1. Ocean Ecosystem Modeling and Observations
IARC Contributors:
Ana Aguilar-Islas
David Atkinson
Clara Deal
Meibing Jin
Peter McRoy
Eiji Watanabe
Jingfeng Wu
Collaborating Institutions include:
Antarctic Climate and Ecosystems Cooperative Research Centre, Tasmania, Australia
Genwest Systems, Seattle, Washington, USA
Graduate School of Fisheries, Hokkaido University, Hokodate, Japan
Korean Polar Research Institute, Republic of Korea
Los Alamos National Laboratory (LANL), Los Alamos, New Mexico, USA
NOAA, Great Lakes Regional Laboratory, Ann Arbor, Michigan, USA
School of Fisheries and Ocean Sciences and Institute of Arctic Biology, UAF, Alaska, USA
University of Groningen, The Netherlands
Today I will summarize IARC efforts in modeling Arctic ocean ecosystems and their interactions with biogeochemical cycles and climate
Since I only have 20 minutes, I will focus on our past year’s activities, future plans, and show some of our recent research results.
IARC contributors to this theme…
This work could not have been carried out without our valued US and international collaborators.

2. Ocean Ecosystem Modeling and Observations Theme
Block diagram outlining some of our strengths and how we work together.
Ocean Ecosystem Modeling and Observations Theme
Observations
Modeling
Fe biogeochemistry
Dimethylsulfide (DMS) cycle
Terrestrial C input and fate
CO2 and methane dynamics
Satellite remote sensing
Internet databases
Reanalysis data
Statistical GIS-based model:
Surface seawater DMS
Process-based models:
1-D Physical ice-ocean ecosystem model:
DMS cycling
Fe limitation on productivity
Inorganic C component
3-D Physical ice ocean ecosystem model
3-D Eddy-resolving ice ocean model
Working towards Artic System Model (ASM)
Within this theme are two integrated components. Observations and ecosystem modeling.
Observation include field data and data that provide large temporal and spatial coverage.
Modeling includes new approach for predicting seawater DMS concentrations – statistical/empirical model
We have a hierarchy of process-based/numerical models that we have developed and worked with over the last several years.
Information exchange…
Beyond providing their own data and expertise, beyond IARC – e.g. collegial ties and collaborations provide awareness and access to data, and new modeling collaborations
After a few introductory slides…

3. Ocean ecosystem modeling is a part of the 1st stage integrated theme
We have begun to work also on the corresponding 2nd stage theme
I will illustrate how marine ecosystem feedbacks is integrated among themes

4. Diagram illustrates integration within ocean ecosystem modeling theme and among 2nd stage themes, and what feedbacks to the physical system might be expected.
Role of
Freshwater/Permafrost in the Arctic-global Connection
Global effect of Warming in the Arctic
Radiation
Budget
Increased Precipitation & Warming Permafrost -
CCN +
Fate of Sea Ice in the Arctic Ocean
Global Temperature
Sulfate aerosols +
Radiative
Feedbacks?
Increased Discharge
SO2 +
Decreased Ice/
Increased Open Water
Radiative
Feedbacks?
CO2
DMS
Runoff, C, nutrients
atmosphere
CO2
CH4
+ ? ?
+ ?
Erosion: C, nutrients
ice
ice
CO2
Dissolved inorganic carbon (DIC)
CO2
CH4
marine food web
Increased Erosion Fe
Nutrients
Increased Storminess,
Diminished Sea Ice &
Warming Permafrost
Simplified schematic illustration of marine ecosystem functional relationships and feedbacks.
Illustrates integration among stage 2 themes.
AND – explain what feedbacks to the physical system may be expected
Increased terrestrial C input may be a positive feedback on climate
Decreased ice/more open water will also impact the marine food web and primary productivity – e.g. vertical supply of nutrients
It has been hypothesized that more open water may lead to higher DMS emissions which through impact on cloud albedo may cool climate
All of these climate impacts influence the marine food web and thus the efficiency of the biological carbon pump.
Start with observations lead into modeling.
Next – examples of activities and recent research results w/in ocean ecosystem modeling theme.
--Oceanic regulation of atmospheric CO2 occurs via 2 different carbon pumps: physical (or solubility) pump driven by intermediate and deep water formation, and the biological pump – mediated by vertical supply of nutrients, strength of thermohaline circulation, eff of bio pump, and supply of macro- and micronutrients to the ocean
euphotic
zone
DOC
POC
CaCO3
Export
DOC
POC
CaCO3
DIC
deep
ocean

5. Dissolved inorganic carbon (DIC)
The incorporation of iron into marine ecosystem models has just begun in recent years.
Questions:
How and in what form delivered?
How made available to phytoplankton?
How cycled in marine ecosystem?
CCN +
Radiation
Budget
Sulfate aerosols + -
SO2 +
Radiative
Feedbacks?
Global Temperature
DMS
Radiative
Feedbacks?
CO2
Runoff, C, nutrients
atmosphere
+ ?
CO2
CH4 ?
+ ?
Erosion: C, nutrients
ice
ice
CO2
Dissolved inorganic carbon (DIC)
CO2
CH4
marine food web Fe
Nutrients
euphotic
zone
Fe is an important micro-nutrient that has the potential to limit phytoplankton growth.
Many questions remain about how and in what form iron is delivered to the ocean, how it is made available to phytoplankton, and how it is cycled in the marine ecosytem.
Aguilar-Islas and Wu have studied the sources and role of the micronutrient iron in high latitude waters.
DOC
POC
CaCO3
Export
DOC
POC
CaCO3
DIC
deep
ocean

6. Subsurface Water (Fe deficient
Sources of iron to the surface ocean.
Bering Sea April
Rivers
Biologically
available Fe
Aerosol Deposition
Sediments/
Pore Waters
Remineralization
Deep Mixing/
Upwelling
Sea Ice
Solubilization
Subsurface Water (Fe deficient
relative to nitrate)
Resuspension
Melting
Dissolved Fe
Flocculation
Salinity
Nitrate (mM) 40 30 20 10
200
125 50
Calvin Mordy unpublished data
Basically interested in biologically available Fe
Grey – only important in coastal areas and shallow shelves
Subsurface water Fe deficient relative to nitrate
Focus on aerosol deposition and sea ice sources
Top plot – nitrate at the surface
Bottom plot – high nitrate/def Fe below surface, but could be mixed -- then, situation as in Bering Sea outer shelf and basin
Salinity
Nitrate (mM)
green, 0–50 m
yellow, 50–125 m
red, 125–200 m
gray 200–275 m
blue, 275+ m
Beaufort Sea
May to November
Simpson et al., 2008

7. Sea ice-derived dissolved iron influences the spring algal bloom in the outer shelf and shelf break of the Bering Sea.
(Aguilar-Islas, Wu)
Fe (nM)
FIGURES: Map. Surface distribution of dissolved iron during spring 2007.
Right graph. Vertical profile of dissolved iron and salinity from the mid shelf. 100% ice cover at this station. Salinity shows sea ice had not begun melting. Iron shows high concentrations and sedimentary input.
Left graph. Same parameters from outer shelf station. ~ 80% ice cover at this station. Salinity shows freshening at surface due to melting sea ice. Higher iron at the surface where salinity is lower shows input of iron from melting sea ice.
CONCLUSION: It was estimated that without input from melting sea ice, iron was not sufficiently high for the complete assimilation of available nitrate by large cells in the outer shelf and shelf break. However, in the mid and inner shelf iron subsurface inputs provide sufficient iron in relation to available nitrate.
In the mid and inner shelf sedimentary iron inputs can reach surface waters during spring.
In the outer shelf and shelf break melting sea ice provides additional iron for the complete assimilation of available nitrate by large cells. Aguilar-Islas, Wu, et al. , 2008 (GRL) 7

8. Convergence of nitrate-rich offshore waters with iron-rich coastal waters leads to high productivity in the NW Gulf of Alaska.
(Wu, Aguilar-Islas)
GAK 1
GAK 7
GAK 1
GAK 13
Aguilar-Islas has received a 2009 Early Career Fellowship from EPSCoR Alaska to study riverine input of trace metals into the American coastal Arctic.
Continental input
rich in Fe
GAK 7
GAK 13
Wu et al. (submitted, GRL) 8

9. Aerosol Fe solubility dominated by the colloidal fraction.
Estimates of aerosol iron solubility in seawater reported in the literature ( %)
(Aguilar-Islas, Wu)
% Fe dissolution
(0.02 µm < colloidal < 0.4 µm)
Different
leaching solutions
Aerosols collected
from different areas
In contrast to the Bering Sea, a major source of iron to the remote ocean is atmospheric deposition.
Only the portion of aerosol-derived iron that dissolves following its deposition on the surface ocean is thought to be bioavailable.
They have conducted experiments to estimate the portion of aerosol iron that is soluble in seawater
and to address factors that contribute to the variability of this estimate.
Their results support previous findings that aerosol characteristics rather than experimental protocols result in higher variability of dissolution estimates.
Most of the aerosol iron dissolved in seawater is in the form of small iron colloids rather than in truly soluble form.
Implications on bioavailability – aggregates and sinks
(dissolved is < 0.4 µm)
Most of the aerosol iron dissolved in seawater was in the colloidal size fraction
Aguilar-Islas, Wu, et al. , 2009 (Marine Chemistry)
Aguilar-Islas et al. In press (Marine Chemistry) 9

10. Dissolved inorganic carbon (DIC)
Diminishing arctic sea ice will influence important biogeochemical cycles and the marine ecosystem.
CCN +
Radiation
Budget
Sulfate aerosols + -
SO2 +
Radiative
Feedbacks?
Global Temperature
Radiative
Feedbacks?
DMS
CO2
Runoff, C, nutrients
atmosphere
+ ?
CO2
CO2
CH4 ?
+ ?
Erosion: C, nutrients
ice
ice
CO2
Dissolved inorganic carbon (DIC)
CO2
CH4
marine food web
CO2 Fe
euphotic
zone
Diminishing sea ice will influence…
Activities…
DOC
POC
CaCO3
Export
DOC
POC
CaCO3
DIC
deep
ocean

11. Clouds remain one of the largest uncertainties in climate modeling.
On the relative importance of the functional relationships and feedbacks to a Pan-Arctic perspective and thus to a comprehensive ASM.
Feedbacks that involve clouds are particularly relevant to the Arctic because clouds influence the physical processes most important to the warming of the Arctic and the melting of sea ice.
Clouds remain one of the largest uncertainties in climate modeling.
Cloud properties such as albedo, extent, and duration are determined in large part by cloud condensation nuclei (CCN).
Source of CCN over the summertime Arctic is nucleated particles of marine biogenic origin that grow to CCN size with the aid of aerosol precursor gases, predominantly DMS.
Do not provide reviewer with an appreciation of their relative importance to a Pan-Arctic perspective and thus to a comprehensive ASM
Marginal ice zone
Kettle et al. (1999) DMS climatology updated by Belviso et al. (2004).
Late-spring low stratus offshore Barrow, Alaska.

12. What is the impact of DMS on climate?
Recent climate models (Gunson et al. 2006): - 50% reduction of ocean DMS emission: radiative forcing: +3 W/m2 air temperature: +1.6 °C - doubling of ocean DMS emission: radiative forcing: -2 W/m2 air temperature: -0.9 °C
Model projections (Gabric et al. 2004) impact of warming on the global zonal DMS flux (70 N- 70 S) indicates greatest perturbations to be at high latitudes
Use of a climate model to force ocean DMS model in Barents Sea (Gabric et al. 2005):
- By the time of equivalent CO2 tripling (2080)
zonal annual DMS flux increase: >80%
zonal radiative forcing: W/m2
summer (June-September)

13. Recent DMS modeling and observations.
A statistical (GIS-based) approach.
(M.S. graduate student Humphries, Deal, Atkinson)
Modeled surface [DMS] for month of May.
(Deal, Jin)
Influence of sea ice on marine sulfur biogeochemistry in Community Climate System Model (CCSM), July
Field and laboratory studies help to clarify sub-processes.
DMS data has gaps. Modeling effort is to fill in the gaps – largely due to efforts of grad student.
Improve arctic biogeochemistry – start by adding ice algae to CICE and DMS biogeochemical cycling in sea ice
Focus on the ice? Ice algae are major producers of the DMS precursor, DMSP – highest DMS concentrations in air and water are at ice edge and in marginal ice zone -- ice edge strongest predictors of high DMSP
(DMS in sea ice,
J. Stefels, unpublished data)

14. Ecosystem modeling focus on ice-ocean ecosystem.
IARC ocean DMS ecosystem model
(Jodwalis (Deal) et al. 2001)
IARC ice-ocean ecosystem model applied:
Land-fast ice in Chukchi Sea
(Jin, Deal et al. 2006)
Fluctuating ice zone of Bering Sea
(Jin, Deal et al. 2007; Jin, Deal, McRoy et al. 2008)
Multi-year pack ice Canadian Basin
(Lee, Jin, et al. submitted)
Working towards a more regenerative microbial loop in ice ecosystem model,
IARC contribution
DMS loss and production pathways included in current IARC DMS model (Deal et al. 2001). Model is being updated based on more recent findings (e.g. Deal et al. 2005; Stefels et al. 2007; Vezina et al. in prep.).
CO2
Dissolved inorganic carbon (DIC)
marine food web Fe
and, Fe limitation on phytoplankton growth.

15. Model results show phytoplankton bloom patterns in the southeastern Bering Sea are related to the Pacific Decadal Oscillation (PDO) Index regimes.
Comparison of modeled phytoplankton at the southeastern Bering Sea with
a) daily SeaWiFS data at sea surface;
b) mooring fluorescence data at 12 m.
Response of lower trophic level production to long-term climate change in the southeastern Bering Sea using 1-D ice-ocean ecosystem model.
(Jin, Deal, McRoy)
Modeled monthly mean net primary production (NPP) for years of PDO Index > 1 subtracted by the mean for years of PDO Index < -1. D, F, Ai denote diatoms, flagellates, and ice algae, respectively.
Jin, Deal, McRoy, et al (JGR).

16. By implementing the IARC 1-D ice ecosystem model in the LANL sea ice model, CICE, we have begun to extend its scale. ≥
(Deal, Jin)
Polar map of base ten logarithm mean ice bottom layer Chl a concentration (mg Chl a m-2) for mid-May. The white line is the 15% ice edge contour and the black lines are ice thickness contours of 1, 2, 3 and 4 m, working inward from the ice edge.
(Jin, Deal)
Modeled Chl a reveal spatial patterns of ice algal biomass accumulation consistent with observations.
We have focused our ecosystem model development on the Bering and Chukchi Seas where the most observations through collaborations are availalble and …
Ice concentration (left) and ice algal biomass (right) at the bottom of sea ice on May 13, 1981.

17. Pacific water transport from the Chukchi shelf to the Canada Basin in being investigated with an eddy-resolving coupled sea ice-ocean model.
(Watanabe – AOMIP participant)
Model bathymetry [m]. B.S.: Bering Strait, H.C.: Herald Canyon, C.C. : Central Channel, N.R. Northwind Ridge.
The model used is the Center for Climate System Research Ocean Component Model (COCO) version 3.4 developed at the University of Tokyo.
Model domain… horizontal resolution is 2.5 km so that mesoscale baroclinic eddies are explicitly resolved.
Max eddy size reach about 50 km in the horizontal and 300 m in the vertical.
When he compared the simulated eddies with those in other eddy-resolving models he found the increase of horizontal resolution clearly improves the representation of spatial scale of the eddies.
Watanabe is participating in the present AOMIP activity with Wieslaw Maslowski, Andey Proshutinsky and others.
Center for Climate System Research
Ocean Component Model (COCO),
Ver. 3.4, Univ. of Tokyo
2.5 km horizontal explicitly resolves mesoscale baroclinic eddies
Simulated eddies in vicinity of the Barrow Canyon.
Northward velocity averaged in the top 100 m in August is shaded [cm s-1]. Vectors show ocean velocity averaged in the top 100 m and their unit vector is 50 cm s-1.

18. The distribution of virtual tracer associated with the Pacific water demonstrates that a significant part of the Pacific water passes through the Barrow Canyon during summer.
(Watanabe)
The simulation result shows a significant part of the Pacific water flowing from the Bering Strait passes through the Barrow Canyon,
and then inflows into the Canada Basin by mesoscale baroclinic eddies.
A significant part of the Pacific water passes through the Barrow Canyon during summer.
Plans are to implement a coupled sea ice-ocean-ecosystem model in order to quantify the change in marine primary productivity induced by sea ice loss in the Arctic Ocean.
Seasonal cycle of transport of the virtual Pacific water tracer across the dashed line [Sv].
Vertically integrated concentration of virtual Pacific water tracer in October [m].

19. Plans - IARC Cooperative Agreement
Scientific Goal: Quantify the relative current and possible future influences of arctic marine ecosystems on the global climate system.
Hypotheses
Enhanced DMS emissions from a more ice-free Arctic Ocean will increase cloud reflectivity of incoming solar radiation and counter the initial loss of surface albedo associated with the loss of sea ice.
Changes in arctic marine carbon cycle in response to a warming climate will significantly influence atmospheric CO2 and CH4 levels.
Strategy:
#1 Interface with modeling groups and the other 2nd stage themes.
Retrospective studies with modeling tools.
Experiments with climate model runs for standard IPCC emissions scenarios.
Coupled model experiments with GCM’s.
Provide ice ecosystem – ocean ecosystem module to ASM.
Potentially the potential for the micro-nutrient iron to limit phytoplankton growth.

20. Challenges and Concerns:
More in-situ and sustained observations needed.
Candian BioChem database on web.
Pan-Arctic PP database by P. Matrai on web.
Aguilar-Islas to study Fe in land fast ice.
Sea ice biologist R. Gradinger on IARC team.
Hydrological Atlas of the Bering Sea – Luchin & Panteleev.
Verification of the 3-D physical-ecosystem models and application in all critical areas.
Focus on the Bering-Chukchi-Beaufort Seas Region.
Group regions with similar features.
Take more advantage of in-house expertise.
Funds for students and post-docs.
New modeling and observations post-docs.
Need to work harder to recruit students.