Assimilation of Phytoplankton Functional Group Data into a Global Coupled Ocean-Ecosystem Model

AWI

Himansu Pradhan, Christoph Völker, Svetlana Losa, Astrid Bracher, Lars Nerger

Alfred Wegener Institute Helmholtz Center for Polar and Marine Research

Bremerhaven, Germany

OceanPredict‘19, Halifax, Canada

H. Pradhan et al. – Assimilation of phytoplankton group data

Global configuration80oN - 80oS, 30 layers

Resolution:lon : 2 deglat : 2 deg in North

up to 0.38 deg in South

MITgcmGeneral ocean circulation modelof MIT (Marshall et al., 1997).

REcoM-2Regulated Ecosystem Model – Version 2(Hauck et al., 2013)

Coupled ocean-biogeochemical model

H. Pradhan et al. – Assimilation of phytoplankton group data

Dynamic photo-acclimation:separate pools for chlorophyll and carbon for each phytoplankton class (“quota model”, Geider et al., 1996, 1998)

(V. Schourup-Kristensen et al., 2015)

Research focus:• Improving model representation of phytoplankton by assimilation

of satellite chlorophyll data

REcoM2 – Regulated Ecosystem Model

H. Pradhan et al. – Assimilation of phytoplankton group data

Data Assimilation

• State vector: 8 variables describing diatoms and small phytoplankton

• Ensemble size 20

• Ensemble generation: Spin up for 1 year with perturbed model parameters

• Assimilate each 5-th day

• Period: years 2008-2009

• Apply LESTKF (local error-subspace transform Kalman filter)

• Implemented using PDAF

H. Pradhan et al. – Assimilation of phytoplankton group data

PDAF: A tool for data assimilationPDAF - Parallel Data Assimilation Framework

§ a program library for ensemble data assimilation

§ provide support for parallel ensemble forecasts

§ provide fully-implemented & parallelized filters and smoothers (EnKF, LETKF, NETF, EWPF … easy to add more)

§ easily useable with (probably) any numerical model(applied with NEMO, MITgcm, FESOM, HBM, TerrSysMP, …)

§ run from laptops to supercomputers (Fortran, MPI & OpenMP)

§ first public release in 2004; continuous further development

§ ~370 registered users; community contributions

Open source: Code, documentation & tutorials at

http://pdaf.awi.de

L. Nerger, W. Hiller, Computers & Geosciences 55 (2013) 110-118

H. Pradhan et al. – Assimilation of phytoplankton group data

Assimilation:• Assimilate satellite total chlorophyll (ESA

Ocean color - climate change initiative):ChlTOT= ChlDIA + ChlPHY

• Handle logarithmic concentrationslog(ChlTOT), log(ChlDIA), log(ChlPHY)

• Multivariate update through ensemble cross-covarinces, e.g. Cov(log(ChlTOT), log(ChlDIA))

• How are both phytoplankton groupsinfluenced?

• Validate with satellite and in situ data

Assimilated: Total chlorophyll from ESA OC-CCI

logarithmic observation errors

Total chlorophyll (5 day composite) mg/m3

Assimilation of Total Chlorophyll

H. Pradhan et al. – Assimilation of phytoplankton group data

Verification: Phytoplankton group data SynSenPFT (Losa et al. 2017)

mg/m3Small phytoplankton

Diatoms mg/m3

Assimilated: Total chlorophyll from ESA OC-CCI

logarithmic observation errors

Total chlorophyll (5 day composite) mg/m3

Assimilation of Total Chlorophyll

H. Pradhan et al. – Assimilation of phytoplankton group data

Assimilation Effect on Total Chlorophyll (April 20, 2008)

Pradhan et al., J. Geophy. Res. Oceans, 124 (2019) 470-490

H. Pradhan et al. – Assimilation of phytoplankton group data

Assimilation Effect on Total Chlorophyll (April 20, 2008)

Pradhan et al., J. Geophy. Res. Oceans, 124 (2019) 470-490

H. Pradhan et al. – Assimilation of phytoplankton group data

Effect on Chlorophyll in Phytoplankton Groups

• Assimilation improves groups individually through cross-covariances

• Stronger error-reductions for Diatoms

• In situ data comparison:

(bias and correlation also improved)

logarithmic RMS errors (southern regions)DiatomsSmall phytoplankton

RMSe Free Assim.Diatoms 1.3 0.91Small Phyto. 0.53 0.45

Pradhan et al., J. Geophy. Res. Oceans, 124 (2019) 470-490

H. Pradhan et al. – Assimilation of phytoplankton group data

Assimilate now: Phytoplankton group data SynSenPFT (Losa et al. 2017)

mg/m3

Small phytoplanktonmg/m

3

Diatoms

Assimilation of Phytoplankton Group Data

SynSenPFT: ‘synergistic’ combination of two data sets:

• OCPFT (Losa et al. 2017, based on Hirata 2011)

➜ group chlorophyll concentrations derived from total chlorophyll

• PhytoDOAS (Bracher et al. 2017)

➜ group chlorophyll concentrations derived from spectral signature in

hyperspectral data (from sensor SCIAMACHY on ENVISAT)

Losa, S. N., et al. (2017). Frontiers in Marine Science, 4, 203.

H. Pradhan et al. – Assimilation of phytoplankton group data

Effect of Phytoplankton Group Data AssimilationEstimated concentrations – average for April 2009

Difference: group DA – total Chlorophyll DA (April 2009)

• Strongest changes in Southern Ocean (in particular for diatoms)

• Changes also in North and Equatorial Pacific and Gulf Stream region

Small phytoplankton Diatoms

Small phytoplankton Diatoms

H. Pradhan et al. – Assimilation of phytoplankton group data

00.5

11.5

22.5

Small Phytoplankton

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22.5

RM

S e

rror

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Free RunTot Chl Asml.

Jan Apr Jul Oct Jan Apr Jul Oct Jan|<-------2008-------->|<--------2009------->|

00.5

11.5

22.5

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11.5

22.5

Diatoms

00.5

11.5

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Jan Apr Jul Oct Jan Apr Jul Oct Jan|<-------2008-------->|<--------2009------->|

00.5

11.5

22.5

(a) 80°N - 79°S

(c) 80°N - 40°N

(e) 40°N - 10°N

(i) 10°S - 40°S

(k) 40°S - 79°S

(b) 80°N - 79°S

(d) 80°N - 40°N

(f) 40°N - 10°N

(h) 10°N - 10°S

(j) 10°S - 40°S

(l) 40°S - 79°S

(g) 10°N - 10°S

Assimilation Effect of Phytoplankton Groups

Assimilate Red: total chlorophyll (OC-CCI)

• RMS error with regard to SynSenPFT data

Total chlorophyll:• improves both groups individually

logarithmic RMS errors (southern regions)DiatomsSmall phytoplankton

➜But: self consistentcomparison

Green: group data (SynSenPFT )

Chlorophyll group data :• strongly improves representation

of Diatoms• Also improves small phytoplankton• Changes phytoplankton

community structure in Southern Ocean

H. Pradhan et al. – Assimilation of phytoplankton group data

Verification with independent in situ measurements

• Assimilating total Chlorophyll and group data improves RMS error, bias, andcorrelation

• Larger improvements for group data assimilation (in particular for diatoms)• Differences between different group assimilation cases smaller than from

assimilation of total Chl.• Joint assimilation of PCFT & PhytoDOAS beneficial for diatoms

No Assimilation

Assimilate group dataAssimilate total Chlorophyll

H. Pradhan et al. – Assimilation of phytoplankton group data

Assimilation Effect on Phytoplankton Community

All small phyto.

All diatoms

Switch in groupdominance

Plankton community structure

• Assimilation changes community structure in Southern Ocean (more for total than group assimilation)

• changes also in northern Pacific and Atlantic (larger for group assim.)

• Dominance overall reduced with group assimilation

H. Pradhan et al. – Assimilation of phytoplankton group data

Summary

• Assimilation of total Chlorophyll data

• Improves both groups in REcoM2

• Still high errors for diatoms

• Assimilation of group data

• Strongly improved diatom chlorophyll concentrations

• Joint assimilation of OCPFT and PhytoDOAS data slightly better than with SynSenPFT data

• Group data assimilation modifies phytoplankton community (lessened dominances)

Lars.Nerger@awi.de

Thank you!

Pradhan, H., Völker, C., Losa, S.N., Bracher, A., Nerger, L. (2019). J. Geophys. Res. Oceans, 124, 470-490, doi:10.1029/2018JC014329