European temperature extremes in CMIP5:

European temperature extremes in CMIP5:

present-day biases and future uncertainties

Julien Cattiaux, Hervé Douville, Yannick Peings and Aurélien Ribes.

CNRM/Météo-France, Toulouse, France.

May 31, 2012

Introduction

Objectives

European temperature extremes: understand model biases & uncertainties under future scenarios.

Isolate respective roles of large-scale circulation, clouds, soil processes etc.

Data & Methods

Original methodology to separate dynamicalvs.non-dynamical contributions.

Data: 9 GCMs of CMIP5/CFMIP2 (amip,historicalandrcp85).

Present-day biases in temperature extremes

historicalvs. E-OBS over 1979–2008

DJFM – Probability of Tmin

≤ C 10

(PC10)

Present-day biases in temperature extremes

historicalvs. E-OBS over 1979–2008

JJAS – Probability of Tmax

≥ C 90

(PC90)

North-Atlantic weather regimes

Clustering of dailyZ500anomalies (k-means, 4 classes).

Temperatures well discriminated among the regimes.

Z500 (NCEP2) – DJFM PC10 (E-OBS) – DJFM

∀k xk= Φ(dk) =⇒ X =P

kfk·Φ(dk)

North-Atlantic weather regimes

Clustering of dailyZ500anomalies (k-means, 4 classes).

Temperatures well discriminated among the regimes.

Z500 (NCEP2) – JJAS PC90 (E-OBS) – JJAS

∀k xk= Φ(dk) =⇒ X =P

kfk·Φ(dk)

North-Atlantic weather regimes

Clustering of dailyZ500anomalies (k-means, 4 classes).

Temperatures well discriminated among the regimes.

d

(k ∈ 1..4) x

= Φ(d

)

∀k xk= Φ(dk) =⇒ X =P

kfk·Φ(dk)

Evaluating dynamical contributions

RecallX=P

kf_k·Φ(dk),

=⇒ ∆^mod−obsX =P

k∆fk·Φ(dk)+P

kfk·Φ(∆dk)+P

kfk·∆Φ(dk)+ε

∆fk

Contribution of biases in regimes’

∆dk

Contribution of biases in regimes’

∆Φ

Contribution of

processes.

DJFM JJAS

Evaluating dynamical contributions

RecallX=P

kf_k·Φ(dk),

=⇒ ∆^mod−obsX =P

k∆fk·Φ(dk)+P

kfk·Φ(∆dk)+P

kfk·∆Φ(dk)+ε

∆fk

Contribution of biases in regimes’

∆dk

Contribution of biases in regimes’

∆Φ

Contribution of

processes.

DJFM JJAS

% of days _●

●

●

NAO+

●

●

●

NAO−

●

●

●

BL

●

●

●

AR

●

●

●

bin

●

● ●

NCEP BCC

CCCMA CNRM

IPSLl MIROC

MPIM MRI

NCC

−20

−10 0 10 20 30 40 50 60

% of days

●

●

●

AR

●●

●

BL

●

●

●

AL

●

●●

NAO−

●●

●

bin

●

● ●

NCEP BCC

CCCMA CNRM

IPSLl MIROC

MPIM MRI

NCC

−20

−10 0 10 20 30 40 50 60

Evaluating dynamical contributions

RecallX=P

kf_k·Φ(dk),

=⇒ ∆^mod−obsX =P

k∆fk·Φ(dk)+P

kfk·Φ(∆dk)+P

kfk·∆Φ(dk)+ε

∆fk

Contribution of biases in regimes’

∆dk

Contribution of biases in regimes’

∆Φ

Contribution of

processes.

DJFM JJAS

Evaluating dynamical contributions

RecallX=P

kf_k·Φ(dk),

=⇒ ∆^mod−obsX =P

k∆fk·Φ(dk)+P

kfk·Φ(∆dk)+P

kfk·∆Φ(dk)+ε

∆fk

Contribution of biases in regimes’

∆dk

Contribution of biases in regimes’

∆Φ

Contribution of

processes.

DJFM JJAS

Breakdown of biases in temps extremes

DJFM PC10 (Tmin) Ensemble mean of each term in:

∆X=P

k∆fk·Φ(dk)+P

kf_k·Φ(∆dk)+P

kf_k·∆Φ(dk)+ε

Regimes’

frequencies (∆fk)

Regimes’

structures (∆dk)

Non-dynamical

processes

(∆Φ)

Breakdown of biases in temps extremes

JJAS PC90 (Tmax) Ensemble mean of each term in:

∆X=P

k∆fk·Φ(dk)+P

kf_k·Φ(∆dk)+P

kf_k·∆Φ(dk)+ε

Regimes’

frequencies (∆fk)

Regimes’

structures (∆dk)

Non-dynamical

processes

(∆Φ)

Half-time summary

What I did show

Biases in temperature extremes dominated by biases in non-dynamical processes (especially summer).

What I did not show (today)

Future changes in temperatures extremes (rcp85−historical) dominated by changes in non-dynamical processes.

Biases/changes in regimes frequencies or structures can substantially contribute to the model dispersion (both present and future).

Now?

Understanding non-dynamical processes.

Isolating the contribution of clouds.

Cloud radiative forcing in observations

SRB data over 1984–2007

DJFM

JJAS

Clouds vs. temps: biases in amip

amipvs. SRB over 1984–2007

DJFM

Tmin vs. CC & CRF

◦clt cltisccp

●●

●

●

●

●

●

CCCMA CNRM

IPSLl MOHC

MPIM

MRI

CNRM* NCC IPSLl*

MOHC*

−2 0 2 4

−30

−20

−10 0 10

tasmin amip (K)

clt amip (%)

●●

●

● ● ●

●

CCCMA CNRM

IPSLl MIROC

MOHC MPIM

MRI

−1 −0.8 −0.6 −0.4 −0.2 0.2 0.4 0.6 0.8 1

−2 0 2 4

−15

−10

−5 0 5

tasmin amip (K)

crf amip (W/m2)

JJAS

Tmax vs. CC & CRF

●

●

●

●

●

●

●

CCCMA CNRM

IPSLl MOHC MPIM

MRI NCC

CNRM*

IPSLl*

MOHC*

−2 0 2 4

−30

−20

−10 0 10

tasmax amip (K)

clt amip (%)

●

● ●

●

●

●

●CCCMA CNRM IPSLl

MIROC MOHC

MPIM MRI

−1 −0.8 −0.6 −0.4 −0.2 0.2 0.4 0.6 0.8 1

−2 0 2 4

−15

−10

−5 0 5

tasmax amip (K)

crf amip (W/m2)

Clouds vs. temps: future changes under rcp85

rcp85over 2070–2099 vs.historicalover 1979–2008

DJFM

Tmin vs. CC & CRF

◦clt cltisccp

●

●

●

●

●

●

●

●BCC CCCMA

CNRM IPSLl

MOHC

MPIM MRI

NCC

4 5 6 7

−6

−4

−2 0 2 4

tasmin rcp85 (K)

clt rcp85 (%)

● ●

● ●

●

●

●

●

BCC CCCMA

CNRM IPSLl MOHC MPIM

MRI NCC

−1 −0.8 −0.6 −0.4 −0.2 0.2 0.4 0.6 0.8 1

4 5 6 7

−4

−2 0 2 4

tasmin rcp85 (K)

crf rcp85 (W/m2)

JJAS

Tmax vs. CC & CRF

●

● ●

●

●

●

BCC CCCMA

CNRM

IPSLl MPIM MOHC MRI

NCC

4 5 6 7

−6

−4

−2 0 2 4

tasmax rcp85 (K)

clt rcp85 (%)

● ●

●

●

●

●

●

●

BCC CCCMA

CNRM

IPSLl MOHC MPIM

MRI

NCC

−1 −0.8 −0.6 −0.4 −0.2 0.2 0.4 0.6 0.8 1

4 5 6 7

−4

−2 0 2 4

tasmax rcp85 (K)

crf rcp85 (W/m2)

Concluding remarks

Summary

Original methodology to separate dynamicalvs.non-dynamical contributions to biases/changes.

Dynamical contribution: minor on mean biases/changes, substantial on dispersion/uncertainties.

Non-dynamical contribution: possibly linked to cloud processes in summer.

Work in progress. . .

Refining the relationship between clouds and summer temperature extremes.

Estimating contribution of soil processes (snow in winter, soil moisture in summer).

Extend the methodology to other regions and/or variables (e.g., precipitations).

Thanks.

References

Cattiaux, J., H. Douville, A. Ribes, F. Chauvin, and C. Plante (2012), Towards a better understanding of changes in wintertime cold extremes over Europe: A pilot study with CNRM and IPSL atmospheric models,Climate Dynamics, resubmitted.

Cattiaux, J., H. Douville, Y. Peings, and A. Ribes, European temperature extremes in CMIP5: present-day biases, future uncertainties and relationship with large-scale circulation,to be submitted to Climate Dynamics.

Extremes vs. mean? historical vs. amip?

DJFM

PC10 & Tmin

●●

●

●

●

●

●

●

CCCMABCC

CNRM IPSLl MIROC MPIM

MRI NCC

−10 0 10 20 30

−3

−2

−1 0 1 2 3

PC10 historical (%)

Tmin historical (K)

●

● ●

●

●

●

● ^BCC ●●

CCCMA CNRM

IPSLl MIROC

MOHC

MRI MPIMNCC

−1 −0.8 −0.6 −0.4 −0.2 0.2 0.4 0.6 0.8 1

−3 −2 −1 0 1 2 3

−3

−2

−1 0 1 2 3

Tmin historical (K)

Tmin amip (K)

JJAS

PC90 & Tmax

●

●

●

●

●●

●

BCC

CCCMA

CNRM

IPSLl

MIROC

MPIM MRI

NCC

−10 0 10 20 30

−3

−2

−1 0 1 2 3

PC90 historical (%)

Tmax historical (K) ● ●

●

● ●

●

●

●

BCC CCCMA

CNRM

IPSLl MOHC

MPIM MRI

NCC

−1 −0.8 −0.6 −0.4 −0.2 0.2 0.4 0.6 0.8 1

−3 −2 −1 0 1 2 3

−3

−2

−1 0 1 2 3

Tmax historical (K)

Tmax amip (K)