1. Introduction to Sea Ice Modeling by Clara Deal Focus: Ecosystem and biogeochemical modeling

2. Lecture Outline I. Introduction 1. Why model? 2. Some challenges of modeling II. Upper-ocean mixed layer ecosystem model 1. Eslinger model in Prince William Sound i. Schematic, summary, equations ii. Bering Sea iii. With DMS biogeochemistry 2. Physical-Ecosystem Model (PhEcoM) for Bering and Chukchi Seas III. Adding sea ice and sea ice algae 1. Bering Sea 2. Chukchi shelf 3. Larger-scale

3. Why model? A numerical simulation (model) is a tool to help: ask better questions identify important processes or factors link data intensive process study and time series measurement sites, to larger spatial and temporal scales synthesize and interpret data guide field campaigns and laboratory studies

4. Some Challenges of Modeling Balancing complexity and simplicity Do assumptions about food web extend beyond the local scale? Site-specific nature of parameter determinations in the field Inadequate observational data to test or constrain model

5. Approach based on previous work: Eslinger, D. L. and R.L. Iverson, The effects of convective and wind-driven mixing on spring phytoplankton dynamics in the Southeastern Bering Sea middle shelf domain, Cont. Shelf. Res. 21, 627-650, 2001. Eslinger, D.L., R.T. Cooney, C.P. McRoy, A. Ward, T.C. Kline, P. Simpson, J. Wang and J.R. Allen, Plankton dynamics: observed and modelled responses to physical conditions in Prince William Sound, Alaska, Fish. Oceanogr., 10, 81- 96, 2001. Jodwalis (Deal), C.M., R.L. Benner and D.L. Eslinger, Modeling of dimethylsulfide ocean mixing, biological production, and sea-to-air flux at high latitudes, J. Geophys. Res., 2000. Wang, J., C.J. Deal, and Z. Wan, USER'S GUDIE for A Physical-Ecosystem Model (PhEcoM) In the Subpolar and Polar Oceans, Version 1, IARC-FRSCG Technical Report 02-01 May 2002.

6. Mechanical (wind), convective mixing Temperature Phytoplankton Two compartments: Diatoms Flagellates Zooplankton Three compartments: Large, Small, Other Detritus Nitrate- Nitrite Silicon (diatoms only) Ammonium Light Interactions among variables in 1-D model. Eslinger, D.L., R.T. Cooney, C.P. McRoy, A. Ward, T.C. Kline, P. Simpson, J. Wang and J.R. Allen, Plankton dynamics: observed and modeled responses to physical conditions in Prince William Sound, Alaska, Fish. Oceanogr., 10, 81-96, 2001.

7. Summary of 1-D Model Nitrogen based: all biomasses and uptakes are in mmol/m 3 N Phytoplankton: diatoms, flagellates Zooplankton: small copepods, large copepods, other large zooplankton Nutrients: nitrate-nitrite, ammonium, silicon Other: detritus Vertical mixing controlled by balancing of wind stress, convective mixing, and stratification; turbulent mixing also included One spatial dimension: Depth = 100 m Temporal: March - January Resolution Vertical: 2 m Time: 1 hrs Forcing Data – Wind Velocity, Air Temperature, Sea Surface Temperature, Relative Humidity, Cloud Cover, Light

8. Governing Equations Legend F = Flagellates D = Diatoms Z = Zooplankton G = growth R = respiration Rg = regeneration  = grazing M = mortality A = assimulation Ex = egestion

9. Maximum temperature-dependent phytoplankton X growth rate: where m is the growth rate at 0C, r x is the temperature coefficient, and N frac, Si frac and I frac are unitless ratios expressing nutrient and light limitation. Respiration rate of phytoplankton X, set to 5% of growth rate Mortality and extracellular excretion of phytoplankton X and fecal material (from data in Harrison 1980, see Eslinger and Iverson 2001) Fraction of phytoplankton growth due to nitrate uptake over that due to ammonium uptake. Biological equations (after Wroblewski, 1977) (after Dugdale, 1967) (after Platt et al. 1980)

10. Map showing R/V Mirai, T/S Oshoro-Maru, PMEL surface buoy and PROBES observation locations in Bering Sea x station 12 X PMEL buoy  PROBES transect

11. Eslinger model results vs. field observations (April 10 – July 10)

13. Processes included in 1-D DMS model. DMS loss and production  in Jodwalis, C., R. Benner, and D. Eslinger, 2000, J. Geophys. Res., 105, D11, 14,387-14,399.  Physical processes included in the original model by Eslinger et al., (2001).

15. Sensitivity study indicates which parameters are most important.

16. Mixed-layer dynamics important factor

17. Mechanical (wind), convective and turbulent mixing Temperature Phytoplankton Two compartments: Diatoms Flagellates Zooplankton Three compartments: Small, Large, Other Detritus Nitrate- Nitrite Silicon (diatoms only) Ammonium Light Wang, J., C. Jodwalis Deal, Z. Wan, M. Jin, N. Tanaka and M. Ikeda, USER’S GUIDE for a Physical-Ecosystem Model (PhEcoM) in the Subpolar and Polar Oceans (Version 1), IARC/FRSGC/UAF, 2003. Interactions among variables in Bering Sea 1-D model. Sinking/export

18. Time series of sea water temperature and fluorescence at ~ 12 m (yellow trace) from Mooring M-2 in southeastern Bering Sea. Year 2000 data and model results at ~12 m depth Year 2000 temperature ( o C) model results

19. Time series of sea water temperature and fluorescence at ~ 12 m (yellow trace) from Mooring M-2 in southeastern Bering Sea. Year 1999 data and model results at ~12 m depth Year 1999 temperature ( o C) model results

20. Is SST important factor in the initiation and maintenance of coccolithophore boom in the Bering Sea? 1999: relatively low SST and small coccolithophore bloom 2000: relatively high SST and large, extensive coccolithophore bloom

21. Flagellates (coccolithophore) 1999  April26 Model results. Flagellate (coccolithophore) biomass profiles for model runs 1999 and 2000. Observations. 1999 and 2000 NOAA/ PMEL buoy data. Wind speed, air temperature, and SST.  July 26 Flagellates (coccolithopore) 2000 1999: relatively low SST and small coccolithophore bloom 2000: relatively high SST and large, extensive coccolithophore bloom

22. Sensitivity analysis results showing increasing flagellate (coccolithophore) bloom duration with decreasing zooplankton initial biomass or threshold grazing rate (year 2000).

23. Surface currents of the North Pacific are reproduced by the global MITgcm model with the coarse (~22km) resolution. Map showing major currents for comparison with model results. The Ocean Model (MITgcm): horizontal spherical grid with resolution 1/20x1/30 degrees (eddy permitting) 48 z-levels in the veritcal, 3m resolution in upper 50m and 6m from 50-100m Atmospheric forcing using NCEP/NCAR reanalysis: heat flux, mass (moisture) flux, daily,monthly wind stress, freshwater runoff

24. We are working on including sea ice in the 1-D model, starting with the “under ice bloom”.

25. Modeling Objectives What are the consequences of marine ecosystem responses to climate variability and climate change? Specifically, 1)How do different sea ice conditions and external forcing (i.e. solar radiation) control local rates of primary production? 2)How will projected retreat of sea ice change the production, transport and fate of primary production in the Arctic? The release of climate relevant trace gases? 3)Will a warming climate result in higher primary productivity (or changes in the boom timing and dominant species) in the water column in the Arctic? 4)How is regional atmospheric CO 2 variability linked to changing sea ice conditions?

26. Year 2002 measurements near Barrow. Sea ice began to melt between May 1 and 22. Chl.a maximum in ice bottom (2 cm layer) was observed on May 1 with the value of 0.6 mg/l. That in seawater was observed on Apr. 17 with the value of 3.6 u(micro)g/l. Sea ice algal biomass is greatest in the bottom few cm of sea ice.

27. Selected model parameters and their values. ParametersValueUnitsReference , Initial slope of P B vs. I curve0.174mg C (mg Chla h W m -2 ) –1 Eslinger & Iverson, 2001(from data) , Photoinhibition coefficient0.0058 for diatomsmg C (mg Chla h W m -2 ) –1 Eslinger & Iverson, 2001(from data) no photoinhibition for coccosNanninga & Tyrell, 1996 k(P), Diffuse attenuation coefficientk(P) = k 0 + kp(P/k chl )(m -1 )k 0, kp from Magley, 1990 (PROBES data) K S,nitrate-nitrite, K S,ammonium 2.5 for diatoms  M NEppley et al., 1969 Nutrient half-saturation constant (0.5-2.75) Sambrotto & Goering, 1980 0.1 for E. hux  M N Tyrell & Taylor, 1996 (Eppley et al., 1969) K S,silicon 3.0 for diatoms  M NEslinger et al., 2001 k chl,, Carbon:chlorophyll mass ratio40 g C (g Chl) -1 Eslinger & Iverson, 2001 k N, Carbon:nitrogen mass ratio5.69 g C (g N) -1 Eslinger & Iverson, 2001 µ 0, Growth rate at 0 o C0.06 h -1 Eslinger & Iverson, 2001(Durbin 1974; PROBES data; within Olson & Strom, 2002) r, temperature coefficient0.0633deg –1 Eppley et al., 1969 R o, Respiration rate at 0 o C0.05µ 0 h -1 Yentsch, 1981 (w/in 10% of daily 1 o prod.) P Bs, Maximum photosynthesis rate3.25 mg C (mg Chl h) -1 Eslinger & Iverson, 2001 R g0, Regeneration rate at 0 o C9.23 x 10 -4 h -1 Eslinger & Iverson, 2001 (Harrison, 1980) r g, Regeneration constant3.0 x 10 -2 o C -1 Eslinger & Iverson, 2001 (Harrison, 1980) S k, Sinking rate constant0.22 i.e. tanh[S k K s ]=0.5m d -1 Eslinger & Iverson, 2001 (Harrison, 1980) Smax, Maximum sinking rate4.0 for diatomsm d -1 Eslinger & Iverson, 2001 (PROBES data) Zooplankton and phytoplankton growth limitation parameters to be included (see manual). Parameters specifically for sea ice algaeValueRangeUnitsReference  /P B s, Initial slope of P B vs. I curve/0.22 (0.22 - 0.028)W m -2 Lee et al., unpublished data estimates maximum photosynthesis rate (~15 x larger than for phytoplankton)Arrigo, 2003 (and references therein)  /P B s, Photoinhibition coefficient/0.019 (0.013 - 0.031)W m -2 Lee et al., unpublished data estimates maximum photosynthesis rate (~10 x larger than for phytoplankton)Arrigo, 2003 (and references therein) K S,nitrate-nitrite, K S,ammonium 1.0  M NArrigo, 2003 (and references therein) (4 or 5)Whitledge, personal communications K S,silicon 3.0  M NEslinger et al., 2001 k chl,, Carbon:chlorophyll mass ratio57 (43 - 66)g C (g Chl) -1 Lee et al., unpublished data estimates k N, Carbon:nitrogen mass ratio8.88 (7.2 - 11.1)g C (g N) -1 Lee et al., unpublished data estimates µ 0, Growth rate at 0 o C0.06 h -1 Arrigo, 2003 (and references therein) (0.040 - 0.053)Hegseth, 1992 (Arctic ice algae) r, temperature coefficient0.0633deg –1 Arrigo, 2003 (and references therein) S k, Sinking rate?m d -1 Note: Median potential grazing rates in Central Arctic are at least 1 order of magnitude less than the mean primary production estimates of Arctic sea-ice associated algae (Gradinger, R., 1999). Grazing impact at ice underside in summer is low [1.1%Laptev, 2.6% Greenland Seas] (Werner, 1997).

28. Large-scale model of sea ice primary production “Little is presently known about either the large-scale horizontal distribution of sea ice algae or their contribution to total regional productivity due to the difficulty inherent in sampling ice covered systems” (course text, p. 159). It is also difficult to sample over a seasonal cycle. Antarctic sea ice model during 1989-90 (Arrigo et al., 1997, 1998b).

29. General take home message A fundamental difficulty in developing marine biogeochemical models is the absence of equations of state for biological processes. As a consequence, biological or chemical formulations may be different for models of the same processes within and between environments. However, the most important feature responsible for biogeochemical model accuracy is the fidelity with which physical models replicate the major physical factors controlling biogeochemical cycling.