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Using water stable isotopi measurements tobetter evaluate the atmospheri and land surfaeomponents of limate modelsCamille RisiCIRES, Boulderwith ontribution of:S Bony, D NooneTES: J Worden, J Lee, D Brown,SCIAMACHY: C Frankenberg,MIPAS: G Stiller, M Kiefer, B FunkeACE-FTS: K Walker, P Bernath,FTIR: M Shneider, D Wunh, P Wennberg,V Sherlok, N Deutsher, D Gri�thin-situ: R Uemura, D YakirSWING2: C SturmMIBA: J. Ogée, T. Baria, L. Wingate, N. Raz-YaseefCDG seminar at NCAR, 5 April 2011
Unertainties in limate projetionsglobal
warming
IPCC AR4
multi−model mean
surf
ace
glob
al w
arm
ing
(°C
)
increaseCO2
Introdution 2/27
Unertainties in limate projetionsglobal
warming
climatefeedbacks(clouds,
water vapor)
in climate modelsUncertainties
IPCC AR4
clouds
relative humidity
boundary layershallow convectiondeep convection
multi−model mean
surf
ace
glob
al w
arm
ing
(°C
)
increaseCO2
Introdution 2/27
Unertainties in limate projetions
precipitation change by 2099 (%)
globalwarming
regionalchanges in
precipitation
in climate modelsUncertainties
IPCC AR4
IPCC AR4
clouds
relative humidity
boundary layershallow convectiondeep convection
climatefeedbacks
water vapor)(clouds,
multi−model mean
surf
ace
glob
al w
arm
ing
(°C
)
increaseCO2
Introdution 2/27
Unertainties in limate projetions
precipitation change by 2099 (%)
globalwarming
regionalchanges in
precipitation
in climate modelsUncertainties
atmospherefeedbacks
land−
IPCC AR4
IPCC AR4
clouds
relative humidity
boundary layershallow convectiondeep convection
land surfacehydrological
changeschangeland use
climatefeedbacks
water vapor)(clouds,
multi−model mean
surf
ace
glob
al w
arm
ing
(°C
)
increaseCO2
Introdution 2/27
Unertainties in limate projetions
precipitation change by 2099 (%)
globalwarming
regionalchanges in
precipitation
in climate modelsUncertainties
atmospherefeedbacks
land−
IPCC AR4
IPCC AR4
land surfacehydrology
clouds
relative humidity
boundary layershallow convectiondeep convection
land surfacehydrological
changeschangeland use
climatefeedbacks
water vapor)(clouds,
multi−model mean
surf
ace
glob
al w
arm
ing
(°C
)
increaseCO2
Introdution 2/27
Water isotopi omposition◮ H162 O, HDO, H182 O, H172 O, frationation
HDO
H16
2O
Introdution 3/27
Water isotopi omposition◮ H162 O, HDO, H182 O, H172 O, frationation◮ reords phase hanges
Continental recyclingEvaporation
ConvectionCondensation
Precipitation
HDO
H16
2O
Introdution 3/27
Water isotopi omposition◮ H162 O, HDO, H182 O, H172 O, frationation◮ reords phase hanges
Continental recyclingEvaporation
ConvectionCondensation
Precipitation
HDO
H16
2O
isotopiccomposition
variablesmeteorological
and variability
present−dayclimate processes
physicalfuture climate
Introdution 3/27
Water isotopi omposition◮ H162 O, HDO, H182 O, H172 O, frationation◮ reords phase hanges
Continental recyclingEvaporation
ConvectionCondensation
Precipitation
HDO
H16
2O
isotopiccomposition
processevaluation
variablesmeteorological
and variability
present−dayclimate
combiningdata
processesphysical
future climate
Introdution 3/27
Water isotopi omposition◮ H162 O, HDO, H182 O, H172 O, frationation◮ reords phase hanges
Continental recyclingEvaporation
ConvectionCondensation
Precipitation
HDO
H16
2O
isotopiccomposition
processevaluation
strengthenprojections credibility
variablesmeteorological
and variability
present−dayclimate
combiningdata
processesphysical
future climate
Introdution 3/27
General strategy
controlling isotopicunderstand processes
compositionIntrodution 4/27
General strategy
controlling isotopicunderstand processes
composition
design observable,process−oriented
isotopic diagnostics
Introdution 4/27
General strategy
controlling isotopicunderstand processes
composition
climate modelsapply to
detect bias in models
propose improvements
understand reasons
design observable,process−oriented
isotopic diagnostics
Introdution 4/27
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
Introdution 4/27
Outline
precipitation change by 2099 (%)
1)2)
3)
globalwarming
regionalchanges in
precipitation
in climate modelsUncertainties
atmospherefeedbacks
land−
IPCC AR4
IPCC AR4
land surfacehydrology
clouds
relative humidity
boundary layershallow convectiondeep convection
land surfacehydrological
changeschangeland use
climatefeedbacks
water vapor)(clouds,
multi−model mean
surf
ace
glob
al w
arm
ing
(°C
)
increaseCO2
Introdution 5/27
1) Proesses ontrolling relative humidity◮ tropial/subtropial free tropospheri relative humidity (RH)impats:
◮ water vapor feedbak (Soden et al 2008)◮ louds feedbaks (Sherwood et al 2010)◮ deep onvetion (Derbyshire 2004)
1) Proesses ontrolling humidity 6/27
1) Proesses ontrolling relative humidity◮ tropial/subtropial free tropospheri relative humidity (RH)impats:
◮ water vapor feedbak (Soden et al 2008)◮ louds feedbaks (Sherwood et al 2010)◮ deep onvetion (Derbyshire 2004)
◮ but:◮ signi�ant dispersion in limate models (Sherwood et al 2010)◮ moist bias in the mid/upper troposphere (John and Soden 2005)
1) Proesses ontrolling humidity 6/27
1) Proesses ontrolling relative humidity◮ tropial/subtropial free tropospheri relative humidity (RH)impats:
◮ water vapor feedbak (Soden et al 2008)◮ louds feedbaks (Sherwood et al 2010)◮ deep onvetion (Derbyshire 2004)
◮ but:◮ signi�ant dispersion in limate models (Sherwood et al 2010)◮ moist bias in the mid/upper troposphere (John and Soden 2005)
=⇒ need proess-based evaluation of RH in limate models1) Proesses ontrolling humidity 6/27
1) Proesses ontrolling relative humidity◮ tropial/subtropial free tropospheri relative humidity (RH)impats:
◮ water vapor feedbak (Soden et al 2008)◮ louds feedbaks (Sherwood et al 2010)◮ deep onvetion (Derbyshire 2004)
◮ but:◮ signi�ant dispersion in limate models (Sherwood et al 2010)◮ moist bias in the mid/upper troposphere (John and Soden 2005)
=⇒ need proess-based evaluation of RH in limate models=⇒ Goal: design observational diagnostis to evaluate proessesontrolling RH, detet and understand biases?1) Proesses ontrolling humidity 6/27
Sensitivity tests to RH proesses
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 1996
Wright et al 2009
1) Proesses ontrolling humidity 7/27
Sensitivity tests to RH proesses
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) Proesses ontrolling humidity 7/27
Sensitivity tests to RH proesses
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) Proesses ontrolling humidity 7/27
Sensitivity tests to RH proesses
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 vertial advetion
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) Proesses ontrolling humidity 7/27
Sensitivity tests to RH proesses
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 vertial advetionσq/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) Proesses ontrolling humidity 7/27
Sensitivity tests to RH proesses
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 vertial advetionσq/10
ǫp/2
Couhert et al 2010Folkins and Martin 2005Pierrehumbert 1998Sherwood et al 1996
Wright et al 2009
1) Proesses ontrolling humidity 7/27
Isotopi measurements200
100
0°1000
30°N
P (hPa)
400
600
800
1) Proesses ontrolling humidity 8/27
Isotopi measurementssatellites
(Steinwagner et al 2010)(Nassar et al 2007)
MIPASACE−FTS
200
100
0°1000
30°N
P (hPa)
400
600
800
1) Proesses ontrolling humidity 8/27
Isotopi measurements(Worden et al 2007)TES
satellites
(Steinwagner et al 2010)(Nassar et al 2007)
MIPASACE−FTS
200
100
0°1000
30°N
P (hPa)
400
600
800
1) Proesses ontrolling humidity 8/27
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) Proesses ontrolling humidity 8/27
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) Proesses ontrolling humidity 8/27
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) Proesses ontrolling humidity 8/27
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: olloation; simulations nudged byECMWF; averaging kernels; spatial/temporal variations1) Proesses ontrolling humidity 8/27
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 advetion"LMDZ ontrol
δD
(h)δD
(h)
200hPa, MIPAS
300hPa, ACEδD
(h) SCIAMACHY andground-based FTIRTotal olumn,
δD
(h) Surfae, in-situ
600 hPa, TESδD(h
)1) Proesses ontrolling humidity 9/27
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 advetion"LMDZ ontrol
200hPa, 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
Surfae, in-situ
1) Proesses ontrolling humidity 10/27
What auses the moist biases in GCMs?
−60
−40
−20
0
20
40
60
20 25 30 35 40 45 50 55
Exessive ondensatedetrainementInsu�ientin-situ ondensationvertial advetionExessively di�usiveControl
400 hPa, 15◦N-30◦N mean
relative humidity (%)JJA-DJF∆δD(h)
Sensitivity tests:with LMDZ: ACE/AIRSdata
1) Proesses ontrolling humidity 11/27
What auses the moist biases in GCMs?
−60
−40
−20
0
20
40
60
20 25 30 35 40 45 50 55
Exessive ondensatedetrainementInsu�ientin-situ ondensationvertial advetionExessively di�usiveControl
400 hPa, 15◦N-30◦N mean
relative humidity (%)JJA-DJF∆δD(h)
Sensitivity tests:with LMDZ: ACE/AIRSdata
◮ robustness? additional tests, theoretial understanding1) Proesses ontrolling humidity 11/27
What auses the moist biases in GCMs?
−60
−40
−20
0
20
40
60
20 25 30 35 40 45 50 55
Exessive ondensatedetrainementInsu�ientin-situ ondensationvertial advetionExessively 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, theoretial understanding◮ frequent reason for moist bias=exessively di�usive advetion1) Proesses ontrolling humidity 11/27
What auses the moist biases in GCMs?
−60
−40
−20
0
20
40
60
20 25 30 35 40 45 50 55
Exessive ondensatedetrainementInsu�ientin-situ ondensationvertial advetionExessively di�usiveControl
ECHAMMIROCHadAM GSMGISSSWING2 models:CAM2vertial resolution
400 hPa, 15◦N-30◦N mean
relative humidity (%)JJA-DJF∆δD(h)
Sensitivity tests:with LMDZ: ACE/AIRSdata
◮ robustness? additional tests, theoretial understanding◮ frequent reason for moist bias=exessively di�usive advetion1) Proesses ontrolling humidity 11/27
What impat on humidity projetions?
−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)
tropial average, 200hPa, 2xCO2
ontrol simulationLMDZ testsdi�usive advetionσq /10ǫp/2CMIP3 models
1) Proesses ontrolling humidity 12/27
What impat on humidity projetions?
−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)
tropial average, 200hPa, 2xCO2
ontrol simulationLMDZ testsdi�usive advetionσq /10ǫp/2CMIP3 models
◮ How a moist bias a�et humidity hange projetions dependson the reason for the bias1) Proesses ontrolling humidity 12/27
What impat on humidity projetions?
−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)
tropial average, 200hPa, 2xCO2
ontrol simulationLMDZ testsdi�usive advetionσq /10ǫp/2CMIP3 models
◮ How a moist bias a�et humidity hange projetions dependson the reason for the bias1) Proesses ontrolling humidity 12/27
Summary on relative humidity◮ Water vapor isotope measurements as observationaldiagnostis to understand the reasons for a moist bias inlimate models
1) Proesses ontrolling humidity 13/27
Summary on relative humidity◮ Water vapor isotope measurements as observationaldiagnostis to understand the reasons for a moist bias inlimate models◮ Exessive vertial di�usion during water vaportransport/insu�ient vertial resolution is a widespread auseof moist bias in limate models
1) Proesses ontrolling humidity 13/27
Summary on relative humidity◮ Water vapor isotope measurements as observationaldiagnostis to understand the reasons for a moist bias inlimate models◮ Exessive vertial di�usion during water vaportransport/insu�ient vertial resolution is a widespread auseof moist bias in limate models◮ Understanding this reason is all the more important ashumidity hange projetions depends on the reason for themoist bias
1) Proesses ontrolling humidity 13/27
Summary on relative humidity◮ Water vapor isotope measurements as observationaldiagnostis to understand the reasons for a moist bias inlimate models◮ Exessive vertial di�usion during water vaportransport/insu�ient vertial resolution is a widespread auseof moist bias in limate models◮ Understanding this reason is all the more important ashumidity hange projetions depends on the reason for themoist bias◮ Consequenes on limate hange? -> study feedbaks usingradiative kernel deomposition (Soden et al 2008)1) Proesses ontrolling humidity 13/27
2) Convetive proesses◮ mirophysial proesses? (Emanuel and Pierrehumbert 1996)
reevaporationdowndraftsunsaturated
detrainementonvetive onvetiveasent subsideneradiative100hPa
2) Convetive proesses 14/27
Proesses 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) Convetive proesses 15/27
Proesses along squall lines◮ rain sampled every 5 mins in Niamey during AMMA ampaign◮ interpretation with 2D model of transport/mirophysis
−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) Convetive proesses 15/27
Proesses along squall lines◮ rain sampled every 5 mins in Niamey during AMMA ampaign◮ interpretation with 2D model of transport/mirophysis
−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) Convetive proesses 15/27
Proesses along squall lines◮ rain sampled every 5 mins in Niamey during AMMA ampaign◮ interpretation with 2D model of transport/mirophysis
−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) Convetive proesses 15/27
Convetive/large-sale �uxes
ompensatingsubsidene large-saleasent
reevaporationdetrainementonvetive
large-saleondensation
downdraftsunsaturated
onvetive sheme large-sale
onvetiveasent100hPa
2) Convetive proesses 16/27
Convetive/large-sale �uxes
more di�usive vertial advetionstronger ondensate detrainementless large-sale ondensationless large-sale preipitation
ontrol: AR4
ompensatingsubsidene large-saleasentvertial di�usionreevaporationdetrainementonvetive
large-saleondensation
downdraftsunsaturated
onvetive sheme large-sale
onvetiveasent
Sensitivity tests with LMDZ:
100hPa
2) Convetive proesses 16/27
New TES pro�les
-90 -60 -30 0 30 60800700600500400300200100
90010000 1 2 3 4800700600500400300200100
9001000∆q (g/kg) ∆δD (h)
pressure(hPa)
Amazon, seasonal yle (DJF-JJA)
TES2) Convetive proesses 17/27
New TES pro�les
-90 -60 -30 0 30 60800700600500400300200100
90010000 1 2 3 4800700600500400300200100
9001000∆q (g/kg) ∆δD (h)
pressure(hPa)
Amazon, seasonal yle (DJF-JJA)
TESontrol2) Convetive proesses 17/27
New TES pro�les
-90 -60 -30 0 30 60800700600500400300200100
90010000 1 2 3 4800700600500400300200100
9001000
ontrolvertial advetion more di�usivestronger ondensate detrainmentless in-situ odnensationless in-situ preipitation
∆q (g/kg) ∆δD (h)pressure(hPa)
Amazon, seasonal yle (DJF-JJA)
TES2) Convetive proesses 17/27
Convetive ontribution to water budgetPLSPconv
enrihmentby ondensateevaporation by ondensatedepletionloss100hPa
subsidene vertial di�usionasent,
ondensationonvetivedetrainement
environmental large-salelarge-sale
2) Convetive proesses 18/27
Convetive ontribution to water budget
−0.4 −0.2 0 0.2 0.4 0.6 0.8 1
1.2
1.6
2
1.8
1.4
1
−50
−40
−30
−20
−10
0
10
20 Amazon
∆PLS/∆Ptot DJF-JJA∆
δD
DJF-JJA(h)
∆q
DJF-JJA(g/kg)
600hPaTES data
PLSPconv
enrihmentby ondensateevaporation by ondensatedepletionloss
ontrolvertial advetion more di�usivestronger ondensate detrainmentless large-sale ondensationless large-sale preipitation
100hPa
subsidene vertial di�usionasent,
ondensationonvetivedetrainement
environmental large-salelarge-sale
2) Convetive proesses 18/27
Convetive ontribution to water budget
−0.4 −0.2 0 0.2 0.4 0.6 0.8 1
1.2
1.6
2
1.8
1.4
1
−50
−40
−30
−20
−10
0
10
20 Amazon
∆PLS/∆Ptot DJF-JJA∆
δD
DJF-JJA(h)
∆q
DJF-JJA(g/kg)
600hPaTES data
PLSPconv
enrihmentby ondensateevaporation by ondensatedepletionloss
ontrolvertial advetion more di�usivestronger ondensate detrainmentless large-sale ondensationless large-sale preipitation
100hPa
subsidene vertial di�usionasent,
ondensationonvetivedetrainement
environmental large-salelarge-sale
◮ PLS/Ptot ill-de�ned quantity, but in�uenes loudiness,intra-seas. variability, hemial traor transport2) Convetive proesses 18/27
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
2) Convetive proesses 19/27
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
2) Convetive proesses 19/27
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
ontrolvertial advetion more di�usivestronger ondensate detrainmentless large-sale ondensationless large-sale preipitation
Di�erene 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
2) Convetive proesses 19/27
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
ontrolvertial advetion more di�usivestronger ondensate detrainmentless large-sale ondensationless large-sale preipitation
Di�erene 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
2) Convetive proesses 19/27
Summary on onvetion1000
800
600
400
200100
P (hPa)
environmentalasentunsaturatedreevaporation
large-saleasentin-situondensation
downdraftsonvetivesubsidenemoistening byonvetive detrainement
onvetion vs large-saleunsaturated downdraftsrain reevaporation
2) Convetive proesses 20/27
Summary on onvetion1000
800
600
400
200100
P (hPa)
environmentalasentunsaturatedreevaporation
large-saleasentin-situondensation
downdraftsonvetivesubsidenemoistening byonvetive detrainement
onvetion vs large-saleunsaturated downdraftsrain reevaporation
◮ Perspetives:◮ high frequeny data: e.g. ground-based remote-sensing
2) Convetive proesses 20/27
Summary on onvetion1000
800
600
400
200100
P (hPa)
environmentalasentunsaturatedreevaporation
large-saleasentin-situondensation
downdraftsonvetivesubsidenemoistening byonvetive detrainement
onvetion vs large-saleunsaturated downdraftsrain reevaporation
◮ Perspetives:◮ high frequeny data: e.g. ground-based remote-sensing◮ A-train synergy: TES+CALIPSO/Cloudsat2) Convetive proesses 20/27
Summary on onvetion1000
800
600
400
200100
P (hPa)
environmentalasentunsaturatedreevaporation
large-saleasentin-situondensation
downdraftsonvetivesubsidenemoistening byonvetive detrainement
onvetion vs large-saleboundary layer?unsaturated downdraftsrain reevaporation◮ Perspetives:
◮ high frequeny data: e.g. ground-based remote-sensing◮ A-train synergy: TES+CALIPSO/Cloudsat◮ New physis of LMDZ for AR5 (Rio et al 2009)2) Convetive proesses 20/27
3) Land atmosphere feedbaks
precipitation
atmospheric properties
soil moisture
surface fluxes− evaporation− transpiration− sensible heat flux
− moisture− boundary layer
3) Land-atmosphere feedbaks 21/27
3) Land atmosphere feedbaks◮ model dispersion (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 feedbaks 21/27
3) Land atmosphere feedbaks◮ model dispersion (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 feedbaks 21/27
Partitionning surfae �uxes◮ ORCHIDEE-iso (Risi et al in rev)
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 feedbaks 22/27
Partitionning surfae �uxes◮ ORCHIDEE-iso (Risi et al in rev)
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 feedbaks 22/27
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 feedbaks 23/27
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-exess(h)
δ18
O (h)
monthly, all tropial land pointsPDF of vapor ompositionbare soilevaporationtranspirationtotal vaporoeani vapor
ET P
evapo-transpiration (rcon)% vapor from ontinental3) Land-atmosphere feedbaks 23/27
Water isotopes and ontinental reylingKoster et al 2006
Land-atmosphere feedbaks"hot spots"
derease in preip variane when soil moisture is presribed
3) Land-atmosphere feedbaks 24/27
Water isotopes and ontinental reyling
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 2006orrelation δ18O - rcon, intra-seasonal sale, annual meanLand-atmosphere feedbaks"hot spots"
derease in preip variane when soil moisture is presribed
3) Land-atmosphere feedbaks 24/27
Isotopi signature of land-atmosphere feedbaksET րsoil moisture րmoistureonvergene ր P ր
P րstrong preipitation omposite minus seasonal average:
3) Land-atmosphere feedbaks 25/27
Isotopi signature of land-atmosphere feedbaks
60W120W 0 60E 120E
30S
Eq
30N
60N
−10 −5 −2 2 5 10
ET րsoil moisture րmoistureonvergene ր P րP ր
strong preipitation omposite minus seasonal average:∆rcon (%) JJA
3) Land-atmosphere feedbaks 25/27
Isotopi signature of land-atmosphere feedbaks
60W120W 0 60E 120E
30S
Eq
30N
60N
−10 −5 −2 2 5 10
−20 −10 0 10
−10
0
10
20
ET րsoil moisture րmoistureonvergene ր P րP ր
strong preipitation omposite minus seasonal average:∆rcon (%) JJA
∆δ
18O
v
(h)
positiveontrol bylarge-saleonvergene feedbakland-atmosphere
DJFJJA
∆rcon (%)3) Land-atmosphere feedbaks 25/27
Summary on land-atmosphere feedbaks◮ work in progress:
◮ look at data (in-situ, GOSAT),◮ sensitivity tests: physis-disriminating diagnostis?
3) Land-atmosphere feedbaks 26/27
Summary on land-atmosphere feedbaks◮ work in progress:
◮ look at data (in-situ, GOSAT),◮ sensitivity tests: physis-disriminating diagnostis?
◮ re�ne isotopi diagnostis◮ minimize sensitivity to unrelated atmospheri proesses◮ robustness of the diagnostis? ⇒ model inter-omparisons:ORCHIDEE, isoLSM, soon CLM and ORCHIDEE-multi-layer
3) Land-atmosphere feedbaks 26/27
Summary on land-atmosphere feedbaks◮ work in progress:
◮ look at data (in-situ, GOSAT),◮ sensitivity tests: physis-disriminating diagnostis?
◮ re�ne isotopi diagnostis◮ minimize sensitivity to unrelated atmospheri proesses◮ robustness of the diagnostis? ⇒ model inter-omparisons:ORCHIDEE, isoLSM, soon CLM and ORCHIDEE-multi-layer
◮ relevane for hydrologial projetions◮ Global warming, land use hange (deforestation, irrigation)
3) Land-atmosphere feedbaks 26/27
Conlusion200
400
800
600
100P (hPa)
unsaturateddowndraftsreevaporationsurfaewaterbudget
di�usiveadvetion
ontinentalreyling
detrainementonvetiveonvetionvs large-sale
Conlusion 27/27
Conlusion200
400
800
600
100P (hPa)
unsaturateddowndraftsreevaporationsurfaewaterbudget
di�usiveadvetion
ontinentalreyling
detrainementonvetiveonvetionvs large-sale
◮ Ultimate goal: isotopi diagnostis to evaluate models andtheir projetions:Conlusion 27/27
Conlusion200
400
800
600
100P (hPa)
unsaturateddowndraftsreevaporationsurfaewaterbudget
di�usiveadvetion
ontinentalreyling
detrainementonvetiveonvetionvs large-sale
◮ Ultimate goal: isotopi diagnostis to evaluate models andtheir projetions:◮ new isotopi data
Conlusion 27/27
Conlusion200
400
800
600
100P (hPa)
unsaturateddowndraftsreevaporationsurfaewaterbudget
di�usiveadvetion
ontinentalreyling
detrainementonvetiveonvetionvs large-sale
◮ Ultimate goal: isotopi diagnostis to evaluate models andtheir projetions:◮ new isotopi data◮ new model-data omparison methodologies
Conlusion 27/27
Conlusion200
400
800
600
100P (hPa)
unsaturateddowndraftsreevaporationsurfaewaterbudget
di�usiveadvetion
ontinentalreyling
detrainementonvetiveonvetionvs large-sale
◮ Ultimate goal: isotopi diagnostis to evaluate models andtheir projetions:◮ new isotopi data◮ new model-data omparison methodologies◮ isotopi model inter-omparisonsConlusion 27/27
Conlusion200
400
800
600
100P (hPa)
unsaturateddowndraftsreevaporationsurfaewaterbudget
di�usiveadvetion
ontinentalreyling
detrainementonvetiveonvetionvs large-sale
◮ Ultimate goal: isotopi diagnostis to evaluate models andtheir projetions:◮ new isotopi data◮ new model-data omparison methodologies◮ isotopi model inter-omparisons◮ proess/feedbaks studies omparing models behavior forpresent limate and for projetionsConlusion 27/27
Supl material
Conlusion 28/27
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,bConlusion 29/27
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,bConlusion 30/27
Consequenes on projetions 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)
tropial average, 200hPa, 2xCO2
σq /10ǫp/2
ontrol simulationdi�usive advetion EverythingLMDZ tests preipitates:presentConlusion 31/27
Consequenes on projetions 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)
tropial average, 200hPa, 2xCO2
σq /10ǫp/2 present2xCO2ontrol simulationdi�usive advetion EverythingLMDZ tests preipitates:Conlusion 31/27
Consequenes on projetions 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)
tropial average, 200hPa, 2xCO2
σq /10ǫp/2 present2xCO2ontrol simulationdi�usive advetion EverythingLMDZ tests preipitates:
Additionalupward transportpresentConlusion 31/27
Consequenes on projetions 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)
tropial average, 200hPa, 2xCO2
σq /10ǫp/2 present2xCO2 2xCO2presentontrol simulationdi�usive advetion EverythingLMDZ tests preipitates:
Additionalupward transportConlusion 31/27
Consequenes on projetions 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)
tropial average, 200hPa, 2xCO2
σq /10ǫp/2 present2xCO2 2xCO2presentontrol simulationdi�usive advetion EverythingLMDZ tests preipitates:
Additionalupward transportCMIP3 modelsConlusion 31/27
Estimating ontinental reyling 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 − rconrcon
June-July 2006 in Niamey, daily
dδvoce
dxknown x estimat
edreyling
simulated reylingd ( ron1− ron) /dx = dδv/dx − dδvoe/dxδp − δvConlusion 32/27
Estimating ontinental reyling 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 preipitation
1 − rconrcon
June-July 2006 in Niamey, daily
dδvoce
dxknown x estimat
edreyling
simulated reylingd ( ron1− ron) /dx = dδv/dx − dδvoe/dxδp − δvConlusion 32/27
Estimating ontinental reyling 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 preipitation
1 − rconrcon
June-July 2006 in Niamey, daily
dδvoce
dxknown x estimat
edreyling
simulated reylingd ( ron1− ron) /dx = dδv/dx − dδvoe/dxδp − δv◮ Main limitation in using vapor isotopi measurements forontinental reyling: understanding atmospheri ontrolsConlusion 32/27
Introduction1) Processes controlling humidity2) Convective processes 3) Land-atmosphere feedbacksConclusion
Recommended