The QMEE CDT Project proposal database

Welcome to the QMEE CDT Project proposal database. This is a live list of projects proposals put forward by PIs across the CDT partner institutions

PIs/Supervisors will continue to add projects to this list over the next few months, so do keep checking back! You can search the projects using the box below: simply enter some text and press Search to do a text search across all the database fields. If you want to search more finely, the search tool also allows you to search on particular details of the project descriptions: you will see these finer search options appear if you click on the search box.

Click on the view button next to a project to get the full proposal description. If you want to download project details, either for all projects, or for a subset you have searched for, then click on the 'Download details' button.

Plant-atmosphere interaction in near-real time: eco-evolutionary optimality theory applied to carbon, water and energy cycle forecasting
The terrestrial biosphere regulates the energy, water and carbon exchanges between the land and the atmosphere. Vegetation is extremely sensitive to changes in weather conditions on daily, seasonal and annual timescales; in turn, changes in vegetation properties control CO2 and latent and sensible heat fluxes, with rapid feedbacks to the state of the atmosphere. The European Centre for Medium-Range Weather Forecasis (ECMWF) performs a near-real-time quantification of atmosphere-land CO2 fluxes as a component of its operational Integrated Forecast System (IFS) for weather, and for atmospheric composition as part of the Copernicus Atmosphere Monitoring Service (CAMS). The project’s aim is to make this quantification both simpler and more accurate, by deploying a new ecosystem model that combines satellite observations with eco-evolutionary optimality theory [1] to predict canopy-level conductance and photosynthesis. The principle of the model is that over a time scale of days, photosynthetic characteristics of leaves ‘acclimate’ so as to simultaneously minimize costs (for carbon fixation and water transport) and maximize net carbon uptake [1,2]. The model has shown unprecedented predictive skill for global patterns of carbon isotope discrimination and monthly CO2 fluxes at eddy-covariance stations [2] and is being further developed in a European Space Agency project to monitor primary production using data from the Sentinel-3 mission [3]. The project will undertake a comprehensive assessment of the model’s predictive skill for plant traits related to photosynthetic function, and will incorporate the model into the ECMWF land-surface modelling framework [4]. The resulting global simulations of the terrestrial carbon cycle will be evaluated and, where necessary, improved by comparison to locally measured diurnal and seasonal cycles of CO2 exchange in different biomes and climates, to variations of atmospheric column CO2 concentration observed by satellites and the Total Column Carbon Observing Network, and to near-surface CO2 concentrations measured at remote atmospheric sampling stations. Through its inclusion in the IFS, the model will lead to improved near-real-time forecasts of the physiological state of vegetation and the exchanges of energy, water vapour [5] and CO2 between the atmosphere and land [6,7]. [1] Prentice, I.C. et al. (2014) Ecology Letters 17: 82–91. [2] Wang, H. et al. (2017) Nature Plants 3: 734–741. [3] https://terra-p.vito.be/about/overall-goal-terra-p [4] Balsamo et al. (2009) Journal of Hydrometeorology 10: 623-643. [5] Boussetta et al. (2013) Journal of Geophysical Research 118: 5923–5946. [6] Agustí-Panareda et al. (2014) Forecasting global atmospheric CO2. Atmospheric Chemistry and Physics 14: 11959–11983. [7] Agustí-Panareda et al. (2016) Atmospheric Chemistry and Physics 16: 10399–10418.
Colin Prentice
Sandy Harrison
Anna Agustí-Panareda, ECMWF, anna.agusti-panareda@ecmwf.int; Souhail Boussetta, ECMWF, souhail.boussetta@ecmwf.int; Gianpaolo Balsamo, ECMWF, gianpaolo.balsamo@ecmwf.int
Development of mathematical theory, Computing, Quantitative data analysis, Ecological observations / data collection
Colin Prentice
The project involves: + the application of a new quantitative theory of large-scale plant and ecosystem function + advanced statistical methods for data analysis and data-model comparison involving large biospheric and atmospheric data sets + interfacing with the ECMWF land-surface framework and forecasting system, requiring high-level programming skill
+ The application of optimality theory to model the large-scale behaviour of the terrestrial biosphere is entirely new. + Theory and models originating in ecology and evolution will be used to solve the real-world challenges of how vegetation modulates air quality and climate extremes, and how to improve operational, near-real-time weather and air-quality forecasts.
+ Eco-evolutionary optimality theory is undergoing an explosion of interest as a new approach to understanding the function of organisms and ecosystems on multiple time scales. + The representation of terrestrial carbon cycling in numerical models is one of the largest uncertainties in projections of future climate. Now, for the first time, it will be informed by ecological theory.
+ The project will contribute to the development and improvement of the ECMWF’s integrated forecasting system, which makes near-real-time predictions of physical and biogeochemical exchanges among atmosphere, ocean and land. + Improvements are envisaged in forecast accuracy for air quality and climate.
+ It will contribute to theory about plant behaviour on multiple time and space scales. + It has the potential to revolutionize the representation of terrestrial ecosystem carbon cycling in Earth System Models: one of the largest source of uncertainty in seasonal climate forecasts and climate projections. + It will improve predictions of extreme weather and air-quality events.
The project lies at the interface of plant physiology and ecology, biophysics, and climate and atmospheric science. It also links the academic discipline of ecology to the operational discipline of numerical weather prediction.
Atmospheric physics & chemistry, Climate and climate change, Ecosystem-scale processes and land use
The student will: + hands-on trained in the operational forecast system by ECMWF staff + become conversant with global plant data sources and database software through training by Harrison + learn fundamentals of quantitative ecosystem science from Prentice + be part of an international virtual network on next-gen model development
Training in the ECMWF land-surface modelling framework and integrated forecasting system will take place at ECMWF headquarters in Reading. Training in plant data sources and database software will take place at the University of Reading. Training in ecosystem science will take place at Imperial College (Silwood Park). The virtual network meets via the web.
Yes
2017-09-28 16:48:29