| 04 Mar 2026
Effects of assimilating phytoplankton carbon in marine ecosystem modelling in NEMO4.0.4-MEDUSA2.0-PDAF2.0
Abstract. The state of the marine ecosystem can be estimated by a combination of numerical models and satellite observations through data assimilation (DA) methods. Satellite data representing phytoplankton chlorophyll are typically used in operational marine ecosystem prediction. These data are derived from ocean colour from optical satellite observations. Recently a novel phytoplankton carbon product, from the ESA funded BICEP project available from the UK CEDA Archive, has been derived through an alternate processing of ocean colour. With the novel carbon product, the phytoplankton biomass is represented more directly than relying on the chlorophyll. Here, we investigate the effects of assimilating the new carbon product on the modelling of the marine ecosystem. The investigation is carried out in a newly developed global ensemble DA system for the marine ecosystem using a coupled ocean-biogeochemistry model, NEMO-MEDUSA, and the Parallel Data Assimilation Framework. With the ensemble DA system, the evaluation can take the time-dependent uncertainty of the marine ecosystem and the reliability of the ensemble into account. We demonstrate that, compared with solely assimilating chlorophyll product, with the new carbon product the DA can provide different patterns of adjustments in the phytoplankton concentration and seasonal anomalies. Our findings reveal that simultaneously assimilating both phytoplankton chlorophyll and carbon products in a complex marine ecosystem yields more accurate and balanced estimates of phytoplankton biomass than assimilating a single phytoplankton product.
Dear authors,
I am not a biogeochemical modeler per-se (that is I understand the math behind them and their content but am not running any model on a regular basis). Hence some of my comments may seem out of scope and there are some aspect I cannot comment on.
My major comments are the following:
1. Carbon products and phytoplankton product derived from remote sensing have been derived and used for years. There is no novelty in that and I am surprised you chose to only look at one such product. For example, Behrenfeld et al., 2005, showed how additional information can be gleaned from using a backscattering based C_phyto. In particular, through many manuscript, we have been able to show how phytoplankton photo-acclimation is the major forcing on the chl/C_phyto ratio and how it can inform us, for example, on nutrient limitation (https://egusphere.copernicus.org/preprints/2025/egusphere-2025-4261/ <- it has been accepted). The point here is not to make you cite papers I contributed to but make you aware that the utility of estimate of C_phyto from space has been shown in many works and, in particular, in providing information content additional to Chl.
2. The first test of a good C_phyto product is whether its ratio to Chl is consistent with lab studies of photoacclimation, e.g. is 30<C_phyto/Chl <300 (unless you are dealing with domination by mixotrophy in which case it could go lower, but you don't resolve them in your model.
3. Field measurements of Fchl, as done with Argo floats, can have many biases (see Roesler et al., 2017). One has to be careful on how to use them.
4. Phytoplankton products under clouds are typically wrong as they interpolate Chl rather than carbon and phytoplankton photo-adapt under cloud (https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024GL112274). How they do it for the product you use could bias your model (~70% of the ocean is covered by clouds at any given time).
5. How do you define the 'effectiveness' of DA is key and need to be provided. Obviously DA will force the model to the data.
6. How are you converting C to N? The product is carbon and your model currency is N.
7. To evaluate the distribution of parameter (e.g. histogram of distributions, whether of [Chl] or [Cphyto]), you could compare your model distribution to those of estimate from satellite. The near log-normal distribution should arise in both.
I am attaching an annotated PDF with some more comments.
Dear authors, I am often wrong. If you feel my comments are 'off the mark' feel free to contact me and if I am convinced I will be more than happy to amend my review.