Improved parameterization of snow albedo in Noah coupled with Weather Research and Forecasting: applicability to snow estimates for the Tibetan Plateau

Abstract

Abstract. Snow albedo is important to the land surface energy balance and to the water cycle. During snowfall and subsequent snowmelt, snow albedo is usually parameterized as functions of snow-related variables in land surface models. However, the default snow albedo scheme in the widely used Noah land surface model shows evident shortcomings in land–atmosphere interaction estimates during snow events on the Tibetan Plateau. Here, we demonstrate that our improved snow albedo scheme performs well after including snow depth as an additional factor. By coupling the Weather Research and Forecasting (WRF) and Noah models, this study comprehensively evaluates the performance of the improved snow albedo scheme in simulating eight snow events on the Tibetan Plateau. The modeling results are compared with WRF run with the default Noah scheme and in situ observations. The improved snow albedo scheme significantly outperforms the default Noah scheme in relation to air temperature, albedo and sensible heat flux estimates by alleviating cold bias estimates, albedo overestimates and sensible heat flux underestimates, respectively. This in turn contributes to more accurate reproductions of snow event evolution. The averaged root mean square error (RMSE) relative reductions (and relative increase in correlation coefficients) for air temperature, albedo, sensible heat flux and snow depth reach 27 % (5 %), 32 % (69 %), 13 % (17 %) and 21 % (108 %), respectively. These results demonstrate the strong potential of our improved snow albedo parameterization scheme for snow event simulations on the Tibetan Plateau. Our study provides a theoretical reference for researchers committed to further improving the snow albedo parameterization scheme.

Observational Constraint on the Contribution of Surface Albedo Feedback to the Amplified Tibetan Plateau Surface Warming

“…Both the reanalysis datasets and CMIP6 models show low consensus on $${\Delta}{\mathrm{T}}_{\text{SAF}}$$ and −$${\Delta}{\mathrm{T}}_{(\mathrm{H}+\text{LE})}$$ due to their relatively large inter‐model spread. However, the spread of their sum ($${\Delta}{\mathrm{T}}_{\text{SAF}-(\mathrm{H}+\text{LE})}$$) is considerably smaller than that of $${\Delta}{\mathrm{T}}_{\text{SAF}}$$ and −$${\Delta}{\mathrm{T}}_{(\mathrm{H}+\text{LE})}$$ (Table S5 in Supporting Information ), as models may have compensating biases when simulating the various components of the surface energy budget (Boeke & Taylor, 2016; Liu et al., 2021) and this manifests in their contribution to surface temperature change. For the reanalysis datasets and CMIP6 models overestimating the contribution of SAF to surface warming, the opposite contribution from the turbulent heat fluxes tends to be overestimated as well.…”

Improving predictions of land-atmosphere interactions based on a hybrid data assimilation and machine learning method

“…In the vertical direction, 28 vertical sigma levels from the surface to 50 hPa were used. (Liu et al, 2021;Ma et al, 2022). The physical parameterization schemes selected in this study included the Rapid Radiative Transfer Model longwave and shortwave radiation scheme (Mlawer et al 1997),…”

“…Such surface variability affects the available energy distribution at the land surface and has additional effects on the sensible and latent heat fluxes, surface temperature, and water vapor (Sawada et al, 2015;Wen et al, 2012). Although land surface models (LSMs) have been improved incrementally in the past decades, it is still challenging to effectively couple LSMs with atmospheric models to improve the description of land-atmosphere interactions (Chen and Dudhia, 2001;Liu et al, 2021). The success of the coupling depends not only on the sophisticated physical processes of the LSMs, but also on soil and vegetation characteristics and the accurate characterization of the water vapor fluxes at the land-atmosphere interface (Gentine et al, 2019;Zhang et al, 2021bZhang et al, , 2022.…”

Simulation of the improved parameterization scheme for snow radiation effect over the Tibetan Plateau on the impact of summer rainfall in China

“…The completeness of snow parameterization schemes in numerical models has an important impact on the simulation performance of snow cover, especially in high-latitude and high-altitude regions. Numeric models with varying degrees of complexity have been used to conduct extensive research on snow cover and the impact of snow cover anomalies on weather and climate (Zhou et al 2019;Liu et al 2021).…”

“…Liu et al 2021). Menard et al (2021) noted that the largest error in the mass and energy balance in the model stems from the uncertainty of the selected site-speci c parameters rather than the model structure.…”