V. Noel CNRS/LMD, with results from H. Chepfer, M. Chiriaco, J. Delanoe, J.-C. Dupont, M. Hae!elin,

C. Hoareau, Y. Morille, C. Stubenrauch...

Journée Scienti"que SIRTA 2012 0518

ICE CLOUDSMICRO & MACROPHYSICS

ROLE IN WATER CYCLE

FROM GROUND & SPACE

• High altitudes (up to 14-18km), cold temperatures (0 to -80°C)

• Hard to document in-situ

• in"nitely variable microphysics

• O#en translucent or transparent

• Hard to document remotely

• Unique optical properties

PECULIAR BEASTS

WHY ICE CLOUDS?

Heymsfield et al. 2005

• Hard to detect

• How much are we missing? What are their properties/e!ects?

• How do they relate to atmospheric properties and dynamics?

• Sole clouds with greenhouse e!ect ~ albedo e!ect

• A source of uncertainty for climate prediction

• Role in the global radiative budget?

• Act as dehydrators

• A process neither well documented nor well understood

• crucial in Tropics: last chance for dehydration before air enters stratosphere

• Role in the global water cycle?

• What triggers their formation and drives they dehydration e$ciency?

ICE CLOUDS • OPEN QUESTIONS

• atmospheric pro"ler

• 532/1064 nm: sensitive to optically thinatmospheric features, e.g. dust, carbon aerosols,PM and ice clouds

• polarization sensitive

• allows for phase discrimination, particle shape classi"cation

• basis for most ice cloud studies from SIRTA

• 1998 -> now - large dataset

• complemented with Radar, radiosounding pro"les, imagers, SW/LW

LIDAR NUAGES AEROSOLS (LNA)

ICE CLOUDS AT SIRTA

from Haeffelin et al. 2005

• Level 1

• Level 1

• Level 2

SUBTITLE

ICE CLOUDS FROM LIDAR • EXAMPLE

depolarization ratio

cloud detection & phase discrimination

24 JAN 2006

100% ice

100%liquid

• Drives instantaneous, local albedo & greenhouse e!ect

• Reflects the activity of small-scale processes

ICE CRYSTALS SIZE, SHAPE & ORIENTATION

1. MICROPHYSICS

Heymsfield et al. 2006

• particle shape (Noel et al. 2001, 2002)

• particle size (Chiriaco et al. 2007, 2004; Delanoe et al. 2005, 2007; Tinel et al. 2005)

• ice particle absorption e$ciency,concentrations (Chiriaco et al. 2006)

• cloud thermodynamical phase (Chiriaco et al. 2007, Naud et al. 2010)

ALGORITHMS TO RETRIEVE

MICROPHYSICS • SIRTA RESULTS

liquid

ice

Naud et al. 2010

• Example: depolarization + multiple scattering= crystal shape classi"cation

• Function of temperature, wind, RH

MICROPHYSICS • SIRTA RESULTS

LOCAL SCALE PROPERTIES AT MIDLATITUDES

Total albedo0.36

Noel et al. 2005

Te

mp

era

ture

(°C

)

Ice crystal shape distributionSIRTA 2002-2004

• Cloud altitude, thickness, optical depth

• They drive global-scale radiative impact

• Reflect the activity of large-scale processes

LIDAR RETRIEVALS

2. MACROPHYSICS

base, top

thickness optical depth

• Algorithms for ice cloud detection and identi"cation (Protat et al. 2006, Morille et al. 2007) - now applied to multiple systems in Europe, space obs.

• Statistics on ice cloud altitude, opacity at midlatitudes (Noel et al. 2006, 2007; Plana-Fattori et al. 2009; Protat et al. 2009; Dupont et al. 2010) + temperature, wind, RH using correlated radiosoundings

MACROPHYSICS • SIRTA RESULTS

0 0.5 1 1.5 2 2.5 3 3.50.0001

0.001

0.01

0.1

1

Cloud Thickness (km)

Clou

d O

ptica

l Dep

th (l

og)

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cloud thickness

op

tical

de

pth

4km0

Noel and Haeffelin 200710-3

1

Courtesy Y. Morille

cloud

aerosols

boundary layer

clear-sky

• Spaceborne lidar, orbiting Earth at ~600km14 times a day since 2006 (NASA/CNES)

• 40 pro"les/orbit above SIRTA (6°x2° box)

• Compare and contrast cloud detectionsfrom ground and space: validation, synergy

FROM LOCAL TO GLOBAL: CALIOP

MACROPHYSICS

CALIPSO SIRTA

from Morille et al. 2007 from Haeffelin et al. 2008

• Maps of cloud frequency

• Macrophysical properties

• Correlated with large-scale dynamics

GLOBAL SCALE

MACROPHYSICS

properties. However, convective events are generally smallscale, limited in time, and may influence clouds distant intime and space from the convection event itself. Moreover,information about the convection/subsidence state of theatmosphere is not available at the fine temporal and geo-graphical scale of CALIOP cloud observations. Therefore,in this section we do not attempt to directly relate discreteconvective events or subsidence motions with cloud detec-tions. Instead, we assume the effect of these vertical motionswill be reflected by changes in distributions of cloud prop-erties in regions statistically dominated over months byspecific vertical motion regimes.

4.1. Cirrus Cloud Fraction and VerticalMotion Regimes[30] A common indicator used to describe the intensity of

the vertical motions in the atmosphere is the vertical pres-sure velocity at 500 hPa, hereafter called w500 (as in thework of, e.g., Bony and Dufresne [2005]). In the present

study, values of w500 come from monthly averages ofECMWF reanalyses [Uppala et al., 2005] over the sameperiod as CALIOP observations (June 2006 to December2008) on a 1.125° × 1.125° grid and 21 vertical pressurelevels (from 1000 to 1 hPa) 4 times a day at 0000, 0600,1200 and 1800 UTC. Monthly averages of w500 were usedsince dynamical processes are not represented correctly onshorter timeframes, due to the large variability between twoconsecutive reanalyses in time [Bony and Dufresne, 2005].[31] Negative w500 are linked with upward air mass mo-

tions and positive w500 with subsidence motions. Deepconvection occurs primarily along the tropical convectionbelt, and subsidence in the subtropics, around the Hadleycell (near ±30° of latitude). We posit that areas with monthlymeans w500 < −35 hPa/d are statistically dominated by deepconvection, areas with monthly means w500 > 25 hPa/d bystrong subsidence, and areas with intermediate values byweak convection (−35 to 0 hPa/d) and subsidence (0 to25 hPa/d). These boundaries were obtained by visually

Figure 4. Seasonal maps of cirrus cloud fraction (t > 0.03) between ±60°, in percent.

MARTINS ET AL.: CIRRUS AND SVC FROM CALIOP D02208D02208

7 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

Martins et al. 2011 Clo

ud

Fra

ctio

n (%

)

CALIPSO - map of ice clouds, 2006-2008

• When, where & why does ice form?

• How fast does ice grow and eliminate supersaturation?

A PROCESS NOT SO WELL UNDERSTOOD

3. ICE CLOUDS AS DEHYDRATORS

water vaporanomaly1979-99

2.2

1.6

1

0 time minutes to hours

altit

ude

[km

]ic

e sa

tura

tion

ratio

S

14

13

12

14

13

12

14

13

12

nucleation threshold

in situ

from a talk by Peter et al. (2006)

theory (naive)

• Upper troposphere concentration is very low (~10ppmv)

• Supersaturations can be very local (~100 m scale)

• lead to formation of SubVisible Cirrus (SVC)

• To document what is going on, we need high-resolution measurements of supersaturations at low water vapor concentrations: hard

• Space-based retrievals through recalibration using in-situ data (e.g. AIRS and MOZAIC, Lamquin et al. 2012 ACP) can document large-scale behavior, but have poor resolution

• back to small-scale processes = Ground-based measurements

WATER VAPOR IS HARD TO MEASURE

THE PROBLEM

• Fine-scale description of balance between saturation / cloud formation

• cf. poster C. Hoareau

• data needed at several latitudes

• OPAR (new system 2012/13)

• cf. poster J.-L. Baray

• OHP, RTV: fragmented

• SIRTA: IPRAL

• continuous, automated

• 355, 532, 1064nm

• data acquisition starts in 2014

SIMULTANEOUS PROFILES OF WV & ICE CLOUDS

RAMAN LIDAR

Hoareau et al. 2009 JAOT

• Earth-CARE preparation (launch 2015) - algorithm development (DARDAR, etc)

• Long-term correlated measurements of ice clouds and water vapor

• Direct retrieval of lidar ratio within ice cloud layers

• Assumption-less retrieval of optical depth

• validation/correction of LNA/CALIOP retrievals

LAST WORDS

WHAT NEXT? IPRAL

ICE CLOUDS FROM SIRTA

• More than 10 years of lidar data: getting close to climate scale

• ~20 papers, 4 PhDs about ice clouds using SIRTA data (+ confs)

• more using SIRTA data for e.g. model validation (MM5, ECMWF, ARPEGE, RACMO...): Bouniol et al. 2010, Chiriaco et al. 2006...