Importance Profiles for Water Vapor |
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| Authors: | Brian Mapes Arunchandra S Chandra Zhiming Kuang Paquita Zuidema |
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| Institution: | 1.Rosenstiel School of Marine and Atmospheric Sciences,University of Miami,Miami,USA;2.Department of Earth and Planetary Sciences, School of Engineering and Applied Sciences,Harvard University,Cambridge,USA |
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| Abstract: | Motivated by the scientific desire to align observations with quantities of physical interest, we survey how scalar importance functions depend on vertically resolved water vapor. Definitions of importance begin from familiar examples of water mass I m and TOA clear-sky outgoing longwave flux I OLR, in order to establish notation and illustrate graphically how the sensitivity profile or “kernel” depends on whether specific humidity S, relative humidity R, or ln(R) are used as measures of vapor. Then, new results on the sensitivity of convective activity I con to vapor (with implied knock-on effects such as weather prediction skill) are presented. In radiative-convective equilibrium, organized (line-like) convection is much more sensitive to moisture than scattered isotropic convection, but it exists in a drier mean state. The lesson for natural convection may be that organized convection is less susceptible to dryness and can survive and propagate into regions unfavorable for disorganized convection. This counterintuitive interpretive conclusion, with respect to the narrow numerical result behind it, highlights the importance of clarity about what is held constant at what values in sensitivity or susceptibility kernels. Finally, the sensitivities of observable radiance signals I sig for passive remote sensing are considered. While the accuracy of R in the lower free troposphere is crucial for the physical importance scalars, this layer is unfortunately the most difficult to isolate with passive remote sensing: In high emissivity channels, water vapor signals come from too high in the atmosphere (for satellites) or too low (for surface radiometers), while low emissivity channels have poor altitude discrimination and (in the case of satellites) are contaminated by surface emissions. For these reasons, active ranging (LiDAR) is the preferred observing strategy. |
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