Climate change projections in Guatemala: temperature and precipitation changes according to CMIP6 models

Rivera, Paris

doi:10.1007/s40808-023-01881-5

Cited by 1 publication

(1 citation statement)

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“…In the case of Indonesia, the superiority of the multi-model ensemble in simulating extreme precipitation events was noted (Kurniadi et al, 2023). A recent research over Guatemala located in central American region noted that MRI-ESM-2.0 and IPSL-CM6A-LR GCMs as the top performer for simulating surface air temperature and precipitation (Rivera, 2023). Research carried out in 2023, evaluated the performance 27 GCMs derived from CMIP6 over Southeast Asian territory, identified TaiESM as a most exceptional performer (Pimonsree et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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

Bhanage,

Lee,

Cabrera

et al. 2024

Front. Environ. Sci.

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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.

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Section: Introductionmentioning

confidence: 99%