Abstract
Abstract. In this study, the Weather Research and Forecasting (WRF) model is coupled with the Advanced Canopy–Atmosphere–Soil Algorithm (ACASA), a high-complexity land surface model. Although WRF is a state-of-the-art regional atmospheric model with high spatial and temporal resolutions, the land surface schemes available in WRF, such as the popular NOAH model, are simple and lack the capability of representing the canopy structure. In contrast, ACASA is a complex multilayer land surface model with interactive canopy physiology and high-order turbulence closure that allows for an accurate representation of heat, momentum, water, and carbon dioxide fluxes between the land surface and the atmosphere. It allows for microenvironmental variables such as surface air temperature, wind speed, humidity, and carbon dioxide concentration to vary vertically within and above the canopy. Surface meteorological conditions, including air temperature, dew point temperature, and relative humidity, simulated by WRF-ACASA and WRF-NOAH are compared and evaluated with observations from over 700 meteorological stations in California. Results show that the increase in complexity in the WRF-ACASA model not only maintains model accuracy but also properly accounts for the dominant biological and physical processes describing ecosystem–atmosphere interactions that are scientifically valuable. The different complexities of physical and physiological processes in the WRF-ACASA and WRF-NOAH models also highlight the impact of different land surface models on atmospheric and surface conditions.
Highlights
Though the surface layer represents a very small fraction of the planet – only the lowest 10 % of the planetary boundary layer – it has been widely regarded as a crucial component of the climate system (Stull, 1988; Mintz, 1981; Rowntree, 1991; de Noblet-Ducoudré et al, 2012)
This study introduces the novel coupling of the mesoscale Weather Research and Forecasting (WRF) model with the complex multilayer Advanced Canopy– Atmosphere–Soil Algorithm (ACASA) model to improve the surface and atmospheric representation in a regional context
In addition to the seasonal variation, both WRF-ACASA and WRF-NOAH models are able to capture the distinct characteristics of the warm Central Valley and semiarid region of southern California
Summary
Though the surface layer represents a very small fraction of the planet – only the lowest 10 % of the planetary boundary layer – it has been widely regarded as a crucial component of the climate system (Stull, 1988; Mintz, 1981; Rowntree, 1991; de Noblet-Ducoudré et al, 2012). The interaction between the land surface (biosphere) and the atmosphere is one of the most active and important aspects of the natural system. Vegetation at the land surface introduces complex structures, properties, and interactions to the surface layer. Vegetation heavily modifies surface exchanges of energy, gas, moisture, and momentum, developing the microenvironment in ways that distinguish vegetated surfaces from landscapes without vegetation. Such influences are known to occur on different spatial and temporal scales (Chen and Avissar, 1994; Pielke et al, 2002; Zhao et al, 2001; de Noblet-Ducoudré et al, 2012; Peel et al, 2010). Often near-geostrophically balanced wind patterns are disrupted in the lower atmosphere when wind encounters vegetated surfaces, i.e., the winds slow down and change direction as a result of turbulent flows that develop within and near vegetated canopies (Wieringa, 1986; Pyles et al, 2004; Queck et al, 2012; Belcher et al, 2012)
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