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Global climate models suggest that the arctic climate may be
particularly sensitive to perturbations caused by the build up of
green house gases. The arctic temperature response to a CO2
doubling is predicted to be 2 to 3 times larger than the global
mean. (IPCC 1990). Surface albedo and sea ice coverage are coupled to
cloud cover in complex feedback loops (Curry et al. 1996). Small
changes in radiation balance may induce large changes in ice
cover. Studies of past climate suggest that changes in ice cover may
induce changes in ocean circulations which produce major climate
changes (Broecker et al. 1988). Meanwhile, as climate researchers
have become aware of the importance of the arctic, model
inter-comparisons have shown large model-to-model variations in
predictions of arctic climate (Tao et al. 1996).
The radiation budget of the arctic is strongly modulated by the
presence of clouds. Global circulation models have little chance of
accurately predicting the arctic climate without high fidelity
parameterizations describing cloud radiative properties. Current
models show large variations in predicted arctic cloud cover. A
comparison of average summer cloud cover predicted by 19 GCM models
show values between 30% and 98% (Tao et al. 1996). It is difficult to
evaluate model predictions because existing arctic cloud climatologies
contain large uncertainties. Detailed observations on the complete
annual cycle of cloud properties including the ice/water phase of
cloud particles are needed to gain an understanding of the radiation
balance in the arctic (Curry et al., 1996, Pinto et al. 1999).
It also appears that arctic clouds are particularly susceptible to
modification by air pollution. Hobb and Rangno, 1998, show that cloud
droplet sizes and number density are strongly correlated with subcloud
aerosol content. Information on arctic cloudiness is limited by a
number of factors including the low density of observers, inconstant
reporting of clear sky ice-crystal precipitation(Curry et al. 1996),
the difficulty of discriminating between clouds and the
low-temperature, high-albedo surface with satellite observations
(Rossow and Garder 1993), the the visual difficulty of characterizing
cloud cover during the long polar night (Hann et al. 1995), and the
lack of instrumentation for measuring the vertical profiles of optical
depth in clouds and for determining the ice/water phase of cloud
We are constructing a new version of the University of Wisconsin High
Spectral Resolution Lidar (HSRL) for routine, untended measurement of
arctic clouds and aerosols. The HSRL eliminates a major weakness of
all lidars which have been deployed in the arctic. Traditional lidars
are unable to seperate the effects of varying backscatter cross
section from variations in the extinction suffered by the lidar pulse
on its path to the scattering volume and back to the receiver (see Limitations of Traditional Lidars.
). Any attempt to measure optical depth or backscatter cross section
must rely on assumptions about the relationship between backscatter
intensity and extinction. It can be shown that the assumptions
commonly used are frequently invalid. As a result, it is generally not
possible to calculate believable error estimates for optical depths or
backscatter cross sections. This also makes it difficult to implement
automated processing of traditional lidar data.
In contrast, HSRL data is amenable to automated processing because it
is calibrated by direct reference molecular scattering. The system
measures a separate molecular scattering profile along with the
standard lidar profile. This provides an absolute calibration and
eliminates the need to make assumptions about the nature of the
scattering media. A brief explanation is provided in HSRL Theory.
Curry, J. A., W. B. Rossow, D. Randall, and J. L. Schramm, 1996:
An overview of arctic cloud and radiation characteristics, J. of
Climate, 9, 1731-1764.
DeSolver, D. H., W. H. Smith, P. Piironen, E. W. Eloranta, 1998: A
Methodology for Measuring Cirrus Cloud Visible to Infrared Spectral
Optical Depth Ratios, J. Atmospheric and Oceanic Technology,
Eberhard, W. L., J. M. Interieri, R. J. Alvarez II, and C. J. Grund,
1998: Cloud cover and phase during arctic winter from DABUL lidar, it
Proceedings of the Eight Atmospheric Radiation Measurement (ARM)
Science Team Meeting, Tucson, AZ, March 23-27, 1998, 237-239.
Eloranta, E. W., P. Piironen, 1996: Measurements of cirrus cloud optical
properties wiht the University of Wisconsin high spectral resolution
lidar, it Advances in Atmospheric Remote Sensing with Lidar,
edited A. Ansmann, R. Neuber, R. Raioux and U. Wandinger,
Springer-Verlag, New York, Berlin, Heidelberg.
Eloranta, E. W., 1998: A practical model for the calculation
of multiply scattered lidar returns, Appl. Optics, 37, 2464-2472.
Grund, C. J. and E. W. Eloranta, 1991: The University of
Wisconsin High Spectral Resolution Lidar, Opt. Engineering. 30,
Grund, C. J. and S. P Sandberg, 1996: Depolarization and Backscatter
Lidar for Unattended Operation it Advances in Atmospheric Remote
Sensing with lidar, edited A. Ansmannn, R. Neuber, R. Rairoux and
U. Wandinger, Springer-Verlag, New York, Berlin, Heidelberg.
Hann, C. J., S. G. Warren, and J. L. London, 1995: The effect of
moonlight on observation of cloud cover at night, and application to cloud
climatology, J. of Climate, 8, 1429-1445.
Hobbs, P. V. and A. L. Rangno, 1998: Microstructures of low and
middle-level clouds over the Beaufort Sea, Q. J. R. Meterol. Soc.,
IPCC, 1990: Climate Change: The IPCC Scientific Assesment.
J. T. Houghton, G. J. Jenkins, and J. J. Ephraums, Eds., Cambridge
University Press, 365p.
Klett, J. D., 1981: Stable analytical inversion solution for
processing lidar returns, App. Optics 20, 211-220.
Matrosov, S., A. Heymsfield, R. Kropfli, Snider, Martner,
P. Piironen, and E.W. Eloranta, 1998: Comparisons of ice and cloud
properties obtained by remote and direct methods, Journal of
Atmospheric and Oceanic Technology, 15, 184-196.
Piironen, P. and E. W. Eloranta, 1994: Demonstration of
a high spectral resolution lidar based on an iodine absorption
filter, Opt. Letters 19, 234-236.
Piironen, P., 1995: A High Spectral Resolution Lidar Based on
an Iodine Absorption Filter, Phd Thesis, University of Joensuu, Joensu,
Finland, pp 113.
Pinto, J. O., J. A. Curry, and A. H. Lynch, 1998: Single-column model
simulation of SHEBA in winter, Proceedings of the Eight Atmospheric
Radiation Measurement (ARM) Science Team Meeting, Tucson, AZ, March 23-27,
Rossow, W. B., and L. C. Garder, 1993: Validation of ISCCP cloud
detections, J. of Climate, 6, 2370-2393.
Shipley, S. T., D. H. Tracy, E. W. Eloranta, J. T.
Trauger, J. T. Sroga, F. L. Roesler, and J. W. Weinman, 1983: High Spectral
Resolution Lidar to Measure Optical Scattering Properties of Atmospheric
Aerosols, Appl. Optics, 22, 3716-3724.
Tao, X., J. E. Walsh and W. L. Chapman, 1996: An assesment of
global climate model simulations of arctic air temperatures,
J. of Climate, 9, 1060-1076.
Wylie, D. P., P. Piironen, W. W. Wolf and E. W. Eloranta, 1995:
Understanding Satellite Cirrus Cloud Climatologies with Calibrated Lidar
Optical Depths, J. of the American Meteorological Society, 52,
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UW Lidar // January 24, 1996 //