climate analysis section, cgd, ncar, usa detection and attribution of extreme temperature and...

Climate Analysis Section, CGD, NCAR, USA

Detection and attribution of extreme temperature and drought using an

analogue-based dynamical adjustment technique

Flavio Lehner

Clara DeserLaurent Terray

Outline

1. Motivation for dynamical adjustment

2. Application in a model framework

3. First results for high temperature events

Dynamics are important – February 2015

3

Dynamics are important – February 2015

4

ΔT = 18 °C

The problem of internal variability

5Deser et al. (in review)

DJF temperature trend 1963-2012

The problem of internal variability

6Deser et al. (in review)

DJF temperature trend 1963-2012

Model

CESM1 (CAM5) – fully coupled GCM

DJF temperature trend 1963-2012

The problem of internal variability

7Deser et al. (in review)

DJF temperature trend 1963-2012

Run #1 from the 30-member CESM Large Ensemble

DJF temperature trend 1963-2012

The problem of internal variability

8Deser et al. (in review)

DJF temperature trend 1963-2012

The problem of internal variability

9Deser et al. (in review)

DJF temperature trend 1963-2012

The problem of internal variability

10Deser et al. (in review)

DJF temperature trend 1963-2012

The problem of internal variability

11Deser et al. (in review)

DJF temperature trend 1963-2012

The problem of internal variability

12Deser et al. (in review)

DJF temperature trend 1963-2012

No forced circulation change!

Dynamical adjustment with analogues

13

1. Select a monthly mean field (SAT and SLP) from historical simulation

raw field

Dynamical adjustment with analogues

14

1. Select a monthly mean field (SAT and SLP) from historical simulation

2. Search analogue of SLP in a long control simulation (no forcing)

long control simulation

raw field

Dynamical adjustment with analogues

15

1. Select a monthly mean field (SAT and SLP) from historical simulation

2. Search analogue of SLP in a long control simulation (no forcing)

3. Reconstruct the historical SLP pattern from a linear combination of the closest analogues found in the control simulation

long control simulation

raw field

coef

ficie

nts

Dynamical adjustment with analogues

16

1. Select a monthly mean field (SAT and SLP) from historical simulation

2. Search analogue of SLP in a long control simulation (no forcing)

3. Reconstruct the historical SLP pattern from a linear combination of the closest analogues found in the control simulation

4. Use the same linear coefficients to reconstruct SAT, now using the SLP from the respective month in the historical simulation

long control simulation

raw field

constructed SAT field (dynamically induced)

raw field

coef

ficie

nts

Dynamical adjustment with analogues

17

1. Select a monthly mean field (SAT and SLP) from historical simulation

2. Search analogue of SLP in a long control simulation (no forcing)

3. Reconstruct the historical SLP pattern from a linear combination of the closest analogues found in the control simulation

4. Use the same linear coefficients to reconstruct SAT, now using the SLP from the respective month in the historical simulation

5. This tells us how much of the SAT field comes from SLP variability, i.e., dynamics; the residual is assumed to come from thermodynamics

long control simulation

raw field

constructed SAT field (dynamically induced)

raw field

coef

ficie

nts

− =raw field dynamics thermodynamics

Dynamical adjustment with analogues

18Deser et al. (in review)

DJF temperature trend 1963-2012 [°C/50 years]

Run #7

TotalDynamical contribution

Thermodynamical contribution

Application to high temperature events

19

Raw

Dynamical contribution

Thermo-dynamical

contribution

Lehner et al. (in preparation)

Application to high temperature events

20

Raw

Dynamical contribution

Thermo-dynamical

contribution

Constructed SLP

Lehner et al. (in preparation)

Application to high temperature events

21

Raw

Dynamical contribution

Thermo-dynamical

contribution

Lehner et al. (in preparation)

Application to high temperature events

22

Raw

Dynamical contribution

Thermo-dynamical

contribution

Lehner et al. (in preparation)

Application to high temperature events

23

Raw

Dynamical contribution

Thermo-dynamical

contribution

Lehner et al. (in preparation)

Application to high temperature events

24

5-yr averages

Lehner et al. (in preparation)

Application to high temperature events

25

Partitioning ~50/50

Lehner et al. (in preparation)

Application to high temperature events

26

Partitioning ~50/50

Increase in thermodynamical contribution becomes detectable

(theoretically)

Lehner et al. (in preparation)

Conclusions and outlook

• Removal of dynamical contribution to surface temperature trends and anomalies

• Increased signal-to-noise for climate change studies• Easier to get at mechanisms for thermodynamic

temperature changes (land-atmosphere interactions)

• Next steps:• Observations• Globally• Daily data?• Precipitation?• Drought?

Thank you!

Dynamical adjustment with analogues

29Deser et al. (in review)

30