StraitFlux – precise computations of water strait fluxes on various modeling grids

Abstract. Oceanic transports shape the global climate, but the evaluation and validation of this key quantity based on reanalysis and model data are complicated by the distortion of the used curvilinear ocean model grids towards their displaced north poles. Combined with the large number of different grid types, this has made the exact calculation of oceanic transports a challenging and time-consuming task. Use of data interpolated to standard latitude/longitude grids is not an option, since transports computed from interpolated velocity fields are not mass-consistent. We present two methods for transport calculations on grids with variously shifted north poles, different orientations, and different Arakawa partitions. The first method calculates net transports through arbitrary sections using line integrals, while the second method generates cross sections of the vertical–horizontal planes of these sections using vector projection algorithms. Apart from the input data on the original model grids, the user only needs to specify the start and endpoints of the required section to get the net transports (for the first method) and their cross sections (for the second method). Integration of the cross sections along their depth and horizontal extent yields net transports in very good quantitative agreement with the line integration method. This allows us to calculate oceanic fluxes through almost arbitrary sections to compare them with observed oceanic volume and energy transports at available sections, such as the RAPID array or at Fram Strait and other Arctic gateways, or to compare them amongst reanalyses and to model integrations from the Coupled Model Intercomparison Projects (CMIPs). We implemented our methods in a Python package called StraitFlux. This paper represents its scientific documentation and demonstrates its application on outputs of multiple CMIP6 models and several ocean reanalyses. We also analyze the robustness and computational performance of the tools, as well as the uncertainties in the results. The package is available on GitHub and Zenodo and can be installed using pypi.