Updates to the Noah Land Surface Model in WRF-CMAQ to Improve Simulated Meteorology, Air Quality, and Deposition

Impacts of tiled land cover characterization in the Model for Prediction Across Scales-Atmosphere (MPAS-A)

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; however, there is increased bias and error for incoming solar radiation at the surface. The inclusion of subgrid LCC has implications for reducing systematic temperature biases found in numerical weather prediction models, particularly those that employ a dominant LCC approach.

Projections of Atmospheric Nitrogen Deposition to the Chesapeake Bay Watershed

Atmospheric deposition is among the largest pathways of nitrogen loading to the Chesapeake Bay Watershed (CBW). The interplay between future climate and emission changes in and around the CBW will likely shift the future nutrient deposition abundance and chemical regime (e.g., oxidized vs. reduced nitrogen). In this work, a Representative Concentration Pathway (RCP) from the Community Earth System Model is dynamically downscaled using the Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ) model coupled to the agro-economic Environmental Policy Integrated Climate (EPIC) model. The relative impacts of emission and climate changes on atmospheric nutrient deposition are explored for a recent historical period and a period centered on 2050. The projected regional emissions in CMAQ reflect current federal and state regulations, which use baseline and projected emission years 2011 and 2040, respectively. The historical simulations of 2-m temperature and precipitation have cool and dry biases, and temperature and precipitation are projected to both increase. Ammonium wet deposition agrees well with observations, but nitrate wet deposition is underpredicted. Climate and deposition changes increase simulated future ammonium fertilizer application. In the CBW at 2050, these changes (along with widespread decreases in anthropogenic nitrogen oxide and sulfur oxide emissions, and relatively constant NH3 emissions) decrease total nitrogen deposition by 21%, decrease annual average oxidized nitrogen deposition by 44%, and increase reduced nitrogen deposition by 10%. These results emphasize the importance of decreased anthropogenic emissions on the control of future nitrogen loading to the Chesapeake Bay in a changing climate.