Global monthly sea surface nitrate fields estimated from remotely sensed sea surface temperature, chlorophyll, and modeled mixed layer depth

Arteaga, Lionel; Pahlow, Markus; Oschlies, Andreas

doi:10.1002/2014gl062937

Cited by 18 publications

(11 citation statements)

References 36 publications

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“…We use monthly satellite‐based data to define temperature (sea surface temperature (SST)), light, and nitrate for the surface mixed layer [ Arteaga et al , ]. Light is represented by the median mixed layer light level ( I g ), which approximates the average light intensity experienced by phytoplankton cells in the surface mixed layer ( I g ) [ Behrenfeld et al , ],

I_{g} = \frac{1}{D} \cdot PAR \cdot e^{- K 490 \cdot \frac{MLD}{2}}

…”

Section: Methodsmentioning

confidence: 99%

“…The use of different modeled MLD outputs is intended to cover the whole study period (2005–2010) with the most recent available model (i.e., MLD output is not available from HYCOM before October 2008 and FNMOC‐HIRES before July 2005). Monthly global surface nitrate concentrations are taken from the data set produced by Arteaga et al []. These global nitrate fields are obtained from multiple local linear regressions of satellite‐derived SST, Chl, and model‐based MLD.…”

Section: Methodsmentioning

confidence: 99%

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Modeled Chl:C ratio and derived estimates of phytoplankton carbon biomass and its contribution to total particulate organic carbon in the global surface ocean

Arteaga

Pahlow

Oschlies

2016

Global Biogeochemical Cycles

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Chlorophyll (Chl) is a distinctive component of autotrophic organisms, often used as an indicator of phytoplankton biomass in the ocean. However, assessment of phytoplankton biomass from Chl relies on the accurate estimation of the Chl:carbon(C) ratio. Here we present global patterns of Chl:C ratios in the surface ocean obtained from a phytoplankton growth model that accounts for the optimal acclimation of phytoplankton to ambient nutrient, light, and temperature conditions. The model agrees largely with observed/expected global patterns of Chl:C. Combining our Chl:C estimates with satellite Chl and particulate organic carbon (POC), we infer phytoplankton C concentration in the surface ocean and its contribution to the total POC pool. Our results suggest that the portion of POC corresponding to living phytoplankton is higher in subtropical latitudes and less productive regions (∼30–70%) and decreases to ∼10–30% toward high latitudes and productive regions. An important caveat of our model is the lack of iron limiting effects on phytoplankton physiology. Comparison of our predicted phytoplankton biomass with an independent estimate of total POC reveals a positive correlation between nitrate concentrations and nonphotosynthetic POC in the surface ocean. This correlation disappears when a constant Chl:C is applied. Our analysis is not constrained by assumptions of constant Chl:C or phytoplankton:POC ratio, providing a novel independent analysis of phytoplankton biomass in the surface ocean. These results highlight the importance of accounting for the variability in Chl:C and its application in distinguishing the autotrophic and heterotrophic components in the assemblage of the marine plankton ecosystem.

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I_{g} = \frac{1}{D} \cdot PAR \cdot e^{- K 490 \cdot \frac{MLD}{2}}

…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Modeled Chl:C ratio and derived estimates of phytoplankton carbon biomass and its contribution to total particulate organic carbon in the global surface ocean

Arteaga

Pahlow

Oschlies

2016

Global Biogeochemical Cycles

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“…Prior to this study, there were two main methods for estimating the concentrations of nutrients. Firstly, global-or basin-scale models were employed based on the relationship between nutrients, sea surface temperature (SST), and chlorophyll concentration [34][35][36]. However, such models are not applicable to coastal waters, because there is little change in environmental elements, such as SST, on a small spatial scale.…”

Section: Comparison With Spectrum-based Algorithmsmentioning

confidence: 99%

Satellite Retrieval of Surface Water Nutrients in the Coastal Regions of the East China Sea

et al. 2018

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Due to the tremendous flux of terrestrial nutrients from the Changjiang River, the waters in the coastal regions of the East China Sea (ECS) are exposed to heavy eutrophication. Satellite remote sensing was proven to be an ideal way of monitoring the spatiotemporal variability of these nutrients. In this study, satellite retrieval models for nitrate and phosphate concentrations in the coastal regions of the ECS are proposed using the back-propagation neural network (BP-NN). Both the satellite-retrieved sea surface salinity (SSS) and remote-sensing reflectance (Rrs) were used as inputs in our model. Compared with models that only use Rrs or SSS, the newly proposed model performs much better in the study area, with determination coefficients (R2) of 0.98 and 0.83, and mean relative error (MRE) values of 18.2% and 17.2% for nitrate and phosphate concentrations, respectively. Based on the proposed model and satellite-retrieved Rrs and SSS datasets, monthly time-series maps of nitrate and phosphate concentrations in the coastal regions of the ECS for 2015–2017 were retrieved for the first time. The results show that the distribution of nutrients had a significant seasonal variation. Phosphate concentrations in the ECS were lower in spring and summer than those in autumn and winter, which was mainly due to phytoplankton uptake and utilization. However, nitrate still spread far out into the ocean in summer because the diluted Changjiang River water remained rich in nitrogen.

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“…To ensure that the chosen number of clusters, k, is representative of the system, typically one needs to repeat the technique for various values of k, and, using visual inspection, select the optimal value for k when the resulting clusters become repetitive or contain no additional information. Objective methods have been proposed (e.g., Bankert and Solbrig, 2015), where the average radius of each cluster (the distance from the centroid to the most distant member within a cluster) is computed for decreasing k. Bankert and Solbrig (2015) found that when the number of clusters falls below the optimal k, the average radius grows rapidly. We employ a similar methodology here.…”

Section: Sensitivity To Predefined Number Of Clustersmentioning

confidence: 99%

“…For example, this technique has been used to define atmospheric weather states by identifying cloud regimes (Jakob and Tselioudis, 2003;Rossow et al, 2005;Williams and Webb, 2009;Tselioudis et al, 2013;Bodas-Salcedo et al, 2014;Oreopoulos et al, 2016). Bankert and Solbrig (2015) were able to extract a 3-D cloud representation using cluster analysis. This technique has also been used to characterize water types in lakes (Trochta et al, 2015), hydraulic habitat composition in rivers (Hugue et al, 2016), phenology patterns in forests (Trans Mills et al, 2011), solar variability (Zagouras et al, 2013), ENSO phenomena (Radebach et al, 2013), and regions with characteristic hydrological responses (Halverson and Fleming, 2015), among many other applications.…”

Section: Introductionmentioning

confidence: 99%

The Ocean Carbon States Database: a proof-of-concept application of cluster analysis in the ocean carbon cycle

Latto

Romanou

2018

Earth Syst. Sci. Data

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Abstract. In this paper, we present a database of the basic regimes of the carbon cycle in the ocean, the “ocean carbon states”, as obtained using a data mining/pattern recognition technique in observation-based as well as model data. The goal of this study is to establish a new data analysis methodology, test it and assess its utility in providing more insights into the regional and temporal variability of the marine carbon cycle. This is important as advanced data mining techniques are becoming widely used in climate and Earth sciences and in particular in studies of the global carbon cycle, where the interaction of physical and biogeochemical drivers confounds our ability to accurately describe, understand, and predict CO2 concentrations and their changes in the major planetary carbon reservoirs. In this proof-of-concept study, we focus on using well-understood data that are based on observations, as well as model results from the NASA Goddard Institute for Space Studies (GISS) climate model. Our analysis shows that ocean carbon states are associated with the subtropical–subpolar gyre during the colder months of the year and the tropics during the warmer season in the North Atlantic basin. Conversely, in the Southern Ocean, the ocean carbon states can be associated with the subtropical and Antarctic convergence zones in the warmer season and the coastal Antarctic divergence zone in the colder season. With respect to model evaluation, we find that the GISS model reproduces the cold and warm season regimes more skillfully in the North Atlantic than in the Southern Ocean and matches the observed seasonality better than the spatial distribution of the regimes. Finally, the ocean carbon states provide useful information in the model error attribution. Model air–sea CO2 flux biases in the North Atlantic stem from wind speed and salinity biases in the subpolar region and nutrient and wind speed biases in the subtropics and tropics. Nutrient biases are shown to be most important in the Southern Ocean flux bias. All data and analysis scripts are available at https://data.giss.nasa.gov/oceans/carbonstates/ (DOI: https://doi.org/10.5281/zenodo.996891).

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Global monthly sea surface nitrate fields estimated from remotely sensed sea surface temperature, chlorophyll, and modeled mixed layer depth

Cited by 18 publications

References 36 publications

Modeled Chl:C ratio and derived estimates of phytoplankton carbon biomass and its contribution to total particulate organic carbon in the global surface ocean

Modeled Chl:C ratio and derived estimates of phytoplankton carbon biomass and its contribution to total particulate organic carbon in the global surface ocean

Satellite Retrieval of Surface Water Nutrients in the Coastal Regions of the East China Sea

The Ocean Carbon States Database: a proof-of-concept application of cluster analysis in the ocean carbon cycle

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