2022
DOI: 10.1029/2021ms002862
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Impacts of Sub‐Grid Topographic Representations on Surface Energy Balance and Boundary Conditions in the E3SM Land Model: A Case Study in Sierra Nevada

Abstract: Sub‐grid topographic heterogeneity has large impacts on surface energy balance and land‐atmosphere interactions. However, the impacts of representing sub‐grid topographic effects in land surface models (LSMs) on surface energy balance and boundary conditions remain unclear. This study analyzed and evaluated the impacts of sub‐grid topographic representations on surface energy balance, turbulent heat flux, and scalar (co‐)variances in the Energy Exascale Earth System Model (E3SM) land model (ELM). Three sub‐gri… Show more

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Cited by 19 publications
(22 citation statements)
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“…Then we used the derived CLM-PFT dataset to upscale the CI dataset from 500 m resolution to the resolution of model run by averaging CI values at finer resolution with the same PFT (Hao et al, 2022).…”
Section: Datasetsmentioning
confidence: 99%
“…Then we used the derived CLM-PFT dataset to upscale the CI dataset from 500 m resolution to the resolution of model run by averaging CI values at finer resolution with the same PFT (Hao et al, 2022).…”
Section: Datasetsmentioning
confidence: 99%
“…Fourth, the effects of non-spherical snow grain shape on snow albedo are considered (Hao et al, 2022a). Fifth, a new parameterization of sub-grid topographic effects on solar radiation has been implemented in ELM to account for the impacts of macro-scale shadow, occlusion and multi-scattering between adjacent terrain on surface albedo (Hao et al, 2021;Hao et al, 2022b).…”
Section: Model Descriptionmentioning
confidence: 99%
“…The Energy Exascale Earth System Model (E3SM) Land Model (ELM) (Leung et al, 2020) includes a multi-layer snow scheme to simulate the prognostic snow processes such as snow accumulation, snow interception, snow compaction, and snow melt. Recently, the snow albedo model in ELM was improved to include new radiative transfer solvers with improved accuracy (Dang et al, 2019), add non-spherical snow grain shape (Hao et al, 2022a), account for the internal mixing of light-absorbing particles (LAPs) with snow (Böttcher et al, 2014); Hao et al, 2022a), and incorporate new parameterizations to account for the subgrid topographic effects on solar radiation (Hao et al, 2021;Hao et al, 2022b) (see Section 2.1 for details). With these enhancements and improvements, ELM may skillfully simulate snow dynamics at a regional scale (e.g., WUS).…”
Section: Introductionmentioning
confidence: 99%
“…Such subpixel hierarchical structures parsimoniously capture the heterogeneity by characterizing various physical states at different levels (Lawrence et al., 2019), which would neglect the corresponding heterogeneity within each land unit, soil column, and PFT. Existing studies associated with the subpixel heterogeneity at the PFT level usually assume that very coarse resolution simulations (e.g., 0.5°) ignore the subpixel heterogeneity and 1 km resolution simulations can explicitly account for surface heterogeneity (Hao et al., 2022; Schneider et al., 2017). Different from these hierarchical structures, spatial scaling algorithms depict the subpixel heterogeneity by characterizing physical states at the fine pixel level, which usually assume that 1 km resolution simulations ignore the subpixel heterogeneity and 30 m resolution simulations can explicitly account for surface heterogeneity (Chen et al., 2013; El Maayar and Chen, 2006; Simic et al., 2004; Xie et al., 2021).…”
Section: Introductionmentioning
confidence: 99%
“…These ecosystem models intended for estimating continental and global GPP are often excused at coarse resolutions without considering the subpixel heterogeneity of the surface conditions. Various studies have reported that the ignored subpixel surface heterogeneity could bring scaling errors to the final estimates of water and carbon fluxes (Ahl et al., 2005; Chen et al., 2013; Ershadi et al., 2013; Hao et al., 2022).…”
Section: Introductionmentioning
confidence: 99%