Improved meteorology from an updated WRF/CMAQ modeling system with MODIS vegetation and albedo

Abstract

Realistic vegetation characteristics and phenology from the Moderate Resolution Imaging Spectroradiometer (MODIS) products improve the simulation for the meteorology and air quality modeling system WRF/CMAQ (Weather Research and Forecasting model and Community Multiscale Air Quality model) that employs the Pleim‐Xiu land surface model (PX LSM). Recently, PX LSM WRF/CMAQ has been updated in vegetation, soil, and boundary layer processes resulting in improved 2 m temperature (T) and mixing ratio (Q), 10 m wind speed, and surface ozone simulations across the domain compared to the previous version for a period around August 2006. Yearlong meteorology simulations with the updated system demonstrate that MODIS input helps reduce bias of the 2 m Q estimation during the growing season from April to September. Improvements follow the green‐up in the southeast from April and move toward the west and north through August. From October to March, MODIS input does not have much influence on the system because vegetation is not as active. The greatest effects of MODIS input include more accurate phenology, better representation of leaf area index (LAI) for various forest ecosystems and agricultural areas, and realistically sparse vegetation coverage in the western drylands. Despite the improved meteorology, MODIS input causes higher bias for the surface O3 simulation in April, August, and October in areas where MODIS LAI is much less than the base LAI. Thus, improvements may be needed in the CMAQ dry deposition model for low LAI areas where deposition on the soil surface becomes important.

A photosynthesis‐based two‐leaf canopy stomatal conductance model for meteorology and air quality modeling with WRF/CMAQ PX LSM

“…The evapotranspiration (ET c ) for the canopy is computed as where E ss and E vs are the evaporation from the bare soil surface and vegetation surface and they are estimated based on the current PX LSM approach [ Pleim and Xiu , ; Ran et al ., ]. Both the photosynthesis‐based model and current PX approach use the same estimated E ss and E vs in ET computation.…”

“…The coupled photosynthesis and stomatal conductance (PX‐PSN) approach is implemented in a diagnostic box model with the ET and ozone deposition velocity routines from WRF/CMAQ with PX LSM described by Ran et al . []. This box model is designed to use as many observational data as possible from the FLUXNET L2 standardized data [ Baldocchi , ] for evaluating modeled LH and ozone fluxes from the PX PSN and current Jarvis approach (PX Jarvis) in comparison with observations.…”

“…This paper focuses on the implementation and evaluation of the photosynthesis‐based approach in a diagnostic box model with the PX LSM and CMAQ dry deposition model components that are directly from the updated WRF/CMAQ system presented by Ran et al . []. The questions which the paper addresses are (i) how does the PX LSM with a coupled leaf photosynthesis and stomatal conductance approach influence the performance of latent heat (LH) flux and ozone dry deposition and, (ii) can the photosynthesis approach better represent diurnal variations in LH flux and ozone dry deposition than the current approach, and (iii) how does the photosynthesis approach combined with MODIS leaf area index (LAI) influence LH fluxes?…”

“…The recent study by Ran et al . [] shows the model's persistent tendency to overestimate O 3 in these regions (Figure ). Many components from this complex modeling system including emissions, transport, photochemistry, and land surface exchange may contribute to these errors.…”

An assessment of geographical distribution of different plant functional types over North America simulated using the CLASS–CTEM modelling framework

“…Structural vegetation changes generally occur over seasonal to decadal timescales (Kramer and Kozlowski, 1979), while the slower areal extent changes typically occur on decadal to centennial timescales (Ritchie and Macdonald, 1986). The dynamic behaviour of vegetation affects weather and climate due to its strong control over biophysical processes.…”