Matthew J. Gidden

Fundamental concepts in the Cyclus nuclear fuel cycle simulation framework

Nuclear fuel cycle modeling generality and robustness are improved by a modular, agent based modeling framework.Discrete material and facility tracking rather than fleet-based modeling improve nuclear fuel cycle simulation fidelity.A free, open source paradigm encourages technical experts to contribute software to the Cyclus modeling ecosystem.The flexibility of the Cyclus tool from the simulator user perspective is demonstrated with both open and closed fuel cycle examples. As nuclear power expands, technical, economic, political, and environmental analyses of nuclear fuel cycles by simulators increase in importance. To date, however, current tools are often fleet-based rather than discrete and restrictively licensed rather than open source. Each of these choices presents a challenge to modeling fidelity, generality, efficiency, robustness, and scientific transparency. The Cyclus nuclear fuel cycle simulator framework and its modeling ecosystem incorporate modern insights from simulation science and software architecture to solve these problems so that challenges in nuclear fuel cycle analysis can be better addressed. A summary of the Cyclus fuel cycle simulator framework and its modeling ecosystem are presented. Additionally, the implementation of each is discussed in the context of motivating challenges in nuclear fuel cycle simulation. Finally, the current capabilities of Cyclus are demonstrated for both open and closed fuel cycles.

First forcing estimates from the future CMIP6 scenarios ofanthropogenic aerosol optical properties and an associated Twomeyeffect

Fiedler et al. present a modelling study in which they interpret the future emission scenarios of Riahi et al. (2017) using a simple model implemented in a GCM. The two aspects that provide added value compared to the Riahi et al. paper in my opinion are that geographical distributions are shown here, and that the scaling of Stevens et al. (2017) allows to convert the emissions into forcing values given the assumptions in the simple MACv2-SP approach (with some extra model information added from the simulated cloud fractionand cloud droplet concentration distributions). As far as I understand, one of the co-authors, Gidden, prepares another manuscript for Geophys. Model Devel. that possibly covers the former aspect in a similar way.

Global energy sector emission reductions and bioenergy use: overview of the bioenergy demand phase of the EMF-33 model comparison

We present an overview of results from 11 integrated assessment models (IAMs) that participated in the 33rd study of the Stanford Energy Modeling Forum (EMF-33) on the viability of large-scale deployment of bioenergy for achieving long-run climate goals. The study explores future bioenergy use across models under harmonized scenarios for future climate policies, availability of bioenergy technologies, and constraints on biomass supply. This paper provides a more transparent description of IAMs that span a broad range of assumptions regarding model structures, energy sectors, and bioenergy conversion chains. Without emission constraints, we find vastly different CO2 emission and bioenergy deployment patterns across models due to differences in competition with fossil fuels, the possibility to produce large-scale bio-liquids, and the flexibility of energy systems. Imposing increasingly stringent carbon budgets mostly increases bioenergy use. A diverse set of available bioenergy technology portfolios provides flexibility to allocate bioenergy to supply different final energy as well as remove carbon dioxide from the atmosphere by combining bioenergy with carbon capture and sequestration (BECCS). Sector and regional bioenergy allocation varies dramatically across models mainly due to bioenergy technology availability and costs, final energy patterns, and availability of alternative decarbonization options. Although much bioenergy is used in combination with CCS, BECCS is not necessarily the driver of bioenergy use. We find that the flexibility to use biomass feedstocks in different energy sub-sectors makes large-scale bioenergy deployment a robust strategy in mitigation scenarios that is surprisingly insensitive with respect to reduced technology availability. However, the achievability of stringent carbon budgets and associated carbon prices is sensitive. Constraints on biomass feedstock supply increase the carbon price less significantly than excluding BECCS because carbon removals are still realized and valued. Incremental sensitivity tests find that delayed readiness of bioenergy technologies until 2050 is more important than potentially higher investment costs.