James J. Benedict

Guest Scientist

Affiliate

jjbenedict@lbl.gov

Curriculum Vitae

Biography

James Benedict is currently with Colorado State University.

My postdoctoral research at Lawrence Berkeley National Laboratory (LBNL) centers on the development, testing, and evaluation of global climate models (GCMs) that implement advanced modeling approaches. With the availability of ever-increasing computing power and the demonstrated benefit of accurately representing localized features of the earth system, GCM spatial grids are becoming increasingly finer. Certain statistical assumptions (“parameterizations”) about the behavior of small, unresolved phenomena—for example, cumulus clouds and turbulence—are often dependent on the chosen grid size and can become less accurate as we refine the grid mesh of a traditional GCM. One approach, called “scale-aware parameterization,” allows these statistical assumptions to more accurately accommodate a refinement in model grid resolution. We expect that scale-aware parameterizations, combined with a multiscale approach in which the global grid is locally refined in regions where higher resolution would be beneficial (strong baroclinic zones, complex terrain, regions of active convection), will contribute to improved simulations of the future climate. At LBNL, I will work with a team of climate scientists to (1) develop and test GCMs that use scale-aware cloud parameterizations in a multiscale modeling framework and (2) evaluate the ability of these newly developed GCMs to reproduce statistical properties of the climate system—most importantly, the key multiscale features of organized tropical convection—by comparing model output to high-quality, high-resolution, observation-based data sets.

My past research experience has focused on achieving a better understanding of how certain large-scale weather patterns operate both in nature and in model simulations. This approach not only highlights critical physical processes that characterize the weather phenomenon but can simultaneously reveal model deficiencies that may limit simulation accuracy. At Pennsylvania State University (B.S.), I had the good fortune of working with Dr. Sukyoung Lee and colleagues on a project examining the North Atlantic Oscillation (NAO), a planetary-scale atmospheric feature that influences weekly to monthly weather patterns from eastern North America to Europe. We discovered that synoptic-scale eddy wavebreaking upstream of the North Atlantic region is vital to the growth and maintenance of the NAO and that the NAO decays when these eddies are no longer present. At Colorado State University (M.S., Ph.D.) I worked with Dr. David Randall to analyze a tropical weather phenomenon called the Madden-Julian Oscillation (MJO), a propagating pattern of rainy and dry conditions that is poorly understood and yet greatly impacts a range of processes from monsoons to El Niño to midlatitude weather. My graduate research incorporated the use of a novel approach in atmospheric modeling called “superparameterization” (SP) that improves the way we simulate clouds and their interactions with the environment. Our work demonstrated that the SP version of a GCM reproduces key features of the MJO and that further improvement is achieved when the SP-CAM is coupled to a simplified slab ocean model. My postdoctoral research (2010-2012), with a team lead by Dr. Eric Maloney at Colorado State, spanned a range of topics aimed at improving MJO depiction in GCMs. Our sub-projects included an analysis of the MJO in various configurations of the GFDL AM3, a review of thermodynamic and advective processes of the MJO, and an investigation into the influence that the background atmospheric state had on MJO structure during two recent field experiments.