2017 was the warmest year on record for the global ocean according to an updated Institute of Atmospheric Physics, Chinese Academy of Sciences （IAR CAS： http：//english. iap.cas.cn/） ocean analysis.
The global annual averaged Surface Air Temperature Anomaly （SATA） in 2015 and its rank in the historical instrumental records are analyzed using the CRU, NASA, and NOAA datasets. All datasets indicate that 2015 is t...The global annual averaged Surface Air Temperature Anomaly （SATA） in 2015 and its rank in the historical instrumental records are analyzed using the CRU, NASA, and NOAA datasets. All datasets indicate that 2015 is the warmest year, which is 0.74℃ warmer than normal years from 1961 to 1990 in the HadCRUT4 data-set. The most evident warm anomaly occurs over land, especially at high latitudes. The averaged SATA over land is 1.13 ℃, which is 0.54℃warmer than that over oceans （0.59℃）. Because an El Niffo event occurred in 2015 and 1998 and 1998 is also the warmest year in the twentieth century, these two years are compared to explain the formation of the warmest climate. A statistical approach that is known as the Ensemble Empirical Mode Decomposition （EEMD） is employed to isolate the components with different timescales, which range from interannual to centennial and a long-term trend. In 2015 the developing El Niffo may have contributed an anomaly of 0. 1 0℃, while this value is 0.1 8 ℃ for 1998. The contribution of the decadal-multidecadal variability and beyond to 2015 is 0.64℃, which is significantly larger than that of the interannual anomaly components （0.10 ℃）. This indicates that the warmest climate in 2015 occurred in the context of the tirnescales beyond the interannual.展开更多
The editorial office of Advances of Atmospheric Sciences （AAS）, on behalf of all AAS editors, would like to publicly acknowledge the people listed below who served as reviewers for the journal during 1 September 201...The editorial office of Advances of Atmospheric Sciences （AAS）, on behalf of all AAS editors, would like to publicly acknowledge the people listed below who served as reviewers for the journal during 1 September 2012 to 31 August 2013. We recognize that the time and work of the reviewers is the most important resource in academic publishing. The quality of our journal depends in a crucial way upon the reviewing process and therefore all reviewers＇ time and efforts taken to sustain the quality of the journal are greatly appreciated.展开更多
The editorial office of Advances in Atmospheric Sciences(AAS),on behalf of all AAS editors,would like to publicly acknowledge the people listed below who served as reviewers for the journal during 1 September 2016 to ...The editorial office of Advances in Atmospheric Sciences(AAS),on behalf of all AAS editors,would like to publicly acknowledge the people listed below who served as reviewers for the journal during 1 September 2016 to 31 August 2017.We recognize that the time and work of the reviewers is the most important resource in academic publishing.The quality of our journal depends in a crucial way upon the reviewing process and therefore all reviewers,time and efforts taken to sustain the quality of the journal are greatly appreciated.展开更多
Many previous studies have focused on the impacts of urbanization on regional mean temperatures. Relatively few have analyzed changes in extreme temperatures. Here, we examine the impact of urbanization on extreme war...Many previous studies have focused on the impacts of urbanization on regional mean temperatures. Relatively few have analyzed changes in extreme temperatures. Here, we examine the impact of urbanization on extreme warmest night temperatures from 33 stations in the Bohai area between 1958 and 2009. We compute the Generalized Extreme Value（GEV） distribution of extreme warmest night temperatures and analyze long-term variations in its characteristic parameters. A new classification method based on the factor analysis of changes in extreme night temperatures is developed to detect the efects of urbanization in diferent cities. Of the three parameters that characterize the GEV distribution, the position parameter is the most representative of long-term changes in extreme warmest night temperatures. During the period of rapid urbanization（i.e., after 1978）, all three parameters of the GEV distribution are larger for the urban station group than for the reference station group, so are the magnitudes of their variations, and the urban areas have been experiencing higher extreme warmest night temperatures with larger variability. Diferent types of cities in the Bohai area have all experienced an urban heat island efect, with an average urbanization efect of approximately 0.3 per decade.展开更多
The heating effect （or mass elevation effect, MEE） of the Tibetan Plateau （TP） is intense due to its massive body. Some studies have been undertaken on its role as the heat source in summer and its implications fo...The heating effect （or mass elevation effect, MEE） of the Tibetan Plateau （TP） is intense due to its massive body. Some studies have been undertaken on its role as the heat source in summer and its implications for Asian climate, but little has been known of the im- plications of its MEE for the distribution of mountain altitudinal belts （MABs）. Using air tem- perature data observed and remotely sensed data, MAB/treeline data, and ASTER GDEM data, this paper compares the height of MABs and alpine treelines in the main TP and the surrounding mountains/lowland and explains the difference from the point of view of MEE. The results demonstrate： 1） at same elevation, air temperature and the length of growing season gradually increase from the eastern edge to the interior TP, e.g., at 4500 m （corre- sponding to the mean altitude of the TP）, the monthly mean temperature is 3.58℃ higher （April） to 6.63℃ higher （June） in the interior plateau than in the Sichuan Basin; the 10℃ iso- therm for the warmest month goes upward from the edge to the interior of the plateau, at 4000 m in the Qilian Mts. and the eastern edges of the plateau, and up to 4600-5000 m in Lhasa and Zuogong; the warmth index at an altitude of 4500 m can be up to 15℃-month in the in- terior TP, but much lower at the eastern edges. 2） MABs and treeline follow a similar trend of rising inwards： dark-coniferous forest is 1000-1500 m higher and alpine steppe is about 700-900 m higher in the interior TP than at the eastern edges.展开更多
基金supported by the National Key Research and Development Program of China (Grant Nos. 2017YFA0603202 and 2016YFC1401705)
文摘2017 was the warmest year on record for the global ocean according to an updated Institute of Atmospheric Physics, Chinese Academy of Sciences （IAR CAS： http：//english. iap.cas.cn/） ocean analysis.
基金supported by the National Natural Science Foundation of China[grant number 41421004]the National Key Basic Research and Development Program of China[grant numbers 2016YFA0601802 and 2015CB453202]
文摘The global annual averaged Surface Air Temperature Anomaly （SATA） in 2015 and its rank in the historical instrumental records are analyzed using the CRU, NASA, and NOAA datasets. All datasets indicate that 2015 is the warmest year, which is 0.74℃ warmer than normal years from 1961 to 1990 in the HadCRUT4 data-set. The most evident warm anomaly occurs over land, especially at high latitudes. The averaged SATA over land is 1.13 ℃, which is 0.54℃warmer than that over oceans （0.59℃）. Because an El Niffo event occurred in 2015 and 1998 and 1998 is also the warmest year in the twentieth century, these two years are compared to explain the formation of the warmest climate. A statistical approach that is known as the Ensemble Empirical Mode Decomposition （EEMD） is employed to isolate the components with different timescales, which range from interannual to centennial and a long-term trend. In 2015 the developing El Niffo may have contributed an anomaly of 0. 1 0℃, while this value is 0.1 8 ℃ for 1998. The contribution of the decadal-multidecadal variability and beyond to 2015 is 0.64℃, which is significantly larger than that of the interannual anomaly components （0.10 ℃）. This indicates that the warmest climate in 2015 occurred in the context of the tirnescales beyond the interannual.
文摘The editorial office of Advances of Atmospheric Sciences （AAS）, on behalf of all AAS editors, would like to publicly acknowledge the people listed below who served as reviewers for the journal during 1 September 2012 to 31 August 2013. We recognize that the time and work of the reviewers is the most important resource in academic publishing. The quality of our journal depends in a crucial way upon the reviewing process and therefore all reviewers＇ time and efforts taken to sustain the quality of the journal are greatly appreciated.
文摘The editorial office of Advances in Atmospheric Sciences(AAS),on behalf of all AAS editors,would like to publicly acknowledge the people listed below who served as reviewers for the journal during 1 September 2016 to 31 August 2017.We recognize that the time and work of the reviewers is the most important resource in academic publishing.The quality of our journal depends in a crucial way upon the reviewing process and therefore all reviewers,time and efforts taken to sustain the quality of the journal are greatly appreciated.
基金Supported by the National Basic Research and Development (973) Program of China (2010CB951600)National Science and Technology Support Program of China (2012BAC22B05)+1 种基金National Natural Science Foundation of China (40605021)China Meteorological Administration Special Fund for Climate Change (CCSF201224)
文摘Many previous studies have focused on the impacts of urbanization on regional mean temperatures. Relatively few have analyzed changes in extreme temperatures. Here, we examine the impact of urbanization on extreme warmest night temperatures from 33 stations in the Bohai area between 1958 and 2009. We compute the Generalized Extreme Value（GEV） distribution of extreme warmest night temperatures and analyze long-term variations in its characteristic parameters. A new classification method based on the factor analysis of changes in extreme night temperatures is developed to detect the efects of urbanization in diferent cities. Of the three parameters that characterize the GEV distribution, the position parameter is the most representative of long-term changes in extreme warmest night temperatures. During the period of rapid urbanization（i.e., after 1978）, all three parameters of the GEV distribution are larger for the urban station group than for the reference station group, so are the magnitudes of their variations, and the urban areas have been experiencing higher extreme warmest night temperatures with larger variability. Diferent types of cities in the Bohai area have all experienced an urban heat island efect, with an average urbanization efect of approximately 0.3 per decade.
基金National Natural Science Foundation of China, No.41571099 No.41001278
文摘The heating effect （or mass elevation effect, MEE） of the Tibetan Plateau （TP） is intense due to its massive body. Some studies have been undertaken on its role as the heat source in summer and its implications for Asian climate, but little has been known of the im- plications of its MEE for the distribution of mountain altitudinal belts （MABs）. Using air tem- perature data observed and remotely sensed data, MAB/treeline data, and ASTER GDEM data, this paper compares the height of MABs and alpine treelines in the main TP and the surrounding mountains/lowland and explains the difference from the point of view of MEE. The results demonstrate： 1） at same elevation, air temperature and the length of growing season gradually increase from the eastern edge to the interior TP, e.g., at 4500 m （corre- sponding to the mean altitude of the TP）, the monthly mean temperature is 3.58℃ higher （April） to 6.63℃ higher （June） in the interior plateau than in the Sichuan Basin; the 10℃ iso- therm for the warmest month goes upward from the edge to the interior of the plateau, at 4000 m in the Qilian Mts. and the eastern edges of the plateau, and up to 4600-5000 m in Lhasa and Zuogong; the warmth index at an altitude of 4500 m can be up to 15℃-month in the in- terior TP, but much lower at the eastern edges. 2） MABs and treeline follow a similar trend of rising inwards： dark-coniferous forest is 1000-1500 m higher and alpine steppe is about 700-900 m higher in the interior TP than at the eastern edges.
