Robin Hogan, Julien Delanoë, Nicky Chalmers,Robin Hogan, Julien Delanoë, Nicky Chalmers,

Thorwald Stein, Anthony IllingworthThorwald Stein, Anthony IllingworthUniversity of ReadingUniversity of Reading

Evaluating and Evaluating and improving the improving the representation of representation of clouds in climate clouds in climate models using models using spaceborne radar and spaceborne radar and lidarlidar

Clouds in climate modelsClouds in climate models

14 global models (AMIP)

90N 80 60 40 20 0 -20 -40 -60 -80 90S

0.05

0.10

0.15

0.20

0.25

Latitude

Ver

tical

ly in

tegr

ated

clo

ud w

ater

(kg

m-2) But all

models tuned to give about the same top-of-atmosphere radiation

The properties of ice clouds

are particularly uncertain

• Via their interaction with solar and terrestrial radiation, clouds are one of the greatest sources of uncertainty in climate forecasts

• But cloud water content in models varies by a factor of 10• Need instrument with high vertical resolution…

Spaceborne radar, lidar and Spaceborne radar, lidar and radiometersradiometers

The A-Train– NASA– 700-km orbit– CloudSat 94-GHz radar (launch 2006)– Calipso 532/1064-nm depol. lidar– MODIS multi-wavelength radiometer– CERES broad-band radiometer– AMSR-E microwave radiometer

EarthCARE (launch 2013)– ESA+JAXA– 400-km orbit: more

sensitive– 94-GHz Doppler radar– 355-nm HSRL/depol. lidar– Multispectral imager– Broad-band radiometer– Heart-warming name

EarthCare

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 (2008, 2010)

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

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

Lidar observations

Radar observations

Visible extinction

Ice water content

Effective radius

Lidar forward model

Radar forward model

Example ice Example ice cloud cloud

retrievalsretrievalsDelanoe and Hogan (2010)

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?

Nicky Chalmers

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

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

A-Train A-Train versus versus

modelsmodels• Ice water

content• 14 July 2006• Half an orbit• 150°

longitude at equator

Delanoe et al. (2010)

• Both models lack high thin cirrus• Met Office has too narrow a distribution of in-cloud IWC• ECMWF lacks high IWC values; using this work, ECMWF have

developed a new scheme that performs better

Evaluation of gridbox-mean ice water Evaluation of gridbox-mean ice water contentcontent

In-cloud mean ice water In-cloud mean ice water contentcontent

Cloud structures in particular Cloud structures in particular locations locations

• How can we identify & cure errors in modelling African convection?• Unified Model simulations at a range of resolutions• Evaluate using A-Train retrievals• Also run “CloudSat simulator” to obtain radar reflectivity from model

Moist monsoon flow

African easterly jet Saharan air layer

Mid-level outflow

Parker et al. (QJRMS 2005)Location of African easterly jet

Met Office 40-km model versus Met Office 40-km model versus CloudSatCloudSat• Frequency of occurrence of reflectivity greater than –30 dBZ

• Plot versus “dynamic latitude” (latitude relative to location of AEJ)

• Anvil cirrus too low in model• Little sign of mid-level outflow

Unified Model CloudSat (~01.30 LT) CloudSat (~13.30 LT)

Thorwald Stein

Met Office 4-km model versus Met Office 4-km model versus CloudSatCloudSat

Unified Model CloudSat (~01.30 LT) CloudSat (~13.30 LT)

• Note increase from 38 to 70 levels

• Anvil cirrus now at around the right altitude• Slightly more mid-level cloud• Large overestimate of stratocumulus (and too low)

Thorwald Stein

Ongoing A-Train and EarthCARE Ongoing A-Train and EarthCARE activityactivity• Preparation for EarthCARE

– Professor Anthony Illingworth is the European lead scientist– Professor Robin Hogan is leading the European development of algorithms

exploiting the synergy of instruments on EarthCARE: novel variational retrieval methods for clouds, precipitation and aerosol being developed for EarthCARE and tested on A-Train data

• Past and future projects– Radiative properties of clouds from the A-Train (NERC): Nicky Chalmers (PhD)– Evaluation of models using CloudSat and Calipso (NERC): Julien Delanoe

(finished)– High-resolution model evaluation using CloudSat (NERC): Thorwald Stein– Lidar retrievals of liquid clouds (NCEO): Nicola Pounder– Synergy algorithms for EarthCARE (NCEO): Chris Westbrook– Radiative Transfer for EarthCARE (ESA): Julien Delanoe then Chris Westbrook– Variational Synergy algorithms for EarthCARE (ESA): not yet started

• Future challenges– Assimilate radar and lidar observations into ECMWF model using forward

models developed at University of Reading– Retrieve global cloud fields that are consistent with the radiative

measurements: can diagnose not only what aspects of clouds are wrong in models, but the radiative error associated with each