E3SM Diagnostics Package v2

Contents:

• E3SM Diagnostics Package v2
• Input Data Requirement
• Quick Guides
  • General quick guide for running e3sm_diags v2
  • Acme1 quick guide for running e3sm_diags v2
  • Compy quick guide for running e3sm_diags v2
  • Cori quick guide for running e3sm_diags v2
  • Old Quick Guides for v1
• Examples
  • Running examples v2
  • Model Time-series vs Model Time-series: Historical H1 (2011-2013) vs Historical H1 (1850-1852)
  • Model Time-series vs Model Time-series with CMIP data
  • Model Time-series vs Observation Time-series with CMIP data
  • Model Climatology vs Model Climatology
  • Model Climatology vs Observation Climatology
  • Observation Climatology vs Observation Climatology
• Installation
  • Activate e3sm_unified environment
  • Installation in a conda environment
• Configuration and Running
  • Configuration
  • Running
• Defining Parameters
  • Ways to define parameters
  • Selecting certain parameters
• Available Parameters
  • Parameters for diagnostics
  • Parameters for plotting
• Colormaps
  • Default
  • Precipitation
  • Wind
  • Customizing
• How to Add New Diagnostics Runs
  • Adding Derived Variables
  • Example
• Developer Guide
  • How to Add Diagnostic Sets to E3SM Diagnostics
  • How to Prepare a Release
  • Creating a New Environment from Scratch
• Contributing to E3SM Diags
  • Code
  • Documentation

Overview

This diagnostics package is constructed to support the diagnostics needs of DOE’s Energy Exascale Earth System Model (E3SM) project, formerly known as Accelerated Climate Modeling for Energy (ACME). The ultimate goal of this work is to develop a comprehensive diagnostics package that:

• integrates the basic functionality of NCAR’s AMWG diagnostics package;

• utilizes most updated observational datasets, including remote sensing, reanalysis and in-situ datasets;

• interfaces with diagnostics developed from different E3SM focus groups: atmosphere group, coupled simulation group, land group;

• interacts effectively with the PCMDI’s metrics package and the ARM diagnostics package through a unifying framework: Community Diagnostics Package (CDP).

• is flexible for user-specified diagnostics and configuration for use by other climate models.

Current State (v2 release)

Algorithm and visualization codes for latitude-longitude contour maps, polar contour maps, the accompanying summarizing table and Taylor diagram plots, pressure-latitude zonal mean contour plots, zonal mean line plots, pressure-longitude meridional mean contour plots, area mean time series plots, and Cloud Top Height-Tau joint histograms from COSP cloud simulator output. Plots can be created for annual and seasonal climatologies, and monthly mean time series.

The package also supports custom user diagnostics, by specifying plot type, desired region (global, ocean, land, etc.), pressure levels for variables with the vertical dimension.

For flexibility, the code structure cleanly separates data manipulation (reading input files, processing data, etc) from plotting functions. To satisfy specific user tastes, two graphical back-ends are available:

• matplotlib/ cartopy (mpl)

• CDAT VCS (vcs)

Additional back-ends could be implemented if the need arose.

Latitude-longitude contour map (mpl)	Polar contour map (mpl)
Pressure-latitude zonal mean contour (vcs)	Pressure-longitude meridional mean contour (mpl)
Cloud Top Height-Tau joint histograms (mpl)	Taylor Diagram (mpl)
Summary Table	Zonal mean line plot (vcs)
Area Mean time series (mpl)

Feature availability for each backend

Not all plot sets and feature are currently supported for every backend. The table below summarizes current status.

Plot set or Feature	mpl	vcs
Latitude-longitude contour maps	✔	✔
Polar contour maps	✔	✔
Pressure-latitude zonal mean contour plots	✔	✔
Pressure-longitude meridional mean contour	✔	✘ [1]
Zonal mean line plots	✔	✔
Cloud Top Height-Tau joint histograms	✔	✘ [1]
Area Mean time series plots	✔	✘ [1]
Multi-processing	✔	✔

^[1] Defaults to matplotlib instead.