ARM Diagnostics for Climate Model Evaluation (ADCME)

A Python-based diagnostics package is currently being developed by the ARM Infrastructure Team to facilitate the use of long-term high-frequency measurements from the ARM user facility in evaluating the regional climate simulation of clouds, radiation, and precipitation. This diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots using a fully automated framework (Zhang et al., 2020). Users can then compare their model simulations directly with the ARM observations. The Coupled Model Intercomparison Project (CMIP) model data sets are also included in the package to enable model intercomparison (Zhang et al., 2018; Zheng et al., 2023). The ensemble means of CMIP models can be served as a reference for individual models as well.

The ARM observational data constitute the core content of the diagnostics package. These data products include three types of data sets: 1. Observational data: we use long-term data sets available at SGP, NSA, TWP, ENA, and MAO to build representative climatology. 2. CMIP5 and CMIP6 climate model simulation data sets: these are auxiliary data sets for climate model evaluation. 3. Test model data: sample input files required to run the diagnostics package. 

The Python-based diagnostics package is available at: https://github.com/ARM-DOE/arm-gcm-diagnostics.

In this v4.0 release, we included datasets for newly-added metrics on land-atmosphere coupling at the SGP site.

This data set serves as part of the ARM data-oriented diagnostics package (ARMDIAG) for climate model simulations. The data were prepared for facilitating the use of ARM data sets in climate model evaluation. The data were collected from multiple ARM instrument datastreams and VAP datastreams. Please refer to Table 1 and Table 2 in the technical report. The data sets will be updated as needed (i.e., update of original datastream, new data available, adding more sites).