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Fast and slow climate responses to CO 2 and solar forcing: A linear multivariate regression model characterizing transient climate change
Author(s) -
Cao Long,
Bala Govindasamy,
Zheng Meidi,
Caldeira Ken
Publication year - 2015
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2015jd023901
Subject(s) - forcing (mathematics) , climate change , radiative forcing , environmental science , transient climate simulation , cloud forcing , climatology , climate model , climate commitment , multivariate statistics , precipitation , climate sensitivity , atmospheric sciences , global warming , meteorology , mathematics , effects of global warming , geography , statistics , geology , oceanography
Abstract Climate change in response to a change in external forcing can be understood in terms of fast response to the imposed forcing and slow feedback associated with surface temperature change. Previous studies have investigated the characteristics of fast response and slow feedback for different forcing agents. Here we examine to what extent that fast response and slow feedback derived from time‐mean results of climate model simulations can be used to infer total climate change. To achieve this goal, we develop a multivariate regression model of climate change, in which the change in a climate variable is represented by a linear combination of its sensitivity to CO 2 forcing, solar forcing, and change in global mean surface temperature. We derive the parameters of the regression model using time‐mean results from a set of HadCM3L climate model step‐forcing simulations, and then use the regression model to emulate HadCM3L‐simulated transient climate change. Our results show that 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 under transient CO 2 and/or solar forcing scenarios. Our findings suggest that temporal and spatial patterns of total change for the climate variables considered here can be represented well by the sum of fast response and slow feedback. Furthermore, by using a simple 1‐D heat‐diffusion climate model, we show that the temporal and spatial characteristics of climate change under transient forcing scenarios can be emulated well using information from step‐forcing simulations alone.