An Overview of Biogeochemical Modeling in ROMS
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An Overview of Biogeochemical Modeling in
ROMS
Kate Hedstrom, ARSCSeptember 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 EquationsP 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
• There’s more than one way to damp the oscillations– Change initial conditions– Change grazing function– Change mortality constant
• It’s 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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NO3
Chlorophyll
Largedetritus
Organic matter
N2 NH4 NO3
Water column
SedimentSediment
Phytoplankton
NH4
Mineralization
Uptake
Nitrification
Nitrification
Grazing
Mortality
Zooplankton
Susp.particles
Aerobic mineralizationAerobic mineralizationDenitrificationDenitrification
Katja Fennel’s Model
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Fennel’s 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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North North Atlantic Atlantic ROMSROMS
Climatolo-Climatolo-gical gical heat/fresh-heat/fresh-water water fluxesfluxes
3-day 3-day average average NCEP NCEP windswinds
SSH (m)
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Middle Atlantic Bight (MAB)Middle Atlantic Bight (MAB)
Cape Hatteras
Hudson
Delaware
Chesapeake
Georges Bank
Nantucket Shoals
50, 100, 200, 50, 100, 200, 500 1000 m 500 1000 m isobaths: isobaths: dashed linesdashed lines
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3.3 DIN3.3 DIN0.9 PON0.9 PON
2.9 PON2.9 PON
2.5 DIN2.5 DIN
DNF: 5.3 TNDNF: 5.3 TN
Rivers: 1.8 TNRivers: 1.8 TN
• cross-isobath export of PON, import of DINcross-isobath export of PON, import of DIN
x 10x 101010 mol N y mol N y-1-1
0.4 TN0.4 TN
4.2 TN4.2 TN
• denitrification removes 90% of all N entering denitrification removes 90% of all N entering MABMAB
Fennel et al. Fennel et al. (submitted to (submitted to Global Global Biogeochemical Biogeochemical Cycles)Cycles)
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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 don’t 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
• It’s 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.
PWS
Alaska Peninsula
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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 IBM’s– 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