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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
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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
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Floods in the UKAverage rainfall May – July 2007
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Impressions
British always make the best of it …..
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UK July rainfall and Z500 anomalies
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Teleconnection Z500
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Daily JA temperatures in Holland
Z500 anomaly Z500 anomaly
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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
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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
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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
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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
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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 %)
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Daily values 1958-2000No apparent clusters by simple visual inspection …
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Temperature anomaly ~ EOF1
Clear dependence …
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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
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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:
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Comparison
Linear regression T and Z500 Composite of 5 % hottest days
EAF 1
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Temperature ~ EAF 1
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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
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Other patterns ????
• Test: synthetic temperature timeseries
• T(t) = a1(t) + a2(t)
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EAF1 : sum of both patterns
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Linear regression pattern
Does not reproduce the original patterns as well
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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
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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
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Streamfunction at 500 hPa
Mean
Standard deviation
Mean
Standard deviation
ERA JA 1958-2000 ESSENCE JA 1958-2000
Mean
Standard deviation
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Streamfunction 500 hPa EOFs
1
ERA ESSENCE
2
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Streamfunction 500 hPa EOFs
3
4
ERA ESSENCE
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Streamfunction EAFsERA ESSENCE
1
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T2m versus EAF1 amplitude
ERAESSENCE
ESSENCE
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Climate change in ESSENCE
• Compare 2071-2100 period with 1958-2000• Average across all 17 ensemble members
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Temperature at 2m
Mean Standard deviation
ESSENCE JA climate change
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ESSENCE JA climate change
Streamfunction at 500 hPa
Mean Standard deviation
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Streamfunction 500 hPa EOFs
1
ESSENCE 1958-2000
2
ESSENCE 2051-2100
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ESSENCE 1958-2000ESSENCE 2051-2100
Streamfunction 500 hPa EOFs
3
4
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Streamfunction EAFsESSENCE 1958-2000 ESSENCE 2050-2100
1
2
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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
future
PDF of EAF amplitudes
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Netherlands EAF 1Mean change included Mean change subtracted
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EAF 1 Francepresent
futurepresent EAF projected on futurefuture wrt future climatepresent EAF projected on futurewrt future climate
Pattern not changed; mere shift of PDF
present
future
PDF of EAF amplitudes
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France EAF 1Mean change included Mean change subtracted
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EAF 1 Spainpresent
futurepresent EAF projected on futurefuture wrt future climatepresent EAF projected on futurewrt future climate
Pattern bit changed; PDF changes
present
future
PDF of EAF amplitudes
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Spain EAF 1Mean change included Mean change subtracted
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EAF 1 Greecepresent
futurepresent EAF projected on futurefuture wrt future climatepresent EAF projected on futurewrt future climate
Pattern bit changed; PDF changes
present
future
PDF of EAF amplitudes
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Greece EAF 1Mean change included Mean change subtracted
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EAF 1 Romaniapresent
futurepresent EAF projected on futurefuture wrt future climatepresent EAF projected on futurewrt future climate
Pattern not changed; PDF changes
present
future
PDF of EAF amplitudes
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Romania EAF 1Mean change included Mean change subtracted
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EAF 1 Moscowpresent
futurepresent EAF projected on futurefuture wrt future climatepresent EAF projected on futurewrt future climate
Pattern not changed; PDF mere shift
present
future
PDF of EAF amplitudes
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Moscow EAF 1Mean change included Mean change subtracted
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EAF 1 Polandpresent
futurepresent EAF projected on futurefuture wrt future climatepresent EAF projected on futurewrt future climate
Pattern not changed; PDF slight change
present
future
PDF of EAF amplitudes
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Moscow EAF 1Mean change included Mean change subtracted
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EAF 1 Hamarpresent
futurepresent EAF projected on futurefuture wrt future climatepresent EAF projected on futurewrt future climate
Pattern not changed; PDF bit changed
present
future
PDF of EAF amplitudes
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Hamar EAF 1Mean change included Mean change subtracted
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EAF 1 UKpresent
futurepresent EAF projected on futurefuture wrt future climatepresent EAF projected on futurewrt future climate
Pattern not changed; PDF bit changed
present
future
PDF of EAF amplitudes
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UK EAF 1Mean change included Mean change subtracted
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Conclusions
• EAF method is an objective, robust tool to relate local extremes to large-scale circulation structures
• Useful tool to evaluate climate simulations
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