Sea Ice, Climate Change and Remote Sensing · 2013. 10. 24. · Sea Ice, Climate Change and Remote Sensing Prof. David Barber Canada Research Chair in Arctic System Science Director,

Sea Ice, Climate Change and RemoteSea Ice, Climate Change and Remote

SensingSensing

Prof. David Barber

Canada Research Chair in Arctic System Science

Director, Centre for Earth Observation Science

University of Manitoba

Winnipeg, MB. Canada

www.umanitoba.ca/ceos

ESA Summer School, August, 2006

CEOSCEOS

Lecture outline

1) Arctic Climate Change and Remote Sensing

2) Thermodynamics, Geophysics and AOP/IOPs

3) Dielectrics, scattering and emission modeling

CEOSCEOS

Outline of this talk

• A look at thermodynamic processes

• Snow geophysics

• Sea ice geophysics

• A look at complexity

• Conclusions

CEOSCEOS

CEOSCEOS

CEOSCEOS

Complex Dielectric

!*=!"+j!#

Ice Type

Ice Thickness

Ice Salinity

Ice Temperature

Snow Depth

.

.

Multi-

frequency

& polarized

EM

Signatures

Forward Approach

Inverse Approach

Freeze onset date Radiative

transfer model

The electromagnetic properties of sea ice

IEEE TGARS, ONR ARI special issue. 36(5): 1750-1763

CEOSCEOS

Frequency/Polarization and

Sensor Geometry

!°total =

!°ss

+ "as

(#) *

!°sv

(#') + "s(#')

* !°

is + "

si (#") * !°

iv (#")

Snow Surface

Snow Volume

Ice Surface

Ice Volume

!h and L

Ri, Rw, $s, Wv, Ss, %s,

!h and L

Ri, Ra, Rb, $i, Is,

FrequencyPolarization

# of LooksIncidence Angle

CEOSCEOS

Thermodynamic Processes

CEOSCEOS

snow

Multilayer thermodynamic model

!

"scs

#Ts

#t=#

#zks

#Ts

#z+ I

o

$

% &

'

( )

!

"ici

#Ti

#t=#

#zki

#$i

#z+ I

o

%

& '

(

) *

!

"k( i / s)

#T( i / s)

#z+ (1" $

( i / s) )QSW +QLW " %& (i / s)TAI4"Qs "Ql " Io = L

(i / s)WAI

!

ks

"Ts

"zSI

= ki

"Ti

"zSI

!

"ki#Ti

#z IO

= Qw " LiWIO

ice

Snow/ice

Ice/ocean

Air/snow

Coupled column model

CEOSCEOSBarber et al. 1998

0-5-10

20

0

30

60

90

Depth

(cm

)

-5-10-15-20 -5-10-15 -5-10-15-20-20

Temperature (°C)

-15-5-10-15-20

Average T°

Diurnal !

Show

twave

Flu

x (

K ) 400

200

0

Snow

Sea Ice

5

Winter ablation 1 ablation 2 ablation 3 ablation 4 ablation 5

0-5-10 -15

Temperature is the control

Ice site overview, measurements

Tsnow Tice+water

Tair+Rh

U,V

u’,v’,w’,CO2’,H2O’SW PAR LW

SW LW

Q*

Tsurf

M1234

& T34

Ice sampling

T12

Spectral albedos

and

irradiance profiles

Temperature,

salinity, O-18,

for snow and ice

Nondestructive

transmittance

measurements

and irradiance

profiles.

CEOS

CEOSCEOS

Radiation, Heat and Mass Transfer Processes

of Snow over FYI

snow

ice

QH QELd LuKd Ku

K*o

K*B=Q*is

K*1x=Q*1x

Qso

Qs1x

QsB

Qio

Z=1x

Z=2x

Z=Nx

Z=0x

Z=B

QM

Q* Q*

Density (kg m-3)

Salinity (ppt)

Liquid (% by vol.)

Mass

CEOSCEOS

Snow

CEOSCEOS

0.0

6.0

9.0

12.0

15.0

18.0

Snow

Depth

(!s;

cm

)

10.0 20.0 30.0 40.0

Snow Grain Size (mm-2)

-8 -7 -6 -5 -4

Snow Temperature (Ts; °C)

0.00 0.02 0.04 0.06

Snow Brine Volume (Vb; %/100)

0.0 100 200 300 400 500

Snow Density ("s; Kg·m-3)

Grain Size3.0

Snow-Ice

Air-Snow

"s

#°

Vb

• Variables

• Processes

CEOSCEOS

0 1Scale (mm)

CEOSCEOS

•Snow is a complex crystalline material which forms

from the condensation and sublimation of water

vapour onto a nucleating material.

•Upon deposition the dendritic structures (small

angular crystal pieces which make up the snow flake)

break into fragments (a process known as saltation).

•This saltation process quickly increases the density of

the snow as it is blown across the Arctic sea ice.

•As the dendrites age a process called sintering occurs

(i.e., bonds forming at the points of adjacent

dendrites).

•This process results in an equilibrium density for

snow of about 375 kg·m-3 for snow on Arctic sea

ice.

Dendrites

CEOSCEOS

0 5Scale (mm)

CEOSCEOS

•Under temperature gradient metamorphism there is

a transfer of mass along the temperature gradient.

•This process is typified by the sublimation at the

warm end of the snow grain, transfer along the

vapour pressure gradient, and a corresponding

phase change from vapour back to a solid at the

colder end of the snow grain.

•This process results in a predominantly elongated

crystal structure with the long axis parallel to the

direction of the vapour gradient.

•The metamorphic state which results from this

process is often called kinetic growth snow grain.

Kinetic

structures

CEOSCEOS

0 10Scale (mm)

CEOSCEOS

•When water in liquid phase is low (or absent) equi-

temperature metamorphosis will create larger

grains at the expense of smaller grains due to the

vapour pressures associated with the snow grain

shapes.

•This is the principal process associated with early

spring grain growth or snow ripening.

•When water in liquid phase increases large grains

will combine into polycrystalline aggregates.

•When adjacent equitemperature grains aggregate

large single grain entities result .

•This usually coincides with draining within the

snow pack.

Aggregate

Structures

CEOSCEOS

Snow

Sea Ice

CEOSCEOS

Snow

Sea Ice

0.0

6.0

9.0

12.0

15.0

18.0

Snow

Depth

(!s;

cm

)10.0 20.0 30.0 40.0

Snow Grain Size (mm-2)

-8 -7 -6 -5 -4

Snow Temperature (Ts; °C)

0.00 0.02 0.04 0.06

Snow Brine Volume (Vb; %/100)

0.0 100 200 300 400 500

Snow Density ("s; Kg·m-3)

Grain Size3.0

Snow-Ice

Air-Snow

"s

#°

Vb

CEOSCEOS

Sea Ice

CEOSCEOS

Brine flux

New Sea Ice

CEOSCEOS

CEOSCEOS

CEOSCEOS

Snow is

saline

(Eicken’s chapter in Thomas and Dieckmann (ed) 2004)

First year sea ice microstructure

CEOSCEOS

Ice microstructure, light nilas, Cape Bathurst polynya

Station 124A on Oct 26, 2003

1.5cm

6cm

8cm

5mm thick section

through transmitted

light

1mm thin section

between polarizing

sheets

CEOSCEOS

CEOSCEOS

Crystal structure (FYI, Franklin Bay, May 9, 2004)

• Sea ice has irregularcrystal boundaries

• Growth parallel to (0001)plane is favored

$ Geometric selection tovertical c-axis orientationwith depth.

• Sizes increase with depth(related to growth rate)

• C-axis alignment withcurrents

• Close to bottom of thickFYI irregular c-axisorientations observed(no explanation)

1 cm

2.5 cm

4 cm

19 cm

14 cm

9 cm44 cm

39 cm

34 cm

29 cm 24 cm bottom

CEOSCEOS

Close-ups with microscope

viewed from above

8cm

6cm

Brine channel

Brine tube

Brine pocket Cellular substructure

platelet

Interconnected tubes

CEOSCEOS

1.5cm

4cm

124F

200E

{2.5cm

Very thin ice examples

Calm conditions

Agitated conditions

CEOSCEOS

Multiyear sea ice microstructure

.

10 t

o 3

0 c

m

Bubble

s ra

nge

from

0.1

to 1

.0 m

m

{

MeltP

onds

Hummocks

!' = 2.5-

3.2!" = 0.0-

0.1

}Winebrenner et al. 1989

CEOSCEOSBarber et al. 1998

0-5-10

20

0

30

60

90

Depth

(cm

)

-5-10-15-20 -5-10-15 -5-10-15-20-20

Temperature (°C)

-15-5-10-15-20

Average T°

Diurnal !

Show

twave

Flu

x (

K ) 400

200

0

Snow

Sea Ice

5

Winter ablation 1 ablation 2 ablation 3 ablation 4 ablation 5

0-5-10 -15

Temperature is the control

CEOSCEOS

.

MgCl2.12H2O

1000

500

100

50

10

5

1-10 -20 -30 -40 -50 -600

CaCO3.6H2O

Na2SO4.10H2O

MgCl2.8H2O

NaCl.2H2O

MgCl2.12H2O

CaCO3.6H2O

Na2SO4.10H2O

MgCl2.8H2O?

KCl

Na+

Cl-

SO4-

H2O

Mg+++Ca++K++rest

Cl-

H2O

TEMPERATURE (°C)

WE

IGH

T R

AT

IO (

g/k

g)

Ice

Bri

ne

NaCl.2H2O

KCl

Bri

ne

Ice

Sa

lts

Temp effect on the partial fractions of brine/ice and air

Phase diagram of sea ice showing the relationships between ice in solid phase, brine and

solid salts. After Assur, 1958.

CEOSCEOS

Some effects of temperature

Solid salts precipitate in brine

pockets (Light et al., 2002)

Brine tubes become

fragmented when

cooled (CASES, 10

Apr, 2004)

1m

m

Freezing Warming

Brine clusters merge

(CASES, May 16,

2004)

Gas bubbles

form within

tubes.

(CASES,

May 16)

CEOSCEOS

. .

Winter EarlyMelt

MeltOnset

AdvancedMelt

Dec April May/June June/July(Approximate time frames)

Brine Volume

Electrical Properties

Ice Strength PropertiesD

B

A Thermodynamics

C

Dep

th (

cm)

0

1.0

Str

eng

th (

MP

a)

12

34

1 Vertical Tensile2 Horizontal Tensile

4 Flexural

5 Compressive

3 Shear

5

2.0

70

0.1

0.4

Per

mit

tiv

ity

(

e ' )

Lo

ss (

e''

)

Ice Surface

Basal Snow Layer

Bulk Ice Volume

1

2

3

1.5

Permittivity

Loss12

0

-50

30

-10 -5 -2.5-7.5-20

Temperature oC

Amax

Bmin

Cmin

Cm

Cma x D

min

Snow

-100

Bm

Bmax

0

100

200

Bri

ne

Vo

lum

e (p

pt)

12

3

2.5

Dm

40

Basal Snow Layer

1

2

Ice

Am

Amin

Barber et al. 1996

The Rule of 5’s

CEOSCEOS

CEOSCEOS

Processes leading to melt pond

maturity over FYI. The melt

pond – albedo feedback is

initiated by an increase in the

atmospheric heat flux (L),

stimulating snow ablation and

the development of melt ponds.

During initial pond formation

the albedo of young ponds is

dictated by pond depth and the

scattering properties of the

frazil ice layer. Ablation of the

frazil layer is a function of the

wind induced mechanical

weathering, and solar

insolation (Wm-2). As the melt

ponds matured, the albedo is

dictated by pond depth and the

optical properties of the

columnar ice volume

CEOSCEOS

The Effect of Frazil Ice On Albedo

• The evolution of melt ponds through the melt season.Bubble densities quickly reduced after initial pondinghowever a ring of high bubble density is found alongpond fringes during times of advance.

CEOSCEOS

Pond Onset

CEOSCEOS

Pond Development

CEOSCEOS

Mature Ponds

CEOSCEOS

Pond Drainage

CEOSCEOS

-18

-16

-14

-12

-10

-8

-6

-4

-2

0

165 168 171 174 177 180 183 186 189 192 195

Year Day

Sig

ma N

aught (d

B)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Alb

edo/P

ond F

raction

_º Albedo Pond Fraction 2 per. Mov. Avg. (_º)

a b c d e

Time Series !, "º and Pond Fraction

IOP and AOP of the seasonal ice cover

29.04.2005

Melting new snow

Early melt pond with 1cm of water

New snow collecting at rougher surface

Measurements - Spectral albedo site M3

CEOS

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

350 450 550 650 750 850 950 1050

m1_0407

m1_0411

m1_0412

m1_0415

m1_0418

m1_0422

m1_0424

m1_04290

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

350 450 550 650 750 850 950 1050

m2_0407

m2_0411

m2_0412

m2_0418

m2_0422

m2_0424

m2_0429

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

350 450 550 650 750 850 950 1050

m3_0411

m3_0412

m3_0415

m3_0418

m3_0422

m3_0424

m3_04290

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

350 450 550 650 750 850 950 1050

m4_0411

m4_0412

m4_0415

m4_0418

m4_0422

m4_0424

m4_0429

Spectral albedos from Button Bay, HB2005, April 7 to April 29, M-sites

M1 – small bare ice area M2 – hummock with ~15cm soft surface layer

M4 – hummock with ~5cm soft surface layerM3 – large bare ice area – early melt pond

1cm of water

on ice surface

resulted in lowest

observed albedo

Hummock (or white ice) sites

had clearly smaller variability

than bare ice (or blue ice).

CEOSCEOS

Spectral irradiance profiles and transmittance

0

20

40

60

80

100

120

140

160

180

200

220

240

260

0.1 1 10 100

650nm

600nm

550nm

500nm

450nm

400nm

CEOS

0

1

2

3

4

5

6

7

350 400 450 500 550 600 650 700 750

200

180

160

150

130

100

80

60

40

30

Diffuse attenuation

coefficient Kd(%) calculated

from irradiance profile

T1_0421 (hummock)

dep

th

water

ice

What about spatial variability?17 April 1998

25 June 1998

4 August 1998

What about AOP’s and IOP’s

at the bottom of the ice?

CEOS

Vertical profiles in sea ice for (a) temperature

(daily mean), (b) salinity and (c) density, with (d)

corresponding calculations of brine and air

inclusion volume fractions. Note the cut in scale

on the latter. The emphasis is on the bottom part

where high temperature and salinities resulted in

an off the scale increase in volume fractions.

Horizontal microstructure sections of a sea ice

sample taken on 9 May. The numbers on the

right-hand corner of each image indicate the

height above the ice-water interface from which

the section was extracted. Processing by:

1. Edge detect

2. Torn edges

CEOS

(a) The vertical downwelling irradiance profiles

integrated over PAR wavelengths and normalized

to bottom irradiance values. The corresponding

diffuse downwelling irradiance attenuation spectra

for the (b) interior ice and (c) bottom 10-cm

bottom layer, with comparisons to (d) particulate

absorption coefficient.

Average chlorophyll-a

concentrations measured on four

occasions using three different

methods to extract samples; ice

core drilled from surface (core),

4-cm thick ice puck taken by

diver from below (puck), and

bottommost algae layer sampled

by diver using syringe “slurp gun”

CEOSCEOS

A brief look at Complexity?

Snow

CEOSCEOS

Sea Ice Modeled Processes

snow

ice

atmosphere

ocean (mixed layer)

T = -1.8°C if ice exists

Kd Ld

Fa

Lu Ku Fs Fl

T = sfc temp

T = -1.8 C

Q*

Flux(1)

Flux(2)

Kd = downwelling SW flux

Ku = upwelling SW flux

Fa = absorbed SW flux

Ld = downwelling LW flux

Lu = upwelling LW flux

Fs = sensible heat flux

Fl = latent heat flux

Q* = net sfc flux

Flux(1)=snow-ice conductive

flux

Flux(2)=ice-ocean conductive

flux

Multiple layers (49)

Sfc nrg balance

Conductive fluxes

CEOSCEOS

80

75

70

65

605 55 105 155 205 255 305 355

Duration of OpenWater (Days)

Anomaly Position (Day of Year)

Role of snow in complexity

Effect of Moving a 5 day (20cm) Snowfall

Anomaly on Open Water Duration

(model run 1961-1990).

CEOSCEOS

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

107

110

113

116

119

122

125

128

131

134

137

140

143

146

149

152

155

158

161

164

167

170

173

176

Julian Day

Alb

ed

o

albedo(obs)

albedo(model)

Modeled vs. Observed Albedo

CEOSCEOS

Hourly vs. Daily Forcing

0

0.5

1

1.5

2

2.5

107

127

147

167

187

207

227

247

267

287

307

327

347 2

22

42

62 82

102

122

142

162

Julian Day

Th

ickn

ess (

m)

Ice (day)

Snow (day)

Ice (hr)

Snow (hr)

Ice

Snow

BU = 202

(July 21)BU = 189

(July 8)

FU = 289

(Oct 16)FU = 297

(Oct 24)

CIS Data: BU = July 9-16

FU = Oct. 15-22

Simulation: April 17, 1992 - June 19, 1993

Open water duration

difference = 21 days

CEOSCEOS

Bias of ‘land based’ versus ‘on ice’ forcing

0

0.1

0.2

0.3

0.4

0.5

0.6

107

111

115

119

123

127

131

135

139

143

147

151

155

159

163

167

171

175

179

Julian Day

Th

ickn

ess (

m)

Snow (day)

Snow (CONT)

Snow (SFS)

Snow (obs)

Q* Q* Tsfc Tsfc

CONT/obs SFS/obs CONT/obs SFS/obs

R-Square 0.44 0.56 0.91 0.98

Mean Error -2.5 -2 2.5 -0.08

St. Dev. 30 25 3.1 1.7

SFS more realistic snow & ice ablationHanesiak et al.

Conclusions

CEOSCEOS

Conclusions

1. Need to know geophysics and thermodynamics to determine

scattering and response to forcing

2. Dynamic vs Thermodynamic processes are NB

3. Many feedbacks exist and processes are not yet well understood

(and thus not modelled).

4. System is very sensitive to changes in snow thickness, distribution

and deposition (timing of sea ice formation is critical)

5. Assumptions of current processes applicable to the future