arctic climate change – structure and mechanisms

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

Vertical structure

Hartman (1994)

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)

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

•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!