The Probable Maximum Precipitation (PMP) is the theoretical greatest depth of precipitation for a given storm duration, particular area, and geographic location.

This study examines the effects of climate change on PMP values by intercomparing CMIP5 model output from various modeling groups for the future (2071-2100) to the modeled historical period (1971-2000.) The focus of the current project examines the changes in the ratio of precipitable water to the saturation vapor pressure and the effects of potentially changing vertical velocities on the resulting precipitable water maximums.

The Python-based Climate Data Analysis Toolkit (CDAT) developed at Lawrence Livermore National Laboratory, was instrumental to this research as its functionality allowed simple methods to:

• retreive the appropriate model data
• create thirty-year climatologies of each variable starting with daily data
• manipulate the resulting data slabs into new variables such as precipitable water
• perform statistical analysis, and
• produce figures and maps for the project.

This presentation will show examples of the CDAT functionality highlighting the ease with which the work was completed and some difficulties encountered using the toolkit as well.