How thick is the ice?

What scale?

How thick is the ice?

July – melt August – melt September – freezeup

April – pre-melt May – pre-melt June – melt

What time?

How thick is the ice? Young ice

First year ice Multiyear ice

New ice

What age?

How thick is the ice?

What kind?

What does ice thickness mean to you?

The distance from the top of the ice to the bottom

The great integrator

Ice strength

A barrier to heat, moisture, momentum

The key parameter (even bigger than albedo)

Storage of fresh water

An insulator

g(h) – the thickness distribution

Light transmission

Mass balance Ice deformation

In situ observations of ice thickness

High resolution over small scales

Jackie Richter-Menge, Don Perovich

Drilling holes

Most direct, most accurate, most information

Point measurement of:

• Snow depth = 30 cm

• Freeboard = 40 cm

• Ice thickness = 400 cm

Uncertainty of 1 cm

•Direct measurement

• Accurate

• Snow, ice, freeboard

• Simple equipment

• Hard work

• Slow, limited coverage

Ice Transect Site 11

-130

-110

-90

-70

-50

-30

-10

10

30

50

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96 102

108

114

120

126

132

138

144

Distance (m)

Ice P

rofi

le (

cm

)

Melt Pond Ice Ocean

Ice

Ocean

Pond

Pond morphology matters

Melt pond survey Important for

• Mass balance

• Mechanical properties

• Salinity balance

• Light transmittance

• Mechanical drilling

• 150 m long line

• Every 2 m

• 1.5 hour

Ridge surveys

Ridges are trouble

0 20 40 60 80

-700

-600

-500

-400

-300

-200

-100

0

Snow

Ice

Ocean

Po

sitio

n (

cm

)

Position

• Mechanical drilling

• 100 m long line

• Every 5 m

• Mean thickness = 410 cm

• Range from 160 cm to 760 cm

• Took 2.5 days

Ridges and rubble fields

Rubble fields are worse. Want to borrow my drill?

Hot water drilling

Two dimensional look at heterogeneity

•100 m x 100 m grid

• Every 10 m

• Mean thickness = 300 cm

• Range from 180 cm to 651 cm

• Took 2 days

• Direct measurement

• Accurate

• Snow, ice, freeboard

• More coverage

• Complicated, heavy equipment

• Major setup effort

• Jackie hates it…a lot

Sea ice mass balance

Simple, but powerful observation – attributes change

• Simple concept • ice growth • surface melt • bottom melt

• Equipment • ablation stakes and thickness gauges • autonomous buoys

Above

Below

-300

-250

-200

-150

-100

-50

0

50

De

pth

(cm

)

Oct 15 Dec 15 Feb 15 Apr 15 Jun 15 Aug 15

An array of mass balance sites

In this case all ice thinned over annual cycle 10/25 12/25 2/25 4/25 6/25 8/250

100

200

300

400

500

Ice T

hic

kness (

cm

)

-120 -100 -80 -60 -40 -20 0 -120 -100 -80 -60 -40 -20 00 20 40 60 80 100 1200.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Surface melt

58 cm

Bottom melt

62 cm

Fra

ctio

n

Growth

50 cm

Autonomous measurements of thickness and mass balance

Ice

Ocean

Ice

Snow

-20

-19

-18

-17

-16

-15

-14

-13

-12

-11

-10

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

-350

-300

-250

-200

-150

-100

-50

0

50

-50

-40

-30

-20

-10

0

10

Air

Tem

pera

ture

(oC

)

Sep Nov Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov-2.0

-1.5

-1.0

-0.5

Wate

r T

em

pera

ture

(oC

)

960

980

1000

1020

1040

1060

Baro

metr

ic p

ressure

(m

b)

Depth

(cm

)

• Position

• Air temperature

• Barometric pressure

• Ice temperatures

• Upper ocean temperatures

• Snow accumulation and ablation

• Ice growth

• Surface and bottom ice melt

2006 2007 2008

Sea ice mass balance buoys

Electromagnetic induction

Cover more area, with less resolution

50 100 150 200 250 300 35050

100

150

200

250

300

350

Drill

hole

thic

kness (

cm

)

EM-31 thickness (cm)

• Easy to operate

• Lightweight, portable

• More coverage (km)

• In situ remote sensing

• Calibration needed

• 6 m maximum thickness

• Footprint 1.35 x distance

In situ ice thickness surveys

Snow depth and ice thickness over kilometers

Add a magnaprobe!

Transarctic ice thickness observations

Sampling across the Arctic

26 August

17 September

Spatial surveys EM-31

First year ice near the end of the melt season

0

10

20

30

40

0

200

400

600

Sn

ow

de

pth

(cm

)

Ice

th

ickn

ess (

cm

)

0 400 800 1200 1600-600

-500

-400

-300

-200

-100

0

100

Snow Ice

Count 312 312

Average 0.0 149

Median 0.0 133

Std dev 0.0 38.9

Minimum 0.0 91

Maximum 0.0 318

% > 6m 0.0%

Dis

tance F

rom

Sea L

evel (c

m)

Distance (m)

83 17.9 N, 171 53.3 W

26 Aug 2005

Spatial surveys EM-31

Multiyear at the beginning of freezeup

0

10

20

30

40

0

200

400

600

Sn

ow

de

pth

(cm

)

Ice

th

ickn

ess (

cm

)

0 600 1200 1800-600

-500

-400

-300

-200

-100

0

100

Snow Ice

Count 368 362

Average 15 164

Median 15 150

Std dev 4.9 58.9

Minimum 1 93

Maximum 32 463

% > 6m 1.6%

Dis

tan

ce

Fro

m S

ea

Le

ve

l (c

m)

Distance (m)

87 28.3 N, 57 35.3 E

17 Sep 2005

0

5

10

15

20

0 5 10 15 20 25 300

100

200

300

400

Average

Median

Snow

depth

(cm

)

Site number

Ice thic

kness (

cm

)Transarctic thickness surveys

8% of sample was ridges more than 6 m thick

0 100 200 300 400 500 6000

5

10

15

20

Mean 179

Median 157

Mode 119

Std Dev 73

Minimum 24

Maximum > 600

Count 7738

Perc

ent

Ice thickness (cm)

> 6 m

August – September 2005

Ship-based ice watch

Rough estimate, but multiple ships

• Many variables

• Standardized format

• Software widely available

• Many ships including tourism

• Excellent Antarctic dataset

• Very rough estimate

• Limited by season

• Sampling is biased

Ship-based ice watch

Large in situ areal coverage

• In transit ice description • Thickness plus concentration, ridges, ponds, ice type, etc

0.0

0.1

0.2

0.3

0.4

0.5

Aug 9 Aug 16 Aug 23 Aug 30 Sep 6 Sep 13 Sep 20 Sep 270.0

0.5

1.0

1.5

2.0

2.5

3.0

0.0

0.2

0.4

0.6

0.8

1.0

Po

nd

fra

ctio

n

Ice

th

ickn

ess (

m)

Nilas

Brash

First year

Multiyear

Open water

Ice

co

nce

ntr

atio

n

EM-31

In situ thickness and remote sensing

Connecting the scales

• Measure surface elevation of snow, ice and open water

• Derive sea ice freeboard

• Assume equilibrium of floating ice cover

• Infer ice thickness, a function of:

– Snow, ice and water density – Snow depth – Ice freeboard

• Ice thickness uncertainty primarily influenced by errors snow depth

Sea Ice Thickness: Airborne and Satellite Altimetry An inferred measurement

Open Water

Snow Freeboard

Photo Credit: Andrew Roberts, SEDNA 2007

Snow

Sea Ice Ice

Draft

Sea

Surface

Radar Altimeter:

Ice Surface LiDAR Altimeter:

Snow surface

Ice

Freeboard

Snow Depth

Ice

Thickness

• Measure surface elevation of snow, ice and open water

• Derive sea ice freeboard

• Assume equilibrium of floating ice cover

• Infer ice thickness, a function of:

– Snow, ice and water density – Snow depth – Ice freeboard

• Ice thickness uncertainty primarily influenced by errors snow depth

Sea Ice Thickness: Airborne and Satellite Altimetry An inferred measurement

Open Water

Snow Freeboard

Photo Credit: Andrew Roberts, SEDNA 2007

Snow

Sea Ice Ice

Draft

Sea

Surface

Radar Altimeter:

Ice Surface LiDAR Altimeter:

Snow surface

Ice

Freeboard

Snow Depth

Ice

Thickness Snow depth measurements

Snow depth

Magnaprobe (Sturm et al.)

• Snow depth • Point measurement • GPS • Automatically logs obs • Easy to use • Quick • cm accuracy

Snow pit

• Detailed snow characterization • Point measurement • Density, grain size, salinity, layers • Tedious

ICEX 2011: US Navy Ice Exercise Supporting Arctic Submarine Operations

March 2011, Alaskan Beaufort Sea

Drill holes

ICEX2011: Coordinated Measurement Campaign

Ground-based

NRL Twin Otter

IceBridge NASA P3

US Navy Submarine

Snow depth and ice thickness

ICEX2011: Coordinated around a 9-km-long survey line

North End

Reflector site

Devil’s Cauldron

South End

Calibration drill hole

Widely varying ice

Multiyear ice

Undeformed first year

Refrozen lead

First-year ridge

NASA P3 Over Flight of ICEX2011 Survey Line

NASA P3 Over Flight of ICEX2011 Survey Line

The Adventure Ends…

…The Analysis Begins

Radar

Undeformed FYI Deformed FYI MYI

Improving in situ observations for cal/val

Surface roughness impacts snow radar return signal

Flight path

First return

Radar footprint

Snow depth: In situ versus radar

Newman et al., 2014, JGR

Positio

n (

m)

Undeformed ice: 12 ± 5 cm

Improving in situ observations for cal/val

THE GRID: Provides 2D footprint of snow depth

-200 -150 -100 -50 0 50 100 150 200

-300

-200

-100

0

100

-30

-15

0

15

30

-200 -150 -100 -50 0 50 100 150 200

-200 -150 -100 -50 0 50 100 150 200

Position (m)

Near Shore Site

Snow Depth (cm)2.000

14.00

26.00

38.00

50.00

62.0070.00

= Reflector Position

Snow Depth Ice Bottom

Drill Hole

Rubble: 30 ± 16 cm

0

35

70

cm

March 2014: Barrow offshore

March Madness2014

Rich resource of in situ, airborne and satellite observations

Eureka: FYI/MYI

Prepared by S. Farrell

ECCC Operation IceBridge 2016 Campaign Snow on Sea Ice Survey

Stephen Howell1, Joshua King1, Peter Toose1, Arvids Silis1, Chris Derksen1,

Jack Landy2, and Thomas Newman3

1Environment and Climate Change Canada

2University of Manitoba, Centre for Earth Observation Science 3Laboratory for Satellite Altimetry / Sea Ice Research Group, NOAA Center for Weather and

Climate Prediction

Smooth First Year Ice Eureka Sound

Ideal site: no drift, variable conditions, accessible

Smooth first year ice Snow Depths: n=8361 avg = 15.3 cm STDV = 7.3 cm

Intensive Observation Site

ECCC 2016 Eureka Campaign By the numbers

• 8 intensive survey sites measured – 2 on multi-year ice; 6 on first-year ice

• 54,287 GPS-tagged snow depths measured

• 22 snow pits providing detailed characterization

• 21 manually augured ice thickness measurements

• 260+km of GPS-tagged EM31 ice thickness measurements

• 255+ million LiDAR measurements of snow on sea ice surface roughness

https://github.com/kingjml/ECCC-Eureka-2014-Snow-on-Sea-Ice-Campaign

In situ: Good news and bad news

What is the evolution of the ice thickness distribution?

We can examine in detail

We can cover meters to kilometers

We can measure undeformed ice easily

We have a wealth of data

We can answer many questions

But we have to be there

But not hundreds of kilometers

But ridges are problematic

But it has to be accessible

But we need a network

•Develop a climatological record of sea ice thickness based on

in situ surface-based observations of the Arctic Ocean

55,000 points from 1894 to present

Arctic sea ice thickness archive

• Highly accurate and long-term records (Nansen/Fram -> present)

• Broader regional sampling than submarines (later discussion)

• Field Experiments: Position, date, ice thickness, snow depth, ice type, transects, grids

Ben Holt (JPL) is building this archive

benjamin.m.holt@jpl.nasa.gov

Summary: In situ thickness

Many detailed observations, but not everywhere, always

1 10 100 1000 10000 100000 1000000

Re

so

lutio

n

Coverage (m)

Hig

he

r Low

er

Satellite

EM-31

Aircraft

Hot water Drill

• Always measure every hole

• Drilling holes is most accurate

• Snow depth, freeboard, ice thickness

• In situ autonomous for thickness changes

• Electromagnetic surveys provide coverage

• Snow depth, ice thickness

• Price is increased “fuzziness”

• Need to organize and archive data Submarine

Questions?

Remote Ice Thickness Observations Submarines

SCICEX cruise tracks, 1993-2003

Data release area defined by the Arctic Submarine Laboratory

From Rothrock and Wensnahan (2007):

• Bias in ice draft obs: + 29 cm

• STD: ± 25 cm

Remote Ice Thickness Observations Submarines

Kwok and Cunningham, 2015

Rothrock et al., 1999

• Changes: 1950s-1970s to 1990s • Early evidence of thinning Arctic

sea ice cover

• Extends satellite-derived record of sea ice thickness

Remote Ice Thickness Observations Submarines

Register for data alerts: https://nsidc.org/scicex

New data:

• 2011, 2012, 2014, 2016 • 6 traverses • Alaska North Pole • More to come • 65+ year record!