Arctic Minimum 2007 A Climate Model Perspective

What makes these two special?

Do models ever have 1 year decline as great as observed from September 2006 to 2007?

Is there evidence for tipping points in models?

What controls sea ice sensitivity in models?

Only 2 of ~20 models have any ensemblemembers that can keep up with 1979-2006 trend

Faster than Forecast? Stroeve et al 2007

Low extent models tend to retreat faster (N)

5 models with ITD tend to retreat faster (Y), but with considerable spread

April

(mid-21st minus late 20th c)

(Y!)

TREND in atmospheric heat flux is negligiblein the two models (N!)

(Don’t know how much variability it explains)

Cloud TRENDS are not unusual in thetwo models (N)

Ice thickness in the late 20th c is high in the two models (N)

red = observationscolors = 7 SRES A1B runs w/ CCSM3black = ensemble mean

Holland, Bitz, & Tremblay 2006T85 (1.4 deg) in atmosphere and land0.5-1 deg in ocean and sea ice

1) Increase in absorbed shortwave 2) Increase in Ocean Heat Transport through Fram Strait Two strong positive feedbacks?

Ocean Transport

Absorbed Sunlight

CCSM3 Single Year Decline at Least as Big

as Observed in 2007

CCSM3 2001-2050 A1B Scenario - 7 Runs

Even with TRENDskew is positive, though probably not significant

4 X in 350 years the drop is as big as observed

in 2006-2007

In any decade

Obs

erve

d 20

06-2

007

Histogram of 1 yr September Sea Ice Change

red = observationscolors = 7 SRES A1B runs w/ CCSM3black = ensemble mean

Same runs only smoothed

A1B Scenario

A1B Scenario

2000 Commitment

2020 Commitment

2030 Commitment

Histogram of 400 yr CCSM3 1990s Control

No significant skewso positive and negative

1 year changes are equally likely

Histogram of 1 yr September Sea Ice Change

Histogram of 320 yr CCSM3 Pre-Industrial Contol

Variance is ~2/3 of modern(Because thickness is

~50% greater)

Histogram of 1 yr September Sea Ice Change

Autocorrelation of 400 yr CCSM3 1990s Control

September ice cover

observed

R=0.5 (0.7 skipping 2 outliers) Correlation Coefficient of linear trend 2004-2035 and mean from 1980-1999 Model uncertainty grows

Climate Models from IPCC AR4 (CMIP3)

Trends in sea ice thickness depend

on the mean state

R=-0.86

Model uncertainty shrinks

all Feedbacks - ∆H No Ice-Albedo

Feedback - ∆H0

“GAIN”G= ∆H / ∆H0 = 1.25 (on average)

∆H Sea Ice Thickness Change from doubling CO2

Summary

Two models that keep up with forecast have unusually high increase in ocean heat transport

Sea ice anomalies like 2007 occur about 1% of the time in 21st century CCSM3 runs. Anomalies are not negatively skewed and there is little memory. Anomalies increase in size as ice thins.

Sea Ice albedo feedback causes sea ice to thin 10-30% faster

Although positive, the feedback is not large enough to cause much uncertainty in thickness prediction

Instead model errors are probably more a function of error in the mean state. (Present day thickness spread in AR4 models is 1-3m)

For a blackbody Earth-like planet

∆To ≈ 1.2 K

= feedback factor

= gain

Now with additional physical processes

∆T = ∆To + f ∆T

When CO2 has been doubled

September Ice Extent in one ensemble member

Holland, Bitz, and Tremblay, 2006

A1B Scenario with CCSM3

106 km2

SeptemberConcentration

4

2

0

-2

-4hPa

5

2.5

0

-2.5

-5hPa

CCSM3 JJA Composite 2007 JJA Reanalysis

Sea Level Pressure

Cloud anomalies toosee Culather et al for more details