Uploaded February 2021 | Updated September 2026, 3 weeks ago
Tipping events, or small magnitude changes with state-altering effects, are anticipated in the Arctic with the potential to disrupt the global Earth system. As a first step in quantifying uncertainties in simulated Arctic climate response, Sandia researchers performed a global sensitivity analysis using a fully coupled ultralow-resolution configuration of the Energy Exascale Earth System Model (E3SM). The parameter variations showed significant impact on the Arctic climate state with the largest impact coming from atmospheric parameters related to cloud parameterizations, as well as significant parameter interactions. To investigate the most influential factors in predicting September average Arctic sea ice extent Sandia researchers also trained machine learning models (MLMs) on observational and simulation data from five historical ensembles generated by E3SM. Monthly averaged data from June, July, and August for a selection of atmosphere, ocean, and sea ice variables were used for the training. Significant differences between feature importance in the simulation data and observational data were found that merit further investigation.
SAND2021-1403V
Tipping events, or small magnitude changes with state-altering effects, are anticipated in the Arctic with the potential to disrupt the global Earth system. As a first step in quantifying uncertainties in simulated Arctic climate response, Sandia researchers performed a global sensitivity analysis using a fully coupled ultralow-resolution configuration of the Energy Exascale Earth System Model (E3SM). The parameter variations showed significant impact on the Arctic climate state with the largest impact coming from atmospheric parameters related to cloud parameterizations, as well as significant parameter interactions. To investigate the most influential factors in predicting September average Arctic sea ice extent Sandia researchers also trained machine learning models (MLMs) on observational and simulation data from five historical ensembles generated by E3SM. Monthly averaged data from June, July, and August for a selection of atmosphere, ocean, and sea ice variables were used for the training. Significant differences between feature importance in the simulation data and observational data were found that merit further investigation.
SAND2021-1403V
