Modern precipitation analysis for paleoclimate scientists

The ability to correctly interpret, and in some cases quantify, paleoclimate records of past hydrological changes requires a full understanding of modern climate variability in both the spatial and temporal domains. For example, precipitation isotopes are influenced by moisture source and along trajectory rainout as well as regional rainfall amount. In the temporal domain, knowing how seasonal and interannual variability changes local precipitation dynamics is important in moving from simplistic wet/dry interpretations of proxies towards more dynamical reconstructions. Finally, understanding the response of proxy systems to changes helps reveal the sensitivity of different proxies to different parts of the climate system and at different times and timescales. These processes can be different for each proxy system in every location.

To aid the proper identification of which parts of the climate system influence paleoclimate proxies in a particular location we present a toolbox for modern climate analyses geared towards paleoclimate scientists wishing to understand climate variability at their site. Coded in Python the toolbox combines functionalities of Hysplit, Pysplit, WebWimp, KNMI Climate Explorer and others. Spatial features include identifying source moisture regions and cumulative rainout using trajectory analysis and precipitation correlation analysis to understand the area a proxy record represents. Temporal assessments of seasonality, ENSO variability and soil moisture fluxes help identify the likely time periods and timescales being recorded by proxy records. We demonstrate the use of this toolbox on several cave sites in the Dominican Republic as we seek to understand what speleothem carbonate stable isotopes are recording in the climate system.