parameterising primary production and convection in a 3d model

14
45 th Liège Colloquium May 13 – 17, 2013 Fabian Große 1 *, Johannes Pätsch 2 and Jan O. Backhaus 2 1 Research Group Scientific Computing, Department of Informatics, University of Hamburg 2 Institute of Oceanography, University of Hamburg * Corresponding author: [email protected] Parameterising Primary Production and Convection in a 3D Model

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Fabian Große 1 *, Johannes Pätsch 2 and Jan O. Backhaus 2 1 Research Group Scientific Computing, Department of Informatics, University of Hamburg 2 Institute of Oceanography, University of Hamburg * Corresponding author: [email protected]. - PowerPoint PPT Presentation

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Page 1: Parameterising Primary Production and Convection in a 3D Model

45th Liège ColloquiumMay 13 – 17, 2013

Fabian Große1*, Johannes Pätsch2 and Jan O. Backhaus2

1 Research Group Scientific Computing, Department of Informatics, University of Hamburg2 Institute of Oceanography, University of Hamburg

* Corresponding author: [email protected]

Parameterising Primary Production and Convection in a 3D Model

Page 2: Parameterising Primary Production and Convection in a 3D Model

Source: Quadfasel (unpublished)

Introduction: ARGO measurements

• convection as driving mechanism (Backhaus et al., 1999)

Source: Quadfasel (unpublished)

Page 3: Parameterising Primary Production and Convection in a 3D Model

Introduction: Phytoconvection

Source: Backhaus (2003)

• mean spatial aspect ratio of 2.5:1 (horizontal vs. vertical scale) (Kämpf & Backhaus, 1998)

• convective cycle takes 1-2 days (D’Asaro, 2008)

• same probability of residence in the euphotic zone for each phytoplankton particle

Source: Janout (2003)

2.5:1MLD

MLD

Page 4: Parameterising Primary Production and Convection in a 3D Model

Phytoconvection in a 3D Model

• hydrostatic approximation requires parameterisation

• Steele (1962):

• MLD-dependent sliding function between standard and phytoconvection

• Phytoconvection = upward and downward displace-ment of phytoplankton within a convective cell

PB … growth rate

Page 5: Parameterising Primary Production and Convection in a 3D Model

Model Setup and Simulations

• 3D physical-biogeochemical model ECOHAM4 (Lorkowski et. al., 2012)

• 20 km horizontal resolution• 5-1000 m vertical resolution (24

layers)• physics initialised from climatology• initialisation for biochemistry from

standard simulation of 1995• simulation period: 1996

• comparison of 2 simulations:• Standard• Phytoconvection

position of 1D analysis

Page 6: Parameterising Primary Production and Convection in a 3D Model

Results – Part I: 1D Analysis• Standard run

• low winter concentrations within mixed layer

• near-surface bloom in April• high concentrations until

autumn within mixed layer

• Phytoconvection run• high winter concentrations

within mixed layer• deep maximum in April• high concentrations until

autumn within mixed layer

dept

h [m

]

mg

chl-a

m-3

dept

h [m

]

mg

chl-a

m-3

MLDsim

chlorophyll-a

chlorophyll-a

Page 7: Parameterising Primary Production and Convection in a 3D Model

dept

h [m

]

mg

chl-a

m-3

dept

h [m

]

mg

chl-a

m-3

Results – Part I: 1D Analysis

Data source: BODC

dept

h [m

]

chlorophyll a [mg m-3]

MLDsim

MLDobschlorophyll-a

chlorophyll-a

MLDsim

Page 8: Parameterising Primary Production and Convection in a 3D Model

Results – Part I: 1D Analysis

MLDsim

MLDobs

Data source: BODC

dept

h [m

]

chlorophyll a [mg m-3]

MLDsim

MLDobs

• Standard run:• significantly lower

concentrations throughout whole water column

• Phytoconvection run:• upper layer concentrations

in good agreement with observations

• low chlorophyll-a below mixed layer

• depth of chlorophyll-a gradient ≠ MLD

Page 9: Parameterising Primary Production and Convection in a 3D Model

Results – Part II: 3D analysisPrimary production

April - standard

g C

m-2

mon

th-1

g C

m-2

mon

th-1

April - phytoconvection

Chlorophyll-a (depth-integrated)April - standard

mg

chl-a

m-2

mg

chl-a

m-2

April - phytoconvection

Page 10: Parameterising Primary Production and Convection in a 3D Model

Results – Part III: Carbon fluxes Air-sea flux

Export (below 500m)

mm

ol C

m-2

mon

th-1

mm

ol C

m-2

mon

th-1

Page 11: Parameterising Primary Production and Convection in a 3D Model

Summary & Conclusion• parameterisation of phytoconvection:

• observed upper layer chlorophyll-a concentrations reproduced• strong influence of convection on primary production and

carbon export production

• sliding function allows continuous transition from winter to summer regime

• problems during decline of mixed layer in spring

• applied MLD criterion (Tsurf – T > 0.4K) not suitable to:• detect haline stratification• distinguish between convective and frictionional mixing

Page 12: Parameterising Primary Production and Convection in a 3D Model

Outlook

• improvement of sliding function:→ include turbulent mixing depth (Taylor & Ferrari, 2011)

• replace MLD criterion (Tsurf – T > 0.4K)

• apply parameterisation on model area with more regions of deep winter convection for better data basis

• include results from tank experiments investigating phytoplankton adaptation to different dark-light cycles

Page 13: Parameterising Primary Production and Convection in a 3D Model

Titelmasterformat durch Klicken bearbeiten

Vielen Dank für Ihre Aufmerksamkeit.

Parametrisierung von Primärproduktion und winterlicher Konvektion in einem 3D Modell

Thank you for your attention.

[email protected]

45th Liège ColloquiumMay 13 – 17, 2013

Page 14: Parameterising Primary Production and Convection in a 3D Model

References• D’Asaro, Eric A.. Convection and the seeding of the North Atlantic bloom.

Journal of Marine Systems, 69:233–237, 2008.• Backhaus, J., Wehde, H., Hegseth, E., and Kämpf, J. ‘Phyto-convection’: the

role of oceanic convection in primary production. Marine Ecology. Progress Series, 189:77–92, 1999.

• Backhaus, J., Hegseth, E., Wehde, H., Irigoien, X., Hatten, K., and Logemann, K. Convection and primary production in winter. Marine Ecology Progress Series, 251:1–14, 2003.

• Janout, M. Biological parameterization of convection in a mixed layer model. Pages 1–87, 2003.

• Lorkowski, I., Pätsch, J., Moll, A., and Kühn, W. Interannual variability of carbon fluxes in the North Sea from 1970 to 2006 - Competing effects of abiotic and biotic drivers on the gas-exchange of CO2. Estuarine, Coastal and Shelf Science, 2012.

• Taylor, J. and Ferrari, R. Shutdown of turbulent convection as a new criterion for the onset of spring phytoplankton blooms. Limnology and

Oceanography, 56(6):2293, 2011.