PMID- 38019299
OWN - NLM
STAT- MEDLINE
DCOM- 20231130
LR  - 20231225
IS  - 1573-2959 (Electronic)
IS  - 0167-6369 (Linking)
VI  - 195
IP  - 12
DP  - 2023 Nov 29
TI  - Future precipitation and near surface air-temperature projection using CMIP6 
      models based on TOPSIS method: case study, Sistan-and-Baluchestan Province of 
      Iran.
PG  - 1548
LID - 10.1007/s10661-023-12084-x [doi]
AB  - Based on surface air temperature and precipitation, the current study examines 
      the climate fluctuations over Sistan-and-Baluchestan Province, Iran's 
      second-largest province. This area suffers from insufficient direct observations 
      and a lack of climatic investigation. Three datasets were utilized including in 
      situ data, gridded data (1984-2013), outputs of historical runs (during 
      1984-2013), and projections under the SSP5-8.5, SSP3-7.0, and SSP1-2.6 scenarios 
      (in 2020-2049) of twenty-six Global Climate Models (GCMs) from the latest Coupled 
      Model Intercomparison Project (CMIP6). The models' performance has been evaluated 
      and ranked using the Technique for Order Preference by Similarity to Ideal 
      Solution (TOPSIS) in Multi-Criteria Decision Making (MCDM) technique including 
      eight metrics in both seasonal and annual scales. The surface air temperature 
      showed an increasing trend in seasonal and annual scales during 1984-2013, while 
      the monthly precipitation trend increased for September-October-November and 
      decreased for the other seasons and annual scale during 1984-2013. The top-ranked 
      models for simulating surface air temperature (precipitation) were CESM2 
      (GFDL-ESM4), IPSL-CM6A-LR (UKESM1-0-LL), ACCESS-CM2 (GFDL-ESM4), and MIROC-ES2L 
      (MPI-ESM1-2-LR) models in DJF, MAM, JJA, and SON seasons, respectively, while 
      ACCESS-CM2 (CNRM-CM6-1-HR) model outperformed others in annual scale. 
      Bias-corrected outputs of the top-ranked CMIP6 GCMs showed an increasing trend 
      for surface air temperature in all seasons (from a 0.7 K increase in 
      December-January-February season under SSP3-7.0 scenario to a 2.5 K increase in 
      June-July-August season under SSP5-8.5 scenario) for period 2020-2049, comparing 
      with that in 1984-2013 period. Bias-corrected monthly precipitation projected by 
      top-ranked CMIP6 GCMs indicated both increasing and decreasing trends depending 
      on selected season and scenario. This varied from a 5 mm/month decrease within 
      December-January-February season under SSP5-8.5 scenario to a 13 mm/month 
      increase during the March-April-May season under SSP1-2.6 scenario in 2020-2050, 
      comparing with that from previous years.
CI  - (c) 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
FAU - Pegahfar, Nafiseh
AU  - Pegahfar N
AD  - Iranian National Institute for Oceanography and Atmospheric Science, Research 
      Center of Atmospheric Sciences, Tehran, Iran. pegahfar@inio.ac.ir.
LA  - eng
PT  - Journal Article
DEP - 20231129
PL  - Netherlands
TA  - Environ Monit Assess
JT  - Environmental monitoring and assessment
JID - 8508350
SB  - IM
MH  - Iran
MH  - Temperature
MH  - *Environmental Monitoring
MH  - *Benchmarking
MH  - Seasons
OTO - NOTNLM
OT  - CMIP6 GCMs
OT  - Climate change
OT  - Historical run
OT  - Precipitation
OT  - SSP1-2.6
OT  - SSP3-7.0
OT  - SSP5-8.5
OT  - Sistan-and-Baluchestan Province
OT  - Surface air temperature
EDAT- 2023/11/29 12:43
MHDA- 2023/11/30 06:43
CRDT- 2023/11/29 11:05
PHST- 2023/02/19 00:00 [received]
PHST- 2023/11/02 00:00 [accepted]
PHST- 2023/11/30 06:43 [medline]
PHST- 2023/11/29 12:43 [pubmed]
PHST- 2023/11/29 11:05 [entrez]
AID - 10.1007/s10661-023-12084-x [pii]
AID - 10.1007/s10661-023-12084-x [doi]
PST - epublish
SO  - Environ Monit Assess. 2023 Nov 29;195(12):1548. doi: 10.1007/s10661-023-12084-x.