Variation in emission metrics due to variation in CO<sub>2</sub> and temperature impulse response functions

Abstract

Abstract. Emission metrics are used to compare the climate effect of the emission of different species, such as carbon dioxide (CO2) and methane (CH4). The most common metrics use linear impulse response functions (IRFs) derived from a single more complex model. There is currently little understanding on how IRFs vary across models, and how the model variation propagates into the metric values. In this study, we first derive CO2 and temperature IRFs for a large number of complex models participating in different intercomparison exercises, synthesizing the results in distributions representing the variety in behaviour. The derived IRF distributions differ considerably, which is partially related to differences among the underlying models, and partially to the specificity of the scenarios used (experimental setup). In a second part of the study, we investigate how differences among the IRFs impact the estimates of global warming potential (GWP), global temperature change potential (GTP) and integrated global temperature change potential (iGTP) for time horizons between 20 and 500 yr. Within each derived CO2 IRF distribution, underlying model differences give similar spreads on the metrics in the range of −20 to +40% (5–95% spread), and these spreads are similar among the three metrics. GTP and iGTP metrics are also impacted by variation in the temperature IRF. For GTP, this impact depends strongly on the lifetime of the species and the time horizon. The GTP of black carbon shows spreads of up to −60 to +80% for time horizons to 100 yr, and even larger spreads for longer time horizons. For CH4 the impact from variation in the temperature IRF is still large, but it becomes smaller for longer-lived species. The impact from variation in the temperature IRF on iGTP is small and falls within a range of ±10% for all species and time horizons considered here. We have used the available data to estimate the IRFs, but we suggest the use of tailored intercomparison projects specific for IRFs in emission metrics. Intercomparison projects are an effective means to derive an IRF and its model spread for use in metrics, but more detailed analysis is required to explore a wider range of uncertainties. Further work can reveal which parameters in each IRF lead to the largest uncertainties, and this information may be used to reduce the uncertainty in metric values.

Metrics for linking emissions of gases and aerosols to global precipitation changes

“…4, and cover a wide range of climate sensitivities (0.49 to 1.06 K (W m −2 ) −1 ) and timescales of climate response, although we note that model uncertainty range may not fully straddle the true uncertainty range. Olivié and Peters (2013) used these fits to explore the sensitivity of the GTP calculations. Figure 9 shows the mean and standard de- Figure 9.…”

Accounting for the climate–carbon feedback in emission metrics

“…This gives Typically, the IRF for atmospheric CO 2 is taken from Joos et al (2013), those for other greenhouse gases are exponential decay functions with a constant e-folding time taken as the "perturbation lifetime" given by Myhre et al (2013), the radiative forcing functions come from Ramaswamy et al (2001) with updated radiative efficiencies from Myhre et al (2013), and the climate IRF is taken from Boucher and Reddy (2008). Note, however, that updates of the climate IRF based on CMIP5 models are available in the literature (Geoffroy et al, 2013;Olivié and Peters, 2013) but they have not been widely used so far.…”

Evaluating the climate and air quality impacts of short-lived pollutants

“…Therefore, it does not retain the memory of short-lived emissions in the same way as the GWP. Difficulties with the GTP include its dependence on the climate sensitivity and on the method of incorporating the ocean's thermal response (Shine et al, 2007;Fuglestvedt et al, 2010;Olivié and Peters, 2013).…”