An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models
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 (SS...
Published in: | Journal of Geophysical Research: Oceans |
---|---|
Main Authors: | , |
Format: | Text |
Language: | unknown |
Published: |
W&M ScholarWorks
2015
|
Subjects: | |
Online Access: | https://scholarworks.wm.edu/vimsarticles/247 https://scholarworks.wm.edu/context/vimsarticles/article/1246/viewcontent/2015JC011018.pdf |
id |
ftwilliammarycol:oai:scholarworks.wm.edu:vimsarticles-1246 |
---|---|
record_format |
openpolar |
spelling |
ftwilliammarycol:oai:scholarworks.wm.edu:vimsarticles-1246 2024-06-23T07:49:37+00:00 An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models Lee, Younjoo J. Friedrichs, Marjorie A.M. 2015-01-01T08:00:00Z application/pdf https://scholarworks.wm.edu/vimsarticles/247 doi: 10.1002/2015JC011018 https://scholarworks.wm.edu/context/vimsarticles/article/1246/viewcontent/2015JC011018.pdf unknown W&M ScholarWorks https://scholarworks.wm.edu/vimsarticles/247 doi: 10.1002/2015JC011018 https://scholarworks.wm.edu/context/vimsarticles/article/1246/viewcontent/2015JC011018.pdf VIMS Articles Arctic Ocean net primary productivity model skill assessment subsurface chlorophyll-a maximum ocean color model remote sensing Biological Sciences Peer-Reviewed Articles Marine Biology text 2015 ftwilliammarycol https://doi.org/10.1002/2015JC011018 2024-06-05T03:30:42Z 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. Text Arctic Arctic Ocean Phytoplankton Sea ice W&M ScholarWorks Arctic Arctic Ocean Journal of Geophysical Research: Oceans 120 9 6508 6541 |
institution |
Open Polar |
collection |
W&M ScholarWorks |
op_collection_id |
ftwilliammarycol |
language |
unknown |
topic |
Arctic Ocean net primary productivity model skill assessment subsurface chlorophyll-a maximum ocean color model remote sensing Biological Sciences Peer-Reviewed Articles Marine Biology |
spellingShingle |
Arctic Ocean net primary productivity model skill assessment subsurface chlorophyll-a maximum ocean color model remote sensing Biological Sciences Peer-Reviewed Articles Marine Biology Lee, Younjoo J. Friedrichs, Marjorie A.M. An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models |
topic_facet |
Arctic Ocean net primary productivity model skill assessment subsurface chlorophyll-a maximum ocean color model remote sensing Biological Sciences Peer-Reviewed Articles Marine Biology |
description |
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. |
format |
Text |
author |
Lee, Younjoo J. Friedrichs, Marjorie A.M. |
author_facet |
Lee, Younjoo J. Friedrichs, Marjorie A.M. |
author_sort |
Lee, Younjoo J. |
title |
An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models |
title_short |
An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models |
title_full |
An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models |
title_fullStr |
An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models |
title_full_unstemmed |
An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models |
title_sort |
assessment of phytoplankton primary productivity in the arctic ocean from satellite ocean color/in situ chlorophyll-a based models |
publisher |
W&M ScholarWorks |
publishDate |
2015 |
url |
https://scholarworks.wm.edu/vimsarticles/247 https://scholarworks.wm.edu/context/vimsarticles/article/1246/viewcontent/2015JC011018.pdf |
geographic |
Arctic Arctic Ocean |
geographic_facet |
Arctic Arctic Ocean |
genre |
Arctic Arctic Ocean Phytoplankton Sea ice |
genre_facet |
Arctic Arctic Ocean Phytoplankton Sea ice |
op_source |
VIMS Articles |
op_relation |
https://scholarworks.wm.edu/vimsarticles/247 doi: 10.1002/2015JC011018 https://scholarworks.wm.edu/context/vimsarticles/article/1246/viewcontent/2015JC011018.pdf |
op_doi |
https://doi.org/10.1002/2015JC011018 |
container_title |
Journal of Geophysical Research: Oceans |
container_volume |
120 |
container_issue |
9 |
container_start_page |
6508 |
op_container_end_page |
6541 |
_version_ |
1802640152139399168 |