An Overview of Biogeochemical Modeling in ROMS Kate Hedstrom, ARSC September 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 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

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

Katja Fennel’s Model NH4 NO3 N2 NH4 NO3 Water column Sediment 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

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

Climatolo-gical heat/fresh-water fluxes SSH (m) North Atlantic ROMS Climatolo-gical heat/fresh-water fluxes 3-day average NCEP winds

Middle Atlantic Bight (MAB) Georges Bank Nantucket Shoals Hudson Delaware Chesapeake Cape Hatteras 50, 100, 200, 500 1000 m isobaths: dashed lines

x 1010 mol N y-1 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

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 -X-axis is cross-shelf distance from GAK1 -Y-axis is depth -All shelf areas, iron has a value of 2 (not limiting) -Gradient between coastal and oceanic surface waters, so that oceanic surface waters is depleted -(Iron input from runoff included)

No Iron Limitation – NO3 Alaska Peninsula PWS Kodiak I. -With no iron limitation term, during late May/ early June (after spring blooms), nitrate is depleted in the surface in all areas (offshore areas should retain high surface nutrients at all times)

Iron Limitation – NO3 Alaska Peninsula PWS With the iron limitation scheme implemented, the oceanic areas no longer show iron depeletion in the surface waters as there is no bloom of phytoplankton due to lowered iron concentrations.

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