Climate change is expected to have a substantial influence on a broad spectrum of natural and human systems, yet many of the methods and approaches currently used to evaluate the impacts, adaptation, and vulnerability to climate change are insufficient. The large majority of climate change assessments have focused on how a local/regional process or system may be affected by a perturbed climate. These traditional assessments usually have not explicitly considered the evolution over time of individual system components, such as climate, other biophysical factors, economic impacts, individual decision-making, and policy formulation. Few assessments have examined temporal changes in the interactions among these components, and traditional assessments typically have not incorporated geographic differences in potential impacts and the interactions among geographic regions. International market systems are characterized by multiple production regions distributed worldwide that are likely to be differentially impacted by climate change. Furthermore, the temporal evolution of the linkages among production regions via international trade needs to be realistically considered along with geographic differences in adaptation strategies and policies. Given these demanding requirements, few industry-wide assessments have been attempted that emphasize the need for enhanced methods for evaluating the potential impacts of climate change on international industries. This international collaborative research project will develop and evaluate an integrated framework for climate change assessments for international market systems that simultaneously and explicitly considers spatial and temporal dynamics of natural and human systems at multiple scales from the local to the global and from the individual to an industry. The investigators will pay special emphasis to industries with long-term investments, with much of their attention given to studies of the tart cherry industry in the central U.S. and central Europe. They will combine dynamic modeling of temporally evolving system components with static modeling for those components where dynamic modeling is not feasible. A chain of linked models will assess the potential impact of a changing climate on a market system for each of a series of future time slices; succeeding time slices will be connected by projections in adaptation options, economic factors such as consumer preferences, and regional development patterns. The model chain will include a hybrid approach to the downscaling of future climate projections, a production model, an individual-level decision-making model, and an international trade model. Numerous technical and implementation challenges will be investigated and addressed using an example industry involving a specialized perennial agricultural commodity as proof of concept. In addition to the technical advances made possible by the proposed framework, this research will contribute to the development and growth of a diverse pool of undergraduate and graduate students with expertise in international, interdisciplinary research and will provide informal education to the general public on potential impacts of climate change.
This project will advance basic understanding of the potential impacts of climate variability and change on dynamic, interactive worldwide activities and systems. It will enhance methodologies for the assessment of climate change impacts, adaptation and vulnerability, it will improve the characterizations of past, current, and future climates, and it will incorporate individual decision making and adaptation into assessment processes. It also will provide a novel approach for evaluating the overall uncertainty, or "meta-uncertainty," of assessment outcomes. More broadly, the framework developed by the project can potentially be transformative in terms of how climate change assessments are conducted, and it could help shape international environmental policy formulation to take into account the spatial interactions of worldwide activities. This project is supported by the NSF Dynamics of Coupled Natural and Human Systems (CNH) Program.
