A modified impulse-response representation of the global near-surface air temperature and atmospheric concentration response to carbon dioxide emissions

Millar, Richard; Nicholls, Zebedee; Friedlingstein, Pierre; Allen, Myles

doi:10.5194/acp-17-7213-2017

“…Historical temperatures were also constrained using the Had-CRUT4 dataset without infilling (Morice et al, 2012), along with the GISTEMP (Hansen et al, 2010), Berkeley Earth (Berkeley Earth, 2017) and NOAA (Zhang et al, 2017) observational datasets. The linear 1880-2016 trends are 0.91 ± 0.18 K, 0.99±0.22 K, 1.07±0.16 K and 0.93±0.24 K respectively.…”

Section: Historical Temperature Constraintmentioning

confidence: 99%

FAIR v1.3: a simple emissions-based impulse response and carbon cycle model

Smith

¹

,

Forster

²

,

Allen

³

et al. 2018

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Abstract. Simple climate models can be valuable if they are able to replicate aspects of complex fully coupled earth system models. Larger ensembles can be produced, enabling a probabilistic view of future climate change. A simple emissions-based climate model, FAIR, is presented, which calculates atmospheric concentrations of greenhouse gases and effective radiative forcing (ERF) from greenhouse gases, aerosols, ozone and other agents. Model runs are constrained to observed temperature change from 1880 to 2016 and produce a range of future projections under the Representative Concentration Pathway (RCP) scenarios. The constrained estimates of equilibrium climate sensitivity (ECS), transient climate response (TCR) and transient climate response to cumulative CO 2 emissions (TCRE) are 2.86 (2.01 to 4.22) K, 1.53 (1.05 to 2.41) K and 1.40 (0.96 to 2.23) K (1000 GtC) −1 (median and 5-95 % credible intervals). These are in good agreement with the likely Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) range, noting that AR5 estimates were derived from a combination of climate models, observations and expert judgement. The ranges of future projections of temperature and ranges of estimates of ECS, TCR and TCRE are somewhat sensitive to the prior distributions of ECS/TCR parameters but less sensitive to the ERF from a doubling of CO 2 or the observational temperature dataset used to constrain the ensemble. Taking these sensitivities into account, there is no evidence to suggest that the median and credible range of observationally constrained TCR or ECS differ from climate model-derived estimates. The range of temperature projections under RCP8.5 for 2081-2100 in the constrained FAIR model ensemble is lower than the emissions-based estimate reported in AR5 by half a degree, owing to differences in forcing assumptions and ECS/TCR distributions.

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“…2.1.1 Carbon dioxide and carbon cycle 30 The carbon cycle component in FAIR v1.0 is described in detail by Millar et al (2017b) and an overview is provided here.…”

Section: Emissions To Concentrationsmentioning

confidence: 99%

“…The various feedback strengths are nevertheless very model dependent (Friedlingstein 15 et al, 2006). The causes of model diversity in feedback strengths was explored only superficially in CMIP5 owing to the number of earth system models available and the experimental design (Taylor et al, 2012;Millar et al, 2017b). Only CO 2 -emission driven experiments under historical and RCP8.5 forcing compared to pre-industrial control emissions were core experiments in CMIP5 for emissions-driven earth system models (Taylor et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…It was introduced for the purposes of calculating global warming potentials, built on the impulse response model of Boucher and Reddy (2008). Millar et al (2017b) showed that this AR5 model does not sufficiently capture the time-evolving dependency 30 of carbon sinks with cumulative carbon emissions and temperatures. They introduced the Finite Amplitude Impulse Response (FAIR) model (version 1.0) that tracks the time-integrated airborne fraction of carbon and uses this to determine the efficiency of carbon sinks.…”

Section: Introductionmentioning

confidence: 99%

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FAIR v1.1: A simple emissions-based impulse response and carbon cycle model

Smith

¹

,

Forster

²

,

Allen

³

et al. 2017

Preprint

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Abstract. Simple climate models can be valuable if they are able to replicate aspects of complex fully coupled earth system models. Larger ensembles can be produced, enabling a probabilistic view of future climate change. A simple emissions-based climate model, FAIR, is presented which calculates atmospheric concentrations of greenhouse gases and effective radiative 10(median and 5-95% credible intervals) are in good agreement, with tighter uncertainty bounds, than AR5 (1.5 to 4.5 K, 1.0 to 2.5 K, and 0.8 to 2.5 K respectively). The ranges of future projections of temperature and ranges of estimates of ECS, TCR and TCRE are moderately sensitive to the historical temperature dataset used to constrain, prior distributions of ECS/TCR parameters, aerosol radiative forcing relationship and ERF from a doubling of CO 2 . Taking these sensitivities into account, there is no evidence to suggest that the median and credible range of observationally constrained TCR or ECS differ from 15 climate model-derived estimates. However, the range of temperature projections under the RCP scenarios for 2081-2100 in the constrained FAIR model ensemble are lower than the emissions-based estimates reported in AR5.

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“…Pymagicc is a Python interface for the Fortran-based reduced-complexity climate carbon cycle model MAGICC (Meinshausen, Raper, and Wigley 2011 Pymagicc also facilitates comparisons with other recently published simple climate models available from or written in Python, such as OSCAR 6 (Gasser et al (2017)), Pyhector 7 (Willner, Hartin, and Gieseke (2017), Hartin et al (2015)), and FAIR 8 (Millar et al (2017).) It can be installed using pip from the Python Package Index.…”

Section: Discussionmentioning

confidence: 99%