Tarkan A Bilge
Research Interests
My main research interests are polar climate and natural variability. In particular I am interested in solar influence on weather and climate.
My current research is targeted at improving understanding of the vertical temperature and wind response to the 11-year solar cycle, and associated mechanisms. Relating to this, I am also working to understand the importance and application of different statistical methods in this area of attribution. My past research has also focused on sea-ice variability and examining and quantifying the influence of solar forcing on regional sea-ice conditions.
Work Experience
Ocean/Sea-ice Modeller, British Antarctic Survey January 2023 – Present
Chief Engineer (Climate Prediction), University of Bergen September 2021 – December 2022
Climate Scientist, UK Met Office September 2019 – August 2021
Publications
Bilge, T. A., Fournier, N., Mignac, D., Hume-Wright, L., Bertino, L., Williams, T., & Tietsche, S. (2022). An Evaluation of the Performance of Sea Ice Thickness Forecasts to Support Arctic Marine Transport. Journal of Marine Science and Engineering, 10(2), 265. https://doi.org/10.3390/jmse10020265
Smith, D., Gillett, N., Simpson, I., Athanasiadis, P., Baehr, J., Bethke, I., Bilge et al. Attribution of multi-annual to decadal changes in the climate system: The Large Ensemble Single Forcing Model Intercomparison Project (LESFMIP). Frontiers in Climate 2022
Conferences
Bilge, T. A. (2022) Generalised Additive Models for Investigating Signals of Solar Influence. HEPPA-SOLARIS 2022, 15 th June 2022, Bergen.
Bilge, T. A. (2021) Performance of Sea Ice Thickness Forecasts to Support Arctic Activities. Arctic Frontiers, 3 rd February 2021, Virtual.
Education
UCL Master of Science (MSc), Physics
University of St Andrews Bachelor of Science (BSc), Physics
Skills
- Coupled climate modelling; designing and running experiments, validation
- Programming; Python, R, Fortran, Mathematica and shell scripting
- Machine learning; self-organising maps, k-means clustering, hyperparameter optimisation
- Statistical methods and modelling; linear models, generalised additive models, principal component analysis, Fourier analysis