Month of Max for PFT Algorithms, Chl & PAR (left ) & climate models (right) (Black Arrow is PFT µ; σ2 = 1- Length)

Month of Max for PFT Algorithms, Chl & PAR (left ) & climate models (right) (Black Arrow is PFT µ; σ2 = 1- Length)

Primary - secondary bloom month of max. difference, +ve = primary bloom leads

Frequency, Year-1 Year

PHYTOPLANKTON PHENOLOGY FROM OCEAN COLOR ALGORITHMS AND EARTH SYSTEM MODELS

Kostadinov, T. S.1,*, A. Cabré2, H. Vedantham3, I. Marinov2, A. Bracher4, R. Brewin5, A. Bricaud6, N. Hardman-Mountford7, T. Hirata8, A. Fujiwara9, C. Mouw10, S. Roy11, J. Uitz6

*Corresponding author: tkostadi@richmond.edu

Introduction Phytoplankton Functional Types (PFTs) are

groups of phytoplankton with similar biogeochemical roles. They closely correspond to phytoplankton size classes (PSCs). Over the last decade or so, numerous

PFT/PSC algorithms have been developed. Here we inter-compare emergent

phenology since: The algorithms use different theoretical

bases and retrieve variables on different scales. Phase differences can further confound

direct comparison. We derive phenological parameters from 10

PFT satellite algorithms, Chl, PAR and 7 CMIP5 climate models using the Discrete Fourier Transform to model the seasonal cycle. We then detect the signal’s local maxima via peak analysis. We use % microplankton or diatoms (or the

closest available variable) from the PFT algorithms and diatom carbon biomass from climate models that have it. We quantify and compare 1) seasonal

amplitude, 3) percent seasonal variance, 2) month of maximum, 4) bloom duration, and 5) secondary bloom characteristics if present.

Seasonal Amplitude and Percent Seasonal Variance

This work is supported by NASA OBB Grant #NNX13AC92G to IM & TSK. We thank the NASA OBPG and the International PFT Intercomparison Project team members for providing ocean color and PFT data. We also thank Tilman Dinter, Toru Hirawake, Svetlana Milutinovic, Danica Fine, and David Shields for their help. We acknowledge the World Climate Research Programme's Working Group on Coupled

Modelling, the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison, and the Global Organization for Earth System Science Portals which are involved in CMIP, and we thank the climate modeling

groups for producing and making available their model output.

San Francisco, CA June 15-18, 2015

Input Satellite Data Sets *SeaWiFS monthly SMI of Rrs(λ), 2003-2007 SCIAMACHY monthly data, 2003-2007

SeaWiFS monthly OC4v6 Chl & PAR, 2003-2007

Regionally Binned Analysis

Participating Algorithms & Models

= Circular statistics – ∆t b/n two months

bbp(λ) slope η

Seasonal Amplitude

Algorithm Publication(s)

Acronym Variables(s) Units Input Data

Algorithm Category/Basis

Alvain et al. (2005,2008) PHYSAT Frequency of detection of diatoms

% of days

SW10* Multiple PFTs, Rrs(λ) second-order anomalies.

Bracher et al. (2009), Sadeghi et al. ( 2012)

PhytoDOAS

Diatoms [Chl-a] mg m-3 SCIAMACHY

Multiple PFTs, differential absorption from hyperspectral

data. Brewin et al. (2010, 2011) BR10 Microplankton –

fraction of Chl-a % SW10* Size structure, abundance-based.

Ciotti and Bricaud (2006), Bricaud et al. (2012)

CB06 1 – Sf, where Sf = fraction of small phytoplankton

% SW10* Size structure, absorption spectral-based.

Fujiwara et al. (2011) FUJI11 Microplankton – fraction of Chl-a

% SW10* Size structure, absorption and backscattering spectral-based.

Hirata et al. (2011) OC-PFT Microplankton – fraction of Chl-a

% SW10* Size structure, abundance-based.

Kostadinov et al. (2009, 2010)

KSM09 Microplankton - volume fraction

% SW10* Size structure, backscattering spectral-based.

Roy et al. (2011, 2013) ROY13 Microplankton – fraction of Chl-a

% SW10* Size-structure, based on absorption at 676 nm.

Uitz et al. (2006) UITZ06 Microplankton – fraction of Chl-a

% SW10* Size structure, abundance-based.

Mouw and Yoder (2010) MY2010 Sfm, fraction large phytoplankton

% SW10* Size structure, absorption spectral-based.

O'Reilly et al. (1998, 2000) OC4v6 Chlorophyll concentration

mg m-3 SW10* Band-ratio algorithm.

7 CMIP5 models (C biomass due to diatoms, mg m-3): CESM1-BGC, GFDL-ESM2G, GFDL-ESM2M,

GISS-E2-H-CC, GISS-E2-R-CC, HadGEM2-ES, IPSL-CM5A-MR

Month of Maximum

DFT takes dot products of sinusoids (sin & cos) with the data, deriving both amplitude

and phase at component frequencies.

Mon

th o

f max

imum

for t

ime

serie

s #1

Difference is positive when time series #1 leads in

time

SeaWiFS PAR Month of Maximum

Mean Month of Maximum for 7 CMIP5 Models

Nor

th A

tlant

ic D

rift

(NA

DR

)

Nor

th A

tlant

ic

Subt

ropi

cal G

yre

– W

est (

NA

SW)

Time, years

Frequency, Year-1 Month

Spe

ctra

l Pow

er D

ensi

ty, lo

g10(

units

2 *ye

ar)

Mic

ro/d

iato

ms,

% o

r [C

hl],

mg

m-3

[1] Dept. of Geography and the Environment, Univ. of Richmond, Richmond, VA, USA. [2] Dept. of Earth & Environmental Science, Univ. of Pennsylvania, Philadelphia, PA, USA. [3] Kapteyn Astronomical Institute, Faculty of Mathematics and Natural Sciences, Astronomy, University of Groningen, Groningen, the Netherlands. [4] Alfred-Wegener-Institute for Polar and Marine Research, Bremerhaven, Germany. [5] Plymouth Marine Laboratory (PML), Plymouth, UK. [6] Laboratoire

d’Océanographie de Villefranche, CNRS, Université Pierre et Marie Curie, Villefranche-sur-Mer, France. [7] CSIRO Oceans and Atmosphere Flagship, Wembley, Western Australia, Australia. [8] Faculty of Environmental Earth Science, Hokkaido Univ., Sapporo, Japan. [9] Arctic Research Center, National Institute of Polar Research, Tachikawa, Tokyo, Japan. [10] Dept. of Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, MI, USA. [11] Dept. of

Geography and Environmental Science, University of Reading, UK.

n indexes through the data points

k indexes through the frequencies, 0:fs/N:fs

Signal Modeling via DFT

)2sin()2cos(ˆ 0 ftbftaa nn ππ −+=xZ∈∈ ff ],6;1[

Bloom Duration & Secondary Blooms

NAD

R K

SM

09 fr

actio

n m

icro

(m

ean

rem

oved

)

Number of algorithms with valid phenological analysis

Longhurst (1998) provinces analyzed

Amplitude (half-height) of primary peak, log10 scale

Mean month of maximum for 10 PFT algorithms

Mean month of maximum for 7 CMIP5 models

Month of max. data – models ∆, +ve = data leads

Month of max. ensemble - algorithm ∆, +ve = ensemble leads

Month of maximum for time series #2

Example signal modeling and peak analysis of a PFT time series

Validation of month of maximum

Month of maximum for DFT-modeled SeaWiFS PAR

Ensemble mean % seasonal variance - algorithms

Ensemble mean % seasonal variance - models

Data – models percent variance ∆, %

# PFT algorithms w/ % seasonal variance < 30%

# CMIP5 models w/ % seasonal variance < 30%

% variance explained by seasonal harmonics for PAR

Ensemble mean bloom duration for PFT algorithms, days

Ensemble mean bloom duration for CMIP5 models, days

Duration difference (data – models), days

Fraction of algorithms with a single annual peak

Fraction of algorithms with two annual peaks

Fraction of CMIP5 models with a single annual peak

Mean fractional prominence of secondary blooms - algorithms

Mean fractional prominence of secondary blooms - models

Mean fractional prominence of secondary blooms - Chl

Spe

ctra

l Pow

er D

ensi

ty, lo

g10(

units

2 *ye

ar)

Month of maximum retrieval becomes

unreliable if modeled seasonal component

of signal explains less than 30% of its

variance

Comparison with direct peak analysis

of monthly climatology, without

DFT modeling

Percent seasonal variance

% p

ixel

s w

ith >

2 m

onth

s ∆

(D

FT-d

irect

)

Mic

ro/d

iato

ms,

% o

r [C

hl],

mg

m-3

Month Frequency, Year-1

Peaks are identified using the MATLAB® findpeaks function

Heinzel et al. (2002) Moody and Johnson (2001)

www.mathworks.com

Slide Number 1

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