circulation is almost the opposite in the negative phases of the PIOAM. This anomalous circulation and moisture can modulate the activity of the MJO. The stronger moistening over the Indian Ocean induced by zonal and vertical moisture advection leads to the stronger MJO activity over the Indian Ocean in the negative phase of the PIOAM. During the positive phase of the PIOAM, the MJO propagates farther east over the central Pacific owing to the stronger moistening there, which is mainly attributable to the meridional and vertical moisture advection, especially low-frequency background state moisture advection by the MJO’s meridional and vertical velocities.
In this study, the impacts of the tropical Pacific–Indian Ocean associated mode (PIOAM) on Madden–Julian Oscillation (MJO) activity were investigated using reanalysis data. In the positive (negative) phase of the PIOAM, the amplitudes of MJO zonal wind and outgoing longwave radiation are significantly weakened (enhanced) over the Indian Ocean, while they are enhanced (weakened) over the central and eastern Pacific. The eastward propagation of the MJO can extend to the central Pacific in the positive phase of the PIOAM, whereas it is mainly confined to west of 160°E in the negative phase. The PIOAM impacts MJO activity by modifying the atmospheric circulation and moisture budget. Anomalous ascending (descending) motion and positive (negative) moisture anomalies occur over the western Indian Ocean and central-eastern Pacific (Maritime Continent and western Pacific) during the positive phase of the PIOAM. The anomalous circulation is almost the opposite in the negative phases of the PIOAM. This anomalous circulation and moisture can modulate the activity of the MJO. The stronger moistening over the Indian Ocean induced by zonal and vertical moisture advection leads to the stronger MJO activity over the Indian Ocean in the negative phase of the PIOAM. During the positive phase of the PIOAM, the MJO propagates farther east over the central Pacific owing to the stronger moistening there, which is mainly attributable to the meridional and vertical moisture advection, especially low-frequency background state moisture advection by the MJO’s meridional and vertical velocities.
2020, 37(12): 1389-1404.
doi: 10.1007/s00376-020-0107-6
Abstract:
In February 2019, a month-long persistent precipitation event occurred in the Yangtze–Huaihe River basin. The geopotential height field that affected the duration of this frontal rainfall was divided into a high-latitude part and a low-latitude part for analysis. In the high-latitude part, a two-wave structure led to quasi-stationary circulation, and the change of both the blocking high pressure and Arctic Oscillation phase caused cold air to invade South China continuously and changed the frontal position. In mid-to-low latitudes, the persistent precipitation showed quasi-biweekly oscillation characteristics. The so-called “subtropical high–precipitation–anticyclone” (SHPA) feedback mechanism blocked the circulation systems in the mid-to-low latitudes and provided a continuous supply of water vapor for precipitation. As for the effect of sea surface temperature, the western North Pacific anomalous anticyclone stimulated by El Niño strengthened the intensity of the southerly wind and provided support for the redevelopment of the anticyclone system in the SHPA feedback mechanism. The sea surface temperature anomaly in the South China Sea provided sensible heating for precipitation, and convergent rising airflow was conducive to the occurrence of precipitation. Additionally, the SHPA mechanism provides a reliable basis for the prediction of persistent precipitation in winter in the mid-to-low latitudes.
In February 2019, a month-long persistent precipitation event occurred in the Yangtze–Huaihe River basin. The geopotential height field that affected the duration of this frontal rainfall was divided into a high-latitude part and a low-latitude part for analysis. In the high-latitude part, a two-wave structure led to quasi-stationary circulation, and the change of both the blocking high pressure and Arctic Oscillation phase caused cold air to invade South China continuously and changed the frontal position. In mid-to-low latitudes, the persistent precipitation showed quasi-biweekly oscillation characteristics. The so-called “subtropical high–precipitation–anticyclone” (SHPA) feedback mechanism blocked the circulation systems in the mid-to-low latitudes and provided a continuous supply of water vapor for precipitation. As for the effect of sea surface temperature, the western North Pacific anomalous anticyclone stimulated by El Niño strengthened the intensity of the southerly wind and provided support for the redevelopment of the anticyclone system in the SHPA feedback mechanism. The sea surface temperature anomaly in the South China Sea provided sensible heating for precipitation, and convergent rising airflow was conducive to the occurrence of precipitation. Additionally, the SHPA mechanism provides a reliable basis for the prediction of persistent precipitation in winter in the mid-to-low latitudes.
2020, 37(12): 1405-1416.
doi: 10.1007/s00376-020-0098-3
Abstract:
Using rain-gauge-observation daily precipitation data from the Global Historical Climatology Network (V3.25) and the Chinese Surface Daily Climate Dataset (V3.0), this study investigates the fidelity of the AHPRODITE dataset in representing extreme precipitation, in terms of the extreme precipitation threshold value, occurrence number, probability of detection, and extremal dependence index during the cool (October to April) and warm (May to September) seasons in Central Asia during 1961–90. The distribution of extreme precipitation is characterized by large extreme precipitation threshold values and high occurrence numbers over the mountainous areas. The APHRODITE dataset is highly correlated with the gauge-observation precipitation data and can reproduce the spatial distributions of the extreme precipitation threshold value and total occurrence number. However, APHRODITE generally underestimates the extreme precipitation threshold values, while it overestimates the total numbers of extreme precipitation events, particularly over the mountainous areas. These biases can be attributed to the overestimation of light rainfall and the underestimation of heavy rainfall induced by the rainfall distribution–based interpolation. Such deficits are more evident for the warm season than the cool season, and thus the biases are more pronounced in the warm season than in the cool season. The probability of detection and extremal dependence index reveal that APHRODITE has a good capability of detecting extreme precipitation, particularly in the cool season.
Using rain-gauge-observation daily precipitation data from the Global Historical Climatology Network (V3.25) and the Chinese Surface Daily Climate Dataset (V3.0), this study investigates the fidelity of the AHPRODITE dataset in representing extreme precipitation, in terms of the extreme precipitation threshold value, occurrence number, probability of detection, and extremal dependence index during the cool (October to April) and warm (May to September) seasons in Central Asia during 1961–90. The distribution of extreme precipitation is characterized by large extreme precipitation threshold values and high occurrence numbers over the mountainous areas. The APHRODITE dataset is highly correlated with the gauge-observation precipitation data and can reproduce the spatial distributions of the extreme precipitation threshold value and total occurrence number. However, APHRODITE generally underestimates the extreme precipitation threshold values, while it overestimates the total numbers of extreme precipitation events, particularly over the mountainous areas. These biases can be attributed to the overestimation of light rainfall and the underestimation of heavy rainfall induced by the rainfall distribution–based interpolation. Such deficits are more evident for the warm season than the cool season, and thus the biases are more pronounced in the warm season than in the cool season. The probability of detection and extremal dependence index reveal that APHRODITE has a good capability of detecting extreme precipitation, particularly in the cool season.
2020, 37(12): 1417-1434.
doi: 10.1007/s00376-020-0070-2
Abstract:
Under recent Arctic warming, boreal winters have witnessed severe cold surges over both Eurasia and North America, bringing about serious social and economic impacts. Here, we investigated the changes in daily surface air temperature (SAT) variability during the rapid Arctic warming period of 1988/89–2015/16, and found the daily SAT variance, mainly contributed by the sub-seasonal component, shows an increasing and decreasing trend over eastern Eurasia and North America, respectively. Increasing cold extremes (defined as days with daily SAT anomalies below 1.5 standard deviations) dominated the increase of the daily SAT variability over eastern Eurasia, while decreasing cold extremes dominated the decrease of the daily SAT variability over North America. The circulation regime of cold extremes over eastern Eurasia (North America) is characterized by an enhanced high-pressure ridge over the Urals (Alaska) and surface Siberian (Canadian) high. The data analyses and model simulations show the recent strengthening of the high-pressure ridge over the Urals was associated with warming of the Barents–Kara seas in the Arctic region, while the high-pressure ridge over Alaska was influenced by the offset effect of Arctic warming over the East Siberian–Chukchi seas and the Pacific decadal oscillation (PDO)–like sea surface temperature (SST) anomalies over the North Pacific. The transition of the PDO-like SST anomalies from a positive to negative phase cancelled the impact of Arctic warming, reduced the occurrence of extreme cold days, and possibly resulted in the decreasing trend of daily SAT variability in North America. The multi-ensemble simulations of climate models confirmed the regional Arctic warming as the driver of the increasing SAT variance over eastern Eurasia and North America and the overwhelming effect of SST forcing on the decreasing SAT variance over North America. Therefore, the regional response of winter cold extremes at midlatitudes to the Arctic warming could be different due to the distinct impact of decadal SST anomalies.
Under recent Arctic warming, boreal winters have witnessed severe cold surges over both Eurasia and North America, bringing about serious social and economic impacts. Here, we investigated the changes in daily surface air temperature (SAT) variability during the rapid Arctic warming period of 1988/89–2015/16, and found the daily SAT variance, mainly contributed by the sub-seasonal component, shows an increasing and decreasing trend over eastern Eurasia and North America, respectively. Increasing cold extremes (defined as days with daily SAT anomalies below 1.5 standard deviations) dominated the increase of the daily SAT variability over eastern Eurasia, while decreasing cold extremes dominated the decrease of the daily SAT variability over North America. The circulation regime of cold extremes over eastern Eurasia (North America) is characterized by an enhanced high-pressure ridge over the Urals (Alaska) and surface Siberian (Canadian) high. The data analyses and model simulations show the recent strengthening of the high-pressure ridge over the Urals was associated with warming of the Barents–Kara seas in the Arctic region, while the high-pressure ridge over Alaska was influenced by the offset effect of Arctic warming over the East Siberian–Chukchi seas and the Pacific decadal oscillation (PDO)–like sea surface temperature (SST) anomalies over the North Pacific. The transition of the PDO-like SST anomalies from a positive to negative phase cancelled the impact of Arctic warming, reduced the occurrence of extreme cold days, and possibly resulted in the decreasing trend of daily SAT variability in North America. The multi-ensemble simulations of climate models confirmed the regional Arctic warming as the driver of the increasing SAT variance over eastern Eurasia and North America and the overwhelming effect of SST forcing on the decreasing SAT variance over North America. Therefore, the regional response of winter cold extremes at midlatitudes to the Arctic warming could be different due to the distinct impact of decadal SST anomalies.
2020, 37(12): 1435-1450.
doi: 10.1007/s00376-020-0144-1
Abstract:
Based on the reforecast data (1999–2010) of three operational models [the China Meteorological Administration (CMA), the National Centers for Environmental Prediction of the U.S. (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF)] that participated in the Subseasonal to Seasonal Prediction (S2S) project, we identified the major sources of subseasonal prediction skill for heatwaves over the Yangtze River basin (YRB). The three models show limited prediction skills in terms of the fraction of correct predictions for heatwave days in summer; the Heidke Skill Score drops quickly after a 5-day forecast lead and falls down close to zero beyond the lead time of 15 days. The superior skill of the ECMWF model in predicting the intensity and duration of the YRB heatwave is attributable to its fidelity in capturing the phase evolution and amplitude of high-pressure anomalies associated with the intraseasonal oscillation and the dryness of soil moisture induced by less precipitation via the land–atmosphere coupling. The effects of 10–30-day and 30–90-day circulation prediction skills on heatwave predictions are comparable at shorter forecast leads (10 days), while the biases in 30–90-day circulation amplitude prediction show close connection with the degradation of heatwave prediction skill at longer forecast leads (> 15–20 days). The biases of intraseasonal circulation anomalies further affect precipitation anomalies and thus land conditions, causing difficulty in capturing extremely hot days and their persistence in the S2S models.
Based on the reforecast data (1999–2010) of three operational models [the China Meteorological Administration (CMA), the National Centers for Environmental Prediction of the U.S. (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF)] that participated in the Subseasonal to Seasonal Prediction (S2S) project, we identified the major sources of subseasonal prediction skill for heatwaves over the Yangtze River basin (YRB). The three models show limited prediction skills in terms of the fraction of correct predictions for heatwave days in summer; the Heidke Skill Score drops quickly after a 5-day forecast lead and falls down close to zero beyond the lead time of 15 days. The superior skill of the ECMWF model in predicting the intensity and duration of the YRB heatwave is attributable to its fidelity in capturing the phase evolution and amplitude of high-pressure anomalies associated with the intraseasonal oscillation and the dryness of soil moisture induced by less precipitation via the land–atmosphere coupling. The effects of 10–30-day and 30–90-day circulation prediction skills on heatwave predictions are comparable at shorter forecast leads (10 days), while the biases in 30–90-day circulation amplitude prediction show close connection with the degradation of heatwave prediction skill at longer forecast leads (> 15–20 days). The biases of intraseasonal circulation anomalies further affect precipitation anomalies and thus land conditions, causing difficulty in capturing extremely hot days and their persistence in the S2S models.
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