ipcc ar4 - sorbonne-universite.frcrlmd/these/seminaire_ncar.pdf · ipcc ar4 ipcc ar4 clouds...

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


IPCC AR4

IPCC AR4

clouds

relative humidity


climatefeedbacks

water vapor)(clouds,

multi−model mean

surf

ace

glob

al w

arm

ing

(°C

)

increaseCO2

Introdution 2/27



globalwarming

regionalchanges in

precipitation


atmospherefeedbacks

land−

IPCC AR4

IPCC AR4

clouds

relative humidity


land surfacehydrological

changeschangeland use

climatefeedbacks


multi−model mean

surf

ace

glob

al w

arm

ing

(°C

)

increaseCO2

Introdution 2/27



globalwarming

regionalchanges in

precipitation


atmospherefeedbacks

land−

IPCC AR4

IPCC AR4

land surfacehydrology

clouds

relative humidity




climatefeedbacks


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




Precipitation

HDO

H16

2O

isotopiccomposition

variablesmeteorological

and variability

present−dayclimate processes

physicalfuture climate

Introdution 3/27




Precipitation

HDO

H16

2O

isotopiccomposition

processevaluation


and variability

present−dayclimate

combiningdata

processesphysical

future climate

Introdution 3/27




Precipitation

HDO

H16

2O

isotopiccomposition

processevaluation

strengthenprojections credibility


and variability

present−dayclimate

combiningdata

processesphysical

future climate

Introdution 3/27

General strategy

controlling isotopicunderstand processes

compositionIntrodution 4/27

General strategy


composition

design observable,process−oriented

isotopic diagnostics

Introdution 4/27

General strategy


composition

climate modelsapply to

detect bias in models

propose improvements

understand reasons



Introdution 4/27

General strategy


composition

consequences onfuture changes

quantify, prioritizeuncertainties

climate modelsapply to

detect bias in models

propose improvements

understand reasons



Introdution 4/27

Outline


1)2)

3)

globalwarming

regionalchanges in

precipitation


atmospherefeedbacks

land−

IPCC AR4

IPCC AR4

land surfacehydrology

clouds

relative humidity




climatefeedbacks


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



◮ 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




=⇒ 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



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


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



800

600

200

100

0°

400

1000

P (hPa)

30°N



in clouds

large−scale

rain evaporation


vertical mixing

circulation

dehydration

3 reasonsfor a

moist bias

lateral and

ontrol: AR4 version (19 levels)


pression(hPa)

3020 40 50 60 70 801000





800

600

200

100

0°

400

1000

P (hPa)

30°N



in clouds

large−scale

rain evaporation


vertical mixing

circulation

dehydration

3 reasonsfor a

moist bias

lateral and

ontrol: AR4 version (19 levels)di�usive vertial advetion


pression(hPa)

3020 40 50 60 70 801000





800

600

200

100

0°

400

1000

P (hPa)

30°N



in clouds

large−scale

rain evaporation


vertical mixing

circulation

dehydration

3 reasonsfor a

moist bias

lateral and

ontrol: AR4 version (19 levels)di�usive vertial advetionσq/10


pression(hPa)

3020 40 50 60 70 801000





800

600

200

100

0°

400

1000

P (hPa)

30°N


LMDZ−iso (Risi et al 2010):

in clouds

large−scale

rain evaporation


vertical mixing

circulation

dehydration

3 reasonsfor a

moist bias

lateral and


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


Isotopi measurements200

100

0°1000

30°N

P (hPa)

400

600

800


Isotopi measurementssatellites

(Steinwagner et al 2010)(Nassar et al 2007)

MIPASACE−FTS

200

100

0°1000

30°N

P (hPa)

400

600

800


Isotopi measurements(Worden et al 2007)TES

satellites


MIPASACE−FTS

200

100

0°1000

30°N

P (hPa)

400

600

800



satellites

total column:(Frankenberg et al 2009)

SCIAMACHY


MIPASACE−FTS

200

100

0°1000

30°N

P (hPa)

400

600

800




SCIAMACHY

satellites

ground−basedFTIR

total column(5 TCCON/NDACC sites:

2 US, 2 Australia,1 New−Zealand)

Schneider et al 2009)

profile(Canaries islands:


MIPASACE−FTS

200

100

0°1000

30°N

P (hPa)

400

600

800




SCIAMACHY

satellites

ground−basedFTIR




profile(Canaries islands:in situ

(GNIP−vapor,Angert et al 2007, Uemura et al 2007)


MIPASACE−FTS

200

100

0°1000

30°N

P (hPa)

400

600

800




SCIAMACHY

satellites

ground−basedFTIR




profile(Canaries islands:in situ

(GNIP−vapor,Angert et al 2007, Uemura 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


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



−60

−40

−20

0

20

40

60

20 25 30 35 40 45 50 55


400 hPa, 15◦N-30◦N mean



◮ robustness? additional tests, theoretial understanding1) Proesses ontrolling humidity 11/27


−60

−40

−20

0

20

40

60

20 25 30 35 40 45 50 55


ECHAMMIROCHadAM GSMGISSSWING2 models:CAM2

400 hPa, 15◦N-30◦N mean



◮ robustness? additional tests, theoretial understanding◮ frequent reason for moist bias=exessively di�usive advetion1) Proesses ontrolling humidity 11/27


−60

−40

−20

0

20

40

60

20 25 30 35 40 45 50 55


ECHAMMIROCHadAM GSMGISSSWING2 models:CAM2vertial resolution

400 hPa, 15◦N-30◦N mean



◮ 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


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)


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


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


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


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




convectiveascent


ascent



cold poolfrontgust



−8−7.5

−7−6.5

0 50 100 150 200 250

15km

200km




rainenrichmentthroughreevaporation

convectiveascent


ascent



cold poolfrontgust



−8−7.5

−7−6.5

0 50 100 150 200 250

15km

200km




subsidenceof dry anddepleted airrainenrichmentthroughreevaporation

convectiveascent


ascent



cold poolfrontgust


Convetive/large-sale �uxes

ompensatingsubsidene large-saleasent

reevaporationdetrainementonvetive

large-saleondensation

downdraftsunsaturated

onvetive sheme large-sale

onvetiveasent100hPa


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


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)


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)


TES2) Convetive proesses 17/27

Convetive ontribution to water budgetPLSPconv

enrihmentby ondensateevaporation by ondensatedepletionloss100hPa

subsidene vertial di�usionasent,

ondensationonvetivedetrainement

environmental large-salelarge-sale


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





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


100hPa




◮ 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



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)




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


Di�erene 15◦S-15◦N minus

δD (h)


∆δD

(h)30◦S-30◦N at 200hPa

∆q

(g/kg)

moistening by detrainement(g/kg/day)

ACE: 30hMIPAS: 50h



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


Di�erene 15◦S-15◦N minus

δD (h)


∆δD

(h)30◦S-30◦N at 200hPa

∆q

(g/kg)

moistening by detrainement(g/kg/day)

ACE: 30hMIPAS: 50h


Summary on onvetion1000

800

600

400

200100

P (hPa)

environmentalasentunsaturatedreevaporation

large-saleasentin-situondensation

downdraftsonvetivesubsidenemoistening byonvetive detrainement

onvetion vs large-saleunsaturated downdraftsrain reevaporation



800

600

400

200100

P (hPa)





◮ Perspetives:◮ high frequeny data: e.g. ground-based remote-sensing



800

600

400

200100

P (hPa)





◮ Perspetives:◮ high frequeny data: e.g. ground-based remote-sensing◮ A-train synergy: TES+CALIPSO/Cloudsat2) Convetive proesses 20/27


800

600

400

200100

P (hPa)




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


soil moisture




3) Land atmosphere feedbaks◮ model dispersion (Koster et al, Guo et al 2006)

uncertaintiesin atmospheric

component

uncertaintiesin land surface

component

precipitation


soil moisture



continentalrecycling

partitionning


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


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


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


Isotopi signature of land-atmosphere feedbaksET րsoil moisture րmoistureonvergene ր P ր

P րstrong preipitation omposite minus seasonal average:


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


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?




◮ 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




◮ 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)


Conlusion200

400

800

600

100P (hPa)

unsaturateddowndraftsreevaporationsurfaewaterbudget

di�usiveadvetion

ontinentalreyling

detrainementonvetiveonvetionvs large-sale

Conlusion 27/27

Conlusion200

400

800

600

100P (hPa)


di�usiveadvetion

ontinentalreyling


◮ Ultimate goal: isotopi diagnostis to evaluate models andtheir projetions:Conlusion 27/27

Conlusion200

400

800

600

100P (hPa)


di�usiveadvetion

ontinentalreyling


◮ Ultimate goal: isotopi diagnostis to evaluate models andtheir projetions:◮ new isotopi data

Conlusion 27/27

Conlusion200

400

800

600

100P (hPa)


di�usiveadvetion

ontinentalreyling


◮ 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)


di�usiveadvetion

ontinentalreyling


◮ 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)


di�usiveadvetion

ontinentalreyling


◮ 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)


σq /10ǫp/2

ontrol simulationdi�usive advetion EverythingLMDZ tests preipitates:presentConlusion 31/27


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


∆

RH(%/K)

Pressure(hPa)


σq /10ǫp/2 present2xCO2ontrol simulationdi�usive advetion EverythingLMDZ tests preipitates:Conlusion 31/27


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


∆

RH(%/K)

Pressure(hPa)


σq /10ǫp/2 present2xCO2ontrol simulationdi�usive advetion EverythingLMDZ tests preipitates:

Additionalupward transportpresentConlusion 31/27


200

250

300

350



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


∆

RH(%/K)

Pressure(hPa)


σq /10ǫp/2 present2xCO2 2xCO2presentontrol simulationdi�usive advetion EverythingLMDZ tests preipitates:

Additionalupward transportConlusion 31/27


200

250

300

350



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


∆

RH(%/K)

Pressure(hPa)


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


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


dδvoce

dxknown x estimat

edreyling

simulated reylingd ( ron1− ron) /dx = dδv/dx − dδvoe/dxδp − δvConlusion 32/27


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


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

ipcc ar4 - sorbonne-universite.frcrlmd/these/seminaire_ncar.pdf · ipcc ar4 ipcc ar4 clouds...

Documents