Compression and Conditional Emulation of Climate Model Output

TL;DR

This paper introduces a statistical compression and emulation method for climate model data that efficiently reduces storage needs while preserving the ability to generate realistic data and quantify uncertainties.

Contribution

It presents a novel approach combining summary statistics and conditional statistical models for effective compression and emulation of climate datasets.

Findings

Accurately models spatial nonstationarity in temperature data

Achieves significant data compression with preserved variability

Enables fast data reconstruction and uncertainty quantification

Abstract

Numerical climate model simulations run at high spatial and temporal resolutions generate massive quantities of data. As our computing capabilities continue to increase, storing all of the data is not sustainable, and thus it is important to develop methods for representing the full datasets by smaller compressed versions. We propose a statistical compression and decompression algorithm based on storing a set of summary statistics as well as a statistical model describing the conditional distribution of the full dataset given the summary statistics. The statistical model can be used to generate realizations representing the full dataset, along with characterizations of the uncertainties in the generated data. Thus, the methods are capable of both compression and conditional emulation of the climate models. Considerable attention is paid to accurately modeling the original dataset--one year of daily mean temperature data--particularly with regard to the inherent spatial nonstationarity in global fields, and to determining the statistics to be stored, so that the variation in the original data can be closely captured, while allowing for fast decompression and conditional emulation on modest computers.