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Daniela Flocco, Daniel Feltham, David Schreder Centre for Polar Observation and Modelling University College London

Daniela Flocco, Daniel Feltham, David Schr eder Centre for Polar Observation and Modelling University College London

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Page 1: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London

Daniela Flocco, Daniel Feltham, David Schreder

Centre for Polar Observation and Modelling

University College London

Page 2: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London

MODELS VS OBSERVATIONS

Stroeve et al., 2007

Page 3: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London

ALBEDO AND ICE-ALBEDO FEEDBACK

Global warming is intensified in polar regions due to

the albedo feedback mechanism [IPCC, 2007]: since

ice and snow are highly reflective compared to other

surface types, a reduction in the ice or snow coverage

due to melting results in enhanced absorption of solar

radiation, which leads to further melt and local

warming (snow = 0.8, ocean = 0.1, grass = 0.3).

Page 4: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London

Using ERS satellite data, strong correlation is found between ice thickness and length of previous melt season (Laxon et al, Nature, 2003)

The fraction of first year ice is increasing in time

Pond area is larger over thin undeformed ice

SEA ICE MASS BALANCE

Page 5: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London

MELT PONDS

Photo Credit: ICESCAPE project - NASA/Kathryn Hansen

July 4, 2010: Arctic sea ice and melt ponds in the Chukchi Sea.

Page 6: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London

MELT PONDS FORMATION

Melt ponds form on Arctic sea ice during summer (rarely seen in Antarctic) when the snow/ice surface melts due to absorbed solar, short wave radiation and freeze in September

Pond coverage ranges from 5% to 50% of the sea ice cover

Albedo of pond-covered ice (0.15 - 0.45) is lower than the albedo of bare sea ice or snow covered ice (0.52 - 0.87)

In terms of the heat budget of the Arctic, a change in mean albedo of 0.1 (e.g. a change in pond fraction of 20%) is equivalent to a change in sea ice extent of 10%.

Underice refrozen ponds are insulated by an ice layer and take up to 2-3 months to freeze completely

Page 7: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London

GCM-COMPATIBLE MELT POND MODELRequirements of constructing a melt pond model for use in existing GCMs places strong constraints on the form the model can take: main difficulty is that GCMs do not determine the sea ice topography.

Modern GCMs contain a thickness distribution function g(h).

FractionalArea

Thickness

g1

g2

g3

g4

g5

gi =1∑

Flocco and Feltham, 2007

Page 8: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London

HEIGHT AND DEPTH DISTRIBUTIONFUNCTIONSNEED: information about the surface height to redistribute surface water.ANSWER: We introduce surface height α(h) and basal depth β(h) distributions, which give the relative area of ice of a given surface height or basal depth.

α

β

hsl

NOTE: α(h) and β(h) are derived from the thickness distribution g(h) but do not describe the topography.

Referenceheight

Page 9: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London

DELTA EDDINGTON and “TOPO” POND’S SCHEME

“Retained” melt water fraction: 0.15 + 0.7ai for hi > 1 cm

Refreezing: Vp = Vpe−0.005(−2−Tsfc) for Tsfc < −2◦

Area ap = (Vp/0.8)0.5 < 1

Depth hp = 0.8ap < 0.9hi

Volume Vp = aphp

Snow ap =(1−hs/0.03)ap

Rransport Vp, ap

Retained melt water fraction: 0.15 + 0.7ai for hi > 1 cm

Reduce Vp by fraction of ice

area that ridged Melt water fills thinnest

categories first, may overflow

Snow occupies space according to ρs. If the ice is permeable, pond can drain to sea level

Vip = refrozen “lid” on pond

newly forming: use Fsfc

thickening: use Stefan solution for

melting: use meltt

ρiLdH

dt=K i

∂T

∂z

(Elizabeth Hunke talk at NCAR)

Page 10: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London

MELT POND AREA AND DEPTH RETRIEVAL

fulli

nn

fullsurf h

Ice Bare of AreaSnow of Area.

)h( VolumeVolume Totalh +

++⋅

−=

∑=1

60

snowpond

Asnow

category i

category i-1

category i-2

A pond (i-1) A bare ice (i+1)

hfull

hsurfl

Flocco and Feltham (JGR, 2007)

Page 11: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London

ICE EXTENT ANOMALYIc

e e

xte

nt

anom

aly

(%

)

Page 12: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London

ICE VOLUME ANOMALYIc

e v

olu

me a

nom

aly

(10

3 k

m3)

Page 13: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London

DIFFERENTIAL ICE GROWTH

Sea ice growth difference between the two pond’s schemes runs

Cm

/month

RED: Sea ice growth greater in new scheme

Page 14: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London

POND AREA AND ICE FRACTION

Ice thickness category

Pondarea

I IIIII VIV

Pond area is larger over thin undeformed ice

Sea ice

conce

ntra

tion %

Page 15: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London

ALBEDO

May Jun July

Page 16: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London

POND AREA JUN-SEPTEMBER 2007

Page 17: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London

SEA ICE EXTENT: SEPTEMBER 2007

Page 18: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London

CONCLUSIONS

We study melt ponds to quantify their importance for the increased melting rate due to global warming

Global Circulation Models are sensitive to the presence of a melt pond routine

Initial runs suggest that incorporation of melt ponds might allow simulations of the 2007 minimum sea ice extent

The melt pond routine is being incorporated into the latest version of CICE, which is the sea ice model used also by the UK Met Office

Page 19: Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London