Abstract. Climate simulation codes, such as the Community Earth System Model (CESM), are especially complex and continually evolving. Their ongoing state of development requires frequent software verification in the form of quality assurance to both preserve the quality of the code and instill model confidence. To formalize and simplify this previously subjective and computationally expensive aspect of the verification process, we have developed a new tool for evaluating climate consistency. Because an ensemble of simulations allows us to gauge the natural variability of the model's climate, our new tool uses an ensemble approach for consistency testing. In particular, an ensemble of CESM climate runs is created, from which we obtain a statistical distribution that can be used to determine whether a new climate run is statistically distinguishable from the original ensemble. The CESM ensemble consistency test, referred to as CESM-ECT, is objective in nature and accessible to CESM developers and users. The tool has proven its utility in detecting errors in software and hardware environments and providing rapid feedback to model developers.

Recently the number of main-sequence and subgiant stars exhibiting solar-like oscillations that are resolved into individual mode frequencies has increased dramatically. While only a few such data sets were available for detailed modeling just a decade ago, the Kepler mission has produced suitable observations for hundreds of new targets. This rapid expansion in observational capacity has been accompanied by a shift in analysis and modeling strategies to yield uniform sets of derived stellar properties more quickly and easily. We use previously published asteroseismic and spectroscopic data sets to provide a uniform analysis of 42 solar-type Kepler targets from the Asteroseismic Modeling Portal. We find that fitting the individual frequencies typically doubles the precision of the asteroseismic radius, mass, and age compared to grid-based modeling of the global oscillation properties, and improves the precision of the radius and mass by about a factor of three over empirical scaling relations. We demonstrate the utility of the derived properties with several applications.

High-resolution climate simulations require tremendous computing resources and can generate massive datasets. At present, preserving the data from these simulations consumes vast storage resources at institutions such as the National Center for Atmospheric Research (NCAR). The historical data generation trends are economically unsustainable, and storage resources are already beginning to limit science objectives. To mitigate this problem, we investigate the use of data compression techniques on climate simulation data from the Community Earth System Model. Ultimately, to convince climate scientists to compress their simulation data, we must be able to demonstrate that the reconstructed data reveals the same mean climate as the original data, and this paper is a first step toward that goal. To that end, we develop an approach for verifying the climate data and use it to evaluate several compression algorithms. We find that the diversity of the climate data requires the individual treatment of variables, and, in doing so, the reconstructed data can fall within the natural variability of the system, while achieving compression rates of up to 5:1.

The Kepler space telescope yielded unprecedented data for the study of solar-like oscillations in other stars. The large samples of multi-year observations posed an enormous data analysis challenge that has only recently been surmounted. Asteroseismic modeling has become more sophisticated over time, with better methods gradually developing alongside the extended observations and improved data analysis techniques. We apply the latest version of the Asteroseismic Modeling Portal (AMP) to the full-length Kepler data sets for 57 stars, comprising planetary hosts, binaries, solar-analogs, active stars, and for validation purposes, the Sun. From an analysis of the derived stellar properties for the full sample, we identify a variation of the mixing-length parameter with atmospheric properties. We also derive a linear relation between the stellar age and a characteristic frequency separation ratio. In addition, we find that the empirical correction for surface effects suggested by Kjeldsen and coworkers is adequate for solar-type stars that are not much hotter (T eff < ∼ 6200 K) or significantly more evolved (log g > ∼ 4.2, ∆ν > ∼ 80 µHz) than the Sun. Precise parallaxes from the Gaia mission and future observations from TESS and PLATO promise to improve the reliability of stellar properties derived from asteroseismology.

Abstract. High-resolution Earth system model simulations generate enormous data volumes, and retaining the data from these simulations often strains institutional storage resources. Further, these exceedingly large storage requirements negatively impact science objectives, for example, by forcing reductions in data output frequency, simulation length, or ensemble size. To lessen data volumes from the Community Earth System Model (CESM), we advocate the use of lossy data compression techniques. While lossy data compression does not exactly preserve the original data (as lossless compression does), lossy techniques have an advantage in terms of smaller storage requirements. To preserve the integrity of the scientific simulation data, the effects of lossy data compression on the original data should, at a minimum, not be statistically distinguishable from the natural variability of the climate system, and previous preliminary work with data from CESM has shown this goal to be attainable. However, to ultimately convince climate scientists that it is acceptable to use lossy data compression, we provide climate scientists

The pure methods in a program are those that exhibit functional or side effect free behaviour, a useful property in many contexts. However, existing purity investigations present primarily static results. We perform a detailed examination of dynamic method purity in Java programs using a JVM-based analysis. We evaluate multiple purity definitions that range from strong to weak, consider purity forms specific to dynamic execution, and accomodate constraints imposed by an example consumer application, memoization. We show that while dynamic method purity is actually fairly consistent between programs, examining pure invocation counts and the percentage of the bytecode instruction stream contained within some pure method reveals great variation. We also show that while weakening purity definitions exposes considerable dynamic purity, consumer requirements can limit the actual utility of this information.

No abstract

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. Since 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 modifications 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 software verification (i.e., quality assurance) 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. The capability to determine consistency without BFB results boosts model confidence and provides the flexibility needed, for example, for more aggressive code optimizations and the use of heterogeneous execution environments. Since ocean and atmosphere models have differing characteristics in term of dynamics, spatial variability, and timescales, 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. Then the percentage of points that have scores greater than a specified threshold indicates whether the new model simulation is statistically distinguishable from the ensemble simulations. Both ensemble size and composition are important. Our experiments indicate that the new POP ensemble consistency test (POP-ECT) tool is capable of distinguishing cases that should be statistically consistent with the ensemble and those that should not, as well as providing a simple, subjective and systematic way to detect errors in CESM-POP due to the hardware or software stack, positively contributing to quality assurance for the CESM-POP code.