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

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

Motivation 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) K IPCC 2007 R. Stöckli 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS

Motivation 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) K IPCC 2007 R. Stöckli 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS

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

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

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

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

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

Extremes: PDFs of T max 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS Eastern Europe

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

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

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

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

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

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

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

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

Δ T=0 Δ persistence T 2. CTL Extremes: HWDI time 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS

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

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

T Extremes: HWDI time ΔTΔT CT L EXP 10% 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS

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

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

Trends in T max T max : CTL :CTL 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS

Trends in T max : mechanisms? T max : CTL :CTL 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS global-dimming global-brightening due to aerosol trends

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

Trends in T max : mechanisms? T max clouds SM : CTL :CTL 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS

no trend Trends in T max : link to SM T max clouds SM : IAV : CTL 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS

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

Trends in T max (extremes) extremes mean : IAV : CTL 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS

Trends in T max (extremes) extremes mean Stronger than those in mean : IAV : CTL 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS

Trends in T max (extremes) extremes mean Stronger influence of SM : IAV : CTL 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS

Conclusions SM asymmetrically effects the PDFs of Tmax, and dampens temperature variability 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 T max extremes hwdi* 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 T max extremes Trends in T max are likely due to trends in clouds, whereas SM acts as an amplifier hwdi* 1. BACKGROUND 2. RESULTS 3. CONCLUSIONS