climate analysis section, cgd, ncar, usa detection and attribution of extreme temperature and...
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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
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Dynamics are important – February 2015
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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
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1. Select a monthly mean field (SAT and SLP) from historical simulation
raw field
Dynamical adjustment with analogues
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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
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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
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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
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Raw
Dynamical contribution
Thermo-dynamical
contribution
Lehner et al. (in preparation)
Application to high temperature events
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Raw
Dynamical contribution
Thermo-dynamical
contribution
Constructed SLP
Lehner et al. (in preparation)
Application to high temperature events
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Raw
Dynamical contribution
Thermo-dynamical
contribution
Lehner et al. (in preparation)
Application to high temperature events
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Raw
Dynamical contribution
Thermo-dynamical
contribution
Lehner et al. (in preparation)
Application to high temperature events
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Raw
Dynamical contribution
Thermo-dynamical
contribution
Lehner et al. (in preparation)
Application to high temperature events
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5-yr averages
Lehner et al. (in preparation)
Application to high temperature events
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Partitioning ~50/50
Lehner et al. (in preparation)
Application to high temperature events
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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)
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