Hossain, M.M.; Anwar, A.H.M.F.; Garg, N.; Prakash, M.; Bari, M.A. CMIP5 Decadal Precipitation over an Australian Catchment. Hydrology2024, 11, 24.
Hossain, M.M.; Anwar, A.H.M.F.; Garg, N.; Prakash, M.; Bari, M.A. CMIP5 Decadal Precipitation over an Australian Catchment. Hydrology 2024, 11, 24.
Hossain, M.M.; Anwar, A.H.M.F.; Garg, N.; Prakash, M.; Bari, M.A. CMIP5 Decadal Precipitation over an Australian Catchment. Hydrology2024, 11, 24.
Hossain, M.M.; Anwar, A.H.M.F.; Garg, N.; Prakash, M.; Bari, M.A. CMIP5 Decadal Precipitation over an Australian Catchment. Hydrology 2024, 11, 24.
Abstract
The fidelity of the decadal experiment in Coupled Model Intercomparison Project Phase-5 (CMIP5) has been examined, over different climate variables for different temporal and spatial scales, in many previous studies. However, most of the studies were for the temperature and temperature-based climate indices. A quite limited study was conducted on precipitation of decadal experiment and no attention was paid to a catchment level. This study evaluates the performances of eight GCMs (MIROC4h, EC-EARTH, MRI-CGCM3, MPI-ESM-MR, MPI-ESM-LR, MIROC5, CMCC-CM, and CanCM4) for the monthly hindcast precipitation of decadal experiment over the Brisbane River catchment in Queensland, Australia. First, the GCMs datasets were spatially interpolated onto a spatial resolution of 0.050×0.050 (5 km× 5 km) matching with the grids of observed data and then were cut for the catchment. Next, model outputs are evaluated for temporal skills, dry and wet periods, and total precipitation (over time and space) based on the observed values. Skill test results reveal that model performances varied over the initialization years and showed comparatively higher scores from the initialization year 1990 and onward. Models with finer spatial resolutions show comparatively better performances as opposed to the models of coarse spatial resolutions where MIROC4h outperformed followed by EC-EARTH and MRI-CGCM3. Comparing the skills, models are divided into three categories (Category-I: MIROC4h, EC-EARTH, and MRI-CGCM3; Category-II: MPI-ESM-LR and MPI-ESM-MR; and Category-III: MIROC5, CanCM4, and CMCC-CM). Three multimodel ensembles’ mean (MMEMs) are formed using the arithmetic mean of Category-I (MMEM1), Category-I and II (MMEM2), and all eight models (MMEM3). The performances of MMEMs are also assessed using the same skill tests and MMEM2 performed best which suggests evaluating the models before the formation of MMEM.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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