95418 LLNL true https://github.com/LLNL/MDAS OS S 2022-08-30 MSU Disentanglement Analysis Software MDAS https://doi.org/10.11578/dc.20221017.3 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. 0.1 United States S&T/PLS LLNL-CODE-840617 2022-10-17 2022-10-17 false wasim1@llnl.gov false Stephen Po-Chedley D Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States) USDOE National Nuclear Security Administration (NNSA) AC52-07NA27344 true Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States) true MIT License