The E3SM Diagnostics Package (E3SM Diags) is a Python-based Earth System Model (ESM) evaluation tool (with Python module name e3sm_diags), developed by the Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) project. E3SM Diags provides tools for evaluating E3SM native output, as well as ESM data on regular latitude longitude grids, such as output from Coupled Model Intercomparison Project (CMIP) class models. E3SM Diags is modeled after the National Center for Atmospheric Research (NCAR) Atmospheric Model Working Group (AMWG) diagnostics package. During the version 1 release, E3SM Diags established a set of core diagnostics to evaluate the mean physical climate from model simulations. As of version 2.7, more process-oriented and phenomenon-based evaluation diagnostics orchestrated from the E3SM and broader scientific communities have been implemented, such as analysis of the Quasi-biennial Oscillation (QBO), El Niño - Southern Oscillation (ENSO), streamflow, diurnal cycle of precipitation, tropical cyclones, ozone and aerosols. An in-situ dataset from DOE’s Atmospheric Radiation Measurement (ARM) program has been integrated into the package for evaluating cloud and precipitation processes. Most recently, additional evaluation to compare performance metrics against major releases of E3SM model and CMIP6 class models is implemented to benchmark and document model performance. In addition, the model versus model capability has been extended to include many more land and river variables to facilitate evaluating simulations with bio-geochemistry focus.

E3SM Diags is designed with flexibility to allow for the addition of new observational datasets and new diagnostic algorithms. It features customizable figures; streamlined installation, configuration, and execution; and multiprocessing for faster computation. An up-to-date observational data repository is maintained by its developers, where recent datasets are added to the repository as they become available. Several applications for the E3SM Diags module to fit a diverse set of use cases from the scientific community will be introduced in this presentation. New development to support aerosol cloud interaction metrics and short-term data evaluation on unstructured grids are being planned in its next phase.

This work is performed under the auspices of the U. S. Department of Energy by Lawrence Livermore National Laboratory under contract No. DE-AC52-07NA27344.