Nutrient distributions simulated in an Ocean General Circulation Model

Kazuhiro Misumi1, Daisuke Tsumune1, Takeshi Yoshimura1, Yoshikatsu Yoshida1, Frank O. Bryan2, Keith Lindsay2, Keith J. Moore3, Scott C. Doney4

and Michio Aoyama5

1 Central Research Institute of Electric Power Company (CRIEPI), Japan2 The National Center for Atmospheric Research (NCAR), USA 3 University of California Irvine, USA4 Woods Hole Oceanographic Institution, USA5 Meteorological Research Institute, Japan

Benefit of model-data comparison

Table of contents

• Introduction of the BEC model

(Biogeochemical Elemental Cycling)

– the marine ecosystem component of the NCAR Community Climate System Model

(details are given in Moore et al., 2004; Moore & Doney2007; Moore and Braucher 2008)

• Nutrient distributions simulated by the BEC model

INTRODUCTION OF THE BEC MODEL

The basic equation for simulating distributions of geochemical tracers

calculated by the POP ocean model

by the BEC model

Schematic of the BEC model

Modified from Doney et al. (2008)

• 5 types of nutrients• Carbonate system• 4 types of phytoplankton(coccolithophores are implicitlyincluded in pico/nano plankton)

• Chlorophyll synthesis• 1 type of zooplankton• 2types of detritus

Ex.) The prognostic equation for pico/nano phytoplankton carbon

Csp, Czoo: pico/nano & zooplankton carbonLlim, Nlim: light & nutrient limitations for pico/nano phyto.Pphysio: physiological parameters

The BEC model represents difference of the functional types byusing the different limiting nutrients, physiological parametersand route of elements

The features of the each functional type of phytoplankton

Pico/nano phytoplankton– light, NO3, NH4, PO4 & Fe limitations

– fixed C/N/P uptake ratio (117/16/1)

– variable Fe/C uptake ratio

– implicitly including coccolithophores

– exporting POM & CaCO3

– efficient nutrient uptake under nutrient limiting conditions

– strong grazing from zooplankton

dominates under nutrient limiting conditions.

The features of the each functional type of phytoplankton

Diatoms– light, NO3, NH4, PO4, Fe & SiO3 limitations

– fixed C/N/P uptake ratio (117/16/1)

– variable Fe/C & Si/C uptake ratios

– exporting POM & biogenic Si

– less grazing from zooplankton

cause blooming under light & nutrient replete conditions.

The features of the each functional type of phytoplankton

Diazotrophs– light, PO4 & Fe limitations

– different C/N/P (329/45/1) (Letelier and Karl, 1996)

– variable Fe/C uptake ratio

– low maximum photosynthesys rate

– no aggregation

– N2 fixation

do not dominate, but important for as bioavailable N producer

Ex.) Simulated variation of carbon biomass for the each functional type

Thus the model simulates temporal & spatialvariaitons of the each plankton

Denitrification & N budget

When [O2] fall below 4uM, nitrate is used forremineralization with

A minimum nitrate concentration (32 uM) whichdenitrification did not occur was set to preventexcessive N inventory drift.

In this calculation, denitrification exceeded N fixation by35 TgN/yr at the last year of the calculation.

NUTRIENT DISTRIBUTIONS SIMULATED BY THE BEC MODEL

Experimental settings

Ocean model & Resolutions:POP2, 1.125۫ in lon & 0.5 ۫ in lat with 60 vertical levels

Initial values:T, S Jan. of PHC2PO4, NO3, SiO3, O2 WOA98 annual meanFe Result from the low res. BEC model

Surface Forcings:Climatological surface fluxes of heat, water and momentum computed from NCAR-NCEP reanalysis (Large and Yeager, 2004)

This calculation simulates the present climatological state.The results presented here are from the last year of 576 year simulation.

Temp & maximum MLD

Surface PO4 & NO3

Taylor diagramsannual mean spatial seasonal variations

Since models are developed referring to these metrics based on observed data,precise observational data will contribute to further model improvement.

Cross section of PO4

in WOCE A16 & P14

Cross section of NO3

in WOCE A16 & P14

Comparison of N/P in WOCE A16

Comparison of N/P in WOCE P14

Summary

• Since models are developed referring to the metrics based on observed data, precise observational nutrient data will contribute to further model improvement.

• To improve the processes incorporated in models, direct comparison between in situ data with simulated results is important.