2021
DOI: 10.3354/cr01646
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Abstract: Using climate scenarios from only 1 or a small number of global climate models (GCMs) in climate change impact studies may lead to biased assessment due to large uncertainty in climate projections. Ensemble means in impact projections derived from a multi-GCM ensemble are often used as best estimates to reduce bias. However, it is often time consuming to run process-based models (e.g. hydrological and crop models) in climate change impact studies using numerous climate scenarios. It would be interesting to inv… Show more

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Cited by 2 publications
(2 citation statements)
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References 44 publications
(23 reference statements)
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“…The results implied that the model ensemble can be an effective tool in the case of reproducing the observed Tmax at the 16 selected weather stations over Guangdong. Previous studies have also found the use of model ensemble reduced more the bias in the evaluation than using only a single GCM, thereby improving the reliability of climate projections (Webber et al, 2018;Qian et al, 2020;Ma et al, 2021). Similarly, it can be found that the developed approach also demonstrates significant performance in reproducing the observed Tmax from the four GCMs at the 16 selected weather stations (Figure 5).…”
Section: Evaluation Of the Statistical Downscaling Modelmentioning
confidence: 55%
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“…The results implied that the model ensemble can be an effective tool in the case of reproducing the observed Tmax at the 16 selected weather stations over Guangdong. Previous studies have also found the use of model ensemble reduced more the bias in the evaluation than using only a single GCM, thereby improving the reliability of climate projections (Webber et al, 2018;Qian et al, 2020;Ma et al, 2021). Similarly, it can be found that the developed approach also demonstrates significant performance in reproducing the observed Tmax from the four GCMs at the 16 selected weather stations (Figure 5).…”
Section: Evaluation Of the Statistical Downscaling Modelmentioning
confidence: 55%
“…In detail, Tmax is somewhat overestimated by the CNRM-CM5 from March to April (e.g., Guangning and Shanwei), and underestimated by the MIROC5 during July to August (e.g., Nanxiong and Zijin). This is mainly due to the uncertainty of the model, which may be induced by differences in the model parameterization, emission concentration scenarios, and internal climate variability (Zhai et al, 2019;Ma et al, 2021). Therefore, it is essentially recommended to present the Tmax with the model ensemble from multiple GCMs' outputs to reduce potential biases.…”
Section: Evaluation Of the Statistical Downscaling Modelmentioning
confidence: 99%