Computer models are essential tools in the earth system sciences. They underpin our search for understanding the earth system functioning and support decision-making across spatial and temporal scales. Predictions of computer models though are conditional on a range of assumptions and input data that are often largely uncertain due to, among others, our limited understanding of earth systems processes and interactions, the simplified representation of spatial heterogeneity in our models, and errors and gaps in the data. To understand the implications of uncertainty and environmental variability on the identification and use of earth system models, we can rely on increasingly powerful Uncertainty and Sensitivity Analysis methods.
In this short course we will:

1) use a set of literature examples to demonstrate the benefits of using Uncertainty and Sensitivity Analysis to support the calibration, evaluation, and simplification of earth systems models and their use to inform decision-making
2) discuss some of the key methodological choices in the set-up of Uncertainty and Sensitivity Analysis and provide guidelines and best-practice examples on how to make such choices
The course will focus on Monte-Carlo methods for uncertainty propagation and Global Sensitivity Analysis (GSA) techniques, such as those discussed in (1) and (2). The course is intended for researchers and practitioners who already have experience of using these techniques as well as beginners.
For those who wants to get some hands-on understanding of GSA before and/or after the course, we have prepared some online tutorials in the form of interactive Jupyter Notebooks (these can be run from browser, no need to install any software):

https://mybinder.org/v2/gh/AndresPenuela/SAFE-Notebooks/HEAD