Using Statistical Downscaling to Project the Future Climate of Hong Kong
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Using Statistical Downscaling to Project the Future Climate of Hong Kong

Using Statistical Downscaling to Project the Future Climate of Hong Kong


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About the Book

This dissertation, "Using Statistical Downscaling to Project the Future Climate of Hong Kong" by Chi-shing, Calvin, Cheung, 張志成, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Climate in Hong Kong is very likely to be modified due to global climate change. In this study the output of General Circulation Models (GCMs) was statistically downscaled to produce future climate projections for the time periods 2046 -2065 and 2081 -2100 for Hong Kong. The future climate projections are based on two emission scenarios provided by the Intergovernmental Panel on Climate Change (IPCC). The emission scenarios, A1B (rapid economic growth with balanced energy technology) and B1 (global environmental sustainability), make assumptions on future human development, and the resulting emissions of greenhouse gases. This study established a method to evaluate GCMs for use in statistical downscaling and utilised six GCMs, selected from the 3rd phase of the Coupled Model Intercomparison Project (CMIP3). They were evaluated based upon their performance in simulating past climate in the southeast China region on three aspects: 1) monthly mean temperature; 2) sensitivity to greenhouse gases and 3) climate variability. Three GCMs were selected for statistical downscaling and climate projection in this study. Downscaling was undertaken by relating large scale climate variables, from NCEP/NCAR reanalysis, a gridded data set incorporating observations and climate models, to local scale observations. Temperature, specific humidity and wind speed were downscaled using multiple linear regressions methods. Rain occurrence was determined using logistic regression and rainfall volume from a generalised linear model. The resultant statistical models were subsequently applied to future climate projections. Overall, all three GCMs, via statistical downscaling, show that daily average, minimum and maximum temperatures, along with specific humidity, will increase under future climate scenarios. Comparing the model ensemble mean projections with current climate (1981 -2010), the annual average temperature in Hong Kong is projected to increase by 1.0 C (B1) to 1.6 C (A1B) in 2046 -2065, and by 1.4 C (B1) to 2.2 C (A1B) in 2081 -2100. Furthermore, the projections in this study show an increase of high temperature extremes (daily average temperature >= 29.6 C), by three to four times in 2046 -2065 and four to five times in 2081 -2100. The projections of rainfall indicate that annual rainfall will increase in the future. Total annual rainfall is projected to increase by 4.9% (A1B) to 8% (B1) in 2046 -2065, and by 8.7% (B1) to 21.5% (A1B) in 2081 -2100. However, this change in rainfall is seasonally dependent; summer and autumn exhibit an increase in rainfall whilst spring and winter exhibit decreases. In order to test one possible impact of this change in climate, the downscaled climate variables were used to estimate how outdoor thermal comfort (using the Universal Thermal Comfort Index) might change under future climate scenarios in Hong Kong. Results showed that there will be a shift from 'No Thermal Stress' towards 'Moderate Heat Stress' and 'Strong Heat Stress' during the period 2046 -2065, becoming more severe for the later period (2081 -2100). The projections of future climate presented in this study will be important when assessing potential climate change impacts, along with adaptation and mitigation options, in Hong Kong. DOI: 10.5353/th_b5194728 Subjects: Weather forecasting - China - Hong Kong - Statistical methods


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Product Details
  • ISBN-13: 9781361370476
  • Publisher: Open Dissertation Press
  • Publisher Imprint: Open Dissertation Press
  • Height: 279 mm
  • No of Pages: 366
  • Weight: 848 gr
  • ISBN-10: 1361370475
  • Publisher Date: 27 Jan 2017
  • Binding: Paperback
  • Language: English
  • Spine Width: 19 mm
  • Width: 216 mm


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