[1] Thirty-three snowpack models of varying complexity and purpose were evaluated across a wide range of hydrometeorological and forest canopy conditions at five Northern Hemisphere locations, for up to two winter snow seasons. Modeled estimates of snow water equivalent (SWE) or depth were compared to observations at forest and open sites at each location. Precipitation phase and duration of above-freezing air temperatures are shown to be major influences on divergence and convergence of modeled estimates of the subcanopy snowpack. When models are considered collectively at all locations, comparisons with observations show that it is harder to model SWE at forested sites than open sites. There is no universal ''best'' model for all sites or locations, but comparison of the consistency of individual model performances relative to one another at different sites (and vice versa). Calibration of models at forest sites provides lower errors than uncalibrated models at three out of four locations. However, benefits of calibration do not translate to subsequent years, and benefits gained by models calibrated for forest snow processes are not translated to open conditions.

ABSTRACT. Many snow models have been developed for various applications such as hydrology, global atmospheric circulation models and avalanche forecasting. The degree of complexity of these models is highly variable, ranging from simple index methods to multi-layer models that simulate snow-cover stratigraphy and texture. In the framework of the Snow Model Intercomparison Project (SnowMIP), 23 models were compared using observed meteorological parameters from two mountainous alpine sites.The analysis here focuses on validation of snow energy-budget simulations. Albedo and snow surface temperature observations allow identification of the more realistic simulations and quantification of errors for two components of the energy budget: the net short-and longwave radiation. In particular, the different albedo parameterizations are evaluated for different snowpack states (in winter and spring). Analysis of results during the melting period allows an investigation of the different ways of partitioning the energy fluxes and reveals the complex feedbacks which occur when simulating the snow energy budget. Particular attention is paid to the impact of model complexity on the energy-budget components. The model complexity has a major role for the net longwave radiation calculation, whereas the albedo parameterization is the most significant factor explaining the accuracy of the net shortwave radiation simulation.

Eddy-covariance and biometeorological methods show significant net annual carbon uptake in an old-growth Douglas-fir 64 forest in southwestern Washington, USA. These results contrast with previous assumptions that old-growth forest ecosystems are in carbon equilibrium. The basis for differences between conventional biomass-based carbon sequestration estimates and the biometeorologic estimates are discussed. Annual net ecosystem exchange was comparable to younger ecosystems at the same latitude, as quantified in the AmeriFlux program. Net ecosystem carbon uptake was significantly correlated with photosynthetically active radiation and air temperature, as well as soil moisture and precipitation. Optimum ecosystem photosynthesis occurred at relatively cool temperatures (5°-10°C). Understory and soil carbon exchange always represented a source of carbon to the atmosphere, with a strong seasonal cycle in source strength. Understory and soil carbon exchange showed a Q 10 temperature dependence and represented a substantial portion of the ecosystem carbon budget. The period of main carbon uptake and the period of soil and ecosystem respiration are out of phase, however, and driven by different climatic boundary conditions. The period of strongest ecosystem carbon uptake coincides with the lowest observed values of soil and ecosystem respiration. Despite the substantial contribution of soil, the overall strength of the photosynthetic sink resulted in the net annual uptake. The net uptake estimates here included two correction methods, one for advection and the other for low levels of turbulence.

SUMMARYThe University of California, Davis (UCD), Advanced Canopy-Atmosphere-Soil Algorithm (ACASA) is presented and its output is compared with a comprehensive set of observations at six diverse sites. ACASA is a multi-layer canopy-surface-layer model that solves the steady-state Reynolds-averaged fluid flow equations to the third-order. These equations include an explicit representation of the steady-state, horizontally homogeneous, diabatic set of vector and scalar fluxes and flux transports. ACASA includes a fourth-order, near-exact technique to calculate leaf, stem, and soil surface temperatures and surface energy fluxes at various levels within the canopy. Plant physiological response to micro-environmental conditions is also included using Ball-Berryhon Caemmerer-Farquhar formulations. Observed energy fluxes and microenvironmental conditions from a grass field in the Netherlands, deciduous and coniferous forests in Canada, tropical pasture and forest in Brazil, and an ancient temperate rainforest in the USA are compared with simulated values.Results indicate that simulated and observed estimates of monthly to annual means of all suiface fluxes agree within 9.5% confidence thresholds for all six sites. Observed and simulated hourly estimates of net radiation are also in excellent agreement for all sites considered. Observed and simulated hourly sensible-and latentheat flux estimates are in very good statistical agreement in most cases. Differences that exist between ACASA and observed sensible-and latent-heat flux estimates are of the same magnitudes as observational uncertainties. Estimates of observed and simulated hourly values of canopy and ground heat storage are within 95% statistical confidence limits of agreement with observations in most cases. Simulated and measured values of daytime intracanopy mean wind speed, temperature, and specific humidity agree with 95% confidence within both a tropical and temperate rainforest at all levels. Results also indicate that, in general, ACASA produces flux estimates closer to observations with significantly less scatter than does the Biosphere-Atmosphere Transfer Scheme. Sensitivity tests show that reducing the vertical resolution, linearizing surface temperature calculations, andlor simplifying the treatment of surface-layer turbulence each altered mean sensible-and latent-heat flux estimates by amounts that are statistically significant in many cases. Results show that simplifying the model alters flux predictions i n manners not simply related to vegetation character, and that using ACASA at its full complexity for all vegetation regimes is warranted. Increasing the vertical resolution beyond 20 layers improved flux predictions at tropical locations but had little impact elsewhere.