Simulating changes to the Earth’s energy, water and carbon cycles is a key goal of climate and earth system models. However we need to know the regional fluxes and transports of these quantities much more accurately from observations to provide strong constraints for models such as those used at the Met Office for climate predictions, and to inform developments across the wider global modelling community. This is now recognized by IPCC who will have separate chapters on the energy, water and carbon cycles in the next Assessment Report.
We have developed an energy-water cycle coupled inverse method in the department 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 now extended the NEWS study getting better results over the oceans by improving the error estimates used for the satellite derived fluxes, and by using additional ocean transport estimates based on ship measurements (Thomas etal., in revision J Climate Earth Energy Imbalance special issue). Further work supported by the National Centre for Earth Observations has extended the model to study interannual variability from 2001-11 and shows 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 a 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, and hence the resultant land surface temperatures (LST), which are now well measured from satellite. 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 and provide additional datasets that can be used with our inverse method, and in the process this will couple the land carbon sink to the energy and water cycles. The aim of the PhD will therefore be to use these new satellite observations as constraints to improve our global 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 framework which could lead to many new applications.
Specific Training opportunities:
The student will benefit from in house training on remote sensing and land surface / earth system modelling from experts in the Meteorology department and from NCEO and NCAS within UoR. 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 Associate Prof Quaife (UoR) and Drs Hemming and King (Met Office).
The Fluxnet course http://www.fluxcourse.org
is an annual 2 week course run in the US providing training in access and analysis from the global network of in situ flux sites from around the world covering heat water and carbon flux measurements, as well as other vegetation characteristics (end of 1st year).
The biennial ESA Earth Observation summer school at ESRIN (Frascati) provides an excellent platform to learn about all aspects of Earth observation data and their assimilation into models. This would offer a broad perspective on the use of EO data and an opportunity to present first results from the student’s own project (year 3).
The student will also spend 2x1 week periods in each of the first 2 years working at the Met Office. This will provide ‘hands on’ experience running and evaluating JULES using a range of land surface (including Fluxnet) and EO data.
Applicants should hold or expect to gain a minimum of a 2:1 Bachelor Degree, Masters Degree with Merit, or equivalent in (ideally) 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/