Soil thermal conductivity (STC) is an important physical parameter in modeling land surface processes. Previous studies on evaluations of STC schemes are mostly based on direct measurements of local conditions, and their recommendations cannot be used as a reference in selecting STC schemes for land modeling use. In this work, seven typical STC schemes are incorporated into the Common Land Model to evaluate their applications in land surface modeling. Statistical analyses show that the Johansen (1975) scheme and its three derivatives, Côté and Konrad (2005, https://doi.org/10.1139/t04-106), Balland and Arp (2005, https://doi.org/10.1139/s05-007), and Lu et al. (2007, https://doi.org/10.2136/sssaj2006.0041), are significantly superior to the other schemes with respect to both STC estimations and their applications in modeling soil temperature and the partitioning of surface available energy in the Common Land Model. The Balland and Arp (2005, https://doi.org/10.1139/s05-007) scheme ranks at the top among the selected schemes. Uncertainty analyses based on single-point and global simulations both show that the differences in STC estimations can induce significant differences in simulated soil temperature in arid/semiarid and seasonally frozen regions, especially at deep layers. The hydrology-related variables are slightly affected by STC variations, but slight changes in these variables can induce notable changes in soil temperature by altering soil thermal properties. These results emphasize the important role of STC in modeling soil thermodynamics and suggest the necessity of further developing STC schemes based on land modeling applications. According to the evaluation analyses, we recommend the Balland and Arp (2005, https://doi. org/10.1139/s05-007) scheme as a more suitable selection for use in land surface models.Plain Language Summary Soil thermal conductivity (STC) is essential for simulating soil temperature and heat flux in land surface models. Previous studies have proposed quite a few parameterization schemes to estimate STC. Although many works have tried to determine which scheme performed best, most of them were based on direct measurements from specific experimental conditions or local soil samples, and few studies have considered the performances of STC schemes in land surface modeling. Here, we present an evaluation of seven typical STC schemes based on their land modeling applications. The results show that four of the schemes, the Johansen (1975) scheme, Côté and Konrad (2005, https://doi.org/10.1139/t04-106) scheme, Balland and Arp (2005, https://doi.org/10.1139/s05-007) scheme, and Lu et al. (2007, https://doi.org/10.2136/sssaj2006.0041) scheme, perform comparatively better than the other schemes and that the Balland and Arp (2005, https://doi.org/10.1139/s05-007) scheme ranks at the top.Uncertainty analyses demonstrate that soil thermodynamics are strongly sensitive to STC estimates, especially in dry and partly frozen regions. This relationship highlights the important role of STC in modeli...

Land-atmosphere interaction is a crucial component of numerical weather and climate models. Predictions of these numerical models are sensitive to surface-air exchanges (i.e., surface fluxes) of energy, mass, and momentum. The surface fluxes are always subgrid-scale (SGS) processes that cannot be explicitly resolved but must be parameterized. The surface flux parameterization in almost all current numerical models is based on the Monin-Obukhov similarity theory (MOST; Monin & Obukhov, 1954).The MOST has been a fundamental theory for studying the atmospheric surface layer (ASL) since its proposal (Monin & Obukhov, 1954). According to the MOST, the statistical structure of the surface layer, under horizontally homogeneous and quasi-stationary conditions, is governed by four independent parameters: height above ground, surface friction velocity, kinematic surface heat flux, and a buoyancy parameter. The MOST has been widely tested with field measurements for mean wind and mean temperature gradients (e.g.

The needs to modeling the Earth's terrestrial water cycle at the scale of human activities, for example, agricultural practices, monitoring the Earth's terrestrial water system, and predicting extreme weather events under global climate change have motivated hyper-resolution modeling of land surface processes (Milly et al., 2008;Singh et al., 2015;Wood et al., 2011). In addition, it becomes feasible to develop hyper-resolution land surface models (LSMs) due to advances in high-performance computing and availability of hyper-resolution land surface data sets (Bierkens et al., 2015;Famiglietti et al., 2015).Hyper-resolution modeling of land surface processes at hillslope scales (kilometers) requires a better representation of spatial heterogeneities in topography, vegetation, and soils at meter scales. In current LSMs at scales of 50-100 km, these spatial heterogeneities at subgrid scales are highly parameterized or even ignored (Bierkens et al., 2015;Fan et al., 2019;Wood et al., 2011). As an example, local topographic gradients within a grid cell are absent, and lateral flux exchanges between grids are ignored in most current LSMs (Clark et al., 2015;Lawrence et al., 2019;Swenson et al., 2019). These models may work well at the coarser resolution of 50-100 km through calibration of model parameters but fail when operating at kilometer scales, at which the horizontal hydraulic gradients become comparable to the vertical gradients (Bierkens et al., 2015;Krakauer et al., 2014). At hillslope