Skip to content

ARM diagnostics

Overview

The ARM data-oriented metrics and diagnostics package (ARM Diags) was developed to facilitate the use of ARM data in climate model evaluation and model intercomparison (Zhang et al., 2020)1. It includes ARM data sets, compiled from multiple ARM data products, and a Python-based analysis toolkit for computation ad visualization. It also includes simulation data from models participating the CMIP, which allows climate-modeling groups to compare a new, candidate version of their model to existing CMIP models. The ARM Diags has been applied in several model evaluation studies to help address a range of issues in climate models (Zheng et al., 2023;2 Emmenegger et al., 2022;3 Zhang et al., 20184). The Majority of ARM Diags sets are ported into E3SM Diags (Zhang et al., 2022)5 for routine evaluation of the model.

To enable the use of ARM Diags

To enable using ARM Diags for a simulation, often, a new tape that output at high-frequency over limited-area (nearest grid box to supported ARM site) needs to be included in the namelist file, an example as follows:

fincl7 = 'PS','Q','T','Z3','CLOUD','CONCLD','CLDICE',
   'CLDLIQ','FREQR','REI','REL','PRECT','TMQ','PRECC',
   'TREFHT','QREFHT','OMEGA','CLDTOT','LHFLX','SHFLX',
   'FLDS','FSDS','FLNS','FSNS','FLNSC','FSDSC','FSNSC',
   'AODVIS','AODABS','LS_FLXPRC','LS_FLXSNW',
   'LS_REFFRAIN','ZMFLXPRC','ZMFLXSNW','CCN1','CCN2',
   'CCN3','CCN4','CCN5','num_a1','num_a2','num_a3',
   'num_a4','so4_a1','so4_a2','so4_a3','AREL','TGCLDLWP',
   'AQRAIN','ANRAIN','FREQR','PRECL','RELHUM'
fincl7lonlat='262.5e_36.6n','203.4e_71.3n','147.4e_2.0s',
   '166.9e_0.5s','130.9e_12.4s','331.97e_39.09n'

Note that in this example fincl7 should set to write output at hourly (nhtfrq = -1). And here additional variables are included for ARM simulator analysis. The ARM site information is shown below:

    "sgpc1": ["97.5W 36.4N Oklahoma ARM"],

    "nsac1": ["156.6W 71.3N Barrow ARM"],

    "twpc1": ["147.4E 2.1S Manus ARM"],

    "twpc2": ["166.9E 0.5S Nauru ARM"],

    "twpc3": ["130.9E 12.4S Darwin ARM"],

    "enac1": ["28.0E 39.1N Graciosa Island ARM"],

Diagnostics and metrics currently implemented in the ARM Diags

Statistical Metrics Variables Time sampling
Line plots and Taylor diagrams for annual cycle variability of each variable Precipitation, column water vapor, surface energy budget components, near-surface temperature and specific humidity, surface pressure, total cloud fraction, and aerosol optical depth. Monthly mean
Contour and vertical profiles of annual cycle and diurnal cycle of cloud fraction Vertical profiles of cloud fraction Hourly mean
Line and harmonic dial plots of diurnal cycle of precipitation Surface precipitation rate Hourly mean
Probability density function (PDF) plots of precipitation rate Surface precipitation rate Hourly mean
CCN Annual Cycles CCN number concentrations at 0.1%, 0.2%, 0.5% and 1.0% supersaturation levels Hourly mean
Aerosol Annual Cycles Total aerosol number concentration Hourly mean
Aerosol Chemical Annual Cycles Organic, sulfate, nitrate, ammonium, chloride mass concentration Hourly mean
Process-oriented metrics Variables Time sampling
Convection Onset 1. Surface precipitation rate
2. Column Precipitable Water Vapor		 Hourly mean
Aerosol-CCN Activation 1. Total aerosol number concentration
2. CCN number concentrations at different supersaturation levels (0.1%, 0.2%, 0.5% and 1.0)		 Hourly mean

  1. C. Zhang, S. Xie, C. Tao, S. Tang, T. Emmenegger, J. D. Neelin, K. A. Schiro, W. Lin, and Z. Shaheen. The ARM Data-Oriented Metrics and Diagnostics Package for Climate Models: A New Tool for Evaluating Climate Models with Field Data. Bulletin of the American Meteorological Society, 101(10):E1619–E1627, October 2020. URL: https://journals.ametsoc.org/view/journals/bams/101/10/bamsD190282.xml (visited on 2024-03-29), doi:10.1175/BAMS-D-19-0282.1. ↩

  2. Xiaojian Zheng, Cheng Tao, Chengzhu Zhang, Shaocheng Xie, Yuying Zhang, Baike Xi, and Xiquan Dong. Assessment of CMIP5 and CMIP6 AMIP Simulated Clouds and Surface Shortwave Radiation Using ARM Observations over Different Climate Regions. Journal of Climate, 36(24):8475–8495, November 2023. URL: https://journals.ametsoc.org/view/journals/clim/36/24/JCLI-D-23-0247.1.xml (visited on 2024-03-29), doi:10.1175/JCLI-D-23-0247.1. ↩

  3. Todd Emmenegger, Yi-Hung Kuo, Shaocheng Xie, Chengzhu Zhang, Cheng Tao, and J. David Neelin. Evaluating Tropical Precipitation Relations in CMIP6 Models with ARM Data. Journal of Climate, 35(19):6343–6360, October 2022. URL: https://journals.ametsoc.org/view/journals/clim/35/19/JCLI-D-21-0386.1.xml (visited on 2024-03-29), doi:10.1175/JCLI-D-21-0386.1. ↩

  4. Chengzhu Zhang, Shaocheng Xie, Stephen A. Klein, Hsi‐yen Ma, Shuaiqi Tang, Kwinten Van Weverberg, Cyril J. Morcrette, and Jon Petch. CAUSES: Diagnosis of the Summertime Warm Bias in CMIP5 Climate Models at the ARM Southern Great Plains Site. Journal of Geophysical Research: Atmospheres, 123(6):2968–2992, March 2018. URL: https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2017JD027200 (visited on 2024-03-29), doi:10.1002/2017JD027200. ↩

  5. Chengzhu Zhang, Jean-Christophe Golaz, Ryan Forsyth, Tom Vo, Shaocheng Xie, Zeshawn Shaheen, Gerald L. Potter, Xylar S. Asay-Davis, Charles S. Zender, Wuyin Lin, Chih-Chieh Chen, Chris R. Terai, Salil Mahajan, Tian Zhou, Karthik Balaguru, Qi Tang, Cheng Tao, Yuying Zhang, Todd Emmenegger, Susannah Burrows, and Paul A. Ullrich. The E3SM Diagnostics Package (E3SM Diags v2.7): a Python-based diagnostics package for Earth system model evaluation. Geoscientific Model Development, 15(24):9031–9056, December 2022. URL: https://gmd.copernicus.org/articles/15/9031/2022/ (visited on 2024-03-29), doi:10.5194/gmd-15-9031-2022. ↩