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.
- Developers:
-
Po-Chedley, Stephen [1]
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Release Date:
- 2022-08-30
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Version:
- 0.1
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE National Nuclear Security Administration (NNSA)Primary Award/Contract Number:AC52-07NA27344
- Code ID:
- 95418
- Site Accession Number:
- LLNL-CODE-840617
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Country of Origin:
- United States
Citation Formats
Po-Chedley, Stephen D.
MSU Disentanglement Analysis Software.
Computer Software.
https://github.com/LLNL/MDAS.
USDOE National Nuclear Security Administration (NNSA).
30 Aug. 2022.
Web.
doi:10.11578/dc.20221017.3.
Po-Chedley, Stephen D.
(2022, August 30).
MSU Disentanglement Analysis Software.
[Computer software].
https://github.com/LLNL/MDAS.
https://doi.org/10.11578/dc.20221017.3.
Po-Chedley, Stephen D.
"MSU Disentanglement Analysis Software." Computer software.
August 30, 2022.
https://github.com/LLNL/MDAS.
https://doi.org/10.11578/dc.20221017.3.
@misc{
doecode_95418,
title = {MSU Disentanglement Analysis Software},
author = {Po-Chedley, Stephen D.},
abstractNote = {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.},
doi = {10.11578/dc.20221017.3},
url = {https://doi.org/10.11578/dc.20221017.3},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20221017.3}},
year = {2022},
month = {aug}
}