CLM GCM ranking

## **Abstract** \n\nThe dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. \n\n\n\nA table showing GCM (Global climate models) rankings for the Clarence-Moreton bioregion.\n\n## **Purpose** \n\nThe ranking of GCM was created to select the model that produced median changes in precipitation. The selected model was used to predict future rainfall for the years 2013 to 2102.\n\n## **Dataset History** \n\nA total of 15 global climate model (GCM) was tested to identify the best GCM to predict future climate data for the Clarence-Moreton Bioregion. The objective in developing a future climate series was to choose the set of GCM's seasonal scaling factors that give the median change in mean annual precipitation. Four seasonal scaling factors for the seasons: summer (December-February), autumn (March-May), winter (June-August) and spring (September-November) were used to predict changes in precipitation in the Clarence-Moreton Bioregion under future climate. For each GCM the change in mean seasonal precipitation for 1 degree global warming was calculated. These seasonal changes were summed to get the change in mean annual precipitation. Based on resulting changes in mean annual precipitation for a 1 degree global warming the GCMs were ranked in ascending order. The GCM that produced median change in annual mean precipitation was selected to predict the future precipitation in the Clarence-Moreton Bioregion for the years 2013 to 2102.\n\n## **Dataset Citation** \n\nBioregional Assessment Programme (2016) CLM GCM ranking. Bioregional Assessment Derived Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/fc1071a3-5c30-4bf4-b288-428df83a1c32.\n\n## **Dataset Ancestors** \n\n* **Derived From** [SEACI GCM scaling](https://data.gov.au/data/dataset/bde1e7b7-5bb3-483c-8616-c4176fde8818)\n\n

**摘要**

本数据集由生物区域评估计划（Bioregional Assessment Programme）基于多源数据集衍生而来。本元数据声明的「谱系（Lineage）」字段中已标注其源数据集，而生成该衍生数据集的具体流程则见于本元数据声明的「历史（History）」字段。

本数据集包含展示克拉伦斯-莫雷顿生物区域（Clarence-Moreton bioregion）全球气候模型（Global Climate Models, GCM）排名的表格。

**数据集用途**

本次GCM排名的构建目的是筛选出降水变化量处于中位数水平的模型，所筛选出的模型将用于预测2013年至2102年的未来降雨量。

**数据集编制历史**

本次研究共测试了15个全球气候模型（GCM），以筛选出适用于克拉伦斯-莫雷顿生物区域未来气候数据预测的最优模型。构建未来气候序列的核心目标是选取能使年平均降水量变化量处于中位数水平的GCM季节缩放因子。本次研究采用夏季（12月-次年2月）、秋季（3月-5月）、冬季（6月-8月）及春季（9月-11月）这四个季节的缩放因子，对克拉伦斯-莫雷顿生物区域未来气候情景下的降水变化进行预测。针对每个GCM，研究人员计算了全球升温1℃时其平均季节降水量的变化量，并将各季节的变化量求和，得到年平均降水量的总变化量。基于全球升温1℃时的年平均降水量变化量，研究人员对所有GCM按升序进行排名，最终选取年平均降水量变化量处于中位数水平的GCM，用于预测2013年至2102年克拉伦斯-莫雷顿生物区域的未来降水量。

**数据集引用格式**

生物区域评估计划（Bioregional Assessment Programme）. (2016) CLM GCM排名. 生物区域评估衍生数据集. 查阅日期：2017年9月28日，http://data.bioregionalassessments.gov.au/dataset/fc1071a3-5c30-4bf4-b288-428df83a1c32.

**数据集溯源**

* **衍生来源** [SEACI GCM缩放](https://data.gov.au/data/dataset/bde1e7b7-5bb3-483c-8616-c4176fde8818)