WRF-ACASA Coupling—Predicting the Future Carbon Cycle

The increasing incidents of record high temperature along with the growing intensity and frequency of severe weather events reported throughout the world in recent years signal the undergoing global warming scenario. Carbon dioxide is widely recognized to be a major contributor to this climate change. Therefore, it is important to accurately estimate the carbon exchange rate between the terrestrial biosphere and the atmosphere. Land surface models (LSMs) are often used to simulate carbon dioxide flux, and they are becoming increasingly important in estimating future climate, impact. This study couples a sophisticated process-based surface layer model ACASA (Advanced Canopy-Atmosphere-Soil Algorithm, developed in the University of California, Davis) with the newest version of mesoscale model WRF (the Weather Research & Forecasting Model, developed by NCAR and several other agencies).

The ACASA model is coupled to the WRF model as a surface-layer scheme to replace WRF's pre-existing ones due to ACASA's more complex and realistic representations in physical and physiological parameters. The WRF model, driven by North American Regional Reanalysis data (NCAR-NCEP), is run down to its planetary boundary layer, where ACASA is called. As a multilayer, steady-state model, ACASA incorporates higher-order representations of vertical temperature variations, CO₂ concentration, radiation, wind speed, turbulent statistics, and plant physiology [3,4]. Surface temperature in ACASA is estimated for 10 leaf angle classes (9 sunlit and 1 shaded) using surface energy balance principles and a fourth order solution technique [2]. This approach, with different leaf angle distributions, will radically improve the estimation of gross primary production and consequently improve carbon flux [1]. The model also employs numerous species-specific parameters (for example: Q₁₀s for leaves, root, and stems; photosynthetical coefficients; canopy element reflectivity and transmissivity) to accurately represent physiological processes within different ecosystem canopies. Surface fluxes (heat, CO₂, H₂O, and momentum) as well as 20 other state variables such as air temperature, leaf temperature, and specific humidity are updated every 30 minutes.

This study is also part of a project to develop a cyberinfrastructure system for data assimilation and model management. The cyberinfrastructure system will allow utilization of meteorological data, land cover/species data, population density, and other heterogeneous and disparate forms of data to be incorporated into the WRF-ACASA model. As a result, the WRF-ACASA coupling will be able to identify how multiple environmental factors, in particularly climate variability, population density, and species distribution, impact future carbon cycle prediction across a wide geographical range. This study is still in progress, and preliminary results are presented at this meeting.

[1]Alton, P. B., R. Ellis, S. O. Los, and P. R. North (2007), Improved global simulations of gross primary product based on a separate and explicit treatment of diffuse and direct sunlight, J. Geophys.

[2]Paw U, K.T. and W. Gao. 1988. Applications of solutions to non-linear energy budget equations. Agricultural and Forest Meteorology. 43:121-145.

[3]Pyles, R. D., 2000: The development and testing of the UCD Advanced Canopy–Atmosphere–Soil Algorithm (ACASA) for use in climate prediction and field studies. Ph.D. dissertation, University of California, Davis, 194 pp.

[4]Pyles, R. D., B. C. Weare, and K. T. Paw U, 2000: The UCD Advanced-Canopy–Atmosphere–Soil Algorithm (ACASA): Comparisons with observations from different climate and vegetation regimes. Quart. J. Roy. Meteor. Soc., 126, 2951–2980.