Roman Olson

Roman Olson
Assistant Research Fellow

Research question

The main research question is how we can use complex statistical tools and climate models to make better climate projections. His research interests include soil moisture, El-Nino - Southern Oscillation, heat waves, and other extreme events. 

Education
  • 2003-2007: York University, Ontario, Canada. B. Sc. in Environmental Science
  • 2007-2010: The Pennsylvania State University, USA. M. Sc. in Geosciences. M. Sc. Thesis title: "What is the skill of climate parameter estimation methods? A case study with global average observational constraints".
  • 2010-2013: The Pennsylvania State University, USA. Ph. D. in Geosciences. Ph. D. Dissertation title: “How well can historical temperature observations constrain climate sensitivity?”
Work Experience
  • Aug 2018~ : Research Associate, IBS Center for Climate Physics, Busan, Korea
  • Nov 2017-Jul 2018: Research Professor, Department of Atmospheric Sciences, Yonsei University, Korea
  • Apr 2016 - Oct 2017: Postdoctoral Research Associate: Department of Atmospheric Sciences, Yonsei University, Korea
  • Jan 2014 - Apr 2016: Postdoctoral Research Associate, Climate Change Research Center, UNSW Australia, Australia
  • Aug 2013 - Dec 2013: Postdoctoral Scholar, Earth and Environmental Systems Institute, The Pennsylvania State University, USA
Research Interests
  • River runoff predictability
  • Climate modeling 
  • Climate extremes
  • Ensemble modeling 
  • El Niño - Southern Oscillation
Publications
  1.  G. Di Virgilio, J. Evans, A. Di Luca, R. Olson,  D. Argüeso, J. Kala, J. Andrys, P. Hoffman, J. Katzfey, and B. Rockel: Evaluation of ERA-Interim-driven CORDEX regional climate model simulations over Australia. In review at Climate Dynamics.
  2. R. Olson, S.-I. An, Y. Fan, W. Chang, and J. P. Evans: Testing and prediction under non-exclusive hypotheses: Application to Arctic sea ice projections. In review at Nature Communications.
  3. Y. Fan, R. Olson, J. P. Evans, J.-S. Shin, and S.-I. An: Bayesian quantile based multimodel projections with application to Korean heatwave data. In review at Journal of Geophysical Research - Atmospheres.
  4. R. Olson, W. Chang, K. Keller, M. Haran, K. L. Ruckert, and S.-I. An: Stilt – easy emulation of time series climate model output in multidimensional parameter space. In review at The R Journal.
  5. R. Olson, S.-I. An, Y. Fan and J. P. Evans: Multi-Model weighted projections accounting for skill in non-linear trend, variability, and autocorrelation. In review forEnvironmental Modelling & Software.
  6. F. Ji, J. P. Evans, A. Di Luca, N. Jiang, R. Olson, L. Fita, D. Argueso, L. T.-C. Chang, Y. Scorgie and M. Riley (2018): Projected change in characteristics of near surface temperature inversions for Southeast Australia. Accepted to Climate Dynamics.
  7. R. Olson, S.-I. An, Y. Fan, J. P. Evans and L. Caesar (2017): North Atlantic observations sharpen meridional overturning projections. Climate Dynamics, doi:10.1007/s00382-017-3867-7.
  8. J. Shin, R. Olson and S.-I. An (2017): Heat wave projections for the Korean Peninsula in the 21st century”. Asia-Pacific Journal of Atmospheric Sciences, doi:https://doi.org/10.1007/s13143-017-0059-7
  9. Y. Fan, R. Olson and J. P. Evans (2017): A Bayesian posterior predictive framework for weighting ensemble regional climate models. Geoscientific Model Development, 10, 2321-2332, doi: 10.5194/gmd-10-2321-2017
  10. Evans, J. P.,  D. Argüeso, R. Olson, A. Di Luca (2016): Future projections of extreme rainfall in south-east Australia. Theoretical and Applied Climatology, doi:10.1007/s00704-016-1949-9.
  11. Olson, R., J. P. Evans, A. Di Luca and D. Argüeso (2016):  The NARCliM project: model agreement and significance of climate projections. Climate Research, 69, 209-227.
  12. Olson, R., Fan, Y. and Evans, J. P. (2016): A simple method for Bayesian Model Averaging of regional climate projections: application to southeast Australian temperatures. Geophysical Research Letters, 43, doi:10.1002/2016GL069704.
  13. Evans, J. P., R. Olson, L. Fita,  D. Argüeso and A. Di Luca (2015): NARCliM model performance including teleconnections with climate modes. 21st International Congress on Modeling and Simulation, Gold Coast, Australia, 29 Nov – 4 Dec 2015.
  14. Evans, J. P., D. Argüeso, R. Olson, A. Di Luca (2015): NARCliM extreme precipitation indices report. NARCliM Technical Note 6, 109 pp, Report to the NSW Office of Environment and Heritage, Sydney, Australia.
  15. Olson, R., J. P. Evans, D. Argüeso and A. Di Luca (2014): NARCliM Climatological Atlas. NARCliM Technical Note 4, 423 pp, NARCliM Consortium, Sydney, Australia.
  16.  Chang, W., M. Haran, R. Olson, and K. Keller (2014): “A composite likelihood approach to computer model calibration with high-dimensional spatial data”. Statistica Sinica, 25, pp. 243-259.
  17. Chang, W., M. Haran, R. Olson, and K. Keller (2014): Fast dimension-reduced climate model calibration. (2nd place winner of the 2014 American Statistical Association Section on the Statistics and the Environment Student Paper Competition). Annals of Applied Statistics, 8(2),  pp. 649-673.
  18. Olson, R., R. Sriver, M. Haran, W. Chang, N. M. Urban, and K. Keller (2013): What is the effect of unresolved internal climate variability on climate sensitivity estimates? Journal of Geophysical Research - Atmospheres, 118, doi:10.1002/jgrd.50390
  19. Sriver, R., N. Urban, R. Olson, and K. Keller (2012): Towards a physically plausible upper bound of sea-level projections. Climatic Change115, pp. 893-902.
  20. Tuana, N., R. Sriver, T. Svoboda, R. Olson, P. Irvine, J. Haqq-Misra, and K. Keller (2012): Towards integrated ethical and scientific analysis of geoengineering: A research agenda. Ethics, Policy & Environment. 15(2), pp. 136-155.
  21. Bhat, K. S., M. Haran, R. Olson, and K. Keller (2012): Inferring likelihoods and climate system characteristics from climate models and multiple tracers. Environmetrics23(4), pp. 345-362.
  22. Olson, R., R. Sriver, M. Goes, N. M. Urban, H. D. Matthews, M. Haran, and K. Keller (2012): A climate sensitivity estimate using Bayesian fusion of instrumental observations and an Earth System model, Journal of Geophysical Research - Atmospheres117(D04103), doi:10.1029/2011JD016620.