Using machine learning to constrain the atmospheric dynamics contribution to regional climate change

Lead Research Organisation: Imperial College London
Department Name: Physics

Abstract

"Global warming is the key metric in the public perception of climate change but regional changes, for example in weather extremes or rainfall, have a more direct impact on people's lives. These are particularly difficult to predict, however, so increasing confidence in regional impacts is arguably one of the most important challenges in present-day science. A large part of the uncertainty in regional projections arises from the complexity of atmospheric dynamics and its response to increasing atmospheric greenhouse gas concentrations.

The goal of this project is to use machine learning to build a data-driven mathematical framework for regional climate change that goes substantially beyond the simple global warming picture.

This framework will:

1) necessarily include both the thermodynamic and the dynamical response of the Earth system to greenhouse gas forcing. Here, we refer to thermodynamic mechanisms as those primarily driven by local changes in the energy budget, which is well reflected in variables such as surface temperature. Dynamical mechanisms broadly refer to shifts or modifications in the strength of the global atmospheric circulation, or changes in the remote coupling between regions of the atmosphere, which are referred to as teleconnections. Both components are intrinsically coupled due to the redistribution of energy (thermodynamics) within the Earth system as part of the circulation (dynamics).

2) put emphasis on an attempt to separate dynamical and thermodynamic drivers of regional change. Each driver will be evaluated using data from observations (e.g. satellite data) and the output of state-of-the-art climate models, i.e. sophisticated computer models used to make climate change projections.

Taking this framework as a basis, a first aim is to introduce novel metrics for regional climate change. Such metrics should be easily visualised and understood by non-experts, but better reflect the uncertainty in, and the importance of, the dynamical response including measures for extreme events such as heat waves, storms, droughts and floods. In addition, by focusing on certain world regions, the underlying physical drivers of uncertainty will be identified and tested concerning their potential to increase confidence in regional climate change projections.

The project will involve the modification and then application of machine learning algorithms to large climate datasets, for example to data from climate model simulations that is used to inform the Intergovernmental Panel on Climate Change (IPCC) and to data published by the European Centre for Medium-Range Weather Forecasts (ECMWF) or NASA. The ideal candidate should be able to demonstrate a keen interest in the physics of the Earth system and in testing out a number of different supervised and unsupervised machine learning algorithms. All coding will be carried out in Python. Good programming experience and familiarity with some machine learning packages (scikit-learn, TensorFlow etc) would be an advantage, but are not essential. Depending on the student's interests, high-resolution numerical models could be used to test the result of reducing uncertainty in coarser climate model projections on much finer spatial resolutions (e.g. county-to-city scale).
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Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
NE/R011613/1 30/09/2017 30/09/2023
2123640 Studentship NE/R011613/1 30/09/2018 29/06/2022 Carl Thomas
 
Description In my research I have studied atmospheric blocking patterns. These are complex large-scale weather patterns which block the path of the jet stream. They are associated with heat waves in summer and cold snaps in winter. Blocking is poorly understood, and the effect of climate change is not clear. In my research I have developed a new method to study blocking using unsupervised machine learning. I have shown that this method performs better than previous methods used. I have also developed a new data set which has classified these blocking patterns over European summer.

These results show the potential for unsupervised learning in atmospheric science.
Exploitation Route The new blocking index identification and accompanying data set can be used by others. A recent reviewer of the current article has stated that the work "provides a quite "revolutionary" and unique dataset to work with", and this can be used to develop understanding of the long-standing problem of objective blocking identification. This can work towards understanding blocking patterns better, and help solve the long-standing problem in climate science of how climate change is impacting the dynamics of the atmosphere. This long-standing issue is a key source of uncertainty in future projections of extreme events, and solving this issue will lead to better information for climate policy, particularly with regard to adaptation.
Sectors Environment

URL https://wcd.copernicus.org/preprints/wcd-2021-1/
 
Title 100 summers of blocking classification over Europe for UKESM1-0-LL pre-industrial control 
Description A unique classification of regional blocking events in a modern climate model. This has been used to develop and verify a new blocking index that has improved skill at classifying blocking events, particularly in climate models. This can help answer the outstanding question of how blocking events respond to climate change. 
Type Of Material Database/Collection of data 
Year Produced 2021 
Provided To Others? Yes  
Impact This dataset can help answer the outstanding question of how blocking events respond to climate change, and could be used to study the occurrence of blocking events with other methods. 
URL https://doi.org/10.5281/zenodo.4436225
 
Title ERA5 JJA European blocking classification 1979-2019 
Description A unique classification of regional blocking events created from the ERA5 reanalysis for the period 1979-2019, covering European summer. This has been used to develop and verify a new blocking index that has improved skill at classifying blocking events, particularly in climate models. This can help answer the outstanding question of how blocking events respond to climate change. 
Type Of Material Database/Collection of data 
Year Produced 2021 
Provided To Others? Yes  
Impact This dataset can help answer the outstanding question of how blocking events respond to climate change, and could be used to study the occurrence of blocking events with other methods. 
URL https://doi.org/10.5281/zenodo.4436206