Climate – Bridging human impacts

A new integrated computational model reduces uncertainty in climate predictions by bridging Earth systems with energy and economic models and large-scale human impact data.


Research scientists at the Climate Change Science Institute at Oak Ridge National Laboratory are once again assembling sessions at this year’s AGU 2017 National Meeting in New Orleans, Louisiana, December 11-15. Session topics range from biogeochemistry to Earth science data to the energy-water nexus.

Mitigate, Adapt, or Suffer – Connecting Global Change to Local Impacts and Solutions

Katharine Hayhoe, professor and director of the Climate Science Center at Texas Tech University, visited the City of Oak Ridge and Oak Ridge National Laboratory (ORNL). Her talks focused on the science and policy of climate change, the kind of impacts we may see globally and locally, what options and information we must have to prepare for these changes, and ways that scientists and non-scientists can effectively discuss these issues with the general public.

Modeling the natural world with Anthony Walker

During the workday, Anthony Walker spends considerable time designing models to advance our understanding of Earth’s biological systems. In the evenings and on weekends, he takes a more hands-on approach to the natural world, whether working in his garden or out kayaking on area waterways. The terrestrial ecosystem modeler has tackled a variety of tasks since arriving at Oak Ridge National Laboratory 4 years ago, including leading an international group of scientists exploring how forests respond to elevated levels of carbon dioxide (CO2).

Simulating Extreme Storms over the Alabama-Coosa-Tallapoosa River Basin

PMP is defined as the largest rainfall depth that could physically occur under a series of adverse atmospheric conditions. It is the design standard of highly important energy-water infrastructures such as dams and nuclear power plants. To understand how PMP may respond to projected future climate forcings, a physics-based numerical weather simulation model was used to estimate PMP across various durations and areas.