{"metadata":{"code_id":95418,"site_ownership_code":"LLNL","open_source":true,"repository_link":"https://github.com/LLNL/MDAS","project_type":"OS","software_type":"S","official_use_only":{},"developers":[{"email":"","orcid":"","first_name":"Stephen","last_name":"Po-Chedley","middle_name":"D","affiliations":["Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)"]}],"contributors":[],"sponsoring_organizations":[{"organization_name":"USDOE National Nuclear Security Administration (NNSA)","funding_identifiers":[],"primary_award":"AC52-07NA27344","DOE":true}],"contributing_organizations":[],"research_organizations":[{"organization_name":"Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)","DOE":true}],"related_identifiers":[],"award_dois":[],"release_date":"2022-08-30","software_title":"MSU Disentanglement Analysis Software","acronym":"MDAS","doi":"https://doi.org/10.11578/dc.20221017.3","description":"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.","programming_languages":[],"version_number":"0.1","country_of_origin":"United States","project_keywords":[],"licenses":["MIT License"],"recipient_org":"S&T/PLS","site_accession_number":"LLNL-CODE-840617","date_record_added":"2022-10-17","date_record_updated":"2022-10-17","is_file_certified":false,"last_editor":"wasim1@llnl.gov","is_limited":false,"links":[{"rel":"citation","href":"https://www.osti.gov/doecode/biblio/95418"}]}}