Download MARINE_ECO_ISAB_030209

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
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
CO2 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
2nd 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 +
SO2 +
Radiative
Feedbacks?
DMS
atmosphere
Decreased Ice/
Increased Open Water
?
CO2
CO2 Dissolved inorganic carbon (DIC)
CO2
marine food web
Export
deep
ocean
DOC
POC
CaCO3
DOC
POC
CaCO3
DIC
Increased Discharge
CH4
+?
ice
Fe
euphotic
zone
Radiative
Feedbacks?
CO2
+?
ice
Increased Precipitation
& Warming Permafrost
Fate of Sea Ice in
the Arctic Ocean
Nutrients
CH4
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
SO2 +
Radiative
Feedbacks?
DMS
CO2
+?
atmosphere
Radiative
Feedbacks?
?
ice
CO2
CO2 Dissolved inorganic carbon (DIC)
CO2
marine food web
euphotic
zone
Export
deep
ocean
+?
ice
Fe
DOC
POC
CaCO3
DOC
POC
CaCO3
DIC
CH4
Nutrients
CH4
Sources of iron to the surface ocean.
Bering Sea
April
Aerosol Deposition
200
Solubilization
Biologically
available Fe
Rivers
Melting
Dissolved Fe
Solubilization
Nitrate (mM)
40
Sea Ice
30
125
20
50
10
Remineralization
Resuspension
Salinity
Calvin Mordy unpublished data
Deep Mixing/
Upwelling
Sediments/
Pore Waters
(Fe deficient
relative to nitrate)
Beaufort Sea
May to November
Simpson et al., 2008
Nitrate (mM)
Subsurface
Water
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 (nM)
• 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 1
GAK 13
Continental input
rich in Fe
GAK 7
GAK 13
Wu et al. (submitted, GRL)
Spoke and Jickells (1996)
Siefert et al. (1999)
Spoke et al. (1994)
Johanson et al. (2000)
Visser et al. (2003)
Bonnet and Guieu (2004)
Chen and Siefert (2003)
Desboeufs et al. (2001)
Zhu et al. (1993)
Chen (2004)
Baker et al. (2005)
Johanson et al. (2000)
Bonnet and Guieu (2004)
Siefert et al. (1999)
Chen and Siefert (2004)
Spoke and Jickells (1997)
Desboeufs et al. (2001)
Edwards and Sedwick (2001)
Chen (2004)
Zhuang et al. (1992)
Baker et al. (2005)
Chen and Siefert (2003)
Edwards and Sedwick (2000)
Aerosol Fe solubility dominated by
the colloidal fraction.
Estimates of aerosol iron
solubility in seawater
reported in the literature
(0.01-90%)
(Aguilar-Islas, Wu)
20
40
60
80
100
% Fe dissolution
Different
leaching solutions
Aerosols collected
from different areas
14.00
Accumulated % Dissolved Fe
Accumulated % Dissolved Fe
3.5
3
2.5
2
1.5
1
30
60
Time (min)
90
8
4
0
12.00
0
4
8
12
% DFe Solubility
10.00
(dissolved is < 0.4 µm)
8.00
6.00
Most of the aerosol iron
dissolved in seawater was
in the colloidal size fraction
4.00
2.00
0.00
0
12
% CFe Solubility
0
(0.02 µm < colloidal < 0.4 µm)
16
0
30
60
Time (min)
90
Aguilar-Islas,
Wu,In
et press
al. , 2009
(MarineChemistry)
Chemistry)
Aguilar-Islas
et al.
(Marine
16
Diminishing arctic sea ice will influence important biogeochemical
cycles and the marine ecosystem.
CCN +
Radiation
Budget
Sulfate aerosols +
-
Global
Temperature
SO2 +
Radiative
Feedbacks?
Radiative
Feedbacks?
DMS
CO2
+?
atmosphere
CO2
?
ice
ice
CO2 Dissolved inorganic carbon (DIC)
CO2
CO2
Fe
marine food web
euphotic
zone
Export
deep
ocean
CO2
DOC
POC
CaCO3
DOC
POC
CaCO3
DIC
CH4
+?
CH4
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?
 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: -7.4 W/m2
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.
cloud albedo
condensation
DMS (nM)
0
50
100
depth (cm)
40
50
60
70
80
oxidation
MSA
150
10
30
condensation
acidification
DMSO
0
20
SO42-
04-Dec
09-Dec
dry & wet deposition
Field and laboratory
studies help to
clarify sub-processes.
DMS
snow
14-Dec
19-Dec
25-Dec
30-Dec
90
Salinity
Light
DMSO
?
enzymatic cleavage
DMSP algae
Temperature
?
excretion
& lysis
Nutrients
uptake
DMSP dissolved
ingestion
100
(DMS in sea ice,
J. Stefels, unpublished data)
photochemical and
biological oxidation
DMS
sloppy
feeding
demethylation?
?
enzymatic cleavage
DMSP grazers
sea ice
DMS
biological oxidation
DMSO
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, McRoy et al. 2008)
Multi-year pack ice Canadian Basin
(Lee, Jin, et al. submitted)
CO2 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 SeaWiFS data at sea surface;
b) mooring fluorescence data at 12 m.
(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. 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 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.
Challenges and Concerns:
More in-situ and sustained observations needed.
http://saga.pmel.noaa.gov/dms/
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.