Dr Ying Chen
Telephone: 01392 723624
Extension: (Streatham) 3624
1) Climate change, in particular, aerosol-cloud-climate interactions (ACI)
The overarching goal of this theme is to help climate models improve ACI representation and hence improve climate projection. ACI is a leading uncertainty since IPCC 2001 untile the recent IPCC AR6 2021, one primary reason of this slow progress is becasue the ACI large-scale signal has never been directly observed (can only observe neither perturbed or unperturbed clouds at one time), therefore we are lacking of realiable constraints for models. To help community overcame this gap, I pioneering a machine-learning approach to disentangle ACI signal from confoundors and derive robust constraints for models. This will open a new horizon for climate modelling, reduce the largest uncertianty in climate models and leading to more reliable climate sensitivity estimate and hence advancing climate projection and impacts estimation.
2) Air pollution, formation mechanism and mitigation stratigies.
The overarching goal of this theme is to optimize mitigation strategies for air pollution, which is one of the five leading threaten to public health according WHO. To achieve this goal: i) I develop fundermental understanding and its representation in models to improve process-level modelling; ii) I develop and improve observation method to advance our ability in understanding of aerosol key parameters, and hence better constrain and improve modelling; iii) I combine model with machine-learning to develop emulator to mimic explicit models to enable "scan" of thousands of mitigation strategies to opimize "effective .vs. feasible .vs. economic" decision for policy makers. My contribution in this theme has been awarded "2nd Price for Nature Science Award" by Ministry of Education, China, and also contributed into mitigation strategies in MoES, India.
3) Utilize clean energy to mitigate climate change and air pollution
The overarching goal of theme is to realize the destinlations of Theme 1&2 via facilitating clean energy utilization. One very large challenge for large-scale utilize of wind and solar power is their nature of fluctation, which is in confliction with electric system's high requirement of stability. To address this problem, I pioneered coupling numerical weather prediction model (and machine-learning technique) with electric dispatching system to optimize the dispatching strategies, therefore enabling large-scale utilization of wind&solar power whilist keeping system safe. My contribution in this theme has enable China cuting CO2 emisison for 50M ton/year, my patents help previous employer secure £5M contracts per year. I was awarded the "2nd Price of Science and Technology Achievement of Jiangsu Province, China"
Co-supervise students in Exeter:
Trish Nowak, PhD student, EPSRC CDT funded. Topic: nature dust aerosol's impacts on climate, health, and ecosystem.
Three MRes students, course: MTHM005 Programme, with topics of climate impacts from dust, biomass burning, and volcanic aerosols.