If you have any problems related to the accessibility of any content (or if you want to request that a specific publication be accessible), please contact (email@example.com). We will work to respond to each request in as timely a manner as possible.
An Investigation Of Cloud Processes Relevant To Climate Sensitivity
AdvisorMitchell, David L
StatisticsView Usage Statistics
Based on the Fourth Assessment Report of the IPCC, cloud feedbacks are the largest source of uncertainty in predicting the Earth's climate response to CO2 radiative forcing. Various global climate models estimate that this response amplifies the initial CO2 forcing by a factor between 2 and 5. The research in this dissertation aims to better understand cloud processes that have been identified as critical for understanding cloud feedbacks. The three topics pursued are characterization of the ice fall speed in cirrus clouds, ice nucleation in cirrus clouds and ground-based remote sensing of cloud properties in ice clouds and mixed phase clouds.Studies have shown that the ice fall speed determines one of the most important climate feedback processes. The mass weighted ice fall speed (Vm) in mid-latitude cirrus clouds is computed from in-situ measurements of ice particle area and number concentration by the 2 Dimensional Stereo (2D-S) probe. Vm is parameterized in terms of cloud temperature (T) and ice water content (IWC) and also by relating Vm to ice particle effective diameter (De). Mid-latitude cirrus cloud data is also used to investigate two classes of ice nucleation: homogeneous and heterogeneous processes. Better understanding the role these two categories play in ice production is crucial for predicting cirrus cloud properties and the impact of aerosol particles on cirrus clouds in GCMs. The temperature dependence of the ice particle size distribution, number concentration-to-ice water content ratio, ice particle shape and fall speed all provide important clues revealing the relative roles of these nucleation categories.Lastly an exploratory remote sensing methodology is presented to retrieve cloud properties in ice clouds and mixed-phase clouds by using data from the Atmospheric Emitted Radiance Interferometer (AERI) and the Milli-Meter Cloud Radar (MMCR) in the Arctic.