2022 Vol. 27, No. 5
Display Method:
2022, 27(5): 559-577.
doi: 10.3878/j.issn.1006-9585.2021.21097
Abstract:
Globally, there have been frequent occurrences of extreme forest fire incidents in recent years. As one kind of the compound extreme events, the occurrence and spread of forest fires are closely associated with meteorological conditions. Under global warming, investigating the changing characteristics of forest fire risk can provide valuable scientific information for forest fire prevention activities in the context of carbon neutrality. In this study, the daily forest fire danger index (FFDI) was used to measure fire weather, and the applicability and spatial distribution of this index were analyzed. In addition, the linear trend in FFDI and related meteorological factors in two major forest areas during 1961–2020 were analyzed. Finally, the ensemble empirical mode decomposition method was used to reveal the multi-time scale characteristics of FFDI in the two forest areas. The results show that the spatial distribution of FFDI has obvious regional characteristics on seasonal and annual time scales. Northeast China has a high FFDI during spring and autumn, whereas southwest China has a high FFDI during spring and winter. These seasonal variations show a good corresponding relationship with the forest fire prevention period in the two forest regions. The number of stations showing a significant increasing trend in FFDI in each season is around 10%–20%, with the highest in spring (21%). The linear trends in FFDI in the northeastern forest area are not significant in all seasons; however, among relevant meteorological factors, the daily maximum temperature and average wind speed respectively show a significant warming trend and a significant weakening trend in all seasons. The FFDI in all seasons of the southwestern forest area showed a significant increasing trend at a level of 0.1 at least, among which the trends during the spring and winter fire prevention periods were 0.09/10 a (P<0.1) and 0.05/10 a (P<0.1), respectively. The summer, autumn, and winter periods showed a significant warming and drying trend (P<0.05). The interannual variability contributes more than 70% to the evolution of FFDI in the two forest areas. The nonlinear trend of FFDI in the spring and autumn fire prevention periods in northeast China showed a rapid rise at first and then a decline. The nonlinear trend of FFDI in the spring fire season in southwest China changed from a stable stage in the last century to a rapidly increasing trend in the 21st century, whereas the overall trend of FFDI during the winter fire season increased steadily. Therefore, the situation of fire risk in forests of southwest China is increasingly becoming severe.
Globally, there have been frequent occurrences of extreme forest fire incidents in recent years. As one kind of the compound extreme events, the occurrence and spread of forest fires are closely associated with meteorological conditions. Under global warming, investigating the changing characteristics of forest fire risk can provide valuable scientific information for forest fire prevention activities in the context of carbon neutrality. In this study, the daily forest fire danger index (FFDI) was used to measure fire weather, and the applicability and spatial distribution of this index were analyzed. In addition, the linear trend in FFDI and related meteorological factors in two major forest areas during 1961–2020 were analyzed. Finally, the ensemble empirical mode decomposition method was used to reveal the multi-time scale characteristics of FFDI in the two forest areas. The results show that the spatial distribution of FFDI has obvious regional characteristics on seasonal and annual time scales. Northeast China has a high FFDI during spring and autumn, whereas southwest China has a high FFDI during spring and winter. These seasonal variations show a good corresponding relationship with the forest fire prevention period in the two forest regions. The number of stations showing a significant increasing trend in FFDI in each season is around 10%–20%, with the highest in spring (21%). The linear trends in FFDI in the northeastern forest area are not significant in all seasons; however, among relevant meteorological factors, the daily maximum temperature and average wind speed respectively show a significant warming trend and a significant weakening trend in all seasons. The FFDI in all seasons of the southwestern forest area showed a significant increasing trend at a level of 0.1 at least, among which the trends during the spring and winter fire prevention periods were 0.09/10 a (P<0.1) and 0.05/10 a (P<0.1), respectively. The summer, autumn, and winter periods showed a significant warming and drying trend (P<0.05). The interannual variability contributes more than 70% to the evolution of FFDI in the two forest areas. The nonlinear trend of FFDI in the spring and autumn fire prevention periods in northeast China showed a rapid rise at first and then a decline. The nonlinear trend of FFDI in the spring fire season in southwest China changed from a stable stage in the last century to a rapidly increasing trend in the 21st century, whereas the overall trend of FFDI during the winter fire season increased steadily. Therefore, the situation of fire risk in forests of southwest China is increasingly becoming severe.
2022, 27(5): 578-590.
doi: 10.3878/j.issn.1006-9585.2022.21058
Abstract:
The performance of a precipitation forecast model is related to many factors. In addition to research areas and research data characteristics, it is also affected by the model's algorithm, statistical simulation methods, and performance metrics. This paper is based on the daily rainfall, average temperature, and average relative humidity of 28 stations in Heilongjiang Province in China from 2015 to 2019, using Monte Carlo statistical simulation methods such as Hold-out, Bootstrap, and machine learning methods. For the first time, this paper systematically studied the performances of daily precipitation forecast models in Heilongjiang Province in the summer and the spatial distribution characteristics of the model performances. The results show that for the entire study area, the overall prediction performance of a BP (Back Propagation) neural network and support vector machine is not significantly different, and the value of the area under ROC cuvre is higher than 76%, which is significantly better than that of the decision tree. The prediction performance of the model estimated by Bootstrap is always better than that of Hold-out, and it helps improve the fidelity of the evaluation results. For a single station in the study area, except for certain stations, the value of accuracy and the area under ROC cuvre of the support vector machine are higher than 80%, and the spatial distribution trend is larger in the southeast and smaller in the northwest. This trend is basically consistent with the distribution of precipitation frequency. The overall prediction effect of the SVM (Support Vector Machine) model is better in the Xiaokingan and Zhangguangcai Mountains, followed by the Sanjiang and Songnen Plains. The sensitivity is higher in mountainous areas than in plain areas. The central and southern regions are larger, followed by the eastern region and then the western and northern regions. The spatial distribution of specificity is simply the opposite of that of sensitivity.
The performance of a precipitation forecast model is related to many factors. In addition to research areas and research data characteristics, it is also affected by the model's algorithm, statistical simulation methods, and performance metrics. This paper is based on the daily rainfall, average temperature, and average relative humidity of 28 stations in Heilongjiang Province in China from 2015 to 2019, using Monte Carlo statistical simulation methods such as Hold-out, Bootstrap, and machine learning methods. For the first time, this paper systematically studied the performances of daily precipitation forecast models in Heilongjiang Province in the summer and the spatial distribution characteristics of the model performances. The results show that for the entire study area, the overall prediction performance of a BP (Back Propagation) neural network and support vector machine is not significantly different, and the value of the area under ROC cuvre is higher than 76%, which is significantly better than that of the decision tree. The prediction performance of the model estimated by Bootstrap is always better than that of Hold-out, and it helps improve the fidelity of the evaluation results. For a single station in the study area, except for certain stations, the value of accuracy and the area under ROC cuvre of the support vector machine are higher than 80%, and the spatial distribution trend is larger in the southeast and smaller in the northwest. This trend is basically consistent with the distribution of precipitation frequency. The overall prediction effect of the SVM (Support Vector Machine) model is better in the Xiaokingan and Zhangguangcai Mountains, followed by the Sanjiang and Songnen Plains. The sensitivity is higher in mountainous areas than in plain areas. The central and southern regions are larger, followed by the eastern region and then the western and northern regions. The spatial distribution of specificity is simply the opposite of that of sensitivity.
2022, 27(5): 591-603.
doi: 10.3878/j.issn.1006-9585.2021.21081
Abstract:
This paper focuses on the precipitation projection of the Heihe River basin with downscaling for 2011–2100 using Coupled Model Intercomparison Project Phase 5 multimodel ensemble, combined with European Center for Medium-Range Weather Forecasts reanalysis data and meteorological stations observation in the Heihe River basin. Grid precipitation projection is mapped onto observatory sites through three downscaling methods for bias corrections, which include the model drift removal (MDR), multivariate linear regression (MLR), and Bayesian model average (BMA). Results show that an overestimate of the 15-model ensemble precipitation in the Heihe River basin has not yet been totally removed after MDR is removed, owing to the presence of a nonstationary bias. However, it works well on bias correction if MLR and BMA are used in downscaling with the factors of v-wind, specific humidity, and geopotential height on 700 hPa. The test demonstrates that BMA has a good estimate on averaged precipitation, but it gives a low variance and correlation coefficient with the meteorological stations observation. Conversely, MLR can produce a good variance in precipitation and a high correlation coefficient, but a negative precipitation estimate often appears in the lower reaches of the river, especially in cold and dry seasons. These problems have been overcome to a great extent as soon as the model precipitation is introduced into the downscaling models. Moreover, the test also shows that BMA is in favor of the bias correction in the upper reaches of the river, whereas MLR is good at the site-precipitation estimate in the middle and lower reaches or the whole river basin. The precipitation projection with downscaling shows that the averaged precipitation at 14 sites of the basin would decrease in comparison with that of the 1971–2000 observation, with the rates of −9.7%, −12.5, and −12.1% for 2011–2040, 2041–2070, and 2071–2100, respectively. The rates of projected precipitations are 1.4%, 1.6%, and 2.3% in the upper reaches; −16.3%, −21.4%, and −22.6% in the middle reaches; and 13.0%, 4.2%, and 21.4% in the lower reaches for the three periods of projection, respectively. The projection shows that the precipitation would be increasing slowly in the upper reaches of the Heihe River basin, decreasing significantly in the middle reaches and for 2011–2040, and then increasing remarkably for 2041–2100 in the lower reaches. This implies that the water shortage problem would be intensified in the middle reaches, a farmland area with climate warming under RCP4.5 scenarios. A strategic adjustment is recommended to the structure of the agriculture and economics around the middle reaches for adapting to future climate change along the Heihe River basin.
This paper focuses on the precipitation projection of the Heihe River basin with downscaling for 2011–2100 using Coupled Model Intercomparison Project Phase 5 multimodel ensemble, combined with European Center for Medium-Range Weather Forecasts reanalysis data and meteorological stations observation in the Heihe River basin. Grid precipitation projection is mapped onto observatory sites through three downscaling methods for bias corrections, which include the model drift removal (MDR), multivariate linear regression (MLR), and Bayesian model average (BMA). Results show that an overestimate of the 15-model ensemble precipitation in the Heihe River basin has not yet been totally removed after MDR is removed, owing to the presence of a nonstationary bias. However, it works well on bias correction if MLR and BMA are used in downscaling with the factors of v-wind, specific humidity, and geopotential height on 700 hPa. The test demonstrates that BMA has a good estimate on averaged precipitation, but it gives a low variance and correlation coefficient with the meteorological stations observation. Conversely, MLR can produce a good variance in precipitation and a high correlation coefficient, but a negative precipitation estimate often appears in the lower reaches of the river, especially in cold and dry seasons. These problems have been overcome to a great extent as soon as the model precipitation is introduced into the downscaling models. Moreover, the test also shows that BMA is in favor of the bias correction in the upper reaches of the river, whereas MLR is good at the site-precipitation estimate in the middle and lower reaches or the whole river basin. The precipitation projection with downscaling shows that the averaged precipitation at 14 sites of the basin would decrease in comparison with that of the 1971–2000 observation, with the rates of −9.7%, −12.5, and −12.1% for 2011–2040, 2041–2070, and 2071–2100, respectively. The rates of projected precipitations are 1.4%, 1.6%, and 2.3% in the upper reaches; −16.3%, −21.4%, and −22.6% in the middle reaches; and 13.0%, 4.2%, and 21.4% in the lower reaches for the three periods of projection, respectively. The projection shows that the precipitation would be increasing slowly in the upper reaches of the Heihe River basin, decreasing significantly in the middle reaches and for 2011–2040, and then increasing remarkably for 2041–2100 in the lower reaches. This implies that the water shortage problem would be intensified in the middle reaches, a farmland area with climate warming under RCP4.5 scenarios. A strategic adjustment is recommended to the structure of the agriculture and economics around the middle reaches for adapting to future climate change along the Heihe River basin.
Historical Changes of High Temperature, Heat Waves, and Drought in Ecological Fragile Zones in China
2022, 27(5): 604-618.
doi: 10.3878/j.issn.1006-9585.2021.21044
Abstract:
High temperatures (HT), heat waves (HW), and droughts are the most important extreme weather and climate events affecting terrestrial ecosystems. Previous research focused on their changes in the whole of China, regions based on geographical divisions, or a single region in China. The historical changes of the extreme events in ecologically fragile zones (EFZs) in China still remain under debate. This study analyzes the spatial−temporal changes in HT, HW, and drought in the EFZs in China between 1980 and 2014 using observational daily maximum surface air temperature and monthly standardized precipitation evapotranspiration index datasets. It has been revealed that the frequency of both HT and HW increased between 1980 and 2014 over nearly all EFZs in China, and the long-term trends of HT and HW exhibited similar spatial patterns, with a significant increase in the central and western EFZs of northern China and the eastern EFZs of southern China. The area fraction with a significant increase was the highest in the southwest karst rocky desertification EFZs and the lowest in the southern agriculture and pasture EFZs. All EFZs showed increasing HT and HW frequency except for the southern agriculture and pasture EFZs, and the trends were significant except for the northern agriculture, pasture, forest, and grassland EFZs. In addition, after the mid-1990s, the frequency and interannual variability of HT and HW in the northern EFZs increased rapidly. Moreover, the EFZs in eastern China had a trend of dryness and increased drought events, while the rest of the EFZs had a trend of wetness and decreased extreme drought events, where only the trend of drought events in the southwest karst rocky desertification EFZ is significant.
High temperatures (HT), heat waves (HW), and droughts are the most important extreme weather and climate events affecting terrestrial ecosystems. Previous research focused on their changes in the whole of China, regions based on geographical divisions, or a single region in China. The historical changes of the extreme events in ecologically fragile zones (EFZs) in China still remain under debate. This study analyzes the spatial−temporal changes in HT, HW, and drought in the EFZs in China between 1980 and 2014 using observational daily maximum surface air temperature and monthly standardized precipitation evapotranspiration index datasets. It has been revealed that the frequency of both HT and HW increased between 1980 and 2014 over nearly all EFZs in China, and the long-term trends of HT and HW exhibited similar spatial patterns, with a significant increase in the central and western EFZs of northern China and the eastern EFZs of southern China. The area fraction with a significant increase was the highest in the southwest karst rocky desertification EFZs and the lowest in the southern agriculture and pasture EFZs. All EFZs showed increasing HT and HW frequency except for the southern agriculture and pasture EFZs, and the trends were significant except for the northern agriculture, pasture, forest, and grassland EFZs. In addition, after the mid-1990s, the frequency and interannual variability of HT and HW in the northern EFZs increased rapidly. Moreover, the EFZs in eastern China had a trend of dryness and increased drought events, while the rest of the EFZs had a trend of wetness and decreased extreme drought events, where only the trend of drought events in the southwest karst rocky desertification EFZ is significant.
2022, 27(5): 619-629.
doi: 10.3878/j.issn.1006-9585.2022.21012
Abstract:
Rainbows are a weather landscape resource with great ornamental value but no effective observation data. To know the distribution of rainbow resources in China and provide meteorological services for local tourism and other industries, on the basis of relevant theories, four factors necessary for rainbows are determined, which are the number of thunderstorms, the solar altitude, the terrain, and the visibility affecting rainbow viewing quality. A conceptual model of visible rainbow evaluation is constructed based on these factors. Using this model, the distribution of rainbow resources is evaluated based on the national meteorological observation data and latitude data from 1980 to 2010, and the following conclusions are drawn: (1) The rainbow resources in South China, southern Yunnan and the Qinghai–Tibet Plateau are relatively rich. Among them, the possibility of a rainbow appearing in different seasons in South China is at the forefront of the whole country, but few rainbows appear in winter. (2) the abundance of rainbow resources is smaller in the northern region than in the south, but in some places, it is very high in the national ranking because of the comprehensive influence of solar altitude, topography, visibility, and thunderstorm weather. For example, the western part of Xinjiang and some parts of northern North China have the advantage of developing rainbow ecotourism. Particularly in Zhaosu, Xinjiang, from May to September, the number and ornamental value of rainbows ranked first in China. (3) inspection shows that the conceptual model can be used to evaluate or rank the richness of rainbow resources throughout the country and can be further applied to the rainbow forecasting model, which will improve the accuracy of rainbow predictions.
Rainbows are a weather landscape resource with great ornamental value but no effective observation data. To know the distribution of rainbow resources in China and provide meteorological services for local tourism and other industries, on the basis of relevant theories, four factors necessary for rainbows are determined, which are the number of thunderstorms, the solar altitude, the terrain, and the visibility affecting rainbow viewing quality. A conceptual model of visible rainbow evaluation is constructed based on these factors. Using this model, the distribution of rainbow resources is evaluated based on the national meteorological observation data and latitude data from 1980 to 2010, and the following conclusions are drawn: (1) The rainbow resources in South China, southern Yunnan and the Qinghai–Tibet Plateau are relatively rich. Among them, the possibility of a rainbow appearing in different seasons in South China is at the forefront of the whole country, but few rainbows appear in winter. (2) the abundance of rainbow resources is smaller in the northern region than in the south, but in some places, it is very high in the national ranking because of the comprehensive influence of solar altitude, topography, visibility, and thunderstorm weather. For example, the western part of Xinjiang and some parts of northern North China have the advantage of developing rainbow ecotourism. Particularly in Zhaosu, Xinjiang, from May to September, the number and ornamental value of rainbows ranked first in China. (3) inspection shows that the conceptual model can be used to evaluate or rank the richness of rainbow resources throughout the country and can be further applied to the rainbow forecasting model, which will improve the accuracy of rainbow predictions.
Macroscopic Characteristics and Formation Mechanisms of Arctic Clouds Based on CloudSat-CALIPSO Data
2022, 27(5): 630-642.
doi: 10.3878/j.issn.1006-9585.2021.21152
Abstract:
On the basis of CloudSat-CALIPSO (CloudSat-Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite inversion data and ERA5 (ECMWF Reanalysis v5) monthly average reanalysis data, the temporal and spatial distributions of the total cloud amount and stratocumulus clouds (Sc) with the highest frequency in the Arctic are analyzed, and the possible reasons for the formation of Sc are discussed. The results show that the total cloud amount in the Arctic region is the largest in autumn, and the increase in cloud amount is more obvious in the Kara Sea–Chukchi Sea region. Factors such as the relatively large sea–air temperature difference, strong upward movement of the boundary layer forced by the surface latent heat flux, and high relative humidity are the main causes of cloud formation and maintenance in this area. In addition, a large amount of Sc is observed in the Arctic, mainly in the Norwegian Sea–Barents Sea, which is almost without sea-ice cover all year round. The lower tropospheric stability (LTS) in this area is negatively correlated with the amount of Sc. The greater the LTS is, the smaller the amount of Sc. This phenomenon is different from what is observed in tropical, mid-latitude regions. The factors affecting the formation of Sc in this sea area can be attributed to the open ocean reducing the LTS through surface–atmosphere coupling and the turbulent surface flux of heat and humidity, which promote the formation of Sc and increase the coverage of Sc.
On the basis of CloudSat-CALIPSO (CloudSat-Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite inversion data and ERA5 (ECMWF Reanalysis v5) monthly average reanalysis data, the temporal and spatial distributions of the total cloud amount and stratocumulus clouds (Sc) with the highest frequency in the Arctic are analyzed, and the possible reasons for the formation of Sc are discussed. The results show that the total cloud amount in the Arctic region is the largest in autumn, and the increase in cloud amount is more obvious in the Kara Sea–Chukchi Sea region. Factors such as the relatively large sea–air temperature difference, strong upward movement of the boundary layer forced by the surface latent heat flux, and high relative humidity are the main causes of cloud formation and maintenance in this area. In addition, a large amount of Sc is observed in the Arctic, mainly in the Norwegian Sea–Barents Sea, which is almost without sea-ice cover all year round. The lower tropospheric stability (LTS) in this area is negatively correlated with the amount of Sc. The greater the LTS is, the smaller the amount of Sc. This phenomenon is different from what is observed in tropical, mid-latitude regions. The factors affecting the formation of Sc in this sea area can be attributed to the open ocean reducing the LTS through surface–atmosphere coupling and the turbulent surface flux of heat and humidity, which promote the formation of Sc and increase the coverage of Sc.
2022, 27(5): 643-652.
doi: 10.3878/j.issn.1006-9585.2021.21068
Abstract:
Understanding of shallow convective clouds (SCCs) is important for forecasting deep, convective thunderstorms and studying radiation balance and climate change. Because an SCC has very small horizontal and vertical lengths and a short lifetime, the ways to measure SCCs remain limited. In this paper, we use 2B-CLDCLASS-LIDAR and 2B-GEOPRO from CloudSat, CALIPSO, and MODIS (Moderate-resolution Imaging Spectroradiometer) for 2009–2010 to study SCCs. The test case shows that this method is reasonable and feasible. The analysis shows that the occurrence frequency of SCCs above Inner Mongolia grassland has obvious seasonal variations and is greatest in summer and autumn. The horizontal distribution of its occurrence frequency shows a low trend in the northwest and high trend in the southeast, which is closely related to the altitude gradient. The average cloud base height of SCCs obviously differs between winter and summer, being highest in August and lowest in December. SCCs in different regions show that their cloud base heights trend releated with altitude.
Understanding of shallow convective clouds (SCCs) is important for forecasting deep, convective thunderstorms and studying radiation balance and climate change. Because an SCC has very small horizontal and vertical lengths and a short lifetime, the ways to measure SCCs remain limited. In this paper, we use 2B-CLDCLASS-LIDAR and 2B-GEOPRO from CloudSat, CALIPSO, and MODIS (Moderate-resolution Imaging Spectroradiometer) for 2009–2010 to study SCCs. The test case shows that this method is reasonable and feasible. The analysis shows that the occurrence frequency of SCCs above Inner Mongolia grassland has obvious seasonal variations and is greatest in summer and autumn. The horizontal distribution of its occurrence frequency shows a low trend in the northwest and high trend in the southeast, which is closely related to the altitude gradient. The average cloud base height of SCCs obviously differs between winter and summer, being highest in August and lowest in December. SCCs in different regions show that their cloud base heights trend releated with altitude.
2022, 27(5): 653-668.
doi: 10.3878/j.issn.1006-9585.2021.21109
Abstract:
The WRFDA-Chem system with the atmospheric chemistry three-dimensional variational (3DVAR) algorithm was developed and applied in the Rapid Refresh Multi-scale Analysis and Prediction System-Chem (RMAPS-Chem), and experiments were conducted with and without the assimilation of the hourly surface PM2.5 and PM10 mass concentration in November 2016 to analyze the impacts of data assimilation on forecasting. The 6-h cycle assimilation results demonstrate that the assimilation of the surface PM2.5 and PM10 observations significantly improved the model performance of PM2.5 and PM10 initial fields with an increase in the correlation by 0.27–0.37 and a reduction in the root mean square error (RMSE) of about 40%. The improvement of the PM2.5 and PM10 forecasts was acquired for over 24 h with the initial analyzed field; the RMSE of the 24-h forecast PM2.5 (PM10) was reduced by 25% (10%), and the correlation of PM2.5 (PM10) increased by 14% (25%), respectively. The increase in the data assimilation (DA) cycling frequency (from 6-h to hourly DA cycle) could further improve the PM2.5 and PM10 forecast. In future operational applications, additional experiments on the data quality control/filtering in the system should be considered to obtain an optimized assimilation performance. Since the biases reflected the combining results of model uncertainties from various aspects, better understanding and diagnosis of model uncertainties should be aimed to promote the synergistic development of the model and the data assimilation system in the future.
The WRFDA-Chem system with the atmospheric chemistry three-dimensional variational (3DVAR) algorithm was developed and applied in the Rapid Refresh Multi-scale Analysis and Prediction System-Chem (RMAPS-Chem), and experiments were conducted with and without the assimilation of the hourly surface PM2.5 and PM10 mass concentration in November 2016 to analyze the impacts of data assimilation on forecasting. The 6-h cycle assimilation results demonstrate that the assimilation of the surface PM2.5 and PM10 observations significantly improved the model performance of PM2.5 and PM10 initial fields with an increase in the correlation by 0.27–0.37 and a reduction in the root mean square error (RMSE) of about 40%. The improvement of the PM2.5 and PM10 forecasts was acquired for over 24 h with the initial analyzed field; the RMSE of the 24-h forecast PM2.5 (PM10) was reduced by 25% (10%), and the correlation of PM2.5 (PM10) increased by 14% (25%), respectively. The increase in the data assimilation (DA) cycling frequency (from 6-h to hourly DA cycle) could further improve the PM2.5 and PM10 forecast. In future operational applications, additional experiments on the data quality control/filtering in the system should be considered to obtain an optimized assimilation performance. Since the biases reflected the combining results of model uncertainties from various aspects, better understanding and diagnosis of model uncertainties should be aimed to promote the synergistic development of the model and the data assimilation system in the future.
2022, 27(5): 669-678.
doi: 10.3878/j.issn.1006-9585.2022.21145
Abstract:
Based on the hourly reanalysis data of ERA5 from 1981 to 2020, the temporal and spatial variation characteristics of human physiological thermal response in the Yangtze River Delta under climate change were analyzed by using the Universal Thermal Climate Index (UTCI). The results show that the occurrence frequency of climate conditions with no thermal stress is 43.77%, the occurrence frequency of climate conditions with heat stress and cold stress is 22.42% and 33.81%, respectively. The occurrence frequency of climate conditions with intensity heat stress and intensity cold stress is 8.38% and 1.58%, respectively. In terms of spatial distribution, the occurrence frequency of climate conditions with no thermal stress in the Yangtze River Delta shows a zonal distribution of more in the south and less in the north, meanwhile, the characteristics are diagnosed as more in the mountainous areas and less in the plains, more in the coastal areas and less in the inland areas. The intense heat stress mainly occurs in the plains of western and southern Anhui, plan of Jiaxing and Shaoxing, and basin of Jinhua and Quzhou in Zhejiang. Intensity cold stress mainly occurred in the northern Jiangsu, northern Anhui and coastal areas of northern Zhoushan. Under the background of global warming, the interdecadal variation of UTCI value in the Yangtze River Delta shows an upward trend, increasing from 13.83℃ during 1981−1990 to 14.75℃ during 2011−2020. The physiological thermal response of human body indicates a character of the decrease in cold stress, the increase in heat stress, and no thermal stress remains basically unchanged. The increase of periods with no thermal stress in spring basically offset the decrease of periods with no thermal stress in summer.
Based on the hourly reanalysis data of ERA5 from 1981 to 2020, the temporal and spatial variation characteristics of human physiological thermal response in the Yangtze River Delta under climate change were analyzed by using the Universal Thermal Climate Index (UTCI). The results show that the occurrence frequency of climate conditions with no thermal stress is 43.77%, the occurrence frequency of climate conditions with heat stress and cold stress is 22.42% and 33.81%, respectively. The occurrence frequency of climate conditions with intensity heat stress and intensity cold stress is 8.38% and 1.58%, respectively. In terms of spatial distribution, the occurrence frequency of climate conditions with no thermal stress in the Yangtze River Delta shows a zonal distribution of more in the south and less in the north, meanwhile, the characteristics are diagnosed as more in the mountainous areas and less in the plains, more in the coastal areas and less in the inland areas. The intense heat stress mainly occurs in the plains of western and southern Anhui, plan of Jiaxing and Shaoxing, and basin of Jinhua and Quzhou in Zhejiang. Intensity cold stress mainly occurred in the northern Jiangsu, northern Anhui and coastal areas of northern Zhoushan. Under the background of global warming, the interdecadal variation of UTCI value in the Yangtze River Delta shows an upward trend, increasing from 13.83℃ during 1981−1990 to 14.75℃ during 2011−2020. The physiological thermal response of human body indicates a character of the decrease in cold stress, the increase in heat stress, and no thermal stress remains basically unchanged. The increase of periods with no thermal stress in spring basically offset the decrease of periods with no thermal stress in summer.
