Arctic climate change – structure and mechanisms
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Arctic climate change – structure and mechanisms
Nils Gunnar Kvamstø, Input from: Øyvind Byrkjedal, Igor Ezau, Asgeir Sorteberg, Ivar Seierstad and David Stephenson
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Arctic zonal temperature anomalies (within 60º-90ºN latitudinal zone)
• Winter, summer, and annual anomalies, 1881-2003 period• All linear trends significant at the 0.01 level• (available from CDIAC, Lugina et al. 2003, updated).
Courtesy P.Groisman
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Northern Hemisphere temperature anomalies
• Winter, summer, and annual anomalies, 1881-2003 period• All linear trends significant at the 0.01 level• (available from CDIAC, Lugina et al. 2003, updated).
Courtesy P.Groisman
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Johannessen et al. 2003
Arctic vs. Global Change
DJF Zonal mean Ts anomalies
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DJF MAM
JJA SON
∆Ts
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Vertical structure
Hartman (1994)
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Seasonal cycle of Arctic temperature profiles
Hartman (1994)
Inversion
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DJF MAM
JJA SON
Vertical structure of recent Arctic warming
Graversen et al 2008, Nature
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Cross-section cold air outbreak, arctic front, Shapiro & Fedor 1989
Sea Ice
Isentropesheight
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Vertical structure
Hartmann and Wendler J. Clim (2003)
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Change in mean winter temperature from 1957-58 to 2003-04 for decoupled (left) and coupled (right) PBL cases. After Hartmann and Wendler (2003).
SAT is heavily sensitive to the relative strengths of surface inversions
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POLAR AMPLIFICATION
• GHG forcing considered to be quite uniform, why polar amplification?
• Ice-albedo feedback
• Cloud feedback
• ”Dynamic feedback”
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Fixed albedo experiment –> Albedo feedback
Hall (2004)
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Fixed cloud experiment -> Cloud feedback
Vavrus (2004)
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15Alexeev, Langen, Bates (2005)
Ghost forcing -> Dynamical feedback
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Ghost forcing -> Dynamical feedback
Alexeev, Langen, Bates (2005)
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___ ENSEMBLE MEAN
ºC
0
2
4
6
8
10
1920 1940 1960 1980 2000 2020 2040 2060 2080
SRES A1B (CO2 ENDS AT 700 ppm)
4-10ºC
2 Projected changes
CHANGES IN ARCTIC TEMPERATURES FROM 15 CLIMATE MODELS
Sorteberg and Kvamstø (2006)
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Why is the spread so large?
• Insufficient formulation of processes in GCMs?
• Internal atmospheric variability?
• Differences in external forcing (GHG, aerosols)?
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LARGE DIFFERENCES IN PROJECTED CLIMATE CHANGE EVEN WHEN SAME FORCING IS USED:
19 CMIP2 MODELS : ZONAL TRENDS IN T2mYEAR 31-60 (ºC/DECADE)
Sorteberg and Kvamstø (2006)
Is this spread entirely due to different models?
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BCM SPREAD vs MULTIMODEL SPREAD
ANNUAL 5 MEMBER ENSEMBLE MEAN T2m CHANGE
YEAR 1-30 (C)
Sorteberg and Kvamstø (2006)
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BCM ENSEMBLE SPREAD IN ANNUAL T2m ZONAL MEAN TEMPERATURE CHANGE RELATIVE TO MULTIMODEL SPREAD (%)
60%
40%
20%
YEAR 1-30
Sorteberg and Kvamstø (2006)
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Role of internal variability w.r.t. multi model spread
Temperature Precipitation
Sorteberg and Kvamstø (2006)
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Year 61-80
<∆T>
<∆P>
Ensemble mean change
Sorteberg and Kvamstø (2006)
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Year 61-80
σ∆T
σ∆P
Ensemble spread
Sorteberg and Kvamstø (2006)
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Year 61-80
S/N; T
S/N; P
Signal to noise ratio
Sorteberg and Kvamstø (2006)
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Spreads dependence on ensemble size
95% confidence in annual means:
<ΔT>±0.2K <ΔP>±0.1mm/day
What contributes to the large Arctic T variability?
Sorteberg and Kvamstø (2006)
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CHANGE IN ICELANDIC LOW AT 2CO2
DJF
DJF: ARCTIC TEMP CHANGE
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THE ICELANDIC LOW: A MAJOR PLAYER ATMOSPHERIC HEAT TRANSPORT INTO THE ARCTIC
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Surface air temperature change (AR4)
A2
B2 Kattsov, Walsh
DJF (1954 – 2003)
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Can we trust projected changes? (even with large ensemble sizes)
• Generally too cold troposphere
• Too warm SAT
• Underestimation of precipitation
• Systematic biases in surface pressure distribution (Beaufort high)
• Model problems connected to poles (Randall et al. BAMS, 1999)
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HIRLAM and ARPEGE comparison with Sodankylä Data http://netfam.fmi.fi/
• Models are missing cold events – model SAT is too warm• Climate variability, diurnal cycle and blocking events are underpredicted
T2m is a heavily used climate parameter.How is the ABL represented in GCMs?
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Mixing profiles in NERSC LES (dashed) and ARPEGE – large discrepancy in shallow Arctic PBLs
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A model resolution problem
An analysis of observations and LES data showsthat the standard closure type in todays GCMs e.g.
are not applicable on vertical resolutions > 10-50m
H: If implemented correctly it should work well
z
uk
zz
wu
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90L:
• 90 vertical layers
• 70 layers increased resolution from 600hPa and below
•10m resolution in the lowest 60 m
31L:
• 31 vercikal layers (standard)
• lowest layer at ca 70m
hPaTest of H
Far too costly – Alt: use analytical functions
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Simulated vertical temperature profile vs observed data (SHEBA)
90L
31L
Obs
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Response in Surface Temperature by season (90L-31L)
djf
jja
mam
son
Moderate improvement.Local processes important, butlarge-scale dynamics is playinga significant role as well!
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5
1 10)ln(
i
p
jjjii tyx
),(~ GammaY
Daily SLP anomalies in Bergen
Highpass filtered SLP variance (2-10d)
const
2
where
GLM:
Seasonality Local SLP + 9 leading PCs
Predictors:
Monthly storminess Y:
Data analysis
Seierstad et al (2007)
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Can teleconnection patterns provide additional explanation for variations in storminess?
ΔY (%) due to 1σ change in predictors
Yes! But, restricted to local, mostly high latitude areas.
Seierstad et al (2007)
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Given limited resources, modellershave to make priorities
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Response Surface flux for DJF (90L-31L)
sensible latent
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•Winds (days – weeks)
•Ocean Currents (years to decades)
•Rivers (years to decades)
•Terrestrial cryosphere (centuries and longer)
This is a highly non-linear coupled system
Macdonald et al., 2003
Complexity of the Arctic Climate System
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Thank you for your attention!