School of Earth and Environment
INSTITUTE FOR CLIMATE & ATMOSPHERIC SCIENCE
Analysis of effective radiative forcing
and precipitation methodology
Piers Forster & Tom Richardson
[email protected], [email protected]
Tim Andrews + PDRMIP Team
Introduction
• Usefulness of previous CMIP exercises hampered by lack of forcing information
• Effective radiative forcing (ERF) easier to diagnose than traditional radiative
forcing and more representative of eventual temperature response
• However ERF depends on the methods of calculation and there is as yet no
agreed method
• Similarly precipitation response to forcing can be split into adjustment and
feedback components which is vital for understanding different responses, but
there is no definitive method to make the separation
• Here we examine different methods of calculating radiative forcing and
precipitation adjustment and feedback repsonses.
Forcing calculation methods
ERF_reg – linear regression TOA flux change against global mean surface air
temperature
ERF_fSST – difference between TOA flux between perturbed and control fSST
simulations
ERF_nudge – modified ERF_fSST with nudging techniques
ERF_trans – modified ERF_fSST to provide transient estimates of ERF
IRF – instantaneous radiative forcing using double call
ERF_fSST and ERF_reg
Comparison of results
F_reg vs F_SST individual models
F_reg vs F_SST individual models
F_reg vs F_SST individual models
F_reg vs F_SST individual models
F_reg vs F_SST individual models
F_reg vs F_SST individual models
F_reg vs F_SST individual models
F_reg vs F_SST individual models
F_reg vs F_SST individual models
F_reg vs F_SST individual models
ERF_trans
Precip Adjustment and Feedback methods
Yr1 – isolate adjustment based on timescale, taking just the first year of abrupt
forcing simulation
Regression – regress precipitation change versus global surface air temperature
change in abrupt forcing simulation
fSST – isolate adjustment using fSST simulation
Comparison of Precip Adjustment and
Feedback Results
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Yr1 methods incorporates substantial surface temp change
fSST and regression method generally agree within uncertainties globally
fSST method exhibits less uncertainty for individual models
Regression method affected by regression length
Precip Zonal results
• Regionally significant differences arise between methods
• Regression results highly dependent on length
Conclusions
ERF:
• ERF_fSST exhibits less uncertainty than regression method
• 30-year integrations sufficient to reduce 5-95% confidence interval in
global ERF_fSST to 0.1W m-2
• ERF_fSST only weakly dependent on methodological choices
•  Impact, RFMIP will choose PI controls of SST and Sea ice for base case
Precip Adjustment and Feedback:
• fSST method exhibits less uncertainty in precipitation adjustment and
feedback components, and less dependent on methodological choices
• fSST methods provides more consistent and clear mechanistic
decomposition