Determination Of Surface Albedo Research Articles

Determination of surface albedo and snow/ice content variation using the MODIS data in the past two decades (2001–2020)

Determination of surface albedo and snow/ice content variation using the MODIS data in the past two decades (2001–2020)

A new fractional snow‐covered area parameterization for the Community Land Model and its effect on the surface energy balance

One function of the Community Land Model (CLM4) is the determination of surface albedo in the Community Earth System Model (CESM1). Because the typical spatial scales of CESM1 simulations are large compared to the scales of variability of surface properties such as snow cover and vegetation, unresolved surface heterogeneity is parameterized. Fractional snow‐covered area, or snow‐covered fraction (SCF), within a CLM4 grid cell is parameterized as a function of grid cell mean snow depth and snow density. This parameterization is based on an analysis of monthly averaged SCF and snow depth that showed a seasonal shift in the snow depth–SCF relationship. In this paper, we show that this shift is an artifact of the monthly sampling and that the current parameterization does not reflect the relationship observed between snow depth and SCF at the daily time scale. We demonstrate that the snow depth analysis used in the original study exhibits a bias toward early melt when compared to satellite‐observed SCF. This bias results in a tendency to overestimate SCF as a function of snow depth. Using a more consistent, higher spatial and temporal resolution snow depth analysis reveals a clear hysteresis between snow accumulation and melt seasons. Here, a new SCF parameterization based on snow water equivalent is developed to capture the observed seasonal snow depth–SCF evolution. The effects of the new SCF parameterization on the surface energy budget are described. In CLM4, surface energy fluxes are calculated assuming a uniform snow cover. To more realistically simulate environments having patchy snow cover, we modify the model by computing the surface fluxes separately for snow‐free and snow‐covered fractions of a grid cell. In this configuration, the form of the parameterized snow depth–SCF relationship is shown to greatly affect the surface energy budget. The direct exposure of the snow‐free surfaces to the atmosphere leads to greater heat loss from the ground during autumn and greater heat gain during spring. The net effect is to reduce annual mean soil temperatures by up to 3°C in snow‐affected regions.

Determination of Surface Albedo from Meteorological Satellite

Determination of Surface Albedo from Meteorological Satellite

Measuring snow and glacier ice properties from satellite

Satellite remote sensing is a convenient tool for studying snow and glacier ice, allowing us to conduct research over large and otherwise inaccessible areas. This paper reviews various methods for measuring snow and glacier ice properties with satellite remote sensing. These methods have been improving with the use of new satellite sensors, like the synthetic aperture radar (SAR) during the last decade, leading to the development of new and powerful methods, such as SAR interferometry for glacier velocity, digital elevation model generation of ice sheets, or snow cover mapping. Some methods still try to overcome the limitations of present sensors, but future satellites will have much increased capability, for example, the ability to measure the whole optical spectrum or SAR sensors with multiple polarization or frequencies. Among the methods presented are the satellite‐derived determination of surface albedo, snow extent, snow volume, snow grain size, surface temperature, glacier facies, glacier velocities, glacier extent, and ice sheet topography. In this review, emphasis is put on the principles and theory of each satellite remote sensing method. An extensive list of references, with an emphasis on studies from the 1990s, allows the reader to delve into specific topics.

Determination of surface albedo from satellites

The dynamical features of the climate system result from the interaction of the atmosphere with the surface. The hope for improving climate prediction, on seasonal and interannual time scales, is based on the premise that slowly varying boundary conditions force well defined predictable patterns of general circulation. It is now recognized that in models of surface climate over land, surface properties should not be regarded as constants and that it is important to specify a realistic value of the surface albedo ∗ ∗ Albedo is defined as the ratio of reflected to incident electromagnetic radiation in the total solar spectrum or in a certain wavelength interval. Reflectivity refers to a specific wavelength and direction. The term “albedo” is used rather freely in the literature. No attempt will be made to define it as appropriate in each reviewed case. in order to correctly estimate the amount of solar radiation absorbed at the surface. The albedo is also considered by some as an internal dependent parameter of climate, since it time integrates the effects of changes in more variable quantities, such as rainfall. The global nature of the links between forcing and response imply global monitoring of the parameters which control the transfer and feedback of the energy at the surface/atmosphere interface. Satellites can play an important role in resolving the difficult problem of properly representing the average surface albedo over large areas. However, satellites measure only the earth-atmosphere reflectance in narrow spectral intervals, narrow solid angles, and in most cases at a fixed local time. To derive from these observations the effective surface albedo, one has to compute the total reflected planetary flux, integrated over the whole solar spectrum, over all the viewing angles, and over time. A transformation from top of the atmosphere to the surface is also required. In this presentation the implications of atmospheric corrections, bidirectional reflectance, and transformations from narrow spectral bands to the total solar spectrum for albedo derivations will be addressed. Also, the various attempts to derive surface reflectivity and surface albedo from satellites will be reviewed.