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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.

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Abstract.A state-of-the-art regional model, the Weather Research and Forecasting (WRF) model (Skamarock et al., 2008) coupled with a chemistry component (Chem) (Grell et al., 2005), is coupled with the snow, ice, and aerosol radiative (SNICAR) model that includes the most sophisticated representation of snow metamorphism processes available for climate study. The coupled model is used to simulate black carbon (BC) and dust concentrations and their radiative forcing in seasonal snow over North China in January-February of 2010, with extensive field measurements used to evaluate the model performance. In general, the model simulated spatial variability of BC and dust mass concentrations in the top snow layer (hereafter BCS and DSTS, respectively) are consistent with observations. The model generally moderately underestimates BCS in the clean regions but significantly overestimates BCS in some polluted regions. Most model results fall within the uncertainty ranges of observations. The simulated BCS and DSTS are highest with > 5000 ng g −1 and up to 5 mg g −1 , respectively, over the source regions and reduce to < 50 ng g −1 and < 1 µg g −1 , respectively, in the remote regions. BCS and DSTS introduce a similar magnitude of radiative warming (∼ 10 W m −2 ) in the snowpack, which is comparable to the magnitude of surface radiative cooling due to BC and dust in the atmosphere. This study represents an effort in using a regional modeling framework to simulate BC and dust and their direct radiative forcing in snowpack.Although a variety of observational data sets have been used to attribute model biases, some uncertainties in the results remain, which highlights the need for more observations, particularly concurrent measurements of atmospheric and snow aerosols and the deposition fluxes of aerosols, in future campaigns.

The Weather Research and Forecasting (WRF) model version 3.0 developed by the National Center for Atmospheric Research (NCAR) includes three land surface schemes: the simple soil thermal diffusion (STD) scheme, the Noah scheme, and the Rapid Update Cycle (RUC) scheme. We have recently coupled the sophisticated NCAR Community Land Model version 3 (CLM3) into WRF to better characterize land surface processes. Among these four land surface schemes, the STD scheme is the simplest in both structure and process physics. The Noah and RUC schemes are at the intermediate level of complexity. CLM3 includes the most sophisticated snow, soil, and vegetation physics among these land surface schemes. WRF simulations with all four land surface schemes over the western United States (WUS) were carried out for the 1 October 1995 through 30 September 1996. The results show that land surface processes strongly affect temperature simulations over the (WUS). As compared to observations, WRF-CLM3 with the highest complexity level significantly improves temperature simulations, except for the wintertime maximum temperature. Precipitation is dramatically overestimated by WRF with all four land surface schemes over the (WUS) analyzed in this study and does not show a close relationship with land surface processes.

Abstract. This paper presents a simple snow model for climate studies. There are three prognostic variables in the model: specific enthalpy, snow water equivalent, and snow depth. This model is developed on the basis of up-to-date comprehensive and complex snow schemes but with substantial simplification and improvement. The effects of vapor on snow processes have been analyzed in the paper. On the basis of the analysis, vapor's contribution in the mass equation is eliminated, and an effective conductivity coefficient, which includes a simple parameterization for vapor diffusion effect, is used to describe its contribution in the energy equation to simplify the computation. Specific enthalpy is used in the energy balance equation. Using enthalpy rather than temperature greatly simplifies the computational procedure for the phase change calculation in the snow process. This approach, along with a one-step test scheme that avoids iterations, saves computational time, which is important for general circulation model (GCM) simulations. The layering scheme is a critical part in the model. After many tests, it is found that three layers with an appropriate layering scheme are adequate for most cases. Preliminary testing using Russian and French snow data shows that the three-layer model is able to produce reasonable and consistent results.

Climate projections from three atmosphere-ocean climate models with a range of low to mid-high temperature sensitivity forced by the Intergovernmental Panel for Climate Change SRES higher, middle, and lower emission scenarios indicate that, over the 21 st century, extreme heat events for major cities in heavily air-conditioned California will increase rapidly. These increases in temperature extremes are projected to exceed the rate of increase in mean temperature, along with increased variance. Extreme heat is defined here as the 90 percent exceedance probability suggests that peak electricity demand will challenge current supply, as well as future planned supply capacities when population and income growth are taken into account.

In this study, we investigated the effects of water stress on the growth and yield of summer maize (Zea mays L.) over four phenological stages: Seedling, jointing, heading, and grain-filling. Water stress treatments were applied during each of these four stages in a water-controlled field in the Guanzhong Plain, China between 2013 and 2016. We found that severe water stress during the seedling stage had a greater effect on the growth and development of maize than stress applied during the other three stages. Water stress led to lower leaf area index (LAI) and biomass owing to reduced intercepted photosynthetically active radiation (IPAR) and radiation-use efficiency (RUE). These effects extended to the reproductive stage and eventually reduced the unit kernel weight and yield. In addition, the chlorophyll content in the leaf remained lower, even though irrigation was applied partially or fully after the seedling stage. Severe and prolonged water stress in maize plants during the seedling stage may damage the structure of the photosynthetic membrane, resulting in lower chlorophyll content, and therefore RUE, than those in the plants that did not experience water stress at the seedling stage. Maize plants with such damage did not show a meaningful recovery even when irrigation levels during the rest of the growth period were the same as those applied to the plants not subjected to water stress. The results of our field experiments suggest that an unrecoverable yield loss could occur if summer maize were exposed to severe and extended water stress events during the seedling stage.