Assimilation of remotely-sensed optical properties to improve marine biogeochemistry modelling

In this paper we evaluate whether the assimilation of remotely-sensed optical data into a marine ecosystem model improves the simulation of biogeochemistry in a shelf sea. A localized Ensemble Kalman filter was used to assimilate weekly diffuse light attenuation coefficient data, Kd(443) from SeaWiFs, into an ecosystem model of the western English Channel. The spatial distributions of (unassimilated) surface chlorophyll from satellite, and a multivariate time series of eighteen biogeochemical and optical variables measured in situ at one long-term monitoring site were used to evaluate the system performance for the year 2006. Assimilation reduced the root mean square error and improved the correlation with the assimilated Kd(443) observations, for both the analysis and, to a lesser extent, the forecast estimates, when compared to the reference model simulation. Improvements in the simulation of (unassimilated) ocean colour chlorophyll were less evident, and in some parts of the Channel the simulation of this data deteriorated. The estimation errors for the (unassimilated) in situ data were reduced for most variables with some exceptions, e.g. dissolved nitrogen. Importantly, the assimilation adjusted the balance of ecosystem processes by shifting the simulated food web towards the microbial loop, thus improving the estimation of some properties, e.g. total particulate carbon. Assimilation of Kd(443) outperformed a comparative chlorophyll assimilation experiment, in both the estimation of ocean colour data and in the simulation of independent in situ data. These results are related to relatively low error in Kd(443) data, and because it is a bulk optical property of marine ecosystems. Assimilation of remotely-sensed optical properties is a promising approach to improve the simulation of biogeochemical and optical variables that are relevant for ecosystem functioning and climate change studies.

Investigating the inter-relationships between water attenuated irradiance, primary production and DMS(P)

Both solar irradiance and primary production have been proposed as independent controls on seawater dimethyl sulphide (DMS) and dimethylsulphoniopropionate (DMSP) concentrations. However, irradiance also drives photosynthesis, and thus influences a complex set of inter-related processes that modulate marine DMS. We investigate the potential inter-relationships between the rate of primary production (carbon assimilation), water-attenuated irradiance and DMS/DMSP dynamics by applying correlation analysis to a high resolution, concurrently sampled in situ data set from a range of latitudes covering multiple biogeochemical provinces from 3 of the 4 Longhurst biogeochemical domains. The combination of primary production (PP) and underwater irradiance (Iz) within a multivariate regression model is able to explain 55% of the variance in DMS concentrations from all depths within the euphotic zone and 66% of the variance in surface DMS concentrations. Contrary to some previous studies we find a variable representing biological processes is necessary to better account for the variance in DMS. We find that the inclusion of Iz accounts for variance in DMS that is independent from the variance explained by PP. This suggests an important role for solar irradiance (beyond the influence of irradiance upon primary production) in mediating the relationship between the productivity of the ecosystem, DMS/DMSP production and ambient seawater DMS concentrations.

Combining metagenomics with metaproteomics and stable isotope probing reveals metabolic pathways used by a naturally occurring marine methylotroph

A variety of culture-independent techniques have been developed that can be used in conjunction with culture-dependent physiological and metabolic studies of key microbial organisms, in order to better understand how the activity of natural populations influences and regulates all major biogeochemical cycles. In this study, we combined DNA-stable isotope probing with metagenomics and metaproteomics to characterize an as yet uncultivated marine methylotroph that actively incorporated carbon from 13C-labeled methanol into biomass. By metagenomic sequencing of the heavy DNA, we retrieved virtually the whole genome of this bacterium and determined its metabolic potential. Through protein-stable isotope probing, the RuMP cycle was established as the main carbon assimilation pathway, and the classical methanol dehydrogenase-encoding gene mxaF, as well as three out of four identified xoxF homologues were found to be expressed. This proof-of-concept study is the first in which theculture-independent techniques of DNA- and protein-stable isotope probing have been used to characterize the metabolism of a naturally-ocurring Methylophaga-like bacterium in the marine environment (i.e. M. thiooxydans L4) and thus provides a powerful approach to access the genome and proteome of uncultivated microbes involved in key processes in the environment

Remote sensing of assimilation number for marine phytoplankton

Estimating primary production at large spatial scales is key to our understanding of the global carbon cycle. Algorithms to estimate primary production are well established and have been used in many studies with success. One of the key parameters in these algorithms is the chlorophyll-normalised production rate under light saturation (referred to as the light saturation parameter or the assimilation number). It is known to depend on temperature, light history and nutrient conditions, but assigning a magnitude to it at particular space-time points is difficult. In this paper, we explore two models to estimate the assimilation number at the global scale from remotely-sensed data that combine methods to estimate the carbon-to-chlorophyll ratio and the maximum growth rate of phytoplankton. The inputs to the algorithms are the surface concentration of chlorophyll, seasurface temperature, photosynthetically-active radiation af the surface of the sea, sea surface nutrient concentration and mixed-layer depth. A large database of in situ estimates of the assimilation number is used to develop the models and provide elements of validation. The comparisons with in situ observations are promising and global maps of assimilation number are produced.