Nanjing University of Information Science and Technology

About: Nanjing University of Information Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Precipitation & Aerosol. The organization has 14129 authors who have published 17985 publications receiving 267578 citations. The organization is also known as: Nan Xin Da.

Representation of the Antarctic Circumpolar Current in the CMIP5 climate models and future changes under warming scenarios

Abstract: The representation of the Antarctic Circumpolar Current (ACC) in the fifth Coupled Models Intercomparison Project (CMIP5) is generally improved over CMIP3. The range of modeled transports in the historical (1976–2006) scenario is reduced (90–264 Sv) compared with CMIP3 (33–337 Sv) with a mean of 155 ± 51 Sv. The large intermodel range is associated with significant differences in the ACC density structure. The ACC position is accurately represented at most longitudes, with a small (1.27°) standard deviation in mean latitude. The westerly wind jet driving the ACC is biased too strong and too far north on average. Unlike CMIP3 there is no correlation between modeled ACC latitude and the position of the westerly wind jet. Under future climate forcing scenarios (2070–2099 mean) the modeled ACC transport changes by between −26 to +17 Sv and the ACC shifts polewards (equatorwards) in models where the transport increases (decreases). There is no significant correlation between the ACC position change and that of the westerly wind jet, which shifts polewards and strengthens. The subtropical gyres strengthen and expand southwards, while the change in subpolar gyre area varies between models. An increase in subpolar gyre area corresponds with a decreases in ACC transport and an equatorward shift in the ACC position, and vice versa for a contraction of the gyre area. There is a general decrease in density in the upper 1000 m, particularly equatorwards of the ACC core.

Comparing the diurnal and seasonal variabilities of atmospheric and surface urban heat islands based on the Beijing urban meteorological network

Abstract: This study compared the diurnal and seasonal cycles of atmospheric and surface urban heat islands (UHIs) based on hourly air temperatures (Ta) collected at 65 out of 262 stations in Beijing and land surface temperature (Ts) derived from Moderate Resolution Imaging Spectroradiometer in the years 2013–2014. We found that the nighttime atmospheric and surface UHIs referenced to rural cropland stations exhibited significant seasonal cycles, with the highest in winter. However, the seasonal variations in the nighttime UHIs referenced to mountainous forest stations were negligible, because mountainous forests have a higher nighttime Ts in winter and a lower nighttime Ta in summer than rural croplands. Daytime surface UHIs showed strong seasonal cycles, with the highest in summer. The daytime atmospheric UHIs exhibited a similar but less seasonal cycle under clear-sky conditions, which was not apparent under cloudy-sky conditions. Atmospheric UHIs in urban parks were higher in daytime. Nighttime atmospheric UHIs are influenced by energy stored in urban materials during daytime and released during nighttime. The stronger anthropogenic heat release in winter causes atmospheric UHIs to increase with time during winter nights, but decrease with time during summer nights. The percentage of impervious surfaces is responsible for 49%–54% of the nighttime atmospheric UHI variability and 31%–38% of the daytime surface UHI variability. However, the nighttime surface UHI was nearly uncorrelated with the percentage of impervious surfaces around the urban stations.

Satellite-based estimates of decline and rebound in China's CO 2 emissions during COVID-19 pandemic.

Abstract: Changes in CO2 emissions during the COVID-19 pandemic have been estimated from indicators on activities like transportation and electricity generation. Here, we instead use satellite observations together with bottom-up information to track the daily dynamics of CO2 emissions during the pandemic. Unlike activity data, our observation-based analysis deploys independent measurement of pollutant concentrations in the atmosphere to correct misrepresentation in the bottom-up data and can provide more detailed insights into spatially explicit changes. Specifically, we use TROPOMI observations of NO2 to deduce 10-day moving averages of NO x and CO2 emissions over China, differentiating emissions by sector and province. Between January and April 2020, China's CO2 emissions fell by 11.5% compared to the same period in 2019, but emissions have since rebounded to pre-pandemic levels before the coronavirus outbreak at the beginning of January 2020 owing to the fast economic recovery in provinces where industrial activity is concentrated.