Quantifying rapid adjustments using radiative kernels Chris Smith, Gunnar Myhre, Ryan Kramer, Piers Forster, Bjørn Samset, Toshi Takemura + PDRMIP authors

Effective radiative forcing We define IRF at the top of the atmosphere even for GHGs, for consistency with ERF definition Adapted from IPCC AR5 WG1, Ch. 8, p. 669

Then, ERF = IRF + adjustments Formally: 𝐸=𝐼+ 𝐴 𝑇 + 𝐴 𝑞 + 𝐴 𝛼 + 𝐴 𝑐 +𝑟 Rapid adjustments Aim for a clean separation of GMST-driven feedbacks from forcing-driven rapid adjustments Then, ERF = IRF + adjustments Formally: 𝐸=𝐼+ 𝐴 𝑇 + 𝐴 𝑞 + 𝐴 𝛼 + 𝐴 𝑐 +𝑟 temperature albedo residual humidity clouds

Non-cloud adjustments Assume TOA radiative fluxes R change linearly w.r.t. small perturbations in T, q and α Then 𝐴 𝑥 = 𝜕𝑅 𝜕𝑥 ∆𝑥 for x in {T, q, α} Δx represents change in state between perturbed and base climate. Separate T into stratosphere, troposphere and surface 𝜕𝑅 𝜕𝑥 is the radiative kernel (month[, height], lat, lon). Make clear sky and all sky kernels using the SOCRATES (HadGEM) radiation code from one year of piControl run.

Change in vertical profiles Fixed SST experiment atmospheric temperature change in HadGEM2

HadGEM2 non-cloud kernels

Non-cloud adjustments

Estimated as a residual; assumes either Clouds (one method) Estimated as a residual; assumes either IRF all-sky and IRF clear-sky are known, and r is calculated from IRF clear-sky; or IRF all-sky is known and r is assumed to be zero Previous work used IRF from double-call 4×CO2 or assumed it from SRES A1B Need more direct approach for PDRMIP 𝐸=𝐼+ 𝐴 𝑇 + 𝐴 𝑞 + 𝐴 𝛼 + 𝐴 𝑐 +𝑟 𝐸 0 = 𝐼 0 + 𝐴 𝑇,0 + 𝐴 𝑞,0 + 𝐴 𝛼,0 +𝑟

Full analysis limited to models where double-call IRFs exist Clouds and IRF Therefore we need estimates of IRF from double-call simulations from PDRMIP models 2% solar is easy CO2 and CH4 experiments could substitute global values into SOCRATES radiation code Aerosols are too spatially variable to do this Full analysis limited to models where double-call IRFs exist HadGEM2, CAM4 and MIROC-SPRINTARS

ERF and adjustments Large residual for 2×CO2 2×CO2 TOA IRF is too low in SOCRATES Hansen et al. (1997) says 2.62 W m-2

Comparison of kernels Both the differences in kernel climatology and the kernel truncation height can affect the results. Note different scale

Comparison of kernels The upper truncation level of the kernel has a significant effect on the stratospheric adjustment to CO2. This is the other cause, other than low IRF, of the large residual for CO2 forcing.

Another method for cloud adjustments ISCCP simulator kernel can be used if model has ISCCP diagnostics Based on what satellites see from TOA I ran in HadGEM2 Zelinka et al., 2012, J. Clim., 10.1175/JCLI-D-11-00248.1

ISCCP simulator cloud adjustments Net TOA cloud adjustments show reasonable agreement between residual and ISCCP simulator LW and SW individual values can disagree substantially

Estimates of clear-sky IRF, assuming r=0 Good agreement between residual and double-call IRF for all experiments except 2×CO2 Evidence of why IRF cannot be assumed except for solar Concentration based: dark shading Emissions-based: light shading GISS is an outlier for 5×SO4

Add in more kernels into the kernel intercomparison Things to do Add in more kernels into the kernel intercomparison Determine a better value of TOA IRF for 2×CO2 Use partial radiative perturbation in each model in a different radiative transfer code?

A request to modelling groups IRFs from fixed-SST experiments would be really useful both all-sky and clear-sky for all fixed-SST forcing experiments + base run Cloud diagnostics calculated from the ISCCP simulator would be desirable too

Conclusions Rapid adjustments are model-specific especially due to cloud adjustments Tropospheric adjustments are significant even for GHGs Knowing IRF is critical to assessing model responses Kernels lead to small residuals except for CO2 but reasons for deficiencies are known Clouds dominate the uncertainty