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Postdoctoral Research Associate

Employer
Princeton University
Location
Princeton, New Jersey

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Position Type
Postdoc
Employment Type
Full Time
Institution Type
Four-Year Institution

Job Details


Princeton University

The Atmospheric and Oceanic Sciences Program at Princeton University, in association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks to fill up to three postdoctoral or more senior research positions in a research initiative aiming at advancing the fundamental understanding of the roles of clouds and radiation in affecting Earth's climate and weather, and evaluating/improving their representation in GFDL climate/weather models. The recently developed GFDL climate models (CM4 and ESM4) are among the best-performing CMIP6 models in terms of mean climate and variability. They use the same FV3 dynamical core as the current NOAA/NWS weather forecast model. CM4 also forms the basis of a prediction model (SPEAR) and can be configured into a limited-domain cloud-resolving model (CRM) for process-level studies. GFDL has a long tradition in conducting cutting-edge research related to clouds/aerosols, radiation, circulation, precipitation and extreme weather/climate events. This search represents a concerted effort to push this prominent research direction to new levels. The first position will be in the area of aerosol-cloud interactions and indirect effects, with focus on understanding the controlling factors of the magnitude and spatiotemporal distribution of model-simulated aerosol indirect effects, using satellite/in-situ observations to validate the model representation of aerosol/cloud processes, and developing/implementing parameterizations of ice nucleation and mixed-phase cloud microphysics. The second position will be in the area of cloud feedbacks, with focus on understanding the key macro- and micro-physical processes in affecting the baseline cloud simulation and the strength of cloud feedbacks, using observations and case studies to constrain cloud feedbacks, and exploring innovative ways to better use process-level models to inform weather/climate model development. The third position will be in the area of atmospheric radiative transfer and cloud radiative effects, with focus on designing a new line-by-line atmospheric radiative transfer code that will serve as a benchmark standard for a new radiative transfer code to be used in weather/climate models, understanding the effect of SST pattern on cloud radiative effects, and improving the model representation of cloud microphysics-radiation interactions. The ideal candidates have to demonstrate a strong background in atmospheric and climate modeling, and climate science, as well as experience in using, developing, and analyzing numerical models and/or large observational datasets. Candidates must have a Ph.D. in atmospheric physics, dynamic meteorology, Earth system science, climate studies, or related fields. The initial appointment is for one year with the possibility of renewal subject to satisfactory performance and available funding. Complete applications, including a cover letter, CV, publication list, a statement of research interests, and contact information of 3 references should be submitted by July 1, 2020 for full consideration. Applicants should apply online to %listings_link%. For more information about the research project and application process, please contact V. Ramaswamy (V. Ramaswamy@noaa.gov) for general inquiries, Yi Ming (Yi.Ming@noaa.gov) for the first position, Ming Zhao (Ming.Zhao@naoa.gov) for the second position, and David Paynter (David.Paynter@noaa.gov) for the third position. 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.

PI125429152

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