Evaluating Statistical Consistency in the Ocean Model Component of the Community Earth System Model (pyCECT v2.0)

作者：Allison H. Baker, Y. Hu, Dorit Hammerling, Y. Tseng, Honglin Xu, Xiaomeng Huang, F. O. Bryan, G. Yang · 年份：2016 · DOI：10.5194/gmd-2016-3 · 被引用次数：3 · 研究领域：Climate variability and models、Meteorological Phenomena and Simulations、Oceanographic and Atmospheric Processes

Abstract. The Parallel Ocean Program (POP), the ocean model component of the Community Earth System Model (CESM), is widely used in climate research. Most current work in CESM-POP focuses on improving the model's efficiency or accuracy, such as improving numerical methods, advancing parameterization, porting to new architectures, or increasing parallelism. Because ocean dynamics are chaotic in nature, achieving bit-for-bit (BFB) identical results in ocean solutions cannot be guaranteed for even tiny code modifications, and determining whether model changes are admissible (i.e. statistically consistent with the original results) is non-trivial. In recent work, an ensemble-based statistical approach was shown to work well for statistical consistency testing on atmospheric model data. The general idea of the ensemble-based statistical consistency testing is to use a qualitative measurement of the variability of the ensemble of simulations as a metric with which to compare future simulations and make a determination of statistical distinguishability. Because ocean and atmosphere models have differing characteristics in term of dynamics and time-scales, we present a new statistical method to evaluate ocean model simulation data that requires the evaluation of ensemble means and deviations in a spatial manner. In particular, the statistical distribution from an ensemble of CESM-POP simulations is used to determine the standard score of any new model solution at each grid point. The...