PhD: Downscaling and Prediction of Rainfall Extremes from Climate Model Outputs (RainX)

University of Glasgow

Glasgow, UK 🇬🇧

Project institution: University of Glasgow

Project supervisor(s): Dr Sebastian Gerhard Mutz (University of Glasgow) and Dr Daniela Castro-Camilo (University of Glasgow)

Overview and Background

In the last decade, Scotland’s rainfall increased by 9% annually and 19% in winter, with more water from extreme events, posing risks to the environment, infrastructure, health, and industry [Sniffer, 2021]. Urgent issues such as flooding, mass wasting, and water quality are closely tied to rainfall extremes [Sniffer, 2021]. Reliable predictions of extremes are, therefore, critical for risk management. Prediction of extremes, which is one of the main focuses of extreme value theory [Friederichs, 2010], is still considered one of the grand challenges by the World Climate Research Programme [Alexander et al., 2016]. This project will address this challenge by developing novel statistical, computationally efficient models that are able to predict rainfall extremes from the output of GPU-optimised climate models.

Methodology and Objectives

General Circulation Models (GCMs) are the primary tools for predicting future climate change [IPCC, 2023; and references therein]. While these GCM simulations are suitable for studies investigating climate dynamics and changes on coarse spatiotemporal scales, their skill in predicting local-scale climate and extremes remains very limited [IPCC, 2023]. Statistical Downscaling (SD) addresses this problem by linking coarse climate information to local-scale observational data [e.g., Hewitson et al., 2014; Mutz et al., 2021] using statistical models, which enables us to “translate” GCM output to predictions that are more relevant for regional impact studies and adaptation measures. This project will leverage recent advances in SD for extremes [Cuba et al., 2024+] to develop a set of algorithms for predicting rainfall extremes in Scotland from the output of the latest GPU-optimised GCM ICON [Giorgetta et al. 2018]. These will be integrated into the user-focused, open-source tool pyESD [Boateng and Mutz, 2023]. Both teaser projects will rely on two datasets: 1) meteorological observations that capture rainfall extremes in Scotland (i.e., the “predictand” dataset), and 2) a dataset used for SD model fitting (i.e., the “predictor” dataset).

Teaser Project 1: “Perfect Prognosis” Approach

In the perfect prognosis approach, SD models are constructed from observation-based datasets for both the predictand and predictors. These models, therefore, capture real-world conditions and relationships, aiding model validation and improving our physical understanding of local-scale predictand variability. Another strength of this approach is the ability to couple the SD models to any GCM or dataset (e.g., Ramon et al., 2021), making them highly transferable. The predictor dataset for Teaser Project 1 will be ERA5 reanalysis data [Hersbach et al., 2020]. The SD models will then be coupled to 21st century simulations conducted with the GCM ICON [Giorgetta et al. 2018] to predict future changes in rainfall extremes in Scotland.

Teaser Project 2: “Model Output Statistics” Approach

Model Output Statistics also uses an observation-based predictand dataset, but the predictor dataset is simulated with climate models (e.g., GCMs). The relationships captured in the resulting SD models do not reflect physical processes as much as in the perfect prognosis approach, and the SD models are fine-tuned to a specific climate model. However, when used in tandem with this climate model, the approach often produces more accurate results and excels at climate model bias correction (e.g., Sachindra et al., 2014). The predictor dataset for Teaser Project 2 will be ICON simulations for the present-day climate. The SD models will then be coupled to 21st century ICON simulations to predict future changes in rainfall extremes in Scotland.

References and Further Reading

  1. Alexander, L.V., Zhang, X., Hegerl, G. & Seneviratne, S.I. (2016). Implementation Plan for WCRP Grand Challenge on Understanding and Predicting Weather and Climate Extremes – the “Extremes Grand Challenge”. Version, June 2016 (click here)
  2. Boateng, D. & Mutz, S. G. (2023). pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information. Geosci. Model Dev., 16, 6479–6514 (click here)
  3. Coles, S. G. (2001). An Introduction to the Statistical Modeling of Extreme Values. London: Springer. Cuba, M.D., Wilkie, C., Scott, M. & Castro-Camilo, D. (2024+). Data fusion for threshold exceedances using a censored Bayesian hierarchical model. To appear
  4. Friederichs, P. (2010). Statistical downscaling of extreme precipitation events using extreme value theory. Extremes, 13, 109-132
  5. Giorgetta, M. A., Brokopf, R., Crueger, T., Esch, M., Fiedler, S., Helmert, J., et al. (2018). ICON-A, the atmosphere component of the ICON Earth system model: I. Model description. Journal of Advances in Modeling Earth Systems, 10, 1613–1637 (click here)
  6. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., et al. (2020). The ERA5 global reanalysis. Q. J. Roy. Meteor. Soc., 146, 1999–2049 (click here)
  7. Hewitson, B. C., Daron, J., Crane, R. G., Zermoglio, M. F. & Jack, C. (2014). Interrogating empirical-statistical downscaling. Clim. Change, 122, 539–554 (click here)
  8. IPCC (2023). Geneva, Switzerland, 35-115 (click here)
  9. Mutz, S. G., Scherrer, S., Muceniece, I. & Ehlers, T. A. (2021). Twenty-first century regional temperature response in Chile based on empirical-statistical downscaling. Clim. Dynam., 56, 2881–2894 (click here)
  10. Ramon, J., Lledó, L., Bretonnière, P.-A., Samsó, M. & Doblas-Reyes, F. J. (2021). A perfect prognosis downscaling methodology for seasonal prediction of local-scale wind speeds. Environ. Res. Lett., 16, 054010 (click here)
  11. Sachindra, D. A., Huang, F., Barton, A. & Perera, B. J. C. (2014). Statistical downscaling of general circulation model outputs to precipitation – part 2: bias-correction and future projections. Int. J. Climatol., 34, 3282–3303 (click here)
  12. Sniffer (2021): ‘Third UK Climate Change Risk Assessment Technical Report: Summary for Scotland’ (click here)

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