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Publication Date
2 March 2021

Integration of ARM Diagnostics into E3SM Diags

Diurnal cycle of March-April-May mean at the South Great Plains Site, comparing a one year experimental run with 3-hourly output (Black) and hourly ARM data (red). Solid lines are first harmonics of the composite diurnal cycle.

The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility provides high-frequency, long-term in-situ measurements that are invaluable in developing and improving climate models. A metrics and diagnostics package called ARM-Diags was developed to further facilitate the use of ground-based ARM measurements in evaluating climate models. With growing interest in improving parameterizations using process-oriented metrics and diagnostics, ARM observations play an important role in the way cloud and precipitation processes are represented in climate models.

E3SM-Diags is a Python-based diagnostics package developed within the E3SM project and used routinely in E3SM development. Most diagnostics suites in E3SM-Diags have focused so far on global analysis. Incorporating comprehensive analyses using observations from selected ARM locations from different climate regions will complement current evaluation capability. Hence, the upcoming E3SM-Diags new release (v2.4.0) integrates ARM-Diags, allowing users to routinely test E3SM output at multiple ARM stations against climatology and time-series files generated from ARM data. Some features from ARM-Diags have already been used in development of E3SM’s physical parameterization.

The v2.4.0 E3SM-Diags release includes the seasonal mean and annual cycle of a range of atmospheric variables from ARM observations including surface air temperature, precipitation, radiation fluxes and surface turbulent fluxes, the metrics of convective onset (Fig. 1), and the diurnal cycle of both cloud fraction and precipitation (Fig. 2). These diagnostics will be run routinely to help the E3SM team evaluate the model’s convection schemes and particularly to understand how the parameters and related physical processes are represented in the model. 


This work is conducted by the ARM infrastructure team at LLNL and the E3SM-Diags team, jointly supported by DOE’s ARM and ESMD programs. Special thanks to David Neelin’s group at UCLA who provided the convection onset diagnostics and Wuyin Lin at BNL who helped finalize the E3SM model configuration and tested its capability for limited region output.

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