Modeling seasonal snowpack evolution in the complex terrain and forested Colorado Headwaters region: A model intercomparison study

Abstract

Correctly modeling snow is critical for climate models and for hydrologic applications. Snowpack simulated by six land surface models (LSM: Noah, Variable Infiltration Capacity, snow-atmosphere-soil transfer, Land Ecosystem-Atmosphere Feedback, Noah with Multiparameterization, and Community Land Model) were evaluated against 1 year snow water equivalent (SWE) data at 112 Snow Telemetry (SNOTEL) sites in the Colorado River Headwaters region and 4 year flux tower data at two AmeriFlux sites. All models captured the main characteristics of the seasonal SWE evolution fairly well at 112 SNOTEL sites. No single model performed the best to capture the combined features of the peak SWE, the timing of peak SWE, and the length of snow season. Evaluating only simulated SWE is deceiving and does not reveal critical deficiencies in models, because the models could produce similar SWE for starkly different reasons. Sensitivity experiments revealed that the models responded differently to variations of forest coverage. The treatment of snow albedo and its cascading effects on surface energy deficit, surface temperature, stability correction, and turbulent fluxes was a major intermodel discrepancy. Six LSMs substantially overestimated (underestimated) radiative flux (heat flux), a crucial deficiency in representing winter land-atmosphere feedback in coupled weather and climate models. Results showed significant intermodel differences in snowmelt efficiency and sublimation efficiency, and models with high rate of snow accumulation and melt were able to reproduce the observed seasonal evolution of SWE. This study highlights that the parameterization of cascading effects of snow albedo and below-canopy turbulence and radiation transfer is critical not only for SWE simulation but also for correctly capturing the winter land-atmosphere interactions.

Sensitivity of Mountain Hydroclimate Simulations in Variable‐Resolution CESM to Microphysics and Horizontal Resolution

“…Understanding the physical processes that affect precipitation and snowpack accumulation in U.S.‐west mountainous regions and how they may be altered with anthropogenic climate change necessitates the use of climate models that can properly characterize land surface heterogeneity and synoptic‐scale storm systems (Huning & Margulis, ). An accurate representation of U.S.‐west orography is particularly important to realistically simulate the capture and storage of available precipitable water from the atmosphere (Ashfaq et al, ; Chen et al, ; Hughes et al, ; Ikeda et al, ; Liu et al, ; Musselman et al, ; Pierce et al, ; Rasmussen et al, ; Walton et al, ). This is due to the importance of mountain range orientation, the mountain slope variation impacts on orographic uplift, the corresponding alterations in the precipitation phase, the resultant transport and location of surface precipitation, the snow‐albedo feedback, and the life cycle of stored mountain snowpack.…”

The incorporation of an organic soil layer in the Noah-MP land surface model and its evaluation over a boreal aspen forest

“…Noah-MP has been implemented in the community Weather Research and Forecasting (WRF) model , which is widely used as a numerical weather prediction and regional climate model for dynamical downscaling in many regions worldwide (Chotamonsak et al, 2012). The performance of Noah-MP was previously evaluated using in situ and satellite data Yang et al, 2011;Cai et al, 2014;Pilotto et al, 2015;Chen et al, 2014). Those evaluation results showed significant improvements in modeling runoff, snow, surface heat fluxes, soil moisture, and surface skin temperature compared to the Noah LSM (Chen et al, 1996;Ek et al, 2003).…”

“…Those evaluation results showed significant improvements in modeling runoff, snow, surface heat fluxes, soil moisture, and surface skin temperature compared to the Noah LSM (Chen et al, 1996;Ek et al, 2003). Recently, Chen et al (2014) compared Noah-MP to Noah and four other LSMs regarding the simulation of snow and surface heat fluxes at a forested site in the Colorado headwaters region, and found a generally good performance L. Chen et al: The incorporation of an organic soil layer in the Noah-MP land surface model of Noah-MP. However, it is challenging to parameterize the cascading effects of snow albedo and below-canopy turbulence and radiation transfer in forested regions as pointed out by Clark et al (2015) and Zheng et al (2015).…”

Study of the Spatiotemporal Characteristics of Meltwater Contribution to the Total Runoff in the Upper Changjiang River Basin

“…Studies have documented the wide spread of model results [19,76,77] when applied to the same numerical experiments, indicating the discrepancies and uncertainties in different parameterizations implemented. Regarding MCR, Siderius et al [15] reported different MCR of Ganges estimated from four different models, particularly in upstream area.…”

“…Among these models, Land Surface Models (LSMs) have been developed to understand the water, energy and carbon cycles of Earth [18]. Many models emphasize on snow/glacier processes due to their important roles in weather and climate [19]. Therefore, snow-related processes including accumulation, sublimation and melt are generally represented using physically based schemes in LSMs by explicitly considering various controlling factors, e.g., climate, topography, vegetation, etc.…”