A Temporal Kernel Method to Compute Effective Radiative Forcing in CMIP5 Transient Simulations

Abstract

ffective radiative forcing (ERF) is calculated as the flux change at the top of the atmosphere after allowing rapid adjustments resulting from a forcing agent, such as greenhouse gases. Rapid adjustments include changes to atmospheric temperature, water vapor, and clouds. Accurate estimates of ERF are necessary in order to understand the drivers of climate change. This work presents a new method of calculating ERF using a kernel derived from the time series of a model variable (e.g., global mean surface temperature) in a model-step change experiment. The top-of-atmosphere (TOA) radiative imbalance has the best noise tolerance for retrieving the ERF of the model variables tested. This temporal kernel method is compared with an energy balance method, which equates ERF to the TOA radiative imbalance plus the scaled surface temperature change. Sensitivities and biases of these methods are quantified using output from phase 5 of the the Coupled Model Intercomparison Project (CMIP5). The temporal kernel method is likely more accurate for models in which a linear fit is a poor approximation for the relationship between temperature change and TOA imbalance. The difference between these methods is most apparent in forcing estimates for the representative concentration pathway 8.5 (RCP8.5) scenario. The CMIP5 multimodel mean ERF calculated for large volcanic eruptions is 80% of the adjusted forcing reported by the IPCC Fifth Assessment Report (AR5). This suggests that about 5% more energy has come into the earth system since 1870 than suggested by the IPCC AR5.