PMID- 37790964
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231005
IS  - 2405-8440 (Print)
IS  - 2405-8440 (Electronic)
IS  - 2405-8440 (Linking)
VI  - 9
IP  - 10
DP  - 2023 Oct
TI  - Evaluating the skills of the CMIP5 global climate models using multicriteria 
      decision-making analysis in Ethiopia.
PG  - e20320
LID - 10.1016/j.heliyon.2023.e20320 [doi]
LID - e20320
AB  - This study evaluates the skills of 30 CMIP5 GCMs and the Multimodel Ensemble 
      (MME) in reproducing the characteristics of observed precipitation (Pr), minimum 
      (T(min)), and maximum (T(max)) temperature over the Middle Awash sub-basin (MASB) 
      in Ethiopia. The MME of the climate variables was generated using the simple 
      arithmetic mean method. The entire analysis was performed on the raw historical 
      GCM simulations (before bias correction) and observed data for the periods 
      1981-2005 based on monthly and annual time series data over the annual and 
      seasonal temporal resolutions. This study considered two approaches. The first 
      one was an evaluation of GCMs employing five statistical performance metrics 
      (SPMs), i.e., mean, CV, PBIAS, RSR, and r. The second approach involves the 
      application of multicriteria decision-making (MCDM) analysis, adopting three SPMs 
      (PBIAS, RSR, and r). The relative weights of the three metrics were determined by 
      the entropy method. Besides, the weighted average and compromise programming 
      techniques were employed to rank and select the best-performing GCMs. The 
      findings from the first approach using five SPMs demonstrate that, for a given 
      variable of interest, a GCM that performs well for one SPM may fail to produce 
      the same for another SPM on the same temporal scale. Likewise, for the same SPM 
      at different resolutions, a GCM may perform well for a one-time scale but poorly 
      for another. These suggested that the results of GCM skills relied mainly on the 
      SPM, time scale, and data formats chosen for analysis. Hence, it is critical to 
      comprehensively evaluate the skill of GCMs using multiple performance metrics 
      over a range of spatial and temporal settings and data formats. In addition, 
      results of the MCDM analysis proved that the ensemble of GCMs, which provide 
      adequate performance in simulating the salient features of Pr, T(min), and T(max) 
      concomitantly across the MASB, encompass CMCC-CMS, BCC-CSM1.1(m), CMCC-CM, 
      BNU-ESM, CanESM2, and MPI-ESM-MR. However, it was observed that different GCMs 
      performed much differently in characterizing various variables over a range of 
      temporal scales and data formats. The MME also proved its superior potential in 
      duplicating the climate of the study area over several individual GCMs. The 
      overall findings attested that instead of aggregating the ranks from the three 
      variables into one, it is recommended to treat each variable independently while 
      developing a subset of best-performing GCMs for ensembling since each GCM 
      responds differently to each variable under a set of conditions. Finally, the 
      approaches and findings from this study will be valuable input for subsequent 
      climate and hydrologic studies in the study area and beyond.
CI  - (c) 2023 The Authors.
FAU - Tesfaye, Endalkachew
AU  - Tesfaye E
AD  - Ethiopian Institute of Water Resources, Addis Ababa University, Addis Ababa, 
      Ethiopia.
FAU - Abate, Brook
AU  - Abate B
AD  - College of Architecture & Civil Engineering, Addis Ababa Science & Technology 
      University, Addis Ababa, Ethiopia.
FAU - Alemayehu, Taye
AU  - Alemayehu T
AD  - Ethiopian Institute of Water Resources, Addis Ababa University, Addis Ababa, 
      Ethiopia.
FAU - Dile, Yihun
AU  - Dile Y
AD  - College of Agriculture & Life Sciences, Texas A & M University, College Station, 
      TX, USA.
LA  - eng
PT  - Journal Article
DEP - 20230920
PL  - England
TA  - Heliyon
JT  - Heliyon
JID - 101672560
PMC - PMC10543453
OTO - NOTNLM
OT  - GCM
OT  - Multicriteria decision-making
OT  - Multimodel ensemble
OT  - Precipitation
OT  - Temperature
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2023/10/04 06:44
MHDA- 2023/10/04 06:45
PMCR- 2023/09/20
CRDT- 2023/10/04 04:14
PHST- 2023/04/06 00:00 [received]
PHST- 2023/09/18 00:00 [revised]
PHST- 2023/09/19 00:00 [accepted]
PHST- 2023/10/04 06:45 [medline]
PHST- 2023/10/04 06:44 [pubmed]
PHST- 2023/10/04 04:14 [entrez]
PHST- 2023/09/20 00:00 [pmc-release]
AID - S2405-8440(23)07528-X [pii]
AID - e20320 [pii]
AID - 10.1016/j.heliyon.2023.e20320 [doi]
PST - epublish
SO  - Heliyon. 2023 Sep 20;9(10):e20320. doi: 10.1016/j.heliyon.2023.e20320. 
      eCollection 2023 Oct.