Uncertainty in Climate Change Modeling: Can Global Sensitivity Analysis Be of Help?

Anderson, Barry; Borgonovo, Emanuele; Galeotti, Marzio; Roson, Roberto

doi:10.1111/risa.12117

Cited by 93 publications

(78 citation statements)

References 95 publications

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“…This has led to a couple of studies using global sensitivity techniques, which vary several uncertain input parameters at a time to explore the interaction effects, in some cases through probabilistic and Monte Carlo methods (Wainwright et al, 2014). Recent studies in energy systems research include van Vuuren et al (2008), Anthoff & Tol (2013), Anderson et al (2014), Branger et al (2015), Pye et al (2015) and Usher (2016) …”

Section: Annexmentioning

confidence: 99%

Impact of technology uncertainty on future low-carbon pathways in the UK

Fais

Keppo

Zeyringer

et al. 2016

Energy Strategy Reviews

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Energy and climate policy-making requires strong quantitative scientific evidence to devise robust and consistent long-term decarbonisation strategies. Energy system modelling can provide crucial insights into the inherent uncertainty in such strategies, which needs to be understood when designing appropriate policy measures.This study contributes to the growing research area of uncertainty analysis in energy system models. We combine consistent and realistic narratives on several technology dimensions with a global sensitivity analysis in a national, bottom-up, optimizing energy system model. This produces structured insights into the impact of low-carbon technology and resource availability on the long-term development of the UK energy system under ambitious decarbonisation pathways. We explore a variety of result metrics to present policy-relevant results in a useful and concise manner. The results provide valuable information on the variability of fuel and technology use across the uncertainty space (e.g. a strong variation in natural gas demand). We demonstrate the complementarities and substitutability of technologies (e.g. the dependency of hydrogen technologies on the availability of CCS). We highlight critical low-carbon options and hedging strategies (e.g. the early decarbonisation of the electricity sector or the stronger use of renewable sources as a hedging against failure in other technologies) and demonstrate timing and path dependencies (e.g. the importance of early decarbonisation action in the presence of multiple technology uncertainty). The results also show how the availability of a given technology can have wider impacts elsewhere in the energy system, thus complicating the management of a long-term energy transition.

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Section: Annexmentioning

confidence: 99%

Impact of technology uncertainty on future low-carbon pathways in the UK

Fais

Keppo

Zeyringer

et al. 2016

Energy Strategy Reviews

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“…For instance the direction (or sign) of change is a question that can be addressed by SA (e.g. Anderson et al (2014)). Another question is the presence of interactions between input factors.…”

Section: Purposes (Settings) Of Samentioning

confidence: 99%

“…Pastres et al, 1999); to support robust decision-making (e.g. Nguyen and de Kok, 2007;Singh et al, 2014;Anderson et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Environmental Models: A Systematic Review with Practical Workflow

Pianosi

Wagener

Rougier

et al. 2014

Vulnerability, Uncertainty, and Risk

256

416

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a b s t r a c tSensitivity Analysis (SA) investigates how the variation in the output of a numerical model can be attributed to variations of its input factors. SA is increasingly being used in environmental modelling for a variety of purposes, including uncertainty assessment, model calibration and diagnostic evaluation, dominant control analysis and robust decision-making. In this paper we review the SA literature with the goal of providing: (i) a comprehensive view of SA approaches also in relation to other methodologies for model identification and application; (ii) a systematic classification of the most commonly used SA methods; (iii) practical guidelines for the application of SA. The paper aims at delivering an introduction to SA for non-specialist readers, as well as practical advice with best practice examples from the literature; and at stimulating the discussion within the community of SA developers and users regarding the setting of good practices and on defining priorities for future research.

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“…As for the key-uncertainty drivers setting, we adopt the three global sensitivity measures synthetically illustrated in Table 2 [for a more detailed overviews the reader is referred to (Anderson et al, 2014;A Saltelli, Ratto, Tarantola, & Campolongo, 2005)]. …”

Section: Comparison Methodology and Sensitivity Methodsmentioning

confidence: 99%

“…The rationale is explained in (Anderson et al, 2014), to which we refer for further details and mathematical aspects. We limit ourselves here to the following observations on the underlying intuition.…”

Section: Evaluation Of Sign Of Changementioning

confidence: 99%

Sensitivity to energy technology costs: A multi-model comparison analysis

et al. 2015

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In the present paper we use the output of multiple expert elicitation surveys on the future cost of key low-carbon technologies and use it as input of three Integrated Assessment models, GCAM, MARKAL_US and WITCH. By means of a large set of simulations we aim to assess the implications of these subjective distributions of technological costs over key model outputs. We are able to detect what sources of technology uncertainty are more influential, how this differs across models, and whether and how results are affected by the time horizon, the metric considered or the stringency of the climate policy. In unconstrained emission scenarios, within the range of future technology performances considered in the present analysis, the cost of nuclear energy is shown to dominate all others in affecting future emissions. Climate-constrained scenarios, stress the relevance, in addition to that of nuclear energy, of biofuels, as they represent the main source of decarbonization of the transportation sector and bioenergy, since the latter can be coupled with Carbon Capture and Storage (CCS) to produce negative emissions.

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Uncertainty in Climate Change Modeling: Can Global Sensitivity Analysis Be of Help?

Cited by 93 publications

References 95 publications

Impact of technology uncertainty on future low-carbon pathways in the UK

Impact of technology uncertainty on future low-carbon pathways in the UK

Sensitivity Analysis of Environmental Models: A Systematic Review with Practical Workflow

Sensitivity to energy technology costs: A multi-model comparison analysis

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