Development of future typical meteorological year (TMY) for major cities in Indonesia: Identification of suitable GCM

¹ Transdisciplinary Science and Engineering Program, Graduate School of Advanced Science and Engineering, Hiroshima University, 1-5-1 Komiyama, Higashi-Hiroshima 739-8529, Hiroshima, Japan
² Graduate School of Science and Engineering, Kagoshima University, 1-21-40, Korimoto, Kagoshima, 890-0065, Japan
³ Center for Planetary Health and Innovation Science (PHIS), The IDEC Institute, Hiroshima University
⁴ Center for Research and Development, Indonesian Agency for Meteorology Climatology and Geophysics (BMKG), Jl. Angkasa 1 No.2, Kec. Kemayoran, Jakarta, 10610, Indonesia
⁵ Center for Applied Climate Information Services, Indonesian Agency for Meteorology Climatology and Geophysics (BMKG), Jl. Angkasa 1 No.2, Kec. Kemayoran, Jakarta, 10610, Indonesia
⁶ Directorate Engineering Affairs for Human Settlements, Ministry of Public Works, and Housing, Jawa Barat, Indonesia

Abstract

Today, with the rapid process of urbanization, the proportion of building energy consumption will continue to increase and speed up the emission of greenhouse gases which can intensify the process of global warming. Thus, building energy conservation has become one of the essential aspects of a sustainable development strategy. A typical meteorological year (TMY) is frequently used in building energy simulation to assess the expected heating and cooling costs in the design of the building. Therefore, by considering the future alternations in climate, it is important to develop future TMY data. To generate the TMY for future climate, the projected weather dataset obtained from GCMs from the IPCC coupled inter comparison project phase 6 (CMIP6) can be helpful. However, a key issue with the use of GCM data is the low resolution and bias of the data. Thus, it is important to identify best suitable GCM for a particular region. Therefore, present study aims to evaluate the performance of 6 global GCMs from the CMIP6 for simulating the surface air temperature over the 29 major cities in Indonesia during 1980-2014. Here, dataset (MERRA-2) was utilized to compare the simulations of GCMs. Further three statistical metrics viz. correlation coefficient, standard deviation and centered root mean square error were computed to check the performance of each GCM against the reanalysis data. For most cities, the correlation coefficient values between the results of GCMs, and the reanalysis dataset ranges from 0.3 to 0.7 whereas the value of standard deviation varies from 0.3 to 1. The result revelled that among all the GCMs MPI-HR is one of the most appropriate choices to simulate the surface air temperature over 8 different cities. However, Nor-MM shows the worse performance over the cities located in Indonesia. For the future period, the input dataset from the best identified GCMs will be downscaled for the generation of TMY for future climate.