Analysis scripts and dataset for Zhang et. al. (2023)
Description
This archive contains post-processed data and scripts for analyses in Zhang et al. (2023) "A Machine Learning Bias Correction of Large-scale Environment of Extreme Weather Events in E3SM Atmosphere Model". These data are derived from the model outputs from the simulations conducted with DOE's E3SM Atmosphere Model Version 2 (EAMv2). There are two groups of simulations. The first group consists of three model simulations were conducted with EAMv2, including one preset-day and two pseudo-global warming simulations with prescribed perturbations on sea surface temperature (SST) and sea ice concentrations (SICs). The second group contains the three same simulations that were post-processed with a machine learning bias correction model. A detailed description of the model and simulations can be found in Zhang et. al. (2023).
Files
Files
(1.7 GB)
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md5:95d69d9f2f0f4db9ced246abb65f4e8d
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md5:f16d62ca53578fdf231481a79d3ccac6
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md5:1a82e9f64c7bc933024d3de0967d9062
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md5:b8814d070547d0c40d61a1ac8734caea
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65.8 MB | Download |