TY  - COMP
TI  - MSU Disentanglement Analysis Software
AB  - 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.
AU  - Po-Chedley, Stephen
DO  - https://doi.org/10.11578/dc.20221017.3
UR  - https://www.osti.gov/doecode/biblio/95418
CY  - United States
PY  - 2022
DA  - 2022-08-30
LA  - English
C1  - Research Org.: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
C2  - Sponsor Org.: USDOE National Nuclear Security Administration (NNSA)
C4  - Contract Number: AC52-07NA27344
ER  -