|From||"Ma, Hsi-Yen" <email@example.com>|
|Date||Mon, 25 Jan 2016 19:38:12 +0000|
Apologies for cross-posting.
We have a climate modeling postdoc job opening at the Lawrence Livermore National Laboratory. The opening features an exciting opportunity to trace the origin of errors in ocean-atmosphere models through novel use of short-term hindcast simulations of the fully coupled climate system. If interested, please apply online through the following link (http://careers-ext.llnl.gov/jobs/4786833-postdoctoral-research-staff-member-cloud-processes-research). Thank you.
Hsi-Yen Ma, PhD
Lawrence Livermore National Laboratory
7000 East Avenue, L103
Livermore, CA 94551-0808
Phone: (925) 422-5958
Fax: (925) 422-7675
Postdoctoral Research Staff Member - Cloud Processes Research
Location: Livermore, CA
Category: Post Docs
Organization: Physical and Life Sciences
Posting Requirement: External Posting
Job ID: 100736
Job Code: Post-Dr Research Staff 1 (PDS.1)
Date Posted: January 22 2016
NOTE: This is a two-year Postdoctoral appointment with the possibility of extension to a maximum of three years. Eligible candidates are recent PhDs within five years of the month of the degree award at time of hire date.
NATURE AND SCOPE OF JOB
The Cloud Processes Research Group within the Atmospheric, Earth and Energy Division (AEED) in the Physical and Life Sciences Directorate has an opening for a Postdoctoral Research Staff Member. The Cloud Processes Research Group is a leader in the study of cloud processes with activities including their diagnosis with observations, their parameterization and evaluation in climate models, and their response to climate change. Climate models are essential tools for climate research and future projections. However, large systematic errors in climate models hinder the fidelity of climate model simulations and predictions. This opening features an exciting opportunity to trace the origin of errors in ocean-atmosphere models through novel use of short-term hindcast simulations of the fully coupled climate system. The prospective candidate will perform coupled climate model simulations and diagnose the error sources in the simulated surface climate using the short-term hindcast technique developed by the DOE Cloud-Associated Parameterizations Testbed (CAPT) project (http://www-pcmdi.llnl.gov/projects/capt/index.php). Potential topics include understanding the correspondence of short-term to long-term errors in the coupled system, particularly for errors in tropical sea surface temperature (SST) and salinity (SSS), and linking them to errors related to atmospheric cloud and precipitation processes. Other potential topics of investigation include long-standing climate errors in the tropics, such as double Inter-Tropical Convergence Zone and the Madden-Julian Oscillation. The postdoctoral researcher will be expected to work collaboratively with scientists in the Cloud Processes Research and Climate Modeling and Analysis Groups. The postdoc will also interact with scientists at other DOE labs, NCAR, and universities through the close collaborations established by CAPT and other major climate programs at LLNL: Program for Climate Model Diagnosis and Intercomparison (PCMDI), Accelerated Climate Modeling for Energy (ACME), Atmospheric System Research (ASR), and Atmospheric Radiation Measurement (ARM). The selected candidate will report to the Cloud Processes Research Group Leader.
- Conduct original research on the processes related to cloud, precipitation and radiation in climate models, as well as processes related to coupled ocean-atmosphere interactions in contributing to the tropical SST and SSS errors using the CAPT approach.
- Contribute to the conception, design, and execution of research plans using both observation and modeling tools.
- Publish research results in technical reports and peer-reviewed journals and present technical results at scientific conferences.
- Travel as required to coordinate research with collaborators.
- Perform all assignments in accordance with ES&H, security, and business practice requirements and policies.
ESSENTIAL SKILLS, KNOWLEDGE AND ABILITIES
- Recent PhD in atmospheric science or closely related field.
- Experience with conducting research in atmospheric science or closely related field.
- Experience with performing and analyzing climate model simulations.
- Expertise in one or more of the following areas: clouds, precipitation and radiation processes, and their representation in climate models; ocean-atmosphere interactions; ocean dynamics and circulation; climate model diagnosis and/or numerical modeling.
- Experience with processing model and observational datasets, modern programming environments, and visualization techniques.
- Experience with collaborating effectively with a team of scientists of diverse backgrounds.
- Demonstrated fundamental verbal and written communication skills as evidenced by published results and presentations.
- Ability to travel to coordinate research with collaborators.
DESIRED SKILLS, KNOWLEDGE, AND ABILITIES
- Experience performing climate model simulations with the Community Earth System Model (CESM) or ACME on high-performance computing platforms.
- Expertise in the coupled ocean-atmosphere interactions; ocean dynamics and circulation.
- Expertise in data assimilation.
- Expertise in the parameterization of clouds and related processes in atmospheric models.
- Experience analyzing and working with cloud observations collected by satellites and/or field programs such as the DOE’s ARM program.
- Familiarity with programing languages including Fortran, Python, NCL, MPI or openMP for supercomputing.
Pre-Placement Medical Exam: None required.
Pre-Employment Drug Test: External applicant(s) selected for this position will be required to pass a post-offer, pre-employment drug test.
Anticipated Clearance Level: None.
LLNL is an affirmative action/ equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, protected veteran status, age, citizenship, or any other characteristic protected by law.
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