Modelling sea ice salinity: 1D, 3D modelling and

implications for ecosystems

Martin Vancoppenollecoll.: T. Fichefet, H. Goosse, C.M. Bitz,

S. Bouillon, G. Madec, M.A. Morales Maqueda, J.-L. Tison, C. Lancelot, B. Delille, F. Jardon, F. Vivier

Approach and questions

What aspects of sea ice desalination are relevant for large-scale sea ice mass balance and ocean circulation?

How to represent ice salinity in models ?

What are the implications for ecosystem models ?

Why modelling ice salinity ?

Simulated change in Arctic sea ice thickness (1979-2006)

Variable salinity – S=5 Albedo + 10%Variable salinity – MY profile

Vancoppenolle et al., Ocean Modelling, 2009b

Thermal properties of sea ice

Thermal properties

Diffusion of heat

Growth / melt rates

Ice-ocean salt / freshwater exchanges

drainage growth / melt

Growing ice Melting ice

Snow ice

Congelation Brine drainage Melt Brine drainage

Lateral meltwater flow

Flushing

Freshwater flux / Salt flux

Virtual salt flux(upper ocean

salinity tendency)

Outline

1D modelling

3D modelling

Perspectives for ecosystems

1D MODELLING

Is Thermal Effect Important ?

S (ppt)

h (m

)

Vancoppenolle, Fichefet and Bitz (GRL 2005)

• Sea ice model with brine thermodynamic effect (Bitz and Lipscomb, 1999) • Run for 50 years using climatological forcing• Using with various salinity profiles

How we model ice salinity (1D) ? Thermal equilibrium

All salt is locked within brine inclusions

Salt transport breaks equilibrium

m = freezing point salinity-temperature ratio [0.054 °C/ppt]

e = brine volume frac. [-] S = ice bulk salinity [ppt] T = ice temperature [°C] s = brine salinty [ppt] vz = percolation velocity

(function of meltwater production) [m/s]

Ds = salt diffusivity in brine [m2/s]

Parameterizing Brine Drainage

Gravity Drainage Convection if Ra is >5

(Notz & Worster, 2008)

Flushing Percolation if

Surface melting min(e) > 5%

30 % of meltwater percolates (Vancoppenolle, Bitz and Fichefet, JGR 2007)

Vancoppenolle, Goosse, et al. (JGR 2010)

1d Simulations of Brine Drainage

Multiyear desalination of Arctic sea ice

(Vancoppenolle, Fichefet and Bitz, JGR 06)

Desalination of Antarctic sea ice (Vancoppenolle, Goosse et al., JGR 10)

Summer desalination of Arctic sea ice(Vancoppenolle, Bitz and Fichefet, JGR 07)

Winter desalination is sensitive to diffusivity parameterization

Turbulent diff.Model (solid)Obs (dash)Molecular Diff.

Winter salinity profileData from Tison et al.

Winter desalination is sensitive to diffusivity parameterization

Antarctic sea ice simulations, June.

Summer desalination is sensitive to model parameters

Simulated surface salinity at Point Barrow (Alaska), June, for several sensitivity exps.

Penetration of penetrating radiation

Fraction of vertical percolation

Brine volume fractionpermeability threshold

0

0.3

0.5

0.2

0.3

0.5

10%

8%5%

Sensitivity to parameters and forcing: summary

Gravity drainage Diffusivity parameters Critical rayleigh number Turbulent diffusivity

Flushing Snow depth Superimposed ice

formation Penetration of radiation

(io) in the ice Fraction of meltwater

that is allowed to percolate vertically

Brine volume fraction permeability threshold

Full-depth convection

Results of a run in Antarctic sea ice with high snowfallSep 17 (1 – thin solid); Sep 24 (2 – dot); Oct 1 (3 – solid thin); Oct 8 (dash)

1 – 2 – 3 – 4

4 – 3 – 2 – 1

1 – 2 – 4 – 3

4 – 3 – 2 - 1

3D MODELLING

Approach• Problems:

– Ice thickness categories– Advection of tracers is expensive

• Approach: develop a simple S equation– 1 equation for vertical mean salinity– Shape of profile function of vertical

mean salinity• Include this simple S equation LIM

– Salt content (S.h) for each ice category

– Horizontal transport (Prather, 86)– Remapping in thickness space

(Lipscomb, JGR01)– Ridging / Rafting

Vancoppenolle et al. (OM 2009a, 2009b)

Comparison at Point Barrow (AK)

Red diam: OBSSolid: Simple eq. Dash: Transport eq.

Simulated Hemispheric Mean Salinitywith a 3D Ice-Ocean Model

Forced by Reanalyses

Hemispheric mean ice salinity simulated by NEMO-LIM3:Arctic (black) and Antarctic (grey).(différences

hémisphériques : percolation, glace blanche, âge de la glace + cycle

saisonnier, variablité

interannuelle)

Comparison to Obs

Ice salinity vs thickness in the model and from Cox and Weeks (1974) regressions computed

using ice core data

S (‰

)

hi(m)

MarJunSepDec

Comparison to observations in various regions(compilation from > 1000 cores)

Geographical Distribution Arctic

Winter maximum In winter, salinity reflects

thickness / age of sea ice Summer low / constant values

Antarctic Weakear contrasts Fall maximum Local maxima due to polynyas

and maximum snow fall

Mar Sep

Sep.Mar

Ice salinity (1979-2006) averaged overice thickness categories

Impact (Arctic)

2 configurations Prescribed salinity Simulated salinity

Ice thicker for varying salinity Differences up to 1m Difference in volume ~ 10%

More ice growth at the ice base (due to reduced energy of melting)

More surface melt(due to reduced specific heat)

More bottom melt(enhanced ice-albedo feedback)

Varying – prescribed salinity

Vancoppenolle et al., 2009b

1979-2008 Annual mean thickness difference

Impact (Antarctic)

2 configurations Prescribed salinity Simulated salinity

Ice thicker for varying salinity Mean volume difference ~ 20%

Importance of ice-ocean interactions Including variations of S

Induce more ice formation with less salt rejection

Reduces vertical mixing in the upper ocean

Reduces the oceanic heat flux Increases sea ice formation

Varying – prescribed salinity

Mean 1979-2009 Ice thickness difference

Vancoppenolle et al., 2009b

Impact on Upper Ocean

Mean 1979-2006 difference in sea surface salinity

Varying - prescribed Varying – prescribed S

ECOSYSTEM MODELLING

Why modelling salinity ?2) Ecosystems

Biophysical couplings associated with ice salinity Nutrient distribution Diatom transport mode Brine salinity inhibition of growth Brine volume fraction

Vertical structure of ice ecosystems

Modelling Sea Ice Ecosystems Energy-conserving

thermodynamics and salt transport

1-stream Beer law with attenuation by chlorophyll-a

Tracer transport Ecosystem model

(diatoms and silicates)

Brine-Ecosystem Coupling Ecosystem variables (n=1, 2)

(diatoms=DAF, silicates=DSi)

C = bulk concentration z = brine concentration e = brine volume fraction

Evolution equation

Physical sources & sinks

A typical run

Comparison to Observations

December Chlorophyll-a profile solid black: simulated (dots = STD)

horizontal lines: obs at ISPOLred : double snow

After calibration of µ, l, ws

Role of diatom transport mode Different scenarios:

(a) diatoms follow brine motion (b) diatoms are locked in ice without

following brine motion (c) diatoms follow brine motion and

stick on brine inclusion walls Observations

Highest biomass is achieved if algae move with brine and stick on brine’s walls.

If algae are not sticky but mobile, they are rejected of the ice as salt, which inhibits the community development.

The nutrient pump increases the availability of nutrients and hence promotes community development.

Total biomass (mg chl-a/m2) as a function of time (months) for the different scenarios of brine-biology interactions. Note the log scale on the y axis.

Role of convection on biomass

Biomass in silica units Dissolved Silica Total Silica

Why nutrients are not limiting in winter?

Salt is rejected from ice due to thermodynamic constrains

on brine salinity Common sense: nutrients should

be rejected from the ice Nutrient fluxes are proportional

to: Ocean-brine gradient of nutrient

concentration Diffusivity (high for growing ice)

Hence, nutrients can be fluxed to the ice.time scales of nutrient uptake is much slower than convection

Conclusions (1) Ice salinity can reasonably well be simulated

1d models: winter convection, full-depth convection, flushing Timing, magnitude and model sensitivity are uncertain

Ice salinity is an important actor of large-scale ice-ocean dynamics

Ice salinity affect thermodynamics in the Arctic and ice-ocean interactions in the Antarctic

Ice salinity is increasing now as the amount of FY ice increases. Hence, the sensitivity of coupled models may depend on how salinity could be represented

Conclusions (2)

Brine-ecosystem multi-layer models are quite different from single-layer based models

Brine dynamics allow to simulate vertical structure in ecosystem in a reasonable way

There are brine dynamics-nutrient interactions Transport mode of diatoms is important Brine salinity limitation is the second

important factor after light limiation

Remaining problems (1)

No thermo-haline coupled term in brine transport equations

Physical inconsistencies in the model (ice/brine density, freezing point, …)

Model results rely on uncertain parameterizations

Remaining problems (2)• Non-destructive method-based time series

Better account of spatial variability (Hajo’s talk) Less errors at high brine volumes (Phillip’s talk)

• How should vertical diffusivity be parameterized ?• Permeability-porosity relation for wide range of T,S• What are the pathways of seawater during surface flooding ?• Full-depth convection: when, how, how often ?• How to represent 3D subfloe-scale circulations ?• What is the impact of ridged ice desalination?• How to represent gas transfer?• How to design a sound model-data comparison

The truth about Louvain-la-Neuve Ice Model

THANKS TO: Cc Bitz, Ralph Timmermann, Steve Ackley, Thierry Fichefet, Hugues Goosse, Gurvan Madec and NEMO team, Jean-Louis Tison, Tony Worby, Hajo Eicken, Bruno Delille, Miguel Angel Morales Maqueda, Bruno Tremblay, Ioulia Nikolskaia , Oiivier Lecomte,

Olivier Lietaer, Sylvain Bouillon, Anne de Montety, Christiane Lancelot and Ivan Grozny + forgotten!

The principle is very basic:

A terrific vibration with maximal resonance…

nice model huh?I found inspiration from an African instrument, but I improved it…

Further reading

Vancoppenolle, M., H. Goosse, A. de Montety, T. Fichefet, B. Tremblay and J.-L. Tison (2010). Modelling brine and nutrient dynamics in Antarctic sea ice : the case of dissolved silica. Journal of Geophysical Research, 115(C2), C02005, doi:/10.1029.2009JC005369.

Vancoppenolle, M., T. Fichefet, H. Goosse, S. Bouillon, G. Madec and M.A. Morales Maqueda (2009a). Simulating the mass balance and salinity of Arctic and Antarctic sea ice. 1. Model description and validation, Ocean Modelling, 27, 33-53, doi:10.1016/j.ocemod.2008.10.005.

Vancoppenolle, M., T. Fichefet, and H. Goosse (2009b). Simulating the mass balance and salinity of Arctic and Antarctic sea ice. 2. Importance of sea ice salinity variations, Ocean Modelling, 27, 54-69.

Vancoppenolle, M. (2008b). Modelling the mass balance and salinity of Arctic and Antarctic sea ice, Phd Thesis, Université Catholique de Louvain, ISBN 978-2-87463-113-9.

Vancoppenolle, M., C. M. Bitz, and T. Fichefet (2007), Summer landfast sea ice desalination at Point Barrow, Alaska: Modeling and observations, Journal of Geophysical Research, 112, C04022, doi:10.1029/2006JC003493.

Vancoppenolle, M., T. Fichefet and C.-M. Bitz (2006) : Modeling the salinity profile of undeformed Arctic sea ice, Geophysical Research Letters, L21501, doi://2006GL028342.

Vancoppenolle, M., T. Fichefet, and C.M. Bitz (2005) : On the sensitivity of undeformed Arctic sea ice to its vertical salinity profile, Geophysical Research Letters, L16502, doi://2005GL023427.