Laura M. Hinkelman

Impact of errors in the downwelling irradiances on simulations of snow water equivalent, snow surface temperature, and the snow energy balance

The forcing irradiances (downwelling shortwave and longwave irradiances) are the primary drivers of snowmelt; however, in complex terrain, few observations, the use of estimated irradiances, and the influence of topography and elevation all lead to uncertainties in these radiative fluxes. The impact of uncertainties in the forcing irradiances on simulations of snow is evaluated in idealized modeling experiments. Two snow models of contrasting complexity, the Utah Energy Balance Model (UEB) and the Snow Thermal Model (SNTHERM), are forced with irradiances with prescribed errors of the structure and magnitude representative of those found in methods for estimating the downwelling irradiances. Relatively modest biases have substantial impacts on simulated snow water equivalent (SWE) and surface temperature (Ts) across a range of climates, whereas random noise at the daily scale has a negligible effect on modeled SWE and Ts. Shortwave biases have a smaller SWE impact, due to the influence of albedo, and Ts impact, due to their diurnal cycle, compared to equivalent longwave biases. Warmer sites exhibit greater sensitivity to errors when evaluated using SWE, while colder sites exhibit more sensitivity as evaluated using Ts. The two models displayed different sensitivity and responses to biases. The stability feedback in the turbulent fluxes explains differences in Ts between models in the negative longwave bias scenarios. When the models diverge during melt events, differences in the turbulent fluxes and internal energy change of the snow are found to be responsible. From this analysis, we suggest model evaluations use Ts in addition to SWE.

An evaluation of NCEP Eta model predictions of surface energy budget and cloud properties by comparison with measured ARM data

Time-series output from the Eta forecast model of the National Weather Services National Centers for Environmental Prediction was evaluated by comparison with measured values for a location in Oklahoma. The measured data were drawn from the archives of the Department of Energys Atmospheric Radiation Measurement program for the southern Great Plains site. Surface energy budget components and cloud indicators were examined for the first half of 1997. Overall, the Eta surface energy budget was found to be nearly balanced, as intended by the model physics, except for one instance when light snowfall occurred in the spring. Despite this balance, an average 50 W m−2 excess in incoming solar radiation was found. Approximately half of this excess was attributed to deficient extinction of shortwave radiation by water vapor and aerosols in the model, while the remainder appeared to be due to cloud treatment errors. The excess shortwave flux was offset by a smaller negative bias in the downwelling infrared flux and the use of slightly high albedos, in addition to errors of lesser magnitude in the latent and sensible heat fluxes. The upwelling infrared flux and ground heat flux were closer to measured values. Ambiguity in the definition of a cloud as well as measurement limitations hindered the analysis of cloud prediction. However, a rough cloud comparison indicated that the Eta model has more difficulty predicting convection than the movement of large storm systems. In addition, cirrus clouds were predicted too frequently in the winter, while low and midlevel clouds were often missed in the spring. This study demonstrates that single-point time-series data can be used effectively both to ascertain the quality of model output and to investigate the treatment of individual physical processes within models.

Using CERES SYN Surface Irradiance Data as Forcing for Snowmelt Simulation in Complex Terrain

AbstractThe benefit of using solar and longwave surface irradiance data from NASA’s Clouds and the Earth’s Radiant Energy System (CERES) synoptic (SYN) satellite product in simulations of snowmelt has been examined. The accuracy of the SYN downwelling solar and longwave irradiances was first assessed by comparison to measurements at NOAA’s Surface Radiation Network (SURFRAD) reference stations and to remote mountain observations. Typical shortwave (longwave) biases had magnitudes less than 30 (10) W m−2, with most standard deviations below 140 (30) W m−2. The performance of a range of snow models of varying complexity when using SYN irradiances as forcing data was then evaluated. Simulated snow water equivalent and runoff from cases using SYN data fell in the range of those from simulations forced with irradiances from well-maintained surface observation sites as well as empirical methods that have been shown to perform well in mountainous terrain. The SYN irradiances are therefore judged to be suitable f...

The Effect of Cumulus Cloud Field Anisotropy on Domain-Averaged Solar Fluxes and Atmospheric Heating Rates

Cumulus clouds can become tilted or elongated in the presence of wind shear. Nevertheless, most studies of the interaction of cumulus clouds and radiation have assumed these clouds to be isotropic. This paper describes an investigation of the effect of fair-weather cumulus cloud field anisotropy on domain-averaged solar fluxes and atmospheric heating rate profiles. A stochastic field generation algorithm was used to produce 20 three-dimensional liquid water content fields based on the statistical properties of cloud scenes from a large eddy simulation. Progressively greater degrees of x–z plane tilting and horizontal stretching were imposed on each of these scenes, so that an ensemble of scenes was produced for each level of distortion. The resulting scenes were used as input to a three-dimensional Monte Carlo radiative transfer model. Domain-averaged transmission, reflection, and absorption of broadband solar radiation were computed for each scene along with the average heating rate profile. Both tilt and horizontal stretching were found to significantly affect calculated fluxes, with the amount and sign of flux differences depending strongly on sun position relative to cloud distortion geometry. The mechanisms by which anisotropy interacts with solar fluxes were investigated by comparisons to independent pixel approximation and tilted independent pixel approximation computations for the same scenes. Cumulus anisotropy was found to most strongly impact solar radiative transfer by changing the effective cloud fraction (i.e., the cloud fraction with respect to the solar beam direction).

Influence of Synoptic Weather Patterns on Solar Irradiance Variability in Northern Europe

AbstractObservations have revealed strong variability of shortwave (SW) irradiance at Earth’s surface on decadal time scales, referred to as global dimming and brightening. Previous studies have attributed the dimming and brightening to changes in clouds and atmospheric aerosols. This study assesses the influence of atmospheric circulation on clouds and SW irradiance to separate the influence of “natural” SW variability from direct and, to some extent, indirect aerosol effects. The focus is on SW irradiance in northern Europe in summer and spring because there is little high-latitude SW irradiance during winter. As a measure of large-scale circulation the Grosswetterlagen (GWL) dataset, a daily classification of synoptic weather patterns, is used. Empirical models of normalized SW irradiance are constructed based on the GWL, relating the synoptic weather patterns to the local radiative climate. In summer, a temporary SW peak in the 1970s and subsequent dimming is linked to variations in the synoptic patte...

A simple algorithm for identifying periods of snow accumulation on a radiometer

Downwelling solar, Qsi, and longwave, Qli, irradiances at the earths surface are the primary energy inputs for many hydrologic processes, and uncertainties in measurements of these two terms confound evaluations of estimated irradiances and negatively impact hydrologic modeling. Observations of Qsi and Qli in cold environments are subject to conditions that create additional uncertainties not encountered in other climates, specifically the accumulation of snow on uplooking radiometers. To address this issue, we present an automated method for estimating these periods of snow accumulation. Our method is based on forest interception of snow and uses common meteorological observations. In this algorithm, snow accumulation must exceed a threshold to obscure the sensor and is only removed through scouring by wind or melting. The algorithm is evaluated at two sites representing different mountain climates: (1) Snoqualmie Pass, Washington (maritime) and (2) the Senator Beck Basin Study Area, Colorado (continental). The algorithm agrees well with time-lapse camera observations at the Washington site and with multiple measurements at the Colorado site, with 70–80% of observed snow accumulation events correctly identified. We suggest using the method for quality controlling irradiance observations in snow-dominated climates where regular, daily maintenance is not possible.

A critical evaluation of modeled solar irradiance over California for hydrologic and land surface modeling

Studies of land surface processes in complex terrain often require estimates of meteorological variables, i.e., the incoming solar irradiance (Qsi), to force land surface models. However, estimates of Qsi are rarely evaluated within mountainous environments. We evaluated four methods of estimating Qsi: the CERES Synoptic Radiative Fluxes and Clouds (SYN) product, MTCLIM, a regional reanalysis product derived from a long-term Weather Research and Forecast simulation, and Mountain Microclimate Simulation Model (MTCLIM). These products are evaluated over the Central Valley and Sierra Nevada mountains in California, a region with meteorology strongly impacted by complex topography. We used a spatially dense network of Qsi observations (n = 70) to characterize the spatial characteristics of Qsi uncertainty. Observation sites were grouped into five subregions, and Qsi estimates were evaluated against observations in each subregion. Large monthly biases (up to 80 W m−2) outside the observational uncertainty were found for all estimates in all subregions examined, typically reaching a maximum in the spring. We found that MTCLIM and SYN generally perform the best across all subregions. Differences between Qsi estimates were largest over the Sierra Nevada, with seasonal differences exceeding 50 W m−2. Disagreements in Qsi were especially pronounced when averaging over high-elevation basins, with monthly differences up to 80 W m−2. Biases in estimated Qsi predominantly occurred with darker than normal conditions associated with precipitation (a proxy for cloud cover), while the presence of aerosols and water vapor was unable to explain the biases. Users of Qsi estimates in regions of complex topography, especially those estimating Qsi to force land surface models, need to be aware of this source of uncertainty.