Implementation and Evaluation of a Unified Turbulence Parameterization Throughout the Canopy and Roughness Sublayer in Noah‐MP Snow Simulations

Abstract

The Noah‐MP land surface model (LSM) relies on the Monin‐Obukhov (M‐O) Similarity Theory (MOST) to calculate land‐atmosphere exchanges of water, energy, and momentum fluxes. However, MOST flux‐profile relationships neglect canopy‐induced turbulence in the roughness sublayer (RSL) and parameterize within‐canopy turbulence in an ad hoc manner. We implement a new physics scheme (M‐O‐RSL) into Noah‐MP that explicitly parameterizes turbulence in RSL. We compare Noah‐MP simulations employing the M‐O‐RSL scheme (M‐O‐RSL simulations) and the default M‐O scheme (M‐O simulations) against observations obtained from 647 Snow Telemetry (SNOTEL) stations and two AmeriFlux stations in the western United States. M‐O‐RSL simulations of snow water equivalent (SWE) outperform M‐O simulations over 64% and 69% of SNOTEL sites in terms of root‐mean‐square‐error (RMSE) and correlation, respectively. The largest improvements in skill for M‐O‐RSL occur over closed shrubland sites, and the largest degradations in skill occur over deciduous broadleaf forest sites. Differences between M‐O and M‐O‐RSL simulated snowpack are primarily attributable to differences in aerodynamic conductance for heat underneath the canopy top, which modulates sensible heat flux. Differences between M‐O and M‐O‐RSL within‐canopy and below‐canopy sensible heat fluxes affect the amount of heat transported into snowpack and hence change snowmelt when temperatures are close to or above the melting point. The surface energy budget analysis over two AmeriFlux stations shows that differences between M‐O and M‐O‐RSL simulations can be smaller than other model biases (e.g., surface albedo). We intend for the M‐O‐RSL physics scheme to improve performance and uncertainty estimates in weather and hydrological applications that rely on Noah‐MP.

Evaluation and Optimization of Snow Albedo Scheme in Noah‐MP Land Surface Model Using In Situ Spectral Observations in the Colorado Rockies

“…Specifically, from 10 April 2019 to 20 April 2019, the optimized simulation shows a 37% bias reduction in broadband albedo relative to the reference simulation. Thus, although the overall statistics highlight only marginal differences between reference and optimized albedo performance, these results support that parameter optimization will likely increase the accuracy of modeled late season ablation which depends on accurate representation of snow albedo (e.g., Abolafia‐Rosenzweig et al., 2021). Overall, we consider the spatial transferability of optimized parameters to the East River site appropriate and nondegrading.…”

“…Broadband snow albedo frequently varies from 0.5 to 0.9, and thus modulates the rate of ablation. Simulated errors in snowpack evolution from the widely used Noah with Multi‐Parameterization (Noah‐MP) LSM (Niu et al., 2011) have been determined to heavily rely on the accuracy of simulated snow albedo (Abolafia‐Rosenzweig et al., 2021; Bryant et al., 2013; Chen et al., 2014; He et al., 2021; Jiang et al., 2019). Furthermore, snow albedo has a significant positive feedback in the climate system: warmer temperatures are expected to reduce snow cover extent, which in turn will decrease the overall land surface albedo favoring even higher land surface temperatures (Gao et al., 2017, 2018; Qu & Hall, 2014).…”

What Causes the Unobserved Early‐Spring Snowpack Ablation in Convection‐Permitting WRF Modeling Over Utah Mountains?

“…For instance, the canopy‐wind interaction and below‐canopy turbulence calculations may be biased, while a stronger aerodynamic resistance to sensible heat throughout the canopy could achieve a similar effect of reducing wind forcing on mitigating the ablation bias. In a companion work (Abolafia‐Rosenzweig et al., 2021), we are improving the Noah‐MP canopy turbulence scheme and test its impact on snowpack simulations. Besides, the canopy radiative transfer process along with canopy properties (e.g., leaf area index, canopy height and vegetation cover) may also be biased in the model, while a stronger blocking of downward solar radiation during the canopy radiative transfer could achieve a similar effect of decreasing solar radiation forcing on reducing the ablation bias.…”

Impacts of snow surface aerodynamic resistance on snow water equivalent simulations in forested regions

“…It is parallel and opposite to the direction of flow. In the simulation of snow processes, r as is a key factor that influences the snowpack energy balance since it governs the efficiency of turbulent transport between the snow surface and the near‐surface reference height (Abolafia‐Rosenzweig et al, 2021; Storck, 2000; Xue et al, 2003). In forested areas, r as can be influenced by vegetation height and structure that can have high spatial variations (e.g., Choudhury & Monteith, 1988; Storck, 2000; Wigmosta et al, 1994).…”