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)
-10 -5 0 +5 +10K
R.
Stö
ckli
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)
-10 -5 0 +5 +10K
IPC
C 2
007
R.
Stö
ckli
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)
-10 -5 0 +5 +10K
IPC
C 2
007
R.
Stö
ckli
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)
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 (1958-2006) (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 (1958-2006) (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 Tmax
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 Tmax
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 Tmax
Soil moisture effects high Tmax values stronger than low ones
1. BACKGROUND 2. RESULTS 3. CONCLUSIONS
Eastern Europe
Extremes: PDFs of Tmax
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 90th-percentile’
1. BACKGROUND 2. RESULTS 3. CONCLUSIONS
Extremes: HWDI
T
time
potential heat waves Long-term
90th-percentile
1. BACKGROUND 2. RESULTS 3. CONCLUSIONS
Extremes: HWDI
SSV-CTL ISV-CTL IAV-CTL PWP-CTL FCAP-CTL +9
-9
1. BACKGROUND 2. RESULTS 3. CONCLUSIONS
Extremes: HWDI
SSV-CTL ISV-CTL IAV-CTL PWP-CTL FCAP-CTL +9
-9
1. BACKGROUND 2. RESULTS 3. CONCLUSIONS
Due to changes in the PDF of Tmax
or due to changes in persistence?
Extremes: HWDI
SSV-CTL ISV-CTL IAV-CTL PWP-CTL FCAP-CTL +9
-9+2.7
-2.7
Lorenz et al., GRL (submitted)
1. BACKGROUND 2. RESULTS 3. CONCLUSIONS
Extremes: HWDI
SSV-CTL ISV-CTL IAV-CTL PWP-CTL FCAP-CTL +9
-9+2.7
-2.7
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 Tmax: mechanisms?
Tmax
1981
-200
6: C
TL
1
959-
1980
:CT
L
1. BACKGROUND 2. RESULTS 3. CONCLUSIONS
global-dimming global-brightening
due to aerosol trends
Trends in Tmax: mechanisms?
Tmax
1981
-200
6: C
TL
1
959-
1980
:CT
L
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 Tmax: mechanisms?
Tmax clouds SM
1981
-200
6: C
TL
1
959-
1980
:CT
L
1. BACKGROUND 2. RESULTS 3. CONCLUSIONS
no trend
Trends in Tmax: link to SM
Tmax clouds SM
1981
-200
6: I
AV
198
1-2
006
: CT
L
1. BACKGROUND 2. RESULTS 3. CONCLUSIONS
no trend
Trends in Tmax: link to SM
Aerosols are kept const. in CLM, hence (mostly) only cloud trends are the cause for Tmax trends, whereas SM act as an amplifier
Tmax clouds SM
1981
-200
6: I
AV
198
1-2
006
: CT
L
1. BACKGROUND 2. RESULTS 3. CONCLUSIONS
Trends in Tmax (extremes)
extremes mean
1981
-200
6: I
AV
1981
-200
6: C
TL
1. BACKGROUND 2. RESULTS 3. CONCLUSIONS
Trends in Tmax (extremes)
extremes mean
Stronger than those in mean
1981
-200
6: I
AV
1981
-200
6: C
TL
1. BACKGROUND 2. RESULTS 3. CONCLUSIONS
Trends in Tmax (extremes)
extremes mean
Stronger influence of SM
1981
-200
6: I
AV
1981
-200
6: C
TL
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 Tmax extremes
hwdi*
1. BACKGROUND 2. RESULTS 3. CONCLUSIONS
Conclusions
1959-1980 1981-2006
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*
1. BACKGROUND 2. RESULTS 3. CONCLUSIONS