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Simulating complex astrophysical reacting flows is computationally expensive—reactions are stiff and typically require implicit integration methods. The reaction update is often the most expensive part of a simulation, which motivates the exploration of more economical methods. In this research note, we investigate how the explicit Runge–Kutta–Chebyshev (RKC) method performs compared to an implicit method when applied to astrophysical reactive flows. These integrators are applied to simulations of X-ray bursts arising from unstable thermonuclear burning of accreted fuel on the surface of neutron stars. We show that the RKC method performs with similar accuracy to our traditional implicit integrator, but is more computationally efficient when run on CPUs.

 

Abstract. We present a newly developed upper-thermocline, open-ocean biogeochemical flux model that is complex and flexible enough to capture open-ocean ecosystem dynamics but reduced enough to incorporate into highly resolved numerical simulations and parameter optimization studies with limited additional computational cost. The model, which is derived from the full 56-state-variable Biogeochemical Flux Model (BFM56; Vichi et al., 2007), follows a biological and chemical functional group approach and allows for the development of critical non-Redfield nutrient ratios. Matter is expressed in units of carbon, nitrogen, and phosphate, following techniques used in more complex models. To reduce the overall computational cost and to focus on upper-thermocline, open-ocean, and non-iron-limited or non-silicate-limited conditions, the reduced model eliminates certain processes, such as benthic, silicate, and iron influences, and parameterizes others, such as the bacterial loop. The model explicitly tracks 17 state variables, divided into phytoplankton, zooplankton, dissolved organic matter, particulate organic matter, and nutrient groups. It is correspondingly called the Biogeochemical Flux Model 17 (BFM17). After describing BFM17, we couple it with the one-dimensional Princeton Ocean Model for validation using observational data from the Sargasso Sea. The results agree closely with observational data, giving correlations above 0.85, except for chlorophyll (0.63) and oxygen (0.37), as well as with corresponding results from BFM56, with correlations above 0.85, except for oxygen (0.56), including the ability to capture the subsurface chlorophyll maximum and bloom intensity. In comparison to previous models of similar size, BFM17 provides improved correlations between several model output fields and observational data, indicating that reproduction of in situ data can be achieved with a low number of variables, while maintaining the functional group approach. Notable additions to BFM17 over similar complexity models are the explicit tracking of dissolved oxygen, allowance for non-Redfield nutrient ratios, and both dissolved and particulate organic matter, all within the functional group framework.