13 research outputs found
Accurate deep-learning estimation of chlorophyll-a concentration from the spectral particulate beam-attenuation coefficient
DiïŹerent techniques exist for determining chlorophyll-a concentration as a proxy of phytoplankton abundance. In this study, a novel method based on the spectral particulate beam-attenuation coeïŹcient (cp) was developed to estimate chlorophyll-a concentrations in oceanic waters. A multi-layer perceptron deep neural network was trained to exploit the spectral features present in cp around the chlorophyll a absorption peak in the
red spectral region. Results show that the model was successful at accurately retrieving chlorophyll-a concentrations using cp in three red spectral bands,irrespective of time or location and over a wide range of chlorophyll-a concentrations
A neural network-based method for merging ocean color and Argo data to extend surface bio-optical properties to depth: Retrieval of the particulate backscattering coefficient
The present study proposes a novel method that merges satellite ocean color bio-optical products with Argo temperature-salinity profiles to infer the vertical distribution of the particulate backscattering coefficient (bbp). This neural network-based method (SOCA-BBP for Satellite Ocean-Color merged with Argo data to infer the vertical distribution of the Particulate Backscattering coefficient) uses three main input components: (1) satellite-based surface estimates of bbp and chlorophyll a concentration matched up in space and time with (2) depth-resolved physical properties derived from temperature-salinity profiles measured by Argo profiling floats and (3) the day of the year of the considered satellite-Argo matchup. The neural network is trained and validated using a database including 4725 simultaneous profiles of temperature-salinity and bio-optical properties collected by Bio-Argo floats, with concomitant satellite-derived products. The Bio-Argo profiles are representative of the global open-ocean in terms of oceanographic conditions, making the proposed method applicable to most open-ocean environments. SOCA-BBP is validated using 20% of the entire database (global error of 21%). We present additional validation results based on two other independent data sets acquired (1) by four Bio-Argo floats deployed in major oceanic basins, not represented in the database used to train the method; and (2) during an AMT (Atlantic Meridional Transect) field cruise in 2009. These validation tests based on two fully independent data sets indicate the robustness of the predicted vertical distribution of bbp. To illustrate the potential of the method, we merged monthly climatological Argo profiles with ocean color products to produce a depth-resolved climatology of bbp for the global ocean
Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design
Numerical models of ocean biogeochemistry are becoming the major tools used to detect
and predict the impact of climate change on marine resources and to monitor
ocean health. However, with the continuous improvement of model structure
and spatial resolution, incorporation of these additional degrees of freedom
into fidelity assessment has become increasingly challenging. Here, we
propose a new method to provide information on the model predictive skill in a concise
way. The method is based on the conjoint use of a k-means clustering
technique, assessment metrics, and Biogeochemical-Argo (BGC-Argo) observations. The k-means
algorithm and the assessment metrics reduce the number of model data points
to be evaluated. The metrics evaluate either the model state accuracy or the
skill of the model with respect to capturing emergent properties, such as the deep
chlorophyll maximums and oxygen minimum zones. The use of BGC-Argo
observations as the sole evaluation data set ensures the accuracy of the
data, as it is a homogenous data set with strict sampling methodologies and
data quality control procedures. The method is applied to the Global Ocean Biogeochemistry Analysis and Forecast system of the Copernicus Marine
Service. The model performance is evaluated using the model efficiency
statistical score, which compares the modelâobservation misfit with the
variability in the observations and, thus, objectively quantifies whether the
model outperforms the BGC-Argo climatology. We show that, overall, the model
surpasses the BGC-Argo climatology in predicting pH, dissolved inorganic
carbon, alkalinity, oxygen, nitrate, and phosphate in the mesopelagic and
the mixed layers as well as silicate in the mesopelagic layer. However,
there are still areas for improvement with respect to reducing the modelâdata misfit for
certain variables such as silicate, pH, and the partial pressure of CO2
in the mixed layer as well as chlorophyll-a-related, oxygen-minimum-zone-related, and particulate-organic-carbon-related metrics. The method proposed
here can also aid in refining the design of the BGC-Argo network, in
particular regarding the regions in which BGC-Argo observations should be enhanced to
improve the model accuracy via the assimilation of BGC-Argo data or
process-oriented assessment studies. We strongly recommend increasing the
number of observations in the Arctic region while maintaining the existing
high-density of observations in the Southern Oceans. The model error in
these regions is only slightly less than the variability observed in
BGC-Argo measurements. Our study illustrates how the synergic use of
modeling and BGC-Argo data can both provide information about the performance of models
and improve the design of observing systems.</p
Introduction to the French GEOTRACES North Atlantic Transect (GA01): GEOVIDE cruise
The GEOVIDE cruise, a collaborative project within the framework of the international GEOTRACES programme, was conducted along the French-led section in the North Atlantic Ocean (Section GA01), between 15 May and 30 June 2014. In this special issue (https://www.biogeosciences.net/special_issue900.html), results from GEOVIDE, including physical oceanography and trace element and isotope cyclings, are presented among 18 articles. Here, the scientific context, project objectives, and scientific strategy of GEOVIDE are provided, along with an overview of the main results from the articles published in the special issue
On the vertical distribution of the chlorophyll <i>a</i> concentration in the Mediterranean Sea: a basin-scale and seasonal approach
The distribution of the chlorophyll a concentration ([Chl a]) in the
Mediterranean Sea, mainly obtained from satellite surface observations or
from scattered in situ experiments, is updated by analyzing a database of
fluorescence profiles converted into [Chl a]. The database, which includes
6790 fluorescence profiles from various origins, was processed with a
specific quality control procedure. To ensure homogeneity between the
different data sources, 65 % of fluorescence profiles have been
intercalibrated on the basis of their concomitant satellite [Chl a]
estimation. The climatological pattern of [Chl a] vertical profiles in four
key sites of the Mediterranean Sea has been analyzed. Climatological results
confirm previous findings over the range of existing [Chl a] values and
throughout the principal Mediterranean trophic regimes. They also provide new
insights into the seasonal variability in the shape of the vertical [Chl a]
profile, inaccessible through remote-sensing observations. An analysis based on
the recognition of the general shape of the fluorescence profile was also
performed. Although the shape of [Chl a] vertical distribution
characterized by a deep chlorophyll maximum (DCM) is ubiquitous during
summer, different forms are observed during winter, thus suggesting that
factors affecting the vertical distribution of the biomass are complex and
highly variable. The [Chl a] spatial distribution in the Mediterranean Sea
mimics, on smaller scales, what is observed in the global ocean. As already
evidenced by analyzing satellite surface observations, midlatitude- and
subtropical-like phytoplankton dynamics coexist in the Mediterranean Sea.
Moreover, the Mediterranean DCM variability appears to be characterized by
patterns already observed on the global scale
A neural network approach to estimate water column nutrient concentrations and carbonate system parameters in the Mediterranean Sea: CANYON-MED.
Vertical distribution of chlorophyll <I>a</I> concentration and phytoplankton community composition from in situ fluorescence profiles: a first database for the global ocean
In vivo chlorophyll <I>a</I> fluorescence is a proxy of chlorophyll <I>a</I> concentration, and is
one of the most frequently measured biogeochemical properties in the ocean.
Thousands of profiles are available from historical databases and the
integration of fluorescence sensors to autonomous platforms has led to a
significant increase of chlorophyll fluorescence profile acquisition. To our
knowledge, this important source of environmental data has not yet been
included in global analyses. A total of 268 127 chlorophyll fluorescence
profiles from several databases as well as published and unpublished
individual sources were compiled. Following a robust quality control
procedure detailed in the present paper, about 49 000 chlorophyll
fluorescence profiles were converted into phytoplankton biomass (i.e.,
chlorophyll <I>a</I> concentration) and size-based community composition (i.e.,
microphytoplankton, nanophytoplankton and picophytoplankton), using a method
specifically developed to harmonize fluorescence profiles from diverse
sources. The data span over 5 decades from 1958 to 2015, including
observations from all major oceanic basins and all seasons, and depths
ranging from the surface to a median maximum sampling depth of around 700 m.
Global maps of chlorophyll <I>a</I> concentration and phytoplankton community
composition are presented here for the first time. Monthly climatologies
were computed for three of Longhurst's ecological provinces in order to
exemplify the potential use of the data product. Original data sets (raw
fluorescence profiles) as well as calibrated profiles of phytoplankton
biomass and community composition are available on open access at PANGAEA,
Data Publisher for Earth and Environmental Science.
<br><br>
Raw fluorescence profiles: <a href="http://doi.pangaea.de/10.1594/PANGAEA.844212" target="_blank">http://doi.pangaea.de/10.1594/PANGAEA.844212</a> and
<br><br>
Phytoplankton biomass and community composition: <a href="http://doi.pangaea.de/10.1594/PANGAEA.844485" target="_blank">http://doi.pangaea.de/10.1594/PANGAEA.844485</a
Biogeochemical-Argo float oservations of seasonal dynamics and disturbance of phytoplankton biomass in a South Pacific island wake
International audienc