Framework for generating high-resolution Hong Kong local climate projections to support building energy simulations

Abstract

Finer resolution climate model projections are essential for designing regional building energy consumption and adaptation strategies under changing climate conditions. However, projections from Global Climate Models (GCMs) are typically coarse in resolution and subject to biases and uncertainty. To address this, the present study uses bilinear interpolation and morphing statistical downscaling to obtain high spatial (around 10 km) and temporal (hourly) resolution weather data, for more accurate estimations of future residential building energy consumption under climate change. An empirical quantile mapping bias-correction technique is applied to adjust the projection data from 44 GCMs under four representative Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The bias-corrected data are validated against meteorological observations from the Hong Kong Observatory's King's Park station. The hourly data are then converted to typical meteorological year data and used as input for EnergyPlus to predict future energy consumption patterns in public rental housing in Hong Kong. Case studies under the four SSPs show that climate change will significantly impact residential building energy use. Energy consumption is projected to increase by up to 14.0% for harmony-type buildings, 12.8% for trident-type buildings, and 12.4% for slab-type buildings by the end of the century under the SSP5-8.5 scenario, highlighting the urgent need for adaptive building design and energy policy measures.