Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model
Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model
复制标题
DOI：
10.5194/gmd-10-1789-2017
复制
发表时间：
2016-08
影响因子：
5.1
通讯作者：
D. Williamson;A. Blaker;B. Sinha
中科院分区：
文献类型：
--
作者：
D. Williamson;A. Blaker;B. Sinha
摘要。在本文中，我们讨论了气候模型的调整，并从统计科学文献中提出了一种迭代自动调整方法。这种方法，我们在这里称之为迭代重新聚焦（尽管也称为历史匹配），避免了基于成本函数优化的自动调整过程的许多常见陷阱，主要是由于只使用部分观测而导致的气候模型的过度调整。这种避免来自于寻求排除我们确信不能重现观察的参数选择，而不是寻求最接近它们的模型（一个有过度调整风险的过程）。我们评论了气候模式调整的状态，并通过以2°分辨率运行的NEMO（欧洲海洋模型核）ORCA2全球海洋模型的三波迭代重新聚焦来说明我们的方法。我们展示了在模型的标准配置中，全球平均温度和盐度的异常如何在某些深度超过观测值的10个标准差，并展示了在不影响模型空间性能的情况下，通过迭代重新聚焦可以减轻这种情况的程度。我们展示了如何通过同时扰动多个参数来实现模型改进，并说明了在更高分辨率下使用低分辨率集成来调整NEMO ORCA配置的潜力。
Abstract. In this paper we discuss climate model tuning and present an iterative automatic tuning method from the statistical science literature. The method, which we refer to here as iterative refocussing (though also known as history matching), avoids many of the common pitfalls of automatic tuning procedures that are based on optimisation of a cost function, principally the over-tuning of a climate model due to using only partial observations. This avoidance comes by seeking to rule out parameter choices that we are confident could not reproduce the observations, rather than seeking the model that is closest to them (a procedure that risks over-tuning). We comment on the state of climate model tuning and illustrate our approach through three waves of iterative refocussing of the NEMO (Nucleus for European Modelling of the Ocean) ORCA2 global ocean model run at 2° resolution. We show how at certain depths the anomalies of global mean temperature and salinity in a standard configuration of the model exceeds 10 standard deviations away from observations and show the extent to which this can be alleviated by iterative refocussing without compromising model performance spatially. We show how model improvements can be achieved by simultaneously perturbing multiple parameters, and illustrate the potential of using low-resolution ensembles to tune NEMO ORCA configurations at higher resolutions.