Dr. Gregory M. FlatoCanadian Centre for Climate Modelling and Analysis. M.S., (University of Alberta, Edmonton)PhD (Dartmouth College)

Research Interests:• Global coupled climate modelling• Sea-ice dynamics and thermodynamics• Role of the cryosphere in climate

The Arctic in Global Climate Models and Projections of Future Change

Gregory M. Flato

Canadian Centre for Climate Modelling and AnalysisMeteorological Service of Canada

Outline

• Arctic climate and its variability

• Global climate models

• Representation of Arctic climate and climate processes in global models

• Projections of future climate change

• Summary

Arctic Climate

Observed Surface Temperature (oC)Winter

Fyfe (2004)Based on NCEP Reanalysis

Annual Mean temperature anomalyTime series: 1850-present

Jones and Moberg (2003)

John Walsh – U. Illinois

Atmospheric Circulation

Mean Sea-Level Pressure (hPa)Winter Summer

Fyfe (2004)

International Arctic Buoy Program

Sea-Ice Circulation

• Variations in transport, deformation, growth and melt all contribute to observed variability and recent decline in ice coverage.

Courtesy of J. Walsh, U. Illinois.

• Transport of ice is balanced by net growth or melt.

• the associated salt or freshwater fluxes impact ocean mixing and circulation.

Courtesy of M. Hilmer, IfM, Kiel

Jones (2001)

Ocean Circulation

Surface layer Atlantic layer

• There are many important feedbacks and connections between these climate components in the Arctic.

• A model provides a framework for synthesizing our understanding of this complex system, and provides a tool for making quantitative projections of future change.

• However, the Arctic is part of, and interacts with, the global climate system, so it can’t be considered in isolation.

Global Climate Models

• Based on laws of physics

• Mathematical representation of – 3-D atmosphere: its temperature, humidity, wind, radiative transfer,

cloud formation/dissipation, precipitation, …– 3-D ocean: its temperature, salinity, circulation, mixing, …– Sea-ice: its formation, melt, motion and deformation.– Land surface: its temperature, moisture content, reflectivity,

evapotranspiration, …

The equations are solved numerically on a discrete grid.

A problem peculiar to the Arctic is the convergence of meridians at the North Pole – this causes numerical difficulties, particularly in the ocean model.

http://climate.lanl.gov/Models/POP/index.htm

These examples are from the POP ocean code, used in the NCAR community climate model.

A recent trend is to make use of alternate grid configurations to better resolve ocean (and ice) processes in the Arctic.

Model Intercomparison Projects

• There are perhaps 15 or so global climate models under development around the world.

• Intercomparison projects provide an opportunity to:

• evaluate models in a systematic fashion;

• compare/contrast results from different models;

• and hopefully, to identify reasons for the differences.

Model Name Reference Flux-Adjustment Sea-Ice Variable Sea-Ice Processes

BMRC Power et al. (1993) none thickness thermo-only

CCCma Flato et al. (2000) heat, water mass per unit area thermo-only

CCSR Emori et al. (1999) heat, water water equivalent depth thermo-only

COLA Schneider et al. (1997)

none thickness thermo-only

CSIRO Gordon and O’Farrell (1997)

heat, water, momentum

thickness dynamic-thermodynamic2

MPI_E4 Roeckner et al. (1996)

heat, water (annual mean)

thickness dynamic-thermodynamic

GFDL Manabe et al. (1991)

heat, water thickness drift-thermodynamic3

GISS_M Miller and Jiang (1996)

none mass per unit area thermo-only

GISS_R Russel et al. (1995) none % time grid cell occupied by ice

thermo-only

MRI Tokioka et al. (1996)

heat, water thickness drift-thermodynamic

NCAR_CSM Boville and Gent (1998)

none thickness dynamic-thermodynamic

NCAR_WM Washington and Meehl (1996)

none thickness dynamic-thermodynamic

UKMO Johns et al. (1996) heat, water thickness drift-thermodynamic

Motionless ice with a prognostic equation for ice growth and melt.2 Prognostic equations for growth/melt and ice motion, including representation of internal ice stress.3 Prognostic equation for ice growth/melt, ice motion diagnosed as a function of ocean surface current.

Global Climate Models of the mid 1990s

Flato (2004)

One can look at ensemble mean quantities, or look at individual models …

IPCC (2001)

Model disagreement is largest over area influenced by sea ice.

Just as sea-ice feedbacks amplify climate change, they also amplify model errors and contribute to uncertainty in projections of future climate.

Intermodel standard deviation of surface air temperature (oC)

based on CMIP archive data

MSLP ensemble mean error

Atmosphere-only models Coupled models

Walsh et al. (2002)

CCCma CGCM2NCEP Reanalysis

Annual Mean Sea-Level Pressure

Modelled ice extent in the 12 model CMIP ensemble

10% of models have less ice than this.

Median ice edge.

10% of models have more ice than this.

Interestingly, median model ice edge agrees well with observations.

Flato, 2004

Snow cover ‘error’ in AMIP2 models (late 1990s)

Frei et al., 2003

Frei and Robinson, 1998

Snow cover ‘error’ in AMIP1 models (early 1990s)

Projections of future change• Coupled models are forced with GHG and aerosol

forcing as observed from 1850 to the present, then increasing as per some prescribed future scenario.

Con

cen

trat

ion

(p

pmv)

0

200

400

600

800

1000

1200

1400

1600

IS92a

IPCC A2

IPCC B2

CCCma CGCM2 -- Mean = 1.92oCProjected Surface Air Temperature Change – 2050 vs 1980

Observations1946-56

CCCma Model

1986-96

One can compare the evolution of temperature anomalies over time …

Projected climate warming is enhanced over sea ice; as in the case of ‘control’ climate, this is also the location of largest disagreement.

(But all models predict warming)NH ensemble mean temperature change (C) NH intermodel standard deviation (C)

Based on CMIP archive

Predictions of sea-ice changes likewise vary from model to model.Here we show NH annual mean ice extent from CCCma and Hadley Centre models.

Walsh observationsBoth models underestimate ice extent somewhat.

CCCma model indicates more rapid historical and future decline – not inconsistent with observed decline.

NH Ice Extent and its Change – CMIP2 model ensemble(CO2 increased at 1% per year for 80 years – the time of doubling

0

5

10

15

20

25

BMRC

CCCma1

CCCma2

CCSR

CERFACS

CSIRO

ECHAM3

GFDL

GIS

SIA

PLM

DM

RI

NCARNRL

UKMO

2

UKMO

3

Avera

ge

NH

Initi

al I

ce E

xte

nt (

10

^6 k

m^2

)

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

BM

RC

CC

Cm

a1

CC

Cm

a2

CC

SR

CE

RFA

CS

CS

IRO

EC

HA

M3

GF

DL

GIS

S

IAP

LMD

MR

I

NC

AR

NR

L

UK

MO

2

UK

MO

3

Ave

rage

NH

Ice

Ext

en

t Ch

an

ge

(1

0^6

km

^2)

dynamic (rheology)

no flux-adj. flux adj. thermo-only

dynamic (drift)diagnostic

Initial Ice Extent Ice Extent Change

No obvious connection between error and ice model characteristics

But all models predict a decline

Flato, 2004

– Feedbacks involving the cryosphere lead to amplification of projected climate warming in the Arctic.

– These feedbacks also amplify model errors

– Although global climate models are improving, the Arctic remains a challenge.

– Model errors tend to be larger than elsewhere.– Nevertheless, models universally agree that climate change

will be larger in the Arctic than at lower latitudes.

– The last decade has seen an increased focus on modelling Arctic climate.

– Various intercomparison projects yield quantitative evaluation of model shortcomings.

– Representation of snow in climate models has improved demonstrably.

– More sophisticated sea-ice models are being employed, and alternative grid configurations are being used to improve resolution of Arctic ice and ocean processes.

Summary

The End

Stendel and Christensen, 2002

Model projection of change in permafrost

Figures courtesy J. Fyfe

The ‘Arctic Oscillation’Sea-Level Pressure Temperature

Fyfe et al., 1999

CCCma CGCM1

Observed

Fyfe, 2004

Climate change scenario

Observations to 2002

CCCma modelFyfe et al., 1999

GISS modelShindell et al., 1999

Stratosphere included

No Stratosphere

Arctic Oscillation

900-year time series of NH ice extent from CCCma climate model

1953-98 trend(p <0.1%)

1978-98 trend(p < 2%)

Likelihood of observed trends based on 5000 yr control run of GFDL model

Vinnikov et al. (1999)

Recent trend is not likely a result of natural variability …

• There is substantial interannual variability in Fram Strait outflow, but no obvious trend.

• Correlation with NAO is strong (r=0.7) for the period 1978-1997, but weak (r=0.1) for ‘58-’77 period.

Hilmer and Jung (2000).

• Observations are insufficient to say much about ice thickness variability, but model results give some indication.

• Variability is expected to be large near coastlines, due to wind-driven deformation.

• Submarine observations do provide some evidence for long-term change in thickness.

• 40% decrease between 1958-1976 and 1993-1997.

Rothrock et al., 1999

• However, model results indicate that wind-driven changes in thickness build-up pattern, and limited sampling, may be important.

Holloway and Sou, 2002

Thickness change by middle of 21st century

CCCma

CCCma

Hadley

Hadley

March September

CCCma model projects seasonal Arctic ice cover by mid century.

1971-1990

2041-2060

Ensemble mean thickness Intermodel standard deviation

Composite results for Southern Hemisphere.

10 model ensemble

Ensemble mean thickness Intermodel standard deviation

JJA