A44A-04
Characterizing the Transient Climate Change to Solar and CO2 Forcing Using the Framework of Rapid Adjustment and Slow Feedback

Thursday, 17 December 2015: 16:45
3006 (Moscone West)
Long Cao, Zhejiang University, Hangzhou, China, Ken Caldeira, Carnegie Institution for Science Washington, Washington, DC, United States and Govindasamy Bala, Indian Institute of Science, Bangalore, India
Abstract:
The framework of fast response and slow feedback has emerged as a conceptual paradigm to understand the response of the climate system to external forcings. Fast response refers to rapid climate adjustment to an imposed external forcing that occurs before substantial change in global mean surface temperature, and slow feedback refers to climate response that is associated with a change in surface temperature.

In this study, we investigate the utility of the response-feedback paradigm in representing total climate change by developing a multivariate regression model of climate change. In contrast to most studies that examine response to a single climate forcing factor, we consider responses to two climate forcings applied separately and in combination. In our work, the change in a climate variable is represented by a linear combination of its sensitivity to CO2 forcing, solar forcing, and change in global mean surface temperature. In contrast to approaches pioneered by Gregory et al. (2004) in which regression parameters are derived from time-series data from a single simulation, in our work the parameters of the regression model are derived using time-mean results from a set of HadCM3L climate model step-forcing simulations. The regression model is then used to emulate HadCM3L-simulate climate change under a wide range of transient CO2/solar forcing scenarios.

The regression model emulates well HadCM3L-simulated temporal evolution and spatial distribution of climate change, including surface temperature, precipitation, runoff, soil moisture, cloudiness, and radiative fluxes, suggesting that total climate change can be represented well by the sum of fast response and slow feedback. We also discuss the limitation of the regression model and the underlying linear assumption of climate response.