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The accuracy of snow density in land surface model (LSM) simulations impacts the accuracy of simulated terrestrial water and energy budgets. However, there has been little research that has focused on enhancing snow compaction in operationally used LSMs. A baseline snow simulation with the widely used Noah‐MP LSM systematically overestimates snow depth by 55 mm even after removing daily snow water equivalent (SWE) biases. To reduce uncertainties associated with snow compaction, we enhance the most sensitive Noah‐MP snow compaction parameter—the empirical parameter for compaction due to overburden (Cbd)—such that Cbd is calculated as a function of surface air temperature as opposed to a fixed value in the baseline simulation. This enhancement improves accuracy in simulated snow compaction across the majority of western U.S. (WUS) SNOTEL test sites (biases reduced at 88% of test sites), with modest bias reductions in cooler accumulation periods (biases reduced at 70% of test sites) and substantial improvements during warmer ablation periods (biases reduced at 99% of test sites). Relatively larger improvements during warm conditions are attributable to the default Cbd value being reasonable for cold temperatures (≤−5°C). Improvements in simulated snow depth and density with observations outside of the training sites and optimization periods support that the snow compaction enhancement is transferable in space and time. Differences between enhanced and baseline gridded simulations across the total WUS support that the enhancement can have important impacts on snowpack evolution, snow albedo feedback, and snow hydrology. Plain Language Summary: Snow density, the ratio of water in the snowpack to the snow depth, is a fundamental property of snow that is important to accurately represent in snow simulations. In this research, we develop and evaluate a model enhancement designed to more accurately simulate the snow densification process in a widely used land surface model, Noah‐MP. Evaluations of the baseline snow compaction scheme, without model enhancements, reveals reasonable performance during cold conditions but substantial errors during warm conditions. We update the Noah‐MP snow compaction scheme to calculate the most sensitive snow compaction parameter as a function of surface air temperature. This enhancement provides substantially more accurate representations of snow compaction during warm conditions and provides similar performance relative to the baseline simulation during cold conditions. Improved accuracy in simulating snow densification is consistent between training sites the enhancement was optimized over and sites that were only used for validation both within and outside of the time period of optimization, supporting that improvements associated with using our model update can be expected at places and times distinct from the optimization locations and time periods. Key Points: Noah‐MP snow compaction parameterization is enhanced using SNOTEL snow observationsThe snow compaction enhancement provides substantial improvements in snow density and depth particularly during warm conditionsThe enhanced snow compaction scheme can have notable effects on snowpack evolution and snow albedo feedback [ABSTRACT FROM AUTHOR]

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: (a) updating ice and aerosol optical properties with more realistic and accurate data sets, (b) adding multiple dust types, (c) adding multiple surface downward solar spectra to account for different atmospheric conditions, (d) incorporating a more accurate adding‐doubling radiative transfer solver, (e) adding nonspherical snow grain representations, (f) adding black carbon‐snow and dust‐snow internal mixing representations, and (g) adding a hyperspectral (480‐band vs. 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 have been included in the latest released CLM version. Sensitivity analyses reveal strong impacts of using the new adding‐doubling solver, nonspherical snow grains, aerosol‐snow internal mixing, updated aerosol optics, and different dust types. 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 (2‐m) air temperature over many mid‐latitude mountainous regions and seasonal snowpacks but degraded performance in some northern high‐latitude regions. Plain Language Summary: Snow albedo plays a critical role in the Earth system, affecting land surface energy and water balance and also serving as an important land process that feeds back to the atmosphere. Several recent studies have identified new or improved physical representations of snow albedo processes. In this study, we leverage recent advances in snow albedo modeling to implement a number of relevant new features into the widely used Community Land Model (CLM), which is the land component of the Community Earth System Model (CESM). Specifically, we improve the ice and aerosol optical properties, the treatment of dust types and downward sunlight energy distribution at different wavelength bands, the albedo computation algorithm, and the representations of snow grain shape and how aerosol is mixed with snow grains. These model updates have been included in the latest released CLM version. Overall, the enhanced snow albedo representations improve the simulated snowpack evolution and related land surface conditions in many parts of the globe. Key Points: We enhance CLM5 snow albedo modeling by including more realistic and physical representations of snow‐aerosol‐radiation interactionsThe new adding‐doubling solver, nonspherical snow grains, aerosol‐snow internal mixing, and updated aerosol optics show strong impactsThe enhanced snow albedo representation improves the CLM simulated snowpack evolution and land surface conditions in many global regions [ABSTRACT FROM AUTHOR]

With the highest albedo of the land surface, snow plays a vital role in Earth's surface energy budget and water cycle. Snow albedo is primarily controlled by snow grain properties (e.g., size and shape) and light-absorbing particles (LAPs) such as black carbon (BC) and dust. The mixing state of LAPs in snow also has impacts on LAP-induced snow albedo reduction and surface radiative forcing (RF). However, most land surface models assume that snow grain shape is spherical and LAPs are externally mixed with the snow grains. This study improves the snow radiative transfer model in the Energy Exascale Earth System Model version 2.0 (E3SM v2.0) Land Model (ELM v2.0) by considering non-spherical snow grain shapes (i.e., spheroid, hexagonal plate, and Koch snowflake) and internal mixing of dust–snow, and it systematically evaluates the impacts on the surface energy budget and water cycle over the Tibetan Plateau (TP). A series of ELM simulations with different treatments of snow grain shape, mixing state of BC–snow and dust–snow, and sub-grid topographic effects (TOP) on solar radiation are performed. Compared with two remote sensing snow products derived from the Moderate Resolution Imaging Spectroradiometer, the control ELM simulation (ELM_Control) with the default configurations of spherical snow grain shape, internal mixing of BC–snow, external mixing of dust–snow, and without TOP as well as the ELM simulation with new model features (ELM_New) can both capture the overall snow distribution reasonably. Additionally, ELM_New overall shows smaller biases in snow cover fraction than ELM_Control in spring when snowmelt is important for water management. The estimated LAP-induced RF in ELM_New ranges from 0 to 19.3 W m -2 with the area-weighted average value of 1.5 W m -2 that is comparable to the reported values in existing studies. The Koch snowflake shape, among other non-spherical shapes, shows the largest difference from the spherical shape in spring when snow processes related to the surface energy budget and water cycle have high importance. The impacts of the mixing state of LAP in snow are smaller than the shape effects and depend on snow grain shape. Compared to external mixing, internal mixing of LAP–snow can lead to larger snow albedo reduction and snowmelt, which further affect the surface energy budget and water cycle. The individual contributions of non-spherical snow shape, mixing state of LAP–snow, and local topography impacts on the snow and surface fluxes have different signs and magnitudes, and their combined effects may be negative or positive due to complex and nonlinear interactions among the factors. Overall, the changes in net solar radiation in spring due to individual and combined effects range from -28.6 to 16.9 W m -2 and -29.7 to 12.2 W m -2 , respectively. This study advances understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offers guidance for improving snow simulations and RF estimates in Earth system models under climate change. [ABSTRACT FROM AUTHOR]

The Biosphere‐Atmosphere Transfer Scheme (BATS) ground snow albedo algorithm is commonly used in land‐surface models (LSM), weather forecasting and research applications. This study addresses key uncertainties in BATS simulated ground snow albedo within the Noah‐MP LSM framework through evaluation and optimization of the Noah‐MP BATS ground snow albedo formulation using 2‐band (visible and near‐infrared (NIR)) in situ albedo observations at Rocky Mountain field stations. The Noah‐MP BATS ground snow albedo scheme is extremely sensitive to its input parameters. Namely, an ensemble generated by varying BATS input parameters within potentially plausible ranges provides an average daily range (maximum ensemble member minus minimum ensemble member) of ground snow albedo exceeding 0.45 in visible and NIR bands. Parameter optimization improves agreement between simulated and in situ observed ground snow albedo in visible, NIR and broadband spectrums. Importantly, optimized parameters result in reduced biases relative to observed fresh‐snow albedo and better agreement with observed albedo decay. Our analysis across different sites supports that the optimized BATS ground snow albedo parameters are appropriate to transfer in space and time, at least within the region studied (the central‐southern Rocky Mountains). The primary error source remaining after parameter optimization is that observed fresh‐snow albedo is highly variable, particularly in the NIR spectrum, whereas BATS fresh‐snow albedo is constant, an issue which requires further investigation. This study shows significant correlations between observed fresh‐snow albedo and surface meteorological conditions (e.g., downward shortwave radiation and temperature) which can support future model development that attempts to include a time‐varying formulation for fresh‐snow albedo. Plain Language Summary: Fresh snow reflects up to 90% of incoming sunlight (albedo 0.9), but as it ages, the reflectivity declines to as much as 50% (albedo 0.5). Snow albedo has a strong influence on snowpack evolution, melt rates, and land surface energy balance. The Biosphere‐Atmosphere Transfer Schemes (BATS) snow albedo algorithm is commonly used to simulate snow albedo in climate and weather predictions. This study evaluates the BATS ground snow albedo algorithm and parameters within the widely used Noah‐MP land surface model, through comparisons with observations of snow albedo in the Rocky Mountains. The BATS snow albedo scheme is extremely sensitive to input parameters. We identify an optimal set of input parameters to calculate snow albedo using the ground observations. Additionally, applying the optimized parameters across different sites in the central‐southern Rocky Mountain region shows that optimized parameters are transferable in space and time within this region to sites with similar climate. The primary error source remaining after parameter optimization is the use of constant fresh‐snow albedo in BATS; in reality, observed fresh‐snow albedo is variable. Relationships between fresh‐snow albedo and environmental conditions could inform model development of a time‐varying formulation for fresh‐snow albedo in the future. Key Points: The Biosphere‐Atmosphere Transfer Scheme (BATS) snow albedo formulation is highly sensitive to fresh‐snow albedo and snow age input parametersParameter optimization substantially improves accuracy for BATS simulated ground snow albedo, particularly for ablation periodsOptimized parameters improve accuracy of BATS simulated snow albedo and are transferable in time and space to sites with similar climate [ABSTRACT FROM AUTHOR]

While meteorology and aerosols are identified as key drivers of snow cover (SC) variability in High Mountain Asia, 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 SC from Moderate Resolution Imaging Spectroradiometer across 2003–2018. Our results show a statistically significant 7% rise in the RI of aerosol‐meteorology interactions (AMI) in modulating SC during late snowmelt season (June and 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. Plain Language Summary: Understanding the changes in snow cover (SC) over glaciers in High Mountain Asia (HMA) is important yet challenging. Despite its impact on water resources, physical processes that drive these changes are complex. In particular, large‐scale weather patterns, together with aerosol pollution hotspots in the vicinity, and its steep elevation strongly interact with each other. We use a statistical approach to assess the relevance of these interactions using geophysical data from present day reanalysis and observed SC extent from satellite products for two decades. We find that during the late snowmelt period from June to July, interactions between aerosols and meteorology are significant, specifically in low SC regions. Interactions of individual aerosol species, especially carbonaceous aerosols like black carbon are more important than total aerosol concentration. This approach in quantifying the interactions of these processes can help improve the monitoring and modeling of snow hydrology. Representing these relevant interactions in current models and reanalysis of hydrometeorology can lead to more accurate predictions of the state of snow for critical regions like HMA. Key Points: Interactions between aerosols and meteorology are significant during late snowmelt (June and July) over low snow‐covered regions in High Mountain AsiaSpecies related interactions drive the seasonal variability of the overall relative importanceCarbonaceous aerosols are more relevant than mineral dust during late snowmelt [ABSTRACT FROM AUTHOR]

Accurate prediction of snowpack evolution and ablation is critical to supporting weather and hydrological applications. Convection‐permitting modeling has been shown to well capture observed snowpack evolution over many western United States (U.S.) mountain ranges, but some significant ablation biases still remain. In this study, we conduct process‐level snowpack analyses of a widely used convection‐permitting (4‐km) weather research and forecasting (WRF) modeling product (WRF4km) for the contiguous U.S. to understand the mechanisms causing its unobserved early‐spring snow ablation over Utah mountains. Analyses across Utah Snowpack Telemetry (SNOTEL) sites show that the unobserved snowpack ablation during mid‐February to late‐March in WRF4km is driven by multiple strong melting events. The melting results from the enhanced downward sensible heat flux to snowpack and enhanced ground solar radiation absorption, with generally larger contributions from the former before early March and from the latter after early March. The enhanced downward sensible heat flux to snowpack is mainly due to the enhanced surface heat exchange coefficient induced by high surface wind speeds. The enhanced ground solar radiation absorption is driven by both enhanced surface downward solar radiation and strong melting‐induced snow cover reduction that is caused by deficiencies in Noah‐MP snow‐related parameterizations used in WRF4km. The substantial snow cover reduction during melting decreases surface albedo and hence triggers a positive albedo feedback that further accelerates melting. Our analyses reveal possible deficiencies in WRF and Noah‐MP (e.g., canopy processes and snow albedo) and shed light on future directions for model improvements. Plain Language Summary: Snowpack plays an important role in modulating surface energy and water balance and land‐atmosphere interaction in the Earth system. Snow melting is key to many important hydrological applications by affecting runoff, fresh water availability, and drought, particularly over western United States (U.S.) mountainous regions. Convection‐permitting modeling with a high spatial resolution (e.g., ≤4 km) can accurately capture observed snowpack evolution over many western U.S. mountain ranges, but some important melting biases still remain. In this study, we conduct analyses of the widely used 4‐km weather research and forecasting (WRF) modeling product developed at the National Center for Atmospheric Research to understand the reasons causing its strong early‐spring snow melting over Utah mountains, which is not seen in observations. We find that the strong melting during mid‐February to late‐March in the model is due to (a) the enhanced downward sensible heat flux to snowpack driven by strong surface winds, and (b) the enhanced ground absorption of solar radiation caused by strong surface downward solar radiation and strong snow cover reduction induced by melting. Our results reveal possible deficiencies in model physics (e.g., canopy processes and snow albedo) and shed light on future directions for model improvements. Key Points: WRF 4‐km early‐spring SWE underestimates are due to strong melting caused by enhanced sensible heat to snow and solar radiation absorptionThe enhanced surface heat exchange coefficient and thus sensible heat flux to snowpack are driven by strong surface winds in the WRF modelThe enhanced solar radiation absorption is due to strong surface downward solar radiation and strong melting‐induced snow cover reduction [ABSTRACT FROM AUTHOR]

Accurate precipitation estimates are critical to simulating seasonal snowpack evolution. We conduct and evaluate high‐resolution (4‐km) snowpack simulations over the western United States (WUS) mountains in Water Year 2013 using the Noah with multi‐parameterization (Noah‐MP) land surface model driven by precipitation forcing from convection‐permitting (4‐km) Weather Research and Forecasting (WRF) modeling and four widely used high‐resolution datasets that are derived from statistical interpolation based on in situ measurements. Substantial differences in the precipitation amount among these five datasets, particularly over the western and northern portions of WUS mountains, significantly affect simulated snow water equivalent (SWE) and snow depth (SD) but have relatively limited effects on snow cover fraction (SCF) and surface albedo. WRF generally captures observed precipitation patterns and results in an overall best‐performed SWE and SD in the western and northern portions of WUS mountains, where the statistically interpolated datasets lead to underpredicted precipitation, SWE, and SD. Over the interior WUS mountains, all the datasets consistently underestimate precipitation, causing significant negative biases in SWE and SD, among which the results driven by the WRF precipitation show an average performance. Further analysis reveals systematic positive biases in SCF and surface albedo across the WUS mountains, with similar bias patterns and magnitudes for simulations driven by different precipitation datasets, suggesting an urgent need to improve the Noah‐MP snowpack physics. This study highlights that convection‐permitting modeling with proper configurations can have added values in providing decent precipitation for high‐resolution snowpack simulations over the WUS mountains in a typical ENSO‐neutral year, particularly over observation‐scarce regions. Key Points: Convection‐permitting WRF results capture the observed precipitation, SWE, and snow depth in western and northern portions of WUS mountainsAll datasets produce large negative bias in precipitation, SWE, and snow depth in interior WUS mountains with fair performance for WRFConvection‐permitting modeling has added values in providing precipitation for snow simulations over mountains without enough observations [ABSTRACT FROM AUTHOR]