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Interactions Between Land Use, Fires and Dust as Drivers of Global Climate Change

Funding Program Area(s)
Project Type
University Grant
Project Term
Project Team

Principal Investigator

In many parts of the world, land use may amplify or dampen climate change impacts on fire and dust emissions. Yet in most earth system models, linkages between land use processes and fire and dust are poorly developed, making it difficult for the models to reproduce observed trends. Accurate projections of fire and dust emissions for the future are even more difficult to come by. Here we propose a research plan that will improve the representation of land-use change impacts on future fire and dust emissions in the Energy Exascale Earth System Model (E3SM). We believe new fire and dust model development is essential for making accurate projections of how these processes will change during the 21st century. The time series of fire and dust emissions that we create will help the global earth system modeling community attain more realistic estimates of radiative forcing for different future scenarios.

Our project has the following four objectives:

1.     Create global time series of roads, land fragmentation, livestock density, and agricultural practice for E3SM needed to improve linkages between fires, dust, and land-use change

2.     Improve the mechanistic representation of fire and dust in E3SM and the relationship between these processes and land-use change

3.     Create new fire and dust emissions time series in E3SM and evaluate using DOE-supported benchmarking software,

4.     Examine the separate impacts of land use and climate on fires and dust emissions in a fully coupled E3SM simulation

Our goal is to implement new-innovative couplings between land-use and climate to produce new datasets and use these to develop state-of-the-art parameterizations of emissions from fires and dust, these parameterizations will be coupled within the E3SM, and thus put the E3SM on the frontiers of science, but will also serve the wider community through the production of our new fire and dust model driver datasets.

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