E3SM Diags: A Python Package for Earth System Model Evaluation
First Author
Chengzhu (Jill) Zhang
zhang40@llnl.gov
All Authors
Chengzhu (Jill) Zhang, Jean-Christophe Golaz, Tom Vo and Ryan Forsyth
Topic
Performance - Algorithms - Tools
Project
E3SM
Abstract
The E3SM Diagnostics Package (E3SM Diags) is a Python-based Earth System Model (ESM) evaluation tool developed by 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, 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. 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 biogeochemistry focus.
Most recently sets to support aerosol diagnostics were implemented under collaboration with Atmosphere Group and Aerosol Working Group, including comparison with AERONET data, aerosol budget benchmarking, mixed phase cloud partition diagrams. New sets being added, as well as current effort to migrate E3SM Diags to use xarray based tools (i.e. xCDAT), will be discussed.
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