Evaluation and Optimization of Snow Albedo Scheme in Noah‐MP Land Surface Model Using In Situ Spectral Observations in the Colorado Rockies. (29th September 2022) - Abolafia‐Rosenzweig, Ronnie; He, Cenlin; McKenzie Skiles, S.; Chen, Fei; Gochis, David
Abstract: 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 parameters Parameter optimization substantially improves accuracy for BATS simulated ground snow albedo, particularly for ablation periods Optimized parameters improve accuracy of BATS simulated snow albedo and are transferable in time and space to sites with similar climate