An Overview of Biogeochemical Modeling in

ROMS

Kate Hedstrom, ARSCSeptember 2006

With help from Sarah Hinckley, Katja Fennel, Georgina Gibson, and Hal

Batchelder

Outline

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

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

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

Franks et al. (1986) Model

• Simple NPZ (nutrient, phytoplankton, zooplankton) model

• Three equations in the three state variables

• Closed system - total is conserved

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

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

Original Franks Model

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)

Simple NPZD Attempt

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

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

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

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

Nested physical-biological

model

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)

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

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)

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

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

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

Iron Climatology

No Iron Limitation – NO3

Kodiak I.

PWS

Alaska Peninsula

Iron Limitation – NO3

Alaska Peninsula

PWS

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

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

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

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