基于9个耦合模型比较项目第5阶段（CMIP5）耦合模型的结果，对东北114个站点的温度和降水数据进行了比较和分析。 评估了CMIP5模型对东北地区降水和温度的模拟效果。 研究表明，地球物理流体动力学实验室地球系统模型（GGFDL-ESM2G）对东北地区的降水和温度具有最佳的模拟效果。 基于SPEI指数，分析了东北地区玉米生长期干旱趋势与玉米产量变化率的关系，并估算了东北地区未来的干旱（2020- 2050年）和玉米产量。 东北玉米生长期（5月至9月）的累积标准降水蒸散指数（SPEI）分析表明，东北地区的干旱从1980年到2010年呈加剧趋势，尤其是在21世纪的前十年。 累积的SPEI指数与东北玉米的产量有显着的正相关，对东北玉米的产量有一定的指示作用。 GFDL-ESM2G模型的三个场景表明，在代表浓度路径（RCP）的三个场景下，东北地区的变暖意义重大。 在RCP4.5情景下，东北地区的降水在增加； 在RCP2.6和RCP8.5气候情景中，出现了降水并减少了干旱趋势。 对东北干旱趋势的估计表明，在RCP4.5气候情景下，东北干旱在2020年至2050年呈放缓趋势。在RCP2.6和RCP

鉴于巴西东南部雷电的发生率不断增加，并且这种现象对社会产生了各种影响，因此越来越需要预测这种现象的发生，以最大程度地减少其后果。 在这种情况下，这项工作介绍了在两个IPCC气候变化情景中使用HadGEM2-ES和CSIRO-Mk3.6模型在圣保罗州（巴西东南部）进行闪电投射的方法的发展。 .5和RCP8.5。 由于闪电不是气候模型的输出变量，因此进行了测试以评估作为模型输出的海洋和大气场观测数据与RINDAT和BrasilDAT检测网络产生的闪电之间的关系。 结果，获得0.84的相关性。 在这些预测中，已经证实，尽管在当前气候的很大一部分期间，我们观察到闪电事件低于平均水平，但未来的气候表明，无论是在中低排放情况下，还是异常高于平均水平的事件（RCP4） .5）和高排放情景（RCP8.5），表明圣保罗州的雷电发生方式发生了变化。

#资源达人分享计划#

Use of NWAI-WG data   So far, NWAI-WG data have been used on a collaborative basis in publications (see the attached file). The major reasons are the data were not widely distributed. They were only used in our group and our collaborative networks. There were some cases with requests of the data made after people read Liu and Zou's (2012) paper. You have two options for using the data. Option 1: Collaboration with us. In this case, we will help you to describe the downscaling method and contribute to other parts of the paper such as comments/suggestions on the papers, if the fields are within our expertise. Option 2: Use of the data on your own. While option 1 for collaboration with us is welcome, option 2 is also highly encouraged, particularly, when the data are used for these research disciplines, rather than agricultural related. Thanks to Professor Yu who provides us with his group's web site (www.agrivy.com) as a media for distribution of the data.   Acknowledgment for option 1  “We acknowledge the modelling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP5 multi-model dataset. Support of this dataset is provided by the Office of Science, US Department of Energy. Dr. Ian Macadam of the University of New South Wales downloaded the raw GCM monthly data. ”   Acknowledgment for option 2  “We acknowledge the modelling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP5 multi-model dataset. Support of this dataset is provided by the Office of Science, US Department of Energy. Dr. Ian Macadam of the University of New South Wales downloaded the raw GCM monthly data. Dr. De Li Liu of the NSW Department of Primary Industries used NWAI-WG to downscale downscaled daily data. Also, thanks to AGRIVY (www.agrivy.c

CMIP5数据下载，详细介绍了CMIP5数据的下载步骤，共计25page。

CMIP5第五次报告的数据详细解释，以及各个模式数据下载的地址及变量特性