Robin Hogan, Richard Allan,Robin Hogan, Richard Allan,

Nicky Chalmers, Thorwald Stein, Julien DelanoëNicky Chalmers, Thorwald Stein, Julien DelanoëUniversity of ReadingUniversity of Reading

How accurate are the radiative How accurate are the radiative properties of ice clouds derived properties of ice clouds derived from the CloudSat and Calipso from the CloudSat and Calipso

satellites? satellites?

OverviewOverview

• To understand the role of clouds in climate we need global measurements of their vertically resolved properties

• We have developed an ice-cloud retrieval algorithm combining the CloudSat radar and Calipso lidar– Previously we have used these to evaluate the ice water content

distributions in the Met Office and ECMWF forecast models – For climate, need to be confident in the retrieved radiative

properties• In this talk this is assessed in two ways using instruments in the same

“A-train” of satellites as CloudSat and Calipso:– Comparison with simultaneous MODIS retrievals of ice clouds;

MODIS uses two solar radiances to estimate optical depth and effective radius, from which ice water path is estimated

– Use the radar and lidar retrievals in a radiative transfer model to predict the top-of-atmosphere fluxes; compare with fluxes simultaneous estimated from the broadband CERES radiometer

What do CloudSat and Calipso What do CloudSat and Calipso see?see?

Cloudsat radar

CALIPSO lidar

Target classificationInsectsAerosolRainSupercooled liquid cloudWarm liquid cloudIce and supercooled liquidIceClearNo ice/rain but possibly liquidGround

Delanoe and Hogan (JGR 2010)

• Radar: ~D6, detects whole profile, surface echo provides integral constraint

• Lidar: ~D2, more sensitive to thin cirrus and liquid clouds but attenuated

CloudSat and Calipso CloudSat and Calipso sensitivitysensitivity

4

• In July 2006, cloud occurrence in subzero troposphere was 13.3%• The fraction observed by radar was 65.9%

– Some thin cirrus not detected• The fraction observed by lidar was 65.0%

– Optically thick clouds not fully penetrated• The fraction observed by both was 31.0%

Retrieval Retrieval frameworkframework

1. New ray of data: define state vector

First guess of ice extinction coefficient, a measure of ice particle number concentration and the lidar extinction-to-backscatter ratio

2a. Radar model 2b. Lidar model

Including multiple scattering2c. IR radiance model

Optional use of MODIS

3. Compare to observations

Check for convergence

4. Gauss-Newton iteration

Derive a new state vector

2. Forward model

Not converged

Converged

Proceed to next ray of data5. Calculate retrieval error

• Standard variational approach• Sophisticated forward models used for both single scattering properties of ice

particles and lidar multiple scattering

Details in Delanoe and Hogan (JGR 2010)

Lidar observations

Radar observations

Retrieved visible extinction

Retrieved ice water content

Retrieved effective radius

Lidar forward model

Radar forward model

Example ice cloud

retrievalsDelanoe and Hogan (2010)

Comparison with MODISComparison with MODIS• All ice retrieval algorithms, no

matter what instruments they use, are sensitive to assumed ice particle shape

• In the “VarCloud-BR” version, we try to replicate the MODIS assumption that particles are largely bullet rosettes

• Leads to reasonable agreement in mean optical depth,

• Significant scatter is likely due to 3D radiative transfer effects in the MODIS retrievals

• Our favoured assumption, oblate aggregates (“VarCloud-OA”) leads to poorer agreement due to the inconsistency with the MODIS assumptions

Stein et al. (2010)

Comparison with MODISComparison with MODIS

eicerIWP

2

3

• Effective radius, re, also shows a better agreement when we use the same assumption as MODIS

• Surprisingly, ice water path, IWP, shows much better agreement with oblate aggregate assumption!

• Reason seems to be that MODIS estimates re from radiances that are dominated by the first few optical depths of the cloud where the particles are smaller

• It then (wrongly) assumes re is constant with height, leading to an overestimate in IWP:

Effective radius, re

Ice water path, IWP

• Still not clear which microphysical assumptions are best• An alternative approach is to consider broad-band fluxes…

Evaluation using CERES TOA Evaluation using CERES TOA fluxesfluxes

• Radar-lidar retrieved profiles containing only ice used with Edwards-Slingo radiation code to predict CERES fluxes

• Small biases but large random shortwave error: 3D effects?

ShortwaveBias 4 W m-2, RMSE 71 W m-2

LongwaveBias 0.3 W m-2, RMSE 14 W m-2

Nicky Chalmers

CERES versus a radar-only CERES versus a radar-only retrievalretrieval

• How does this compare with radar-only empirical IWC(Z, T) retrieval of Hogan et al. (2006) using effective radius parameterization from Kristjansson et al. (1999)?

Bias 10 W m-2

RMS 47 W m-2

ShortwaveBias 48 W m-2, RMSE 110 W m-2

LongwaveBias –10 W m-2, RMSE 47 W m-2

Nicky Chalmers

How important are cirrus only seen How important are cirrus only seen by lidar?by lidar?

• Remove lidar-only pixels from radar-lidar retrieval• Change to fluxes is only ~5 W m-2 but lidar still acts to improve

retrieval in radar-lidar region of the cloud

ShortwaveBias –5 W m-2, RMSE 17 W m-2

LongwaveBias 4 W m-2, RMSE 9 W m-2

Nicky Chalmers

Conclusions• Ice cloud properties can be retrieved from CloudSat and

Calipso with an accuracy that enables them to be used to predict shortwave and longwave top-of-atmosphere fluxes within the likely error of CERES

• This gives us more confidence in our ongoing work to use of these retrievals to evaluate models

• Differences in microphysical assumptions can explain differences with other retrievals (e.g. MODIS); work still required to determine the most appropriate assumptions

• Currently we are working on simultaneous retrievals of ice, liquid, rain and aerosol, to be applied both to the A-Train of satellites and to the new EarthCARE satellite

EarthCARE