Monitoring Change in the Arctic - Satellite-based Applications on Climate Change - page 137

There are two peaks in the annual cycle of the surface albedo for the Arctic
region north of 60 N: one in the early spring (about 60%) and the other in autumn
(about 35%). The relatively low wintertime albedo corresponds to the low-latitude
regions with less snow/ice coverage, because the dark, high-latitude areas are
excluded from the statistics. The autumn peak is due to freeze-up, but more open
water areas in autumn result in a lower albedo than in spring. Over the Arctic
Ocean, the spring maximum surface albedo is about 60%, but the autumn second
maximum albedo is only about 23%. The Arctic annual mean surface albedo has a
spatial distribution similar to surface temperature.
9.5 Radiative Fluxes and Cloud Forcing
Very few studies have been performed on the spatial and temporal distribution of
surface radiation in the Arctic. Serreze et al. ( 1998 ) studied a monthly climatology
of the global radiation (downwelling solar radiation) for the Arctic with
measurements from the drifting ice stations, but these in situ measurements do
not provide much information on spatial patterns. Can satellite data be used to
estimate surface radiation fields? The approach is to use satellite-derived cloud
and surface properties, with a radiative transfer model to calculate upwelling and
downwelling shortwave and longwave radiative fluxes. Radiative transfer models
tend to be too slow for use with satellite data on a pixel-by-pixel basis, so alterna-
tive methods are sought. One method that has been very successful is a neural-
network implementation of a two-stream radiative transfer model (Key and
Schweiger 1998 ), which is accurate and computationally efficient.
How well we can estimate the radiation budget from space depends on how well
we can estimate the quantities that directly affect it. Key et al. ( 1997a ) investigated
uncertainties of satellite-derived surface and cloud properties and surface radiation
budget at the high latitudes and how they combine into an overall uncertainty in
radiative fluxes. They concluded that the accuracy in estimating radiation budgets
from satellite is appropriate for a wide range of process studies at monthly time
scales. They found that although improvements in retrievals are desirable, currently
available methods can provide surface net radiation estimate with uncertainties
similar to those of surface-based climatologies.
Clouds affect the climate system primarily through their impact on the surface and
TOA radiation budgets. Clouds attenuate sunlight causing a decrease in the
downwelling shortwave radiation at the surface during the daytime. Clouds also
emit infrared radiation to the surface, resulting in a greater downwelling longwave
flux than in clear conditions. Therefore, clouds have a cooling effect on the surface in
the shortwave, but a warming effect in terms of infrared radiation. Overall the effect
of clouds on the radiation budget depends on the balance between shortwave
and longwave budgets. The cloud radiative effect is also commonly called “cloud