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DOI | 10.1007/s00382-018-4560-1 |
Predictive ability of climate change with the automated statistical downscaling method in a freeze–thaw agricultural area | |
Ouyang W.; Hao F.; Shi Y.; Gao X.; Gu X.; Lian Z. | |
发表日期 | 2019 |
ISSN | 0930-7575 |
起始页码 | 7013 |
结束页码 | 7028 |
卷号 | 52期号:11 |
英文摘要 | Precipitation and temperature in freeze–thaw agricultural area have different patterns under global warming. In this study, a statistical relationship between large-scale changes in climate variables and local weather data was built by applying an automated statistical downscaling (ASD) model in the Sanjiang Plain in China. We evaluated the prediction ability of the ASD model in terms of spatial–temporal changes in freeze–thaw agricultural area, and the temperature and precipitation changes in the twenty-first century under the representative concentration pathway 4.5 (RCP4.5) scenario were estimated. The results revealed that the explained variances in temperature were higher than 0.93 during the calibration and verification periods, which demonstrated good simulation capacity. The R2 of precipitation was acceptable due to the randomness and complexity of daily precipitation. Based on the Geophysical Fluid Dynamics Laboratory Earth System Model with the Generalized Ocean Layer Dynamics component (GFDL-CM3), the regional climate simulation provided good predictions. By 2100, the average, maximum and minimum temperatures in this area could increase by 2.0–2.5 °C, 2.5 °C and 2.5–4.0 °C, respectively. In terms of the spatial distribution, temperatures could increase faster in the northern region and slower in the central and southern regions. The warming trends in summer and winter were more significant than those in spring and autumn. There was no significant change in annual precipitation in the twenty-first century (increased approximately 10 mm by 2100). Precipitation decreased obviously in July and August (approximately 0.4 mm/day), and other months showed an increasing trend (approximately 0.5–0.9 mm/day). There will be large spatial variation of precipitation in the future changes. The results could serve as a reference for assessing non-point source pollution and agricultural management. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature. |
英文关键词 | Automated statistical downscaling method (ASD model); Freeze–thaw agricultural area; Global warming; Non-point source pollution; Prediction ability |
语种 | 英语 |
scopus关键词 | agricultural land; automation; climate prediction; downscaling; freeze-thaw cycle; global warming; nonpoint source pollution; precipitation (climatology); spatial variation; temperature effect; China; Heilongjiang; Sanjiang Plain |
来源期刊 | Climate Dynamics
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文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/146234 |
作者单位 | School of Environment, State Key Laboratory of Water Environment Simulation, Beijing Normal University, Beijing, 100875, China |
推荐引用方式 GB/T 7714 | Ouyang W.,Hao F.,Shi Y.,等. Predictive ability of climate change with the automated statistical downscaling method in a freeze–thaw agricultural area[J],2019,52(11). |
APA | Ouyang W.,Hao F.,Shi Y.,Gao X.,Gu X.,&Lian Z..(2019).Predictive ability of climate change with the automated statistical downscaling method in a freeze–thaw agricultural area.Climate Dynamics,52(11). |
MLA | Ouyang W.,et al."Predictive ability of climate change with the automated statistical downscaling method in a freeze–thaw agricultural area".Climate Dynamics 52.11(2019). |
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