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.

Climate modeling studies and observations do not fully agree on the implications of anthropogenic warming for evapotranspiration (ET), a major component of the water cycle and driver of irrigation water demand. Here we use California as a testbed to assess the ET impacts of changing atmospheric conditions induced by climate change on irrigated systems. Our analysis of irrigated agricultural and urban regions shows that warmer atmospheric temperatures have minimal implications for ET rates and irrigation water demands-about one percent change per degree Celsius warming (~1 %°C-1). By explicitly modeling irrigation, we control for the confounding effect of climate-driven soil moisture changes and directly estimate water demand implications. Our attribution analysis of the drivers of ET response to global anthropogenic warming shows that as the atmospheric temperature and vapor pressure deficit depart from the ideal conditions for transpiration, regulation of stomata resistance by stressed vegetation almost completely offsets the expected increase in ET rates that would otherwise result from abiotic processes alone. We further show that anthropogenic warming of the atmosphere has minimal implications for mean relative humidity (<1.7%°C-1) and the surface energy budget (<0.2%°C-1), which are critical drivers of ET. This study corroborates the growing evidence that plant physiological changes moderate the degree to which changes in potential ET are realized as actual ET.