On a Structural Similarity Index Approach for Floating-Point Data.

Data visualization is typically a critical component of post-processing analysis workflows for floating-point output data from large simulation codes, such as global climate models. For example, images are often created from the raw data as a means for evaluation against a reference dataset or image. While the popular Structural Similarity Index Measure (SSIM) is a useful tool for such image comparisons, generating large numbers of images can be costly when simulation data volumes are substantial. In fact, computational cost considerations motivated our development of an alternative to the SSIM, which we refer to as the Data SSIM (DSSIM). The DSSIM is conceptually similar to the SSIM, but can be applied directly to the floating-point data as a means of assessing data quality. We present the DSSIM in the context of quantifying differences due to lossy compression on large volumes of simulation data from a popular climate model. Bypassing image creation results in a sizeable performance gain for this case study. In addition, we show that the DSSIM is useful in terms of avoiding plot-specific (but data-independent) choices that can affect the SSIM. While our work is motivated by and evaluated with climate model output data, the DSSIM may prove useful for other applications involving large volumes of simulation data.

North Atlantic subtropical mode water formation controlled by Gulf Stream fronts.

The North Atlantic Ocean hosts the largest volume of global subtropical mode waters (STMWs) in the world, which serve as heat, carbon and oxygen silos in the ocean interior. STMWs are formed in the Gulf Stream region where thermal fronts are pervasive and result in feedback with the atmosphere. However, their roles in STMW formation have been overlooked. Using eddy-resolving global climate simulations, we find that suppressing local frontal-scale ocean-to-atmosphere (FOA) feedback leads to STMW formation being reduced almost by half. This is because FOA feedback enlarges STMW outcropping, attributable to the mixed layer deepening associated with cumulative excessive latent heat loss due to higher wind speeds and greater air-sea humidity contrast driven by the Gulf Stream fronts. Such enhanced heat loss overshadows the stronger restratification induced by vertical eddies and turbulent heat transport, making STMW colder and heavier. With more realistic representation of FOA feedback, the eddy-present/rich coupled global climate models reproduce the observed STMWs much better than the eddy-free ones. Such improvement in STMW production cannot be achieved, even with the oceanic resolution solely refined but without coupling to the overlying atmosphere in oceanic general circulation models. Our findings highlight the need to resolve FOA feedback to ameliorate the common severe underestimation of STMW and associated heat and carbon uptakes in earth system models.