## 1 Introduction  
Partitioning of incident solar radiation among various components at the land surface, especially from vegetation and underlying soil for vegetated regions, determines the energy absorbed by vegetation, evapotranspiration, partitioning between surface sensible and latent heat fluxes, and the energy and water exchange between the land surface and the atmosphere [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. How solar radiation reaches underlying soil through between-crown gaps and within-crown gaps is an essential part of micrometeorological, climatological, biogeochemical, and hydrological modeling [11; 12; 13; 14; 15]. Various authors have explored numerical solutions to the partitioning problem, in both one-Dimensional (1-D) and three-Dimensional (3-D) geometries [13; 16; 17; 18; 19]. Though the complexity of the problem fully justifies the need for a 3-D model, 1-D models have been popular because of their ability to approximate it in a relatively simple form and to give reasonable results [16; 17]. In the Community Land Model (version 4.0, CLM4), this process is formulated by a 1-D land surface solar radiation partitioning scheme: each sub-grid land cover type, plant functional type (PFT) patch, and bare soil, is a separate column for energy calculation [18; 20]; a 1-D radiative transfer approximation is employed to simulate the radiative transfer process within canopy at PFT levels [18]. This study aims to address the performance of this modeling and how to improve it. The fraction of absorbed photosynthetically active radiation (FPAR) is employed as the diagnostic parameter for the land surface solar radiation partitioning scheme. FPAR is defined to be the fraction of PAR absorbed by the canopy [21], and hence, should be decided mainly by the radiative transfer process within the canopy. However, it can also be affected by the solar radiation reflected by the ground since plants are not separated from their background in FPAR observations, either at site level or in remote sensing. Below-canopy PAR sensors at flux sites measure not only the PAR transmitted through the canopy, but also the PAR directly incident on the ground; digital information at each pixel in remote sensing represents the averaged spectral information from each surface type within the instantaneous field of view (IFOV). Therefore, FPAR is an appropriate parameter for this study. In order to evaluate model performance, the dynamics of the fraction of absorbed photosynthetically active radiation (FPAR)'s seasonal cycle, diurnal cycle, long-term trends, and spatial patterns are used. The seasonal cycle of FPAR is essentially driven by leaf presence, growth, and foliage, so it can be interpreted as a manifestation of plant phenology. The diurnal FPAR cycle, however, is a more complicated process. It is orchestrated by the angular effect of direct solar radiation, fraction of direct radiation in total solar radiation, and vegetation coverage in the forest. The study of the diurnal cycle of FPAR is a novel utilization of flux tower observations. Diurnal cycle observations represent all aspects of canopy-sun-surrounding relations, while traditional studies, using site level observations, focus on the seasonal cycle and absolute value at the time when measurements are made, although series of these measurements may be collected over multiple days [22; 23; 24; 25; 26]. The long-term trends of FPAR could be impacted by two factors: plant phenology changes, such as leaf area index (LAI) and leaf out time, and plant distribution changes [27]. In addition to temporal dynamics, FPAR spatialpatterns are also evaluated. Spatial patterns are decided mainly by plant types and geographical conditions [28]. As analyzed above, the accuracy of the solar radiation partitioning solutions depends, on not only the performance of the land surface solar radiation partitioning scheme, but also on the accuracy of input information on vegetation (e.g., LAI) and other conditions.