Ocean Ecosystem Modeling and Observations IARC Contributors Ana

Ocean Ecosystem Modeling and Observations IARC Contributors: Ana Aguilar-Islas David Atkinson Clara Deal Meibing Jin Peter Mc. Roy 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

Block diagram outlining some of our strengths and how we work together. Ocean Ecosystem Modeling and Observations Theme Observations Fe biogeochemistry Dimethylsulfide (DMS) cycle Satellite remote sensing Internet databases Reanalysis data Modeling Statistical GIS-based model: Surface seawater DMS Terrestrial C input and fate CO 2 and methane dynamics 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)

Diagram illustrates integration within ocean ecosystem modeling theme and among 2 nd stage themes, and what feedbacks to the physical system might be expected. Radiation Budget Role of Freshwater/Permafrost in the Arctic-global Connection Global effect of Warming in the Arctic - CCN + Global Temperature Sulfate aerosols + SO 2 + Radiative Feedbacks? DMS atmosphere Decreased Ice/ Increased Open Water ? CO 2 Dissolved inorganic carbon (DIC) CO 2 marine food web DOC Export deep ocean POC Ca. CO 3 DOC POC Ca. CO 3 DIC CH 4 Increased Discharge ff o Run Nutrients trie nu , C, +? ice Fe euphotic zone Radiative Feedbacks? CO 2 +? ice Increased Precipitation & Warming Permafrost Fate of Sea Ice in the Arctic Ocean : sion CH 4 Ero utr C, n ient s Increased Erosion Increased Storminess, Diminished Sea Ice & Warming Permafrost

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 + - Global Temperature SO 2 + Radiative Feedbacks? DMS CO 2 +? atmosphere Radiative Feedbacks? ? ice CO 2 Dissolved inorganic carbon (DIC) CO 2 marine food web euphotic zone DOC Export deep ocean POC Ca. CO 3 DOC POC Ca. CO 3 DIC ff o Run +? ice Fe CH 4 Nutrients trie nu , C, : sion CH 4 Ero utr C, n ient s

Sources of iron to the surface ocean. Bering Sea April Aerosol Deposition 200 Solubilization Biologically available Fe Remineralization lat cu c Flo Deep Mixing/ Upwelling Subsurface Water Sea Ice 30 125 20 50 10 Resuspension Salinity Calvin Mordy unpublished data Sediments/ Pore Waters (Fe deficient relative to nitrate) Beaufort Sea May to November Simpson et al. , 2008 Nitrate (m. M) Rivers Melting Dissolved Fe Solubilization Nitrate (m. M) 40 green, 0– 50 m yellow, 50– 125 m red, 125– 200 m gray 200– 275 m blue, 275+ m Salinity

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 (n. M) • 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) • In the mid and inner shelf sedimentary iron inputs can reach surface waters during spring.

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 13 Continental input rich in Fe GAK 7 GAK 13 Wu et al. (submitted, GRL)

Estimates of aerosol iron solubility in seawater reported in the literature (0. 01 -90%) Aerosol Fe solubility dominated by the colloidal fraction. % Fe dissolution Different leaching solutions Aerosols collected from different areas (0. 02 µm < colloidal < 0. 4 µm) (Aguilar-Islas, Wu) (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)

Diminishing arctic sea ice will influence important biogeochemical cycles and the marine ecosystem. CCN + Radiation Budget Sulfate aerosols + - Global Temperature SO 2 + Radiative Feedbacks? DMS CO 2 +? atmosphere CO 2 ? ice CO 2 Dissolved inorganic carbon (DIC) CO 2 Fe marine food web euphotic zone DOC Export deep ocean CO 2 POC Ca. CO 3 DOC POC Ca. CO 3 DIC CH 4 ff o Run nts trie nu , C, +? : sion CH 4 Ero utr C, n ient s

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. Kettle et al. (1999) DMS climatology updated by Belviso et al. (2004). Late-spring low stratus offshore Barrow, Alaska.

What is the impact of DMS on climate? v Recent climate models (Gunson et al. 2006): - 50% reduction of ocean DMS emission: radiative forcing: +3 W/m 2 air temperature: +1. 6 °C - doubling of ocean DMS emission: radiative forcing: -2 W/m 2 air temperature: -0. 9 °C v 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 v Use of a climate model to force ocean DMS model in Barents Sea (Gabric et al. 2005): - By the time of equivalent CO 2 tripling (2080) zonal annual DMS flux increase: >80% zonal radiative forcing: -7. 4 W/m 2 summer (June-September)

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 2009 -2012. Field and laboratory studies help to clarify sub-processes. (DMS in sea ice, J. Stefels, unpublished data)

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) Working towards a more regenerative microbial loop in ice ecosystem model, Fluctuating ice zone of Bering Sea (Jin, Deal et al. 2007; Jin, Deal, Mc. Roy et al. 2008) Multi-year pack ice Canadian Basin (Lee, Jin, et al. submitted) CO 2 Dissolved inorganic carbon (DIC) marine food web Fe and, Fe limitation on phytoplankton growth.

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 Sea. Wi. FS data at sea surface; b) mooring fluorescence data at 12 m. (Jin, Deal, Mc. Roy) 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, Mc. Roy, et al. 2008 (JGR).

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) Ice concentration (left) and ice algal biomass (right) at the bottom of sea ice on May 13, 1981.

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. 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.

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) Vertically integrated concentration of virtual Pacific water tracer in October [m]. Seasonal cycle of transport of the virtual Pacific water tracer across the dashed line [Sv].

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 CO 2 and CH 4 levels. Strategy: #1 Interface with modeling groups and the other 2 nd 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.

Challenges and Concerns: More in-situ and sustained observations needed. http: //saga. pmel. noaa. gov/dms/ Candian Bio. Chem 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.