EGU25-14590, updated on 15 Mar 2025
https://doi.org/10.5194/egusphere-egu25-14590
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Quantifying Internal Variability Uncertainty in Regional Climate Projections using Artificial Intelligence
Steven Sherwood1, Neelesh Rampal1,2, and Peter Gibson3
Steven Sherwood et al.
  • 1University of New South Wales, Climate Change Research Centre, Sydney, Australia (s.sherwood@unsw.edu.au)
  • 2National Institute of Water & Atmospheric Research Ltd (NIWA), Auckland, New Zealand
  • 3National Institute of Water & Atmospheric Research Ltd (NIWA), Wellington, New Zealand

The large computational cost of Regional Climate Models (RCMs) means that only one ensemble member per climate model is typically downscaled; subsequently, internal variability uncertainty is generally not explicitly accounted for in coordinated regional climate downscaling efforts (e.g., CORDEX). Surrogate Artificial Intelligence-based emulators are several orders of magnitude faster than RCMs and have been well-tested in their ability to generate reliable regional climate projections. This study employs a Generative AI-based approach using Generative Adversarial Networks (GANs) to downscale daily precipitation from a large ensemble of climate projections from CanESM5 (n=20) and ACCESS-ESM-1-5 (n=40) at a 12km resolution for New Zealand. We show that this AI-based approach can reproduce key features including rainfall extremes and their increases in future climates with useful accuracy. Similar to previous studies using low-resolution climate models, our results show robust future changes in winter precipitation across the ensemble members, but significant uncertainty during summer. The large ensemble of downscaled climate projections better samples extremely rare localized extreme events, which are not adequately sampled using a single ensemble member. Using this ensemble, we can calculate the relative contributions of internal variability and model structural uncertainty (both GCM and downscaling) in climate projections of local-scale impact-relevant weather events. Overall, our study highlights the significant potential of AI to complete dynamical downscaling and allow quantification of internal variability uncertainty at regional scales.

How to cite: Sherwood, S., Rampal, N., and Gibson, P.: Quantifying Internal Variability Uncertainty in Regional Climate Projections using Artificial Intelligence, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14590, https://doi.org/10.5194/egusphere-egu25-14590, 2025.