Roughness Lengths For Heat Research Articles

Observed and Parameterized Roughness Lengths for Momentum and Heat Over Rough Ice Surfaces

AbstractTurbulent heat fluxes, that is, the sensible heat flux and latent heat flux, are important sources/sinks of energy for surface melt over glaciers and ice sheets. Therefore, credible simulations of for example, future Greenland Ice Sheet mass loss need an accurate description of these fluxes. However, the parameterization of surface turbulent heat fluxes in climate models requires knowledge about the surface roughness lengths for momentum, heat and moisture, which are currently either unknown or tuned to indirect observations. In this study we take advantage of a large data set of eddy covariance observations acquired during multiple years and at multiple sites over the Greenland Ice Sheet. These in‐situ observations are used to develop an improved parameterization for the roughness length for momentum, and update the parameterization for the roughness lengths for heat and moisture over rough ice surfaces. The newly derived parameterizations are implemented in a surface energy balance model that is used to compute surface melt. Sensitivity experiments confirm the high sensitivity of surface melt to the chosen roughness length models. The new parameterization models the sensible heat flux to within 10 W m−2, and the cumulative ice ablation within 10% at three out of four sites. Two case studies demonstrate the important contribution of the turbulent heat fluxes to surface ablation. The presented roughness parameterizations can be implemented in climate models to improve the physical representation of surface roughness over rough snow and ice surfaces, which is expected to improve the modeled turbulent heat fluxes and thus surface melt.

Surface Heat and Moisture Exchange in the Marginal Ice Zone: Observations and a New Parameterization Scheme for Weather and Climate Models

AbstractAircraft observations from two Arctic field campaigns are used to characterize and model surface heat and moisture exchange over the marginal ice zone (MIZ). We show that the surface roughness lengths for heat and moisture over uninterrupted sea ice vary with roughness Reynolds number (; itself a function of the roughness length for momentum, , and surface wind stress), with a peak at the transition between aerodynamically smooth (<0.135) and aerodynamically rough (>2.5) regimes. A pre‐existing theoretical model based on surface‐renewal theory accurately reproduces this peak, in contrast to the simple parameterizations currently employed in two state‐of‐the‐art numerical weather prediction models, which are insensitive to . We propose a new, simple parameterization for surface exchange over the MIZ that blends this theoretical model for sea ice with surface exchange over water as a function of sea ice concentration. In offline tests, this new scheme performs much better than the existing schemes for the rough conditions observed during the ‘Iceland Greenland Seas Project’ field campaign. The bias in total turbulent heat flux across the MIZ is reduced to only 13 W m−2 for the new scheme, from 48 and 80 W m−2 for the Met Office Unified Model and ECMWF Integrated Forecast System schemes, respectively. It also performs marginally better for the comparatively smooth conditions observed during the ‘Aerosol‐Cloud Coupling and Climate Interactions in the Arctic’ field campaign. The new surface exchange scheme has the benefit of being physically‐motivated, comparatively accurate and straightforward to implement, although to reap the full benefits an improvement to the representation of sea ice topography via is required.

Assessing the simulated soil hydrothermal regime of the active layer from the Noah-MP land surface model (v1.1) in the permafrost regions of the Qinghai–Tibet Plateau

Abstract. Extensive and rigorous model intercomparison is of great importance before model application due to the uncertainties in current land surface models (LSMs). Without considering the uncertainties in forcing data and model parameters, this study designed an ensemble of 55 296 experiments to evaluate the Noah LSM with multi-parameterization (Noah-MP) for snow cover events (SCEs), soil temperature (ST) and soil liquid water (SLW) simulation, and investigated the sensitivity of parameterization schemes at a typical permafrost site on the Qinghai–Tibet Plateau (QTP). The results showed that Noah-MP systematically overestimates snow cover, which could be greatly resolved when adopting the sublimation from wind and a semi-implicit snow/soil temperature time scheme. As a result of the overestimated snow, Noah-MP generally underestimates ST, which is mostly influenced by the snow process. A systematic cold bias and large uncertainties in soil temperature remain after eliminating the effects of snow, particularly in the deep layers and during the cold season. The combination of roughness length for heat and under-canopy (below-canopy) aerodynamic resistance contributes to resolving the cold bias in soil temperature. In addition, Noah-MP generally underestimates top SLW. The runoff and groundwater (RUN) process dominates the SLW simulation in comparison to the very limited impacts of all other physical processes. The analysis of the model structural uncertainties and characteristics of each scheme would be constructive to a better understanding of the land surface processes in the permafrost regions of the QTP as well as to further model improvements towards soil hydrothermal regime modeling using LSMs.

Evaluating and improving simulations of diurnal variation in land surface temperature with the Community Land Model for the Tibetan Plateau.

This study evaluated and improved the ability of the Community Land Model version 5.0 (CLM5.0) in simulating the diurnal land surface temperature (LST) cycle for the whole Tibetan Plateau (TP) by comparing it with Moderate Resolution Imaging Spectroradiometer satellite observations. During daytime, the model underestimated the LST on sparsely vegetated areas in summer, whereas cold biases occurred over the whole TP in winter. The lower simulated daytime LST resulted from weaker heat transfer resistances and greater soil thermal conductivity in the model, which generated a stronger heat flux transferred to the deep soil. During nighttime, CLM5.0 overestimated LST for the whole TP in both two seasons. These warm biases were mainly due to the greater soil thermal inertia, which is also related to greater soil thermal conductivity and wetter surface soil layer in the model. We employed the sensible heat roughness length scheme from Zeng, Wang & Wang (2012), the recommended soil thermal conductivity scheme from Dai et al. (2019), and the modified soil evaporation resistance parameterization, which was appropriate for the TP soil texture, to improve simulated daytime and nighttime LST, evapotranspiration, and surface (0–10 cm) soil moisture. In addition, the model produced lower daytime LST in winter because of overestimation of the snow cover fraction and an inaccurate atmospheric forcing dataset in the northwestern TP. In summary, this study reveals the reasons for biases when simulating LST variation, improves the simulations of turbulent fluxes and LST, and further shows that satellite-based observations can help enhance the land surface model parameterization and unobservable land surface processes on the TP.

Assessment of surface exchange coefficients in the Noah‐MP land surface model for different land‐cover types in China

AbstractThe parameterization of surface exchange coefficients (Ch) representing land–atmosphere coupling strength plays a key role in land surface modelling. Previous studies have found that land–atmosphere coupling in land surface models (LSMs) is largely overestimated, which affects the predictability of weather and climate evolution. To improve the representation of land–atmosphere interactions in LSMs, this study investigated the impact of dynamic canopy‐height‐dependent coupling strength in an offline Noah LSM with multi‐parameterization options (Noah‐MP) when applied to China. Comparison with the default Noah‐MP LSM showed that the dynamic scheme significantly improved the Ch calculations and realistically reduced the biases of simulated surface energy and water components against observations. It is noteworthy that the improvements brought about by the dynamic scheme differed across land‐cover types. The scheme was superior in reproducing the observed Ch as well as surface energy and water variables for short vegetation (grass, crops, and shrubs), whereas the improvement for tall canopy vegetation (forest) was not significant although the estimations were reasonable. The improved version benefits from the treatment of the roughness length for heat. Further, the scheme can better reproduce the observed surface thermal components, while the improvement in hydraulic variables (such as soil moisture) remains limited. Overall, the dynamic coupling scheme markedly affects the simulation of land–atmosphere interactions, and altering the dynamics of surface coupling has the potential to improve the representation of land–atmosphere interactions and, thus, the further development of LSM.

Improving the Noah‐MP Model for Simulating Hydrothermal Regime of the Active Layer in the Permafrost Regions of the Qinghai‐Tibet Plateau

AbstractSoil hydrothermal regime of the active layer in the permafrost regions of the Qinghai‐Tibet Plateau (QTP) is important to the underlying permafrost and the climate change dynamics in Asia. However, a large bias still exists in current land surface models in the representation of soil temperature and moisture. This study assessed and augmented the Noah land surface model with multiparameterization options (Noah‐MP) for simulating soil hydrothermal dynamics at the Tanggula (alpine meadow) and Beiluhe (alpine swamp) stations located in the permafrost regions of the QTP. The results showed that the default Noah‐MP tended to underestimate soil temperature and moisture. Specifically, the default model overestimated the snow depth and duration due to the low snow sublimation rate. This resulted in a cold deviation in the soil temperature at two stations. Such underestimation was reduced by introducing a scheme that considered the sublimation loss from wind. Moreover, the remaining cold bias in the soil profiles of two stations was greatly resolved by a combined scheme of roughness length for heat (Z0h) and undercanopy aerodynamic resistance (ra,g). A soil thermal conductivity scheme, which can produce more realistic soil thermal conductivity in frozen soil, further improved the deep soil temperature simulation. The consideration of soil organic matter could mitigate the underestimation of the shallow soil moisture to some extent, but this improvement was more obvious at the Tanggula station, which had coarser mineral soil than the Beiluhe station.

Surface and Atmospheric Driven Variability of the Single‐Layer Urban Canopy Model Under Clear‐Sky Conditions Over London

AbstractUrban canopy models (UCMs) are parametrization schemes that are used to improve weather forecasts in urban areas. The performance of UCMs depends on understanding potential uncertainty sources that can generally originate from the (a) urban surface parameters, (b) atmospheric forcing, and (c) physical description. Here, we investigate the relative importance of surface and atmospheric driven model sensitivities of the single‐layer urban canopy model when fully interactive with a 1‐D configuration of the Weather Research and Forecasting model (WRF). The impact of different physical descriptions in UCMs and other key parameterization schemes of WRF is considered. As a case study, we use a 54‐hr period with clear‐sky conditions over London. Our analysis is focused on the surface radiation and energy flux partitioning and the intensity of turbulent mixing. The impact of changes in atmospheric forcing and surface parameter values on model performance appears to be comparable in magnitude. The advection of potential temperature, aerosol optical depth, exchange coefficient and roughness length for heat, surface albedo, and the anthropogenic heat flux are the most influential. Some atmospheric forcing variations have similar impact on the key physical processes as changes in surface parameters. Hence, error compensation may occur if one optimizes model performance using a single variable or combinations that have potential for carryover effects (e.g., temperature). Process diagrams help differences to be understood in the physical description of different UCMs, boundary layer, and radiation schemes and between the model and the observations.

Estimation of Turbulent Heat Fluxes via Assimilation of Air Temperature and Specific Humidity into an Atmospheric Boundary Layer Model

AbstractA number of studies have used time series of air temperature and specific humidity observations to estimate turbulent heat fluxes. These studies require the specification of surface roughness lengths for heat and momentum (that are directly related to the neutral bulk heat transfer coefficientCHN) and/or ground heat flux, which are often unavailable. In this study, sequences of air temperature and specific humidity are assimilated into an atmospheric boundary layer model within a variational data assimilation (VDA) framework to estimateCHN, evaporative fraction (EF), turbulent heat fluxes, and atmospheric boundary layer (ABL) height, potential temperature, and humidity. The developed VDA approach needs neither the surface roughness parameterization (as it is optimized by the VDA approach) nor ground heat flux measurements. The VDA approach is tested over the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) site in the summers of 1987 and 1988. The results indicate that the estimated sensible and latent heat fluxes agree fairly well with the corresponding measurements. For FIFE 1987 (1988), the daily sensible and latent heat fluxes estimates have a root-mean-square error of 25.72 W m−2(27.77 W m−2) and 53.63 W m−2(48.22 W m−2), respectively. In addition, the ABL height, specific humidity, and potential temperature estimates from the VDA system are in good agreement with those inferred from the radiosondes both in terms of magnitude and diurnal trend.

REMOTE SENSING OF GLOBAL MONTHLY EVAPOTRANSPIRATION WITH AN ENERGY BALANCE (EB) MODEL

Abstract. A global monthly evapotranspiration (ET) product without spatial-temporal gaps for 2000–2017 is delivered by using an energy balance (EB) algorithm and MODIS satellite data. It provides us with a moderate resolution estimate of ET without spatial-temporal gaps on a global scale. The model is driven by monthly remote sensing land surface temperature and ERA-Interim meteorological data. A global turbulent exchange parameterization scheme was developed for global momentum and heat roughness length calculation with remote sensing information. The global roughness length was used in the energy balance model, which uses monthly land-air temperature gradient to estimate the turbulent sensible heat, and take the latent heat flux as a residual of the available energy. This study produced an ET product for global landmass, at a monthly time step and 0.05-degree spatial resolution. The performance of ET data has been evaluated in comparison to hundreds flux sites measurements representing a broad range of land covers and climates. The ET product has a mean bias of 3.3 mm/month, RMSE value of 36.9 mm/month. The monthly ET product can be used to study the global energy and hydrological cycles at either seasonal or inter-annual temporal resolution.

Land Surface Parameter and State Perturbations in the Global Ensemble Forecast System

AbstractThe National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) is underdispersive near the surface, a common characteristic of ensemble prediction systems. Here, several methods for increasing the spread are tested, including perturbing soil initial conditions, soil tendencies, and surface parameters, with physically based perturbations. Perturbations are applied to the soil initial conditions based on empirical orthogonal functions (EOFs) of differences between normalized soil moisture states from two land surface models (LSMs). Perturbations to roughness lengths for heat and momentum, soil hydraulic conductivity, stomatal resistance, vegetation fraction, and albedo are applied, with the amplitude and perturbation scales based on previous research. Soil moisture and temperature tendencies are also perturbed using a stochastic perturbation scheme. The results show that surface perturbations, through their impact on 2-m temperature spread, have a modest positive impact on the skill of short-range ensemble forecasts. However, adjusting the forecasts using an estimate of the systematic bias shows that bias correction has a greater impact on the forecast reliability than surface perturbations, indicating that systematic bias in the model needs to be addressed as well.

Significant differences exist in lake-atmosphere interactions and the evaporation rates of high-elevation small and large lakes

Lakes impact atmosphere boundary layer processes and are thus important for catchment scale climate modeling and regional water and heat budgets. To explore the differences in lake-atmosphere interaction parameters, meteorological variables and turbulent heat fluxes in small and large water bodies, we collected eddy covariance observations and meteorological data during ice-free periods of the Lake small Nam-Co (“small lake”) in 2012–2013 and Lake Nam-Co (“large lake”) in 2015–2016 on the Tibetan Plateau. Significant differences exist in their lake-atmosphere interaction processes due to differences in their inherent attributes and environmental backgrounds. Relative to the “small lake”, the maximum surface temperature of the “large lake” in summer is approximately 3 °C lower; “large lake” also has a larger wind speed, a higher monthly average air temperature and delayed peaks of the seasonal variation of water and air temperature. The typical values of the roughness length and standard bulk transfer coefficient for momentum are approximately 80% and 21% higher, respectively, for the “large lake”. The typical values of the roughness lengths for heat and water are one order of magnitude lower in the “large lake” while the corresponding standard bulk transfer coefficients are only 7% lower. The latent and sensible heat fluxes of the two lakes have quite different seasonal variations, with evaporation peaking in November for the “large lake” and in June for the “small lake”. The estimated evaporation during the ice-free season of the “large lake” (approximately 981±18 mm) is also higher than that (812 mm) of the “small lake” and this is mainly related to the observed lower Bowen ratio in the “large lake”.

Assessment of the Sensitivity to the Thermal Roughness Length in Noah and Noah-MP Land Surface Model Using WRF in an Arid Region

Atmospheric models are known to underestimate land surface temperature and, by association, 2 m air temperature over dry arid regions during the day due to the treatment of the thermal roughness length also known as roughness length of heat. The thermal roughness length can be controlled by the Zilitinkevich parameter, known as Czil, which is a tunable parameter within the models. Three different scenarios with the WRF model are run to test the impact of the Czil parameter on the simulations using two land surface models: the Noah and Noah-MP models. In this study, a modified version of the Noah-MP model is tested, in which the Czil parameter, and, therefore, the thermal roughness length varies depending on the land cover and vegetation height. The model domain is over the United Arab Emirates (UAE) where the major land cover type is desert. The following configurations are tested: the Noah model with Czil = 0.1, Noah model with Czil = 0.5 and the Noah-MP model with Czil = 0.5 over desert. Results of 2 m air temperature are verified against three stations in the UAE. Mean gross error of the diurnal 2 m temperature was reduced by up to 1.48 and 1.54 °C in the 24 and 48 h forecasts, respectively. This reduced the cold bias in the model. This improvement in air temperature showed to improve the diurnal cycle of relative humidity at the three monitoring stations as well as the duration of the sea breeze in some cases.

Effect of roughness lengths on surface energy and the planetary boundary layer height over high-altitude Ngoring Lake

The special climate environment creates a distinctive air-lake interaction characteristic in the Tibetan Plateau (TP) lakes, where the variations of surface roughness lengths also differ somewhat from those of other regions. However, how different categories of roughness lengths affect the lake surface energy exchange and the planetary boundary layer height (PBLH) remains unclear in the TP lakes. In this study, we used a tuned Weather Research and Forecasting (WRF) model version 3.6.1 to investigate the responses of the freeze-up date, turbulent fluxes, meteorological variables, and PBLH to surface roughness length variations in Ngoring Lake. Of all meteorological variables, the lake surface temperature responded to roughness length variations most sensitively; increasing roughness lengths can put the lake freeze-up date forward. The effect of momentum roughness length on wind speed was significantly affected by the fetch length. The increase in the roughness length for heat can induce the increment of the nightly PBLH in most months, especially for the central lake area in autumn. The primary factors that contribute to sensible heat flux (H) and latent heat flux (LE) were the roughness lengths for heat and momentum during the ice-free period, respectively. Increasing roughness length for heat can increase the nightly PBLH, and decreasing roughness length for moisture can also promote growth of the PBLH, but there was no obvious correlation between the momentum roughness length and the PBLH.

Comparison of model land skin temperature with remotely sensed estimates and assessment of surface‐atmosphere coupling

AbstractThe coupling between land surface and the atmosphere is a key feature in Earth System Modeling for exploiting the predictability of slowly evolving geophysical variables (e.g., soil moisture or vegetation state), and for correctly representing rapid variations within the diurnal cycle, particularly relevant in data assimilation applications. In this study, land surface temperature (LST) estimated from Meteosat Second Generation (MSG) is used to assess the European Centre for Medium‐Range Weather Forecasts (ECMWF) skin temperature, which can be interpreted as a radiative temperature of the model surface. It is shown that the ECMWF model tends to slightly overestimate skin temperature during nighttime and underestimate daytime values. Such underestimation of daily amplitudes is particularly pronounced in (semiarid) arid regions, suggesting a misrepresentation of surface energy fluxes in those areas. The LST estimated from MSG is used to evaluate the impact of changes in some of the ECMWF model surface parameters. The introduction of more realistic model vegetation is shown to have a positive but limited impact on skin temperature: long integration leads to an equilibrium state where changes in the latent heat flux and soil moisture availability compensate each other. Revised surface roughness lengths for heat and momentum, however, lead to overall positive impact on daytime skin temperature, mostly due to a reduction of sensible heat flux. This is particularly relevant in nonvegetated areas, unaffected by model vegetation. The reduction of skin conductivity, a parameter which controls the heat transfer to ground by diffusion, is shown to further improve the model skin temperature.

Simulation of Forest Evapotranspiration Using Time-Series Parameterization of the Surface Energy Balance System (SEBS) over the Qilian Mountains

We propose a long-term parameterization scheme for two critical parameters, zero-plane displacement height (d) and aerodynamic roughness length (z0m), that we further use in the Surface Energy Balance System (SEBS). A sensitivity analysis of SEBS indicated that these two parameters largely impact the estimated sensible heat and latent heat fluxes. First, we calibrated regression relationships between measured forest vertical parameters (Lorey’s height and the frontal area index (FAI)) and forest aboveground biomass (AGB). Next, we derived the interannual Lorey’s height and FAI values from our calibrated regression models and corresponding forest AGB dynamics that were converted from interannual carbon fluxes, as simulated from two incorporated ecological models and a 2009 forest basis map These dynamic forest vertical parameters, combined with refined eight-day Global LAnd Surface Satellite (GLASS) LAI products, were applied to estimate the eight-day d, z0m, and, thus, the heat roughness length (z0h). The obtained d, z0m and z0h were then used as forcing for the SEBS model in order to simulate long-term forest evapotranspiration (ET) from 2000 to 2012 within the Qilian Mountains (QMs). As compared with MODIS, MOD16 products at the eddy covariance (EC) site, ET estimates from the SEBS agreed much better with EC measurements (R2 = 0.80 and RMSE = 0.21 mm·day−1).

Supplementary data of "Impacts of mesic and xeric urban vegetation on outdoor thermal comfort and microclimate in Phoenix, AZ".

An advanced Markov-Chain Monte Carlo approach called Subset Simulation is described in Au and Beck (2001) [1] was used to quantify parameter uncertainty and model sensitivity of the urban land-atmospheric framework, viz. the coupled urban canopy model-single column model (UCM-SCM). The results show that the atmospheric dynamics are sensitive to land surface conditions. The most sensitive parameters are dimensional parameters, i.e. roof width, aspect ratio, roughness length of heat and momentum, since these parameters control the magnitude of sensible heat flux. The relative insensitive parameters are hydrological parameters since the lawns or green roofs in urban areas are regularly irrigated so that the water availability for evaporation is never constrained.

Quantifying the parameters that control turbulent land–atmosphere energy exchange over the Dunhuang Gobi land surface, northwest China

Land–atmosphere interactions in the northwest arid and semi-arid areas of China are important because of their influence on energy exchange at both regional and global scales. In this paper, regular meteorological observations, as well as flux eddy covariance methods, are used to measure land–atmosphere energy exchange. Experiments were conducted over the Dunhuang Gobi, a typical arid region of northwest China, during the summer of 2008. Surface fluxes were measured directly using an eddy-correlation system, while the vertical gradients of wind and temperature were also observed at four levels using a meteorological tower. Based on the observation data, the integral mean of surface albedo over the Gobi was found to be 0.238. In addition, the mean values of momentum, heat roughness lengths, and soil thermal conductivity were, 0.045, 0.005 cm, 0.199 and 0.201 W (m K)−1, respectively. The Monin–Obukhov similarity functions for momentum and heat were analyzed and new empirical formulae were developed.

Comparison of land skin temperature from a land model, remote sensing, and in situ measurement

Land skin temperature (Ts) is an important parameter in the energy exchange between the land surface and atmosphere. Here hourly Ts from the Community Land Model Version 4.0, MODIS satellite observations, and in-situ observations in 2003 were compared. Compared with the in-situ observations over four semi-arid stations, both MODIS and modeled Ts show negative biases, but MODIS shows an overall better performance. Global distribution of differences between MODIS and modeled Ts shows diurnal, seasonal, and spatial variations. Over sparsely vegetated areas, the model Ts is generally lower than the MODIS observed Ts during the daytime, while the situation is opposite at nighttime. The revision of roughness length for heat and the constraint of minimum friction velocity from Zeng et al. [2012] bring the modeled Ts closer to MODIS during the day, and have little effect on Ts at night. Five factors contributing to the Ts differences between the model and MODIS are identified, including the difficulty in properly accounting for cloud cover information at the appropriate temporal and spatial resolutions, and uncertainties in surface energy balance computation, atmospheric forcing data, surface emissivity, and MODIS Ts data. These findings have implications for the cross-evaluation of modeled and remotely sensed Ts, as well as the data assimilation of Ts observations into Earth system models.

Improvements in Sensible Heat-Flux Parametrization in the High-Resolution Regional Model (HRM) Through the Modified Treatment of the Roughness Length for Heat

We discuss the impact of the differential treatment of the roughness lengths for momentum and heat (\(z_{0\mathrm{m}}\) and \(z_{0\mathrm{h}}\)) in the flux parametrization scheme of the high-resolution regional model (HRM) for a heterogeneous terrain centred around Thiruvananthapuram, India (8.5°N, 76.9°E). The magnitudes of sensible heat flux (H) obtained from HRM simulations using the original parametrization scheme differed drastically from the concurrent in situ observations. With a view to improving the performance of this parametrization scheme, two distinct modifications are incorporated: (1) In the first method, a constant value of 100 is assigned to the \(z_{0\mathrm{m}}/z_{0\mathrm{h}}\) ratio; (2) and in the second approach, this ratio is treated as a function of time. Both these modifications in the HRM model showed significant improvements in the H simulations for Thiruvananthapuram and its adjoining regions. Results obtained from the present study provide a first-ever comparison of H simulations using the modified parametrization scheme in the HRM model with in situ observations for the Indian coastal region, and suggest a differential treatment of \(z_{0\mathrm{m}}\) and \(z_{0\mathrm{h}}\) in the flux parametrization scheme.

Surface Skin Temperature and the Interplay between Sensible and Ground Heat Fluxes over Arid Regions

Abstract Over arid regions, two community land models [Noah and Community Land Model (CLM)] still have difficulty in realistically simulating the diurnal cycle of surface skin temperature. Based on theoretical arguments and synthesis of previous observational and modeling efforts, three revisions are developed here to address this issue. The revision of the coefficients in computing roughness length for heat significantly reduces the underestimate of daytime skin temperature but has a negligible effect on nighttime skin temperature. The constraints of the minimum friction velocity and soil thermal conductivity help improve nighttime skin temperature under weak wind and dry soil conditions. These results are robust in both Noah and CLM, as well as in Noah, with 4 versus 10 soil layers based on in situ data at the Desert Rock site in Nevada with a monthly averaged diurnal amplitude of 31.7 K and the Gaize site over Tibet, China, with an amplitude of 44.6 K. While these revisions can be directly applied to CLM or other land models with subgrid tiles (including bare soil), suggestions are also made on their application to Noah and other land models that treat bare soil and vegetated area together in a model grid cell. It is suggested that the challenging issue of measuring and simulating surface sensible heat flux under stable conditions should be treated as a land–atmosphere coupled issue, involving the interplay of ground and sensible heat fluxes in balancing the net radiation over arid regions, rather than as an atmospheric turbulence issue alone. The implications of such a coupling perspective are also discussed.