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Robin Hogan, Julien Delanoë, Nicky Chalmers, Thorwald Stein, Anthony Illingworth University of Reading. Evaluating and improving the representation of clouds in climate models using spaceborne radar and lidar. Clouds in climate models.

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Robin Hogan, Julien Delanoë, Nicky Chalmers, Thorwald Stein, Anthony Illingworth

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Robin hogan julien delano nicky chalmers thorwald stein anthony illingworth

Robin Hogan, Julien Delanoë, Nicky Chalmers,

Thorwald Stein, Anthony Illingworth

University of Reading

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


Clouds in climate models

Clouds in climate models

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

14 global models (AMIP)

0.25

0.20

0.15

Vertically integrated cloud water (kg m-2)

The properties of ice clouds are particularly uncertain

0.10

0.05

90N

80

60

40

20

0

-20

-40

-60

-80

90S

Latitude

  • 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 radiometers

Spaceborne radar, lidar and radiometers

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

  • 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


What do cloudsat and calipso see

What do CloudSat and Calipso see?

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

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

Cloudsat radar

CALIPSO lidar

Insects

Aerosol

Rain

Supercooled liquid cloud

Warm liquid cloud

Ice and supercooled liquid

Ice

Clear

No ice/rain but possibly liquid

Ground

Target classification

Delanoe and Hogan (2008, 2010)


Example ice cloud retrievals

Example ice cloud retrievals

Lidar observations

Visible extinction

Lidar forward model

Ice water content

Radar observations

Effective radius

Radar forward model

Delanoe and Hogan (2010)


Evaluation using ceres toa fluxes

Evaluation using CERES TOA fluxes

  • 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?

Longwave

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

Shortwave

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

Nicky Chalmers


A train versus models

A-Train versus models

  • Ice water content

  • 14 July 2006

  • Half an orbit

  • 150° longitude at equator

Delanoe et al. (2010)


Robin hogan julien delano nicky chalmers thorwald stein anthony illingworth

Evaluation of gridbox-mean ice water content

  • 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

In-cloud mean ice water content


Cloud structures in particular locations

Cloud structures in particular locations

Mid-level outflow

African easterly jet

Saharan air layer

Moist monsoon flow

  • 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

Location of African easterly jet

Parker et al. (QJRMS 2005)


Met office 40 km model versus cloudsat

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

Met Office 40-km model versus CloudSat

Unified Model

CloudSat (~01.30 LT)

CloudSat (~13.30 LT)

Thorwald Stein


Met office 4 km model versus cloudsat

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)

Met Office 4-km model versus CloudSat

Unified Model

CloudSat (~01.30 LT)

CloudSat (~13.30 LT)

Thorwald Stein


Ongoing a train and earthcare activity

Ongoing A-Train and EarthCARE activity

  • 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


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