ARM Metrics and Diagnostics to Support Climate Model Evaluation and Development
Poster PDF
Authors
Shaocheng Xie — Lawrence Livermore National Laboratory
Chengzhu Zhang — Lawrence Livermore National Laboratory
Yuying Zhang — Lawrence Livermore National Laboratory
Qi Tang — Lawrence Livermore National Laboratory
Shuaiqi Tang — Pacific Northwest National Laboratory
Xiao Chen — Lawrence Livermore National Laboratory
Category
ARM infrastructure
Description
A set of metrics, diagnostics, and tools that utilize long-term high frequency measurements from the DOE Atmospheric Radiation Measurement (ARM) program has been developed for evaluating the regional simulation of clouds, radiation and precipitation in climate models. The metrics are computed and visualized automatically in a python-based package that aims to serve as an easy entry point for evaluating climate simulations using the ARM data, as well as the CMIP5 multi-model simulations. Process-oriented diagnostics focusing on individual cloud and precipitation-related phenomena and the land-atmosphere (LA) coupling strength are developed for providing insights into model deficiencies in representing relevant physical processes. These include a convection onset diagnostic that links precipitation intensity and frequency to column water vapor in the environment and a novel diagnostic that can be used to quantify the land-atmosphere coupling strength through correlating the surface evaporative fraction and impacting land and atmosphere variables. In addition, an ARM radar simulator was developed for climate models to improve model cloud evaluation with detailed ARM radar observations. We have applied these diagnostics and tools to evaluate the representation of clouds, convection, and land-atmosphere coupling in climate models such as the DOE’s Energy Exascale and Earth System Model (E3SM) and/or CMIP5 models with data collected at ARM SGP and TWP sites. Detailed results will be presented at the meeting.
(This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.)
