Transcript Monday_4_Reynolds_ICESCOCC.pptx

Towards community-based
approaches to estimating NPP &
NCP from remotely-sensed optical
properties
Rick A. Reynolds
Scripps Institution of Oceanography
University of California San Diego
[email protected]
NPP models
 Optical measurements are a key component to estimating
NPP & NCP at multiple temporal and spatial scales
 General form of NPP models
Net Production = [Biomass * Light absorption * Quantum yield] – (Respiration)
700
𝑁𝑃𝑃 = 𝐶 ∗
400
∗
𝐸𝑜 𝜆 𝑎𝑝ℎ
𝜆 𝑑𝜆 ∗ 𝜙𝑐 − 𝑅
Can be coupled with additional models to estimate NCP
 All phytoplankton
“Biomass” is considered equal!
 Differences in community composition, photophysiology, and size structure are
ignored
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The ocean is not homogenous
57 biogeochemical provinces identified by Longhurst et al. (1995)
81 provinces classified from satellite data (Oliver and Irwin, 2008)
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Can optics be used to identify communities?
 Significant advances in
discriminating oceanic
communities from
optical measurements
 Several approaches
 abundance based
 single species blooms
 dominant functional
groups
 size structure
 Potential for mapping
communities and
improving NPP & NCP
model estimates
available at www.ioccg.org
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Example community-based approach to NPP
 Chla distributions
partitioned into 3
distinct phytoplankton
size classes based on
total abundance
 Micro (diatoms and
dinoflagellates)
 Nano (prymnesiophytes)
 Pico (prokaryotes and
picoeukaryotes)
Uitz et al. 2006; IOCCG 2014
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Example community-based approach to NPP
 Extended to NPP
TOTAL
 Class-specific
photophysiology
 NPP partitioned among size
classes
Dec-Feb climatology of NPP
for 1998-2007
Micro
 SO results seem
reasonable
 Micro 30-50% of NPP in
spring-summer
 Nano dominate seasonal
blooms
Nano
 But
 No SO data in
parameterization
 Valid with changing ocean?
Pico
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Uitz et al. 2010
Optical-based classifications

Discrimination of
communities through
HCA of optical data

Pigment-based clusters
used as a reference

Non-bloom conditions
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 Chla range 0.1-0.6 mg m-3
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Torrecilla et al. 2011
Good agreement between pigments and optics

High degrees of similarity between
classifications derived from pigments and
optics

Phytoplankton absorption coefficient
better than Rrs

Best results obtained using derivatives of
high spectral resolution data
Pigment-based clusters
′′
𝑎𝑝ℎ
(𝜆) -based clusters
Dominant marker pigments
Station
Fuco ≈ MV-Chlb
A
DV-Chla > Zea
B
DV-Chla ≈ Zea
C1, C2, C3, C4
19’-Hexfuco > Zea
D
19’-Hexfuco > Fuco
E
Zea ≈ 19’-Hexfuco
F
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Torrecilla et al. 2011
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IOPs and planktonic community
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Specific absorption spectra of
major phytoplankton pigments
 Particle IOPs closely
linked
to planktonic constituents
 spectral absorption coefficient
directly linked to
phytoplankton pigments and
cell size
 spectral scattering coefficient
sensitive to particle size
distribution
 Can be obtained from in
situ from sensors on
various platforms, or from
ocean color inversion
models
Bricaud et al. 2004
Model simulations of particle backscattering shape
in relation to particle size distribution
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Kostadinov et al. 2009
suggests that IOPs can
discriminate particle
assemblages
 7 planktonic assemblages
identified in Chukchi and
Beaufort Seas, each with
distinct biogeochemical
characteristics
Beaufort
Sea
Alaska
Mackenzie R.
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Input to hierarchical cluster analysis
216 normalized spectra of bbp(l):ap(l)
Dendrogram obtained from HCA of bbp(l):ap(l)
Ward’s linkage distance
 Recent work in Arctic
Particle backscattering to absorption
(dimensionless)
IOPs as community indicators
Canada
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Neukermans et al. 2014 Ocean Sciences Meeting
Approach applicable to S. Ocean?
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Relative contributions to anw(440)
S. Ocean
Arctic
0.0 1.0
 Phytoplankton are
generally dominant
contributors to
nonwater absorption in
SO
0.0 1.0
0.12
0.19
0.63
0.5
0.5
0.5
0.5
0.19
1.0
0.0
1.0
0.0
0.25
0.5
1.0
0.0
0.5
CDOM
0.62
0.0
1.0
CDOM
Reynolds et al., IOCCG in press
 Regional variability in
particle size distribution
and backscattering has
been observed
bbp(555) [m-1]
10-1
10-2
Ross Sea
APFZ
10-3
10-4
10-2
10-1
100
101
102
Chl [mg m-3]
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Reynolds et al. 2001
Towards community-based approach
 Optics-derived
Satellite-derived
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Modeled
community indicators
can be used to
 assess community
distributions
 improve NPP and NCP
estimates
 validate or assimilate into
numerical models
 monitor changes in SO
environment and
biodiversity
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Ward et al. 2012
Outstanding questions
 What communities can we identify optically in the S.
Ocean?
 Do current approaches work in the SO?
 How do optical communities relate to other indicators of
community structure? (genomics, pigments, size structure)
 Consistent with environmental patterns? (macro and
micronutrients, light and mixing, grazing pressure)
 Can we associate these communities with specific biogeochemical
behavior?
 Do these community
distributions change over time and
space?
 Are there any trends in planktonic community distributions?
 What are the potential implications?
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Acknowledgements
 Collaborators
 M. Babin, G. Mitchell, G. Neukermans, J. Piera, D. Stramski,
E. Torrecilla, J. Uitz
 NASA
Programs
 Ocean Biology and Biogeochemistry
 Biodiversity and Ecological Forecasting
 Cryospheric Sciences
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