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Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading Sean Milton, Malcolm Brooks Met Office, Exeter
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Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

Mar 28, 2015

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Page 1: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

Evaluating the Met Office global forecast model using GERB data

Richard Allan, Tony Slingo

Environmental Systems Science Centre, University of Reading

Sean Milton, Malcolm Brooks

Met Office, Exeter

Page 2: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

Objectives

• Improve experience with satellite datasets including GERB

• Timely Model Evaluation• Understanding of physical processes

Page 3: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

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Mean model bias: 2006

All-sky Clear-sky

Page 4: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

All-sky Clear-sky

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Convective cloud

Surface albedo

Mineral dust aerosol

Marine stratocumulus

Cirrus outflow

Page 5: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

All-sky Clear-sky

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Mineral dust aerosol

Page 6: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

Dust impact on longwave radiation

• Large perturbation to Met Office model OLR during summer over west Sahara

• Correlates with high mineral dust aerosol optical depth

Model minus GERB OLR: July 2006, 12-18 UTC

Page 7: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

18 UTC model-GERB OLR over West Sahara

GERBIL – aircraft campaign (Jim Haywood)

GERB Intercomparison of Longwave Radiation: June 2007 http://www.faam.ac.uk/public/campaigns/index.html#gerbil

Page 8: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

Consistent with calculations of dust longwave radiative effect

Clear-sky OLR bias (Wm-2) in 2003

Calculations:

Direct radiative effect

Direct plus shortwave feedback effect

Haywood et al. (2005) JGR 110, D05105

Page 9: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

- Major dust source for Amazon

- Large component from March 2004 dust storms

Page 10: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

March 2004: an interesting month

Loeb et al. (2007) J. Climate, 20, p.582

March 2006 was interesting too…

Page 11: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

All-sky Clear-sky

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Marine stratocumulus

Convective cloud

Cirrus outflow Radiative biases in the Met Office global model

Page 12: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

Marine Stratocumulus

Page 13: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

• Curious banding structure– Transition across

model levels• Cloud reflectivity

bias– Model low-altitude

stratiform clouds are too reflective

Page 14: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

Changes in albedo bias (ocean)

• Model upgrade (March 2006) reduced but did not remove albedo bias– Compensating errors: ITCZ/stratocumulus

Page 15: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

Stratocumulus composites

Page 16: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

Cloud liquid water path

Bias: model minus GERB; SSM/I; SEVIRI

Albedo Liquid Water Path Cloud

Reduction in model bias from June to July 2006 - relates to cloud liquid water

Page 17: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

LWPWentz: overestimate for low cloud fraction?

TMIWentz/MODIS LWPOvercast boundary layer clouds: good agreement

Horváth and Davies (2007) JGR 112, D01202

Page 18: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

Convective cloud

5th June 2006

Page 19: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

Model evaluation: near-real time

• Change in model minus GERB flux differences

• Relate to change in model physics implementation

13th March | 14th March

Model SW albedo

2005 2006

Page 20: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

Convective Decay Time-scale

• Unrealistically low levels of convective cloud

• On-off; common problem in models

• Simple fix…

Page 21: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

• Increased convective cloud cover

• But is the physics any better?

• Future work: Comparisons with CloudSat

Page 22: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

Improved shortwave reflectivity

Page 23: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

Gulf of Guinea Model CloudSat

19th July 2006

5th July 2006

Page 24: Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

Conclusions

• Top down-bottom up approach– Good for feedback to modellers

• Mineral dust aerosol– Shortwave absorption; longwave radiative effect– Large effect of single events

• Marine stratocumulus– Reflectivity and seasonal variability: issues

• Deep convection– Intermittent in models; issues with detrainment