Socio-Environmental Systems Modelling https://sesmo.org/ <p><em>SESMO seeks to transform society and socio-environmental decision-making through model-based research that integrates multiple issues, domain expertise and interest groups&nbsp;</em></p> International Environmental Modelling and Software Society en-US Socio-Environmental Systems Modelling 2663-3027 18 Politically relevant solar geoengineering scenarios https://sesmo.org/article/view/18127 <p>Solar geoengineering, also known as Solar Radiation Modification (SRM), has been proposed to alter Earth’s radiative balance to reduce the effects of anthropogenic climate change. SRM has been identified as a research priority, as it has been shown to effectively reduce surface temperatures, while substantial uncertainties remain around side effects and impacts. Global modeling studies of SRM have often relied on idealized scenarios to understand the physical processes of interventions and their widespread impacts. These extreme or idealized scenarios are not directly policy-relevant and are often physically implausible (such as imposing global solar reduction to counter the warming of an instantaneous quadrupling of CO<sub>2</sub>). The climatic and ecological impacts of politically relevant and potentially plausible SRM approaches have rarely been modeled and assessed. Nevertheless, commentators and policymakers often falsely assume that idealized or extreme scenarios are proposed solutions to climate change. This paper proposes 18 scenarios that appear to be broadly plausible from political and Earth System perspectives and encompass futures that could be both warnings or perhaps desirable. We place these scenarios into four groups following broader strategic contexts: (1) Global Management; (2) Regional Emergencies; (3) Coordinated Regional Interventions; and (4) Reactive Global Interventions. For each scenario, relevant model experiments are proposed. Some may be performed with existing setups of global climate models, while others require further specification. Developing and performing these model experiments – and assessing likely resulting impacts on society and ecosystems – would be essential to inform public debate and policymakers on the real-world issues surrounding SRM.</p> Andrew Lockley Yangyang Xu Simone Tilmes Masahiro Sugiyama Dale Rothman Adrian Hindes Copyright (c) 2022 Andrew Lockley, Yangyang Xu, Simone Tilmes, Masahiro Sugiyama, Dale Rothman, Adrian Hindes http://creativecommons.org/licenses/by-nc/4.0 2022-07-22 2022-07-22 4 18127 18127 10.18174/sesmo.18127 RICE50+: DICE model at country and regional level https://sesmo.org/article/view/18038 <p>Benefit-cost Integrated Assessment Models (IAMs) have been largely used for optimal policies and mitigation pathways countering climate change. However, the available models are relatively limited in the representation of regional heterogeneity. This is despite strong evidence of significant variation of local mitigation costs and benefits, institutional capacity, environmental and economic priorities. Here, I introduce RICE50+, a benefit-cost optimizing IAM with more than 50 independently deciding regions or countries. Its core foundation is the DICE model, improved with several original contributions. These include new calibrations on actual mitigation cost data, full integration of recent empirically based impact functions, alternative socioeconomic reference projections as well as normative preferences, including welfare specifications explicitly featuring inequality aversion. Due to its high level of regional detail, the model can support researchers in better investigating the role of heterogeneity in international cooperation, cross-country inequalities, and climate change impacts under a variety of mitigation pathways and scenarios.</p> Paolo Gazzotti Copyright (c) 2022 Paolo Gazzotti http://creativecommons.org/licenses/by-nc/4.0 2022-04-13 2022-04-13 4 18038 18038 10.18174/sesmo.18038 Towards a global behavioural model of anthropogenic fire: The spatiotemporal distribution of land-fire systems https://sesmo.org/article/view/18130 <p>Landscape fire regimes are created through socio-ecological processes, yet in current global models the representation of anthropogenic impacts on fire regimes is restricted to simplistic functions derived from coarse measures such as GDP and population density. As a result, fire-enabled dynamic global vegetation models (DGVMs) have limited ability to reproduce observed patterns of fire, and limited prognostic value. At the heart of this challenge is a failure to represent human agency and decision-making related to fire. This paper outlines progress towards a global behavioural model that captures the categorical differences in human fire use and management that arise from diverse land use objectives under varying socio-ecological contexts. We present a modelled global spatiotemporal distribution of what we term ‘land-fire systems’ (LFSs), a classification that combines land use systems and anthropogenic fire regimes. Our model simulates competition between LFSs with a novel bootstrapped classification tree approach that performs favourably against reference multinomial regressions. We evaluate model outputs with the human appropriation of net primary production (HANPP) framework and find good overall agreement. We discuss limitations to our methods, as well as remaining challenges to the integration of behavioural modelling in DGVMs and associated model-intercomparison protocols.</p> Oliver Perkins Sarah Matej Karlheinz Erb James Millington Copyright (c) 2022 Oliver Perkins, Sarah Matej, Karlheinz Erb, James Millington http://creativecommons.org/licenses/by-nc/4.0 2022-05-23 2022-05-23 4 18130 18130 10.18174/sesmo.18130 Upscaling in socio-environmental systems modelling: Current challenges, promising strategies and insights from ecology https://sesmo.org/article/view/18112 <p>Sustainability challenges in socio-environmental systems (SES) are inherently multiscale, with global-level changes emerging from socio-environmental processes that operate across different spatial, temporal, and organisational scales. Models of SES therefore need to incorporate multiple scales, which requires sound methodologies for transferring information between scales. Due to the increasing global connectivity of SES, upscaling – increasing the extent or decreasing the resolution of a modelling study – is becoming progressively more important. However, upscaling in SES models has received less attention than in other fields (e.g., ecology or hydrology) and therefore remains a pressing challenge. To advance the understanding of upscaling in SES, we take three steps. First, we review existing upscaling approaches in SES as well as other disciplines. Second, we identify four main challenges that are particularly relevant to upscaling in SES: 1) heterogeneity, 2) interactions, 3) learning and adaptation, and 4) emergent phenomena. Third, we present an approach that facilitates the transfer of existing upscaling methods to SES, using two good practice examples from ecology. To describe and compare these methods, we propose a scheme of five general upscaling strategies. This scheme builds upon and unifies existing schemes and provides a standardised way to classify and represent existing as well as new upscaling methods. We demonstrate how the scheme can help to transparently present upscaling methods and uncover scaling assumptions, as well as to identify limits for the transfer of upscaling methods. We finish by pointing out research avenues on upscaling in SES to address the identified upscaling challenges.</p> Gunnar Dressler Jürgen Groeneveld Jessica Hetzer Anja Janischewski Henning Nolzen Edna Rödig Nina Schwarz Franziska Taubert Jule Thober Meike Will Tim Williams Stephen Björn Wirth Birgit Müller Copyright (c) 2022 Gunnar Dressler, Jürgen Groeneveld, Jessica Hetzer, Anja Janischewski, Henning Nolzen, Edna Rödig, Nina Schwarz, Franziska Taubert, Jule Thober, Meike Will, Tim Williams, Stephen Björn Wirth, Birgit Müller http://creativecommons.org/licenses/by-nc/4.0 2022-07-28 2022-07-28 4 18112 18112 10.18174/sesmo.18112 Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses https://sesmo.org/article/view/18155 <p>Sensitivity analysis is now considered a standard practice in environmental modeling. Several open-source libraries, such as the Sensitivity Analysis Library (SALib), have been published in the recent past aimed at simplifying the application of sensitivity analyses. Still, there remain issues in software usability and accessibility, as well as a lack of guidance in the interpretation of sensitivity analysis results. This paper describes the changes made and planned to SALib to advance the ease with which modelers may conduct sensitivity analysis and interpret results. We further offer our perspectives from the past 7 years of maintaining SALib for the consideration of those aspiring to launch their own software for sensitivity analysis, develop methodology, or those otherwise interested in becoming involved in a project like SALib. These include the value of a community of practice to foster best practices for sensitivity analysis, the potential for collaboration across different software (for sensitivity analysis) platforms, and the need to specifically support the software development that underpins computational science.</p> Takuya Iwanaga William Usher Jonathan Herman Copyright (c) 2022 Takuya Iwanaga, Will Usher, Jon Herman http://creativecommons.org/licenses/by-nc/4.0 2022-05-31 2022-05-31 4 18155 18155 10.18174/sesmo.18155 Perspectives on confronting issues of scale in systems modeling https://sesmo.org/article/view/18156 <p>Issues of scale pervade every aspect of socio-environmental systems (SES) modeling. They can stem from the context of both the modeling process, and the purpose of the integrated model. A webinar hosted by the National Socio-Environmental Synthesis Center (SESYNC), The Integrated Assessment Society (TIAS) and the journal Socio-Environmental Systems Modelling (SESMO) explored how model stakeholders can address issues of scale. Four key considerations were raised: (1) being aware of our influence on the modeling pathway, and developing a shared language to overcome cross-disciplinary communication barriers; (2) that localized effects may aggregate to influence behavior at larger scales, necessitating the consideration of multiple scales; (3) that these effects are “patterns” that can be elicited to capture understanding of a system (of systems); and (4) recognition that the scales must be relevant to the involved stakeholders and decision makers. Key references in these four areas of consideration are presented to complement the discussion of confronting scale as a grand challenge in socio-environmental modeling. By considering these aspects within the integrated modeling process, we are better able to confront the issues of scale in socio-environmental modeling.</p> Takuya Iwanaga Patrick Steinmann Amir Sadoddin Derek Robinson Val Snow Volker Grimm Hsiao-Hsuan Wang Copyright (c) 2022 Takuya Iwanaga, Patrick Steinmann, Amir Sadoddin, Derek T. Robinson, Val Snow, Volker Grimm, Hsiao-Hsuan Wang http://creativecommons.org/licenses/by-nc/4.0 2022-04-27 2022-04-27 4 18156 18156 10.18174/sesmo.18156