Postdoctoral position on subseasonal predictions using the GFDL SPEAR model
The Atmospheric and Oceanic Sciences Program at Princeton University in cooperation with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) seeks a motivated postdoctoral or more senior researcher to join a team working on subseasonal predictions. The incumbent will be part of a team aiming to improve our physical understanding of the prediction and predictability of various phenomena on the subseasonal timescale and to improve the performance of the GFDL SPEAR subseasonal prediction system.
The incumbent is responsible for testing two novel schemes for improving ensemble spread and mean states in the GFDL SPEAR forecast model and evaluating their impact on subseasonal prediction skills. The incumbent will (a) examine and evaluate a stochastic physics scheme and its impacts on climate simulation and prediction, (b) work on optimizing an atmospheric mean-state adjustment scheme to correct the mean state drift issue and then test its impacts on the subseasonal predictions. The outcome will also be expected to improve our understanding of the origins of model biases so as to provide guidance for the development of the GFDL forecast models as well as the GFDL next-generation atmospheric model, AM5. The selected candidate will have a Ph.D. degree in meteorology, climate sciences, or a related field, and will possess one or more of the following attributes: (a) strong computational skills, including experience using comprehensive climate models, (b) strong background in weather and climate predictions, and (c) strong diagnostic skills in analyzing simulated and observed data sets. This is a two-year position with renewal contingent upon satisfactory performance and continued funding. The successful candidate will be based at the Geophysical Fluid Dynamics Laboratory (GFDL) in Princeton, New Jersey, to work with team members in the Weather and Climate division and have close collaboration with the GFDL Seasonal to Decadal Variability and Predictability division. For further information, please contact Baoqiang Xiang (firstname.lastname@example.org), Lucas Harris (email@example.com), and Tom Knutson (firstname.lastname@example.org).
Ph.D is required. Complete applications, including a cover letter, CV, publication list, 3 letters of recommendation and a one-to-two page statement of professional interests. Applicants should apply online at https://www.princeton.edu/acad-positions/position/32345. Review of applications will begin as soon as they are received and continue until the position is filled. Princeton is interested in candidates who, through their research, will contribute to the diversity and excellence of the academic community.
This position is subject to Princeton University's background check policy. Princeton University is an equal opportunity/affirmative action employer and all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status, or any other characteristic protected by law.