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Impacts of tiled land cover characterization in the Model for Predictions Across Scales-Atmosphere (MPAS-A)
Campbell, P., J. Bash, J. Herwehe, AND R. Gilliam. Impacts of tiled land cover characterization in the Model for Predictions Across Scales-Atmosphere (MPAS-A). JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, 125(15):e2019JD032093, (2020). https://doi.org/10.1029/2019JD032093
The land cover characterization (LCC), i.e., the physical characteristics of Earth’s land surface (vegetated, wetlands, water, ice, or urban/impervious), is inherently heterogeneous, in some areas extreme, and is rapidly changing due to recent and projected future fluctuations in the LCC for both developed and developing countries. Changes in LCC due to human activities (e.g., deforestation, industrialization, agriculture, urban sprawl) produce physical changes in land surface albedo, latent (LH) and sensible heat (SH) fluxes, and atmospheric aerosol and greenhouse gas concentrations. Consequently, LCC changes have accounted for approximately half of the human-caused global radiative forcing from 1850 to the present day.
Parameterization of subgrid-scale variability of land cover characterization (LCC) is an active area of research, and can improve model performance compared to the dominant (i.e., most abundant tile) approach. The “Noah” land surface model implementation in the global Model for Predictions Across Scales-Atmosphere (MPAS-A), however, only uses the dominant LCC approach that leads to oversimplification in regions of highly heterogeneous LCC (e.g., urban/suburban settings). Thus, in this work we implement a subgrid tiled approach as an option in MPAS-A, version 6.0, and assess the impacts of tiled LCC on meteorological predictions for two gradually refining meshes (92-25 and 46-12 km) focused on the conterminous U.S for January and July 2016. Compared to the dominant approach, results show that using the tiled LCC leads to pronounced global changes in 2-m temperature (July global average change ~ -0.4 K), 2-m moisture, and 10-m wind speed for the 92-25 km mesh. The tiled LCC reduces mean biases in 2-m temperature (July U.S. average bias reduction ~ factor of 4) and specific humidity in the central and western U.S. for the 92-25 km mesh, improves the agreement of vertical profiles (e.g., temperature, humidity, and wind speed) with observed radiosondes, and there is a general decrease in error for precipitation in the U.S.; however, there is increased bias and error for incoming solar radiation at the surface. The inclusion of subgrid LCC has implications for reducing systematic warm biases found in numerical weather prediction models.
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