The new IKARUS-Model is a time-step dynamical bottom-up linear optimization model where each time interval is optimized by itself using the heritage from all periods before. Contrary to perfect-foresight models, this model does not take into account future changes in each time-step optimization. It therefore shows a more realistic character of prognosis and projection. Aspects like reaction on sudden changes (e.g. of energy prices), flexibility of technical scenarios, lost opportunities etc. can be examined. Interactions with macroeconomic I/O-models or dependencies on elasticities, technological learning etc. are possible. Recent calculations for Germany up to 2030 show that consistent and plausible future energy scenarios can be produced and analyzed.

<p>The assessment of long-term greenhouse gas emissions scenarios and societal transformation pathways is a key component of the IPCC Working Group 3 (WG3) on the Mitigation of Climate Change. A large scientific community, typically using integrated assessment models and econometric frameworks, supports this assessment in understanding both near-term actions and long-term policy responses and goals related to mitigating global warming. WG3 must systematically assess hundreds of scenarios from the literature to gain an in-depth understanding of long-term emissions pathways, across all sectors, leading to various levels of global warming. Systematic assessment and understanding the climate outcomes of each emissions scenario, requires coordinated processes which have developed over consecutive IPCC assessments. Here, we give an overview of the processes involved in the systematic assessment of long-term mitigation pathways as used in recent IPCC Assessments<sup>1</sup> and being further developed for the IPCC 6<sup>th</sup> Assessment Report (AR6). The presentation will explain how modelling teams can submit scenarios to AR6 and invite feedback to the process.</p><p>Following discussions amongst IPCC Lead Authors to define the scope of scenarios desired and variables requested, a call for scenarios to support AR6 was launched in September 2019. Modelling teams have registered and submitted scenarios through Autumn 2019 using a new and secure online submission portal, from which authorised Lead Authors can interrogate the scenarios interactively.</p><p>This analysis is underpinned by the open-source software pyam, a Python package specifically designed for analysis and visualisation of integrated assessment scenarios<sup>2</sup>. Submitted scenarios are automatically checked for errors and processed using a new climate assessment pipeline. The climate assessment involves infilling and harmonization<sup>3</sup> of emissions data, then the scenarios are processed through Simple Climate Models, using the OpenSCM framework<sup>4</sup>, to give probabilistic climate implications for each scenario – atmospheric concentrations, radiative forcing and global mean temperature. The climate assessment accounts for updated climate sensitivity estimates from CMIP6 and WG1,s scenarios are categorized according to climate outcomes and distinguish between timing and levels of net-negative emissions, emissions peak and temperature overshoot. Scenarios are also categorized by other indicators, for consistent use across WG3 chapters, such as: population and GDP; Primary and Final energy use; and shares of renewables, bioenergy and fossil fuels.</p><p>The automated framework also facilitates bolt-on analyses, such as estimating the population impacted by biophysical climate impacts<sup>5</sup>, and estimates of avoided damages with the social cost of carbon<sup>6</sup>.</p><p>Upon publication of the WG3 AR6 report, all scenario data used in the WG3 Assessment will be publicly available on a Scenario Explorer, an online tool for interrogating and visualizing the data that supports the report. In combination, this framework brings new levels of consistency, transparency and reproducibility to the assessment of scenarios in IPCC WG3 and will be a key resource for the climate community in understanding the main drivers of different transformation pathways.</p><ol><li>Huppmman et al 2018, Nature Climate Change</li> <li>Gidden and Huppmann, 2019, Journal of Open Source Software</li> <li>Gidden et al 2018 Environ. Model. Softw</li> <li>Nicholls et al 2020</li> <li>Byers et al 2018 Environmental Research Letters</li> <li>Ricke et al 2018 Nature Climate Change</li> </ol>

Abstract. The energy-water-land nexus represents a critical leverage future policies must draw upon to reduce trade-offs between sustainable development objectives. Yet, existing long-term planning tools do not provide the scope or level of integration across the nexus to unravel important development constraints. Moreover, existing tools and data are not always made openly available or are implemented across disparate modeling platforms that can be difficult to link directly with modern scientific computing tools and databases. In this paper, we present the Nexus Solutions Tool (NEST): a new open modeling platform that integrates multi-scale energy-water-land resource optimization with distributed hydrological modeling. The new approach provides insights into the vulnerability of water, energy and land resources to future socioeconomic and climatic change and how multi-sectoral policies, technological solutions and investments can improve the resilience and sustainability of transformation pathways while avoiding counterproductive interactions among sectors. NEST can be applied at different spatial and temporal resolutions, and is designed specifically to tap into the growing body of open access geospatial data available through national inventories and the earth system modeling community. A case study analysis of the Indus River Basin in South Asia demonstrates the capability of the model to capture important interlinkages across system transformation pathways towards the United Nations' Sustainable Development Goals, including the intersections between local and regional transboundary policies and incremental investment costs from rapidly increasing regional consumption projected over the coming decades.

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