Optimising the Design of Ensembles to Support Science and Society (ODESSS)

Lead Research Organisation: London School of Economics and Political Science
Department Name: Grantham Research Inst on Climate Change

Abstract

This project will build the foundational techniques needed to understand what is necessary from an ensemble of computer model simulations to provide robust reliable knowledge. Such knowledge includes progress in scientific understanding of complex multi-component systems on the one hand, and guidance for societal decisions on the other. The focus is on climate models, where ensembles are a core element of research activities. The research will involve, indeed requires, integrating expertise across a range of disciplines.

Global climate models (GCMs) are complex, high-dimensional, discretized systems. They use the latest computer technology to solve a large number of simultaneous differential equations. Different disciplines and researchers view them in radically different ways and hence have very different perspectives on how to explore their errors and uncertainties. For physicists they are interpreted as representing physical understanding so the term "model error" encompasses their failure to effectively represent what we know about physical processes. For nonlinear dynamicists they are high dimensional systems of nonlinear equations so there is an expectation that profoundly different results could potentially arise as a consequence of uncertainty in both initial conditions and model formulation (model errors) of even the smallest degree. For risk analysts and forecasters they are generators of timeseries from which errors can be judged by relation to historic observations, although physicists and statisticians might be concerned that the extrapolatory nature of the climate change problem undermines such an assessment. For "users" such as adaptation planners and policy makers they provide climate projections which represent the starting point for their own work; any uncertainties provided by the scientists are assumed to be reliable estimates of the best current knowledge. This variety of perspectives leads to many different ways of interpreting the errors and uncertainties, and creates conflicting demands on the models themselves.

Projection uncertainties are typically quantified from ensembles of simulations which come in a variety of shapes and sizes. These ensembles are used to explore the impact of initial condition uncertainty (ICU - the consequence of not knowing the current state of the climate system when trying to make simulations of the future) and of model uncertainty (MU - the consequence of our models being different from reality). Today's ensembles (and models) have been built under the constraint of limited computational capacity so their designs start from the question: "what's the best we can do with today's technology?" By contrast, one of the unique and innovative aspects of this project is that its starting point is "what type and size of ensembles are necessary to provide the information we want?" It will develop designs for "aspirational ensembles" i.e. ensembles that are necessary to answer a particular set of questions without regard to current computational limitations. From this foundation it will evaluate the best way to approach the trade-offs necessary in building practical ensembles which DO allow for current computational limitations.

The approach taken will be twofold. First will be to use low-dimensional nonlinear systems to study the consequences of nonlinearity for ensemble design in climate like situations. Second will be to build a collaborative, collective picture of the demands and constraints on model ensembles from a wide range of different disciplinary and national perspectives.

The project will provide a solid foundation for future ensemble designs and will inform the widely debated computational conflict between uncertainty exploration, resolution and complexity. The latter two of these are much studied but there has been no comprehensive assessment of former. This project will fill that gap from the perspective of what is needed rather than what is available.
 
Description The nonlinear, chaotic nature of climate means that climate predictions are inherently probabilistic. These probabilities can be deduced within models by running multiple simulations from similar but not identical starting conditions This project has demonstrated, in a simple chaotic model, how these probability distributions can themselves depend on large scale features of how such models are initialised. It has also illustrated how this effect relates to conventional mathematical concepts such as an attractor or a pull-back attractor. It is hoped that an understanding of this effect will influence how future climate projections are made with complex climate models, and how the simulations of such models are interpreted.
Exploitation Route It has the potential to influence the design of ensemble experiments with complex climate models - experiments designed to provide information about future climate and the risks of tipping points.
Sectors Environment

 
Title Model output used in the manuscript "The evolution of a non-autonomous chaotic system under non-periodic forcing: a climate change example" 
Description This *.rar file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (Van Veen et al, 2001; Daron and Stainforth, 2013). To run these simulations, we used the E-forth ensemble generator (de Melo Viríssimo and Stainforth, in preparation), which is a MATLAB toolboox that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (de Melo Viríssimo and Stainforth, 2023). These model outputs are presented and discussed in the Preprint "The evolution of a non-autonomouys chaotic system under non-periodic forcing: a climate change example". The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to Daron and Stainforth (2013). All files uploaded were generated from simulations run by the authors. For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me. References: Van Veen et al. (2003): https://doi.org/10.1034/j.1600-0870.2001.00241.x Daron and Stainforth (2013): https://dx.doi.org/10.1088/1748-9326/8/3/034021 de Melo Viríssimo and Stainforth (2023): https://doi.org/10.5194/egusphere-egu23-14755 de Melo Viríssimo and Stainforth (2023): in preparation Note: This version (v1.1) is the same version as v1.0 but with the correct README file. 
Type Of Material Database/Collection of data 
Year Produced 2023 
Provided To Others? Yes  
Impact This dataset supports the results of the associated paper. How it was generated will hopefully support the design of future model ensembles. 
URL https://zenodo.org/record/8367809
 
Description AMOC Prediction and low order coupled models 
Organisation Indian Institute of Science Bangalore
Department Divecha Centre for Climate Change
Country India 
Sector Charity/Non Profit 
PI Contribution Understanding of the role of initial condition uncertainty in climate predictions and in low dimensional models under climate-change-like scenarios.
Collaborator Contribution A deeper understanding of the complexity of the response of low-dimensional models to climate change - particularly the potential for riddled basins of response. This work has specifically considered the response of the atlantic meridional overturning circulation (AMOC) in low-dimensional models.
Impact A joint-authored presentation at the European Geophysical Union conference 2023. A paper is in preparation.
Start Year 2017
 
Description STEM for Britain 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Policymakers/politicians
Results and Impact Poster on "IMPROVING CLIMATE PREDICTION AND IMPACT ASSESSMENT WITH MATHEMATICS" selected as a finalist in the STEM for Britain 2024 awards and presented to MPs in the Houses of Parliament.
Year(s) Of Engagement Activity 2024
URL https://stemforbritain.org.uk/