Modeling Earth Systems News Today : Breaking News, Live Updates & Top Stories | Vimarsana

Climate models are a key technology in predicting the impacts of climate change. By running simulations of the Earth's climate, scientists and policymakers can estimate conditions like sea level rise, flooding, and rising temperatures, and make decisions about how to appropriately respond. But current climate models struggle to provide this information quickly or affordably enough to be useful on smaller scales, such as the size of a city.

A climate modeling downscaling method developed by MIT scientists leverages a machine-learning technique called adversarial learning to produce simulations faster and at finer resolutions, making them relevant to use on local levels for assessing risks of extreme weather.

A series of test cases designed to confirm the accuracy of ocean models could help improve our understanding of large-scale climate processes.

Tuning parameterizations of turbulent mixing and of the fall velocity of precipitation and cloud ice alleviates long-standing biases in climate simulations.

<p>Most modern ocean models focus on two categories of waves: a barotropic system, which has a fast wave propagation speed, and a baroclinic system, which has a slow wave propagation speed. To help address the challenge of simulating these two modes simultaneously, a team from DOE&rsquo;s Oak Ridge, Los Alamos and Sandia National Laboratories has developed a new solver algorithm that reduces the total run time of the Model for Prediction Across Scales-Ocean, or MPAS-Ocean, E3SM&rsquo;s ocean circulation model, by 45%.&nbsp;</p>