CondiDiag1.0: a flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM)

Abstract. Numerical models used in weather and climate prediction take into account

a comprehensive set of atmospheric processes (i.e., phenomena) such as the

resolved and unresolved fluid dynamics, radiative transfer,

cloud and aerosol life cycles, and mass or energy exchanges with

the Earth's surface.

In order to identify model deficiencies and improve predictive skills,

it is important to obtain process-level understanding of the

interactions between different processes.

Conditional sampling and budget analysis are powerful tools

for process-oriented model evaluation, but they often require

tedious ad hoc coding and large amounts of instantaneous model output,

resulting in inefficient use of human and computing resources.

This paper presents an online diagnostic tool that addresses

this challenge by monitoring model variables in a generic manner

as they evolve within the time integration cycle. The tool is convenient to use.

It allows users to select sampling conditions and

specify monitored variables at run time.

Both the evolving values of the model variables and their increments

caused by different atmospheric processes can be monitored and archived.

Online calculation of vertical integrals is also supported.

Multiple sampling conditions can be monitored in a single simulation

in combination with unconditional sampling.

The paper explains in detail the design and implementation of the tool in

the Energy Exascale Earth System Model (E3SM) version 1.

The usage is demonstrated through three examples:

a global budget analysis of dust aerosol mass concentration,

a composite analysis of sea salt emission and its dependency

on surface wind speed,

and a conditionally sampled relative humidity budget.

The tool is expected to be easily portable to

closely related atmospheric models that use the same or

similar data structures

and time integration methods.