Using ensemble-mean climate scenarios for future crop yield projections: a stochastic weather generator approach

Ma, Di; Jing, Qi; Xu, Yue-Ping; Cannon, Alex J.; Shang, Jiali; Semenov, Mikhail A.; Qian, Budong

doi:10.3354/cr01646

Cited by 2 publications

(2 citation statements)

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%

“…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%

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A Stepwise-Clustered Simulation Approach for Projecting Future Heat Wave Over Guangdong Province

Huang¹,

Huang

Li³

et al. 2021

Front. Ecol. Evol.

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A heat wave is an important meteorological extreme event related to global warming, but little is known about the characteristics of future heat waves in Guangdong. Therefore, a stepwise-clustered simulation approach driven by multiple global climate models (i.e., GCMs) is developed for projecting future heat waves over Guangdong under two representative concentration pathways (RCPs). The temporal-spatial variations of four indicators (i.e., intensity, total intensity, frequency, and the longest duration) of projected heat waves, as well as the potential changes in daily maximum temperature (i.e., Tmax) for future (i.e., 2006–2095) and historical (i.e., 1976–2005) periods, were analyzed over Guangdong. The results indicated that Guangdong would endure a notable increasing annual trend in the projected Tmax (i.e., 0.016–0.03°C per year under RCP4.5 and 0.027–0.057°C per year under RCP8.5). Evaluations of the multiple GCMs and their ensemble suggested that the developed approach performed well, and the model ensemble was superior to any single GCM in capturing the features of heat waves. The spatial patterns and interannual trends displayed that Guangdong would undergo serious heat waves in the future. The variations of intensity, total intensity, frequency, and the longest duration of heat wave are likely to exceed 5.4°C per event, 24°C, 25 days, and 4 days in the 2080s under RCP8.5, respectively. Higher variation of those would concentrate in eastern and southwestern Guangdong. It also presented that severe heat waves with stronger intensity, higher frequency, and longer duration would have significant increasing tendencies over all Guangdong, which are expected to increase at a rate of 0.14, 0.83, and 0.21% per year under RCP8.5, respectively. Over 60% of Guangdong would suffer the moderate variation of heat waves to the end of this century under RCP8.5. The findings can provide decision makers with useful information to help mitigate the potential impacts of heat waves on pivotal regions as well as ecosystems that are sensitive to extreme temperature.

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Section: Evaluation Of the Statistical Downscaling Modelmentioning

confidence: 55%

Section: Evaluation Of the Statistical Downscaling Modelmentioning

confidence: 99%