Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> 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.
Highlights
Atmospheric general circulation models (AGCMs) used in climate research and weather prediction are simplified mathematical representations of the complex physical and chemical processes driving the evolution of the Earth’s atmosphere
It allows users to select sampling conditions and specify monitored variables at run time. Both 10 the evolving values of the model variables and their increments caused by different atmospheric processes can be monitored and archived
– If the metric and the quantities of interest (QoIs) are both 3D but have different numbers of vertical layers, masking will be skipped, meaning this specific QoI will be captured for output as if no conditional sampling had happened
Summary
Atmospheric general circulation models (AGCMs) used in climate research and weather prediction are simplified mathematical representations of the complex physical and chemical processes driving the evolution of the Earth’s atmosphere. While an AGCM might only calculate RHI once or a few times during each time step, a detailed budget analysis of the terms influencing RHI can provide useful insights into the atmospheric processes that contribute to or compete with ice cloud formation These types of diagnostic variables appear frequently in AGCMs, and supporting budget analyses for them would require inserting many new model variables and output, which often leads to a dilemma in source code management: that if a user throws away the ad hoc coding after their study is completed, other users interested in similar topics will need to 50 reinvent the wheel or at least re-do the coding; on the other hand, if users commit study-specific code to the model’s central repository, clutter will accumulate quickly.
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