Bayesian Methods For Climate Impact Uncertainty Quantification (Ref: 4386)

In our Impact Science research programme, IBM Research are exploring how AI can be used to help build resilience to climate change by quantifying the risk of extreme climate events. One specific area of focus is the development of hybrid physics/data-driven climate impact models, which leverage both traditional simulations and a rapidly-increasing volume of in-situ and remote-sensed earth observation data. A key gap identified in the research to date is an appropriate methodology to address heterogeneities across models and data in resolution, domain, form and, most significantly, uncertainty. Failure to correctly combine and propagate uncertainties when building and deploying models can result in over-confident predictions which are at risk of being considered unreliable and inaccurate.

The PhD project will address this gap using Bayesian hierarchical modelling, which models the real world climate (and its potential impacts) as a latent, unobserved process, and observations and simulations as imperfect versions of this real world. The aim of the research is to enable hybrid models to produce reliable and well-calibrated probabilities of high-impact climate events, informed by all available sources of information, including simulation models and observational data sets. The methodology will be tested for real world applications in development at IBM.

The main work components will consist of:
* Development and application of methods to infer the error structures and interdependencies of relevant climate datasets and models (hosted in IBM PAIRS data repository and associated modelling infrastructure), using parametric statistical inference, and, where possible, expert knowledge;
* Development and application of Bayesian methods to propagate uncertainties in input datasets and models, and their relationships, to predictions of high-impact climate event probabilities (e.g. flood, drought, heatwave), in a mathematically coherent way;
* Application of the methods...