GLO 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 Gloucester subregion.\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 Gloucester subregion. 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 Gloucester subregion 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 Gloucester subregion for the years 2013 to 2102.\n\n## **Dataset Citation** \n\nBioregional Assessment Programme (XXXX) GLO GCM ranking. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/212c9416-a8bb-48c0-a8e5-83fa375883b0.\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。本元数据声明中的“谱系”字段标注了其源数据集，衍生该数据集的具体处理流程则在元数据声明的“历史”字段中予以说明。
本数据集包含一张展示格洛斯特子区域全球气候模式（Global climate models, GCM）排名的表格。

## 用途
本全球气候模式排名的生成目的是筛选出降水量变化量处于中位数的模式，所选模式将用于预测2013年至2102年的未来降雨量。

## 数据集历史
本次研究共测试了15个全球气候模式（Global climate models, GCM），以筛选出适配格洛斯特子区域未来气候数据预测的最优模式。构建未来气候序列的核心目标，是选取可实现年平均降水量中位数变化量的全球气候模式季节缩放因子集合。研究针对夏季（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）四类季节，分别采用季节缩放因子，预测格洛斯特子区域未来气候情景下的降水量变化。针对每一个全球气候模式，计算全球升温1℃时的平均季节降水量变化量，将各季节变化量求和后得到年平均降水量变化量。基于全球升温1℃下的年平均降水量变化量结果，按升序对全球气候模式进行排序，选取年平均降水量变化量处于中位数的模式，用于预测2013年至2102年格洛斯特子区域的未来降水量。

## 数据集引用
生物区域评估计划（Bioregional Assessment Programme）（XXXX）. GLO全球气候模式排名. 生物区域评估衍生数据集. 2018年7月18日查阅，http://data.bioregionalassessments.gov.au/dataset/212c9416-a8bb-48c0-a8e5-83fa375883b0.

## 数据集溯源
* **数据衍生自** [SEACI GCM缩放数据集](https://data.gov.au/data/dataset/bde1e7b7-5bb3-483c-8616-c4176fde8818)