A few days ago, a colleague from UCD Mathematics, Joseph Biello (of MJO fame), and I published a paper in npj Climate and Atmospheric Science.  It’s available here.  We attempted to use well understood aspects of the statistical properties of precipitation to derive a simple model for global precipitation.  Then we populated model parameters with data from satellites and from cloud simulations.  The nice thing is that our model is analytic, and because its constructed from physically understood pieces, it’s very easy to use as a sandbox.  In the paper, we ask what possible future states of precipitation may look like given some arbitrary surface warming.  The cool (or not so cool depending on your perspective) result is that many possible future hydrologies are possible.  Yet models seems to predict a limited range of possible responses in the properties of precipitation.  This could mean two things.  1) There are some consistencies among models that are physically based that we have not identified or 2) the suite of models is not large enough to span the possible responses.  My gut feeling is that its probably a little of both.  What I think is neat about this paper is that we have created an analytic, data-driven model of the climatology of precipitation properties.  We’re hardly the first to do such a thing, but this one is mine.