Postdoctoral Position: Diagnosing S2S Precipitation Biases and Errors Associated with Extratropical Cyclones and Storm Tracks over the Continental United States Using the GFDL SPEAR Model
The Atmospheric and Oceanic Sciences Program at Princeton University, in association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks a postdoctoral or more senior researcher to investigate subseasonal-to-seasonal (S2S) precipitation biases and errors associated with extratropical cyclones and storm tracks over the Continental United States using the GFDL SPEAR model through data analysis and global modeling experiments. This project will focus on (1) diagnosing extratropical cyclone related precipitation biases in GFDL's SPEAR model simulations, (2) evaluating the model bias in winter cyclone frequency/intensity using reanalysis data and examining the linkage between cyclone frequency/intensity bias and precipitation bias at the S2S time scale, (3) diagnosing the cyclone related synoptic precipitation structural errors in model simulations using observations and identifying the key processes causing these structural errors, and (4) conducting model sensitivity studies on how these biases may be reduced with respect to model resolution, sea surface temperature biases, and biases in tropical forcing, the large-scale circulation and model physics. The incumbent will be encouraged to use novel diagnostic techniques and new physical insights to explore these topics.
This is a joint project between GFDL and Stony Brook University. The successful applicant will be based at the GFDL in Princeton, New Jersey, and will work with Dr. Xiaosong Yang (Xiaosong.Yang@noaa.gov). The postdoc will also collaborate with Prof. Edmund Chang at Stony Brook University. The selected candidate must have a Ph.D. in Atmospheric Science, Oceanography, or a closely related field, and have one or more of the following attributes: (a) a strong background in climate dynamics, especially extratropical dynamics, (b) experience using and analyzing climate models, and (c) strong diagnostic skills in analyzing large data sets. The initial appointment is for one year with the possibility of renewal subject to satisfactory performance and available funding.
Princeton is interested in candidates who, through their research, will contribute to the diversity and excellence of the academic community. Complete applications include a CV, publication list, 3 letters of recommendation and a one-to-two page research statement. Applicants should apply online at https://www.princeton.edu/acad-positions/position/27902. Review of applications will begin November 1, 2022 and continue until the position is filled. For additional information, contact Xiaosong Yang (Xiaosong.Yang@noaa.gov). This position is subject to the 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.