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Incorporating Stochastic Weather Generators into Studies on Climate Impacts: Methods and Uncertainties

  • By adopting various stochastic weather generators, different research groups in their recent studies have realized the importance of the effects of climatic variability on crop growth and development. The conventional assessments derived climate change scenarios from General Circulation Models (GCMs) ex periments, however, are incapable of helping to understand this importance. The particular interest here is to review the general methodological scheme to incorporate stochastic weather generator into climate im pact studies and the specific approaches in our studies, and put forward uncertainties that still exist. A variety of approaches have been taken to develop the parameterization program and stochastic ex periment, and adjust the parameters of a typical stochastic weather generator called WGEN. Usually, the changes in monthly means and variances of weather variables between controlled and changed climate are used to perturb the parameters to generate the intended daily climate scenarios. We establish a parameterization program and methods for stochastic experiment of WGEN in the light of outputs of short-term climate prediction models, and evaluate its simulations on both temporal and spatial scales. Also, we manipulated parameters in relation to the changes in precipitation to produce the intended types and qualitative magnitudes of climatic variability. These adjustments yield various changes in climatic vari ability for sensitivity analyses. The impacts of changes in climatic variability on maize growth, final yield, and agro-climatic resources in Northeast China are assessed and presented as the case studies through the above methods. However, this corporation is still equivocal due to deficiencies of the generator and unsophisticated manipulation of parameters. To detect and simulate the changes in climatic variability is one of the indis pensable ways to reduce the uncertainties in this aspect.
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    [2] Zhe HAN, Feifei LUO, Shuanglin LI, Yongqi GAO, Tore FUREVIK, Lea SVENDSEN, 2016: Simulation by CMIP5 Models of the Atlantic Multidecadal Oscillation and Its Climate Impacts, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 1329-1342.  doi: 10.1007/s00376-016-5270-4
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    [7] HUANG Anning, ZHANG Yaocun, GAO Xinfang, 2007: Impacts of Coastal SST Variability on the East Asian Summer Monsoon, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 259-270.  doi: 10.1007/s00376-007-0259-7
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    [10] LI Shu, WANG Tijian, ZHUANG Bingliang, HAN Yong, 2009: Indirect Radiative Forcing and Climatic Effect of the Anthropogenic Nitrate Aerosol on Regional Climate of China, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 543-552.  doi: 10.1007/s00376-009-0543-9
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    [13] Wansuo DUAN, Lichao YANG, Mu MU, Bin WANG, Xueshun SHEN, Zhiyong MENG, Ruiqiang DING, 2023: Recent Advances in China on the Predictability of Weather and Climate, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1521-1547.  doi: 10.1007/s00376-023-2334-0
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Manuscript History

Manuscript received: 10 September 2001
Manuscript revised: 10 September 2001
通讯作者: 陈斌, bchen63@163.com
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Incorporating Stochastic Weather Generators into Studies on Climate Impacts: Methods and Uncertainties

  • 1. Chinese Academy of Meteorological Sciences, Beijing 100081,Chinese Academy of Meteorological Sciences, Beijing 100081

Abstract: By adopting various stochastic weather generators, different research groups in their recent studies have realized the importance of the effects of climatic variability on crop growth and development. The conventional assessments derived climate change scenarios from General Circulation Models (GCMs) ex periments, however, are incapable of helping to understand this importance. The particular interest here is to review the general methodological scheme to incorporate stochastic weather generator into climate im pact studies and the specific approaches in our studies, and put forward uncertainties that still exist. A variety of approaches have been taken to develop the parameterization program and stochastic ex periment, and adjust the parameters of a typical stochastic weather generator called WGEN. Usually, the changes in monthly means and variances of weather variables between controlled and changed climate are used to perturb the parameters to generate the intended daily climate scenarios. We establish a parameterization program and methods for stochastic experiment of WGEN in the light of outputs of short-term climate prediction models, and evaluate its simulations on both temporal and spatial scales. Also, we manipulated parameters in relation to the changes in precipitation to produce the intended types and qualitative magnitudes of climatic variability. These adjustments yield various changes in climatic vari ability for sensitivity analyses. The impacts of changes in climatic variability on maize growth, final yield, and agro-climatic resources in Northeast China are assessed and presented as the case studies through the above methods. However, this corporation is still equivocal due to deficiencies of the generator and unsophisticated manipulation of parameters. To detect and simulate the changes in climatic variability is one of the indis pensable ways to reduce the uncertainties in this aspect.

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