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The ParallelOcean Program (POP), the ocean model component of the Community Earth System Model (CESM), is widely used in climate research. Most current work inCESM-POP focuses on improving the model's efficiency or accuracy, such asimproving numerical methods, advancing parameterization, porting to newarchitectures, or increasing parallelism. Since ocean dynamics are chaoticin nature, achieving bit-for-bit (BFB) identical results in ocean solutionscannot be guaranteed for even tiny code modifications, and determiningwhether modifications are admissible (i.e., statistically consistent with theoriginal results) is non-trivial. In recent work, an ensemble-basedstatistical approach was shown to work well for software verification (i.e.,quality assurance) on atmospheric model data. The general idea of theensemble-based statistical consistency testing is to use a qualitativemeasurement of the variability of the ensemble of simulations as a metricwith which to compare future simulations and make a determination ofstatistical distinguishability. The capability to determine consistencywithout BFB results boosts model confidence and provides the flexibilityneeded, for example, for more aggressive code optimizations and the use ofheterogeneous execution environments. Since ocean and atmosphere modelshave differing characteristics in term of dynamics, spatial variability, andtimescales, we present a new statistical method to evaluate ocean modelsimulation data that requires the evaluation of ensemble means and deviationsin a spatial manner. In particular, the statistical distribution from anensemble of CESM-POP simulations is used to determine the standard score ofany new model solution at each grid point. Then the percentage of points thathave scores greater than a specified threshold indicates whether the newmodel simulation is statistically distinguishable from the ensemblesimulations. Both ensemble size and composition are important. Ourexperiments indicate that the new POP ensemble consistency test (POP-ECT)tool is capable of distinguishing cases that should be statisticallyconsistent with the ensemble and those that should not, as well as providinga simple, subjective and systematic way to detect errors in CESM-POP due tothe hardware or software stack, positively contributing to quality assurancefor the CESM-POP code

Evaluating statistical consistency in the ocean model component of the Community Earth System Model (pyCECT v2.0)