[ES_JOBS_NET] 2-yr postdoc - constraining cloud parameterizations using data assimilation - NASA GISS/William Paterson University

[Griffiths, Michael]

Dear colleagues,


The Department of Environmental Science at William Paterson University, in collaboration with Columbia University/NASA GISS and the University of Arizona, invites applications for a 2-year NSF funded postdoc to work on a project entitled Constraining cloud and convective parameterizations using paleoclimate data assimilation.



Links to the application and attached description can be found here.<https://wpunj.hiretouch.com/job-details?jobID=1431&job=post-doctoral-associate-grant-funded>



Best wishes,

Michael

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Michael L. Griffiths
Professor
Department of Environmental Science
William Paterson University
Wayne, NJ 07470
Past Climate and Ecosystems Lab<http://mickgriffiths.com/>
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Responsibilities

  *   Working with NASA scientists, conduct fully coupled, isotope enabled simulations for the Last Glacial Maximum (LGM), mid-Holocene (MH), and Pre-Industrial (PI) time periods using a wide ensemble of cloud and convective parameter sets (i.e., perturbed parameter ensemble, or PPE) in an updated (CMIP6-CMIP7) version of the NASA GISS climate model, GISS-E3.
  *   Perform paleoclimate data assimilation on these PPEs using sea surface temperature and water isotope proxies.
  *   Using the posteriors from the LGM, MH, and PI assimilations, identify which cloud and convection parameters provide the best match to the proxy data, then conduct present-day and doubled CO2 experiments with these parameter sets to calculate a narrowed estimate of ECS.
  *   Prepare manuscripts for publication in peer-reviewed journals.
  *   Present findings at conferences/seminars and participate in various outreach activities.
  *   Supervise and mentor undergraduate and graduate students at William Paterson University, University of Arizona, and Columbia University.

Minimum Qualifications

  *   PhD in atmospheric sciences, climate sciences, earth system sciences or related field.
  *   Experience working with climate model output and large datasets.
  *   Knowledge and understanding of climate proxies and PSMs.
  *   Proficiency in coding with Python, R, MATLAB, IDL, Julia, or similar data analysis software.

Preferred Qualifications

  *   Familiarity with speleothem proxies and associated PSMs.
  *   Experience with data assimilation, machine learning, and related statistical methods.
  *   FORTRAN experience welcomed.





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