Predicting the land ice contribution to sea level rise with Gaussian process emulation

Changes in the cryosphere are the leading component of global sea level rise. There is great uncertainty in what these changes will look like in the coming centuries, partly due to the unknown effects of the climate and ice sheet models used to model these changes. Modelling these contributions are necessary to understand how coastal communities and low-lying states will be affected by climate change; in order to do this, and to quantify the inherent uncertainties to make more informed estimates, statistical methods are required.

 

Here we describe our work building on Edwards et al. (2021) in the use of Gaussian process emulators to predict the land ice contribution to future sea level rise. Rather than building an emulator of an ensemble of ice sheet models, we emulate each model individually, allowing us to better understand the inherent biases and internal variability within each model. We then compare our combined estimates with our previous results to test how treating each model individually affects our predictions. 

 

We predict changes for different Shared Socioeconomic Pathways (SSPs), to investigate how different future levels of greenhouse emissions will affect sea level rise this century. We also explore differences in sensitivity of the models to different inputs, building a range of sea level predictions. In particular, sensitivity to the basal melt parameter in Antarctica has a significant effect on the upper tail of our distributions; further analysis of other inputs will also be explored.