Return to the Index.

Scientific Motivation

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 particles.

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.

References

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, 16, 251-262.

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, 6-12.

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., 124, 2035-2071.

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, 1998, 605-610.

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, 4327-4343.

Return to the Index.

UW Lidar // January 24, 1996 // root@lidar.ssec.wisc.edu