A comparison of CMIP6 and CMIP5 projections for precipitation to observational data: the case of Northeastern Iran
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ORIGINAL PAPER
A comparison of CMIP6 and CMIP5 projections for precipitation to observational data: the case of Northeastern Iran Yasin Zamani 1 & Seyed Arman Hashemi Monfared 1
&
Mehdi Azhdari moghaddam 1 & Mohsen Hamidianpour 2
Received: 15 June 2020 / Accepted: 21 September 2020 # Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract The present study aimed to assess the performance of CMIP6 and CMIP5 projects in projecting mean precipitation at annual, summer, autumn, winter, and spring timescales in the north and northeast of Iran over the period 1987–2005 using relative bias, correlation coefficient, root mean square error, relative error, and the Taylor diagram. This is the first attempt to compare CMIP6 and CMIP5 data in an arid region at a seasonal and annual scale. The results showed that the precipitations simulated by the ensembles of CMIP6 and CMIP5 models were different. The relative bias for winter was lower at all stations in CMIP6 than in CMIP5, so CMIP6 performed better in this respect. CMIP6 outperformed CMIP5 in projecting annual and spring precipitation in 60 and 69% of the stations, respectively. Whereas CMIP6 overestimated precipitation in 70% of the stations, CMIP5 underestimated it in 77% of the stations. CMIP5 models exhibited better performance in 70% of the stations only in autumn. In most seasons and stations, CMIP6 CGMs’ ensemble outperformed CMIP5. The results of HadGEM2-ES from CMIP5 and CESM2 from CMIP6 were more accurate than the models’ ensembles in both projects. Overall, CMIP6 models exhibited better performance than CMIP5 models.
1 Introduction A good source for quantitative prediction of climate in the twenty-first century is the simulation of general circulation models (GCMs) collected by the coupled model intercomparison project (CMIP) (Eyring et al. 2016; Baker and Huang 2014). Given the global concerns over the impacts of climate change on water resources and hydrology, it is imperative to model precipitation variations (Arnell 2004; Duan and Mei 2014). Sensitive systems, e.g., the environment, water resources, and agriculture, are vulnerable to anthropogenic climate change (Sivakumar 2011; Chen et al. 2011; Xu et al. 2015; Kundzewicz et al. 2008). Therefore, it is crucial to assess regional impacts and adaptability to climate change and predict the impacts of greenhouse gas (GHG) emissions on climate (Hussain et al. 2020; Zamani et al. 2019). GCMs
* Seyed Arman Hashemi Monfared [email protected] 1
Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Sistan and Baluchestan, Iran
2
Department of Geography and Environmental Planning, University of Sistan and Baluchestan, Zahedan, Sistan and Baluchestan, Iran
perform the climate projection with high accuracy (Yarnal et al. 2001; Koutroulis et al. 2016). Before using the outputs of GCMs, the performance of the models should be evaluated with respect to some reference data (observational data), and the models should be compared to reveal their relative bias. The capability
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