Science project 5: Volcanic impact on surface climate (VolClim)

Science goals

When the next large climate relevant volcano erupts, large changes in the Earth system are to be expected which will have an impact on seasonal and decadal climate predictability. It is therefore essential to ensure that we are prepared for future volcanic events under present day but also under future warmer climate. An assessment of the climate impact of potential future volcanic eruptions is however only possible if the main processes and pathways through which volcanoes affect climate variability and predictability are fully understood. This is in particular crucial for processes that are controlled largely by dynamical changes (Northern Hemisphere Winter circulation, tropical hydroclimate). Artificial Intelligence/Machine Learning (AI/ML) has become a powerful tool for prediction in many domains. A natural question to ask is whether these data-driven methods could be used to predict the climate response to volcanic eruptions in advance. Here, we combine our previous and ongoing work on the volcanic impact on climate with our expertise with AI/ML techniques.

In VolClim, we will develop a volAI system which consists of three neural networks based on deep learning applications:

 1) predAI to predict the surface climate response to volcanic eruptions;

 2) ensAI to enlarge single model run to ensemble size, and

 3) resAI. to combine with a super resolution approach large ensembles of low resolution model runs with an ultra high resolution run.

predAI and resAI will be based on a convolutional neural network (CNN), ensAI on a generative adversarial network (GAN). As all approaches are data driven, several training periods are necessary to investigate the process and results to improve certain prediction capabilities of the volAI.

Applying volAI, we will investigate if the seasonal to annual impact on tropical hydroclimate and NH surface winter climate of a potential future volcano is more predictable or precise, if AI/ML methods get trained using multi ESM data.


Participants:

Claudia Timmreck (PI, MPI-M, Hamburg), Christopher Kadow (Co-I, DKRZ, Hamburg), Johannes Meuer (Ph.D. student, DKRZ, Hamburg), Gabriele Hegerl (Mercator Fellow Univ. of Edinburgh, Edinburgh, UK)

Selected publications

Azoulay, A., Schmidt, H., and Timmreck, C.: The Arctic polar vortex response to volcanic forcing of different strengths, J. Geophys. Res., 126, e2020JD034450, doi.org/10.1029/2020JD034450, 2021.

D'Agostino, R. & Timmreck, C.: Sensitivity of regional monsoons to idealised equatorial volcanic eruption of different sulfur emission strengths. Environmental Research Letters, 17: 054001. doi:10.1088/1748-9326/ac62af, 2022.

lling, S., Kadow, C., Pohlmann, H., and Timmreck, C.: Assessing the impact of a future volcanic eruption on decadal predictions, Earth Syst. Dynam., 9, 701-715, doi.org/10.5194/esd-9-701-2018, 2018.

Kadow, C., Hall, D.M., and Ulbrich, U.: Artificial intelligence reconstructs missing climate information. Nat. Geosci. 13, 408–413, doi.org/10.1038/s41561-020-0582-5, 2020.

Timmreck, C., Pohlmann, H., Illing, S., and Kadow, C.: The impact of stratospheric volcanic aerosol on decadal scale predictability, Geophys. Res. Lett., 43, 834 – 842, doi: 10.1002/2015GL06743, 2016.