introduction limitations of cloud data for evaluating climate models

12
1 Using Reanalyses in combination with Earth Radiation Budget data to evaluate climate model simulated cloud radiative properties Richard P. Allan, Tony Slingo, Mark A. Ringer Hadley Centre for Climate Prediction and Research Introduction Limitations of cloud data for evaluating climate models Use well-calibrated, stable monthly mean radiation budget data Use method of Cess et al. (2001) N=-SWCF/LWCF Evaluation of climate model cloud properties

Upload: eamon

Post on 17-Jan-2016

43 views

Category:

Documents


0 download

DESCRIPTION

Using Reanalyses in combination with Earth Radiation Budget data to evaluate climate model simulated cloud radiative properties Richard P. Allan, Tony Slingo, Mark A. Ringer Hadley Centre for Climate Prediction and Research. Introduction Limitations of cloud data for evaluating climate models - PowerPoint PPT Presentation

TRANSCRIPT

Page 1: Introduction Limitations of cloud data for evaluating climate models

1

Using Reanalyses in combination with Earth Radiation Budget data

to evaluate climate model simulated cloud radiative

propertiesRichard P. Allan, Tony Slingo, Mark A.

RingerHadley Centre for Climate Prediction and

Research Introduction

– Limitations of cloud data for evaluating climate models

– Use well-calibrated, stable monthly mean radiation budget data

– Use method of Cess et al. (2001)

– N=-SWCF/LWCF

– Evaluation of climate model cloud properties

Page 2: Introduction Limitations of cloud data for evaluating climate models

2

From Cess et al. (2001), J. Clim, 14, p.2129

Page 3: Introduction Limitations of cloud data for evaluating climate models

3

Limitations

Convergence of points at Net CF=0, -SWCF/LWCF=1.0

» Plot N verses -SWCF?

Cloud altitude, and therefore N, strongly dependent on dynamical regime

» sample by dynamic regime?

» use reanalyses

CF dependent on cloud amount although N is not (to 1st order)

» scale by total cloud fraction?

Page 4: Introduction Limitations of cloud data for evaluating climate models

4

N verses -SWCF (Allan et al., 2002)

Page 5: Introduction Limitations of cloud data for evaluating climate models

5

Page 6: Introduction Limitations of cloud data for evaluating climate models

6

Page 7: Introduction Limitations of cloud data for evaluating climate models

7

Page 8: Introduction Limitations of cloud data for evaluating climate models

8

Page 9: Introduction Limitations of cloud data for evaluating climate models

9

MODEL EVALUATION

• Changes in N-SWCF distribution dependent on change in dynamic regime

• Large scale anomalous N in 1998 (and 1994) relate to large-scale changes in tropical radiation budget (Wielicki et al. 2001)

• Cess cloud ratio technique useful. But need to

• sample by dynamic regime

• Account for Cloud Forcing dependence on changes in cloud amount

Currently using technique to assess simulation of tropical radiation budget in latest climate model version, HadGEM and ERA-40.

Apply scaling factor to Cloud Forcing: assuming to first order linear in cloud amount...

Page 10: Introduction Limitations of cloud data for evaluating climate models

10

Evaluation vesions of climate model and ERA40. N verses -SWCF: (a) ERBS/ERA40, (b) HadAM3, (c) HadAM4, (d) New Dynamics, (e) ERBS/NCEP, (f) ERA40. Unscaled by cloud amount.

Page 11: Introduction Limitations of cloud data for evaluating climate models

11

...SCALED by cloud amount.

Page 12: Introduction Limitations of cloud data for evaluating climate models

12

Conclusions

Anomalous cloud forcing ratio in tropical west Pacific during 1998– (1) change in dynamic regime

– (2) large-scale tropical changes (Wielicki et al. 2001)

– Model can simulate (1) but not (2)

Cloud forcing ratio useful if – sample by dynamic regime

– scale by cloud amount