About the Project
We have developed an energy-water cycle coupled inverse method in the department, Thomas et al (2019), which uses many independently observed satellite datasets and their errors to develop closed heat and water budgets on a global scale following an earlier NASA Energy and Water cycle Study (NEWS) L’Ecuyer et al (2015), Rodell et al (2015), see http://www.nasa-news.org. We have extended the NEWS study with better results over the oceans by improving the errors used for the satellite derived fluxes, and by using additional ocean transport estimates based on ship measurements. Further work supported by the National Centre for Earth Observations has extended the model to study interannual variability from 2001-11, showing a better seasonal cycle of continental warming and a better land water cycle constrained by precipitation, runoff data and water storage estimates from GRACE gravity data. Interannual variability over Africa is one current focus.
This PhD project will focus on improving the land surface processes. On land, soil moisture and vegetation properties largely determine how much energy the surface can store on seasonal timescales, and hence the resultant land surface temperatures (LST), which are now well measured from satellites. Water, sunlight and temperature also determine photosynthesis and biomass growth, taking up CO2 from the Earth’s atmosphere. Biomass growth and CO2 uptake can also be monitored from satellite measurements providing additional datasets that can be used with our inverse method. The aim is to couple the land carbon sink to the energy and water cycles, and to test the resulting model in some key regions of interest; possibilities include Africa and China. The PhD student will therefore use new satellite observations as constraints to improve surface flux estimates. The inverse method will be extended to include carbon budgets alongside the water and energy budgets to produce a truly coupled Earth system cycling analysis with many new applications, including testing Earth and Climate circulation models.
The student will benefit from in house training in remote sensing and land surface / earth system modelling from experts in the Meteorology department and from NCEO and NCAS within Reading. The NCEO has a large community of carbon cycle modellers who are available for advice. NERC Advanced training programs in aspects of Earth System modelling will also be used to help the student become familiar with community methods and models. Specific training on the Joint UK Land-Environment Simulator (JULES) community land surface model will be provided through the annual JULES training workshop and support from co-supervisors who have expertise in this model.
The student is expected to attend the 2-week Fluxnet course (www.fluxcourse.org) in the US at the end of their 1st year and the ESA Earth Observation summer school at ESRIN (Frascati) in year 3.
The student will also spend time in each of the first 2 years working at the Met Office.
Applicants should hold or expect to gain a minimum of a 2:1 Bachelor Degree, Masters Degree with Merit, or equivalent in Physics, Mathematics, Meteorology, Physical Oceanography, Plant Science or a closely related environmental or physical science. We will provide training on modelling and computer programming to motivated candidates as needed, however confidence in solving numerical problems computationally would be an advantage.
To apply, please follow the instructions at https://research.reading.ac.uk/scenario/apply/
The project has CASE funding from the Met Office.
Due to UKRI rules, the DTP can only fund a very limited number of international students. We will only consider applications from international students with an outstanding academic background placing them in the top 10% of their cohort.
Rodell et al (2015) The observed state of the water cycle in the early 21st century. J Climate
Thomas et al (2019) Global Energy and Water Cycle Fluxes from Remote sensing data. J. Climate EEI Special Issue.
Why not add a message here
Based on your current searches we recommend the following search filters.
Based on your current search criteria we thought you might be interested in these.