Abstract: Integrated Assessment Models (such as, notably, GCAM) use a regional approach: the planet is subdivided into some number of regions (for GCAM, 283 regions for agricultural production and 32 regions for other commodities), and these are used as the units of analysis when the model is run. This has a number of advantages, most notably computational tractability and data availability, but also that the units match those in which policies are established (e.g. trade between countries or blocks of countries). However, data availability and computational power are both changing; moreover, the impacts of a changing climate are being felt- and decisions about responses to them made- at smaller scales. We can reasonably ask if a different resolution might not be more appropriate for modeling climate-human feedbacks.One potential avenue is to take the city as the unit of analysis. Some advantages to this approach are presented and discussed here. Urbanism is increasing, and cities are representing daily life for a higher proportion of the world's population each day. Cities represent the economic drivers of the planet: the changes in consumption and production, even when that plays out across agricultural landscapes far removed from cities, are increasingly driven by cities. Responses to climate challenges will play out in cities, whether this occurs by rising water levels or by changes in demand for agricultural products, or by altering the efficiency and distribution of consumption of energy for buildings and transportation.In this presentation, I sketch a proposed modeling framework in which cities are central, and represent nodes on a network. The modeled network of cities has a number of dimensions along which adaptation in response to climate challenges is possible, and these modify the human system's impact on the climate system in a true feedback loop. I outline what this modeling program might look like, given the current state of modeling urban systems in the social science work, and especially with respect to agent-based modeling, considering both the prospect of cities as agents and of cities comprising simulated human agents. An important component will be asking how the results of such a modeling activity might usefully be compared to results from IAMs such as GCAM, and whether the change in focal point leads to genuinely novel insight or whether the approaches might mutually reinforce one another. I additionally consider the data, technical, and computational challenges of a truly agent-based approach at global scales.

Abstract: Food production is responsible for over 70% of freshwater use by humans and is the primary cause of land conversion globally. The global system of food production and trade is complex, with interdependencies between natural and socioeconomic conditions in importing and exporting regions, as well as the physical and socioeconomic infrastructure linking the two. Given this complexity, policy or environmental changes can have non-linear and cascading impacts for water and land resources along the supply chain. As the world becomes more globalised and more urbanised our dependence on this complex system increases. Thus, in order to achieve sustainable food security, we require an understanding of the complexity of the food production and trade system.In this paper we set out a framework for modelling the complex feedbacks between food production policy and water and land resource use and the how these are linked globally via trade. Our framework couples a multi-agent policy network with a model of the physical environment based on the global hydrological model PCR-GLOBWB and the dynamic vegetation model LPJ-GUESS. Cities are nodes in our network and are linked via physical trade infrastructure. Our framework provides a template for new type of Earth System model that captures the complex feedbacks between policy and environmental change and how these are linked globally via trade.

Abstract: One of the crucial aspects of global climate change, and global change more generally is the representation of ecosystems. Forest in particular plays a critical role in the regulation of atmospheric CO2. However, the dynamics of vegetated systems depends upon the presence of animals: current earth system models may include a global vegetation model, but the herbivores and the carnivores that depend upon them and help to shape them are typically absent. At the same time the ability of humans to create long-term sustainable societies arguably requires the maintenance of healthy ecosystems, and this needs a consideration of the whole ecosystem dynamics rather just the vegetated part. The ability to maintain wild-capture fisheries, for example, depends upon being able to assess the health of the carnivorous fish that are the main focus of fishing fleets, yet we have little idea of how fishing affects the resilience or long term stability of ecosystems in the ocean, especially in the face of increasing acidification from CO2 input. The Madingley model is the first model to include in a general way all of the living systems on the earth, including the animal component. The model is agent-based, but because of the vast numbers of animals that exist in certain categories (zooplankton for example) it uses a cohort representation in which a single agent may represent just one individual or many millions. In order to keep the model as general as possible, the cohorts may also represent multiple species that occupy the same functional group, so that our limited knowledge of the specifics does not prevent a meaningful representation of the dynamics. Grid-based climate driving fields and land-surface representations are used to link the individuals to environmental factors, with a variety of configurations that allow for the exploration of different scales. This also allows for scenario-based experiments to be performed in which the effects of human harvesting of natural capital on the ecosystem dynamics can be explored. However, since the model is already agent-based it also forms an ideal platform onto which global-scale agent-based dynamical human systems can be built while including all the relevant feedbacks between the atmosphere, ocean, biosphere and human systems. The latter present challenges both in coupling the model to existing climate GCMs, but also in exploring sufficiently generic but computationally tractable societal dynamics that go beyond the simple economic and equilibrium assumptions that still dominate most integrated assessments. The presentation will give a description the current state of the model and that ways it is currently being developed to include human factors.