Monitoring Change in the Arctic - Satellite-based Applications on Climate Change - page 132

improve reanalysis wind fields. A 30-year data set of AVHRR polar winds was
created for climate studies and used in reanalyses (Dworak and Key 2009 ). An
analysis of this historical AVHRR wind product has shown that the AVHRR winds
were, on average, slower in regions of positive vorticity (troughs and cyclones) and
faster in regions of negative vorticity (ridges and anticyclones) than the European
Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-40).
Furthermore, AVHRR is noticeably faster than ERA-40 in jet streaks, an indication
that AVHRR has stronger winds in jet streams overall. Therefore, the use of the
historical AVHRR wind product in future reanalyses should result in more accurate
wind fields (Fig. 9.5 ).
9.3 Clouds
Clouds affect Arctic climate primarily though the absorption, emission, and scat-
tering of radiation (Wang and Key 2003 ; Wang and Key 2005a , b ). Cloud detection
and characterization play a crucial role in satellite retrievals of other climate
variables. However, the detection of clouds in the polar regions is arguably more
difficult than any place else on Earth. Clouds are often warmer than the surface due
to ubiquitous low-level temperature inversions. In addition to a low thermal con-
trast between clouds and the surface, clouds, snow, and ice have similar reflectances
in the visible portion of the spectrum (Key and Barry 1989 ). Nevertheless,
reasonably accurate cloud detection can be done with a variety of spectral and
temporal tests optimized for high-latitude conditions (Frey et al. 2008 ). Cloud
particle phase uses near-infrared reflectances (daytime) and infrared brightness
temperature differences to separate ice and liquid (“water”) clouds (Key and Intrieri
2000 ). Cloud optical depth and particle effective radius retrievals use absorbing and
nonabsorbing wavelengths, where the absorbing wavelength is more sensitive to
particle size and the nonabsorbing wavelength is more sensitive to optical depth.
Cloud temperature is calculated from the infrared window brightness temperature,
adjusted for surface emission if the cloud transmittance is greater than 1%. For
more algorithm details, see Key ( 2002 ).
There are two major satellite-derived, multi-decadal, polar-specific data sets that
can be used to monitor and study atmospheric characteristics in the polar regions:
the AVHRR Polar Pathfinder (APP) (Fowler et al. 2000 ; Meier et al. 1997 ) and the
TOVS Path-P products. Other global data sets such as the International Satellite
Cloud Climatology Project (ISCCP) cloud data set (Rossow et al. 1996 ) and
PATMOS-x can also be employed, but they are not optimized for polar studies,
and the uncertainties are generally higher than in the APP and TOVS Path-P
products. The APP data set was extended (hereinafter “APP-x”) to include the
retrievals of cloud fraction, cloud optical depth, cloud particle phase and size, cloud
top temperature and pressure, surface skin temperature, surface broadband albedo,
and radiative fluxes as well as cloud radiative effects (“cloud forcing”) (Wang and