Earth System Modeling
The Earth System Modeling group of the Climate Change Science Institute at Oak Ridge National Laboratory strives to simulate the climate system in a way that faithfully depicts the observed world. The group tackles challenges from quantifying uncertainties and increasing model resolution to integrating models of ocean, atmosphere, land, and sea ice.
Earth system models can serve as predictive tools, but they have shortcomings. It remains difficult to capture key phenomena and statistics and achieve accurate projections on subcontinental scales. If the modeling framework does a good job of mirroring the past and present at higher resolutions, researchers will gain confidence in its ability to make reliable projections about the future.
The ESM group focuses on determining the benefit of exploiting very-high-resolution global models to support the investigation of regional climate phenomena, especially those related to the hydrological cycle. In the development and deployment of an ultra-high-resolution model it will be important to test if high-resolution models a) are necessary to simulate nonlinear phenomena and interactions on the small scale that have feedbacks on large-scale climate behavior and b) accurately simulate local- to regional-scale phenomena.
CCSI researchers explore modeling and simulation spaces as comprehensively as possible using ORNL’s unique computing resources, including Titan, one of the world’s fastest supercomputers. They collaborate with the larger Earth system modeling community to assemble and experiment with a full, coupled climate system model that includes high-resolution ocean, atmosphere, land, and sea ice components. The models capture ocean motions down to about 50 kilometers and atmospheric motion scales down to about 120 kilometers—approximately eight times more detail than available with standard modeling frameworks. Moreover, CCSI experts are embedding regional models into global models to look at phenomena on scales smaller than 10 kilometers.
High resolution models will enable researchers to better capture the seasonal cycle of temperature and precipitation distributions, longer-term modes of climate variability such as the El Niño–Southern Oscillation climate pattern, and extremes such as hurricanes, intense storms, heat waves, and droughts.