the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
SuCCESs – a global IAM for exploring the interactions between energy, materials, land-use and climate systems in long-term scenarios (model version 2024-10-23)
Abstract. SuCCESs is a bottom-up Integrated Assessment Model (IAM) that represents energy production and use, materials production, land-use and climate globally. The primary use-case for SuCCESs is to calculate long-term scenarios until 2100 that consider the interactions between these systems, for example the greenhouse gas emissions from energy, materials and land-use and their impact on climate change. The four systems are hard-linked in SuCCESs and scenarios are solved through intertemporal optimization by minimizing discounted system costs to satisfy projected demand and other constraints, e.g. climate targets. This yields a long-term equilibrium solution between the modelled systems. This article introduces the model logic and structure, describes the overall representation of each system, and provides an evaluation by comparing the scenarios produced by SuCCESs with different end-of-century radiative forcing targets to those from other IAMs. Towards this end, and to demonstrate the capability of SuCCESs for large-scale scenario exploration, we also conduct a sensitivity analysis employing Monte Carlo sampling with a 1000-member scenario ensemble for each radiative forcing target. Last, we discuss some practical aspects and different ways of using the model in long-term scenario analyses.
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Status: open (until 05 Jan 2025)
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RC1: 'Comment on gmd-2024-196', Anonymous Referee #1, 26 Nov 2024
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The study introduces SuCCESs, a bottom-up Integrated Assessment Model (IAM) that integrates energy, materials, land-use, and climate systems globally to explore long-term scenarios through 2100. By hard-linking these systems, SuCCESs captures interactions such as greenhouse gas emissions and their climate impacts. Scenarios are solved using intertemporal optimization, minimizing system costs while meeting demand and climate constraints, yielding equilibrium solutions. The article details the model structure, evaluates its performance against other IAMs under varying radiative forcing targets, and demonstrates its capacity for large-scale scenario exploration through a Monte Carlo sensitivity analysis with a 1000-member ensemble. Below are specific comments and questions to enhance clarity and address potential uncertainties.
Below are specific comments and questions to enhance clarity and address potential uncertainties:
Major:
- The climate system in SuCCESs endogenously includes only three GHG emissions (CO2, CH4, and N2O). How do exogenous inputs for radiative forcing from other sources influence the outcomes of mitigation scenarios? Could the authors discuss the potential uncertainties introduced by these exogenous factors?
- Given the varying levels of detail in the representation of the energy, land, material, and climate systems, could the authors provide guidance on scenarios or cases where SuCCESs excels (e.g., scenarios benefiting from its lightweight design and detailed system representation)? In which scenarios or cases might the model produce higher uncertainties, and why? Such guidance would be valuable for potential users of the model.
- Lines 373–374: “Even small to moderate variations in input parameters (up to ±20%) can result in a diverse set of scenarios, spanning in some cases a broader range than those observed in the SSP scenario ensemble.” How does this model’s sensitivity to input parameters compare with other IAMs? Is this sensitivity within a reasonable range?
- The calibration, extensions, and exogenous inputs mentioned in the article are based on SSP2, e.g. in Lines 164, 233, 243, and 313. Additionally, the comparison with other IAMs and the sensitivity analysis are also based on SSP2. Does the model already include other SSPs (i.e., SSP1, SSP3, SSP4, and SSP5)? If so, and if it is not too much effort, how does the model perform under these SSPs compared to other IAMs? Providing this information would be helpful for potential users. However, I understand that comparing SuCCESs' performance under other SSPs with other IAMs may require significant effort. Even just clarifying whether the model includes other SSPs in the article would still be very useful.
Minor:
- Line 74, does the assumption imply that all demands are fixed? What are the potential impacts of this assumption on the model results, especially in scenarios where demand might vary dynamically?
- Line 74, how does the model account for supply elasticity? Please clarify how flexible responses in supply are modeled across different systems.
- Figure 2, why are wind and solar energy sources not included in the figure?
- Line 344: “SuCCESs does not include traditional bioenergy use.” How might the exclusion of traditional bioenergy affect future projections of energy use and related emissions? Please discuss the implications.
- Line 334, how is land use constrained in the model? Additional details on the mechanisms or assumptions for land-use constraints would be useful.
- Does the energy and material system provide all three major greenhouse gases (CO2, CH4, and N2O) to the climate system? If so, how are these emissions generated by the energy sector? Additional details on this linkage would be helpful.
- Figures 5, 6, and 7, the shaded areas representing the range of results from other IAMs in the SSP database are not easy to distinguish. Could the authors consider revising the visualization methods to make these areas clearer for readers?
Citation: https://doi.org/10.5194/gmd-2024-196-RC1
Data sets
Results and plotting scripts for the manuscript Tommi Ekholm https://zenodo.org/records/13981206
Model code and software
SuCCESs Integrated Assessment Model Tommi Ekholm, Nadine-Cyra Freistetter, Tuukka Mattlar, Theresa Schaber, and Aapo Rautiainen https://zenodo.org/records/13981520
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