Mind the gap: The impact of missing data on the calculation of phytoplankton phenology metrics

Annual phytoplankton blooms are key events in marine ecosystems and interannual variability in bloom timing has important implications for carbon export and the marine food web. The degree of match or mismatch between the timing of phytoplankton and zooplankton annual cycles may impact larval surviv...

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Published in	Journal of Geophysical Research: Oceans Vol. 117; no. C8
Main Authors	Cole, Harriet, Henson, Stephanie, Martin, Adrian, Yool, Andrew
Format	Journal Article
Language	English
Published	Washington, DC Blackwell Publishing Ltd 01.08.2012 American Geophysical Union
Subjects	Anthropogenic factors biogeochemical model Biological oceanography Chemical oceanography Data processing Earth sciences Earth, ocean, space Exact sciences and technology Geobiology Geophysics Marine Marine ecosystems NOBM ocean color remote sensing Oceanography Phenology Phytoplankton phytoplankton phenology Pneumoviridae Remote sensing Seasonal variations seasonality metrics spring bloom Trophic levels Zooplankton color aerosols clouds annual variations Winter Trophic level carbon phytoplankton plankton export models NASA Time series zooplankton Satellite observation Subpolar zone Sun Interannual variation Missing data Phenology ecosystems Timing SeaStar satellite seasonal variations errors Comparative study
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Abstract	Annual phytoplankton blooms are key events in marine ecosystems and interannual variability in bloom timing has important implications for carbon export and the marine food web. The degree of match or mismatch between the timing of phytoplankton and zooplankton annual cycles may impact larval survival with knock‐on effects at higher trophic levels. Interannual variability in phytoplankton bloom timing may also be used to monitor changes in the pelagic ecosystem that are either naturally or anthropogenically forced. Seasonality metrics that use satellite ocean color data have been developed to quantify the timing of phenological events which allow for objective comparisons between different regions and over long periods of time. However, satellite data sets are subject to frequent gaps due to clouds and atmospheric aerosols, or persistent data gaps in winter due to low sun angle. Here we quantify the impact of these gaps on determining the start and peak timing of phytoplankton blooms. We use the NASA Ocean Biogeochemical Model that assimilates SeaWiFS data as a gap‐free time series and derive an empirical relationship between the percentage of missing data and error in the phenology metric. Applied globally, we find that the majority of subpolar regions have typical errors of 30 days for the bloom initiation date and 15 days for the peak date. The errors introduced by intermittent data must be taken into account in phenological studies. Key Points Global maps of seasonality metrics and the associated uncertainty are presented Bloom start and peak date errors are 30 and 15 days respectively in most regions The error in bloom start date has a directional bias that changes with latitude
AbstractList	Annual phytoplankton blooms are key events in marine ecosystems and interannual variability in bloom timing has important implications for carbon export and the marine food web. The degree of match or mismatch between the timing of phytoplankton and zooplankton annual cycles may impact larval survival with knock‐on effects at higher trophic levels. Interannual variability in phytoplankton bloom timing may also be used to monitor changes in the pelagic ecosystem that are either naturally or anthropogenically forced. Seasonality metrics that use satellite ocean color data have been developed to quantify the timing of phenological events which allow for objective comparisons between different regions and over long periods of time. However, satellite data sets are subject to frequent gaps due to clouds and atmospheric aerosols, or persistent data gaps in winter due to low sun angle. Here we quantify the impact of these gaps on determining the start and peak timing of phytoplankton blooms. We use the NASA Ocean Biogeochemical Model that assimilates SeaWiFS data as a gap‐free time series and derive an empirical relationship between the percentage of missing data and error in the phenology metric. Applied globally, we find that the majority of subpolar regions have typical errors of 30 days for the bloom initiation date and 15 days for the peak date. The errors introduced by intermittent data must be taken into account in phenological studies. Key Points Global maps of seasonality metrics and the associated uncertainty are presented Bloom start and peak date errors are 30 and 15 days respectively in most regions The error in bloom start date has a directional bias that changes with latitude Annual phytoplankton blooms are key events in marine ecosystems and interannual variability in bloom timing has important implications for carbon export and the marine food web. The degree of match or mismatch between the timing of phytoplankton and zooplankton annual cycles may impact larval survival with knock-on effects at higher trophic levels. Interannual variability in phytoplankton bloom timing may also be used to monitor changes in the pelagic ecosystem that are either naturally or anthropogenically forced. Seasonality metrics that use satellite ocean color data have been developed to quantify the timing of phenological events which allow for objective comparisons between different regions and over long periods of time. However, satellite data sets are subject to frequent gaps due to clouds and atmospheric aerosols, or persistent data gaps in winter due to low sun angle. Here we quantify the impact of these gaps on determining the start and peak timing of phytoplankton blooms. We use the NASA Ocean Biogeochemical Model that assimilates SeaWiFS data as a gap-free time series and derive an empirical relationship between the percentage of missing data and error in the phenology metric. Applied globally, we find that the majority of subpolar regions have typical errors of 30 days for the bloom initiation date and 15 days for the peak date. The errors introduced by intermittent data must be taken into account in phenological studies. Annual phytoplankton blooms are key events in marine ecosystems and interannual variability in bloom timing has important implications for carbon export and the marine food web. The degree of match or mismatch between the timing of phytoplankton and zooplankton annual cycles may impact larval survival with knock-on effects at higher trophic levels. Interannual variability in phytoplankton bloom timing may also be used to monitor changes in the pelagic ecosystem that are either naturally or anthropogenically forced. Seasonality metrics that use satellite ocean color data have been developed to quantify the timing of phenological events which allow for objective comparisons between different regions and over long periods of time. However, satellite data sets are subject to frequent gaps due to clouds and atmospheric aerosols, or persistent data gaps in winter due to low sun angle. Here we quantify the impact of these gaps on determining the start and peak timing of phytoplankton blooms. We use the NASA Ocean Biogeochemical Model that assimilates SeaWiFS data as a gap-free time series and derive an empirical relationship between the percentage of missing data and error in the phenology metric. Applied globally, we find that the majority of subpolar regions have typical errors of 30 days for the bloom initiation date and 15 days for the peak date. The errors introduced by intermittent data must be taken into account in phenological studies. Key Points * Global maps of seasonality metrics and the associated uncertainty are presented * Bloom start and peak date errors are 30 and 15 days respectively in most regions * The error in bloom start date has a directional bias that changes with latitude Annual phytoplankton blooms are key events in marine ecosystems and interannual variability in bloom timing has important implications for carbon export and the marine food web. The degree of match or mismatch between the timing of phytoplankton and zooplankton annual cycles may impact larval survival with knock‐on effects at higher trophic levels. Interannual variability in phytoplankton bloom timing may also be used to monitor changes in the pelagic ecosystem that are either naturally or anthropogenically forced. Seasonality metrics that use satellite ocean color data have been developed to quantify the timing of phenological events which allow for objective comparisons between different regions and over long periods of time. However, satellite data sets are subject to frequent gaps due to clouds and atmospheric aerosols, or persistent data gaps in winter due to low sun angle. Here we quantify the impact of these gaps on determining the start and peak timing of phytoplankton blooms. We use the NASA Ocean Biogeochemical Model that assimilates SeaWiFS data as a gap‐free time series and derive an empirical relationship between the percentage of missing data and error in the phenology metric. Applied globally, we find that the majority of subpolar regions have typical errors of 30 days for the bloom initiation date and 15 days for the peak date. The errors introduced by intermittent data must be taken into account in phenological studies. Global maps of seasonality metrics and the associated uncertainty are presented Bloom start and peak date errors are 30 and 15 days respectively in most regions The error in bloom start date has a directional bias that changes with latitude
Author	Henson, Stephanie Yool, Andrew Martin, Adrian Cole, Harriet
Author_xml	– sequence: 1 givenname: Harriet surname: Cole fullname: Cole, Harriet email: harriet.cole@noc.soton.ac.uk, harriet.cole@noc.soton.ac.uk organization: Ocean and Earth Science, National Oceanography Centre Southampton, University of Southampton, Southampton, UK – sequence: 2 givenname: Stephanie surname: Henson fullname: Henson, Stephanie organization: National Oceanography Centre, University of Southampton, Southampton, UK – sequence: 3 givenname: Adrian surname: Martin fullname: Martin, Adrian organization: National Oceanography Centre, University of Southampton, Southampton, UK – sequence: 4 givenname: Andrew surname: Yool fullname: Yool, Andrew organization: National Oceanography Centre, University of Southampton, Southampton, UK
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Copyright	2012. American Geophysical Union. All Rights Reserved. 2015 INIST-CNRS Copyright American Geophysical Union 2012
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Keywords	color aerosols clouds annual variations Winter Trophic level carbon phytoplankton plankton export models NASA Time series zooplankton Satellite observation Subpolar zone Sun Interannual variation Missing data Phenology ecosystems Timing SeaStar satellite seasonal variations errors Comparative study
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Snippet	Annual phytoplankton blooms are key events in marine ecosystems and interannual variability in bloom timing has important implications for carbon export and...
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SubjectTerms	Anthropogenic factors biogeochemical model Biological oceanography Chemical oceanography Data processing Earth sciences Earth, ocean, space Exact sciences and technology Geobiology Geophysics Marine Marine ecosystems NOBM ocean color remote sensing Oceanography Phenology Phytoplankton phytoplankton phenology Pneumoviridae Remote sensing Seasonal variations seasonality metrics spring bloom Trophic levels Zooplankton
Title	Mind the gap: The impact of missing data on the calculation of phytoplankton phenology metrics
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