%0Computer Program
%TMSU Disentanglement Analysis Software
%XThis 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.
%APo-Chedley, Stephen
%Rhttps://doi.org/10.11578/dc.20221017.3
%Uhttps://www.osti.gov/doecode/biblio/95418
%CUnited States
%D2022
%GEnglish
%2USDOE National Nuclear Security Administration (NNSA)
%1AC52-07NA27344
2022-08-30