Horizon CDT Research Highlights

Research Highlights

On the modelling of future urban climate for sustainability assessment

  Lucen Li (2015 cohort)   www.nottingham.ac.uk/~psxll3

Although lots of researchers are looking at climate modelling and some of them are enlightening and reliable, but there are only few researchers looking at urban climate modelling and prediction. Hideki did a study of urban thermal environment in Tokyo in summer of the 2030s (Hideki et al. 2015). But they failed to take Urban Canopy Model and the heat island effect into consideration. In my study, the aim is to build an urban climate model to investigate and predict the thermal environment in Shanghai and Chengdu in China. However, the heat island effect will be taken into consideration. The main purpose of this model is to predict future city climate and the energy flow under the effect of heat island in the city, which can help with the decision making regarding building the heating and cooling system in order to reduce the energy cost and increase the city sustainability. Another thing to look at is that since the study is about to predict thermal environment of urban city in future, which is absolutely different from the city of today, the change of future city like extension and distribution should be taken into consideration.

For recently study, most of the researchers are using WRF (Skamarock. 2005) as a research tool to predict future climate. As a successful model, WRF is a suitable tool to use but since it is mainly designed for global climate research, the scale in WRF system is too large for a city. Therefore it is necessary to use downscaling methods in order to apply WRF for a regional climate research. Different downscaling methods may lead to different research scale(Fan et al. 2005), so far I‘ve seen two kinds of scale settings, 3km by 3km and 5km by 5km, respectively. Smaller the scale is, better the simulation will be, but consequently more factors will be taken into consideration. For instance the heat island effect and city canopy model, even the city transport model may have an effect on the urban climate change. The choice of which downscaling method should be used haven’t been decide yet. The data in Chinese city is not sufficient and easy to collection. But so far learning downscaling methods and how to use WRF model could be a first step for the whole research schedule.

The aim of my project is to build a model of the climate change for cities in future. Several mid-term objectives must be established in order to achieve this ambitious goal:

  1. Understand the tools and the algorithm like WRF model and downscaling methods, however downscaling method is not a well-developed methods, which means it could be optimized to perform better.

  2. Identity how will city look like in the future.

  3. Identity a city model that will take heat island effect into consideration and predict energy flow in the future city context.

  4. Use the real data of Shanghai and Chengdu to provide a consulting view of the future of these two city.


1.Kikumoto, Hideki, Ryozo Ooka, and Yusuke Arima. "A study of urban thermal environment in Tokyo in summer of the 2030s under influence of global warming." Energy and Buildings (2015).

2.Skamarock, William C., et al. A description of the advanced research WRF version 2. No. NCAR/TN-468+ STR. National Center For Atmospheric Research Boulder Co Mesoscale and Microscale Meteorology Div, 2005.

3.Fan, L. J., C. B. Fu, and D. L. Chen. "Review on creating future climate change scenarios by statistical downscaling techniques." Adv Earth Sci 20.3 (2005): 320-329.

4.Kusaka, H., Hara, M., & Tkane, Y. (2012). Urban climate projection by the WRF modelat 3-km horizontal grid increment: dynamical downscaling and predicting heatstress in the 2070’s August for Tokyo, Osaka, and Nagoya Metropolises. Journalof the Meteorological Society of Japan, 90B, 47–63.

This work was carried out at the International Doctoral Innovation Centre (IDIC). The authors acknowledge the financial support from Ningbo Education Bureau, Ningbo Science and Technology Bureau, China's MOST, and the University of Nottingham. The work is also partially supported by EPSRC grant no EP/L015463/1.