An Overview of Biogeochemical Modeling in ROMS

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An Overview of Biogeochemical Modeling in ROMS

• Kate Hedstrom, ARSC
• September 2006
• With help from Sarah Hinckley, Katja Fennel,
  Georgina Gibson, and Hal Batchelder

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Outline

• Simple NPZ model
• Mid-Atlantic Bight model
• Gulf of Alaska model
• GLOBEC Northeast Pacific
• ROMS options

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Annual Phytoplankton Cycle

• Strong vertical mixing in winter, low sun angle
  keep phytoplankton numbers low
• Spring sun and reduced winds contribute to
  stratification, lead to spring bloom
• Stratification prevents mixing from bringing up
  fresh nutrients, plants become nutrient limited,
  also zooplankton eat down the plants

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Annual Cycle Continued

• In the fall, the grazing animals have declined or
  gone into winter dormancy, early storms bring in
  nutrients, get a smaller fall bloom
• Winter storms and reduced sun lead to reduced
  numbers of plants in spite of ample nutrients
• We want to model these processes as simply as
  possible

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Franks et al. (1986) Model

• Simple NPZ (nutrient, phytoplankton, zooplankton)
  model
• Three equations in the three state variables
• Closed system - total is conserved

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The Equations

• P Change nutrient uptake - mortality(P) -
  grazing
• Z Change growth efficiency grazing -
  mortality(Z)
• N Change - nutrient uptake mortality(P)
  mortality(Z) (1 - growth efficiency) grazing

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Goal

• Initial conditions are low P, Z, high N
• We want the model to produce a strong spring
  bloom, followed by reduced numbers for both P and
  N
• Hope to find a steady balance between growth of P
  and grazing by Z after the bloom

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Original Franks Model
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NPZ Results

• Theres more than one way to damp the
  oscillations
• Change initial conditions
• Change grazing function
• Change mortality constant
• Its not clear if any of these methods is right
• Current practice is to add detritus falling out
  of the mixed layer (NPZD model)

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Simple NPZD Attempt
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NPZD Summary

• The Franks model is attempting to describe the
  ocean mixed layer at one point
• Next is a 1-D vertical profile of the biology,
  including light input at the surface
• Final goal is a full 3-D model with the physical
  model providing temperature, currents, seasonal
  cycle, etc.
• Each component is advected and diffused in the
  same manner as temperature

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More Modern Models

• Seven, ten, or more variables
• Large and small zooplankton
• Large and small phytoplankton
• More (specific) nutrients
• Detritus
• Still missing some fields such as gelatinous
  zooplankton (salmon food)
• Models are tuned for specific regions, specific
  questions

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Katja Fennels Model
Nitrification
Water column
Mineralization
NH4
NO3
Uptake
Phytoplankton
Grazing
Chlorophyll
Zooplankton
Mortality
Large detritus
Susp. particles
Nitrification
N2
NH4
NO3
Denitrification
Aerobic mineralization
Organic matter
Sediment
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Fennels Model

• Fasham-type model
• In shallow coastal waters, the sinking particles
  hit the bottom where nutrient remineralization
  can occur
• Add a benthos which contributes a flux of NH4 as
  a bottom boundary condition
• Her goal is to track the nitrogen fluxes and
  extrapolate to carbon

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Nested physical-biological model
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SSH (m)
North Atlantic ROMS Climatolo-gical
heat/fresh-water fluxes 3-day average NCEP winds
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Middle Atlantic Bight (MAB)
Georges Bank
Nantucket Shoals
Hudson
Delaware
Chesapeake
Cape Hatteras
50, 100, 200, 500 1000 m isobaths dashed lines
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• denitrification removes 90 of all N entering
  MAB

• cross-isobath export of PON, import of DIN

DNF 5.3 TN
4.2 TN
3.3 DIN
0.9 PON
Rivers 1.8 TN
2.5 DIN
2.9 PON
0.4 TN
Fennel et al. (submitted to Global
Biogeochemical Cycles)
x 1010 mol N y-1
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Gulf of Alaska

• Sarah Hinckley and Liz Dobbins built a full NPZD
  model for the shelf and tested it in 1-D
• They then ran a 3-D Gulf of Alaska with that
  model (3 km resolution)
• The shelf waters bloomed, as did the offshore
  waters

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The Iron Story

• They believe that offshore waters dont bloom due
  to iron limitation
• Iron is brought to the coastal ocean via river
  sediments
• Not enough iron data to do a full model of it
• Trace quantities of iron are difficult to measure
  on steel ships

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More Iron

• People have done experiments at sea, seeding the
  water with iron - the ocean does bloom after
• Adding an iron component to their NPZD model
  allowed Sarah and Liz to obtain more realistic
  looking results
• Its not a full iron model, containing nudging to
  a climatology

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Iron Climatology
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No Iron Limitation NO3

• Kodiak I.

Alaska Peninsula
PWS
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Iron Limitation NO3
Alaska Peninsula
PWS
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Northeast Pacific GLOBEC

• Goal is one model that runs off California and
  Gulf of Alaska
• Get the fish prey right, then move on to IBMs
• Track individuals as Lagrangian particles -
  euphausiids and juvenile coho salmon
• Include behavior - swimming vertically
• Growth depends on food availability, temperature
• Use stored physical and NPZ fields from ROMS

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ROMS Biology Options

• NPZD
• Fasham-type
• Two phytoplankton-class model (Lima and Doney)
• Small
• Diatoms
• Silica and carbon
• Multiple phytoplankton-class model (ECOSIM)
• Four phytoplankton classes
• Internal carbon and nitrate for each
• No zooplankton

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Computer Issues

• Adding biology doubles or triples the computer
  time needed for the physical model
• These models need more time for more grid points
  (480x480x30 for CGOA) and also a shorter timestep
  if the grid spacing is small
• They can take weeks of computer time, using many
  processors in parallel

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Conclusions

• Biological modeling is an area of active research
• Coupled biogeochemical systems are hot too
• Still things to figure out, including the rest of
  the annual cycle, interannual variability and
  other changes