We enhance the Community Land Model (CLM) snow albedo modeling by implementing several new features with more realistic and physical representations of snow-aerosol-radiation interactions. Specifically, we incorporate the following model enhancements: (1) updating ice and aerosol optical properties with more realistic and accurate datasets, (2) adding multiple dust types, (3) adding multiple surface downward solar spectra to account for different atmospheric conditions, (4) incorporating a more accurate adding-doubling radiative transfer solver, (5) adding nonspherical snow grain representation, (6) adding black carbon-snow and dust-snow internal mixing representations, and (7) adding a hyperspectral (480-band versus the default 5-band) modeling capability. These model features/enhancements are included as new CLM physics/namelist options, which allows for quantification of model sensitivity to snow albedo processes and for multi-physics model ensemble analyses for uncertainty assessment. The model updates will be included in the next CLM version release. Sensitivity analyses reveal stronger impacts of using the new adding-doubling solver, nonspherical snow grains, and aerosol-snow internal mixing than the other new features/enhancements. These enhanced snow albedo representations improve the CLM simulated global snowpack evolution and land surface conditions, with reduced biases in simulated snow surface albedo, snow cover, snow water equivalent, snow depth, and surface temperature, particularly over northern mid-latitude mountainous regions and polar regions.

The widely-used Noah-MP land surface model (LSM) currently adopts snow albedo parameterizations that are semi-physical in nature with nontrivial uncertainties. To improve physical representations of snow albedo processes, a state-of-the-art snowpack radiative transfer model, the latest version of Snow, Ice, and Aerosol Radiative (SNICAR) model, is integrated into Noah-MP in this study. The coupled Noah-MP/SNICAR represents snow grain properties (e.g., shape and size), snow aging, and physics-based snow-aerosol-radiation interaction processes. We compare Noah-MP simulations employing the SNICAR scheme and the default semi-physical Biosphere-Atmosphere Transfer Scheme (BATS) against in-situ snow albedo observations at three Rocky Mountain field stations. The agreement between simulated and in-situ observed ground snow albedo in the broadband, visible, and near-infrared spectra is enhanced in Noah-MP/SNICAR simulations relative to Noah-MP/BATS simulations. The SNICAR scheme improves the temporal variability of modeled broadband snow albedo, with a nearly twofold higher correlation with observations (r=0.66) than the default BATS snow albedo scheme (r=0.37). The underestimated variability in Noah-MP/BATS is a result of inadequate physical linkage between snow albedo and environmental/snowpack conditions, which is substantially improved by the SNICAR scheme. Importantly, the Noah-MP/SNICAR model, with constraints of snow grain size from the MODIS snow covered area and grain size (MODSCAG) satellite data, physically represents and quantifies the snow albedo and absorption of shortwave radiation in response to snow grain size, non-spherical snow shapes, and light-absorbing particles (LAPs). The coupling framework of the Noah-MP/SNICAR model provides a means to reduce the bias in simulating snow albedo.

In the present work, the sensitivity of near-surface air temperature and building energy consumption to different rooftop mitigation strategies in the urban environment is evaluated by means of numerical simulations in idealized urban areas, covering a large spectra of possible urban structures, for typical summer and winter conditions. Rooftop mitigation stategies considered include cool roofs, green roofs and rooftop photovoltaic panels. In particular, the latter two rooftop technologies are simulated using two novel parameterization schemes, incorporated in the mesoscale model Weather Research and Fore-5 casting (WRF), coupled with a multilayer urban canopy parameterization and a building energy model (BEP+BEM). Results indicate that near-surface air temperature within the city is reduced by all the RMSs during the summer period: cool roofs are the most efficient in decreasing air temperature (up to 1°C on average), followed by irrigated green roofs with grass vegetation and photovoltaic panels. Green roofs reveal to be the most efficient strategy in reducing the energy consumption by air conditioning systems, up to 45%, because of their waterproof insulating layer, while electricity produced by photovoltaic 10 panels overcomes energy demand by air conditioning systems. During wintertime, green roofs maintain a higher near-surface air temperature than standard roofs, because of their higher thermal capacity and the consequent release of sensible heat during nighttime. On the other hand, photovoltaic panels (during nighttime) and cool roofs (during daytime) reduce near-surface air temperature, resulting in a reduced thermal comfort. Green roofs are the most efficient rooftop mitigation strategy in reducing energy consumption by heating, and are able to reduce the energy demand up to 40% for low rise buildings, while cool roofs 15 always increase consumption due to the decreased temperature. The results presented here show that the novel parameterization schemes implemented in the WRF model can be a valuable tool to evaluate the effects of mitigation strategies in the urban environment. Moreover, this study demonstrates that all rooftop technologies present multiple benefits for the urban environment , showing that green roofs are the most efficient in increasing thermal comfort and diminish energy consumption, while photovoltaic panels can reduce the dependence on fossil fuel consumption through electricity generation.

While meteorology and aerosols are identified as key drivers of snow cover variability in High Mountain Asia (HMA), complex non-linear interactions between them are not adequately quantified. Here, we attempt to unravel these interactions through a simple relative importance (RI) analysis of meteorological and aerosol variables from ERA5/CAMS-EAC4 reanalysis against satellite-derived snow cover from MODIS across 2003-2018. Our results show a statistically significant 7% rise in the RI of aerosol-meteorology interactions (AMI) in modulating snow cover during late snowmelt season (June-July), notably over low snow-covered (LSC) regions. Sensitivity tests further reveal that the importance of meteorological interactions with individual aerosol species are more prominent than total aerosols over LSC regions. We find that the RI of AMI for LSC regions is clearly dominated by carbonaceous aerosols, on top of the expected importance of dynamic meteorology. These findings clearly highlight the need to consider AMI in hydrometeorological monitoring, modeling, and reanalyses.

This paper describes and evaluates novel parameterizations for accounting for the effect of rooftop mitigation strategies on the urban environment, in the context of the mesoscale model Weather Research and Forecasting (WRF), coupled with a urban canopy parameterization and a building energy model (BEP+BEM). Through the new implementation, the sensitivity of near-surface air temperature and building energy consumption to different rooftop mitigation strategies is evaluated by means of numerical simulations in idealized urban areas, for typical summer and winter conditions. Rooftop mitigation strategies considered include cool roofs, green roofs and rooftop photovoltaic panels. Results indicate that near-surface air temperature is reduced by all the RMSs during the summer period: cool roofs are the most efficient in decreasing air temperature (up to 1 °C on average), followed by green roofs and photovoltaic panels. Green roofs reveal to be the most efficient strategy in reducing the energy consumption by air conditioning systems, up to 45%, while electricity produced by photovoltaic panels overcomes energy demand by air conditioning systems. During wintertime, green roofs maintain a higher near-surface air temperature than standard roofs. On the other hand, photovoltaic panels and cool roofs reduce near-surface air temperature, resulting in a reduced thermal comfort. The results presented here show that the novel parameterization schemes implemented in the WRF model can be a valuable tool to evaluate the effects of mitigation strategies in the urban environment. The new model is available as part of the public release of WRF in version 4.3.