Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES
Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES
复制标题
DOI：
10.5194/gmd-2021-205
复制
发表时间：
2021-08
影响因子：
5.1
通讯作者：
Evan Baker;A. Harper;D. Williamson;P. Challenor
中科院分区：
文献类型：
--
作者：
Evan Baker;A. Harper;D. Williamson;P. Challenor
抽象的。陆地表面模型通常被整合到全球气候预测中，但随着其空间分辨率的提高，使用它们来帮助地方政策决策的前景变得更具吸引力。如果要使用这些复杂的模型来做出局部决策，则需要对不确定性进行完全量化，但只以高分辨率运行一次模拟的计算成本可能会阻碍适当的分析。统计仿真是一种越来越常见的技术，用于开发快速近似模型，这种方法既能保持精度，又能为近似提供全面的不确定性界限。在这项工作中，我们开发了一个陆面模式的统计仿真框架，该框架承认输入模式的强迫数据，提供高分辨率的预测。我们使用英国联合陆地环境模拟器(Jules)作为这一战略的案例研究，并进行了初步的敏感性分析和参数调整，以展示其能力。Jules可能是最复杂的陆地表面模型之一，因此我们在这里的成功表明，所有类型的陆地表面模型都可以取得令人难以置信的收益。
Abstract. Land surface models are typically integrated into global climate projections, but as their spatial resolution increases the prospect of using them to aid in local policy decisions becomes more appealing. If these complex models are to be used to make local decisions, then a full quantification of uncertainty is necessary, but the computational cost of running just one simulation at high resolution can hinder proper analysis. Statistical emulation is an increasingly common technique for developing fast approximate models in a way that maintains accuracy but also provides comprehensive uncertainty bounds for the approximation. In this work, we develop a statistical emulation framework for land surface models which acknowledges the forcing data fed into the model, providing predictions at a high resolution. We use The Joint UK Land Environment Simulator (JULES) as a case study for this strategy, and perform initial sensitivity analysis and parameter tuning to showcase its capabilities. JULES is perhaps one of the most complex land surface models, and so our success here suggests incredible gains can be made for all types of land surface model.