met-jobs@lists.reading.ac.uk
June 2015
Message 54

[Periods|Index by:DateThreadSubjectAuthor|Date:PreviousNext|Thread:(Previous)(Next)|List Information]

[Met-jobs] Job Vacancy - CSSP-China Global Model Evaluation & Development Scientist, Met Office Exeter UK

From Sean Milton <sean.milton1@gmail.com>
To met-jobs@lists.reading.ac.uk
Cc Sean Milton <sean.milton@metoffice.gov.uk>
Date Tue, 16 Jun 2015 19:35:29 +0100

More Details and application process available from  http://careers.metoffice.gov.uk/current-vacancies

Location

 Exeter HQ

Contract type

 Permanent

Salary range

 26010.00  - 35040.00  GBP per Year

Opening date

 09-06-2015

Closing date

 23-06-2015
mologo.jpg
Salary Information

Scientist - £26,010 - £29,100 
Senior Scientist £30,529 - £35,040

Background

The Climate Science for Service Partnership: China (CSSP China) is a scientific research programme - led in the UK by the Met Office - that will help build the basis for services to protect against climate variability and prepare for a changing climate. The Met Office is supported in this endeavour by the Newton Fund, which aims to develop science and innovation partnerships that promote the economic development and welfare of developing countries. The Met Office is a delivery partner on behalf of UK government for the Newton Fund. 
  
This CSSP post sits within the Global Model Evaluation and Development team (GMED) in Foundation Science at the Met Office, which comprises four sub-teams totalling about 24 scientists. GMED is responsible for evaluation of global model performance across all timescales from weather to climate and the pull through of science developments into global model prediction systems for the benefit of weather and climate customers. The post will be working in CSSP work package 4 on “Development of Models and Climate Projection systems” and specifically engaging in Global Climate Model Evaluation & Development activities in collaboration with partners in China. 

Job Purpose

Global climate models of the coupled atmosphere, ocean, land and sea-ice systems are central to the delivery of climate services in China and other regions worldwide. These models continue to be subject to considerable uncertainties associated with the modelling of key physical and dynamical processes (e.g. clouds, convection, land surface characteristics, orographic forcing etc.). Identifying the potential sources of model systematic errors in physical processes and ultimately improving model formulation are at the heart of a climate programme to improve climate services. The broad aims of this post are to study the key model systematic errors that impact important modes of climate variability affecting East Asia and China on the 5-40 year timescale (e.g. teleconnections from the deep tropics towards the extra-tropics), assist in developing novel diagnostic techniques to elucidate these sources of systematic error growth, and interact closely with model development teams in the Met Office and China, and with UK academic partners involved in CSSP to “pull through” robust improvements to the global climate models.   

Job Responsibilities
  • In collaboartion with other CSSP work packages develop improved understanding of the drivers of East Asian regional Climate Variability and the current status of global model performance and key systematic errors in the Met Office Unified Model (MetUM) and in partner models in China. 
  • Participate in the development of novel diagnostic techniques and their application to pin down possible sources of model errors affecting East Asia. These techniques could include: 
  • Statistical and dynamical diagnostics of tropical to extra-tropical teleconnections (e.g. correlation maps, Rossby wave sources, wave activity fluxes). 
  • Use of perturbed parameter ensembles (PPE), being developed as part of the climate projection system development in WP4, to study sensitivity of East Asian climate variability to parametrization uncertainty. 
  • Seamless prediction capability - Use of short-range NWP model experiments (atmosphere and coupled) to study the initial growth of model errors (e.g. Rodwell and Palmer (2007); Martin et. al. (2010)) 
  • Process based evaluation of model processes against new observations (in-situ, satellite data) 
  • Use of higher resolution (vertical & horizontal) climate models and convective resolving regional models to understand key processes.    
  • Nudging - relaxing models to reanalysis in key regions of error growth (e.g. Tropics) and studying impacts on East Asia. 
  • Engaging with model developers and contributing to evaluation of new versions of the Met Office global coupled climate model as they are developed (e.g. see Williams et. al. (2015)), with a focus on East Asia.  
Essential Qualifications, Skills & Abilities
  1. An honours degree (2:1 or above) in a physical science, mathematics or other related discipline. 
  2. Scientist & Senior Scientist : Ability to plan and carry out postgraduate level research and development in a related science discipline as demonstrated by publication record and to show initiative in resolving problems with limited supervision. 
    Senior Scientist: Evidence of leading and delivering complex scientific research and development , taking ownership and initiative in resolving problems. 
  3. Knowledge of the Unified Model or similar global model, its dynamics and parametrizations and experience in running large scale models on HPC platforms. 
  4. Scientist & senior Scientist :Good general computing skills (Fortran, Unix, IDL/Iris or similar), with proven expertise in handling and analysing large datasets. 
    Senior Scientist : Strong evidence of developing and testing complex software for scientific application 
  5. Ability to work effectively both as an independent scientist but also as part of a larger team involving colleagues at the Met Office, China, or in UK Academia. 
  6. Good communication skills - demonstrated ability to present scientific research at conferences etc. and lead/contribute to peer reviewed scientific papers.
Desirable Qualifications, Skills & Abilities
  • A PhD in a geophysical science. 
  • Knowledge of advanced statistical and climate diagnostic techniques (e.g. Von Storch and Zwiers (1999)). 
  • Experience in coding diagnostics using Python and/or the Iris framework. 
     
Additional Supplementary Information

**References** 

Martin, G M, S F Milton, C A Senior, M E Brooks, S Ineson, T Reichler, and J Kim (2010): "Analysis and Reduction of Systematic Errors Through a Seamless Approach to Modeling Weather and Climate." Journal of Climate 23, no. 22 : doi:10.1175/2010JCLI3541.1. http://journals.ametsoc.org/doi/abs/10.1175/2010JCLI3541.1
  
Rodwell, M J, and T N Palmer (2007): "Using Numerical Weather Prediction to Assess Climate Models." Q J R Meteorol Soc 133, no. 622 : doi:10.1002/qj.23. http://doi.wiley.com/10.1002/qj.23
  
von Storch, H. and Zwiers, F. (1999): Statistical analysis in climate research. Cambridge University Press. 
  
Williams, K. D., C. M. Harris, A. Bodas-Salcedo, J. Camp, R. E. Comer, D. Copsey, D. Fereday, and others. "The Met Office Global Coupled Model 2.0 (GC2) Configuration." Geosci. Model Dev. Discuss. 8, no. 1 (2015): 521-565. 



Go to: Periods · List Information · Index by: Date (or Reverse Date), Thread, Subject or Author.