CO_(2)is one of the most important greenhouse gases(GHGs)in the earth’s atmosphere.Since the industrial era,anthropogenic activities have emitted excessive quantities of GHGs into the atmosphere,resulting in climate ...CO_(2)is one of the most important greenhouse gases(GHGs)in the earth’s atmosphere.Since the industrial era,anthropogenic activities have emitted excessive quantities of GHGs into the atmosphere,resulting in climate warming since the 1950s and leading to an increased frequency of extreme weather and climate events.In 2020,China committed to striving for carbon neutrality by 2060.This commitment and China’s consequent actions will result in significant changes in global and regional anthropogenic carbon emissions and therefore require timely,comprehensive,and objective monitoring and verification support(MVS)systems.The MVS approach relies on the top-down assimilation and inversion of atmospheric CO_(2)concentrations,as recommended by the Intergovernmental Panel on Climate Change(IPCC)Inventory Guidelines in 2019.However,the regional high-resolution assimilation and inversion method is still in its initial stage of development.Here,we have constructed an inverse system for carbon sources and sinks at the kilometer level by coupling proper orthogonal decomposition(POD)with four-dimensional variational(4DVar)data assimilation based on the weather research and forecasting-greenhouse gas(WRF-GHG)model.Our China Carbon Monito ring and Verification Support at the Regional level(CCMVS-R)system can continuously assimilate information on atmospheric CO_(2)and other related information and realize the inversion of regional and local anthropogenic carbon emissions and natural terrestrial ecosystem carbon exchange.Atmospheric CO_(2)data were collected from six ground-based monito ring sites in Shanxi Province,China to verify the inversion effect of regio nal anthropogenic carbon emissions by setting ideal and real experiments using a two-layer nesting method(at 27 and 9 km).The uncertainty of the simulated atmospheric CO_(2)decreased significantly,with a root-mean-square error of CO_(2)concentration values between the ideal value and the simulated after assimilation was close to 0.The total anthropogenic carbon emissions in Shanxi Province in 2019 from the assimilated inversions were approximately 28.6%(17%-38%)higher than the mean of five emission inventories using the bottomup method,showing that the top-down CCMVS-R system can obtain more comprehensive information on anthropogenic carbon emissions.展开更多
Based on China’s monthly precipitation data from 1950 to 2000 and by using the Z-index, 4 categories of flood were estimated. Variation and change of flood in South China were analyzed in terms of percentage areas of...Based on China’s monthly precipitation data from 1950 to 2000 and by using the Z-index, 4 categories of flood were estimated. Variation and change of flood in South China were analyzed in terms of percentage areas of flood. This study reveals that flood areas in South China had a slightly decreasing trend in the latest 50 years. During the winter half year, however, it displayed an increasing trend, especially since the 1990’s. It is also found that flood areas decreased during the summer half year from April to September, but increased during summer, especially since the 1990’s. In the annually first season of precipitation, the flood area has a decreasing trend, but it has a strongly increasing trend in the annually second season. The gradual wet trend during the winter-half year results in wetter climate condition for South China, which will be more favorable for spreading some of the epidemic pathogenic bacterium, crop diseases and insect pests.展开更多
The limit of numerical prediction and ensemble prediction can be further understood by the study of the forecast jump. By using the ensemble average forecast and control forecast product output data for the United Sta...The limit of numerical prediction and ensemble prediction can be further understood by the study of the forecast jump. By using the ensemble average forecast and control forecast product output data for the United States National Environmental Prediction Center (NCEP) global ensemble forecast system (GEFS), and the concept of Jumpiness index from Zsoter et al., we analyzed the statistical characteristics of forecast jump. Results show that, on average, in the NCEP ensemble forecast product, the time average prediction jump index increases with the increase of the forecast aging, and the actual forecast experience can reflect this phenomenon. The consistency of ensemble average forecast is better than the corresponding control forecast. Also, in summer, the frequency of “forecast jump” phenomenon is fluctuating by 17.5%.展开更多
基金supported by the General Project of Top-Design of Multi-Scale Nature-Social ModelsData Support and Decision Support System for NSFC Carbon Neutrality Major Project(42341202)the Basic Scientific Research Fund of the Chinese Academy of Meteorological Sciences(2021Z014)。
文摘CO_(2)is one of the most important greenhouse gases(GHGs)in the earth’s atmosphere.Since the industrial era,anthropogenic activities have emitted excessive quantities of GHGs into the atmosphere,resulting in climate warming since the 1950s and leading to an increased frequency of extreme weather and climate events.In 2020,China committed to striving for carbon neutrality by 2060.This commitment and China’s consequent actions will result in significant changes in global and regional anthropogenic carbon emissions and therefore require timely,comprehensive,and objective monitoring and verification support(MVS)systems.The MVS approach relies on the top-down assimilation and inversion of atmospheric CO_(2)concentrations,as recommended by the Intergovernmental Panel on Climate Change(IPCC)Inventory Guidelines in 2019.However,the regional high-resolution assimilation and inversion method is still in its initial stage of development.Here,we have constructed an inverse system for carbon sources and sinks at the kilometer level by coupling proper orthogonal decomposition(POD)with four-dimensional variational(4DVar)data assimilation based on the weather research and forecasting-greenhouse gas(WRF-GHG)model.Our China Carbon Monito ring and Verification Support at the Regional level(CCMVS-R)system can continuously assimilate information on atmospheric CO_(2)and other related information and realize the inversion of regional and local anthropogenic carbon emissions and natural terrestrial ecosystem carbon exchange.Atmospheric CO_(2)data were collected from six ground-based monito ring sites in Shanxi Province,China to verify the inversion effect of regio nal anthropogenic carbon emissions by setting ideal and real experiments using a two-layer nesting method(at 27 and 9 km).The uncertainty of the simulated atmospheric CO_(2)decreased significantly,with a root-mean-square error of CO_(2)concentration values between the ideal value and the simulated after assimilation was close to 0.The total anthropogenic carbon emissions in Shanxi Province in 2019 from the assimilated inversions were approximately 28.6%(17%-38%)higher than the mean of five emission inventories using the bottomup method,showing that the top-down CCMVS-R system can obtain more comprehensive information on anthropogenic carbon emissions.
基金Project "Statistics of drought in China since 1950 and analysis of its characteristics"(SZ2003C-04)
文摘Based on China’s monthly precipitation data from 1950 to 2000 and by using the Z-index, 4 categories of flood were estimated. Variation and change of flood in South China were analyzed in terms of percentage areas of flood. This study reveals that flood areas in South China had a slightly decreasing trend in the latest 50 years. During the winter half year, however, it displayed an increasing trend, especially since the 1990’s. It is also found that flood areas decreased during the summer half year from April to September, but increased during summer, especially since the 1990’s. In the annually first season of precipitation, the flood area has a decreasing trend, but it has a strongly increasing trend in the annually second season. The gradual wet trend during the winter-half year results in wetter climate condition for South China, which will be more favorable for spreading some of the epidemic pathogenic bacterium, crop diseases and insect pests.
基金jointly supported by the Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDA19070401]the National Natural Science Foundation of China[grant numbers 41790473 and41421004]the Fundamental Research Funds for the Central Universities
文摘The limit of numerical prediction and ensemble prediction can be further understood by the study of the forecast jump. By using the ensemble average forecast and control forecast product output data for the United States National Environmental Prediction Center (NCEP) global ensemble forecast system (GEFS), and the concept of Jumpiness index from Zsoter et al., we analyzed the statistical characteristics of forecast jump. Results show that, on average, in the NCEP ensemble forecast product, the time average prediction jump index increases with the increase of the forecast aging, and the actual forecast experience can reflect this phenomenon. The consistency of ensemble average forecast is better than the corresponding control forecast. Also, in summer, the frequency of “forecast jump” phenomenon is fluctuating by 17.5%.
