Our present state of understanding of the relationship between upper tropospheric clouds and the climate system is insufficient due primarily to a lack of detailed measurements that span the continuum of cloud genre and dynamical forcing. This shortcoming will be addressed soon with the A-Train satellite constellation that will include the CALIPSO satellite, CloudSat, and Aqua. With a primary goal of developing understanding of the physical processes that maintain cirrus, we propose to exploit the natural synergy of the A-Train measurements to investigate how cirrus are coupled to and evolve within the large scale atmosphere. The basic steps to accomplish this task are as follows,
- Using the A-Train products compiled by the Cloudsat science team, identify cirrus events, and extract from the operational data streams the set of measurements that will allow for determination of the macroscale and microphysical cloud properties
- Apply a suite of new cloud property retrieval algorithms to the cirrus event profiles. Using a common set of assumptions, forward model equations, empirical relationships, and inversion framework, these algorithms have been developed specifically for cirrus measured by the A-Train instruments.
- Using geostationary satellite imagery, construct forward and backward trajectories of the cirrus fields observed by the A-Train and examine the temporal tendencies in the microphysical properties of the cloud field using algorithms applied to multispectral geostationary data along the forward and backward trajectories.
- Using available analysis data sets, combine the cirrus events with the large scale dynamics and build a global database of cirrus events from which dynamical and microphysical composites can be constructed. These composites will then be used as the basis for modeling studies that will seek to understand the physical processes that govern the maintenance of cirrus through their lifecycle. Ultimately this understanding will be used in the critical evaluation of cirrus parameterizations in global models.
A second objective of this proposal, is the use of A-Train clouds in the evaluation of GCM-predicted clouds from the NCAR CAM3 GCM. The CAM3 is initialized every 24 hours by the U.S. DOE Climate Change Prediction Program (CCPP) ARM Parameterization Testbed (CAPT) so that model-predicted clouds can be evaluated against observations. We will extract the model output at grid cells along the A-Train track. This will allow development of both case studies and statistics over climatically important regions. This work merging modeling at various scales with analysis of a unique global dataset will allow for greater understanding that will influence climate models through improved physically-based parameterizations.
