Identification of optimal CMIP6 GCMs for future typical meteorological year in major cities of Indonesia using multi-criteria decision analysis

• Published in: Frontiers in environmental science Vol. 12
• Main Authors: Bhanage, Vinayak, Lee, Han Soo, Cabrera, Jonathan Salar, Kubota, Tetsu, Pradana, Radyan Putra, Fajary, Faiz Rohman, Nimiya, Hideyo
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 11.03.2024; Frontiers Media S.A
• Subjects: Air temperature; Analytic hierarchy process; analytical hierarchical process; Cities; Climate change; Climate models; CMIP6; Datasets; Decision analysis; Emissions; Energy consumption; Global climate; Global climate models; Green buildings; Greenhouse gases; Indonesia; Meteorological parameters; Multiple criterion; Performance evaluation; Precipitation; Relative humidity; Statistics; Surface temperature; Temperature; total relative error; typical meteorological year; Urban planning; Weather; Wind; Wind speed; Indonesia
• ISSN: 2296-665X; 2296-665X
• DOI: 10.3389/fenvs.2024.1341807

Many studies often use a single global climate model (GCM) across multiple cities to develop future Typical Meteorological Year (TMY), without emphasizing city-specific selection of GCM. The present investigation employs the Analytical Hierarchy Process (AHP) to assess city-specific GCMs for generating future TMY datasets across 29 Indonesian cities. Six GCMs from the coupled model intercomparison project phase 6 (CMIP6) were evaluated against Modern-Era Retrospective Analysis for Research Applications (MERRA-2) to assess their performance in simulating surface air temperature, precipitation, wind speed, and relative humidity. Six statistical measures were used to recognize the systematic biases. Further, AHP was applied to integrate these statistical measures to calculate the city-specific total relative error for each meteorological parameter. Results of total relative error show that TaiESM, 6-Model Ensemble (6ME), NorMM, and MPI-HR were best for simulating surface air temperature, precipitation, wind speed, and relative humidity in most cities, respectively. TMY recognizes distinctive importance among meteorological parameters. Thus, it is essential to reflect the parameter-specific importance while selecting GCMs for future TMY. Hence, AHP was reapplied on total relative errors accounting for differing weights of each meteorological parameter. Outcomes show that TaiESM, 6ME, and MPI-HR were found suitable for generating future TMY datasets in 18, 5, and 3 cities, respectively, while MPI-LR, NorLM, and NorMM were recommended for Boven Digoel, Medan, and Bengkulu cities, respectively. Using city-specific GCMs ensures precise and cost-effective future TMY generation, assisting urban planners and policymakers in designing environmentally sustainable buildings for anticipated climatic changes.