Humidity is a basic and crucial meteorological indicator commonly measured in several forms, including specific humidity, relative humidity, and absolute humidity. These different forms can be inter-derived based on the saturation vapor pressure (SVP). In past decades, dozens of formulae have been developed to calculate the SVP with respect to, and in equilibrium with, liquid water and solid ice surfaces, but many prior studies use a single function for all temperature ranges, without considering the distinction between over the liquid water and ice surfaces. These different approaches can result in humidity estimates that may impact our understanding of surface-subsurface thermal-hydrological dynamics in cold regions. In this study, we compared the relative humidity (RH) downloaded and calculated from four data sources in Alaska based on five commonly used SVP formulas. These RHs, along with other meteorological indicators, were then used to drive physics-rich land surface models at a permafrost-affected site. We found that higher values of RH (up to 40 %) were obtained if the SVP was calculated with the over-ice formulation when air temperatures were below freezing, which could lead to a 30 % maximum difference in snow depths. The choice of whether to separately calculate the SVP over an ice surface in winter also produced a significant range (up to 0.2 m) in simulated annual maximum thaw depths. The sensitivity of seasonal thaw depth to the formulation of SVP increases with the rainfall rate and the height of above-ground ponded water, while it diminishes with warmer air temperatures. These results show that RH variations based on the calculation of SVP with or without over-ice calculation meaningfully impact physicallybased predictions of snow depth, sublimation, soil temperature, and active layer thickness. Under particular conditions, when severe flooding (inundation) and cool air temperatures are present, care should be taken to evaluate how humidity data is estimated for land surface and earth system modeling

The uncertainty of passive microwave retrievals of snowfall is notoriously high where high-frequency surface emissivity is significantly reduced and varies markedly in response to the changes in snowpack physical properties. Using the dense media radiative transfer theory, this article studies the potential effects of terrestrial snow-cover depth, density, and grain size on high-frequency channels 89 and 166 GHz of the radiometer onboard the Global Precipitation Measurement (GPM) core satellite, which are commonly used to capture snowfall scattering signals. Integrating the inference across all feasible grain sizes, ranges of snowpack density and depth are identified over which snowfall scattering signatures can be time-varying and potentially obscured. Using ten years of reanalysis data, the seasonal vulnerability of snowfall retrievals to the changes in snowpack emissivity in the Northern Hemisphere is mapped in a probabilistic sense and connections are made with the uncertainties of the GPM passive microwave snowfall retrievals. It is found that among different snow classes, relatively light Arctic tundra snow in fall, with a density below 260 kg m(-3), and shallow prairie snow during the winter, with a depth of less than 40 cm, can reduce the surface emissivity and obscure the snowfall passive microwave signatures. It is demonstrated that during winter, the highly vulnerable areas are over Kazakhstan and Mongolia with taiga and prairie snow. In the fall, these areas are largely over tundra and taiga snow in north of Russia and the Arctic Archipelagos as well as prairies in Canada and the Great Plains in the United States.

Numerical experiments with the Brazilian additions to the Regional Atmospheric Modeling System were performed with two nested grids (50 and 10 km horizontal resolution, respectively) with and without the effect of biomass burning for 8 different situations for 96 h integrations. Only the direct radiative effect of aerosols is considered. The results were analyzed in large areas encompassing the BR163 road (one of the main areas of deforestation in the Amazon). mainly where most of the burning takes place. The precipitation change due to the direct radiative impact of biomass burning is generally negative (i.e., there is a decrease of precipitation). However, there are a few cases with a positive impact. Two opposite forcing mechanisms were explored: (a) the thermodynamic forcing that is generally negative in the sense that the aerosol tends to stabilize the lower atmosphere and (b) the dynamic impact associated with the low level horizontal pressure gradients produced by the aerosol plumes. In order to understand the non-linear relationship between the two effects, experiments were performed with 4-fold emissions. In these cases, the dynamic effect overcomes the stabilization produced by the radiative forcing and precipitation increase is observed in comparison with the control experiment. This study suggests that. in general, the biomass burning radiative forcing decreases the precipitation. However, very large concentrations of aerosols may lead to an increase of precipitation due to the dynamical forcing associated with the horizontal pressure gradients. (C) 2009 Elsevier B.V. All rights reserved.