Calibration of a simple and a complex model of global marine biogeochemistry

Abstract

Abstract. The assessment of the ocean biota's role in climate change is often carried out with global biogeochemical ocean models that contain many components and involve a high level of parametric uncertainty. Because many data that relate to tracers included in a model are only sparsely observed, assessment of model skill is often restricted to tracers that can be easily measured and assembled. Examination of the models' fit to climatologies of inorganic tracers, after the models have been spun up to steady state, is a common but computationally expensive procedure to assess model performance and reliability. Using new tools that have become available for global model assessment and calibration in steady state, this paper examines two different model types -a complex seven-component model (MOPS) and a very simple four-component model (RetroMOPS) -for their fit to dissolved quantities. Before comparing the models, a subset of their biogeochemical parameters has been optimised against annual-mean nutrients and oxygen. Both model types fit the observations almost equally well. The simple model contains only two nutrients: oxygen and dissolved organic phosphorus (DOP). Its misfit and large-scale tracer distributions are sensitive to the parameterisation of DOP production and decay. The spatio-temporal decoupling of nitrogen and oxygen, and processes involved in their uptake and release, renders oxygen and nitrate valuable tracers for model calibration. In addition, the non-conservative nature of these tracers (with respect to their upper boundary condition) introduces the global bias (fixed nitrogen and oxygen inventory) as a useful additional constraint on model parameters. Dissolved organic phosphorus at the surface behaves antagonistically to phosphate, and suggests that observations of this tracer -although difficult to measure -may be an important asset for model calibration.

The Flexible Ocean and Climate Infrastructure Version 1 (FOCI1): Mean State and Variability

“…This model is based on the cycling of nutrients (phosphate and nitrate), phytoplankton, zooplankton, detritus, and dissolved inorganic matter (Kriest and Oschlies, 2015). MOPS has been coupled to the Transport Matrix Method (Khatiwala, 2007), with monthly mean transport matrices (TMs), wind speed, temperature and salinity (for air-sea gas exchange), and is calibrated against global climatologies of observed macronutrients and oxygen (Kriest, 2017;Kriest et al, 2017), yielding an objectively optimized set of biogeochemical parameters for a given circulation and metric, and an uncertainty envelope for the optimized biogeochemical parameters. As the parameters can depend on the circulation of the physical ocean model, parameter optimizations have been performed with five different TMs.…”

Optimality-Based Non-Redfield Plankton-Ecosystem Model (OPEMv1.0) in the UVic-ESCM 2.9. Part II: Sensitivity Analysis and Model Calibration

“…Remineralisation rate (ν det ) and phytoplankton subsistence nitrogen quota (Q N 0, phy ) are the two parameters with the strongest correlations for most tracers as well as particulate elemental stoichiometry. The importance of ν det was expected, because it is an important driver of nutrient recycling in the surface ocean (Thomas, 2002;Anderson and Sarmiento, 1994;Eppley and 340 Peterson, 1979), which strongly affects NPP, NCP, Chl, DIC, DFe and N 2 fixation (Kriest et al, 2012). ν det also determines the rate of O 2 consumption, hence also the NO 3 level, due to denitrification in ODZs (Cavan et al, 2017).…”

“…Particle fluxes in marine biogeochemical models tend to agree most closely with sediment trap data for depths of about 1000 m or below (Kriest et al, 2012). Therefore, we integrate NCP from 0 to 980 m (7 th layer of the ocean in the UVic-ESCM), which 225 in steady state is equivalent to POC export flux at 980 m. NPP is sensitive to ν det and Q N 0, phy .…”

Optimality-Based Non-Redfield Plankton-Ecosystem Model (OPEMv1.0) in the UVic-ESCM 2.9. Part I: Implementation and ModelBehaviour

“…The UVic model version 2.9 (Weaver et al, 2001;Eby et al, 2013) in the configuration of Nickelsen et al (2015) with the isopycnal diffusivity modifications by Getzlaff and Dietze (2013), vertically increasing sinking velocity of detritus (Kriest, 2017), and several bug-fixes (some of which were already introduced by Kvale et al, 2017, see Appendix A for the new bug fixes applied here) is referred to as the original UVic in the following. We base our new configurations on this original UVic, except that we use constant half-saturation iron concentrations and omit the upper temperature limit in the zooplankton temperature dependence.…”

“…where v 0 = 6 m d −1 is the sinking velocity at the surface, z is depth and a v = 0.06 d −1 the rate of increase in v sink with depth (Kriest, 2017 (Chien et al, 2019). Symbol descriptions are given in Table 1.…”