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doi: 10.3878/j.issn.1006-9585.2021.221102
Abstract:
To adapt to climate change, China, as the country creating the highest levels of carbon emissions in the world, is formulating a carbon emission reduction plan. The plan under development considers many aspects of emission generation and proposes a goal of achieving carbon neutrality by 2060. With the ever-increasing globalization of trade, the carbon emissions produced locally in China are increasingly impacting other countries worldwide. Thus, the carbon policies negotiated as terms of trade between countries can greatly impact worldwide carbon emission levels. Similarly, setting the level of tariffs based on emission levels will bring about significant changes in the international trade for goods. Consequently, this will likely lead to changes in the treatment of emissions in import and export agreements. This article examines the trade conflicts between China and the United States. In doing so, it simulates the additional tariffs imposed by China and the United States and combines the results of the global trade analysis model and the input–output analysis method. It concludes by quantitatively analyzing the changes in China’s hidden import and export emissions under the proposed tariff changes. The article finds that after China and the United States have hypothetically imposed tariffs, although the trade between China and the United States and the emissions thereof would be greatly reduced, the emissions of China’s exports to the world nonetheless would have significantly increased. In addition, due to the impact of the import market, China’s emissions from imports worldwide would have been significantly reduced, which would lead to an increase in China’s net exports of carbon emissions after tariffs. These exports would be concentrated in energy-intensive industries. Based on a review of the current results, China continues to export a significant amount of emissions to the world despite applicable trade restrictions. Promoting the progress of the new energy industry through trade may more efficiently solve the problem of carbon emission reduction.
To adapt to climate change, China, as the country creating the highest levels of carbon emissions in the world, is formulating a carbon emission reduction plan. The plan under development considers many aspects of emission generation and proposes a goal of achieving carbon neutrality by 2060. With the ever-increasing globalization of trade, the carbon emissions produced locally in China are increasingly impacting other countries worldwide. Thus, the carbon policies negotiated as terms of trade between countries can greatly impact worldwide carbon emission levels. Similarly, setting the level of tariffs based on emission levels will bring about significant changes in the international trade for goods. Consequently, this will likely lead to changes in the treatment of emissions in import and export agreements. This article examines the trade conflicts between China and the United States. In doing so, it simulates the additional tariffs imposed by China and the United States and combines the results of the global trade analysis model and the input–output analysis method. It concludes by quantitatively analyzing the changes in China’s hidden import and export emissions under the proposed tariff changes. The article finds that after China and the United States have hypothetically imposed tariffs, although the trade between China and the United States and the emissions thereof would be greatly reduced, the emissions of China’s exports to the world nonetheless would have significantly increased. In addition, due to the impact of the import market, China’s emissions from imports worldwide would have been significantly reduced, which would lead to an increase in China’s net exports of carbon emissions after tariffs. These exports would be concentrated in energy-intensive industries. Based on a review of the current results, China continues to export a significant amount of emissions to the world despite applicable trade restrictions. Promoting the progress of the new energy industry through trade may more efficiently solve the problem of carbon emission reduction.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2021.21162
Abstract:
Spring frost is one of the most critical extreme events related with agriculture in northern temperate zone. In the context of climate change, research on the previous tendency and probable future change in the spring frost all over China can enhance people’s understanding, and it also has some reference value for adjustment of agricultural structure. Using a non-parametric method that is not sensitive to outliers and takes into account autocorrelation as the method of trend analysis, this study first analyzed the historical changes based on the meteorological observation data from 1960 to 2020. Then, based on the climate data simulated by 24 models in Coupled Model Intercomparison Project Phase 6 and the results of model evaluation, the future trend in spring frost from 2021 to 2100 was analyzed under the moderate radiative forcing scenario (SSP2-4.5), including the spatial distribution and national average anomalies compared with the 1991–2020 climatology. The main conclusions are summarized as follows: 1) From 1960 to 2020, the number of spring frost days of 60.3% stations across China showed a significantly decreasing trend (−3.5 – 0 d(10a)−1), and the last frost date of 40% stations showed a significant advancing trend (−4.3 – 0 d(10a)−1). Moreover, national-averaged anomalies for the number of spring frost days during 1960–2020 in China showed a significantly decreasing trend (−1.3 d(10a)−1), while those for the last frost date showed a significantly advancing trend (−1.7 d(10a)−1). 2) From 2021 to 2100, it is estimated that the number of spring frost days across China will decrease significantly (−1.6 – 0 d(10a)−1), and the last frost day will advance significantly (−1.4 – 0 d(10a)−1). In addition, national-averaged anomalies for the number of spring frost days in China will show a significantly decreasing trend at a rate of −0.8 d(10a)−1, and those for the last frost date will show a significantly advancing trend at a rate of −0.8 d(10a)−1.
Spring frost is one of the most critical extreme events related with agriculture in northern temperate zone. In the context of climate change, research on the previous tendency and probable future change in the spring frost all over China can enhance people’s understanding, and it also has some reference value for adjustment of agricultural structure. Using a non-parametric method that is not sensitive to outliers and takes into account autocorrelation as the method of trend analysis, this study first analyzed the historical changes based on the meteorological observation data from 1960 to 2020. Then, based on the climate data simulated by 24 models in Coupled Model Intercomparison Project Phase 6 and the results of model evaluation, the future trend in spring frost from 2021 to 2100 was analyzed under the moderate radiative forcing scenario (SSP2-4.5), including the spatial distribution and national average anomalies compared with the 1991–2020 climatology. The main conclusions are summarized as follows: 1) From 1960 to 2020, the number of spring frost days of 60.3% stations across China showed a significantly decreasing trend (−3.5 – 0 d(10a)−1), and the last frost date of 40% stations showed a significant advancing trend (−4.3 – 0 d(10a)−1). Moreover, national-averaged anomalies for the number of spring frost days during 1960–2020 in China showed a significantly decreasing trend (−1.3 d(10a)−1), while those for the last frost date showed a significantly advancing trend (−1.7 d(10a)−1). 2) From 2021 to 2100, it is estimated that the number of spring frost days across China will decrease significantly (−1.6 – 0 d(10a)−1), and the last frost day will advance significantly (−1.4 – 0 d(10a)−1). In addition, national-averaged anomalies for the number of spring frost days in China will show a significantly decreasing trend at a rate of −0.8 d(10a)−1, and those for the last frost date will show a significantly advancing trend at a rate of −0.8 d(10a)−1.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2021.21073
Abstract:
In recent decades, there has been a high frequency of droughts and floods in China, which affects the agricultural sector as it is more sensitive to climate changes than other industrial sectors. Therefore, this study aimed to evaluate the sensitivity, exposure, adaptability, and vulnerability of China’s agricultural ecosystem to droughts and floods from 1991 to 2019 via selected indicators and index weights determined by the analytic hierarchy process and entropy method. Results show that the drought-sensitive areas of the agricultural ecosystem in China are distributed in the central and southern provinces, such as Hubei and Hunan. The flood-sensitive areas are distributed in the coastal provinces of Hainan, Shanghai, and Jiangsu. Areas with high exposure to droughts and floods are found in the provinces of Gansu, Henan, and Heilongjiang. Areas with low adaptability to droughts and floods are found in the Southwestern provinces of Tibet, Chongqing, Guizhou, and Yunnan. Generally, the drought and flood vulnerability of the agricultural ecosystem in China tend to be weak from the central areas to the other areas, and Henan and Hubei belong to the central provinces with high vulnerability to droughts and floods. Therefore, to recede and adapt to the high vulnerability in the central provinces, the agricultural ecology structure in China should be adjusted according to local conditions, and measures like flexible planting and feeding and crop protection should be adopted to cope with droughts and floods.
In recent decades, there has been a high frequency of droughts and floods in China, which affects the agricultural sector as it is more sensitive to climate changes than other industrial sectors. Therefore, this study aimed to evaluate the sensitivity, exposure, adaptability, and vulnerability of China’s agricultural ecosystem to droughts and floods from 1991 to 2019 via selected indicators and index weights determined by the analytic hierarchy process and entropy method. Results show that the drought-sensitive areas of the agricultural ecosystem in China are distributed in the central and southern provinces, such as Hubei and Hunan. The flood-sensitive areas are distributed in the coastal provinces of Hainan, Shanghai, and Jiangsu. Areas with high exposure to droughts and floods are found in the provinces of Gansu, Henan, and Heilongjiang. Areas with low adaptability to droughts and floods are found in the Southwestern provinces of Tibet, Chongqing, Guizhou, and Yunnan. Generally, the drought and flood vulnerability of the agricultural ecosystem in China tend to be weak from the central areas to the other areas, and Henan and Hubei belong to the central provinces with high vulnerability to droughts and floods. Therefore, to recede and adapt to the high vulnerability in the central provinces, the agricultural ecology structure in China should be adjusted according to local conditions, and measures like flexible planting and feeding and crop protection should be adopted to cope with droughts and floods.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2021.21105
Abstract:
With the intensification of global climate change, extreme weather events will become more frequent, especially heatwaves, seriously affecting agroecosystems and human health. There have been many controversies about the definition of heatwave events, and understanding the spatial distribution characteristics of heatwave trends needs further improvement. Compared with definitions of absolute or relative temperature, this paper adopts a heatwave indicator that considers the daily temperature range and combines both absolute and relative temperature. The spatial distribution and temporal change characteristics of future heatwave events in China were evaluated based on the results of a multimodel ensemble of nine CMIP6 climate models under three different development scenarios: (1) SSP1-2.6, (2) SSP2-4.5, and (3) SSP5-8.5. Results show that (1) future heatwave events under the SSP1-2.6 scenario peaked around 2050 and then stabilized, while the frequency, days, and longest duration of heatwaves under the SSP2-4.5 scenario showed an increasing trend. The growth trend and severity of heatwaves under the SSP5-8.5 scenario are both the highest. (2) South China and Central China will face a greater risk of heatwave occurrence in the future. The frequency and intensity of heatwaves under the SSP5-8.5 scenario are about twice or more than those of SSP1-2.6, while those of SSP2-4.5 are about 1.5 times those of SSP1-2.6. (3) The occurrence of heatwaves of a larger scale in arid/semiarid regions in the west and arid regions in eastern Inner Mongolia, combined with the definition of heatwaves in this paper, predicts that nocturnal warming is an important feature of global warming. Results of the study help to understand the characteristics of future changes in the frequency and intensity of heatwaves in China under sustainable development and medium forcing scenarios and provide effective references for developing energy conservation and emission reduction programs for regional development.
With the intensification of global climate change, extreme weather events will become more frequent, especially heatwaves, seriously affecting agroecosystems and human health. There have been many controversies about the definition of heatwave events, and understanding the spatial distribution characteristics of heatwave trends needs further improvement. Compared with definitions of absolute or relative temperature, this paper adopts a heatwave indicator that considers the daily temperature range and combines both absolute and relative temperature. The spatial distribution and temporal change characteristics of future heatwave events in China were evaluated based on the results of a multimodel ensemble of nine CMIP6 climate models under three different development scenarios: (1) SSP1-2.6, (2) SSP2-4.5, and (3) SSP5-8.5. Results show that (1) future heatwave events under the SSP1-2.6 scenario peaked around 2050 and then stabilized, while the frequency, days, and longest duration of heatwaves under the SSP2-4.5 scenario showed an increasing trend. The growth trend and severity of heatwaves under the SSP5-8.5 scenario are both the highest. (2) South China and Central China will face a greater risk of heatwave occurrence in the future. The frequency and intensity of heatwaves under the SSP5-8.5 scenario are about twice or more than those of SSP1-2.6, while those of SSP2-4.5 are about 1.5 times those of SSP1-2.6. (3) The occurrence of heatwaves of a larger scale in arid/semiarid regions in the west and arid regions in eastern Inner Mongolia, combined with the definition of heatwaves in this paper, predicts that nocturnal warming is an important feature of global warming. Results of the study help to understand the characteristics of future changes in the frequency and intensity of heatwaves in China under sustainable development and medium forcing scenarios and provide effective references for developing energy conservation and emission reduction programs for regional development.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2021.21087
Abstract:
The daily grid precipitation data from 1961 to 2018 were obtained by interpolating data from more than 2400 national meteorological stations in China. Based on this data set, regression analysis, Morlet wavelet transform, and other methods are employed to analyze the spatial and temporal characteristics of the rainstorm and regional rainstorm in South China; moreover, the variation laws of heavy precipitation are revealed. Results show that from 1961 to 2018, the maximum number of annual rainstorm days and amount of rainstorm rainfall in South China are distributed in the coastal areas of Guangdong, Guangxi, and Fujian, as well as Hainan Province and the northern part of Guangxi. The number of rainstorm days and amount of rainfall are the largest in summer, followed by spring. From the northern part of Guangxi to the junction of Guangxi, Hunan, and Guangdong provinces, the southern part of Guangdong, Fujian, and Hainan provinces, the increasing trend of rainstorm days, rainfall, and intensity are the most significant. The regional mean increasing trend is the highest in summer, followed by autumn. Additionally, the number of regional rainstorm days and processes in South China presents the occurrence of a single peak distribution, which could occur throughout the year. Moreover, the maximum value appears in June. The annual average number of regional rainstorm days and processes are 28 d a−1 and 16.5 a−1, and the increasing rates are 0.15 d a−1 and 0.097 a−1. In four seasons, the increasing rate is the fastest during summer and the slowest in autumn. The average and maximum of a single course duration increase significantly at the rate of 0.015 d a−1 in winter but show a decreasing trend in spring. For the periodic change, the South China rainstorm and regional rainstorm show quasi-three-year, quasi-14-year, and quasi-18-year cycle changes to different degrees in annual and seasonal fluctuations. After 2000, the quasi-18-year long period and quasi-three-year short-period oscillations of annual rainstorms and regional rainstorms are extremely significant.
The daily grid precipitation data from 1961 to 2018 were obtained by interpolating data from more than 2400 national meteorological stations in China. Based on this data set, regression analysis, Morlet wavelet transform, and other methods are employed to analyze the spatial and temporal characteristics of the rainstorm and regional rainstorm in South China; moreover, the variation laws of heavy precipitation are revealed. Results show that from 1961 to 2018, the maximum number of annual rainstorm days and amount of rainstorm rainfall in South China are distributed in the coastal areas of Guangdong, Guangxi, and Fujian, as well as Hainan Province and the northern part of Guangxi. The number of rainstorm days and amount of rainfall are the largest in summer, followed by spring. From the northern part of Guangxi to the junction of Guangxi, Hunan, and Guangdong provinces, the southern part of Guangdong, Fujian, and Hainan provinces, the increasing trend of rainstorm days, rainfall, and intensity are the most significant. The regional mean increasing trend is the highest in summer, followed by autumn. Additionally, the number of regional rainstorm days and processes in South China presents the occurrence of a single peak distribution, which could occur throughout the year. Moreover, the maximum value appears in June. The annual average number of regional rainstorm days and processes are 28 d a−1 and 16.5 a−1, and the increasing rates are 0.15 d a−1 and 0.097 a−1. In four seasons, the increasing rate is the fastest during summer and the slowest in autumn. The average and maximum of a single course duration increase significantly at the rate of 0.015 d a−1 in winter but show a decreasing trend in spring. For the periodic change, the South China rainstorm and regional rainstorm show quasi-three-year, quasi-14-year, and quasi-18-year cycle changes to different degrees in annual and seasonal fluctuations. After 2000, the quasi-18-year long period and quasi-three-year short-period oscillations of annual rainstorms and regional rainstorms are extremely significant.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2021.21085
Abstract:
The Loess Plateau is highly vulnerable to floods and landslides. This paper aims to assess the flood risk in the Quchan River basin located east of the Loess Plateau. A FloodArea model developed by the Gemer company of Germany is seamlessly integrated with ArcGIS in a module form. The principle is based on a two-dimensional unsteady hydrodynamic model, and the calculation is based on a hydrodynamic method. Although rare in history, heavy precipitation struck the Quchan River basin from 4 August to 7 August 2020. The DEM (Digital Elevation Model), roughness, and hourly rainfall in the basin were used to run the FloodArea model. Under the rainstorm scene, an hourly flooding pattern was simulated at a 30-m high resolution. Moreover, land-use types were converted to roughness values because different land-use types have different roughness values. Results show that a flash flood risk is higher in low-lying river areas and gullies of the Quchan River basin than other areas. During this flooding, the maximum flooding depth at the survey site was 3.1 m, close to the observed flooding depth. For the disaster situations of the simulations, the population affected by the flood was 5475, the GDP was 36.15 million Yuan, as well as the disaster areas of cultivated and residential lands, were 20.7 and 0.7 km2, respectively. The affected GDP and land area were consistent with the disaster-related data collected from the survey, but the affected population was lower than those reflected in the survey were. This indicates that the FloodArea model is superior in simulating flooding situations and can be employed in the risk evaluation and early warning of rainstorm and flood disasters in the Quchan River basin.
The Loess Plateau is highly vulnerable to floods and landslides. This paper aims to assess the flood risk in the Quchan River basin located east of the Loess Plateau. A FloodArea model developed by the Gemer company of Germany is seamlessly integrated with ArcGIS in a module form. The principle is based on a two-dimensional unsteady hydrodynamic model, and the calculation is based on a hydrodynamic method. Although rare in history, heavy precipitation struck the Quchan River basin from 4 August to 7 August 2020. The DEM (Digital Elevation Model), roughness, and hourly rainfall in the basin were used to run the FloodArea model. Under the rainstorm scene, an hourly flooding pattern was simulated at a 30-m high resolution. Moreover, land-use types were converted to roughness values because different land-use types have different roughness values. Results show that a flash flood risk is higher in low-lying river areas and gullies of the Quchan River basin than other areas. During this flooding, the maximum flooding depth at the survey site was 3.1 m, close to the observed flooding depth. For the disaster situations of the simulations, the population affected by the flood was 5475, the GDP was 36.15 million Yuan, as well as the disaster areas of cultivated and residential lands, were 20.7 and 0.7 km2, respectively. The affected GDP and land area were consistent with the disaster-related data collected from the survey, but the affected population was lower than those reflected in the survey were. This indicates that the FloodArea model is superior in simulating flooding situations and can be employed in the risk evaluation and early warning of rainstorm and flood disasters in the Quchan River basin.
Vegetation Changes and Their Causes in the Yellow River Basin under the Background of Climate Change
, Available online ,
doi: 10.3878/j.issn.1006-9585.2021.21115
Abstract:
Understanding the spatiotemporal variations of vegetation in the Yellow River basin and their influencing factors is important to formulate policies for the construction of ecological civilization. Based on the MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation index (normalized difference vegetation index, NDVI) data and meteorological observations from 2001 to 2020, this study investigates the spatiotemporal evolution characteristics and driving factors of vegetation through the mean method, unary linear regression, partial correlation analysis, and multivariate residual trend analysis. The results show that the increased NDVI dominates most of the Yellow River basin but with large spatiotemporal variability. In particular, the largest increased NDVI approaches to 0.0496 per 10 years in the middle reaches of the Yellow River basin. In the growing season, areas with significantly positive NDVI increase mainly in the western and southeastern of the YELLOW RIVER BASIN, most evidently in irrigated areas along the Ningxia and Hetao plain. Both precipitation and temperature play an important role in the NDVI changes for most areas of the Yellow River basin. For the Yellow River basin as a whole, contributions from the precipitation and temperature to the NDVI change approach to 32.6% and 15.9%, respectively. Contributions from the precipitation are mainly found in the upper reaches (50.7%), while those from the temperature are mainly seen in the lower reaches (32.3%). On the other hand, human activities and climate change can account for 78% and 22% of the NDVI changes in the Yellow River basin, respectively. In particular, contributions from human activities are more than 80% in the central region of the Loess Plateau. Meanwhile, the drought is also a key driver to cause the increased NDVI changes in the Loess Plateau in central Gansu and the Hedong sand area (with a correlation of 0.6), which is especially higher in the upper reaches of the Yellow River basin.
Understanding the spatiotemporal variations of vegetation in the Yellow River basin and their influencing factors is important to formulate policies for the construction of ecological civilization. Based on the MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation index (normalized difference vegetation index, NDVI) data and meteorological observations from 2001 to 2020, this study investigates the spatiotemporal evolution characteristics and driving factors of vegetation through the mean method, unary linear regression, partial correlation analysis, and multivariate residual trend analysis. The results show that the increased NDVI dominates most of the Yellow River basin but with large spatiotemporal variability. In particular, the largest increased NDVI approaches to 0.0496 per 10 years in the middle reaches of the Yellow River basin. In the growing season, areas with significantly positive NDVI increase mainly in the western and southeastern of the YELLOW RIVER BASIN, most evidently in irrigated areas along the Ningxia and Hetao plain. Both precipitation and temperature play an important role in the NDVI changes for most areas of the Yellow River basin. For the Yellow River basin as a whole, contributions from the precipitation and temperature to the NDVI change approach to 32.6% and 15.9%, respectively. Contributions from the precipitation are mainly found in the upper reaches (50.7%), while those from the temperature are mainly seen in the lower reaches (32.3%). On the other hand, human activities and climate change can account for 78% and 22% of the NDVI changes in the Yellow River basin, respectively. In particular, contributions from human activities are more than 80% in the central region of the Loess Plateau. Meanwhile, the drought is also a key driver to cause the increased NDVI changes in the Loess Plateau in central Gansu and the Hedong sand area (with a correlation of 0.6), which is especially higher in the upper reaches of the Yellow River basin.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2021.21148
Abstract:
Considering the uniqueness and complexity of food security challenges caused by climate change, this article proposes a novel research idea and approach based on the junction of natural sciences and social sciences. Econometric models are used to perform statistical analysis on climate change data and econometric methods to assess the causal relationship between external climate drivers and observed climate change. The author clarifies the “impact of climate change” on food production and estimate China’s climate change risks on food production in the next 30 years, especially at the two key nodes of economic and social development in 2035 and 2050. The article provides a novel research perspective, as well as research content and methodology. The goal is to blend qualitative and quantitative research and provide strong support with scientific projections for policymaking.
Considering the uniqueness and complexity of food security challenges caused by climate change, this article proposes a novel research idea and approach based on the junction of natural sciences and social sciences. Econometric models are used to perform statistical analysis on climate change data and econometric methods to assess the causal relationship between external climate drivers and observed climate change. The author clarifies the “impact of climate change” on food production and estimate China’s climate change risks on food production in the next 30 years, especially at the two key nodes of economic and social development in 2035 and 2050. The article provides a novel research perspective, as well as research content and methodology. The goal is to blend qualitative and quantitative research and provide strong support with scientific projections for policymaking.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2021.21114
Abstract:
In this study, 14 contiguous poverty-stricken areas of China were selected as the study areas. The localized population parameters for the population development environment (PDE) model under different shared socio-economic paths (SSPs) were set by considering China’s population characteristics. Then, the population change characteristics of the 14 contiguous poverty-stricken areas from 2020 to 2040 were estimated based on the PDE model. Combined with the multimodel drought assessment results under shared socioeconomic pathway-representative concentration pathway (SSP–RCP) scenarios, the change characteristics of drought exposed population in the future were compared with a baseline period (1995–2014). The results show that: (1) the population of China’s contiguous poverty-stricken areas under SSP1, SSP4, and SSP5 scenarios show a downward trend; the population under the SSP2 remains stable and that under the SSP3 continues to grow; the population under each SSP scenario is projected to be 230–266 million in 2040, accounting for 16.7%–18.1% of China’s population. (2) In terms of the age structure, the proportion of the elderly population in 2040 under SSP1, SSP4, and SSP5 scenarios are relatively higher, and the number of freshmen is very small, which may result in an aging problem; the age structure is relatively balanced under the SSP2 scenario, the newborn population is high, and the working population is relatively large under the SSP3. (3) The annual average drought frequency and exposed population from 2020 to 2040 under all the SSP–RCP scenarios except for SSP3–7.0 are projected to increase compared with the baseline period; the spatial pattern of the change of population exposed to drought is relatively consistent under different SSP–RCP scenarios; more than 60% of the regions are projected to increase compared with the reference period, among which the increased range is the highest in the southwest and central regions. In contrast, the exposed population in Dabie Mountain Area is projected to decrease slightly. (4) Different age groups are affected by drought to varying degrees; children are more exposed to drought under the SSP3–7.0 scenario, while the elderly are more affected under the SSP5–8.5 scenario.
In this study, 14 contiguous poverty-stricken areas of China were selected as the study areas. The localized population parameters for the population development environment (PDE) model under different shared socio-economic paths (SSPs) were set by considering China’s population characteristics. Then, the population change characteristics of the 14 contiguous poverty-stricken areas from 2020 to 2040 were estimated based on the PDE model. Combined with the multimodel drought assessment results under shared socioeconomic pathway-representative concentration pathway (SSP–RCP) scenarios, the change characteristics of drought exposed population in the future were compared with a baseline period (1995–2014). The results show that: (1) the population of China’s contiguous poverty-stricken areas under SSP1, SSP4, and SSP5 scenarios show a downward trend; the population under the SSP2 remains stable and that under the SSP3 continues to grow; the population under each SSP scenario is projected to be 230–266 million in 2040, accounting for 16.7%–18.1% of China’s population. (2) In terms of the age structure, the proportion of the elderly population in 2040 under SSP1, SSP4, and SSP5 scenarios are relatively higher, and the number of freshmen is very small, which may result in an aging problem; the age structure is relatively balanced under the SSP2 scenario, the newborn population is high, and the working population is relatively large under the SSP3. (3) The annual average drought frequency and exposed population from 2020 to 2040 under all the SSP–RCP scenarios except for SSP3–7.0 are projected to increase compared with the baseline period; the spatial pattern of the change of population exposed to drought is relatively consistent under different SSP–RCP scenarios; more than 60% of the regions are projected to increase compared with the reference period, among which the increased range is the highest in the southwest and central regions. In contrast, the exposed population in Dabie Mountain Area is projected to decrease slightly. (4) Different age groups are affected by drought to varying degrees; children are more exposed to drought under the SSP3–7.0 scenario, while the elderly are more affected under the SSP5–8.5 scenario.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2021.21026
Abstract:
Based on the observational data from 1961 to 2013 and the minimum temperature data of 32 climate models in the Coupled Model Intercomparison Project Phase 6 (CMIP6), this paper evaluates the individual model and multi-model ensemble average ability to simulate the frequency of winter cold waves in China. We select the best models in different regions of China to provide theoretical support for future cold wave frequency projection and climate model improvement over China. The results reveal that CMIP6 global climate models can well reproduce the spatial characteristics of the cold wave frequency that gradually decreases from north to south. EC-Earth3-Veg has the best ability to stimulate the cold wave frequency over China. Most models can simulate the downward trend of the cold wave frequency, but the ability to simulate the magnitude of trend changes is relatively limited. Compared with the single model, the multi-model equal weight average and multi-model median average improve the simulation effect of the spatial pattern of the winter cold wave frequency in northern China and southern China, respectively. However, the simulation of the trend is generally low.
Based on the observational data from 1961 to 2013 and the minimum temperature data of 32 climate models in the Coupled Model Intercomparison Project Phase 6 (CMIP6), this paper evaluates the individual model and multi-model ensemble average ability to simulate the frequency of winter cold waves in China. We select the best models in different regions of China to provide theoretical support for future cold wave frequency projection and climate model improvement over China. The results reveal that CMIP6 global climate models can well reproduce the spatial characteristics of the cold wave frequency that gradually decreases from north to south. EC-Earth3-Veg has the best ability to stimulate the cold wave frequency over China. Most models can simulate the downward trend of the cold wave frequency, but the ability to simulate the magnitude of trend changes is relatively limited. Compared with the single model, the multi-model equal weight average and multi-model median average improve the simulation effect of the spatial pattern of the winter cold wave frequency in northern China and southern China, respectively. However, the simulation of the trend is generally low.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2020.20087
Abstract:
By adopting future climate projection data with a resolution of 1° (latitude)× 1° (longitude) driven by regional climate models during the base period from 1981 to 2005 and from 2069 to 2098 under representative concentration pathway (RCP) 4.5 and 8.5 along with temperature, radiation, and precipitation indicators, the spatial distribution characteristics of thermal resources, planting boundary, theoretical growth period, and climatically suitable region of winter wheat in China were analyzed and compared under RCP 4.5 and 8.5. The main findings of this study are as follows: Compared with the base period, there were significant differences in China’s thermal resources, winter wheat planting conditions, and climatically suitable region under RCP 4.5 and 8.5. Most importantly, compared with RCP 4.5, the thermal resources are projected to increase in most areas in China during 2069–2098 under RCP 8.5. The northern and southern boundaries of winter wheat are projected to move northward and eastward; and therefore, the cultivable area would consequently increase. In most areas, the theoretically suitable sowing date would be delayed, theoretical maturity date would shift to an earlier date and, as a result, the potential growing season would be shortened. Meanwhile, allocation of radiation−temperature−precipitation in the potential growing season improved the climate suitability of winter wheat. However, because winter wheat is a chimonophilous crop that is sensitive to high-temperature stress, the negative effects under RCP 8.5, such as extremely hot weather and asymmetric warming, are likely to offset the favorable effects of the allocation of radiation−temperature−precipitation mentioned above; thus probably reducing the planting suitability of winter wheat. Therefore, future research should be devoted to mitigating climate change in order to ensure the security of China’s food production.
By adopting future climate projection data with a resolution of 1° (latitude)× 1° (longitude) driven by regional climate models during the base period from 1981 to 2005 and from 2069 to 2098 under representative concentration pathway (RCP) 4.5 and 8.5 along with temperature, radiation, and precipitation indicators, the spatial distribution characteristics of thermal resources, planting boundary, theoretical growth period, and climatically suitable region of winter wheat in China were analyzed and compared under RCP 4.5 and 8.5. The main findings of this study are as follows: Compared with the base period, there were significant differences in China’s thermal resources, winter wheat planting conditions, and climatically suitable region under RCP 4.5 and 8.5. Most importantly, compared with RCP 4.5, the thermal resources are projected to increase in most areas in China during 2069–2098 under RCP 8.5. The northern and southern boundaries of winter wheat are projected to move northward and eastward; and therefore, the cultivable area would consequently increase. In most areas, the theoretically suitable sowing date would be delayed, theoretical maturity date would shift to an earlier date and, as a result, the potential growing season would be shortened. Meanwhile, allocation of radiation−temperature−precipitation in the potential growing season improved the climate suitability of winter wheat. However, because winter wheat is a chimonophilous crop that is sensitive to high-temperature stress, the negative effects under RCP 8.5, such as extremely hot weather and asymmetric warming, are likely to offset the favorable effects of the allocation of radiation−temperature−precipitation mentioned above; thus probably reducing the planting suitability of winter wheat. Therefore, future research should be devoted to mitigating climate change in order to ensure the security of China’s food production.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2021.21063
Abstract:
This study evaluated the ability of 28 CMIP6 (coupled model intercomparison project6) models that simulate the interannual variation and change of the climate mean state of the Diurnal Temperature Range (DTR) in China and different regional and seasonal scales using the CRU_TS V4.04 observation data as the benchmark. The results showed that the cmip6 models can reflect the declining trend of the DTR at a centennial time scale in the interannual variation. The correlation coefficient between the model and the observation is 0.1–0.7, root mean square error is 0.6–1.5, and TS is 0.2–0.7. The correlation coefficient between the MRI-ESM2-0 model and the observation is the highest (0.65), root mean square error (0.8) is the lowest, and TS (0.67) is the highest. This indicates that the MRI-ESM2-0 model has the best simulation ability. At a 30-year climate mean scale, the CMIP6 models accord with the observed spatial distribution characteristics of the DTR, which is high in northern China, low in southern China, high in western China, high in eastern China, high in inland China, low in coastal areas, high in the plateau, and low in the plain basin. CMIP6 models can basically reproduce the declining trend over a large area of China in the climate mean state, and the DTR variation in different regions and seasons are also well simulated, with the EC-Earth3 model exhibiting the best performance. However, the individual model is easy to overestimate or underestimate the DTR variation to some extent. The multi-model ensemble can simulate some characteristics of the DTR in the interannual variation and change of the climate mean state, which is better than the single model for the spring and winter simulation.
This study evaluated the ability of 28 CMIP6 (coupled model intercomparison project6) models that simulate the interannual variation and change of the climate mean state of the Diurnal Temperature Range (DTR) in China and different regional and seasonal scales using the CRU_TS V4.04 observation data as the benchmark. The results showed that the cmip6 models can reflect the declining trend of the DTR at a centennial time scale in the interannual variation. The correlation coefficient between the model and the observation is 0.1–0.7, root mean square error is 0.6–1.5, and TS is 0.2–0.7. The correlation coefficient between the MRI-ESM2-0 model and the observation is the highest (0.65), root mean square error (0.8) is the lowest, and TS (0.67) is the highest. This indicates that the MRI-ESM2-0 model has the best simulation ability. At a 30-year climate mean scale, the CMIP6 models accord with the observed spatial distribution characteristics of the DTR, which is high in northern China, low in southern China, high in western China, high in eastern China, high in inland China, low in coastal areas, high in the plateau, and low in the plain basin. CMIP6 models can basically reproduce the declining trend over a large area of China in the climate mean state, and the DTR variation in different regions and seasons are also well simulated, with the EC-Earth3 model exhibiting the best performance. However, the individual model is easy to overestimate or underestimate the DTR variation to some extent. The multi-model ensemble can simulate some characteristics of the DTR in the interannual variation and change of the climate mean state, which is better than the single model for the spring and winter simulation.
Prediction of Inter-annual Signal of Global Mean Surface Temperature Based on Deep Learning Approach
, Available online ,
doi: 10.3878/j.issn.1006-9585.2021.20141
Abstract:
Global Mean Surface Temperature (GMST) research and prediction is still an essential theoretical basis for climate change and disaster prevention. Because GMST series contains multi-scale variation characteristics that are highly complex and nonlinear, the Ensemble Empirical Mode Decomposition (EEMD) was adopted to effectively decompose the GMST time series to Intrinsic Mode Functions (IMFs), which obtains different scales and different characteristics. The machine learning model Autoregressive Integrated Moving Average Model (ARIMA) and the deep learning model long short-term memory (LSTM) present substantial advantages in predicting long-term, complex, and nonlinear time series to carry out GMST inter-annual signal prediction research. The results reveal that the deep learning model fits and predicts the sub-sequences with strong long-term correlation (IMF2-6). IMF1, which represents the inter-annual scale change of GMST is affected by the Pacific Ocean and the Atlantic Ocean multi-climate signal. Three climate indexes should be added as forecast precursor factors into the prediction model to predict IMF1 more accurately. This paper finally selected the LSTM(ENSO) model that considers real-time ENSO to predict the inter-annual GMST signal in advance by comparing multiple sets of GMST data and found that 2020 will have a greater probability of becoming one of the hottest years in history.
Global Mean Surface Temperature (GMST) research and prediction is still an essential theoretical basis for climate change and disaster prevention. Because GMST series contains multi-scale variation characteristics that are highly complex and nonlinear, the Ensemble Empirical Mode Decomposition (EEMD) was adopted to effectively decompose the GMST time series to Intrinsic Mode Functions (IMFs), which obtains different scales and different characteristics. The machine learning model Autoregressive Integrated Moving Average Model (ARIMA) and the deep learning model long short-term memory (LSTM) present substantial advantages in predicting long-term, complex, and nonlinear time series to carry out GMST inter-annual signal prediction research. The results reveal that the deep learning model fits and predicts the sub-sequences with strong long-term correlation (IMF2-6). IMF1, which represents the inter-annual scale change of GMST is affected by the Pacific Ocean and the Atlantic Ocean multi-climate signal. Three climate indexes should be added as forecast precursor factors into the prediction model to predict IMF1 more accurately. This paper finally selected the LSTM(ENSO) model that considers real-time ENSO to predict the inter-annual GMST signal in advance by comparing multiple sets of GMST data and found that 2020 will have a greater probability of becoming one of the hottest years in history.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2021.21027
Abstract:
Simulations for China’s annual average maximum and minimum surface air temperature by CMIP6 models were evaluated, referring to observations from CN05.1. Results show that the annual average maximum and minimum surface air temperature in China from 1961 to 2014 had increasing trends. The maximum surface air temperature increased at a rate of 2.15 ℃/100 a. The growth rate of the minimum air temperature was 3.92 °C/100 a, which was about twice the growth rate of the maximum air temperature. CMIP6 models can simulate trends over long time scales, but there were large differences in the simulation ability of different models. The dispersion between models reached 0.38 °C/100 a (maximum air temperature) and 0.41 °C/100 a (minimum air temperature). BCC-ESM1 and EC-Earth3 had the best performance in simulating the trends of the maximum and minimum air temperature, respectively. CMIP6 models can well simulate the spatial distribution of the climatological maximum and minimum air temperature in China. Proportions of grid points where most of the model simulations correlated positively with observations were 82% (maximum air temperature) and 97% (minimum air temperature) in China. Simulation results of the maximum and minimum air temperature in the whole of eastern China had obvious geographical characteristics with a standard deviation within 3 ℃, showing a high consistency. The variation was significant in the western region and reached more than 6 °C in the Tibetan Plateau. GISS-E2-1-G and MRI-ESM2-0 can well simulate the main EOF (empirical orthogonal function) modes and principal components of the maximum and minimum air temperature in China in 1961–2014. In summary, CMIP6 models can well simulate the spatial distribution of the climatological maximum and minimum air temperature and interannual trends of the maximum and minimum air temperature in China.
Simulations for China’s annual average maximum and minimum surface air temperature by CMIP6 models were evaluated, referring to observations from CN05.1. Results show that the annual average maximum and minimum surface air temperature in China from 1961 to 2014 had increasing trends. The maximum surface air temperature increased at a rate of 2.15 ℃/100 a. The growth rate of the minimum air temperature was 3.92 °C/100 a, which was about twice the growth rate of the maximum air temperature. CMIP6 models can simulate trends over long time scales, but there were large differences in the simulation ability of different models. The dispersion between models reached 0.38 °C/100 a (maximum air temperature) and 0.41 °C/100 a (minimum air temperature). BCC-ESM1 and EC-Earth3 had the best performance in simulating the trends of the maximum and minimum air temperature, respectively. CMIP6 models can well simulate the spatial distribution of the climatological maximum and minimum air temperature in China. Proportions of grid points where most of the model simulations correlated positively with observations were 82% (maximum air temperature) and 97% (minimum air temperature) in China. Simulation results of the maximum and minimum air temperature in the whole of eastern China had obvious geographical characteristics with a standard deviation within 3 ℃, showing a high consistency. The variation was significant in the western region and reached more than 6 °C in the Tibetan Plateau. GISS-E2-1-G and MRI-ESM2-0 can well simulate the main EOF (empirical orthogonal function) modes and principal components of the maximum and minimum air temperature in China in 1961–2014. In summary, CMIP6 models can well simulate the spatial distribution of the climatological maximum and minimum air temperature and interannual trends of the maximum and minimum air temperature in China.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2021.21113
Abstract:
The ecological environments of relative poverty areas in China are relatively fragile. Additionally, meteorological disasters, e.g., heavy rains and floods, occur frequently in these areas. Thus, a quantitative and scientific evaluation of characteristic changes of precipitation extremes in relative poverty areas at different global warming levels can provide a scientific basis for the formulation of strategies to prevent these areas from returning to poverty due to meteorological disasters. Here, we investigated changes in characteristics of precipitation extremes, i.e., frequency, intensity, and duration, under 1.5°C and 2.0°C global warming levels, across relative poverty areas in China. We used fourteen global climate models under four different emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) from the latest Sixth phase Coupled Model Intercomparison Project for analysis. Run-theory was also used to analyze the characteristics of extreme precipitation events. At 1.5°C warming, the frequency, intensity, and duration of the precipitation extremes were predicted to increase 60.91%, 88.19%, and 81.07% over the entire region, respectively, relative to a reference period (1995–2014). For 2°C warming, changes in precipitation extreme characteristics were predicted to increase 55.78%, 85.24%, and 79.33% over the entire region, respectively. The central and western regions of the relative poverty areas were expected to be more susceptible to precipitation extremes compared with the eastern parts for both 1.5°C and 2.0°C warming levels. These changes in frequency and duration were predominant in the Tibet region, which is of great concern. The additional 0.5°C of warming (from 1.5°C to 2.0°C) will lead to fewer areas affected by precipitation extremes for the studied areas. However, these extreme events will be more severe and have longer durations in the affected regions. These findings necessitate the initiation of urgent mitigation and adaptation measures to combat precipitation-related extreme events across relative poverty areas in China.
The ecological environments of relative poverty areas in China are relatively fragile. Additionally, meteorological disasters, e.g., heavy rains and floods, occur frequently in these areas. Thus, a quantitative and scientific evaluation of characteristic changes of precipitation extremes in relative poverty areas at different global warming levels can provide a scientific basis for the formulation of strategies to prevent these areas from returning to poverty due to meteorological disasters. Here, we investigated changes in characteristics of precipitation extremes, i.e., frequency, intensity, and duration, under 1.5°C and 2.0°C global warming levels, across relative poverty areas in China. We used fourteen global climate models under four different emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) from the latest Sixth phase Coupled Model Intercomparison Project for analysis. Run-theory was also used to analyze the characteristics of extreme precipitation events. At 1.5°C warming, the frequency, intensity, and duration of the precipitation extremes were predicted to increase 60.91%, 88.19%, and 81.07% over the entire region, respectively, relative to a reference period (1995–2014). For 2°C warming, changes in precipitation extreme characteristics were predicted to increase 55.78%, 85.24%, and 79.33% over the entire region, respectively. The central and western regions of the relative poverty areas were expected to be more susceptible to precipitation extremes compared with the eastern parts for both 1.5°C and 2.0°C warming levels. These changes in frequency and duration were predominant in the Tibet region, which is of great concern. The additional 0.5°C of warming (from 1.5°C to 2.0°C) will lead to fewer areas affected by precipitation extremes for the studied areas. However, these extreme events will be more severe and have longer durations in the affected regions. These findings necessitate the initiation of urgent mitigation and adaptation measures to combat precipitation-related extreme events across relative poverty areas in China.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2021.21061
Abstract:
Based on National Oceanic and Atmospheric Administration (NOAA) daily optimum interpolation sea surface temperature V2 observation data and various physical sciences laboratory climate observation indexes from 1982 to 2019, this paper adopts statistical methods such as least square regression, high-low pass filtering, and correlation analysis to analyze the multi-timescale evolution characteristics of the global Marine Heatwaves (MHWs) frequency, duration, total days, and maximum intensity and the regulation effect of different climate signals on its evolution. Research shows that the MHW frequency linearly grows the fastest in the equatorial western Pacific. After removing the global warming trend, the interannual and interdecadal changes in the global mean MHWs have obvious regional variation characteristics, and all the dominant regions are modulated by the climate signals of multiple timescales. This study analyzes the correlation between the MHW properties and different climate signals in five key sea areas (equatorial central and eastern Pacific Ocean, northeast Pacific Ocean, western Indian Ocean, northwest Atlantic Ocean, and mid-high latitude Southern Ocean). Results show that the frequency of the MHWs in the five key sea areas is mainly modulated by interannual climate signals. The interdecadal climate signal mainly provides a background state, and its influence on the frequency evolution of MHWs in the key areas is not as significant as that of the interannual climate signal.
Based on National Oceanic and Atmospheric Administration (NOAA) daily optimum interpolation sea surface temperature V2 observation data and various physical sciences laboratory climate observation indexes from 1982 to 2019, this paper adopts statistical methods such as least square regression, high-low pass filtering, and correlation analysis to analyze the multi-timescale evolution characteristics of the global Marine Heatwaves (MHWs) frequency, duration, total days, and maximum intensity and the regulation effect of different climate signals on its evolution. Research shows that the MHW frequency linearly grows the fastest in the equatorial western Pacific. After removing the global warming trend, the interannual and interdecadal changes in the global mean MHWs have obvious regional variation characteristics, and all the dominant regions are modulated by the climate signals of multiple timescales. This study analyzes the correlation between the MHW properties and different climate signals in five key sea areas (equatorial central and eastern Pacific Ocean, northeast Pacific Ocean, western Indian Ocean, northwest Atlantic Ocean, and mid-high latitude Southern Ocean). Results show that the frequency of the MHWs in the five key sea areas is mainly modulated by interannual climate signals. The interdecadal climate signal mainly provides a background state, and its influence on the frequency evolution of MHWs in the key areas is not as significant as that of the interannual climate signal.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2021.21009
Abstract:
Using the Ozone Monitoring Instrument derived satellite data (OMHCHO_003), Rizhao in Shandong Province was chosen as a typical eastern coastal city to investigate the characteristics of its vertical column density (VCD) changing trends for formaldehyde from 2014 to 2020. The spatial distribution of VCD for formaldehyde presents an obvious seasonal characteristic. The high value of VCD for formaldehyde in the summer is about 1.5 times the average value in other seasons. In summer, the high VCD value for formaldehyde is mainly located in Ju County, northwest of Rizhao. The VCD for formaldehyde in Rizhao has increased significantly over the last seven years. Moreover, its increasing rate in the Donggang District reached 0.20\begin{document}$ \times $\end{document} ![]()
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Using the Ozone Monitoring Instrument derived satellite data (OMHCHO_003), Rizhao in Shandong Province was chosen as a typical eastern coastal city to investigate the characteristics of its vertical column density (VCD) changing trends for formaldehyde from 2014 to 2020. The spatial distribution of VCD for formaldehyde presents an obvious seasonal characteristic. The high value of VCD for formaldehyde in the summer is about 1.5 times the average value in other seasons. In summer, the high VCD value for formaldehyde is mainly located in Ju County, northwest of Rizhao. The VCD for formaldehyde in Rizhao has increased significantly over the last seven years. Moreover, its increasing rate in the Donggang District reached 0.20
, Available online ,
doi: 10.3878/j.issn.1006-9585.2021.20112
Abstract:
The mass concentration changes of air pollutants are mainly controlled through changes in meteorological conditions and anthropogenic emissions. In the first half of 2020, under the context of the impact of the “COVID-19” pandemic, the mass concentrations of the six major pollutants in the Sichuan Basin have changed compared with those in previous years. To distinguish these changes from the impact of meteorological conditions and anthropogenic emission changes, the influence of meteorological condition changes on the mass concentration of pollutants in the Sichuan Basin is analyzed. The results show that the overall air quality from January to June 2020 was better than that from January to June 2019. Among the meteorological parameters that mainly affect the concentration of pollutants, the average wind speed in the Chengdu Plain and northeastern Sichuan was the same as in previous years, whereas the average wind speed in southern Sichuan was lower than that in previous years. Starting from March 2020, the total precipitation in various cities in the basin has been significantly lower. The lowest precipitation in May was the most serious. Correspondingly, the relative humidity was lowest in May. The average temperature in 2020 was higher than that in 2019; in particular, the average temperature in May and June was approximately 5°C higher. Starting from March or April, the total monthly hours of sunshine, which reflect the amount of solar radiation, were significantly longer than those in previous years. The numerical simulation results obtained using fixed pollution sources and only changing the initial meteorological conditions show that PM2.5 and PM10 were similar. Moreover, the concentrations in January and February were lower than those in 2019, the concentrations in March and April were higher than those in previous years, and the concentrations in May and June were lower than those in previous years. The main deviation area was in southern Sichuan. SO2 is likely to spread in January and February but tends not to spread in March in northeastern Sichuan. The meteorological condition of NO2 was relatively unfavorable in February, and the meteorological condition of CO was relatively unfavorable in April. Furthermore, the meteorological conditions in the basin in April involved ozone diffusion, and the meteorological conditions in the basin in May and June played a strong role in promoting the O3 pollution process. Quantitative analysis of the contribution rate of pollutant concentration in each city determined that, because of the impact of the “COVID-19” pandemic, the contribution of particulate matter and gaseous pollutants other than ozone was negative in January to April, and the resumption of work and production in May and June increased the concentration of pollutants in the basin. Because of the considerably increased anthropogenic emissions and unfavorable weather conditions, serious O3 pollution is observed in the basin.
The mass concentration changes of air pollutants are mainly controlled through changes in meteorological conditions and anthropogenic emissions. In the first half of 2020, under the context of the impact of the “COVID-19” pandemic, the mass concentrations of the six major pollutants in the Sichuan Basin have changed compared with those in previous years. To distinguish these changes from the impact of meteorological conditions and anthropogenic emission changes, the influence of meteorological condition changes on the mass concentration of pollutants in the Sichuan Basin is analyzed. The results show that the overall air quality from January to June 2020 was better than that from January to June 2019. Among the meteorological parameters that mainly affect the concentration of pollutants, the average wind speed in the Chengdu Plain and northeastern Sichuan was the same as in previous years, whereas the average wind speed in southern Sichuan was lower than that in previous years. Starting from March 2020, the total precipitation in various cities in the basin has been significantly lower. The lowest precipitation in May was the most serious. Correspondingly, the relative humidity was lowest in May. The average temperature in 2020 was higher than that in 2019; in particular, the average temperature in May and June was approximately 5°C higher. Starting from March or April, the total monthly hours of sunshine, which reflect the amount of solar radiation, were significantly longer than those in previous years. The numerical simulation results obtained using fixed pollution sources and only changing the initial meteorological conditions show that PM2.5 and PM10 were similar. Moreover, the concentrations in January and February were lower than those in 2019, the concentrations in March and April were higher than those in previous years, and the concentrations in May and June were lower than those in previous years. The main deviation area was in southern Sichuan. SO2 is likely to spread in January and February but tends not to spread in March in northeastern Sichuan. The meteorological condition of NO2 was relatively unfavorable in February, and the meteorological condition of CO was relatively unfavorable in April. Furthermore, the meteorological conditions in the basin in April involved ozone diffusion, and the meteorological conditions in the basin in May and June played a strong role in promoting the O3 pollution process. Quantitative analysis of the contribution rate of pollutant concentration in each city determined that, because of the impact of the “COVID-19” pandemic, the contribution of particulate matter and gaseous pollutants other than ozone was negative in January to April, and the resumption of work and production in May and June increased the concentration of pollutants in the basin. Because of the considerably increased anthropogenic emissions and unfavorable weather conditions, serious O3 pollution is observed in the basin.
2021 Issue 6
Display Method:
2021, 26(6): 591-607.
doi: 10.3878/j.issn.1006-9585.2021.20113
Abstract:
Based on the atmospheric data from NCEP, sea surface temperature (SST) data from the Hadley Center, sea surface heat fluxes and related physical data from OAFlux, and cloud data from NCAR/NOAA, the characteristics of heat fluxes were analyzed over the tropical Pacific and then the different characteristics and influential factors of turbulent heat fluxes between two types of ENSO events were investigated. The main conclusions are as follows: The maximum amplitude of the net heat flux variation is found over the equatorial Pacific, which is mainly caused by variations of the latent heat flux and shortwave radiation flux. Using the SST indexes associated with two types of ENSO events, partial regressions are applied to investigate the influence of SST on the heat fluxes in every evolution stage. Negative anomalies of the net heat flux can be captured over the equatorial Pacific during the two types of ENSO events. However, the range and the amplitude of the anomalies are much broader and stronger over the equatorial Pacific under the partial regression of Niño3 than those of El Niño Modoki index (EMI). Besides, the SST of the maturing periods has the greatest impact on the contemporaneous turbulence heat fluxes over the equatorial eastern Pacific in both events.
Based on the atmospheric data from NCEP, sea surface temperature (SST) data from the Hadley Center, sea surface heat fluxes and related physical data from OAFlux, and cloud data from NCAR/NOAA, the characteristics of heat fluxes were analyzed over the tropical Pacific and then the different characteristics and influential factors of turbulent heat fluxes between two types of ENSO events were investigated. The main conclusions are as follows: The maximum amplitude of the net heat flux variation is found over the equatorial Pacific, which is mainly caused by variations of the latent heat flux and shortwave radiation flux. Using the SST indexes associated with two types of ENSO events, partial regressions are applied to investigate the influence of SST on the heat fluxes in every evolution stage. Negative anomalies of the net heat flux can be captured over the equatorial Pacific during the two types of ENSO events. However, the range and the amplitude of the anomalies are much broader and stronger over the equatorial Pacific under the partial regression of Niño3 than those of El Niño Modoki index (EMI). Besides, the SST of the maturing periods has the greatest impact on the contemporaneous turbulence heat fluxes over the equatorial eastern Pacific in both events.
2021, 26(6): 608-620.
doi: 10.3878/j.issn.1006-9585.2021.20156
Abstract:
The physical and chemical characteristics of atmospheric aerosol particles in urban areas are complex. It is of great significance to clarify the fine chemical composition and mixing state of a single particle for air pollution traceability and fine control. This study used passive aerosol samplers to collect samples in the urban area and steel park of Rizhao, Shandong Province. The collected samples were analyzed using an intelligent scanning electron microscope environmental particle analysis system (IntelliSEM EPAS). The results show that atmospheric particulates in Rizhao City mainly comprise irregular carbonaceous particles (C-rich), sulfur-containing particles (Ca-S, Na-S-Ca), and mineral particles. Among them, the contribution of C-rich particles in urban samples is 53.5%, which is 2.5 times higher than that in steel park samples. In addition, the number of particles >1 μm is 9.0%, which is 1.7 times that of the samples from the steel park. Urban residents’ activities and industrial processes are the main sources of atmospheric particulate matter, especially secondary fine particles. The quantity contribution of sulfur-containing particles in the samples from the steel park is 72.9%, the quality contribution of sulfur-containing particles is 30.9%, and the quality contribution of iron-rich particles is 5.3%, which are 1.8 times, 3.6 times, and 2.9 times higher than those of urban samples, respectively, indicating that the main sources of atmospheric particles in the steel park include primary pollutants discharged by iron and steel enterprises and secondary fine particles generated through them.
The physical and chemical characteristics of atmospheric aerosol particles in urban areas are complex. It is of great significance to clarify the fine chemical composition and mixing state of a single particle for air pollution traceability and fine control. This study used passive aerosol samplers to collect samples in the urban area and steel park of Rizhao, Shandong Province. The collected samples were analyzed using an intelligent scanning electron microscope environmental particle analysis system (IntelliSEM EPAS). The results show that atmospheric particulates in Rizhao City mainly comprise irregular carbonaceous particles (C-rich), sulfur-containing particles (Ca-S, Na-S-Ca), and mineral particles. Among them, the contribution of C-rich particles in urban samples is 53.5%, which is 2.5 times higher than that in steel park samples. In addition, the number of particles >1 μm is 9.0%, which is 1.7 times that of the samples from the steel park. Urban residents’ activities and industrial processes are the main sources of atmospheric particulate matter, especially secondary fine particles. The quantity contribution of sulfur-containing particles in the samples from the steel park is 72.9%, the quality contribution of sulfur-containing particles is 30.9%, and the quality contribution of iron-rich particles is 5.3%, which are 1.8 times, 3.6 times, and 2.9 times higher than those of urban samples, respectively, indicating that the main sources of atmospheric particles in the steel park include primary pollutants discharged by iron and steel enterprises and secondary fine particles generated through them.
2021, 26(6): 621-636.
doi: 10.3878/j.issn.1006-9585.2021.20146
Abstract:
The predictive ability of the Climate Forecast System version 2 (CFSv2) for the Antarctic Oscillation (AAO) is evaluated in the three boreal spring months (March, April, and May) of the period 1983–2019. Results show that the CFSv2 model has a good predictive capability for the spatial patterns of the AAO in the three spring months. Specifically, the CFSv2 model can effectively predict the interannual variability of the AAO in March but not in April and May. Results indicate that the tropical central and eastern Pacific sea surface temperature anomalies and the eastern Australia sea surface temperature anomalies are likely to be the predictable sources of the AAO in March. On the one hand, a significant relationship exists between El Niño–Southern Oscillation (ENSO) and the AAO in March, and it weakens in April and May. In March, ENSO can excite the Pacific–South American wave train from tropical Pacific to the South Pacific, thereby influencing the interannual variability of the simultaneous AAO by affecting the South Pacific sea surface temperature anomalies and the low-level cyclonic circulation anomalies. On the other hand, the March sea surface temperature anomaly east of Australia triggers an active Rossby wave train in the core area of the subtropical jet, which propagates away from eastern Australia toward the southeast and arrives at the mid-high latitudes of the South Pacific. Consequently, the 30°S westerly wind weakens, and the 60°S high latitude in the Southern Hemisphere strengthens and eventually leads to changes in the AAO. CFSv2’s predictive capability for March AAO is higher than that of April and May because this model can reproduce not only the relationship between March AAO and ENSO and the sea surface temperature east of Australia but also the physical processes between them.
The predictive ability of the Climate Forecast System version 2 (CFSv2) for the Antarctic Oscillation (AAO) is evaluated in the three boreal spring months (March, April, and May) of the period 1983–2019. Results show that the CFSv2 model has a good predictive capability for the spatial patterns of the AAO in the three spring months. Specifically, the CFSv2 model can effectively predict the interannual variability of the AAO in March but not in April and May. Results indicate that the tropical central and eastern Pacific sea surface temperature anomalies and the eastern Australia sea surface temperature anomalies are likely to be the predictable sources of the AAO in March. On the one hand, a significant relationship exists between El Niño–Southern Oscillation (ENSO) and the AAO in March, and it weakens in April and May. In March, ENSO can excite the Pacific–South American wave train from tropical Pacific to the South Pacific, thereby influencing the interannual variability of the simultaneous AAO by affecting the South Pacific sea surface temperature anomalies and the low-level cyclonic circulation anomalies. On the other hand, the March sea surface temperature anomaly east of Australia triggers an active Rossby wave train in the core area of the subtropical jet, which propagates away from eastern Australia toward the southeast and arrives at the mid-high latitudes of the South Pacific. Consequently, the 30°S westerly wind weakens, and the 60°S high latitude in the Southern Hemisphere strengthens and eventually leads to changes in the AAO. CFSv2’s predictive capability for March AAO is higher than that of April and May because this model can reproduce not only the relationship between March AAO and ENSO and the sea surface temperature east of Australia but also the physical processes between them.
2021, 26(6): 637-647.
doi: 10.3878/j.issn.1006-9585.2021.21055
Abstract:
In the context of global climate change, the frequent occurrence of heatwave events not only affects human production activities but also threatens personal safety. From the perspective of human health, this paper uses daily meteorological data of 657 weather stations and remote sensing data from 2000 to 2019 for discussing the distribution of outdoor heat risk during the warm season (May–October) in China based on the apparent temperature index, which considers air temperature, humidity, wind speed, and radiation. Then, the 1-km resolution outdoor heat-risk distribution is mapped. When the results were compared with the results of the high-temperature classification using temperature as a single indicator, it showed that the heat-risk zones based on the apparent temperature were more detailed and consistent with human thermal perception than the risk zones delineated by temperature alone. This will help more outdoor workers who are vulnerable to potential heat risks to anticipate and make better protective measures in advance. First, the heat-risk levels follow the pattern of decreasing from south to north and east to west, as does the number of thermal-risk days. Regions with the highest heat-risk level and longest thermal-risk days include the middle−lower Yangtze River, Guangdong, Guangxi, and Hainan Provinces, with heat-risk days possibly exceeding 140. The number of days with thermal risk ranges from 100–120 days in northern China and 50–90 days in the northern areas. Second, the thermal-risk levels in all provinces peaked in June–August. Moreover, high temperatures can occur locally, and the Turpan region of Xinjiang and the valley of southern Tibet experience different levels of thermal risk throughout the warm season. This study fully considers several elements that affect outdoor thermal comfort. Assigning risk levels can provide guidance to outdoor workers on heat avoidance to decrease their suffering from heat-related injuries. It can also provide a scientific basis for developing more detailed heat subsidy measures in different regions of China.
In the context of global climate change, the frequent occurrence of heatwave events not only affects human production activities but also threatens personal safety. From the perspective of human health, this paper uses daily meteorological data of 657 weather stations and remote sensing data from 2000 to 2019 for discussing the distribution of outdoor heat risk during the warm season (May–October) in China based on the apparent temperature index, which considers air temperature, humidity, wind speed, and radiation. Then, the 1-km resolution outdoor heat-risk distribution is mapped. When the results were compared with the results of the high-temperature classification using temperature as a single indicator, it showed that the heat-risk zones based on the apparent temperature were more detailed and consistent with human thermal perception than the risk zones delineated by temperature alone. This will help more outdoor workers who are vulnerable to potential heat risks to anticipate and make better protective measures in advance. First, the heat-risk levels follow the pattern of decreasing from south to north and east to west, as does the number of thermal-risk days. Regions with the highest heat-risk level and longest thermal-risk days include the middle−lower Yangtze River, Guangdong, Guangxi, and Hainan Provinces, with heat-risk days possibly exceeding 140. The number of days with thermal risk ranges from 100–120 days in northern China and 50–90 days in the northern areas. Second, the thermal-risk levels in all provinces peaked in June–August. Moreover, high temperatures can occur locally, and the Turpan region of Xinjiang and the valley of southern Tibet experience different levels of thermal risk throughout the warm season. This study fully considers several elements that affect outdoor thermal comfort. Assigning risk levels can provide guidance to outdoor workers on heat avoidance to decrease their suffering from heat-related injuries. It can also provide a scientific basis for developing more detailed heat subsidy measures in different regions of China.
2021, 26(6): 648-662.
doi: 10.3878/j.issn.1006-9585.2021.20160
Abstract:
The surface albedo represents the ability of the earth’s surface to reflect solar radiation and is a key parameter that affects the balance of surface radiation energy. Based on moderate resolution imaging spectroradiometer (MODIS) data, spatial and temporal variations of the land surface albedo and the influence of the types of land use, terrain, land surface parameters, and climate factors in the Huaihe River basin from 2005 to 2015 were analyzed using grid trend, abnormal change, correlation, and gray correlation analyses. The results show that the annual average land surface albedo of the Huaihe River basin displayed a spatial distribution pattern of “high in the north and east, low in the south and west”, varying from 0.043–0.223 with an average of 0.145. Low-value areas were mainly concentrated in regions where water is densely distributed, mountainous and hilly areas, and with a small standard deviation. High-value areas were mainly concentrated in the central and eastern plain of the basin with a relatively large standard deviation. The land surface albedo showed an increasing trend with seasonal differences in 61.5% of the area. The largest was in summer, followed by spring, and finally by autumn. In winter, the fluctuation range is larger owing to the impact of snow cover and farmland use mode. The land surface albedo positively correlated with the NDVI(Normalized Difference Vegetation Index), LST(Land Surface Temperature), air temperature, and precipitation in most areas of Huaihe River Basin, accounting for 90.23%, 82.32%, 85.41%, and 93.70%, respectively. Under different types of land use (except water), the order of the annual average gray correlation degree between the land surface albedo and the influencing factors are as follows: NDVI>air temperature>LST>precipitation, and the order of the spatial gray correlation degree is: NDVI> precipitation >LST> air temperature> elevation.
The surface albedo represents the ability of the earth’s surface to reflect solar radiation and is a key parameter that affects the balance of surface radiation energy. Based on moderate resolution imaging spectroradiometer (MODIS) data, spatial and temporal variations of the land surface albedo and the influence of the types of land use, terrain, land surface parameters, and climate factors in the Huaihe River basin from 2005 to 2015 were analyzed using grid trend, abnormal change, correlation, and gray correlation analyses. The results show that the annual average land surface albedo of the Huaihe River basin displayed a spatial distribution pattern of “high in the north and east, low in the south and west”, varying from 0.043–0.223 with an average of 0.145. Low-value areas were mainly concentrated in regions where water is densely distributed, mountainous and hilly areas, and with a small standard deviation. High-value areas were mainly concentrated in the central and eastern plain of the basin with a relatively large standard deviation. The land surface albedo showed an increasing trend with seasonal differences in 61.5% of the area. The largest was in summer, followed by spring, and finally by autumn. In winter, the fluctuation range is larger owing to the impact of snow cover and farmland use mode. The land surface albedo positively correlated with the NDVI(Normalized Difference Vegetation Index), LST(Land Surface Temperature), air temperature, and precipitation in most areas of Huaihe River Basin, accounting for 90.23%, 82.32%, 85.41%, and 93.70%, respectively. Under different types of land use (except water), the order of the annual average gray correlation degree between the land surface albedo and the influencing factors are as follows: NDVI>air temperature>LST>precipitation, and the order of the spatial gray correlation degree is: NDVI> precipitation >LST> air temperature> elevation.
2021, 26(6): 663-677.
doi: 10.3878/j.issn.1006-9585.2021.20161
Abstract:
Numerical simulation is an effective method to study the urban climate effect, and the parameters are very important for simulation. Using satellite and surface meteorological observations, parameter sensitivity tests of the simulation of extremely high temperatures in the Beijing–Tianjin–Hebei urban agglomeration are conducted, and localized parameter configurations such as surface albedo, surface emissivity, and anthropogenic heat for the model (WRF/BEP/BEM) in the Beijing–Tianjin–Hebei urban agglomeration are determined. It is shown that the locally optimized surface albedo, surface emissivity, and anthropogenic heat have a significant effect on the simulation results of extremely high temperatures in Beijing–Tianjin–Hebei. By localizing these parameters, surface maximum temperature simulation errors are reduced by more than 0.5°C in more than 65% of urban sites. Since 2010, five extreme high-temperature processes have been successfully simulated in the Beijing–Tianjin–Hebei region with the optimized WRF/BEP/BEM. Standard deviations of the extremely high temperature simulated in the model from the observation are about 1.4°C, 0.8°C, 0.9°C, 1.0°C, and 0.7°C, which are decreased by 26.3%, 61.9%, 40.0%, 41.2%, and 36.3%, respectively, compared with the standard deviations of ERA5. After optimization, the model simulation error is obviously reduced, and it can be further applied to the study of the Beijing–Tianjin–Hebei urbanization mechanism under extremely high temperatures.
Numerical simulation is an effective method to study the urban climate effect, and the parameters are very important for simulation. Using satellite and surface meteorological observations, parameter sensitivity tests of the simulation of extremely high temperatures in the Beijing–Tianjin–Hebei urban agglomeration are conducted, and localized parameter configurations such as surface albedo, surface emissivity, and anthropogenic heat for the model (WRF/BEP/BEM) in the Beijing–Tianjin–Hebei urban agglomeration are determined. It is shown that the locally optimized surface albedo, surface emissivity, and anthropogenic heat have a significant effect on the simulation results of extremely high temperatures in Beijing–Tianjin–Hebei. By localizing these parameters, surface maximum temperature simulation errors are reduced by more than 0.5°C in more than 65% of urban sites. Since 2010, five extreme high-temperature processes have been successfully simulated in the Beijing–Tianjin–Hebei region with the optimized WRF/BEP/BEM. Standard deviations of the extremely high temperature simulated in the model from the observation are about 1.4°C, 0.8°C, 0.9°C, 1.0°C, and 0.7°C, which are decreased by 26.3%, 61.9%, 40.0%, 41.2%, and 36.3%, respectively, compared with the standard deviations of ERA5. After optimization, the model simulation error is obviously reduced, and it can be further applied to the study of the Beijing–Tianjin–Hebei urbanization mechanism under extremely high temperatures.
2021, 26(6): 678-690.
doi: 10.3878/j.issn.1006-9585.2021.21006
Abstract:
Spatial distributions of the monthly–seasonal precipitation in China are similar to the atmospheric circulation teleconnections. Based on the summer precipitation data of China in recent 57 years, this paper investigates the main spatial mode characteristics and interdecadal changes of the spatial teleconnection pattern of the summer precipitation in China. Further, this study evaluates and improves the prediction ability of BCC_CSM, ECMWF_ SYSTEM4, and NCEP_CFSV2 climate models for the summer precipitation in China. Results show that there are four significant spatial teleconnection patterns of summer precipitation in China: (1) North China: lower reaches of the Yangtze River, (2) East China: Central and Northern China, (3) South China: Yangtze River basin, and (4) Southwest China: Central Northeast China. The climate models can only predict the large-scale precipitation distributions; however, the precipitation remote correlation between different regions cannot be well predicted, and there are many false teleconnections. To improve the precipitation prediction ability of the model, a correction scheme is constructed by taking the precipitation teleconnection pattern in the actual situation as the constraint condition to correct the precipitation teleconnection pattern’s distribution in the model. Results show that the correction can effectively improve the model’s ability for predicting the precipitation of Central Northeast China and the lower reaches of the Yangtze River. Four-year return test results show that the average anomaly consistency rate of model prediction increased from 47% to 58%, the root mean square error decreased from 153 to 120 mm, and the average PS (Prediction Score) score increased from 64 to 73.
Spatial distributions of the monthly–seasonal precipitation in China are similar to the atmospheric circulation teleconnections. Based on the summer precipitation data of China in recent 57 years, this paper investigates the main spatial mode characteristics and interdecadal changes of the spatial teleconnection pattern of the summer precipitation in China. Further, this study evaluates and improves the prediction ability of BCC_CSM, ECMWF_ SYSTEM4, and NCEP_CFSV2 climate models for the summer precipitation in China. Results show that there are four significant spatial teleconnection patterns of summer precipitation in China: (1) North China: lower reaches of the Yangtze River, (2) East China: Central and Northern China, (3) South China: Yangtze River basin, and (4) Southwest China: Central Northeast China. The climate models can only predict the large-scale precipitation distributions; however, the precipitation remote correlation between different regions cannot be well predicted, and there are many false teleconnections. To improve the precipitation prediction ability of the model, a correction scheme is constructed by taking the precipitation teleconnection pattern in the actual situation as the constraint condition to correct the precipitation teleconnection pattern’s distribution in the model. Results show that the correction can effectively improve the model’s ability for predicting the precipitation of Central Northeast China and the lower reaches of the Yangtze River. Four-year return test results show that the average anomaly consistency rate of model prediction increased from 47% to 58%, the root mean square error decreased from 153 to 120 mm, and the average PS (Prediction Score) score increased from 64 to 73.
2021, 26(6): 691-702.
doi: 10.3878/j.issn.1006-9585.2021.20165
Abstract:
Since 1981, the Chinese Meteorological Archives has collected the phenological records of plants, insects, birds, and amphibious animals. These records were observed over a long time by agrometeorological stations in many provinces. After a series of steps involving data cleaning, quality control, and other qualified processing, the Phenophase Dataset of plants and animals in China (1981–2018) was made. The subset of herbaceous plants has 13936 rows with 42 species. The subset of woody plants has 36495 rows with 111 species. The third subset comprises birds, insects, and amphibious animals and has 15513 rows with 18 species. Results of the data analysis were consistent with the existing climate change data.
Since 1981, the Chinese Meteorological Archives has collected the phenological records of plants, insects, birds, and amphibious animals. These records were observed over a long time by agrometeorological stations in many provinces. After a series of steps involving data cleaning, quality control, and other qualified processing, the Phenophase Dataset of plants and animals in China (1981–2018) was made. The subset of herbaceous plants has 13936 rows with 42 species. The subset of woody plants has 36495 rows with 111 species. The third subset comprises birds, insects, and amphibious animals and has 15513 rows with 18 species. Results of the data analysis were consistent with the existing climate change data.
Since 1996 Bimonthly
Supervisor: Chinese Academy of Sciences
Sponsors by: Institute of Atmospheric Physics, Chinese Academy of Sciences/Chinese Meteorological Society
Editor: Wang Zifa
Email: qhhj@mail.iap.ac.cn
ISSN 1006-9585
CN 11-3693/P
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