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The atmospheric carbon dioxide (CO 2) mixing ratio and its carbon isotope (δ13 C-CO 2) composition contain important CO 2 sink and source information spanning from ecosystem to global scales. The observation and simulation for both CO 2 and δ13 C-CO 2 can be used to constrain regional emissions and better understand the anthropogenic and natural mechanisms that control δ13 C-CO 2 variations. Such work remains rare for urban environments, especially megacities. Here, we used near-continuous CO 2 and δ13 C-CO 2 measurements, from September 2013 to August 2015, and inverse modeling to constrain the CO 2 budget and investigate the main factors that dominated δ13 C-CO 2 variations for the Yangtze River delta (YRD) region, one of the largest anthropogenic CO 2 hotspots and densely populated regions in China. We used the WRF-STILT model framework with category-specified EDGAR v4.3.2 CO 2 inventories to simulate hourly CO 2 mixing ratios and δ13 C-CO 2 , evaluated these simulations with observations, and constrained the total anthropogenic CO 2 emission. We show that (1) top-down and bottom-up estimates of anthropogenic CO 2 emissions agreed well (bias < 6 %) on an annual basis, (2) the WRF-STILT model can generally reproduce the observed diel and seasonal atmospheric δ13 C-CO 2 variations, and (3) anthropogenic CO 2 emissions played a much larger role than ecosystems in controlling the δ13 C-CO 2 seasonality. When excluding ecosystem respiration and photosynthetic discrimination in the YRD area, δ13 C-CO 2 seasonality increased from 1.53 ‰ to 1.66 ‰. (4) Atmospheric transport processes in summer amplified the cement CO 2 enhancement proportions in the YRD area, which dominated monthly δs (the mixture of δ13 C-CO 2 from all regional end-members) variations. These findings show that the combination of long-term atmospheric carbon isotope observations and inverse modeling can provide a powerful constraint on the carbon cycle of these complex megacities. [ABSTRACT FROM AUTHOR]

Anthropogenic CO2 emissions from cities represent a major source contributing to the global atmospheric CO2 burden. Here, we examined the enhancement of atmospheric CO2 mixing ratios by anthropogenic emissions within the Yangtze River Delta (YRD), China, one of the world’s most densely populated regions (population greater than 150 million). Tower measurements of CO2 mixing ratios were conducted from March 2013 to August 2015 and were combined with numerical source footprint modeling to help constrain the anthropogenic CO2 emissions. We simulated the CO2 enhancements (i.e., fluctuations superimposed on background values) for winter season (December, January, and February). Overall, we observed mean diurnal variation of CO2 enhancement of 23.5~49.7 μmol mol−1, 21.4~52.4 μmol mol−1, 28.1~55.4 μmol mol−1, and 29.5~42.4 μmol mol−1 in spring, summer, autumn, and winter, respectively. These enhancements were much larger than previously reported values for other countries. The diurnal CO2 enhancements reported here showed strong similarity for all 3 years of the study. Results from source footprint modeling indicated that our tower observations adequately represent emissions from the broader YRD area. Here, the east of Anhui and the west of Jiangsu province contributed significantly more to the anthropogenic CO2 enhancement compared to the other sectors of YRD. The average anthropogenic CO2 emission in 2014 was 0.162 (± 0.005) mg m−2 s−1 and was 7 ± 3% higher than 2010 for the YRD. Overall, our emission estimates were significantly smaller (9.5%) than those estimated (0.179 mg m−2 s−1) from the EDGAR emission database. [ABSTRACT FROM AUTHOR]

How rising temperature and changing solar radiation affect evaporation of natural water bodies remains poor understood. In this study, evaporation from Lake Taihu, a large (area 2400 km) freshwater lake in the Yangtze River Delta, China, was simulated by the CLM4-LISSS offline lake model and estimated with pan evaporation data. Both methods were calibrated against lake evaporation measured directly with eddy covariance in 2012. Results show a significant increasing trend of annual lake evaporation from 1979 to 2013, at a rate of 29.6 mm decade according to the lake model and 25.4 mm decade according to the pan method. The mean annual evaporation during this period shows good agreement between these two methods (977 mm according to the model and 1007 mm according to the pan method). A stepwise linear regression reveals that downward shortwave radiation was the most significant contributor to the modeled evaporation trend, while air temperature was the most significant contributor to the pan evaporation trend. Wind speed had little impact on the modeled lake evaporation but had a negative contribution to the pan evaporation trend offsetting some of the temperature effect. Reference evaporation was not a good proxy for the lake evaporation because it was on average 20.6 % too high and its increasing trend was too large (56.5 mm decade). [ABSTRACT FROM AUTHOR]