Geoscience Reference
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(
Section 5.6
), processes that maintain the low-level Arctic temperature inversion
(
Section 5.9.2
), and the application of a radiative transfer model to a grid array to
provide fields of surface radiation fluxes and surface albedo from AVHRR data (the
APP-x products; see, for example
Figure 5.4
).
Another good example is provided by the study of Zhang et al. (
2001
), who
examined relationships between atmospheric thickness (1,000 to 500 hPa), mean
atmospheric temperature (surface to 10 km), and total atmospheric water vapor con-
tent (precipitable water, surface to 10 km) on the atmospheric downward longwave
flux at the surface at two sites in Alaska (Barrow and McGrath) during the snow-
melt period. Radiation fluxes in this model were computed using a one-dimensional
atmospheric radiative transfer model for shortwave and longwave radiation com-
bined with a surface energy balance equation. The radiative transfer model (Stamnes
et al.,
1988
) has routines to include the effects of clouds, Arctic haze, carbon diox-
ide, water vapor, ozone and snow. Snow optical properties are based on assumed
mean snow grain radii (200 µm) and density (350 kg m
−3
). The model was driven
by twice-daily atmospheric pressure, temperature, and water vapor concentrations
from radiosonde observations for the years 1980 through 1991.
Figure 9.1
shows some of the results for Barrow. The most striking feature is
the impact of precipitable water in the downward longwave radiation. Longwave
radiation increases logarithmically with an increase in precipitable water. The
impact on the downward longwave radiation is hence much greater at low precip-
itable water values. This compares to a linear relationship between the longwave
flux and mean atmospheric temperature (and the 1,000- to 500-hPa thickness). The
conclusion drawn from this modeling study is that the impacts of changes in pre-
cipitable water (especially at low values of precipitable water) are much greater
than those of temperature. There are a number of empirical formulae to estimate
the downward longwave flux.
Figure 9.1a
shows the longwave flux estimated from
the formula of C. Parkinson and W. Washington (
1979
) based on the near-surface
temperature. Compared to results from the radiative transfer model, the Parkinson
and Washington formula underestimates the radiation flux when the near surface
temperature is relatively high and overestimates the flux when the near-surface tem-
perature is comparatively low.
We next turn to the study of C. Bitz et al. (
1996
). The focus of this effort was
to model the natural variability of the Arctic climate system using a single-col-
umn energy balance model of the atmosphere, sea ice, and upper-ocean. Variability
was induced by forcing the model with synthetic data, including realistic random
perturbations of incoming solar radiation, the meridional transport of atmospheric
energy into the column, cloudiness, and snowfall.
Figure 9.2
provides a schematic.
Conduction of heat through the ice is treated such that the number of ice layers
remains fixed while total ice thickness varies (consequently the layer thicknesses
vary). The sea ice is modeled as a slab with no leads, with the upper ocean treated
as a slab mixed layer. When the ice surface is snow-free, solar radiation penetrates
the sea ice, heating its interior and the ocean mixed layer. Surface albedo depends
on sea ice thickness and snow thickness. The atmospheric model has eighteen lay-
ers and includes heating rates from different atmospheric gases and atmospheric
