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Using water stable isotopi measurements tobetter evaluate the atmospheri and land surfa e omponents of limate modelsCamille RisiCIRES, Boulderwith ontribution of:Analysis: S Bony, D Noone, C CastetTES/SCIAMACHY: J Worden, J Lee, D Brown, C FrankenbergMIPAS:ACE: G Stiller, M Kiefer, B Funke, K Walker, P Bernath,FTIR: M S hneider, D Wun h, P Wennberg, V Sherlo k, N Deuts her, D Gri�thin-situ/MIBA: R Uemura, J. Ogée, T. Baria , L. Wingate, N. Raz-YaseefSWING2: C SturmHydrologi S ien es and Water Resour es Engineering SeminarSeries, 27 April 2011
Un ertainties in limate proje tions
CO2increase
temperatureincrease
climatefeedbacks
IPCC AR4
multi−model mean
surf
ace
glob
al w
arm
ing
(°C
)
Introdu tion 2/31
Un ertainties in limate proje tions
CO2increase
temperatureincrease
climatefeedbacks
Key uncertaintiesin climate models:
IPCC AR4
atmospheric processes
− atmospheric convection− clouds
− boundary layer− relative humidity
multi−model mean
surf
ace
glob
al w
arm
ing
(°C
)
Introdu tion 2/31
Un ertainties in limate proje tions
CO2increase
IPCC AR4
precipitation changeby 2100 (%)
<2/3 agree on sign
temperatureincrease
regionalprecipitation
changesclimatefeedbacks
Key uncertaintiesin climate models:
IPCC AR4
atmospheric processes
− atmospheric convection− clouds
− boundary layer− relative humidity
multi−model mean
surf
ace
glob
al w
arm
ing
(°C
)
Introdu tion 2/31
Un ertainties in limate proje tions
CO2increase
land usechange
IPCC AR4
Boé et Terray2008
precipitation changeby 2100 (%)
<2/3 agree on sign
evapo−transpiration changeby 2100 (mm/d)
<70% agree on sign
temperatureincrease
regionalprecipitation
changes
land surfacehydrological
changesclimatefeedbacks land−atmosphere
feedbacks
Key uncertaintiesin climate models:
IPCC AR4
atmospheric processes
− atmospheric convection− clouds
− boundary layer− relative humidity
multi−model mean
surf
ace
glob
al w
arm
ing
(°C
)
Introdu tion 2/31
Un ertainties in limate proje tions
CO2increase
land usechange
IPCC AR4
Boé et Terray2008
precipitation changeby 2100 (%)
<2/3 agree on sign
evapo−transpiration changeby 2100 (mm/d)
<70% agree on sign
temperatureincrease
regionalprecipitation
changes
land surfacehydrological
changesclimatefeedbacks land−atmosphere
feedbacks
Key uncertaintiesin climate models:
IPCC AR4
atmospheric processes land surface processes− sensitivity of surface fluxes
to soil moisture− atmospheric convection− clouds
− soil/groundwater storage− spatial heterogeneities
− boundary layer− relative humidity
multi−model mean
surf
ace
glob
al w
arm
ing
(°C
)
Introdu tion 2/31
Water isotopi omposition◮ H162 O, HDO, H182 O, H172 O, fra tionation
HDO
H16
2O
Introdu tion 3/31
Water isotopi omposition◮ H162 O, HDO, H182 O, H172 O, fra tionation◮ re ords phase hanges
Continental recyclingEvaporation
ConvectionCondensation
Precipitation
HDO
H16
2O
Introdu tion 3/31
Water isotopi omposition◮ H162 O, HDO, H182 O, H172 O, fra tionation◮ re ords phase hanges
Continental recyclingEvaporation
ConvectionCondensation
Precipitation
HDO
H16
2O
isotopiccomposition
variablesmeteorological
and variability
present−dayclimate processes
physicalfuture climate
Introdu tion 3/31
Water isotopi omposition◮ H162 O, HDO, H182 O, H172 O, fra tionation◮ re ords phase hanges
Continental recyclingEvaporation
ConvectionCondensation
Precipitation
HDO
H16
2O
isotopiccomposition
processevaluation
variablesmeteorological
and variability
present−dayclimate
combiningdata
processesphysical
future climate
Introdu tion 3/31
Water isotopi omposition◮ H162 O, HDO, H182 O, H172 O, fra tionation◮ re ords phase hanges
Continental recyclingEvaporation
ConvectionCondensation
Precipitation
HDO
H16
2O
isotopiccomposition
processevaluation
strengthenprojections credibility
variablesmeteorological
and variability
present−dayclimate
combiningdata
processesphysical
future climate
Introdu tion 3/31
General strategy
controlling isotopicunderstand processes
compositionIntrodu tion 4/31
General strategy
controlling isotopicunderstand processes
composition
design observable,process−oriented
isotopic diagnostics
Introdu tion 4/31
General strategy
controlling isotopicunderstand processes
composition
climate modelsapply to
detect bias in models
propose improvements
understand reasons
design observable,process−oriented
isotopic diagnostics
Introdu tion 4/31
General strategy
controlling isotopicunderstand processes
composition
consequences onfuture changes
quantify, prioritizeuncertainties
climate modelsapply to
detect bias in models
propose improvements
understand reasons
design observable,process−oriented
isotopic diagnostics
Introdu tion 4/31
Outline
CO2increase
land usechange
IPCC AR4
Boé et Terray2008
precipitation changeby 2100 (%)
<2/3 agree on sign
evapo−transpiration changeby 2100 (mm/d)
<70% agree on sign
temperatureincrease
regionalprecipitation
changes
land surfacehydrological
changesclimatefeedbacks land−atmosphere
feedbacks
Key uncertaintiesin climate models:
IPCC AR4
atmospheric processes land surface processes− sensitivity of surface fluxes
to soil moisture− atmospheric convection− clouds
− soil/groundwater storage− spatial heterogeneities
− boundary layer− relative humidity
multi−model mean
surf
ace
glob
al w
arm
ing
(°C
)
Introdu tion 5/31
Outline
3)
2)
1)
CO2increase
land usechange
IPCC AR4
Boé et Terray2008
precipitation changeby 2100 (%)
<2/3 agree on sign
evapo−transpiration changeby 2100 (mm/d)
<70% agree on sign
temperatureincrease
regionalprecipitation
changes
land surfacehydrological
changesclimatefeedbacks land−atmosphere
feedbacks
Key uncertaintiesin climate models:
IPCC AR4
atmospheric processes land surface processes− sensitivity of surface fluxes
to soil moisture− atmospheric convection− clouds
− soil/groundwater storage− spatial heterogeneities
− boundary layer− relative humidity
multi−model mean
surf
ace
glob
al w
arm
ing
(°C
)
Introdu tion 5/31
1) Pro esses ontrolling relative humidity◮ tropi al/subtropi al free tropospheri relative humidity (RH)impa ts:
◮ water vapor feedba k (Soden et al 2008)◮ loud feedba ks (Sherwood et al 2010)
1) Pro esses ontrolling humidity 6/31
1) Pro esses ontrolling relative humidity◮ tropi al/subtropi al free tropospheri relative humidity (RH)impa ts:
◮ water vapor feedba k (Soden et al 2008)◮ loud feedba ks (Sherwood et al 2010)
◮ but:◮ signi� ant dispersion in limate models (Sherwood et al 2010)◮ moist bias in the mid/upper troposphere (John and Soden 2005)
1) Pro esses ontrolling humidity 6/31
1) Pro esses ontrolling relative humidity◮ tropi al/subtropi al free tropospheri relative humidity (RH)impa ts:
◮ water vapor feedba k (Soden et al 2008)◮ loud feedba ks (Sherwood et al 2010)
◮ but:◮ signi� ant dispersion in limate models (Sherwood et al 2010)◮ moist bias in the mid/upper troposphere (John and Soden 2005)
=⇒ need pro ess-based evaluation of RH in limate models1) Pro esses ontrolling humidity 6/31
Sensitivity tests to RH pro esses
800
600
200
100
0°
400
1000
P (hPa)
30°N
condensate detrainementin clouds
large−scale
rain evaporation
boundary layermixing
vertical mixing
circulation
dehydration
lateral and
Couhert et al 2010Folkins and Martin 2005Pierrehumbert 1998Sherwood et al 1996Wright et al 2009
1) Pro esses ontrolling humidity 7/31
Sensitivity tests to RH pro esses
800
600
200
100
0°
400
1000
P (hPa)
30°N
condensate detrainement
LMDZ−iso (Risi et al 2010a):
in clouds
large−scale
rain evaporation
boundary layermixing
vertical mixing
circulation
dehydration
lateral and
ontrol: AR4 version (19 levels)
AIRS datarelative humidity (%)
pression(hPa)
3020 40 50 60 70 801000
100300200400500600700800900Couhert et al 2010Folkins and Martin 2005
Pierrehumbert 1998Sherwood et al 1996Wright et al 2009
1) Pro esses ontrolling humidity 7/31
Sensitivity tests to RH pro esses
800
600
200
100
0°
400
1000
P (hPa)
30°N
condensate detrainement
LMDZ−iso (Risi et al 2010a):
in clouds
large−scale
rain evaporation
boundary layermixing
vertical mixing
circulation
dehydration
3 reasonsfor a
moist bias
lateral and
ontrol: AR4 version (19 levels)
AIRS datarelative humidity (%)
pression(hPa)
3020 40 50 60 70 801000
100300200400500600700800900Couhert et al 2010Folkins and Martin 2005
Pierrehumbert 1998Sherwood et al 1996Wright et al 2009
1) Pro esses ontrolling humidity 7/31
Sensitivity tests to RH pro esses
800
600
200
100
0°
400
1000
P (hPa)
30°N
condensate detrainement
LMDZ−iso (Risi et al 2010a):
in clouds
large−scale
rain evaporation
boundary layermixing
vertical mixing
circulation
dehydration
3 reasonsfor a
moist bias
lateral and
ontrol: AR4 version (19 levels)di�usive verti al adve tion
AIRS datarelative humidity (%)
pression(hPa)
3020 40 50 60 70 801000
100300200400500600700800900Couhert et al 2010Folkins and Martin 2005
Pierrehumbert 1998Sherwood et al 1996Wright et al 2009
1) Pro esses ontrolling humidity 7/31
Sensitivity tests to RH pro esses
800
600
200
100
0°
400
1000
P (hPa)
30°N
condensate detrainement
LMDZ−iso (Risi et al 2010a):
in clouds
large−scale
rain evaporation
boundary layermixing
vertical mixing
circulation
dehydration
3 reasonsfor a
moist bias
lateral and
ontrol: AR4 version (19 levels)di�usive verti al adve tionσq/10
AIRS datarelative humidity (%)
pression(hPa)
3020 40 50 60 70 801000
100300200400500600700800900Couhert et al 2010Folkins and Martin 2005
Pierrehumbert 1998Sherwood et al 1996Wright et al 2009
1) Pro esses ontrolling humidity 7/31
Sensitivity tests to RH pro esses
800
600
200
100
0°
400
1000
P (hPa)
30°N
condensate detrainement
LMDZ−iso (Risi et al 2010):
in clouds
large−scale
rain evaporation
boundary layermixing
vertical mixing
circulation
dehydration
3 reasonsfor a
moist bias
lateral and
AIRS datarelative humidity (%)
pression(hPa)
3020 40 50 60 70 801000
100300200400500600700800900
ontrol: AR4 version (19 levels)di�usive verti al adve tionσq/10
ǫp/2
Couhert et al 2010Folkins and Martin 2005Pierrehumbert 1998Sherwood et al 1996Wright et al 2009
1) Pro esses ontrolling humidity 7/31
Isotopi measurements(Worden et al 2007)TES
satellites
total column:(Frankenberg et al 2009)
SCIAMACHY
(Steinwagner et al 2010)(Nassar et al 2007)
MIPASACE−FTS
200
100
0°1000
30°N
P (hPa)
400
600
800
1) Pro esses ontrolling humidity 8/31
Isotopi measurements(Worden et al 2007)TES
total column:(Frankenberg et al 2009)
SCIAMACHY
satellites
ground−basedFTIR
total column(5 TCCON/NDACC sites:
2 US, 2 Australia,1 New−Zealand)
Schneider et al 2009)
profile(Canaries islands:
(Steinwagner et al 2010)(Nassar et al 2007)
MIPASACE−FTS
200
100
0°1000
30°N
P (hPa)
400
600
800
1) Pro esses ontrolling humidity 8/31
Isotopi measurements(Worden et al 2007)TES
total column:(Frankenberg et al 2009)
SCIAMACHY
satellites
ground−basedFTIR
total column(5 TCCON/NDACC sites:
2 US, 2 Australia,1 New−Zealand)
Schneider et al 2009)
profile(Canaries islands:in situ
(GNIP−vapor,Angert et al 2007, Uemura et al 2007)
(Steinwagner et al 2010)(Nassar et al 2007)
MIPASACE−FTS
200
100
0°1000
30°N
P (hPa)
400
600
800
1) Pro esses ontrolling humidity 8/31
Isotopi measurements(Worden et al 2007)TES
total column:(Frankenberg et al 2009)
SCIAMACHY
satellites
ground−basedFTIR
total column(5 TCCON/NDACC sites:
2 US, 2 Australia,1 New−Zealand)
Schneider et al 2009)
profile(Canaries islands:in situ
(GNIP−vapor,Angert et al 2007, Uemura et al 2007)
(Steinwagner et al 2010)(Nassar et al 2007)
MIPASACE−FTS
200
100
0°1000
30°N
P (hPa)
400
600
800
◮ model-data omparison: ollo ation; simulations nudged byECMWF; averaging kernels; spatial/temporal variations1) Pro esses ontrolling humidity 8/31
Zonal anual mean
data
−180
−160
−140
−120
−100
−80
Eq 30N60S 60N30S
−150
−200
−250
−100
Eq 30N60S 60N30S
−60
Eq 30N60S 60N30S
−250
−200
−150
Eq 30N60S 60N30S−800
−300
−200
−400
−500−600
−700
Eq 30N60S 60N30S−600
−500
−400
−300
LMDZ "ǫp/2"LMDZ "σq /10"LMDZ "di�usive adve tion"LMDZ ontrolδD
(h)δD
(h)
200hPa, MIPAS
300hPa, ACEδD
(h) SCIAMACHY andground-based FTIRTotal olumn,
δD
(h) Surfa e, in-situ
600 hPa, TESδD
(h)1) Pro esses ontrolling humidity 9/31
Zonal Seasonal variations (JJA-DJF)
data
Eq 30N60S 60N30S
0
50
100
−100
−50
Eq 30N60S 60N30S
100
50
0
Eq 30N60S 60N30S−20
0
20
40
60
80
Eq 30N60S 60N30S
−100
0
100
200
Eq 30N60S 60N30S
0
100
−100
−50
50
150
−150
LMDZ "ǫp/2"LMDZ "σq /10"LMDZ "di�usive adve tion"LMDZ ontrol200hPa, MIPAS
∆δD
(h)
∆δD
(h)300hPa, ACEand ground-based FTIR
∆δD
(h)ground-based FTIRSCIAMACHY andTotal olumn
∆δD
(h)
∆δD
(h)
600 hPa, TESand ground-based FTIR
Surfa e, in-situ
1) Pro esses ontrolling humidity 10/31
What auses the moist biases in GCMs?
−60
−40
−20
0
20
40
60
20 25 30 35 40 45 50 55
Ex essive ondensatedetrainementInsu� ientin-situ ondensationverti al adve tionEx essively di�usiveControl 400 hPa, 15◦N-30◦N mean
relative humidity (%)JJA-DJF∆δD(h)
Sensitivity tests:with LMDZ: ACE/AIRSdata
1) Pro esses ontrolling humidity 11/31
What auses the moist biases in GCMs?
−60
−40
−20
0
20
40
60
20 25 30 35 40 45 50 55
Ex essive ondensatedetrainementInsu� ientin-situ ondensationverti al adve tionEx essively di�usiveControl 400 hPa, 15◦N-30◦N mean
relative humidity (%)JJA-DJF∆δD(h)
Sensitivity tests:with LMDZ: ACE/AIRSdata
◮ robustness? additional tests, theoreti al understanding1) Pro esses ontrolling humidity 11/31
What auses the moist biases in GCMs?
−60
−40
−20
0
20
40
60
20 25 30 35 40 45 50 55
Ex essive ondensatedetrainementInsu� ientin-situ ondensationverti al adve tionEx essively di�usiveControl
ECHAMMIROCHadAM GSMGISSSWING2 models:CAM2
400 hPa, 15◦N-30◦N mean
relative humidity (%)JJA-DJF∆δD(h)
Sensitivity tests:with LMDZ: ACE/AIRSdata
◮ robustness? additional tests, theoreti al understanding◮ frequent reason for moist bias=ex essively di�usive adve tion1) Pro esses ontrolling humidity 11/31
What impa t on humidity proje tions?
−2.5
−2
−1.5
−1
−0.5
0
0.5
1
1.5
25 30 35 40 45 50 55 60 65 70 75
AIRS
∆
RH(%/K)
present-day RH (%/K)
tropi al average, 200hPa, 2xCO2 ontrol simulationLMDZ testsdi�usive adve tionσq /10ǫp/2CMIP3 models
1) Pro esses ontrolling humidity 12/31
What impa t on humidity proje tions?
−2.5
−2
−1.5
−1
−0.5
0
0.5
1
1.5
25 30 35 40 45 50 55 60 65 70 75
AIRS
∆
RH(%/K)
present-day RH (%/K)
tropi al average, 200hPa, 2xCO2 ontrol simulationLMDZ testsdi�usive adve tionσq /10ǫp/2CMIP3 models
◮ How a moist bias a�e t humidity hange proje tions dependson the reason for the bias1) Pro esses ontrolling humidity 12/31
What impa t on humidity proje tions?
−2.5
−2
−1.5
−1
−0.5
0
0.5
1
1.5
25 30 35 40 45 50 55 60 65 70 75
AIRS
∆
RH(%/K)
present-day RH (%/K)
tropi al average, 200hPa, 2xCO2 ontrol simulationLMDZ testsdi�usive adve tionσq /10ǫp/2CMIP3 models
◮ How a moist bias a�e t humidity hange proje tions dependson the reason for the bias1) Pro esses ontrolling humidity 12/31
What impa t on limate sensitivity?◮ radiative kernel de omposition (Soden et al 2008)
controldiffusive advection insufficient condensationexcessive détrainement
feed
back
par
amet
er (
W/m
2/K
)
−4
−3
−2
−1
0
1
2
3
surfa
ce albedo
water vapor
lapse ra
te
Planck
clouds
total
Global average
1) Pro esses ontrolling humidity 13/31
What impa t on limate sensitivity?◮ radiative kernel de omposition (Soden et al 2008)
decrease in
humidityupper−tropospheric
controldiffusive advection insufficient condensationexcessive détrainement
feed
back
par
amet
er (
W/m
2/K
)
−4
−3
−2
−1
0
1
2
3
surfa
ce albedo
water vapor
lapse ra
te
Planck
clouds
total
Global average
1) Pro esses ontrolling humidity 13/31
What impa t on limate sensitivity?◮ radiative kernel de omposition (Soden et al 2008)
decrease in
humidityupper−tropospheric
controldiffusive advection insufficient condensationexcessive détrainement
feed
back
par
amet
er (
W/m
2/K
)
−4
−3
−2
−1
0
1
2
3
surfa
ce albedo
water vapor
lapse ra
te
Planck
clouds
total
Global average
1) Pro esses ontrolling humidity 13/31
What impa t on limate sensitivity?◮ radiative kernel de omposition (Soden et al 2008)
decrease in
humidityupper−tropospheric
decrease infrontal clouds
decreasein low−levelsubtropicalmarine clouds
controldiffusive advection insufficient condensationexcessive détrainement
feed
back
par
amet
er (
W/m
2/K
)
−4
−3
−2
−1
0
1
2
3
surfa
ce albedo
water vapor
lapse ra
te
Planck
clouds
total
Global average
1) Pro esses ontrolling humidity 13/31
Summary on relative humidity◮ Water vapor isotope measurements as observationaldiagnosti s to understand the reasons for a moist bias in limate models
1) Pro esses ontrolling humidity 14/31
Summary on relative humidity◮ Water vapor isotope measurements as observationaldiagnosti s to understand the reasons for a moist bias in limate models◮ Ex essive verti al di�usion during water vapor transport is awidespread ause of moist bias in limate models
1) Pro esses ontrolling humidity 14/31
Summary on relative humidity◮ Water vapor isotope measurements as observationaldiagnosti s to understand the reasons for a moist bias in limate models◮ Ex essive verti al di�usion during water vapor transport is awidespread ause of moist bias in limate models◮ Understanding this reason is all the more important ashumidity hange proje tions depends on the reason for themoist bias
1) Pro esses ontrolling humidity 14/31
Summary on relative humidity◮ Water vapor isotope measurements as observationaldiagnosti s to understand the reasons for a moist bias in limate models◮ Ex essive verti al di�usion during water vapor transport is awidespread ause of moist bias in limate models◮ Understanding this reason is all the more important ashumidity hange proje tions depends on the reason for themoist bias◮ Consequen es on limate hange ompli ated by dynami aland loud feedba ks1) Pro esses ontrolling humidity 14/31
2) Conve tive pro esses◮ mi rophysi al pro esses? (Emanuel and Pierrehumbert 1996)
reevaporationdowndraftsunsaturated
detrainement onve tive onve tiveas ent subsiden eradiative100hPa
2) Conve tive pro esses 15/31
Pro esses along squall lines◮ rain sampled every 5 mins in Niamey during AMMA ampaign
−8−7.5
−7−6.5
0 50 100 150 200 250
15km
200km
time (minutes after the start of the rain)
11 August 2006(Risi et al 2010b QJRMS)
convective zone stratiform zone
convectiveascent
front−to−rear flowmeso−scale
ascent
meso−scale descent
rear−to−front flow
cold poolfrontgust
δ18O (h)2) Conve tive pro esses 16/31
Pro esses along squall lines◮ rain sampled every 5 mins in Niamey during AMMA ampaign◮ interpretation with 2D model of transport/mi rophysi s
−8−7.5
−7−6.5
0 50 100 150 200 250
15km
200km
time (minutes after the start of the rain)
11 August 2006(Risi et al 2010b QJRMS)
convective zone stratiform zone
convectiveascent
front−to−rear flowmeso−scale
ascent
meso−scale descent
rear−to−front flow
cold poolfrontgust
δ18O (h)2) Conve tive pro esses 16/31
Pro esses along squall lines◮ rain sampled every 5 mins in Niamey during AMMA ampaign◮ interpretation with 2D model of transport/mi rophysi s
−8−7.5
−7−6.5
0 50 100 150 200 250
15km
200km
time (minutes after the start of the rain)
11 August 2006(Risi et al 2010b QJRMS)
convective zone stratiform zone
rainenrichmentthroughreevaporation
convectiveascent
front−to−rear flowmeso−scale
ascent
meso−scale descent
rear−to−front flow
cold poolfrontgust
δ18O (h)2) Conve tive pro esses 16/31
Pro esses along squall lines◮ rain sampled every 5 mins in Niamey during AMMA ampaign◮ interpretation with 2D model of transport/mi rophysi s
−8−7.5
−7−6.5
0 50 100 150 200 250
15km
200km
time (minutes after the start of the rain)
11 August 2006(Risi et al 2010b QJRMS)
convective zone stratiform zone
subsidenceof dry anddepleted airrainenrichmentthroughreevaporation
convectiveascent
front−to−rear flowmeso−scale
ascent
meso−scale descent
rear−to−front flow
cold poolfrontgust
δ18O (h)2) Conve tive pro esses 16/31
Conve tive/large-s ale �uxes ompensatingsubsiden e large-s aleas ent
reevaporationdetrainement onve tive
large-s ale ondensation
downdraftsunsaturated onve tive s heme large-s ale
onve tiveas ent100hPa
2) Conve tive pro esses 17/31
Conve tive/large-s ale �uxes
more di�usive verti al adve tionstronger ondensate detrainementless large-s ale ondensationless large-s ale pre ipitation ontrol: AR4
ompensatingsubsiden e large-s aleas entverti al di�usionreevaporationdetrainement onve tive
large-s ale ondensation
downdraftsunsaturated onve tive s heme large-s ale
onve tiveas ent
Sensitivity tests with LMDZ:
100hPa
2) Conve tive pro esses 17/31
Conve tive ontribution to water budget−50
−40
−30
−20
−10
0
10
20
−0.4 −0.2 0 0.2 0.4 0.6 0.8 1
Amazon600hPaTES data∆
δD
DJF-JJA(h)
∆PLS/∆Ptot DJF-JJA ontrolverti al adve tion more di�usivestronger ondensate detrainmentless large-s ale ondensationless large-s ale pre ipitation
2) Conve tive pro esses 18/31
Conve tive ontribution to water budget−50
−40
−30
−20
−10
0
10
20
−0.4 −0.2 0 0.2 0.4 0.6 0.8 1
Amazon600hPaTES data∆
δD
DJF-JJA(h)
∆PLS/∆Ptot DJF-JJAPconv PLS
enri hmentby ondensateevaporation by ondensatedepletionloss ontrolverti al adve tion more di�usivestronger ondensate detrainmentless large-s ale ondensationless large-s ale pre ipitation
100hPa
subsiden e verti al di�usionas ent, ondensation onve tivedetrainement
environmental large-s alelarge-s ale
2) Conve tive pro esses 18/31
Conve tive ontribution to water budget−50
−40
−30
−20
−10
0
10
20
−0.4 −0.2 0 0.2 0.4 0.6 0.8 1
Amazon600hPaTES data∆
δD
DJF-JJA(h)
∆PLS/∆Ptot DJF-JJAPconv PLS
enri hmentby ondensateevaporation by ondensatedepletionloss ontrolverti al adve tion more di�usivestronger ondensate detrainmentless large-s ale ondensationless large-s ale pre ipitation
100hPa
subsiden e verti al di�usionas ent, ondensation onve tivedetrainement
environmental large-s alelarge-s ale
◮ PLS/Ptot ill-de�ned quantity, but in�uen es loudiness,intra-seasonal variability, hemi al tra or transport2) Conve tive pro esses 18/31
Summary on onve tion1000
800
600
400
200100
P (hPa)
environmentalas entunsaturatedreevaporation
large-s aleas entin-situ ondensation
downdrafts onve tivesubsiden e onve tion vs large-s aleunsaturated downdraftsrain reevaporation
2) Conve tive pro esses 19/31
Summary on onve tion1000
800
600
400
200100
P (hPa)
environmentalas entunsaturatedreevaporation
large-s aleas entin-situ ondensation
downdrafts onve tivesubsiden e onve tion vs large-s aleunsaturated downdraftsrain reevaporation
◮ Perspe tives:◮ New physi s of LMDZ for AR5 (Rio et al 2009)
2) Conve tive pro esses 19/31
Summary on onve tion1000
800
600
400
200100
P (hPa)
environmentalas entunsaturatedreevaporation
large-s aleas entin-situ ondensation
downdrafts onve tivesubsiden e onve tion vs large-s aleunsaturated downdraftsrain reevaporation
◮ Perspe tives:◮ New physi s of LMDZ for AR5 (Rio et al 2009)◮ Design diagnosti s based on loud pro esses/isotopes link:
◮ High frequen y data: e.g. ground-based remote-sensing◮ A-train synergy: TES+CALIPSO/Cloudsat2) Conve tive pro esses 19/31
Summary on onve tion1000
800
600
400
200100
P (hPa)
environmentalas entunsaturatedreevaporation
large-s aleas entin-situ ondensation
downdrafts onve tivesubsiden e onve tion vs large-s aleunsaturated downdraftsrain reevaporation
◮ Perspe tives:◮ New physi s of LMDZ for AR5 (Rio et al 2009)◮ Design diagnosti s based on loud pro esses/isotopes link:
◮ High frequen y data: e.g. ground-based remote-sensing◮ A-train synergy: TES+CALIPSO/Cloudsat
◮ impa t of misrepresentation of onve tive pro esses onpre ipitation hanges?2) Conve tive pro esses 19/31
3) Land atmosphere feedba ks
precipitation
atmospheric properties
soil moisture
surface fluxes− evaporation− transpiration− sensible heat flux
− moisture− boundary layer
3) Land-atmosphere feedba ks 20/31
3) Land atmosphere feedba ks◮ inter-model spread (Koster et al, Guo et al 2006)
uncertaintiesin atmospheric
component
uncertaintiesin land surface
component
precipitation
atmospheric properties
soil moisture
surface fluxes− evaporation− transpiration− sensible heat flux
− moisture− boundary layer
3) Land-atmosphere feedba ks 20/31
3) Land atmosphere feedba ks◮ inter-model spread (Koster et al, Guo et al 2006)
uncertaintiesin atmospheric
component
uncertaintiesin land surface
component
precipitation
atmospheric properties
soil moisture
surface fluxes− evaporation− transpiration− sensible heat flux
− moisture− boundary layer
continentalrecycling
partitionning
3) Land-atmosphere feedba ks 20/31
Evapo-transpiration partitioning◮ ORCHIDEE-iso land surfa e model (Risi et al in rev,a)
3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 5 5.2−0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
controlstomatal resistance /5no drainage, only surface runoffsoil capacity /2less vegetation coverroot extraction depth /4
Le Bray (France)
observations
D
E/I (%)δ
18O
soil−
δ18O
p
(h)Rs
ET Pmoisturesoil I
3) Land-atmosphere feedba ks 21/31
Evapo-transpiration partitioning◮ ORCHIDEE-iso land surfa e model (Risi et al in rev,a)
3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 5 5.2−0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
controlstomatal resistance /5no drainage, only surface runoffsoil capacity /2less vegetation coverroot extraction depth /4
Le Bray (France)
observations
D
E/I (%)δ
18O
soil−
δ18O
p
(h)Rs
ET Pmoisturesoil I
3) Land-atmosphere feedba ks 21/31
Does it matter for limate hange?P
rese
nt−
day
more bare soil+stronger stomatalresistan eORCHIDEEo�-line simulationsat Le Bray: ontrol
δ18Os (h)-6-5-4-3 1hqsoil (mm)
240260220
5%0
10%0.40.81.2
ET (mm/d)E/ET (%)1020300
3) Land-atmosphere feedba ks 22/31
Does it matter for limate hange?P
rese
nt−
day
clim
ate
chan
ge more bare soil+stronger stomatalresistan eORCHIDEEo�-line simulationsat Le Bray: ontrol
δ18Os (h)-6-5-4-3 1hqsoil (mm)
240260220
5%0
10%0.40.81.2
ET (mm/d)E/ET (%)1020300de reases by 50%0
-100-150-50 40% less
∆qsoil (mm/d)Pre ipitation
de rease3) Land-atmosphere feedba ks 22/31
Does it matter for limate hange?P
rese
nt−
day
clim
ate
chan
ge
Temperaturein reases by 4K60% morein rease
∆ ET (mm/d)0.40.30.20.10more bare soil+stronger stomatalresistan e
ORCHIDEEo�-line simulationsat Le Bray: ontrol
δ18Os (h)-6-5-4-3 1hqsoil (mm)
240260220
5%0
10%0.40.81.2
ET (mm/d)E/ET (%)1020300de reases by 50%0
-100-150-50 40% less
∆qsoil (mm/d)Pre ipitation
de rease3) Land-atmosphere feedba ks 22/31
Impa t on response to pre ipitationFun tional relationships (Koster and Milly 1996)
0 50 100 150 200 250 0
1
2
3
4
ET
ET+R
control
ET(mm)
soil moisture (mm)3) Land-atmosphere feedba ks 23/31
Impa t on response to pre ipitationFun tional relationships (Koster and Milly 1996)
0 50 100 150 200 250 0
1
2
3
4
E remains strongin winter despitelow soil moistureand limits runoff
controlmore bare soil, stronger stomatal resistance
ET
ET+R
ET(mm)
soil moisture (mm)3) Land-atmosphere feedba ks 23/31
Impa t on response to pre ipitationFun tional relationships (Koster and Milly 1996)
0 50 100 150 200 250 0
1
2
3
4
E remains strongin winter despitelow soil moistureand limits runoff
controlmore bare soil, stronger stomatal resistance
ET
ET+R
P
ET(mm)
soil moisture (mm)3) Land-atmosphere feedba ks 23/31
Impa t on response to pre ipitationFun tional relationships (Koster and Milly 1996)
0 50 100 150 200 250 0
1
2
3
4
E remains strongin winter despitelow soil moistureand limits runoff
controlmore bare soil, stronger stomatal resistance
ET
ET+R
P
ET(mm)
soil moisture (mm)3) Land-atmosphere feedba ks 23/31
Impa t on response to pre ipitationFun tional relationships (Koster and Milly 1996)
0 50 100 150 200 250 0
1
2
3
4
E remains strongin winter despitelow soil moistureand limits runoff
controlmore bare soil, stronger stomatal resistance
P decreasesET
ET+R
P
ET(mm)
soil moisture (mm)3) Land-atmosphere feedba ks 23/31
Impa t on response to pre ipitationFun tional relationships (Koster and Milly 1996)
0 50 100 150 200 250 0
1
2
3
4
E remains strongin winter despitelow soil moistureand limits runoff
controlmore bare soil, stronger stomatal resistance
P decreasesET
ET+R
P
ET(mm)
soil moisture (mm)soil moisture de reases less strongly3) Land-atmosphere feedba ks 23/31
Summary on evapo-transpiration partitioning◮ Isotopi measurements an dete t misrepresentation in baresoil evaporation/transpiration partitioning, even if it has noimpa t on traditional observable variables
3) Land-atmosphere feedba ks 24/31
Summary on evapo-transpiration partitioning◮ Isotopi measurements an dete t misrepresentation in baresoil evaporation/transpiration partitioning, even if it has noimpa t on traditional observable variables◮ This partitioning impa ts the land surfa e hydrologi alresponse to limate hange, even if it doesn't impa tpresent-day hydrology
3) Land-atmosphere feedba ks 24/31
Summary on evapo-transpiration partitioning◮ Isotopi measurements an dete t misrepresentation in baresoil evaporation/transpiration partitioning, even if it has noimpa t on traditional observable variables◮ This partitioning impa ts the land surfa e hydrologi alresponse to limate hange, even if it doesn't impa tpresent-day hydrology◮ Perspe tives:
◮ To what extent does it ontribute to inter-model spread inhydrologi al proje tions?3) Land-atmosphere feedba ks 24/31
Summary on evapo-transpiration partitioning◮ Isotopi measurements an dete t misrepresentation in baresoil evaporation/transpiration partitioning, even if it has noimpa t on traditional observable variables◮ This partitioning impa ts the land surfa e hydrologi alresponse to limate hange, even if it doesn't impa tpresent-day hydrology◮ Perspe tives:
◮ To what extent does it ontribute to inter-model spread inhydrologi al proje tions?◮ To what extent does it feedba k on pre ipitation hanges?
3) Land-atmosphere feedba ks 24/31
Isotopi signature of evaporative origin
5 10 15 20 25 30 4050 60 70 80 90
120W 60W 0 60E 120E
60N
30N
Eq
30S
60S
Water tagging:ET P
evapo-transpiration (rcon)% vapor from ontinental3) Land-atmosphere feedba ks 25/31
Isotopi signature of evaporative origin
−30 −24 −18 −12 −6 0
120
−30
0
60
90
30
5 10 15 20 25 30 4050 60 70 80 90
120W 60W 0 60E 120E
60N
30N
Eq
30S
60S
Water tagging:
d-ex ess(h)
δ18
O (h)
monthly, all tropi al land pointsPDF of vapor ompositionbare soilevaporation
transpirationtotal vaporo eani vapor
ET P
evapo-transpiration (rcon)% vapor from ontinental3) Land-atmosphere feedba ks 25/31
Water isotopes and ontinental re y lingKoster et al 2006
Land-atmosphere feedba ks"hot spots"
de rease in pre ip varian e when soil moisture is pres ribed
3) Land-atmosphere feedba ks 26/31
Water isotopes and ontinental re y ling
120E60E060W120W
30S
Eq
30N
60N
60S−0.8
−0.6
−0.4
0.2−0.2
0.4
0.6
0.8
Koster et al 2006 orrelation δ18O - rcon, intra-seasonal s ale, annual meanLand-atmosphere feedba ks"hot spots"
de rease in pre ip varian e when soil moisture is pres ribed
3) Land-atmosphere feedba ks 26/31
Diagnosing land-atmosphere feedba ksET րsoil moisture րmoisture onvergen e ր P ր
P ր
strong pre ipitation omposite minus seasonal average:
3) Land-atmosphere feedba ks 27/31
Diagnosing land-atmosphere feedba ks
30S
Eq
30N
60N
60W120W 0 60E 120E
−10 −5 −2 2 5 10
ET րsoil moisture րmoisture onvergen e ր P ր
P ր
strong pre ipitation omposite minus seasonal average:∆rcon (%) JJA
3) Land-atmosphere feedba ks 27/31
Isotopi signature of feedba ks
60W120W 0 60E 120E
−10 −5 −2 2 5 10
30S
Eq
30N
60N
8 1240−4
−4 0 4 8 12 16
04
−4
8
12
−8−12Am
azon
, DJF
Wes
tern
US
, JJA
−4
0
4
8
12
16
positivefeedba kland-atmosphere ontrol bylarge-s ale onvergen e
∆rcon (%) JJA∆
δD
v(h) Strong pre ipitation ompositeminus seasonal average:
∆rcon (%)
∆rcon (%)∆
δD
v
(h)
3) Land-atmosphere feedba ks 28/31
Could we dis riminate bewteen di�erentsimulated feedba ks?Eq
30N
60N
30S
−10 −5 −2 2 5 10
Eq
30N
60N
30S
60W120W 0 60E 120E
60W120W 0 60E 120E
control
less bare soil
∆rcon (%) JJA, ontrol3) Land-atmosphere feedba ks 29/31
Could we dis riminate bewteen di�erentsimulated feedba ks?Eq
30N
60N
30S
−10 −5 −2 2 5 10
Eq
30N
60N
30S
60W120W 0 60E 120E
60W120W 0 60E 120E
−3 −2 −1 0 1 2 3 4 5−5
−4
−3
−2
−1
0
−8−6−4−2 0 2 4 6
−8 −6 −4 −2 0 2 4 6
control
less bare soil
Am
azon
DJF
Wes
tern
US
, JJA
ontrolless bare soil∆rcon (%) JJA, ontrol
∆rcon (%)
∆δD
v
(h)∆rcon (%)
∆δD
v
(h)
positiveland-atmospherefeedba klarge-s ale onvergen e ontrol by3) Land-atmosphere feedba ks 29/31
Summary on land-atmosphere feedba ks◮ Water vapor isotopes re ord land-atmosphere feedba ks atintra-seasonal s ale and ould help dis riminate betweendi�erent simulations
3) Land-atmosphere feedba ks 30/31
Summary on land-atmosphere feedba ks◮ Water vapor isotopes re ord land-atmosphere feedba ks atintra-seasonal s ale and ould help dis riminate betweendi�erent simulations◮ Perspe tives:
◮ Use data: in-situ, satellite (GOSAT)3) Land-atmosphere feedba ks 30/31
Summary on land-atmosphere feedba ks◮ Water vapor isotopes re ord land-atmosphere feedba ks atintra-seasonal s ale and ould help dis riminate betweendi�erent simulations◮ Perspe tives:
◮ Use data: in-situ, satellite (GOSAT)◮ Robustness of isotopi diagnosti s?
◮ model inter- omparisons: ORCHIDEE, isoLSM, soon CLMand ORCHIDEE-multi-layer3) Land-atmosphere feedba ks 30/31
Summary on land-atmosphere feedba ks◮ Water vapor isotopes re ord land-atmosphere feedba ks atintra-seasonal s ale and ould help dis riminate betweendi�erent simulations◮ Perspe tives:
◮ Use data: in-situ, satellite (GOSAT)◮ Robustness of isotopi diagnosti s?
◮ model inter- omparisons: ORCHIDEE, isoLSM, soon CLMand ORCHIDEE-multi-layer◮ Relevan e for hydrologi al proje tions?
◮ Do some pro esses determine behavior at intra-seasonals ales, and in ontext of- global warming- land use hange (deforestation, irrigation)3) Land-atmosphere feedba ks 30/31
Con lusion200
400
800
600
100P (hPa)
unsaturateddowndraftsreevaporationsurfa ewaterbudget onve tionvs large-s ale ontinentalre y lingadve tiondi�usive
Con lusion 31/31
Con lusion200
400
800
600
100P (hPa)
unsaturateddowndraftsreevaporationsurfa ewaterbudget onve tionvs large-s ale ontinentalre y lingadve tiondi�usive
◮ Ultimate goal: isotopi diagnosti s to evaluate models andtheir proje tions:Con lusion 31/31
Con lusion200
400
800
600
100P (hPa)
unsaturateddowndraftsreevaporationsurfa ewaterbudget onve tionvs large-s ale ontinentalre y lingadve tiondi�usive
◮ Ultimate goal: isotopi diagnosti s to evaluate models andtheir proje tions:◮ new isotopi data
Con lusion 31/31
Con lusion200
400
800
600
100P (hPa)
unsaturateddowndraftsreevaporationsurfa ewaterbudget onve tionvs large-s ale ontinentalre y lingadve tiondi�usive
◮ Ultimate goal: isotopi diagnosti s to evaluate models andtheir proje tions:◮ new isotopi data◮ new model-data omparison methodologies
Con lusion 31/31
Con lusion200
400
800
600
100P (hPa)
unsaturateddowndraftsreevaporationsurfa ewaterbudget onve tionvs large-s ale ontinentalre y lingadve tiondi�usive
◮ Ultimate goal: isotopi diagnosti s to evaluate models andtheir proje tions:◮ new isotopi data◮ new model-data omparison methodologies◮ model inter- omparisonsCon lusion 31/31
Con lusion200
400
800
600
100P (hPa)
unsaturateddowndraftsreevaporationsurfa ewaterbudget onve tionvs large-s ale ontinentalre y lingadve tiondi�usive
◮ Ultimate goal: isotopi diagnosti s to evaluate models andtheir proje tions:◮ new isotopi data◮ new model-data omparison methodologies◮ model inter- omparisons◮ pro ess/feedba ks studies omparing models behavior forpresent limate and for proje tionsCon lusion 31/31
Suplementary material
Supplementary material 32/31
Evaluation against SCIAMACHY120W180W 060W 60E 120E 180E
30S
Eq
30N
30S
Eq
30N
120W180W 060W 60E 120E 180E
δD (h) total olumn-200 -180 -170 -160 -150 -140 -130 -120 -110 -100
-80 -60 -40 -20 -10 10 20 40 60 80∆δD (h) total olumn JJA-DJF
LMDZ ontrolSCIAMACHY data
Risi et al in rev,bSupplementary material 33/31
Evaluation against TES30S
Eq
30N
30S
Eq
30N
120W180W 060W 60E 120E 180E120W180W 060W 60E 120E 180E
−230 −220 −210 −200 −190 −180 −170 −160 −150
120W180W 060W 60E 120E 180E
30S
Eq
30N
120W180W 060W 60E 120E 180E
−80 −50 −30 −20 −10 10 20 30 50 80
TES data LMDZ (-31h)
LMDZTES data δD (h) 600hPa annual mean
∆δD (h) 600hPa JJA-DJF Risi et al in rev,bSupplementary material 34/31
What auses the moist bias?
−60
−40
−20
0
20
40
60
20 25 30 35 40 45 50 55
Ex essive ondensatedetrainementInsu� ientin-situ ondensationverti al adve tionEx essively di�usiveControl
ECHAMMIROCHadAM GSMGISSSWING2 models:CAM2verti al resolution
400 hPa, 15◦N-30◦N mean
relative humidity (%)JJA-DJF∆δD(h)
Sensitivity tests:with LMDZ: ACE/AIRSdata
Supplementary material 35/31
Consequen es on proje tions 150
200
250
300
350 0 50 100 150 200 250 25 30 35 40 45 50 55 60 65 70 75
−2.5
−2
−1.5
−1
−0.5
0
0.5
1
1.5
AIRS Relative humidity (%)present-day RH (%/K)
∆
RH(%/K)
Pressure(hPa)
tropi al average, 200hPa, 2xCO2
σq /10ǫp/2
ontrol simulationdi�usive adve tion EverythingLMDZ tests pre ipitates:presentSupplementary material 36/31
Consequen es on proje tions 150
200
250
300
350
(Harmann and Larson 2002)
fixed anviltemperature
0 50 100 150 200 250 25 30 35 40 45 50 55 60 65 70 75−2.5
−2
−1.5
−1
−0.5
0
0.5
1
1.5
AIRS Relative humidity (%)present-day RH (%/K)
∆
RH(%/K)
Pressure(hPa)
tropi al average, 200hPa, 2xCO2
σq /10ǫp/2 present2xCO2 ontrol simulationdi�usive adve tion EverythingLMDZ tests pre ipitates:
Supplementary material 36/31
Consequen es on proje tions 150
200
250
300
350
(Harmann and Larson 2002)
fixed anviltemperature
0 50 100 150 200 250 25 30 35 40 45 50 55 60 65 70 75−2.5
−2
−1.5
−1
−0.5
0
0.5
1
1.5
AIRS Relative humidity (%)present-day RH (%/K)
∆
RH(%/K)
Pressure(hPa)
tropi al average, 200hPa, 2xCO2
σq /10ǫp/2 present2xCO2 ontrol simulationdi�usive adve tion EverythingLMDZ tests pre ipitates: Additionalupward transportpresentSupplementary material 36/31
Consequen es on proje tions 150
200
250
300
350
(Harmann and Larson 2002)
fixed anviltemperature
0 50 100 150 200 250
less warmanomaly
anomaly warm
25 30 35 40 45 50 55 60 65 70 75−2.5
−2
−1.5
−1
−0.5
0
0.5
1
1.5
AIRS Relative humidity (%)present-day RH (%/K)
∆
RH(%/K)
Pressure(hPa)
tropi al average, 200hPa, 2xCO2
σq /10ǫp/2 present2xCO2 2xCO2present ontrol simulationdi�usive adve tion EverythingLMDZ tests pre ipitates: Additionalupward transport
Supplementary material 36/31
Consequen es on proje tions 150
200
250
300
350
(Harmann and Larson 2002)
fixed anviltemperature
0 50 100 150 200 250
less warmanomaly
anomaly warm
25 30 35 40 45 50 55 60 65 70 75−2.5
−2
−1.5
−1
−0.5
0
0.5
1
1.5
AIRS Relative humidity (%)present-day RH (%/K)
∆
RH(%/K)
Pressure(hPa)
tropi al average, 200hPa, 2xCO2
σq /10ǫp/2 present2xCO2 2xCO2present ontrol simulationdi�usive adve tion EverythingLMDZ tests pre ipitates: Additionalupward transportCMIP3 modelsSupplementary material 36/31
Upper troposphere detrainment120W180W 060W 60E 120E 180E
30S
Eq
30N
60N
60S
-700 -640 -600 -560 -520 -480 -440 -400 -360-320δD (h)
MIPAS data at 200hPa, annual
Supplementary material 37/31
Upper troposphere detrainment120W180W 060W 60E 120E 180E
30S
Eq
30N
60N
60S
120W180W 060W 60E 120E 180E
30S
Eq
30N
60N
60S-700 -640 -600 -560 -520 -480 -440 -400 -360-320
LMDZ ontrol
δD (h)
MIPAS data at 200hPa, annual
Supplementary material 37/31
Upper troposphere detrainment120W180W 060W 60E 120E 180E
30S
Eq
30N
60N
60S
120W180W 060W 60E 120E 180E
30S
Eq
30N
60N
60S
50
0 0.01 0.02 0.03
0.011
0.012
0.013
0.014
0.015
0.04
−5
0
5
10
15
20
−10
-700 -640 -600 -560 -520 -480 -440 -400 -360-320
LMDZ ontrol
ontrolverti al adve tion more di�usivestronger ondensate detrainmentless large-s ale ondensationless large-s ale pre ipitation
Di�eren e 15◦S-15◦N minus
δD (h)
MIPAS data at 200hPa, annual
∆δD
(h)30◦S-30◦N at 200hPa
∆q
(g/kg)
moistening by detrainement(g/kg/day)
ACE: 30hMIPAS: 50h
Supplementary material 37/31
Upper troposphere detrainment120W180W 060W 60E 120E 180E
30S
Eq
30N
60N
60S
120W180W 060W 60E 120E 180E
30S
Eq
30N
60N
60S
50
0 0.01 0.02 0.03
0.011
0.012
0.013
0.014
0.015
0.04
−5
0
5
10
15
20
−10
-700 -640 -600 -560 -520 -480 -440 -400 -360-320
LMDZ ontrol CAMMIROCHadAMGISSECHAMGSM
ontrolverti al adve tion more di�usivestronger ondensate detrainmentless large-s ale ondensationless large-s ale pre ipitation
Di�eren e 15◦S-15◦N minus
δD (h)
MIPAS data at 200hPa, annual
∆δD
(h)30◦S-30◦N at 200hPa
∆q
(g/kg)
moistening by detrainement(g/kg/day)
ACE: 30hMIPAS: 50h
Supplementary material 37/31
Soil water isotopes
30S30N60N60SEq
-4 -2 0 2 4 6 8120W 60W 0 60E 120E
120W 60W 0 60E 120E30S30N60N60SEq
LMDZ-ORCHIDEE
δ18Osoil − δ18Oprecip (h)
GNIP+USNIP+MIBA
Supplementary material 38/31
Estimating evapotranspiration partitioning60N
30N
Eq
30S
60S
1 3 5 7 10 15 20 30 50 70 90 95
120W 60W 0 60E 120E
δ18Osoil − δ18Op
δ18OvRH, T
E/P (%)
isotopi "measurements"estimated from simulated
Supplementary material 39/31
Estimating evapotranspiration partitioning60N
30N
Eq
30S
60S
60N
30N
Eq
30S
60S
1 3 5 7 10 15 20 30 50 70 90 95
120W 60W 0 60E 120E
δ18Osoil − δ18Op
δ18OvRH, T
120W 60W 0 60E 120Esimulated by LMDZ-ORCHIDEE
E/P (%)
isotopi "measurements"estimated from simulated
r=0.91Supplementary material 39/31
Estimating ontinental re y ling 0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
y=x
Risi et al 2010d
1 − rcon
rcon
June-July 2006 in Niamey, daily
dδvoce
dxknown x estimat
edre y ling
simulated re y lingd ( r on1− r on)
/dx =
dδv/dx − dδvo e/dxδp − δvSupplementary material 40/31
Estimating ontinental re y ling 0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
y=x
Risi et al 2010ddδvoce
dxdependslinearly on pre ipitation
1 − rcon
rcon
June-July 2006 in Niamey, daily
dδvoce
dxknown x estimat
edre y ling
simulated re y lingd ( r on1− r on)
/dx =
dδv/dx − dδvo e/dxδp − δvSupplementary material 40/31
Estimating ontinental re y ling 0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
y=x
Risi et al 2010ddδvoce
dxdependslinearly on pre ipitation
1 − rcon
rcon
June-July 2006 in Niamey, daily
dδvoce
dxknown x estimat
edre y ling
simulated re y lingd ( r on1− r on)
/dx =
dδv/dx − dδvo e/dxδp − δv
◮ Main limitation in using vapor isotopi measurements for ontinental re y ling: understanding atmospheri ontrolsSupplementary material 40/31
Monitoring land-atmosphere feedba ksrelated to land use hange or global warming90W 30W60W
-80 -50 -20-5 5 20 50 80 3020105-5-10-20-30
Amazon, JJA
40S20SEq20N
40S20SEq20N
90W 30W60WDeforestation
4xCO2∆rcon (%)pre ipitation hange (%)Supplementary material 41/31
Monitoring land-atmosphere feedba ksrelated to land use hange or global warming90W 30W60W
-80 -50 -20-5 5 20 50 80 3020105-5-10-20-30
-40 -30 -20 -10 0-21-10
∆rcon (%)
Amazon, JJA
40S20SEq20N
40S20SEq20N
90W 30W60WDeforestation
4xCO2∆rcon (%)pre ipitation hange (%)
∆δ
18O
v
(h)
Supplementary material 41/31
Monitoring land-atmosphere feedba ksrelated to land use hange or global warming90W 30W60W
-80 -50 -20-5 5 20 50 80 3020105-5-10-20-30 0 10 20 30 40
-40 -30 -20 -10 0-21-10
∆rcon (%)40S20SEq20N
90W 30W60W90W 30W60W 40S20SEq20N
135462
Amazon, JJA
40S20SEq20N
40S20SEq20N
90W 30W60WDeforestation
4xCO2∆rcon (%)pre ipitation hange (%)
∆δ
18O
v
(h)∆
δ18O
v
(h)
∆rcon (%)Supplementary material 41/31
Impa t on response to temperature
1
2
3
ET
P
ET+R
200 250 150 100
controlmore bare soil, stronger stomatal resistance
ET(mm)
soil moisture (mm)Supplementary material 42/31
Impa t on response to temperature
1
2
3
ET
P
ET+R
E is more sensitivethan T totemperature
200 250 150 100
controlmore bare soil, stronger stomatal resistance
+4K
ET(mm)
soil moisture (mm)Supplementary material 42/31
Impa t on response to temperature
1
2
3
ET
incr
ease
sm
ore
stro
ngly
ET
P
ET+R
E is more sensitivethan T totemperature
200 250 150 100
controlmore bare soil, stronger stomatal resistance
+4K
ET(mm)
soil moisture (mm)Supplementary material 42/31