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Role of soil moisture for climate extremes and trends in Europe

Role of soil moisture for climate extremes and trends in Europe. Eric Jäger and Sonia Seneviratne CECILIA final meeting, 25.November 2009. -10 -5 0 +5 +10K. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS. Motivation. R. Stöckli.

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Role of soil moisture for climate extremes and trends in Europe

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  1. Role of soil moisture for climate extremes and trends in Europe Eric Jäger and Sonia Seneviratne CECILIA final meeting, 25.November 2009

  2. -10 -5 0 +5 +10K 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Motivation R. Stöckli Several major extreme events over Europe in recent years (e.g. heat wave 2003, Schär et al. (2004), Nature)

  3. -10 -5 0 +5 +10K 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Motivation R. Stöckli Several major extreme events over Europe in recent years (e.g. heat wave 2003, Schär et al. (2004), Nature) Likely to become more frequent in future (IPCC 2007; e.g. Meehl and Tebaldi (2004), Science; Christensen and Christensen (2003), Nature) IPCC 2007

  4. -10 -5 0 +5 +10K 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Motivation R. Stöckli Several major extreme events over Europe in recent years (e.g. heat wave 2003, Schär et al. (2004), Nature) Likely to become more frequent in future (IPCC 2007; e.g. Meehl and Tebaldi (2004), Science; Christensen and Christensen (2003), Nature) Land-atmosphere interactions are a substantial contribution (Seneviratne et al. (2006), Nature) IPCC 2007

  5. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Model experiments Regional climate model CLM,50km, driven by ECMWF re-analysis and operational analysis (1958-2006) (see Jaeger et al. [2008; 2009] for a validation of the model)

  6. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Model experiments Regional climate model CLM,50km, driven by ECMWF re-analysis and operational analysis (1958-2006) (see Jaeger et al. [2008; 2009] for a validation of the model) • Interactive SM: • CTL: control simulation • Prescribed SM: • SSV: lowpass filtered SM from CTL (cutoff ~10d) • ISV: lowpass filtered SM from CTL (cutoff ~100d) • IAV: SM climatology from CTL • PWP: SM const. at plant wilting point • FCAP: SM const. at field capacity

  7. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Model experiments Regional climate model CLM,50km, driven by ECMWF re-analysis and operational analysis (1958-2006) (see Jaeger et al. [2008; 2009] for a validation of the model) • Interactive SM: • CTL: control simulation • Prescribed SM: • SSV: lowpass filtered SM from CTL (cutoff ~10d) • ISV: lowpass filtered SM from CTL (cutoff ~100d) • IAV: SM climatology from CTL • PWP: SM const. at plant wilting point • FCAP: SM const. at field capacity Jaeger and Seneviratne., Clim. Dynam. (submitted)

  8. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Assessment of Extremes • Probability density functions of Tmax • Extreme indices (as defined in CECILIA) • Extreme Value Theory (block maxima & peak over threshold, both stationary and non-stationary)

  9. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Assessment of Extremes • Probability density functions of Tmax • Extreme indices (as defined in CECILIA) • Extreme Value Theory (block maxima & peak over threshold, both stationary and non-stationary)

  10. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Extremes: PDFs of Tmax Eastern Europe

  11. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Extremes: PDFs of Tmax Eastern Europe Soil moisture effects high Tmax values stronger than low ones

  12. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Extremes: PDFs of Tmax Soil moisture has a dampening effect on temperature Eastern Europe

  13. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Extremes: HWDI HWDI = heat wave duration index: ‘mean number of consecutive days (at least two) with values above the long-term 90th-percentile’

  14. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Extremes: HWDI T time

  15. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Extremes: HWDI T Long-term 90th-percentile time

  16. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Extremes: HWDI T potential heat waves Long-term 90th-percentile time

  17. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Extremes: HWDI T time

  18. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Extremes: HWDI T 1. ΔT CTL time

  19. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Extremes: HWDI T 2. ΔT=0 Δpersistence CTL time

  20. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Extremes: HWDI hwdi SSV-CTL ISV-CTL IAV-CTL PWP-CTL FCAP-CTL +9 -9

  21. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Extremes: HWDI hwdi SSV-CTL ISV-CTL IAV-CTL PWP-CTL FCAP-CTL +9 -9 Due to changes in the PDF of Tmax or due to changes in persistence?

  22. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Extremes: HWDI T 10% 10% ΔT EXP CTL time

  23. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Extremes: HWDI hwdi*hwdi SSV-CTL ISV-CTL IAV-CTL PWP-CTL FCAP-CTL +9 -9 +2.7 -2.7 Lorenz et al., GRL (submitted)

  24. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Extremes: HWDI hwdi*hwdi SSV-CTL ISV-CTL IAV-CTL PWP-CTL FCAP-CTL +9 Continuous reduction in HWDI, likely due to reduction in persistence associated with a loss of SM memory -9 +2.7 -2.7 Lorenz et al., GRL (submitted)

  25. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Trends in Tmax Tmax 1981-2006: CTL 1959-1980:CTL

  26. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Trends in Tmax: mechanisms? Tmax global-dimming global-brightening due to aerosol trends 1981-2006: CTL 1959-1980:CTL

  27. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Trends in Tmax: mechanisms? Tmax global-dimming global-brightening due to aerosol trends 1981-2006: CTL 1959-1980:CTL BUT: in CLM and ECMWF model aerosols are constant only an indirect effect of aerosols via data assimilation

  28. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Trends in Tmax: mechanisms? Tmax clouds SM 1981-2006: CTL 1959-1980:CTL

  29. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Trends in Tmax: link to SM Tmax clouds SM no trend 1981-2006: IAV 1981-2006: CTL

  30. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Trends in Tmax: link to SM Tmax clouds SM Aerosols are kept const. in CLM, hence (mostly) only cloud trends are the cause for Tmax trends, whereas SM act as an amplifier no trend 1981-2006: IAV 1981-2006: CTL

  31. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Trends in Tmax (extremes) extremesmean 1981-2006: IAV 1981-2006: CTL

  32. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Trends in Tmax (extremes) extremesmean 1981-2006: IAV 1981-2006: CTL Stronger than those in mean

  33. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Trends in Tmax (extremes) extremesmean 1981-2006: IAV 1981-2006: CTL Stronger influence of SM

  34. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Conclusions • SM asymmetrically effects the PDFs of Tmax, and dampens temperature variability

  35. 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Conclusions • SM asymmetrically effects the PDFs of Tmax, and dampens temperature variability • Reduced SM memory causes a reduction in persistence in Tmax extremes hwdi*

  36. 1959-1980 1981-2006 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Conclusions • SM asymmetrically effects the PDFs of Tmax, and dampens temperature variability • Reduced SM memory causes a reduction in persistence in Tmax extremes • Trends in Tmax are likely due to trends in clouds, whereas SM acts as an amplifier hwdi*

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