Modeling micronutrients in the ocean: Modeling micronutrients in the ocean:
The case of IronThe case of Iron
Olivier Aumont
Based on the work by: L. Bopp, A. Tagliabue, J.K. Moore, W. Gregg, P. Parekh,
A. Ridgwell, S. Dutkiewicz, D. Archer, L. Weber, C. Voelker, K. Flynn, ...
LOCEAN, Centre IRD de Bretagne, Plouzané, France
Nozaki Periodic TableMacronutrients
Micronutrients
The case of IronThe case of Iron
Iron: Used for many chemical and biological processes in
phytoplankton cells
N metabolism Nitrate and Nitrite reductase Photosynthesis & Respiration Cytochrome, ferrodoxin, … Process Catalyst …
Iron has been demonstrated to play a critical role in large regions of
the ocean
• Limits primary productivity
• Controls species composition
• Trophic structure
Consequence: Iron is the only micro-nutrient that has been explicitely
included in ocean biogeochemical models so far
(Short)(Short) History History
1931: Gran suggested iron could be limiting in the Southern Ocean
1980: First reliable iron measurements in the open ocean
1980’s: Martin showed that iron stimulates phytoplankton growth in
incubations
1993: Ironex I
1997: First 1D biogeochemical models with explicit iron (Loukos et al,
Johnson et al)
2000: First global 3D model with iron (Archer et al)
2007: At least, 6 global biogeochemical models include a description of
the iron cycle
The Iron cycle in the oceanThe Iron cycle in the ocean
Fecoll
Fe(III)’ FeL
Fe(II)’
Fepart
Dissolved iron
Phyto.Bacteria
Zoo.
SedimentsRivers
Dust
(Simple view)(Simple view)
sinkingsinking
sinkingsinking
ObservationsObservations(Design/validate the models)
Iron fertilization experimentsIron fertilization experiments
EisenEx EiFexEisenEx EiFex
SOIREESOIREE
, II, II
SOFeXSOFeX
SEEDSI &II
SERIESSEEDSI &IISEEDSI &II
SERIESSERIES
IronEx IIronEx I
CYCLOPSCYCLOPSFeEPFeEP
+ Natural system, detailed obs., …
- Small scales, HNLC systems, …
Lab experimentsLab experiments
+ Processes, detailed obs., parameters …
- Artificial systems
In situ measurementsIn situ measurements
+ Large spatio-temp. scales, natural system, …
- Coverage, speciation, parameters, …
Iron distributionIron distribution
Dissolved iron (nM)Dissolved iron (nM) 0-50 m0-50 m
Dissolved iron (nM)Dissolved iron (nM) 500-2000 m500-2000 m
OutlineOutline
IntroductionIntroduction
Iron chemistryIron chemistry
Biological uptakeBiological uptake
External sources of ironExternal sources of iron
Past/Future scenariosPast/Future scenarios
Conclusions/thoughtsConclusions/thoughts
I won't discuss about iron cycle in sediments and in dust
Iron chemistry (I)Iron chemistry (I)
From Archer and Johnson (2000)
The simplest iron modelThe simplest iron model
FeT Fepart
ksc
This model is not used anymore
The Johnson modelThe Johnson model
Fe’ Fepart
ksc
FeL
This model is still commonly usedFrom Archer and Johnson (2000)
< 0.6nM
Iron at 2500m (nM)
Iron chemistry (II)Iron chemistry (II)
Adsorption/coagulation modelAdsorption/coagulation model
Fe’ Fepart
FeL
(Aumont and Bopp, 2006;Moore and Braucher, 2007)
kcoag
kads
Des
orpt
ion/
rem
in.
Better agreement but:Better agreement but:
- Predicted iron concentrations too uniform in the deep ocean
- Parameters at the surface and in the deep ocean differ significantly
- Desorption improves model results but is not demonstrated
Iron SpeciationIron Speciation
Iron speciation modelsIron speciation models
Fe(III)' Fepart
FeLb
Des
orpt
ion/
rem
in.
Fe(II)'
FeLa
(Tagliabue and Arrigo, 2006; Weber et al., 2005;2007)
Tagliabue and Arrigo, 2006
(Tagliabue et al., 2007, sub.)
Iron speciation does matter !! Iron speciation does matter !!
Impacts restricted to the upper ocean
Expensive, many unconstrained parameters and processes
Iron Chemistry: Current state/challengesIron Chemistry: Current state/challenges
What we have learnt from modelsWhat we have learnt from models
Fecoll
Fe(III)’ FeL
Fe(II)’
Fepart
Critical for iron distribution in the deep ocean
Critical for PP and surface Iron
Future challengesFuture challenges
Dynamics of Iron colloids (Thorium, DOM analysis, ...)
Bioavailability of the different forms of operationnally defined dissolved iron
Ligands
Phytoplankton growthPhytoplankton growth
Most biogeochemical models: NPZD-type modelsMost biogeochemical models: NPZD-type models
Phytoplankton growthPhytoplankton growth
N1, N2, ...N1, N2, ... P1, P2, ...P1, P2, ...
D1, D2, ...D1, D2, ... Z1, Z2, ...Z1, Z2, ...
μ = μM L
N (1-exp(- (Chl/C) E/ μ
M)
IronIron
LLNN : Quota or Monod Approach (see for instance the work by Flynn and coauthors : Quota or Monod Approach (see for instance the work by Flynn and coauthors
for discussion on both approaches)for discussion on both approaches)
Constant parameters for iron limitation (except in Flynn, 2001)Constant parameters for iron limitation (except in Flynn, 2001)
Fe/C ratioFe/C ratio
First iron models : constant Fe/C ratios (5-10First iron models : constant Fe/C ratios (5-10mol/mol)
Currently, most (both quota and Monod) models have variable Fe/CCurrently, most (both quota and Monod) models have variable Fe/C
(Loukos et al., 1997; Lefèvre and Watson, 1999 ; Archer and Johnson, 2000; ...)
No luxury uptake, No Fe adsorbed onto the cell walls
Values representative of the open ocean
(Moore et al, 2002)(Moore et al, 2002)
Iron LimitationIron Limitation
All models predict similar patterns for Fe limitationAll models predict similar patterns for Fe limitation
(Aumont and Bopp, 2006) (Moore et al., 2004)Diatoms limiting factors
They reproduce the main characteritics of HNLC regions but ...They reproduce the main characteritics of HNLC regions but ...
1D models and obs. suggest higher K
Iron is not the whole story : light !!!
(Gregg et al., 2003)
Other componentsOther components
Most of the modeling work has concentrated on phytoplanktonMost of the modeling work has concentrated on phytoplankton
Detailed mechanistic models: Flynn, 2001 ; Armstrong 1999 ; ...
In comparison, other components have received much less attentionIn comparison, other components have received much less attention
Bacteria are not modeled. Bacteria are not modeled.
Observations: in competition with phytoplankton for iron
Zooplankton role in iron cycle is neglectedZooplankton role in iron cycle is neglected
Constant Fe/C ratios or passively controled by its diet
Never iron limited, nor affected by the Fe/C of its preys. Ideas from the work
by Mitra et al. (2006,2007)
Iron acquisition only from organic matter (preys) whereas studies have
shown colloids consumption (e.g., Chen and Wang, 2001)
Sediment mobilizationRivers
Dust deposition
Hydrothermal vents
External sources of ironExternal sources of iron
Dust depositionDust deposition
Historically, the external source which has received the first and main Historically, the external source which has received the first and main
attentionattention
(Jickells et al., 2005)
Large uncertainties in dust deposition to the ocean: 290-430 Mt/year
Dust deposition in modelsDust deposition in models
All models include this source, but with very simple parameterizationsAll models include this source, but with very simple parameterizations
Iron is a constant fraction of dust, typically ~3.5%
Solubility is constant, typically between 1% and 5%
Monthly-mean climatological fields
Solubility is not constant (time/space)Solubility is not constant (time/space)
(From Hand et al., 2004)
Dust dissolves not only at the surface Dust dissolves not only at the surface (Moore et al., 2004; Aumont and Bopp, 2006)(Moore et al., 2004; Aumont and Bopp, 2006)
Weak sensitivity to increased iron flux
Increased PP in HNLC region balanced by larger oligotrophic regions and scavenging
Role of Dust DepositionRole of Dust Deposition
Models have been used to estimate the contribution of aelion iron to Models have been used to estimate the contribution of aelion iron to new ironnew iron
Dust scenarios or estimating its impact on PPDust scenarios or estimating its impact on PP
• About 20% to 30% of new iron in the euphotic zone comes from dust
(Archer and Johnson, 2000; Moore et al, 2002 ; Aumont et al., 2003)
• But new iron is not necessarily completely used by phytoplankton
Weak sensitivity to decreased iron flux at the beginning
Strong iron flux over oligotrophic regions
Strong sensitivity to decreased iron flux over long timescales
Iron scavenging not balanced anymore by dust supply
Temporal variability of dust depositionTemporal variability of dust deposition
Dust deposition extremely variable on all timescalesDust deposition extremely variable on all timescales
Dust scenarios or estimating its impact on PPDust scenarios or estimating its impact on PP
Iron deposition at BATS (g Fe/d/m2)
(from INCA2 atmospheric model)
Iron relative variability Chlorophyll absolute variability (mgChl/m3)
Problem : iron variability seems underestimated with 1-2% solubility
Sediment mobilizationSediment mobilization
A significant source to the oceanA significant source to the ocean
Sediments in modelsSediments in models
• Resuspension and diffusion generates high Fe in coastal regions (> 5nM)
• Influence far offshore along some transects (S. Polar Frontal zone, N. Pacific)
(From Moore et al., 2007)
Only very few models include this source(Moore et al., 2004; Aumont and Bopp, 2006;Tagliabue and Arrigo, 2006; Moore and Braucher,
2007)
Estimated magnitude : ~2.10Estimated magnitude : ~2.101010 mol Fe/yr (very, very uncertain !!!!) mol Fe/yr (very, very uncertain !!!!)
Without it, models are much too sensitive to variations in dust deposition
External sources: ThoughtsExternal sources: Thoughts
Dust depositionDust deposition
The main unknown is the solubility (the rest is second order ...)
Processing in surface and deep waters
Dust is not the only major source
Sediment mobilizationSediment mobilization
Potentially as important as dust
We know very very little !!
Oxic/anoxic, Diffusion/Suspension/Irrigation, ...
Rivers ??Rivers ??
Hydrothermal vents ??Hydrothermal vents ??
LGM: The iron hypothesisLGM: The iron hypothesis
Atmospheric pCO2 about 80-100 ppmv lower than preindustrial levelsAtmospheric pCO2 about 80-100 ppmv lower than preindustrial levels
The Iron hypothesis The Iron hypothesis (Martin, 1990)(Martin, 1990)
Ice cores suggest higher dust deposition in the Southern Ocean
Higher aeolian input ⇒ enhanced PP ⇒ enhanced C sequestration
Models Models ?
A predicted CO2 drawdown between ~8 and ~30 ppmv(Archer and Johnson, 2000; Bopp et al., 2003; Parekh et al., 2006)
Compensation between enhanced PP in HNLC regions and decreased PP
due to lower N, P, Si levels
An other Iron effect ?An other Iron effect ?
Lower sea level = lower sediment mobilization
Maximum effect = +17 ppmv (Aumont et al., 2007, in prep.)
Future climateFuture climate
Dust deposition is predicted to decreaseDust deposition is predicted to decrease
40% decrease between 2000 and 2100 (Mahowald et al., 2006)
Impact on the C cycleImpact on the C cycle
Ocean sink is reduced by 0.5 Pg C/yr (Moore et al., 2006)
Atmospheric pCO2 increased by up to 100 atm (Parekh et al., 2006)
With Sediments, the impact is reduced to 0.2 Pg C/yr (Tagliabue et al., 2007,
sub.)
ConclusionsConclusions
Models proved to be useful despite the oversimplications Models proved to be useful despite the oversimplications
They are able to reproduce the basic features of the iron cycleThey are able to reproduce the basic features of the iron cycle
Major uncertainties in the iron cycleMajor uncertainties in the iron cycle
Scavenging/coagulation. In particular, what is the role of the colloids?Scavenging/coagulation. In particular, what is the role of the colloids?
A lot to be learn from other metals, especially Th
Characteristics of the ligands
Biological processes. What is the role of zooplankton/bacteria?Biological processes. What is the role of zooplankton/bacteria?
Iron on particles in the deep ocean
External sources of Iron. What is the role of sediments?External sources of Iron. What is the role of sediments?
Solubility of aelian iron
– Data required both from process studies and from the field (Obvious !!!)Data required both from process studies and from the field (Obvious !!!)
Speciation of iron (truly dissolved, colloidal, particulate, ...)Speciation of iron (truly dissolved, colloidal, particulate, ...)