Work

MSU Disentanglement Analysis Software

Year: 2022

Type: article

Abstract: This software is used to disentangle the forced-versus-unforced components of tropospheric temperature change over the satellite era (after 1979) using maps of surface temperature change as a predictor. In general, the software assembles training datasets (from pre-computed surface temperature trend maps and domain averaged tropospheric warming rates), trains statistical/machine learning (ML) algorithms, applies the trained statistical/ML model to climate model data and observations, and then saves the results. A leave-one-out approach is used in which the statistical/ML models are iteratively trained on (N- 1) climate model and then applied to the remaining climate model (and observations). Each model includes a large ensemble (i.e., >10) of model simulations. The software relies on scikit-learn ridge regression, PLS regression, and neural network algorithms.

Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)

DOI: 10.11578/dc.20221017.3

Author: Po-Chedley, Stephen

Institution: Lawrence Livermore National Laboratory

Language: English

Topic: Climate variability and models

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: green

Funder: Office of Infrastructure and Environment