选择GCM:在R中选择通用循环模型的工具包
Luíz Fernando Esser1, Dayani Bailly1, Marcos Robalinho Lima2
1Universidade Estadual de Maringá, Maringa, Brazil.
Global change biology
|December 30, 2024
概括
为气候变化研究选择通用循环模型 (GCM) 是一个挑战. 我们的新R包,选择GCM,提供了一个强大的框架来评估GCM变异性,大大减少了物种分布建模的计算时间和成本.
科学领域:
- 气候科学 气候科学
- 生态建模 生态建模
背景情况:
- 气候变化研究依赖于通用循环模型 (GCM) 的预测.
- 选择适合的GCM进行研究,特别是物种分布模型 (SDM),缺乏标准化的共识.
- 使用所有可用的GCM在计算上是不可避免的.
研究的目的:
- 引入一种方法框架,用于评估气候变化预测中的GCM变化.
- 为了提供一个可访问的R包,选择GCM,让研究人员分析GCM数据.
- 在管理计算资源的同时实现可靠的预测.
主要方法:
- 开发一个R包,实施GCM评估的方法框架.
- 使用用于集群化,相关性,距离和在GCM预测上的探索性数据分析的功能.
- 使用物种分布模型 (SDM) 的概念验证应用程序.
主要成果:
- 与传统方法相比,选择GCM包显著减少了超过79%的计算时间.
- 在SDM应用程序中实现了与基线的输出相关性大于0.9.
- 该框架支持更广泛的硬件,从而实现了可靠的气候预测.
结论:
- 选择GCM包为选择和分析GCM提供了一种高效且易于使用的工具.
- 为各种研究领域提供更可靠和计算可行的气候变化预测.
- 授权研究人员,无论专业知识如何,进行基于GCM的强有力的分析.
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