Abstract
Updating the urban land cover information has been proved a necessary method for the numerical studies of urban climate and urban atmospheric environment in China, a fast urbanizing country. However, there are uncertainties in the urban land use/cover (ULUC) information in different datasets due to the uncertainties in raw data sources and produce methods. In this study, the Weather Research and Forecasting model is used to simulate the summer climate over the Yangtze River Delta in July and August 2013, when a heatwave episode occurred. Five numerical experiments are designed; one is the baseline experiment (non-urban land cover (NOURB)) in which all urban land covers are removed, and different urban land use/cover (ULUC) experiments from four different global land cover datasets are used in other four experiments. The differences between the ULUC experiments and the NOURB experiment are used to observe the urbanization effects. The results show a remarkable urbanization effect in all the ULUC experiments, and the urbanization influences are most different over the union-urban area, which defined as the area marked as urban area by at least one land cover dataset, because the uncertainties in urban land cover information are greatest in this area. All the ULUC experiments show the regional influence of urbanization as well, which means urbanization effects are not limited in cities. The Climate Change Initiative (CCI) experiment using the European Space Agency CCI 2013 dataset showed the greatest urbanization influence and the highest effect index in all analyzed variables, because of the largest urban land cover area in the analysis domain. The urban land cover area in the Global Land Cover experiment is the smallest among the ULUC experiments, but it distributes in a more concentrated way than in other experiments; it indicates that both city size and city shape affect the regional influence.
Highlights
In the past decades, China has experienced a very fast urbanization process with the rapid economic development (Xiao et al 2014)
It has been found in previous studies that the influences of urban canyon aspect are much less than these of the thermal and radiation parameters, such as albedos of urban facets, in the single-layer urban canopy models (UCMs) (Zhao et al 2014), so the uncertainties caused by the urban canopy physical parameters are taken as a part of the uncertainties caused by urban land cover information in this paper
The underestimation of relative humidity at 2 m (RH2 m) has two reasons: the first one is that the Weather Research and Forecasting (WRF) model overestimated temperature at 2 m (T2 m) as described before, and the second reason is that the specific humidity at 2 m (Q2 m) was underestimated with the biases of − 1.45 g kg−1, − 1.52 g kg−1, − 1.45 g kg−1, and − 1.44 g kg−1 and the root-mean-square error (RMSE) of 2.25 g kg−1, 2.31 g kg−1, 2.28 g kg−1, and 2.27 g kg−1, respectively
Summary
China has experienced a very fast urbanization process with the rapid economic development (Xiao et al 2014). The vegetation land covers are replaced by impervious construction materials, which are of higher conductivities and higher heat capacities, and shed water more rapidly; at the same time, the city buildings have more vertical surfaces and form the so-called “urban canopy layer” (Grimmond 2007) These changes modify the physical, radiation, and hydraulic properties of the land surface, cause the differences of the meteorological environment between urban and non-urban areas (Arnfield 2003). Even the releases of several global land use/land cover datasets enrich the user’s choice, and the updating of the static urban land cover information in the WRF model can improve the model performance (Li et al 2014a, b); most datasets failed to represent the quick urbanization processes in China because of the data resolution, the classification method, or the training database (Yang et al 2017). We used the WRF model as a numerical tool to investigate the impact of the selection of ULUC information from the different global land cover datasets in numerical modeling simulations and the different urbanization effects introduced by such uncertainties
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