Diagnosing organic matter flux profiles

This discussion paper is/has been under review for the journal Biogeosciences (BG). Please refer to the corresponding final paper in BG if available. Abstract The average depth in the ocean at which the majority of sinking organic matter particles remineralise is a fundamental parameter in the oceans role in regulating atmospheric CO 2. Observed spatial patterns in sinking fluxes and relationships between the fluxes of different particles in the modern ocean have widely been used to invoke controlling 5 mechanisms with important implications for CO 2 regulation. However, such analyses are limited by the sparse spatial sampling of the available sediment trap data. Here we explore whether model ocean circulation rates, in the form of a transport matrix, can be used to derive remineralisation rates and sinking particle flux curves from the much more highly resolved observations of dissolved nutrient concentrations. Initially 10 we use the Earth system model GENIE to generate a synthetic tracer dataset to explore the methods and its sensitivity to key sources of uncertainty arising from errors in the tracer observations and in the model circulation. We use a perturbed physics ensemble to generate 54 different estimates of circulation to explore errors associated with model transport rates. We find that reconstructed remineralisation rates are highly sensitive 15 to both errors in observations and our ensemble uncertainty in model circulation rates such that a simple inversion does not provide a robust estimate of particulate flux profiles. Inferred remineralisation rates are particularly sensitive to differences between the " observed " and modelled transport fluxes because remineralisation rates are 3–4 magnitudes smaller than circulation rates. We also find that when inferring particle flux 20 curves from remineralisation rates the cycling of dissolved organic matter also creates biases that have a similar magnitude and spatial variability to flux curves diagnosed using sediment trap data. We end with a discussion on the potential future directions and pitfalls of estimating remineralisation rates using model circulation schemes.