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... Show moreThe 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. Show less