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Dr. Gregory M. Flato Canadian 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. - PowerPoint PPT Presentation
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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