Spatial and Temporal Variability of Temperature in Iran for the Twenty-First Century Foreseen by the CMIP5 GCM Models

Miri, Morteza; Samakosh, Jafar Masoompour; Raziei, Tayeb; Jalilian, Abdollah; Mahmodi, Maryam

doi:10.1007/s00024-020-02631-9

Cited by 8 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The findings for surface air temperature and precipitation have been listed as below: (2019) introduced the CCSM4 (from CMIP5) with the highest correlation coefficients and lowest RMSE for the mean annual precipitation over the whole of Iran. Miri et al (2021) showed the historical temperatures estimated by all CMIP5 models were highly correlated with the observed temperatures all over Iran from 1987 to 2014. In their research, the highest accuracy was found in the mountainous areas of the western part during, while the accuracy decreased in the coastal areas of southern and northern Iran due to the complex topographical structure of the other effective local features that had not been incorporated in the models.…”

mentioning

confidence: 82%

“…Katiraie-Boroujerdy et al (2019) examined the ability of five CMIP5 GCMs for simulating precipitation over Iran and ranked them. Miri et al (2021) used four CMIP5 GCMs to study the temperature variability in the future decades (2015-2059) and found them in parallel with the temporal variations of temperature in the present period and reached the highest temperature variability in winter and somehow in the autumn, mostly in the mountainous areas of Iran. They also predicted that in most parts of Iran, the air temperature would have an increasing tendency in future decades in all four seasons of the year.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of Climate change Over Sistan-and-Baluchestan Province of Iran using CMIP6 GCMs; In terms of Precipitation and Surface Air Temperature

Pegahfar

2022

Preprint

View full text Add to dashboard Cite

Information of probable change in a regional climate system can play an important role in the government’s policies. The present study investigates the climate variations over the Sistan-and-Baluchestan Province, the second biggest province of Iran with scarce direct observations, in basis of surface air temperature and precipitation. To this aim, three datasets of (I) in-situ data, (II) gridded data from Climatic Research Unit (CRU, in 1979-2020), and (III) outputs of historical runs (during 1979-2020) and simulations under the SSP5-8.5 and SSP3-7.0 scenarios (in 2021-2050) of twenty-seven Global Climate Models (GCMs) from the latest Coupled Model Intercomparison Project (CMIP6) were utilized. The climatic analysis was conducted in three steps of (I) evaluation of models' performance comparing with CRU data in 1979-2020, (II) rank the models based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in Multi-Criteria Decision Making (MCDM) technique including eight metrics and (III) analysis of performance of the high-ranked models for 2021-2050. Results showed that CMIP6-GCMs generated lower satisfactory and consistency in simulating precipitation than temperature. The three models of CESM2-WACCM-FV2, MPI-ESM1-2-LR and IITM-ESM (MPI-ESM1-2-LR and UKESM1-0-LL and ACCESS-CM2) outperformed the others in simulating temperature (precipitation), while the MIROC6 (MIROC-ES2L) model performed the weakest. The MPI-ESM1-2-LR, UKESM1-0-LL and ACCESS-CM2 models achieved the highest rank for the both climate variables. Regarding the high-ranked models, analysis of the CMIP6-GCMs for 2021-2050 showed that the increase of 2.5-3.4 (2.2-3.1) K for the maximum temperature in SSP5-8.5 (SSP3-7.0) scenario is probable. Both scenarios showed an increasing (decreasing) trend for temperature (precipitation) in the future. Conclusively, the considered area will experience a remarkable increase in temperature and significant decrease in precipitation amount. It should be noted that the results from the global models will be exacerbated by any mistake in policy making.

show abstract

mentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Assessment of Climate change Over Sistan-and-Baluchestan Province of Iran using CMIP6 GCMs; In terms of Precipitation and Surface Air Temperature

Pegahfar

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In this context, global climate models (GCM) are important tools for understanding and predicting the Earth's complex climate (Kamworapan and Surussavadee 2019). There are several research centers around the world committed to modeling the future climate using global and regional climate models (GCM) and emissions scenarios (Miri et al 2021). These GCM make up Coupled Model Intercomparison Project 5 (CMIP5), featuring mainly as an integrating basis carbon cycle models and dynamic vegetation modules (Taylor et al 2012).…”

Section: Future Climate Trends In Brazilian Regionsmentioning

confidence: 99%

Climate change in Brazil: future scenarios classified by Thornthwaite (1948)

Lima

Aparecido

Lorençone

et al. 2021

Theor Appl Climatol

View full text Add to dashboard Cite

Climate Classification System (CCS) is an important tool for validating climate change models, subsidizing the characterization of new areas suitable or unfit for agricultural activity according to future climate change scenarios. This study aims to classify the climate of the Brazilian territory in the various climate change scenarios of the IPCC through the Thornthwaite system (1948). We used a 30-year historical series of climatic data of average air temperature (°C) and rainfall (mm), obtained from the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources platform (NASA/POWER). Potential evapotranspiration (ETP) was estimated by the method of Camargo (1971); the climatological water balance (CWB) was calculated by the method of Thornthwaite and Mather (1955), using 100 mm of soil water storage capacity. CWB extracts were combined for classification by Thornthwaite (1948). The scenarios used were based on the IPCC (2014) projections and the study of Pirttioja et al. (2015). The Brazilian territory had an average air temperature of 22.20 °C (± 3.20) °C and annual precipitation of 1987 mm (± 725) mm. The climatic classification of Thornthwaite presented 108 climatic classes for the current scenario with a more significant predominance of the classes ArAʹaʹ, B4rAʹaʹ, and B3rAʹaʹ representing 20.54%, 15.62%, and 9.46% of the Brazilian territory, respectively. The climate class ArAʹaʹ had 39.20% in the North and 14.97% in the Midwest. The South region has a predominance of 24.31% for the class ArBʹ3aʹ. In the Southeast and Northeast, the climate classes B2rBʹ3aʹ and DdBʹ2aʹ represented 14.80% and 15.26% of the regions, respectively. The S5 scenario was considered more favorable to establishing crops, with 48.04% of Brazil represented by the climate class ArAʹaʹ. Furthermore, the most catastrophic scenarios for crops were S3 and S4, promoting Brazil a predominance of classes B3rAʹaʹ in 18.02% and B1rAʹaʹ in 21.04%, respectively, favoring the occurrence of arid and dry climates in large part of the Brazilian territory.

show abstract

Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble

Zarrin

Dadashi-Roudbari

2021

Theor Appl Climatol

View full text Add to dashboard Cite

Spatial and Temporal Variability of Temperature in Iran for the Twenty-First Century Foreseen by the CMIP5 GCM Models

Cited by 8 publications

References 24 publications

Assessment of Climate change Over Sistan-and-Baluchestan Province of Iran using CMIP6 GCMs; In terms of Precipitation and Surface Air Temperature

Assessment of Climate change Over Sistan-and-Baluchestan Province of Iran using CMIP6 GCMs; In terms of Precipitation and Surface Air Temperature

Climate change in Brazil: future scenarios classified by Thornthwaite (1948)

Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble

Contact Info

Product

Resources

About