2021 Vol. 26, No. 5
Display Method:
2021, 26(5): 471-481.
doi: 10.3878/j.issn.1006-9585.2020.19156
Abstract:
Qinhuangdao is an important port city located in the northeast of Hebei province, China. In recent years, emission reductions were effective in improving the air quality over the North China Plain. However, a heavy PM2.5 pollution event appeared in Qinhuangdao area in January. (In contrast, continuous pollution that contained PM2.5 particles was evident during January 2019 at Qinhuangdao.) In this paper, the regional air quality model RAMS-CMAQ coupled with the integrated source apportionment method (ISAM) was employed to simulate the PM2.5 pollution process and analyze the impact of local sources on PM2.5 mass concentration. Two periods were defined: (1) Clean period in which the PM2.5 mass concentration over Qinhuangdao was below 75 μg m−3 and (2) pollution period in which the PM2.5 mass concentration over Qinhuangdao was above 75 μg m−3. Contributions of local emission sources to PM2.5 mass concentration between the two periods were then compared, and the regional transport pattern of PM2.5 particles from different areas to four air quality monitoring sites was evaluated. The results showed that a high PM2.5 mass burden was distributed in the south part of Qinhuangdao. During the clean period, PM2.5 concentration was greatly contributed by local areas, with the contribution of 40%–50% in most areas of Qinglong and Lulong, and more than 60% in most areas of Haigang, Funing, Beidaihe, Tiiguan, and Changli.(Local emission sources of Qinglong greatly contributed to the PM2.5 mass burden. Lulong contributed 40%–50% and Haigang, Funing, Beidaihe, Diyiguan, and Changli together contributed more than 60% of the PM2.5 mass burden.) Regional transport during the clean period accounted for 34.7%–41.6% of the concentration of PM2.5 at the four monitoring sites. Compared with the clean period, the PM2.5 mass concentration from local areas decreased by approximately 10% and the effect of regional transport increased during the pollution period. Among the four monitoring sites, the PM2.5 concentration contributed by regional transport at Beidaihe and Diyiguan sites decreased by 1.0% and 2.3%, respectively while the contribution from surrounding regions increased by 2.9% and 2.0% at Shijiance and Jianshedasha sites, respectively.
Qinhuangdao is an important port city located in the northeast of Hebei province, China. In recent years, emission reductions were effective in improving the air quality over the North China Plain. However, a heavy PM2.5 pollution event appeared in Qinhuangdao area in January. (In contrast, continuous pollution that contained PM2.5 particles was evident during January 2019 at Qinhuangdao.) In this paper, the regional air quality model RAMS-CMAQ coupled with the integrated source apportionment method (ISAM) was employed to simulate the PM2.5 pollution process and analyze the impact of local sources on PM2.5 mass concentration. Two periods were defined: (1) Clean period in which the PM2.5 mass concentration over Qinhuangdao was below 75 μg m−3 and (2) pollution period in which the PM2.5 mass concentration over Qinhuangdao was above 75 μg m−3. Contributions of local emission sources to PM2.5 mass concentration between the two periods were then compared, and the regional transport pattern of PM2.5 particles from different areas to four air quality monitoring sites was evaluated. The results showed that a high PM2.5 mass burden was distributed in the south part of Qinhuangdao. During the clean period, PM2.5 concentration was greatly contributed by local areas, with the contribution of 40%–50% in most areas of Qinglong and Lulong, and more than 60% in most areas of Haigang, Funing, Beidaihe, Tiiguan, and Changli.(Local emission sources of Qinglong greatly contributed to the PM2.5 mass burden. Lulong contributed 40%–50% and Haigang, Funing, Beidaihe, Diyiguan, and Changli together contributed more than 60% of the PM2.5 mass burden.) Regional transport during the clean period accounted for 34.7%–41.6% of the concentration of PM2.5 at the four monitoring sites. Compared with the clean period, the PM2.5 mass concentration from local areas decreased by approximately 10% and the effect of regional transport increased during the pollution period. Among the four monitoring sites, the PM2.5 concentration contributed by regional transport at Beidaihe and Diyiguan sites decreased by 1.0% and 2.3%, respectively while the contribution from surrounding regions increased by 2.9% and 2.0% at Shijiance and Jianshedasha sites, respectively.
2021, 26(5): 482-492.
doi: 10.3878/j.issn.1006-9585.2021.20046
Abstract:
In this paper, based on historical and global warming scenarios and observed sea temperature data in the Coupled Model Intercomparison Project Phase 5 (CMIP5) multimodel, changes in the space–time characteristics of the Pacific Decadal Oscillation (PDO) were analyzed. Empirical orthogonal function, regression, and power spectrum analyses were used to study the winter sea–air coupled system in the North Pacific region. Comparing the spatiotemporal characteristics of the PDO from the observed data for the periods 1850–1934 and 1934–2017, we found that, under the background of global warming, the intensity of the PDO is strengthened, and the frequency of the PDO mode is shifted to high frequency (the period is shorter). Then Taylor diagram analysis and power spectrum analysis were used to evaluate the simulation ability of 13 CMIP5 models of the PDO in the 20th century. On this basis, nine well-evaluated models were selected to compare and analyze the spatial and temporal characteristics of the PDO under different warming scenarios. The response of PDO to global warming in the model was consistent with the observed results.
In this paper, based on historical and global warming scenarios and observed sea temperature data in the Coupled Model Intercomparison Project Phase 5 (CMIP5) multimodel, changes in the space–time characteristics of the Pacific Decadal Oscillation (PDO) were analyzed. Empirical orthogonal function, regression, and power spectrum analyses were used to study the winter sea–air coupled system in the North Pacific region. Comparing the spatiotemporal characteristics of the PDO from the observed data for the periods 1850–1934 and 1934–2017, we found that, under the background of global warming, the intensity of the PDO is strengthened, and the frequency of the PDO mode is shifted to high frequency (the period is shorter). Then Taylor diagram analysis and power spectrum analysis were used to evaluate the simulation ability of 13 CMIP5 models of the PDO in the 20th century. On this basis, nine well-evaluated models were selected to compare and analyze the spatial and temporal characteristics of the PDO under different warming scenarios. The response of PDO to global warming in the model was consistent with the observed results.
2021, 26(5): 493-508.
doi: 10.3878/j.issn.1006-9585.2020.20052
Abstract:
Based on the precipitation observation data of 46 stations in the northern China monsoon region (NCMR) and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis datasets, the midsummer rainfall patterns in the NCMR during 1961–2019 are objectively classified into four typical categories: pattern A, pattern B, pattern C, and pattern D. Pattern A (B) corresponds to the positive (negative) anomaly midsummer precipitation in the whole area, while pattern C (D) corresponds to positive (negative) anomaly precipitation in North China (Northeast China). The characteristics of the four rainfall patterns, atmospheric circulation, sea surface temperature (SST), and precursors are investigated with EOF and composite analysis along with other methods. The major circulation characteristics and the evolution of SST, resulting in the four rainfall patterns, are different. Pattern A: The East Asia subtropical westerly jet stream (EAJS) shows a northward trend relative to its normal position. The ridgeline of the western Pacific subtropical high (WPSH) is located to the north of its normal position, accompanying stronger southerly winds. The zonal circulation is dominant in the middle and high latitudes of Eurasia. There are significant convergences (divergences) at the low (high) level in the troposphere and cold and warm air convergence in the NCMR. The negative phase of the North Atlantic SST tripole and the eastern pattern of La Niña develop from the previous winter to summer. Meanwhile, the characteristics of atmospheric circulation and SST evolution of pattern B are basically opposite to those of pattern A. Pattern C (D): EAJS shows an anomalous northward (slight southward) trend relative to its normal position. The longitudinal position of WPSH is located westward (eastward), and their ridgelines both tend to be northward of the normal position. North China is controlled by stronger southwest (northwest) winds, while northeast (southeast) winds prevail over northeast China. The lower troposphere converges (diverges) and the upper troposphere diverges (converges) in North China, while the lower troposphere diverges (converges) and the upper troposphere converges (diverges) in northeast China, accompanying a weak (active) northeast cold vortex. Meanwhile, the warm SST transforms to cold SST (El Niño pattern gradually forms) in the middle eastern tropical Pacific.
Based on the precipitation observation data of 46 stations in the northern China monsoon region (NCMR) and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis datasets, the midsummer rainfall patterns in the NCMR during 1961–2019 are objectively classified into four typical categories: pattern A, pattern B, pattern C, and pattern D. Pattern A (B) corresponds to the positive (negative) anomaly midsummer precipitation in the whole area, while pattern C (D) corresponds to positive (negative) anomaly precipitation in North China (Northeast China). The characteristics of the four rainfall patterns, atmospheric circulation, sea surface temperature (SST), and precursors are investigated with EOF and composite analysis along with other methods. The major circulation characteristics and the evolution of SST, resulting in the four rainfall patterns, are different. Pattern A: The East Asia subtropical westerly jet stream (EAJS) shows a northward trend relative to its normal position. The ridgeline of the western Pacific subtropical high (WPSH) is located to the north of its normal position, accompanying stronger southerly winds. The zonal circulation is dominant in the middle and high latitudes of Eurasia. There are significant convergences (divergences) at the low (high) level in the troposphere and cold and warm air convergence in the NCMR. The negative phase of the North Atlantic SST tripole and the eastern pattern of La Niña develop from the previous winter to summer. Meanwhile, the characteristics of atmospheric circulation and SST evolution of pattern B are basically opposite to those of pattern A. Pattern C (D): EAJS shows an anomalous northward (slight southward) trend relative to its normal position. The longitudinal position of WPSH is located westward (eastward), and their ridgelines both tend to be northward of the normal position. North China is controlled by stronger southwest (northwest) winds, while northeast (southeast) winds prevail over northeast China. The lower troposphere converges (diverges) and the upper troposphere diverges (converges) in North China, while the lower troposphere diverges (converges) and the upper troposphere converges (diverges) in northeast China, accompanying a weak (active) northeast cold vortex. Meanwhile, the warm SST transforms to cold SST (El Niño pattern gradually forms) in the middle eastern tropical Pacific.
2021, 26(5): 509-518.
doi: 10.3878/j.issn.1006-9585.2021.20103
Abstract:
Planetary albedo is defined as the ratio of the reflected and incident shortwave solar radiation at the top of the atmosphere, and it is also a critical parameter for the surface energy budget and global climate change. To improve our understanding of the spatiotemporal characteristics of the global planetary albedo, this work decomposed the atmospheric and surface contributions of the global planetary albedo using the Clouds and the Earth’s Radiant Energy System data, derived the global planetary albedo trends from 2001 to 2018 using the Theil-Sen+Mann-Kendall method, and explored the driving factors of the planetary albedo in typical regions by the correlation analysis method. Results showed that: 1) In mid-low latitudes (<60°), the atmospheric contribution is the dominant factor to the planetary albedo (89.3%±5%), while in high latitudes (>60°), the surface contribution to the planetary albedo increases with latitude (29%±12%). The spatial distribution of the planetary albedo showed that the zonal variations of the planetary albedo were larger than the meridional variations. 2) A decreasing trend of −0.0002/a can be observed in the global planetary albedo from 2001 to 2018, and decreasing trends of −0.00015/a and −0.00004/a can be found in the surface and atmospheric contribution to the planetary albedo from 2001 to 2018, respectively. The decrease of the global planetary albedo can be largely explained by the decrease of the global cloud fraction. 3) For the atmospheric contribution to the planetary albedo, significant increasing trends can be found in regions of the Sahara Desert and other deserts, and significant decreasing trends can be found in the eastern Antarctic and other locations. For the surface contribution to the planetary albedo, significant increasing trends can be found in eastern Antarctica and other locations, and significant decreasing trends can be found in the Arctic Ocean and other locations. Moreover, the variation of the planetary albedo in typical regions can be effectively explained by the variation of the cloud fraction, snow cover, and the normalized difference vegetation index.
Planetary albedo is defined as the ratio of the reflected and incident shortwave solar radiation at the top of the atmosphere, and it is also a critical parameter for the surface energy budget and global climate change. To improve our understanding of the spatiotemporal characteristics of the global planetary albedo, this work decomposed the atmospheric and surface contributions of the global planetary albedo using the Clouds and the Earth’s Radiant Energy System data, derived the global planetary albedo trends from 2001 to 2018 using the Theil-Sen+Mann-Kendall method, and explored the driving factors of the planetary albedo in typical regions by the correlation analysis method. Results showed that: 1) In mid-low latitudes (<60°), the atmospheric contribution is the dominant factor to the planetary albedo (89.3%±5%), while in high latitudes (>60°), the surface contribution to the planetary albedo increases with latitude (29%±12%). The spatial distribution of the planetary albedo showed that the zonal variations of the planetary albedo were larger than the meridional variations. 2) A decreasing trend of −0.0002/a can be observed in the global planetary albedo from 2001 to 2018, and decreasing trends of −0.00015/a and −0.00004/a can be found in the surface and atmospheric contribution to the planetary albedo from 2001 to 2018, respectively. The decrease of the global planetary albedo can be largely explained by the decrease of the global cloud fraction. 3) For the atmospheric contribution to the planetary albedo, significant increasing trends can be found in regions of the Sahara Desert and other deserts, and significant decreasing trends can be found in the eastern Antarctic and other locations. For the surface contribution to the planetary albedo, significant increasing trends can be found in eastern Antarctica and other locations, and significant decreasing trends can be found in the Arctic Ocean and other locations. Moreover, the variation of the planetary albedo in typical regions can be effectively explained by the variation of the cloud fraction, snow cover, and the normalized difference vegetation index.
2021, 26(5): 519-531.
doi: 10.3878/j.issn.1006-9585.2021.20118
Abstract:
In this paper, the ambient circulation and thermal dynamics of a snowfall affecting Beijing and Hebei from 5 to 7 November 2015, were analyzed using the observed data of MICAPS (Meteorological Information Combine Analysis and Process System) and the reanalysis data, 0.25°×0.25° every 6 h, from the European Center for Medium-Range Weather Forecasts. The characteristics and causes of the snowfall were revealed. According to the analysis of the existing circulation, the snowfall was a "return-flow" snowfall under the superposition of two long wave troughs, one long wave ridge, and a series of short wave troughs. The development of a Siberian ridge at 500 hPa and a cyclonic vortex over Inner Mongolia and its weak trough forced the northerly cold air to meet the southwesterly warm air in Hebei. A low-pressure vortex developed at 700 hPa, which was the direct cause of the snowfall. At 500 hPa, several short wave troughs moved eastward, which made the rain and snow persist for an extended period. North China was affected by southerly warm and humid air flow at 700 hPa, easterly flow at 850 hPa, and the easterly wind at the bottom of ground high pressure combined with an inverted trough, which provided favorable conditions for vertical lift and water vapor transport. Upper and low-level jets formed, coupled with upper divergence and positive vorticity and vertical velocity fields, creating the dynamic conditions for snowfall. A moist environment and the convergence of water vapor in the lower layer provided abundant water vapor for snowfall.
In this paper, the ambient circulation and thermal dynamics of a snowfall affecting Beijing and Hebei from 5 to 7 November 2015, were analyzed using the observed data of MICAPS (Meteorological Information Combine Analysis and Process System) and the reanalysis data, 0.25°×0.25° every 6 h, from the European Center for Medium-Range Weather Forecasts. The characteristics and causes of the snowfall were revealed. According to the analysis of the existing circulation, the snowfall was a "return-flow" snowfall under the superposition of two long wave troughs, one long wave ridge, and a series of short wave troughs. The development of a Siberian ridge at 500 hPa and a cyclonic vortex over Inner Mongolia and its weak trough forced the northerly cold air to meet the southwesterly warm air in Hebei. A low-pressure vortex developed at 700 hPa, which was the direct cause of the snowfall. At 500 hPa, several short wave troughs moved eastward, which made the rain and snow persist for an extended period. North China was affected by southerly warm and humid air flow at 700 hPa, easterly flow at 850 hPa, and the easterly wind at the bottom of ground high pressure combined with an inverted trough, which provided favorable conditions for vertical lift and water vapor transport. Upper and low-level jets formed, coupled with upper divergence and positive vorticity and vertical velocity fields, creating the dynamic conditions for snowfall. A moist environment and the convergence of water vapor in the lower layer provided abundant water vapor for snowfall.
2021, 26(5): 532-540.
doi: 10.3878/j.issn.1006-9585.2021.20119
Abstract:
The temporal and spatial variation characteristics of the boundary layer height (BLH) and climate dryness and wetness factor (Aridity Index, AI) and their relationship in extremely arid areas are analyzed based on the ERA5 (the fifth generation reanalysis product of European Centre for Medium-Range Weather Forecasts) reanalysis data from 1950 to 2019. Results show that the BLH exhibits an average value of 695 m. The distribution characteristics of BLH are high in the east and low in the west. In addition, the BLH rises in the east and decreases in the west. The AI has an average value of 0.03. AI values increase in all directions, with the Tarim Basin and Central Gobi as the center of the low values. Meanwhile, the AI decreases in the east and increases in the west. In terms of temporal changes, the BLH fluctuates lower, and the AI increases from 1950 to 1964, 1965 to 1993, and 2010 to 2019; whereas, the BLH rises and the AI decreases from 1993 to 2009. The BLH and AI have opposite trends at different times in different substrata, with 80% of the years showing inverse phase changes. These two values are basically negatively correlated, with the strongest correlation in the western oasis subsurface, where the correlation coefficient reaches −0.79. With the rise of the BLH in the east and its decrease in the west, the AI shows that the scope of extreme arid areas has been moving eastward in the recent 70 years.
The temporal and spatial variation characteristics of the boundary layer height (BLH) and climate dryness and wetness factor (Aridity Index, AI) and their relationship in extremely arid areas are analyzed based on the ERA5 (the fifth generation reanalysis product of European Centre for Medium-Range Weather Forecasts) reanalysis data from 1950 to 2019. Results show that the BLH exhibits an average value of 695 m. The distribution characteristics of BLH are high in the east and low in the west. In addition, the BLH rises in the east and decreases in the west. The AI has an average value of 0.03. AI values increase in all directions, with the Tarim Basin and Central Gobi as the center of the low values. Meanwhile, the AI decreases in the east and increases in the west. In terms of temporal changes, the BLH fluctuates lower, and the AI increases from 1950 to 1964, 1965 to 1993, and 2010 to 2019; whereas, the BLH rises and the AI decreases from 1993 to 2009. The BLH and AI have opposite trends at different times in different substrata, with 80% of the years showing inverse phase changes. These two values are basically negatively correlated, with the strongest correlation in the western oasis subsurface, where the correlation coefficient reaches −0.79. With the rise of the BLH in the east and its decrease in the west, the AI shows that the scope of extreme arid areas has been moving eastward in the recent 70 years.
2021, 26(5): 541-555.
doi: 10.3878/j.issn.1006-9585.2021.20137
Abstract:
Based on the monthly cloud water content of the 20th-century reanalysis version 2c dataset, mathematical-statistical methods are employed to analyze the distribution and variation characteristics of the global cloud water content, including oceans and land from 1960 to 2014, and their relationships with water vapor flux. Results show that: 1) The global cloud water content is unevenly distributed spatially, with the oceans having a higher content than the land at a ratio of approximately 4﹕3. Variations in the trend of cloud water content over the middle and low latitude oceans and land are approximately 0.07 g m−2 (10 a)−1 and −0.04 g m−2 (10 a)−1, respectively. Seasonal differences are reflected mainly as high cloud water content in the Tropical Convergence Zone and the Southern Hemisphere oceans in summer, and the Northern Hemisphere oceans and the Southern Hemisphere land in winter. 2) South America, with the highest cloud water content, has the fastest increasing trend of 0.46 g m−2 (10 a)−1 whereas Africa, with the lowest cloud water content, has the fastest decreasing trend of −0.59 g m−2 (10 a)−1, as shown by a comparison of six continents. 3) The convergence and divergence zones of the water vapor flux divergence field in the middle and lower layers correspond to the high and low-value zones of cloud water content. The variation in the cloud water content and water vapor flux divergence presents a negative correlation, with a correlation coefficient of −0.44. The negative correlation is significant at low latitudes near the equator. Herein, the temporal and spatial patterns of the distribution and change in the cloud water content under the background of global warming are revealed, providing a reference for model parameterization and future climate prediction.
Based on the monthly cloud water content of the 20th-century reanalysis version 2c dataset, mathematical-statistical methods are employed to analyze the distribution and variation characteristics of the global cloud water content, including oceans and land from 1960 to 2014, and their relationships with water vapor flux. Results show that: 1) The global cloud water content is unevenly distributed spatially, with the oceans having a higher content than the land at a ratio of approximately 4﹕3. Variations in the trend of cloud water content over the middle and low latitude oceans and land are approximately 0.07 g m−2 (10 a)−1 and −0.04 g m−2 (10 a)−1, respectively. Seasonal differences are reflected mainly as high cloud water content in the Tropical Convergence Zone and the Southern Hemisphere oceans in summer, and the Northern Hemisphere oceans and the Southern Hemisphere land in winter. 2) South America, with the highest cloud water content, has the fastest increasing trend of 0.46 g m−2 (10 a)−1 whereas Africa, with the lowest cloud water content, has the fastest decreasing trend of −0.59 g m−2 (10 a)−1, as shown by a comparison of six continents. 3) The convergence and divergence zones of the water vapor flux divergence field in the middle and lower layers correspond to the high and low-value zones of cloud water content. The variation in the cloud water content and water vapor flux divergence presents a negative correlation, with a correlation coefficient of −0.44. The negative correlation is significant at low latitudes near the equator. Herein, the temporal and spatial patterns of the distribution and change in the cloud water content under the background of global warming are revealed, providing a reference for model parameterization and future climate prediction.
2021, 26(5): 556-568.
doi: 10.3878/j.issn.1006-9585.2021.20144
Abstract:
Using the precipitation samples from the northeast cold vortex in the Haihe River basin, based on the encrypted observation station data and precipitation forecast data produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), we conducted a sliding time window correlation analysis between 24-h precipitation observation and forecast series to re-establish the precipitation forecast series. Then, we matched the frequency with the gamma cumulative probability distribution curve of 24-h precipitation observation, re-established the 1–3-days short-term forecast series on each of the encrypted observation stations to enable correction of precipitation forecast in the Haihe River basin, and checked the forecast effect of the correction of precipitation forecast. The results showed that the period of ECMWF model precipitation forecast is mainly slower than that of the observation of the northeast cold vortex precipitation forecast in the Haihe River basin. The frequency matching method mainly improves forecasting skills by correcting the precipitation magnitude. TS (Threat Score) of the precipitation calibration forecast has been improved for light rain, heavy rain, and rainstorm. The magnitude and area of precipitation calibration forecast need to be close to the observation station data, particularly for the forecasting of heavy rain and rainstorm. However, the model has poor forecasting skills for convective precipitation caused by the northeast cold vortex. Thus, the precipitation-calibration forecast can effectively improve the forecasting skills for heavy precipitation, resulting in good business application prospects.
Using the precipitation samples from the northeast cold vortex in the Haihe River basin, based on the encrypted observation station data and precipitation forecast data produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), we conducted a sliding time window correlation analysis between 24-h precipitation observation and forecast series to re-establish the precipitation forecast series. Then, we matched the frequency with the gamma cumulative probability distribution curve of 24-h precipitation observation, re-established the 1–3-days short-term forecast series on each of the encrypted observation stations to enable correction of precipitation forecast in the Haihe River basin, and checked the forecast effect of the correction of precipitation forecast. The results showed that the period of ECMWF model precipitation forecast is mainly slower than that of the observation of the northeast cold vortex precipitation forecast in the Haihe River basin. The frequency matching method mainly improves forecasting skills by correcting the precipitation magnitude. TS (Threat Score) of the precipitation calibration forecast has been improved for light rain, heavy rain, and rainstorm. The magnitude and area of precipitation calibration forecast need to be close to the observation station data, particularly for the forecasting of heavy rain and rainstorm. However, the model has poor forecasting skills for convective precipitation caused by the northeast cold vortex. Thus, the precipitation-calibration forecast can effectively improve the forecasting skills for heavy precipitation, resulting in good business application prospects.
2021, 26(5): 569-582.
doi: 10.3878/j.issn.1006-9585.2021.20149
Abstract:
The spatial distribution and temporal variation of the Urban Heat Island Intensity (UHII) are analyzed by using the hourly surface air temperature (SAT) data from automatic weather stations during September 2012−August 2014 and January 2016−December 2017 over four cities, those are Changchun, Beijing, Wuhan, and Guangzhou. The annual mean UHII in built-up areas of these cities are found as 0.96°C, 1.06°C, 0.91°C, and 0.78°C, respectively. The UHII in northern cities is higher in spring and summer than in autumn and winter due to calm and inversion weather and higher anthropogenic heat release than in other seasons. However, the autumn UHII is the most obvious in southern cities beacause the clear sky and crisp air is conducive to the development of heat island, followed by the UHII of winter and summer, and the UHII difference among the four seasons is small. Besides, the diurnal variation of UHII in each city is characterized by a stronger UHII at night than during the day, with UHII beginning to decline (rise) in the early morning (afternoon). The amplitude of UHII diurnal variation is the largest in Wuhan due to several water bodies and the smallest in Guangzhou due to the mild climate. For the UHII diurnal variations, the steadily strong UHII period at night (day) is longer (shorter) in autumn and winter for the northern cities. The UHII difference between north and south climate zones has practical significance for urban planning and urban operations management.
The spatial distribution and temporal variation of the Urban Heat Island Intensity (UHII) are analyzed by using the hourly surface air temperature (SAT) data from automatic weather stations during September 2012−August 2014 and January 2016−December 2017 over four cities, those are Changchun, Beijing, Wuhan, and Guangzhou. The annual mean UHII in built-up areas of these cities are found as 0.96°C, 1.06°C, 0.91°C, and 0.78°C, respectively. The UHII in northern cities is higher in spring and summer than in autumn and winter due to calm and inversion weather and higher anthropogenic heat release than in other seasons. However, the autumn UHII is the most obvious in southern cities beacause the clear sky and crisp air is conducive to the development of heat island, followed by the UHII of winter and summer, and the UHII difference among the four seasons is small. Besides, the diurnal variation of UHII in each city is characterized by a stronger UHII at night than during the day, with UHII beginning to decline (rise) in the early morning (afternoon). The amplitude of UHII diurnal variation is the largest in Wuhan due to several water bodies and the smallest in Guangzhou due to the mild climate. For the UHII diurnal variations, the steadily strong UHII period at night (day) is longer (shorter) in autumn and winter for the northern cities. The UHII difference between north and south climate zones has practical significance for urban planning and urban operations management.
2021, 26(5): 583-590.
doi: 10.3878/j.issn.1006-9585.2021.21041
Abstract:
In order to explore the effect of precipitation distribution on the yield of single cropping late rice in Shanghai, Precipitation Concentration Degree (PCD) and Precipitation Concentration Period (PCP) were used to study the nonuniform distribution characteristics of precipitation during the whole growth period of single cropping late rice from 1971 to 2015. The trend analysis method was used to study the precipitation distribution in the whole growth period of single cropping late rice and the relationship between the precipitation and yield in each growth period. Based on the CMIP5 Global Climate Model scenario’s precipitation forecast data, the precipitation on meteorological yield of single cropping late rice in Shanghai from 2020 to 2045 was estimated. The results showed that the changing trend of PCD in single cropping late rice-growing season was insignificant in the recent 45 years, and the distribution of precipitation was uneven. The PCP mainly concentrated from 27 July to 11 September, the booting and heading stages. The precipitation at tillering, booting, heading, and maturity stages were accounted for 29.9%, 26.2%, 7.1%, and 10.8% of the total growth period, and the precipitation at tillering and booting stages was accounted for more than half of the total growth period. There was a significant negative correlation between the precipitation at the booting stage and the meteorological yield of single-season late rice (p<0.05). In the next 30 years, the negative effect of precipitation on the meteorological yield of single cropping late rice will be slightly larger than the positive effect. That is to say, the effect of reducing production will be greater than that of increasing production. The precipitation variation during the single cropping late rice will impact the yield in the future climate change scenario, and the corresponding adaptive measures should be formulated.
In order to explore the effect of precipitation distribution on the yield of single cropping late rice in Shanghai, Precipitation Concentration Degree (PCD) and Precipitation Concentration Period (PCP) were used to study the nonuniform distribution characteristics of precipitation during the whole growth period of single cropping late rice from 1971 to 2015. The trend analysis method was used to study the precipitation distribution in the whole growth period of single cropping late rice and the relationship between the precipitation and yield in each growth period. Based on the CMIP5 Global Climate Model scenario’s precipitation forecast data, the precipitation on meteorological yield of single cropping late rice in Shanghai from 2020 to 2045 was estimated. The results showed that the changing trend of PCD in single cropping late rice-growing season was insignificant in the recent 45 years, and the distribution of precipitation was uneven. The PCP mainly concentrated from 27 July to 11 September, the booting and heading stages. The precipitation at tillering, booting, heading, and maturity stages were accounted for 29.9%, 26.2%, 7.1%, and 10.8% of the total growth period, and the precipitation at tillering and booting stages was accounted for more than half of the total growth period. There was a significant negative correlation between the precipitation at the booting stage and the meteorological yield of single-season late rice (p<0.05). In the next 30 years, the negative effect of precipitation on the meteorological yield of single cropping late rice will be slightly larger than the positive effect. That is to say, the effect of reducing production will be greater than that of increasing production. The precipitation variation during the single cropping late rice will impact the yield in the future climate change scenario, and the corresponding adaptive measures should be formulated.
