Temperature change vs. cumulative radiative forcing as metrics for evaluating climate consequences of energy system choices

Caldeira, Ken; Myhrvold, Nathan

doi:10.1073/pnas.1206019109

Cited by 8 publications

(8 citation statements)

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“…Such models represent the ocean as a vertical diffusive column [15] and assume that outgoing long-wave radiation varies linearly with surface temperature [16]. In such a model (see examples in [4,14,17]), the top-of-atmosphere net radiative fluxes, which follow equation (1), are applied to a one-dimensional slab representing the heat capacity of the climate system (primarily the oceans), subject to a zero-flux boundary condition at the maximum depth (z max ), which we take to be 4000 m:…”

Section: Curve Fits To Estimate Temporal Parametersmentioning

confidence: 99%

Projections of the pace of warming following an abrupt increase in atmospheric carbon dioxide concentration

Caldeira

Myhrvold

2013

Environ. Res. Lett.

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The temperature response of atmosphere-ocean climate models is analyzed based on atmospheric CO 2 step-function-change simulations submitted to phase 5 of the Coupled Model Intercomparison Project (CMIP5). From these simulations and a control simulation, we estimate adjusted radiative forcing, the climate feedback parameter, and effective climate system thermal inertia, and we show that these results can be used to predict the temperature response to time-varying CO 2 concentrations. We evaluate several kinds of simple mathematical models for the CMIP5 simulation results, including single-and multiple-exponential models and a one-dimensional ocean-diffusion model. All of these functional forms, except the single-exponential model, can produce curves that fit most CMIP5 results quite well for both continuous and step-function CO 2 -change pathways. Choice of model for any particular application would include consideration of factors such as the number of free parameters to be constrained and the conception of the underlying mechanistic model. Smooth curve fits to the CMIP5 simulation results realize approximately half (range 38%-61%) of equilibrium warming within the first decade after a CO 2 concentration increase, but approximately one quarter (range 14%-40%) of equilibrium warming occurs more than a century after the CO 2 increase. Following an instantaneous quadrupling of atmospheric CO 2 , fits to four of the 20 simulation results reach 4 • C of warming within the first decade, but fits to three of the 20 simulation results require more than a century to reach 4 • C.These results indicate the need to reduce uncertainty in the temporal response of climate models and to consider this uncertainty when evaluating the risks posed by climate change.

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Section: Curve Fits To Estimate Temporal Parametersmentioning

confidence: 99%

Projections of the pace of warming following an abrupt increase in atmospheric carbon dioxide concentration

Caldeira

Myhrvold

2013

Environ. Res. Lett.

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“…According to MacMynowski et al [2011] and Caldeira and Myhrvold [2012], temperature change in response to a step forcing in the 1-D model can be expressed as…”

Section: A11 Construction Of a 1-d Heat-diffusion Modelmentioning

confidence: 99%

“…Following MacMynowski et al [2011] and Caldeira and Myhrvold [2012], we consider a 1-D model representing heat diffusion into a semi-infinite slab.…”

Section: A11 Construction Of a 1-d Heat-diffusion Modelmentioning

confidence: 99%

“…Following the method of MacMynowski et al Myhrvold [2012], we consider a 1-D climate model with heat-diffusion into a semi-infinite medium, which has a known form of response function that links temperature change to imposed forcing. The response function of the 1-D model has three parameters, effective radiative forcing (F), climate feedback parameter (λ), and characteristic time scale (τ), which can all be calibrated from step-forcing simulations.…”

Section: Conjoining the Linear Regression Model With A One-dimensionamentioning

confidence: 99%

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Fast and slow climate responses to CO₂ and solar forcing: A linear multivariate regression model characterizing transient climate change

et al. 2015

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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.

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“…Caldeira and Myhrvold (1) argue that the temperature change metric proposed in their recent paper (2) would be more useful to policymakers than the cumulative radiative forcing metric used by Alvarez et al (3). We believe both metrics are useful, although the simplicity, transparency, and relatively low uncertainty of the cumulative radiative forcing metric makes it particularly useful in policy formulation.…”

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confidence: 99%

Reply to Caldeira and Myhrvold: Radiative forcing is a useful, accepted metric to compare climate influence of alternative energy choices

Alvarez¹,

Pacala²,

Winebrake³

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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Temperature change vs. cumulative radiative forcing as metrics for evaluating climate consequences of energy system choices

Cited by 8 publications

References 5 publications

Projections of the pace of warming following an abrupt increase in atmospheric carbon dioxide concentration

Projections of the pace of warming following an abrupt increase in atmospheric carbon dioxide concentration

Fast and slow climate responses to CO₂ and solar forcing: A linear multivariate regression model characterizing transient climate change

Reply to Caldeira and Myhrvold: Radiative forcing is a useful, accepted metric to compare climate influence of alternative energy choices

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Temperature change vs. cumulative radiative forcing as metrics for evaluating climate consequences of energy system choices

Cited by 8 publications

References 5 publications

Projections of the pace of warming following an abrupt increase in atmospheric carbon dioxide concentration

Projections of the pace of warming following an abrupt increase in atmospheric carbon dioxide concentration

Fast and slow climate responses to CO2 and solar forcing: A linear multivariate regression model characterizing transient climate change

Reply to Caldeira and Myhrvold: Radiative forcing is a useful, accepted metric to compare climate influence of alternative energy choices

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Product

Resources

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Fast and slow climate responses to CO₂ and solar forcing: A linear multivariate regression model characterizing transient climate change