r.p.allan@reading.ac.uk© University of Reading 20101

Richard AllanDepartment of Meteorology, University of Reading

Thanks to: Jim Haywood and Malcolm Brooks (Met Office)

Using Geostationary Earth Radiation Budget data to evaluate global models and radiative processes

r.p.allan@reading.ac.uk

All-sky Clear-sky

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Radiative biases in Met Office global NWP model

r.p.allan@reading.ac.uk

All-sky Clear-sky

Sh

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Convective cloud

Radiative biases in Met Office global NWP model

Convective outflow

r.p.allan@reading.ac.uk

All-sky Clear-sky

Sh

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wav

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gw

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Convective cloud

Radiative biases in Met Office global NWP model

Convective outflow

Mineral dust

Surface albedo

r.p.allan@reading.ac.uk

All-sky Clear-sky

Sh

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wav

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on

gw

ave

Convective cloud

Radiative biases in Met Office global NWP model

Convective outflow

Mineral dust

Surface albedo

Marine stratocumulus

r.p.allan@reading.ac.uk

Marine stratocumulus

• Key in determining model uncertainty in climate sensitivity e.g. Bony and Dufresne (2005); Clemment et al. (2010)

• Model biases in physical properties• Commonality: NWP and climate parametrizations

© University of Reading 20106

CERES FM3 (Aqua) -SSF Albedo 13:11

Model albedo 5 June 2006 12:00 GERB albedo

r.p.allan@reading.ac.uk

Model stratocumulus cloud: too bright, too much water - Implications for Cloud Feedback, e.g. Stephens (2010)

© University of Reading 20107

Allan et al. (2007) QJRMS

r.p.allan@reading.ac.uk

NWP model cloud radiative bias: overcast Sc-cover pixels only (2003-2010)

© University of Reading 20108

• LW cloud effect too small (~ -5 Wm-2)• SW cloud effect too large yet too little

stratocumulus cloud cover

r.p.allan@reading.ac.uk

Systemic bias in climate models?

• Radiative bias: climate models minus ERBS

r.p.allan@reading.ac.uk

Mineral dust and surface albedo (model-GERB)

Progressive reduction in model biases through continual evaluation and improvement

See also poster by Margaret Woodage for evaluation of dust scheme in HiGEM model

r.p.allan@reading.ac.uk© University of Reading 200911

GERBILS aircraft campaign Lead by Jim Haywood 18-28 June 2007

r.p.allan@reading.ac.uk© University of Reading 200912

Persistent contrail cirrus

case study

Combining satellite data with models to understand physical processes

r.p.allan@reading.ac.uk© University of Reading 200913

Persistent contrail cirrus

case study

Combining satellite data with models to understand physical processes

r.p.allan@reading.ac.uk© University of Reading 200914

Persistent contrail cirrus

case study

Combining satellite data with models to understand physical processes

r.p.allan@reading.ac.uk© University of Reading 200915

Persistent contrail cirrus

case study

Combining satellite data with models to understand physical processes

r.p.allan@reading.ac.uk© University of Reading 200916

Persistent contrail cirrus

case study

Combining satellite data with models to understand physical processes

r.p.allan@reading.ac.uk© University of Reading 200917

Persistent contrail cirrus

case study

Combining satellite data with models to understand physical processes

r.p.allan@reading.ac.uk© University of Reading 200918

Persistent contrail cirrus

case study

Combining satellite data with models to understand physical processes

r.p.allan@reading.ac.uk© University of Reading 200919

CERES FM3 (Aqua) FLASH fluxes 13:25

Contrail induced cirrus?

Window flux

Inverse greenhouse parameter

SW fluxes (Wm-2)

Using GERB/NWP model estimate radiative effect of contrail cirrus:LW ~ 40 Wm-2

SW up to 80 Wm-2

stratocumulus

LW fluxes (Wm-2)

r.p.allan@reading.ac.uk© University of Reading 200920

Clo

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Contrail region: higher optical thickness, smaller effective radius

SEVIRI cloud properties (0.8 and 1.6 μm channels)

Water cloud

r.p.allan@reading.ac.uk© University of Reading 200921Haywood et al. (2009) JGR

Using GERB-like/SEVIRI and 4km forecast model to quantify contrail radiative effects: example at 14:00, 20 March 2009

r.p.allan@reading.ac.uk

Case II: 25-26/06/2010

r.p.allan@reading.ac.uk

LW and SW cloud radiative effect• Estimated using GERB/SEVIRI minus NCEP clear-sky• LW and SW cloud radiative effect up to 80 Wm-2 (small net effect at

midday)• But would cirrus have formed anyway?

© University of Reading 200923

12-18 UTC

r.p.allan@reading.ac.uk

Summary• Mineral dust aerosol

– Systematic model LW radiative bias at TOA up to 40 Wm-2 – GERBILS field campaign

• Model Cloud– Subtropical stratocumulus too bright, too much liquid water– Cannot tune to TOA radiation; need to get bulk physical

properties right. Issues with LWP retrievals (μ-wave/visible)– Highly sensitive to model changes (e.g. convection)

• Persistent contrail cirrus events– Up to 80 Wm-2 LW and SW cirrus radiative effect– Small net effect, but large dynamical and diurnal forcing– Would cirrus have formed anyway?– Any contrail effect following Iceland volcano aircraft shutdown?

© University of Reading 201024

r.p.allan@reading.ac.uk

Was there any contrail effect following the Iceland Volcano?

© University of Reading 201025

Airspace closure Airspace reopens

r.p.allan@reading.ac.uk

Was there any contrail effect following the Iceland Volcano?

© University of Reading 201026

Airspace closure Airspace reopens

...unlikely! Probably more related to advection of dry air and subsequent encroachment of mid-latitude system.

GERB HR (V003) LW fluxes

r.p.allan@reading.ac.uk

• Major update to model parametrizations 15 July 2010

• Stratocumulus cloud now too extensive

© University of Reading 201027

GERB Model

12 July

19 July

Continuous monitoring

r.p.allan@reading.ac.uk28

LW bias in climate models May-July

Average clear-LW bias over whole region 3-15 Wm-2 depending upon model & satellite dataset/period

r.p.allan@reading.ac.uk

Ongoing evaluation: diurnal cycle of

convection in high resolution models

© University of Reading 200929

Pearson et al. (2010) JGR in press (CASCADE project)