Using satellite-derived backscattering coefficients in addition to chlorophyll data to constrain a simple marine biogeochemical model

H Kettle

doi:10.5194/bg-6-1591-2009

Abstract

Abstract. Biogeochemical models of the ocean carbon cycle are frequently validated by, or tuned to, satellite chlorophyll data. However, ocean carbon cycle models are required to accurately model the movement of carbon, not chlorophyll, and due to the high variability of the carbon to chlorophyll ratio in phytoplankton, chlorophyll is not a robust proxy for carbon. Using inherent optical property (IOP) inversion algorithms it is now possible to also derive the amount of light backscattered by the upper ocean (bb) which is related to the amount of particulate organic carbon (POC) present. Using empirical relationships between POC and bb, a 1-D marine biogeochemical model is used to simulate bb at 490 nm thereby allowing the model to be compared with both remotely-sensed chlorophyll or bb data. Here I investigate the possibility of using bb in conjunction with chlorophyll data to help constrain the parameters in a simple 1-D NPZD model. The parameters of the biogeochemical model are tuned with a genetic algorithm, so that the model is fitted to either chlorophyll data or to both chlorophyll and bb data at three sites in the Atlantic with very different characteristics. Several inherent optical property (IOP) algorithms are available for estimating bb, three of which are used here. The effect of the different bb datasets on the behaviour of the tuned model is examined to ascertain whether the uncertainty in bb is significant. The results show that the addition of bb data does not consistently alter the same model parameters at each site and in fact can lead to some parameters becoming less well constrained, implying there is still much work to be done on the mechanisms relating chlorophyll to POC and bb within the model. However, this study does indicate that including bb data has the potential to significantly effect the modelled mixed layer detritus and that uncertainties in bb due to the different IOP algorithms are not particularly significant.

Highlights

Quantifying the global carbon cycle is crucial for predicting our future climate
For climate change prediction it is crucial to quantify the amount of CO2 that is transferred from the atmosphere to the ocean through the air-sea interface and the amount of carbon that is subsequently exported to the deep ocean
Bbp can be derived from nLw or Rrs using a variety of inherent optical property (IOP) inversion algorithms

Summary

Introduction

Quantifying the global carbon cycle is crucial for predicting our future climate. The oceans play an important role in the carbon cycle as they absorb CO2 from the atmosphere enabling the transport of carbon to the deep ocean through physical and biological processes. For climate change prediction it is crucial to quantify the amount of CO2 that is transferred from the atmosphere to the ocean through the air-sea interface (the air-sea CO2 flux) and the amount of carbon that is subsequently exported to the deep ocean. Validation of ocean carbon cycle models would involve comparison of the simulated air-sea CO2 flux and export production with measured data. These data are not available at the time and space scales necessary. Chl gives an indication of the amount of living phytoplankton in the ocean This is useful since it is algal photosynthesis that removes CO2 from the water allowing more CO2 from the atmosphere to enter the ocean. It is perfectly possible to correctly predict chlorophyll concentrations without correctly predicting carbon concentrations

Methods

Results

Conclusion