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An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models
- Source :
- JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2015, 120 (9), pp.6508-6541. ⟨10.1002/2015JC011018⟩, Journal of Geophysical Research. Oceans, EPIC3Journal of Geophysical Research: Oceans, pp. n/a-n/a, ISSN: 21699275
- Publication Year :
- 2015
- Publisher :
- HAL CCSD, 2015.
-
Abstract
- We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll‐a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed‐layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite‐derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low‐productivity seasons as well as in sea ice‐covered/deep‐water regions. Depth‐resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption‐based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll‐a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic‐relevant parameters.<br />Key Points The models reproduced primary productivity better using in situ chlorophyll‐a than satellite valuesThe models performed well in low‐productivity seasons and in sea ice‐covered/deep‐water regionsNet primary productivity models need to be carefully tuned for the Arctic Ocean
- Subjects :
- 0106 biological sciences
In situ
Chlorophyll a
010504 meteorology & atmospheric sciences
Settore BIO/07
Arctic Ocean
model skill assessment
net primary productivity
ocean color model
remote sensing
subsurface chlorophyll‐a maximum
Oceanography
Atmospheric sciences
Biogeosciences
01 natural sciences
Remote Sensing
chemistry.chemical_compound
Oceanography: Biological and Chemical
Forum for Arctic Modeling and Observational Synthesis (FAMOS): Results and Synthesis of Coordinated Experiments
Geochemistry and Petrology
Phytoplankton
Earth and Planetary Sciences (miscellaneous)
14. Life underwater
Arctic Region
Research Articles
[SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography
0105 earth and related environmental sciences
010604 marine biology & hydrobiology
Arctic and Antarctic oceanography
Primary production
Model Verification and Validation
The arctic
Sea surface temperature
Oceanography: General
Geophysics
chemistry
13. Climate action
Space and Planetary Science
Ocean color
Chlorophyll
Environmental science
Antarctica
Geographic Location
Computational Geophysics
Research Article
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2015, 120 (9), pp.6508-6541. ⟨10.1002/2015JC011018⟩, Journal of Geophysical Research. Oceans, EPIC3Journal of Geophysical Research: Oceans, pp. n/a-n/a, ISSN: 21699275
- Accession number :
- edsair.doi.dedup.....230bd66aa5c0fcbea7b805e5c6248479
- Full Text :
- https://doi.org/10.1002/2015JC011018⟩