ABSTRACT

A map of US surface air temperature, meaning the air temperature recorded 2 meters above the surface, typically has a broad-brush appearance with warmer temperatures to the south. But the temperature of the surface itself can vary dramatically in a few short steps, say on the barefoot walk from a grassy picnic area across hot sand to the cool water at the beach. This mismatch in spatial scale between the patchy land surface and the broad-brush atmosphere poses a challenge for understanding how the two interact: each patch of land interacts with the patch of air directly above it, yet the atmosphere somehow smooths out the patchiness and responds to the aggregate effects of the moisture, heat, and other inputs it receives from the surface.

As a practical matter some of this aggregation must be explicitly coded into weather and climate models as it is not possible to represent every distinct patch of the local land surface on the global grid of a weather or climate model. The standard strategy for such aggregation is to represent surface patchiness through the use of multiple "tiles" within each surface grid square (the grid squares can be 50 kilometers wide or wider). Tiles represent distinct land surface types, for instance paved urban environments, farms, forests, and lakes, each with appropriate values of reflectivity, roughness, drainage, and other parameters that regulate land-atmosphere coupling. The percentage of the grid square covered with each tile is specified, and the exchanges of heat, moisture, momentum, and constituents are summed over the tile fractions to form grid totals which are shared with the atmosphere. Climate simulations generate a tremendous amount of tile data, which could provide a valuable resource for understanding land-atmosphere interactions and their climatic consequences. Yet tile data is rarely archived with climate model output, which generally contains only the grid square totals.

This project seeks to exploit the untapped resource of tile-level data for understanding land-atmosphere coupling and its implications for large-scale climate. The PIs have organized an effort to collect this data as part of the Land Use Model Intercomparison Project (LUMIP) so that results from multiple models can be compared. Data is collected for two specific simulations, one which considers the effects of deforestation assuming a fixed carbon dioxide (CO2) concentration, and another in which CO2 increases substantially following a scenario of future emissions (SSP3).

A key issue in the research is differences in climate that can be related to the representation of soil reservoirs of moisture, heat, and nutrients. In the commonly used "shared column" approach all tiles within a grid box share the same soil reservoir, while the "independent column" approach uses a separate soil reservoir for each tile within a grid box. The PIs argue that the independent column approximation leads to more extreme grid box surface temperature as, for example, a tree-covered tile only has access to soil moisture within its own tile and is thus more prone to drought. Nevertheless this may be a better representation of the hydrology as plants with similar functional traits are often found together, and the extent of water sharing across a grid box may be limited as grid boxes are typically much larger than the distances over which water can percolate and heat can diffuse.

The database of tile outputs from multiple models constitutes an important broader impact of the project. It serves as a resource for the community of researchers working on land-atmosphere coupling and its role in climate, and for model developers seeking to improve models used to provide weather predictions and climate projections for decision makers. The tile data can also be used to assess the likely climatic consequences of climate change over regions with particular ground cover, for instance the data can reveal characteristic differences in water availability between grasslands and forests in a warming climate. The project also supports a graduate student and a postdoc, thereby providing for the future workforce in this research area.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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