Intermodel spread in instantaneous radiative forcing across PDRMIP

Inter-model spread in instantaneous radiative forcing across PDRMIP climate drivers Ryan Kramer Chris Smith, Gunnar Myhre, Brian Soden Tri. MIP Meeting 2018

Estimating TOA IRF with Radiative Kernels Effectve Radiative Forcing (ERF) IRF(clr) = ERF(clr) – Σ RA_x(clr) = (d. R/dx)*dx Instantaneous Radiative Forcing Rapid Adjustments Myhre et al. BAMS, 2016

Model Mean IRF Across Scenarios

Radiative Kernel Validation: Solar Case 1. Had. GEM 2 2. NCAR-CESM 1 -CAM 4 3. Can. ESM 2 4. MPI-ESM 5. GISS-E 2 -R 6. MIROC-SPRINTARS 7. Had. GEM 3 8. NCAR-CESM 1 -CAM 5 9. Nor. ESM 1 10. IPSL-CM 5 A 11. ECHAM-HAM

Solar IRF does not explain ERF spread 1. Had. GEM 2 2. NCAR-CESM 1 -CAM 4 3. Can. ESM 2 4. MPI-ESM 5. GISS-E 2 -R 6. MIROC-SPRINTARS 7. Had. GEM 3 8. NCAR-CESM 1 -CAM 5 9. Nor. ESM 1 10. IPSL-CM 5 A 11. ECHAM-HAM

IRF vs ERF – Greenhouse Gas Forcing

IRF - Aerosols sulx 5 BCx 10 W/m 2 IRF inter-model spread in source regions contributes to global-mean spread

Impact of aerosol treatment on IRF spread sulx 5 BCx 10

IRF vs ERF – Aerosol Forcing

Conclusions • IRF exhibits notable inter-model spread in all forcing scenarios except solar +2% • IRF is a major contributor to ERF spread in all GHG and aerosol cases • Radiative kernels can give us a sense of IRF spread, but we encourage modelers to archive double-call calculations