Abstract 

The ability to rapidly simulate the local climate implications of a large number of future climate policy scenarios is important for planning and implementing adequate climate mitigation and adaptation policies. Given the societal impacts from rising local temperature, we build an emulator of spatially resolved near-surface air temperature responses to carbon dioxide (CO₂) emissions. Near surface air temperature is approximately proportional to cumulative carbon dioxide (CO₂) emissions, allowing for linearization of the temperature response to emissions scenarios. This linearity enables us to diagnose Green’s Functions for the spatial temperature response to CO₂ emissions from pulse simulations conducted as part of the CDRMIP experiments. We then apply this emulator across a wide range emissions scenarios to estimate local temperature responses.

We evaluate this emulator with two CMIP6 experiments: 1) a 1% increase in CO₂ concentration, and 2) an experiment that branches from this after concentrations of 1000 PgC are reached. We find that this emulation approach captures the spatial temperature response to CO₂ emissions within one standard deviation of the CMIP6 range, with some limited accuracy in polar regions where nonlinearities in climate feedbacks dominate and internal variability may influence the Green’s Function. This approach incorporates emissions path dependency, accounting for various timescales of warming due to CO₂ emissions. It is useful for evaluating large ensembles of policy scenarios that are otherwise prohibitively expensive to simulate using earth system models, as it takes less than one second to emulate 90 years of temperature response. We apply this emulator to quantify differing local temperature responses when a global mean of 2ºC is reached, showing that some locations (such as Lagos and Buenos Aires) warm slower than the global mean, while others warm faster (such as Boston and Shanghai). We also evaluate varying CO₂ emissions trajectories with the same cumulative emissions, showing that the resulting temperature changes are path dependent.

Plain-language Summary

The ability to quantify the climate impacts of changes in future emissions due to various climate policies is important for planning and implementing adequate climate mitigation and adaptation. Given the societal impacts from rising local temperature, we build a rapid model that can quantify local temperature responses to changes in carbon dioxide (CO₂) emissions, a key greenhouse gas responsible for climate change. This simple approach takes advantage of a proportional relationship between total CO₂ emissions and temperature, and it takes only one second to quantify temperature impacts over 90 years. We test this approach against the Climate Model Intercomparison Project Phase 6 (CMIP6) experiments, finding some limited accuracy in polar regions. We then use this approach to quantify the local temperature impacts in different cities when a global mean increase of 2 ºC is reached, showing that some cities warm faster than the global mean and others warm slower. We also show that the emissions pathway matters, even if the same total CO₂ emissions are reached. Importantly, this approach can be used to estimate local temperature response to dozens of policy scenarios without the computational power and time needed for running a climate model.

Abstract: Climate change, income and population growth, and changing diets are major stressors for global agricultural markets with implications for land use change. US land use at regional and local scales is directly affected by domestic forces and indirectly through international trade. In order to investigate the effects of several potential forces on land use changes in the US at multiple spatial scales, we advanced the capabilities in representing the interactions between natural and human system through a collaborative effort between two MSD teams. This effort couples a multi-sectoral and multi-regional socio-economic model of the world economy with detailed representation of land use and agricultural systems to an open-source downscaling model which enables translating regional projections of future land use into high-resolution representations of time-evolving land cover. We exemplify the framework over the Mississippi river basin and consider the effects of a range of global drivers and stressors, such as: high or low economic and population growth, more negative or more positive impacts of climate change, and more or less dietary change. The resulting regional land use changes are further translated into more detailed projections of land use changes through the downscaling model. In addition, we examine assumptions, including 1) how global stressors might, in combination, affect regional land use change and 2) how alternative rules and constraints spatialize the regional projections. Our results help better understand the implications of land use change on carbon storage, soil erosion, chemical use, hydrology, and water quality. The employed downscaling model facilitates interoperability among models and across various spatial scales. The presented framework can be readily applied to other basins with little effort.

Abstract: Negative carbon emissions options are required to meet long-term climate goals in many countries. One way to incentivize these options is by paying farmers for carbon sequestered by forests through an emissions trading scheme (ETS). New Zealand has a comprehensive ETS, which includes incentives for farmers to plant permanent exotic forests.

This research uses an economy-wide model, a forestry model and land use change functions to measure the expected proportion of farmers with trees at harvesting age that will change land use from production to permanent forests in New Zealand from 2014 to 2050. We also estimate the impacts on carbon sequestration, the carbon price, gross emissions, GDP and welfare.

When there is forestry land use change, the results indicate that the responsiveness of land owners to the carbon price has a measured impact on carbon sequestration. For example, under the fastest land use change scenario, carbon sequestration reaches 29.93 Mt CO₂e by 2050 compared to 23.41 Mt CO₂e in the no land use change scenario (a 28% increase). Even under the slowest land use change scenario, carbon sequestration is 25.89 Mt CO₂e by 2050 (an 11% increase compared with no land use change). This is because, if foresters decide not to switch to permanent forests in 1 year, carbon prices and ultimately incentives to convert to permanent forests will be higher in future years.

Part of this research was completed while co-author Dominic White was visiting the MIT Joint Program.

Abstract: This Perspective evaluates recent progress in modeling nature–society systems to inform sustainable development. We argue that recent work has begun to address longstanding and often-cited challenges in bringing modeling to bear on problems of sustainable development. For each of four stages of modeling practice—defining purpose, selecting components, analyzing interactions, and assessing interventions—we highlight examples of dynamical modeling methods and advances in their application that have improved understanding and begun to inform action. Because many of these methods and associated advances have focused on particular sectors and places, their potential to inform key open questions in the field of sustainability science is often underappreciated. We discuss how application of such methods helps researchers interested in harnessing insights into specific sectors and locations to address human well-being, focus on sustainability-relevant timescales, and attend to power differentials among actors. In parallel, application of these modeling methods is helping to advance theory of nature–society systems by enhancing the uptake and utility of frameworks, clarifying key concepts through more rigorous definitions, and informing development of archetypes that can assist hypothesis development and testing. We conclude by suggesting ways to further leverage emerging modeling methods in the context of sustainability science.