• Autour des Observations Spatiales, 20120203

• Equipe Intro

• Personnes concernées : G. Cesana, H. Chepfer, C. Hoareau, P. Messina, V. Noel, M. Reverdy, S. Stromatas, S. Turquety...

2

PRESSU

RE    (hP

a)  CALIPSO

Observa5ons  

0 8.13.4 40.516.5

CLOUDFRACTION

CLOUD  OPTICAL  DEPTH  (CALIOP)

-­‐  op&cally  thick  clouds  :          mul&layer-­‐  very  thick  clouds  :          abundance  of  high  clouds  

0

0.1

0.3

0.2

0.4

800

600

200

400

300

500

700

900

LMDZ5-­‐NP  +  SIM

CLOUD  FRACTION

Objec&f  :  Observer  et  comprendre  les  nuages,  leur  évolu5on  naturelle(à  différentes  échelles  spa5o-­‐temporelles)  et  leur  évolu5on  en  réponse  aux    forçages  externes

LEVEL1A-­‐train,  CALIPSO,  GOCCP,PARASOL,  CERES,  CLOUDSAT,  MODIS  COSP

Collabora&on:  CFMIP  &  Atrain

Perspec5ve:  A-­‐train,  Megha-­‐Tropique,  EarthCare,  ADM-­‐Aeolus

D.  Konsta Modèle  (pour  info)  

0

0.1

0.3

0.2

0.40 8.13.4 40.516.5

800

600

200

400

300

500

700

900

0 0.40.2 0.80.6 0 0.40.2 0.80.6CLOUDY  REFLECTANCECLOUDY  REFLECTANCE  (MODIS)

-­‐  op&cally  thin  clouds  :          low  level,  low  CF

Objec&f:Comprendre  la  transi5on  de  la  phase  de  l’eau  dans  les  nuages  et  ses  implica5ons  pour  la  sensibilité  clima5que

OBS GOCCP

LMDZ

LMDZ +

SIM

Latitude

LIQ CLOUDICE CLOUD

Altitude

CALIPSO  L1CALIPSO-­‐GOCCP/LMD  (L2  &  L3)COSP  simulateur  Lidar

Collabora5on:  GEWEX-­‐CACFMIP

Perspec5ve:  Obs  décennales  CALIPSO+Earth-­‐Care

G.  Cesana

Latitude

liquid fraction f(T°)

LMDZ+SIM

OBS GOCCP

-40°C 0°C

4

NUAGES DE GLACE ET VAPEUR D'EAUV. Noel, H. Chepfer, M. Reverdy, C. Hoareau

Q1 Quelles sont les propriétés et la distribution des nuages de glace(y compris subvisibles)

• à l'échelle globale ? aux pôles ? aux Tropiques ?• spatialement ? temporellement ?

• Observations profils L1 CALIOP

• coefficient de retrodiffusion• rapports de dépolarisation / couleur

• moyennage, filtrage, aggrégation

• temporel, géographique

• restitution de propriétés (optiques, microphysiques)

• détection / seuillage

• statistique data mining, identification de populations, classification, interprétation

3° in November 2007 [Hu et al., 2009]; such orientedcrystals are however almost nonexistent in clouds colderthan −40°C [Noel and Chepfer, 2010] and their removalshould not impact the present results. The permitted colorratio range is voluntarily large in order to account forfluctuations in the 1064 nm channel calibration [Hunt et al.,2009]. However, due to these fluctuations, the color ratiowill only be used for cloud filtering purposes.3.1.4. Application of the Cloud Layer Detectionto a Single Orbit[23] As an example, Figure 1 shows the nighttime orbit

1155 UT on 1 January 2007. Figure 1 (top) shows b′532(using a logarithmic color scale) between 60°S and 60°Nfrom ground to 22 km, Figure 1 (middle) shows the result ofthe cloud detection and filtering described above. Cloudsthat completely attenuate the signal (e.g., around 5°N at15 km and 30°–45°N at 10 km) are entirely removed exceptat their edges (because of their lower geometrical thickness)while the highest and thinnest clouds, mostly found in thetropical latitudes, keep an accurate structure. Figure 1(bottom) shows b′532 inside cloud layers after applying thesame selection and filtering, but using cloud boundariesfrom the NL2 data set. There is little difference betweenFigure 1 (middle) and Figure 1 (bottom) except between15°N and 25°N where NL2 algorithms detect a smaller partof the thin high cloud at ∼18 km of altitude.

3.2. Statistical Comparison of Retrieved CloudFraction With CALIOP NASA Level 2 Products[24] The algorithm described in section 3.1 was applied on

2.5 years of CALIOP level 1 observations (June 2006 toDecember 2008). The resulting ensemble of cloud detectionsis called the Subvisible‐Enhanced level 2 (SEL2) data sethereafter. We then define cloud fraction as the ratio betweenthe number of CALIOP profiles in which cirrus clouds weredetected according to the criteria in section 3.1.3 and the totalnumber of profiles in each longitude‐latitude bin of the grid(section 2.2). Zonal variations of total cirrus cloud fraction,for each season, from the NL2 and SEL2 data sets arecompared in Figure 2, and averages are compared for severallatitude bands in Table 1. In the two data sets, high cloudfractions appear at tropical latitudes (45%–55% comparedto 5%–25% in midlatitudes) for all seasons, and cloud frac-tion maximum follows the movements of the IntertropicalConvergence Zone (ITCZ), near the equator in DJF andaround 10°N in JJA. The cloud fraction maximum is lower inJJA (45% in CALIOP NL2 and 51% in SEL2) than in DJF(48% and 55%, respectively). The cloud fraction minimumin the subtropics (∼5%–15%) also moves northward fromDJF to JJA. The cloud fraction is higher at all latitudes inSEL2 than in NL2 data for the optically thin clouds con-sidered in the present study. Differences in cloud layers in the

Figure 1. Time series of b′532 in log scale from CALIPSO level 1 (top) before and (middle) after clouddetection from the algorithm described in the text and (bottom) from CALIPSO level 2 for the nighttimeorbit 1155 UT on 1 January 2007. Only nontotally attenuating clouds, with maximum temperature colderthan −40°C, 0.1 < dp < 0.7, 0.7 < cp < 1.5, base < (tropopause + 1 km), and t > 0.001 were selected in thetwo data sets.

MARTINS ET AL.: CIRRUS AND SVC FROM CALIOP D02208D02208

4 of 19

3° in November 2007 [Hu et al., 2009]; such orientedcrystals are however almost nonexistent in clouds colderthan −40°C [Noel and Chepfer, 2010] and their removalshould not impact the present results. The permitted colorratio range is voluntarily large in order to account forfluctuations in the 1064 nm channel calibration [Hunt et al.,2009]. However, due to these fluctuations, the color ratiowill only be used for cloud filtering purposes.3.1.4. Application of the Cloud Layer Detectionto a Single Orbit[23] As an example, Figure 1 shows the nighttime orbit

1155 UT on 1 January 2007. Figure 1 (top) shows b′532(using a logarithmic color scale) between 60°S and 60°Nfrom ground to 22 km, Figure 1 (middle) shows the result ofthe cloud detection and filtering described above. Cloudsthat completely attenuate the signal (e.g., around 5°N at15 km and 30°–45°N at 10 km) are entirely removed exceptat their edges (because of their lower geometrical thickness)while the highest and thinnest clouds, mostly found in thetropical latitudes, keep an accurate structure. Figure 1(bottom) shows b′532 inside cloud layers after applying thesame selection and filtering, but using cloud boundariesfrom the NL2 data set. There is little difference betweenFigure 1 (middle) and Figure 1 (bottom) except between15°N and 25°N where NL2 algorithms detect a smaller partof the thin high cloud at ∼18 km of altitude.

3.2. Statistical Comparison of Retrieved CloudFraction With CALIOP NASA Level 2 Products[24] The algorithm described in section 3.1 was applied on

2.5 years of CALIOP level 1 observations (June 2006 toDecember 2008). The resulting ensemble of cloud detectionsis called the Subvisible‐Enhanced level 2 (SEL2) data sethereafter. We then define cloud fraction as the ratio betweenthe number of CALIOP profiles in which cirrus clouds weredetected according to the criteria in section 3.1.3 and the totalnumber of profiles in each longitude‐latitude bin of the grid(section 2.2). Zonal variations of total cirrus cloud fraction,for each season, from the NL2 and SEL2 data sets arecompared in Figure 2, and averages are compared for severallatitude bands in Table 1. In the two data sets, high cloudfractions appear at tropical latitudes (45%–55% comparedto 5%–25% in midlatitudes) for all seasons, and cloud frac-tion maximum follows the movements of the IntertropicalConvergence Zone (ITCZ), near the equator in DJF andaround 10°N in JJA. The cloud fraction maximum is lower inJJA (45% in CALIOP NL2 and 51% in SEL2) than in DJF(48% and 55%, respectively). The cloud fraction minimumin the subtropics (∼5%–15%) also moves northward fromDJF to JJA. The cloud fraction is higher at all latitudes inSEL2 than in NL2 data for the optically thin clouds con-sidered in the present study. Differences in cloud layers in the

Figure 1. Time series of b′532 in log scale from CALIPSO level 1 (top) before and (middle) after clouddetection from the algorithm described in the text and (bottom) from CALIPSO level 2 for the nighttimeorbit 1155 UT on 1 January 2007. Only nontotally attenuating clouds, with maximum temperature colderthan −40°C, 0.1 < dp < 0.7, 0.7 < cp < 1.5, base < (tropopause + 1 km), and t > 0.001 were selected in thetwo data sets.

MARTINS ET AL.: CIRRUS AND SVC FROM CALIOP D02208D02208

4 of 19

3° in November 2007 [Hu et al., 2009]; such orientedcrystals are however almost nonexistent in clouds colderthan −40°C [Noel and Chepfer, 2010] and their removalshould not impact the present results. The permitted colorratio range is voluntarily large in order to account forfluctuations in the 1064 nm channel calibration [Hunt et al.,2009]. However, due to these fluctuations, the color ratiowill only be used for cloud filtering purposes.3.1.4. Application of the Cloud Layer Detectionto a Single Orbit[23] As an example, Figure 1 shows the nighttime orbit

1155 UT on 1 January 2007. Figure 1 (top) shows b′532(using a logarithmic color scale) between 60°S and 60°Nfrom ground to 22 km, Figure 1 (middle) shows the result ofthe cloud detection and filtering described above. Cloudsthat completely attenuate the signal (e.g., around 5°N at15 km and 30°–45°N at 10 km) are entirely removed exceptat their edges (because of their lower geometrical thickness)while the highest and thinnest clouds, mostly found in thetropical latitudes, keep an accurate structure. Figure 1(bottom) shows b′532 inside cloud layers after applying thesame selection and filtering, but using cloud boundariesfrom the NL2 data set. There is little difference betweenFigure 1 (middle) and Figure 1 (bottom) except between15°N and 25°N where NL2 algorithms detect a smaller partof the thin high cloud at ∼18 km of altitude.

3.2. Statistical Comparison of Retrieved CloudFraction With CALIOP NASA Level 2 Products[24] The algorithm described in section 3.1 was applied on

2.5 years of CALIOP level 1 observations (June 2006 toDecember 2008). The resulting ensemble of cloud detectionsis called the Subvisible‐Enhanced level 2 (SEL2) data sethereafter. We then define cloud fraction as the ratio betweenthe number of CALIOP profiles in which cirrus clouds weredetected according to the criteria in section 3.1.3 and the totalnumber of profiles in each longitude‐latitude bin of the grid(section 2.2). Zonal variations of total cirrus cloud fraction,for each season, from the NL2 and SEL2 data sets arecompared in Figure 2, and averages are compared for severallatitude bands in Table 1. In the two data sets, high cloudfractions appear at tropical latitudes (45%–55% comparedto 5%–25% in midlatitudes) for all seasons, and cloud frac-tion maximum follows the movements of the IntertropicalConvergence Zone (ITCZ), near the equator in DJF andaround 10°N in JJA. The cloud fraction maximum is lower inJJA (45% in CALIOP NL2 and 51% in SEL2) than in DJF(48% and 55%, respectively). The cloud fraction minimumin the subtropics (∼5%–15%) also moves northward fromDJF to JJA. The cloud fraction is higher at all latitudes inSEL2 than in NL2 data for the optically thin clouds con-sidered in the present study. Differences in cloud layers in the

Figure 1. Time series of b′532 in log scale from CALIPSO level 1 (top) before and (middle) after clouddetection from the algorithm described in the text and (bottom) from CALIPSO level 2 for the nighttimeorbit 1155 UT on 1 January 2007. Only nontotally attenuating clouds, with maximum temperature colderthan −40°C, 0.1 < dp < 0.7, 0.7 < cp < 1.5, base < (tropopause + 1 km), and t > 0.001 were selected in thetwo data sets.

MARTINS ET AL.: CIRRUS AND SVC FROM CALIOP D02208D02208

4 of 19

tropics ∼75% of cirrus clouds are colder than −60°C, whilein midlatitudes this is true for only ∼10% of cirrus clouds. Inthe tropics, SVC are slightly colder on average (−66°C) thancirrus clouds (−63°C) whereas in midlatitudes their averagemidlayer temperature is the same (−52°C). It has to be keptin mind that the midlayer point is lower for a cirrus cloudthan for a SVC, which might explain in part this result.[28] Figure 6 shows distributions of particulate depolar-

ization ratio dp of ice crystals in SVC (dashed lines) andcirrus clouds (solid lines). Values are typical of cirrus clouds[e.g., Sassen and Benson, 2001] and show latitude depen-dence: dp is higher in the tropics (0.4–0.5 in average) than inmidlatitudes (0.3–0.4) for both cloud types. The mean dp islower by 0.03 for SVC compared to cirrus clouds either intropics or midlatitudes, which implies that crystals in SVChave slightly less complex shapes; that is, crystals in SVCare either conceptually simpler, like plates, or complexshapes whose sharp edges were smoothed out by sublima-

tion. Previous lidar studies [e.g., Noel et al., 2006] haveoften concluded the depolarization ratio varies with tem-perature, since it influences crystal nucleation and growthmechanisms and therefore directs particle shape; coldertemperatures are usually linked to higher dp. This is con-sistent with present results that show higher dp for cirrusclouds in the tropics, which are colder, than in midlatitudes(see Figure 5); however this is inconsistent with presentresults that show lower dp for SVCs than for cirrus clouds,which are warmer. This suggests that, at least in SVC,temperature is not the only influence driving particle shapeand depolarization.

4. Cirrus Clouds and Vertical Motionsin the Tropics

[29] In this section we investigate the impact of synopticvertical air motions (convection or subsidence) on cloud

Figure 3. Seasonal maps of subvisible cirrus cloud fraction (0.001 < t < 0.03) between ±60°, in percent.

MARTINS ET AL.: CIRRUS AND SVC FROM CALIOP D02208D02208

6 of 19

NUAGES DE GLACE ET VAPEUR D'EAUV. Noel, H. Chepfer, M. Reverdy, C. Hoareau

Q2 Comment les nuages de glace interagissent avec

• la vapeur d'eau ?

• les injections d'aérosols (éruptions, feux de biomasse) ?

• la convection ?

CALIOP L1 (profils retrodiff/rapports)MLS L2 (profils H2O), AIRS ?

OMI L2 (total NO2/SO2)

METEOSAT, TRMM L4 (imagerie IR)

• Approche :

• traitement + restitution/détection + statistique

• colocalisation géographique / temporelle

• correlations, classification, interprétation

+ WRF, ERA-I, in-situ, ground obs

• ClimServ

• Icare

Q3 Quelles sont les propriétés et la répartition des PSC ?

Q4 Comment interagissent-ils avec leur environnement chimique (HNO3, SO2) et dynamique (ondes de gravité) ?

• CALIOP L1 (profils retrodiff/rapports)• MLS L2 (profils chimie)+ WRF, ERA-I

NUAGES STRATOSPHÉRIQUES POLAIRES (PSC)V. Noel, H. Chepfer, A. Hertzog

90°W 75°W 60°W 45°W 30°W

70°S

60°SGravity waves frequency, JJA 2006-2010

0.0

2.5

5.0

7.5

10.0

12.5

15.0

17.5

20.0

22.5

25.0

Freq

uenc

y [%

]

100°W 80°W 60°W 40°W 20°W 0°E 20°E 40°E 60°E 80°E 100°E0

25

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Ice

PSC

vol

ume

per p

rofil

e -

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NA

T PS

C v

olum

e pe

r pro

file

- km

100°W 80°W 60°W 40°W 20°W 0°E 20°E 40°E 60°E 80°E 100°E0

25

50

75

100

125

Ice

PSC

vol

ume

per p

rofil

e -

km

0

50

100

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200

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NA

T PS

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e pe

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file

- km

WRF

NAT PSCH2O + HNO3

ice PSC

vortex

8

composition chimique et qualité de l’air

Questions scientifiques•  Impact  des  feux  de  végéta&on  sur  la  composi&on  chimique  (global)  &  sur  la  qualité  de  l’air  (Europe)  ?     =>  Emissions,  transport,  évolu&on  de  la  composi&on,  etc.•  Quan&fica&on  de  l’importance  rela&ve  des  sources  naturelles  (dust,  feux,  biog.)  et  anthropiques  sur  la  qualité  de  l’air  en  Euro-­‐Méditerranée  (S.  Stromatas)

•  Transport  à  longue  distance  HN:  impact  des  principales  sources  hautes  la&tudes,  biais  modèles  globaux  

Observations L2  Surface  /  feux:  •  Végéta&on  MODIS  •  Ac&vité  et  puissance  feux  MODIS,  SEVIRI•  Surfaces  brûlées  MODIS    

Approche généraleCalcul  «  boeom-­‐up  »  des  émissions  par  les  feux  :gaz  traces  et  aérosols

à Intégra&on  dans  un  modèle  de  chimie-­‐  transport  (régional  CHIMERE,  global  LMDz-­‐INCA)    

 Gaz  traces  •  IASI:  O3,  CO  &  panaches  feux  NH3,  C2H4,  etc.  •  OMI  /  GOME-­‐2  (NO2+formaldéhyde)+  in  situ  /  surface  :  campagnes,  MOZAIC-­‐IAGOS,  Airbase,  etc.

 Aérosols•  A-­‐Train:  PARASOL,  MODIS,  CALIPSO  L1  •  SEVIRI/MSG  :  à  venir•  IASI  ?•  Surface:  Airbase,  AERONET,  EARLINET    

Evalua&on  panaches  /  niveaux  de  fondè  Comparaison  simula&ons  vs  observa&ons-­‐  Pondéra&on  ver&cale  pour  les  gaz  traces-­‐  Calcul  des  propriétés  op&ques  -­‐  Calcul  du  signal  lidar  (SR,  CR)

Ajustement  des  processus  en  fonc&on  des  différences  constatées.          

Exemple de travaux en cours: analyse de l’impact des feux

MODIS

Projet  APIFLAME  (PRIMEQUAL):  Quan&fier  l’impact  des  feux  sur  l’ozone  et  les  PM  de  surface  région  Euro-­‐Mediterranée  &  intégra&on  dans  les  prévisions  (www.lmd.polytechnique.fr/apiflame)1. Inventaire  régional  des  émissions  2. Evalua&on  u&lisant  observa&ons  satellitaires  dans  les  

panaches  –  cas  d’étude  été  2007  3. Cartographie  :  fréquence  et  intensité  de  la  contribu&on  dans  

la  région  sur  2003-­‐2010

IASI  CO(Turquety  et  al.,  2009)

TOSCA  IASI  /  EECLAT:Exploita&on  des  observa&ons  satellitaires  pour  mieux  contraindre  les  émissions;  applica&on  à  différentes  régions  d’étude  (tropiques,  forêts  boréales)

CHIMERE

PARASOL

SR:  CALIOP  vs  CHIMERE

OMI

CHIMERE

NO2  tropo  

ref  +  feux

advec&on             distribu&on  ver&cale             photochimie

MODIS