An ensemble-based statistical methodology to detect differences in weather and climate model executables

Abstract

Abstract. Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather prediction and climate research. As such, they are subject to continuous changes, thanks to advances in computer systems, numerical methods, more and better observations, and the ever-increasing knowledge about the atmosphere of earth. Many of the changes in today's models relate to seemingly innocuous modifications associated with minor code rearrangements, changes in hardware infrastructure, or software updates. Such changes are meant to preserve the model formulation, yet the verification of such changes is challenged by the chaotic nature of our atmosphere – any small change, even rounding errors, can have a significant impact on individual simulations. Overall, this represents a serious challenge to a consistent model development and maintenance framework. Here we propose a new methodology for quantifying and verifying the impacts of minor changes in the atmospheric model or its underlying hardware/software system by using ensemble simulations in combination with a statistical hypothesis test for instantaneous or hourly values of output variables at the grid-cell level. The methodology can assess the effects of model changes on almost any output variable over time and can be used with different underlying statistical hypothesis tests. We present the first applications of the methodology with the regional weather and climate model COSMO. While providing very robust results, the methodology shows a great sensitivity even to very small changes. Specific changes considered include applying a tiny amount of explicit diffusion, the switch from double to single precision, and a major system update of the underlying supercomputer. Results show that changes are often only detectable during the first hours, suggesting that short-term ensemble simulations (days to months) are best suited for the methodology, even when addressing long-term climate simulations. Furthermore, we show that spatial averaging – as opposed to testing at all grid points – reduces the test's sensitivity for small-scale features such as diffusion. We also show that the choice of the underlying statistical hypothesis test is not essential and that the methodology already works well for coarse resolutions, making it computationally inexpensive and therefore an ideal candidate for automated testing.