Assimilation of Phytoplankton Functional Group Data into a Global...
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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
Transcript of Assimilation of Phytoplankton Functional Group Data into a Global...
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
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Small Phytoplankton
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RM
S e
rror
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Free RunTot Chl Asml.
Jan Apr Jul Oct Jan Apr Jul Oct Jan|<-------2008-------->|<--------2009------->|
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Diatoms
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Jan Apr Jul Oct Jan Apr Jul Oct Jan|<-------2008-------->|<--------2009------->|
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(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)
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
