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Urban areas are global methane (CH4) hotspots. Yet large uncertainties still remain for the CH4 budget of these domains. The Yangtze River Delta (YRD), China, is one of the world's most densely populated regions where a large number of cities are located. To estimate anthropogenic CH4 emissions in YRD, we conducted simultaneous atmospheric CH4 and CO2 mixing ratio measurements from June 2010 to April 2011. By combining these measurements with the Weather Research and Forecasting and Stochastic Time‐Inverted Lagrangian Transport models and a priori Emission Database for Global Atmospheric Research emission inventories, we applied three “top‐down” approaches to constrain anthropogenic CH4 emissions. These three approaches included multiplicative scaling factors, flux ratio, and scale factor Bayesian inversion. The posteriori CH4 flux density estimated from the three approaches showed high consistency and were 36.32 (±9.17), 35.66 (±2.92), and 36.03(±14.25) nmol·m−2·s−1, respectively, for the duration of the study period (November 2010 to April 2011). The total annual anthropogenic CH4 emission was 6.52(±1.59) Tg for the YRD region based on the average of these three approaches. Our emission estimates were 30.2(±17.6)%, 31.5 (±5.6)%, and 30.8 (±27.4)% lower than the a priori Emission Database for Global Atmospheric Research v432 emission inventory estimate. The scale factor Bayesian inversion results indicate that the overestimate was mainly caused by two source categories including fuel exploitation and agricultural soil emissions (rice cultivation). The posteriori flux densities for agricultural soil and fuel exploitation were 10.68 and 6.34 nmol·m−2·s−1, respectively, and were 47.8% and 29.2% lower than the a priori inventory. Agricultural soil was the largest source contribution and accounted for 29.6% of the YRD CH4 budget during the study period.

Abstract. Eddy covariance data are widely used for the investigation of surface–air interactions. Although numerous datasets exist in public depositories for land ecosystems, few research groups have released eddy covariance data collected over lakes. In this paper, we describe a dataset from the Lake Taihu eddy flux network, a network consisting of seven lake sites and one land site. Lake Taihu is the third-largest freshwater lake (area of 2400 km2) in China, under the influence of subtropical climate. The dataset spans the period from June 2010 to December 2018. Data variables are saved as half-hourly averages and include micrometeorology (air temperature, humidity, wind speed, wind direction, rainfall, and water or soil temperature profile), the four components of surface radiation balance, friction velocity, and sensible and latent heat fluxes. Except for rainfall and wind direction, all other variables are gap-filled, with each data point marked by a quality flag. Several areas of research can potentially benefit from the publication of this dataset, including evaluation of mesoscale weather forecast models, development of lake–air flux parameterizations, investigation of climatic controls on lake evaporation, validation of remote-sensing surface data products and global synthesis on lake–air interactions. The dataset is publicly available at https://yncenter.sites.yale.edu/data-access (last access: 24 October 2020) and from the Harvard Dataverse (https://doi.org/10.7910/DVN/HEWCWM; Zhang et al., 2020).

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Does lake evaporation increase or decrease under the scenario of climate warming? This paper aims to answer this question by investigating the controlling mechanism of interannual variations in lake evaporation at a subtropical lake. The research methodology is based on continuous eddy covariance measurement over >7 years and a diagnostic analysis using the surface energy balance principle. The results indicate that lake evaporation was enhanced mainly by increasing energy inputs including solar radiation and incoming longwave radiation and was weakened by surface feedback through outgoing longwave radiation. The incoming longwave radiation was positively correlated with cloud cover. Bowen ratio and surface albedo had slight effect on the change of lake evaporation. The annual lake evaporation can be predicted by the Priestly-Taylor model using a larger coefficient of 1.39 than the original value of 1.26, suggesting that advection or entrainment in the atmospheric boundary layer may play a role in lake evaporation. Special Section:Water-energy-carbon fluxes over terrestrial water surfaces Key Points:• Incoming shortwave and incoming longwave radiation controls the interannual change of lake evaporation • Annual Bowen ratio and incoming and outgoing longwave radiation are predictable functions of temperature change • The Brutsaert formula with a constant relative humidity predicts well the interannual variability in the incoming longwave radiationSupporting Information:• Supporting Information S1 • Table S1