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How to link large-scale circulation structures to local extremes ? Frank Selten and Deb Panja Royal Netherlands Meteorological Institute “Extreme Associated Functions: Optimally Linking Local Extremes to 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 UK daily mean summer temperatures in the Netherlands Floods in the UK Average 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 EuroAtlantic region EOF 1 (12.8 %) EOF 2 ( 11.6 %) Daily values 1958-2000 No 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, …) n <..>p b an adjustable power to emphasize the more extreme anomalies time average over positive anomalies only amplitude of the new pattern Interpretation: find the pattern that for a one standard deviation change gives the largest change in the local temperature 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 ” random perturbations in atmospheric temperatures (< 0.1 K ) initial state from preindustrial control integration 1850 1950 17 simulations 2000 2100 historical concentrations of GHG according to GHGs and sulphate SRES A1b aerosols. Streamfunction at 500 hPa ESSENCE JA 1958-2000 ERA JA 1958-2000 Mean Standard deviation Mean Mean Standard Standard deviation deviation Streamfunction 500 hPa EOFs ERA 1 2 ESSENCE Streamfunction 500 hPa EOFs ERA 3 4 ESSENCE Streamfunction EAFs ERA 1 ESSENCE T2m versus EAF1 amplitude ERA ESSENCE ESSENCE Climate change in ESSENCE • Compare 2071-2100 period with 1958-2000 • Average across all 17 ensemble members Temperature at 2m ESSENCE JA climate change Mean Standard deviation Streamfunction at 500 hPa ESSENCE JA climate change Mean Standard deviation Streamfunction 500 hPa EOFs ESSENCE 2051-2100 1 2 ESSENCE 1958-2000 Streamfunction 500 hPa EOFs ESSENCE 2051-2100 3 4 ESSENCE 1958-2000 Streamfunction EAFs ESSENCE 1958-2000 1 2 ESSENCE 2050-2100 EAF 1 Netherlands present PDF of EAF amplitudes present future present EAF projected on future future wrt future climate present EAF projected on future wrt future climate Pattern not changed; mere shift of PDF future Netherlands EAF 1 Mean change included Mean change subtracted EAF 1 France present PDF of EAF amplitudes present future present EAF projected on future future wrt future climate present EAF projected on future wrt future climate Pattern not changed; mere shift of PDF future France EAF 1 Mean change included Mean change subtracted EAF 1 Spain present PDF of EAF amplitudes present future present EAF projected on future future wrt future climate present EAF projected on future wrt future climate Pattern bit changed; PDF changes future Spain EAF 1 Mean change included Mean change subtracted EAF 1 Greece present PDF of EAF amplitudes present future present EAF projected on future future wrt future climate present EAF projected on future wrt future climate Pattern bit changed; PDF changes future Greece EAF 1 Mean change included Mean change subtracted EAF 1 Romania present PDF of EAF amplitudes present future present EAF projected on future future wrt future climate present EAF projected on future wrt future climate Pattern not changed; PDF changes future Romania EAF 1 Mean change included Mean change subtracted EAF 1 Moscow present PDF of EAF amplitudes present future present EAF projected on future future wrt future climate present EAF projected on future wrt future climate Pattern not changed; PDF mere shift future Moscow EAF 1 Mean change included Mean change subtracted EAF 1 Poland present PDF of EAF amplitudes present future present EAF projected on future future wrt future climate present EAF projected on future wrt future climate Pattern not changed; PDF slight change future Moscow EAF 1 Mean change included Mean change subtracted EAF 1 Hamar present PDF of EAF amplitudes present future present EAF projected on future future wrt future climate present EAF projected on future wrt future climate Pattern not changed; PDF bit changed future Hamar EAF 1 Mean change included Mean change subtracted EAF 1 UK present PDF of EAF amplitudes present future present EAF projected on future future wrt future climate present EAF projected on future wrt future climate Pattern not changed; PDF bit changed future UK EAF 1 Mean 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
