How to link large-scale circulation structures to local extremes ?

Frank Selten and Deb PanjaRoyal Netherlands Meteorological Institute

“Extreme Associated Functions: Optimally Linking Local Extremesto Large-scale Atmospheric Circulation Structures”In discussion in Atmospheric Chemistry and Physics Discussions

Motivation

• Local weather extremes are usually connected to typical large-scale circulation anomalies

Examples: extreme rainfall in the UKdaily mean summer temperatures in the Netherlands

Floods in the UKAverage rainfall May – July 2007

Impressions

British always make the best of it …..

UK July rainfall and Z500 anomalies

Teleconnection Z500

Daily JA temperatures in Holland

Z500 anomaly Z500 anomaly

Motivation

• Local weather extremes are usually connected to typical large-scale circulation anomalies

• Probability of occurrence of these structures impact probability of the local extremes

Motivation

• Local weather extremes are usually connected to typical large-scale circulation anomalies

• Probability of occurrence of these structures impact probability of the local extremes

• Future probability of local extremes depends on the response of circulation to the CO2 forcing

Motivation

• Local weather extremes are usually connected to typical large-scale circulation anomalies

• Probability of occurrence of these structures impact probability of the local extremes

• Future probability of local extremes depends on the response of circulation to the CO2 forcing

• Uncertainties in circulation changes lead to uncertainties in local weather extremes

Identification of circulation structures that are optimally linked

to local extremes enables:

• model evaluation against observations• diagnosis of cause of discrepancies; maybe not

due to circulation but clouds, soil-moisture or radiation deficiencies

• evaluation and intercomparison of simulated changes in extremes

• dynamical understanding of circulation changes which enhances faith in simulated changes

Use information on the extremes

• Example: July and August daily mean temperatures in De Bilt and 500 hPa geopotential height fields over the Euro-Atlantic region

EOF 1 (12.8 %) EOF 2 ( 11.6 %)

Daily values 1958-2000No apparent clusters by simple visual inspection …

Temperature anomaly ~ EOF1

Clear dependence …

Extreme Associated Functions

• Linear combinations of first L EOF amplitudes that have maximum ‘tilt’ r in scatter plot with local temperature (or wind, rain, …)

Interpretation: find the pattern that for a one standard deviation change gives the largest change in the local temperature

n an adjustable power to emphasize the more extreme anomalies<..>p time average over positive anomalies onlyb amplitude of the new pattern

Two possibilities: find c’s that maximize r2 by variational analysis:

= 0

Or find the least-squares solution of the multiple linear regression problem:

The solution is:

Comparison

Linear regression T and Z500 Composite of 5 % hottest days

EAF 1

Temperature ~ EAF 1

Robustness

• Taking only the 30% maximum temperature anomalies leads to the same EAFs

• Varying the power from 1 to 3 leads to qualitatively similar EAFs

• Choosing a smaller geographical region leads to the same EAFs

Other patterns ????

• Test: synthetic temperature timeseries

• T(t) = a1(t) + a2(t)

EAF1 : sum of both patterns

Linear regression pattern

Does not reproduce the original patterns as well

Conclusion

• EAFs are a robust method to link large-scale circulation structures to local extremes; all contributing patterns are sumarized into one

• Next application: validate climate simulations for present day and assess changes in climate scenario simulation

Application to simulated data“ESSENCE project: a 17 member ensemble of climate SRES A1b scenario simulations from perturbed initial conditions using the ECHAM5-MPI-OM model ”

1850 1950 2000 2100

historical concentrations of GHGs and sulphate aerosols.

GHG according toSRES A1b

17 simulations

random perturbations in atmospheric temperatures (< 0.1 K )

initial state from pre-industrial control integration

Streamfunction at 500 hPa

Mean

Standard deviation

Mean

Standard deviation

ERA JA 1958-2000 ESSENCE JA 1958-2000

Mean

Standard deviation

Streamfunction 500 hPa EOFs

1

ERA ESSENCE

2

Streamfunction 500 hPa EOFs

3

4

ERA ESSENCE

Streamfunction EAFsERA ESSENCE

1

T2m versus EAF1 amplitude

ERAESSENCE

ESSENCE

Climate change in ESSENCE

• Compare 2071-2100 period with 1958-2000• Average across all 17 ensemble members

Temperature at 2m

Mean Standard deviation

ESSENCE JA climate change

ESSENCE JA climate change

Streamfunction at 500 hPa

Mean Standard deviation

Streamfunction 500 hPa EOFs

1

ESSENCE 1958-2000

2

ESSENCE 2051-2100

ESSENCE 1958-2000ESSENCE 2051-2100

Streamfunction 500 hPa EOFs

3

4

Streamfunction EAFsESSENCE 1958-2000 ESSENCE 2050-2100

1

2

EAF 1 Netherlandspresent

futurepresent EAF projected on futurefuture wrt future climatepresent EAF projected on futurewrt future climate

Pattern not changed; mere shift of PDF

present

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PDF of EAF amplitudes

Netherlands EAF 1Mean change included Mean change subtracted

EAF 1 Francepresent

futurepresent EAF projected on futurefuture wrt future climatepresent EAF projected on futurewrt future climate

Pattern not changed; mere shift of PDF

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PDF of EAF amplitudes

France EAF 1Mean change included Mean change subtracted

EAF 1 Spainpresent

futurepresent EAF projected on futurefuture wrt future climatepresent EAF projected on futurewrt future climate

Pattern bit changed; PDF changes

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PDF of EAF amplitudes

Spain EAF 1Mean change included Mean change subtracted

EAF 1 Greecepresent

futurepresent EAF projected on futurefuture wrt future climatepresent EAF projected on futurewrt future climate

Pattern bit changed; PDF changes

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PDF of EAF amplitudes

Greece EAF 1Mean change included Mean change subtracted

EAF 1 Romaniapresent

futurepresent EAF projected on futurefuture wrt future climatepresent EAF projected on futurewrt future climate

Pattern not changed; PDF changes

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PDF of EAF amplitudes

Romania EAF 1Mean change included Mean change subtracted

EAF 1 Moscowpresent

futurepresent EAF projected on futurefuture wrt future climatepresent EAF projected on futurewrt future climate

Pattern not changed; PDF mere shift

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PDF of EAF amplitudes

Moscow EAF 1Mean change included Mean change subtracted

EAF 1 Polandpresent

futurepresent EAF projected on futurefuture wrt future climatepresent EAF projected on futurewrt future climate

Pattern not changed; PDF slight change

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PDF of EAF amplitudes

Moscow EAF 1Mean change included Mean change subtracted

EAF 1 Hamarpresent

futurepresent EAF projected on futurefuture wrt future climatepresent EAF projected on futurewrt future climate

Pattern not changed; PDF bit changed

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PDF of EAF amplitudes

Hamar EAF 1Mean change included Mean change subtracted

EAF 1 UKpresent

futurepresent EAF projected on futurefuture wrt future climatepresent EAF projected on futurewrt future climate

Pattern not changed; PDF bit changed

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PDF of EAF amplitudes

UK EAF 1Mean change included Mean change subtracted

Conclusions

• EAF method is an objective, robust tool to relate local extremes to large-scale circulation structures

• Useful tool to evaluate climate simulations