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Abstract
Abstract. Integrated assessment models (IAMs) project future anthropogenic emissions which can be used as input for climate models. However, the full list of climate-relevant emissions is lengthy and most IAMs do not model all of them. Here we present Silicone, an open-source Python package which infers anthropogenic emissions of unmodelled species based on other reported emissions projections. For example, it can infer nitrous oxide emissions in one scenario based on carbon dioxide emissions from that scenario plus the relationship between nitrous oxide and carbon dioxide emissions found in other scenarios. Infilling broadens the range of IAMs available for exploring projections of future climate change, and hence Silicone forms part of the open-source pipeline for assessments of the climate implications of IAM scenarios, led by the Integrated Assessment Modelling Consortium (IAMC). This paper presents a variety of infilling options and outlines their suitability for different cases. We recommend certain infilling techniques as good defaults but emphasise that considering the specifics of the model being infilled will produce better results. We demonstrate the package's utility with three examples: infilling all required gases for a pathway with data for only one emission species, splitting up a Kyoto emissions total into separate gases, and complementing a set of idealised emissions curves to provide a complete, consistent emissions portfolio. The code and notebooks explaining details of the package and how to use it are available on GitHub (https://github.com/GranthamImperial/silicone, last access: 2 November 2020). The repository with this paper's examples and uses of the code to complement existing research is available at https://github.com/GranthamImperial/silicone_examples (last access: 2 November 2020).
Highlights
1.1 General context and problem settingIntegrated assessment models (IAMs) are scientific modelling tools that integrate knowledge from different academic disciplines with the aim of exploring and informing policy decisions (Clarke et al, 2014; Rogelj et al, 2018a; Weyant, 2017)
The best choice is where there is a causal link between the lead and follower variable, if there is a clear understanding of the implications of this link for the relative behaviour of the two variables; for instance, black carbon and carbon monoxide are both produced by incomplete combustion
We use the data from the IPCC Special Report on Global Warming of 1.5 ◦C (Huppmann et al, 2018) as our database of scenarios and compare the correlations between the different variables
Summary
Integrated assessment models (IAMs) are scientific modelling tools that integrate knowledge from different academic disciplines with the aim of exploring and informing policy decisions (Clarke et al, 2014; Rogelj et al, 2018a; Weyant, 2017) They are widely used in climate change research to combine insights from energy, economy, agricultural, and natural sciences, with the aim of creating scenarios that explore how societal decisions can affect projected greenhouse gases and other emissions, as well as their related climate outcomes (Clarke et al, 2014; Huppmann et al, 2018; Riahi et al, 2017; Rogelj et al, 2018b). A complete set of these climate forcers is required to accurately estimate the overall climatic effects of a given scenario (Meinshausen et al, 2011; Smith et al, 2018), as a large number of supposedly minor emissions may collectively exert a significant radiative forcing (Meinshausen et al, 2017; O’Neill et al, 2016).
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