A reconstructed total precipitation framework

Matthew R Igel,Joseph A Biello

doi:10.1038/s41612-019-0090-8

Matthew R Igel, Joseph A Biello

Open Access

https://doi.org/10.1038/s41612-019-0090-8

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Abstract

Climate change is expected to alter the statistical properties of precipitation. There are two related but consequentially distinct theories for changes to precipitation that have received some consensus: (1) the time-and-space integrated global total precipitation should increase with longwave cooling as the surface warms, (2) the most intense precipitation rates should increase at a faster rate related to the increase in vapor saturation. Herein, these two expectations are combined with an analytic integration of three conceptually independent properties of the tropical hydrological cycle, the intensity, probability, and frequency of precipitation. The total precipitation in both a cloud-resolving model and tropical Global Precipitation Measurement mission data is decomposed and reconstructed with the analytic integral. By applying (1) and (2) to the precipitation characteristics from the model and observations to form a warming proxy model, it is suggested that a wide range of future distributions of precipitation intensity, probability, and frequency are possible.

Highlights

Changes due to climate warming in the intensity and location of precipitation are some of the most potentially impactful to human and ecological systems
Our knowledge of how the hydrological cycle may respond to climate warming remains incomplete and largely derived from highly parameterized models, models that exhibit commendable skill in many respects and which have been thoroughly compared to available data
To make use of these numbered postulates, we will decompose the global, long-term mean precipitation rate, I

Summary

Introduction

Changes due to climate warming in the intensity and location of precipitation are some of the most potentially impactful to human and ecological systems. Our knowledge of how the hydrological cycle may respond to climate warming remains incomplete and largely derived from highly parameterized models, models that exhibit commendable skill in many respects and which have been thoroughly compared to available data. Such a reliance on models has limited the predictions made of hydrological changes due to warming to those that climate models are capable of simulating or, more accurately, those that climate models happen to simulate. It is tempting to assume that the range of possible responses of precipitation to climate warming are completely suggested by the ensemble of models. The predicted changes are in one sense often very general and physically based, but in another sense, are rather limited in that they are the cumulative result of similar structural assumptions and parameterizations among the ensemble of models

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