Machine learning approaches to constrain and understand the role of clouds in climate change (ML4CLOUDS)

As a defining challenge of our time, climate change has led to the 2015 Paris Agreement whose central policy goal is to keep global warming well below 2 degrees Celsius. The substantial remaining uncertainty in physical climate change projections, however, means that there is a very wide window of the dates within which this threshold might be passed. Assuming continuous greenhouse gas emissions, it could be within the next decade, or it might not be until well into the second half of this century. To inform their decision-making, policymakers urgently need this uncertainty reduced. Our research proposal, ML4CLOUDS, addresses the leading role of clouds in this uncertainty, and the coupled implications for climate variability.

Clouds are ubiquitous phenomena covering around two thirds of Earth's surface at any time and, as such, play key roles in our climate system. Crucially, clouds are the single most important uncertainty factor in global warming projections under increasing atmospheric carbon dioxide (CO2) concentrations. Clouds are also key modulators of the main modes of climate variability, such as the El Niño Southern Oscillation (ENSO), which in turn drive regional climate and weather extremes. A better understanding of the response of clouds and their interactions with the atmospheric circulation and global warming has therefore been highlighted as one of the 7 Grand Challenges by the World Climate Research Programme. Constraining cloud-related uncertainties, and understanding the underlying physical drivers, would consequently be invaluable to society.

The fundamental role of clouds primarily arises from their interaction with Earth's energy budget. Low-altitude clouds are highly reflective for sunlight (having a cooling effect on climate), while upper tropospheric clouds trap radiation emitted from the Earth (having a warming effect). Cloud formation itself releases latent heat to the atmosphere. It is the overall impacts of these processes on atmospheric temperature and the hydrological cycle that make clouds so important for the behaviour and evolution of the climate system.

ML4CLOUDS aims to provide a better understanding of the complex physical control mechanisms driving cloud formation. This will improve our ability to predict how Earth's cloud cover will change under human influences such as increasing atmospheric CO2 and aerosol pollution, and thus reduce uncertainty in global warming. This reduction in cloud-related uncertainty will also feed back on our ability to model and comprehend present-day climate variability, and on how we expect the main climate modes, such as ENSO, to change in the future.

We will achieve these goals through a novel approach incorporating artificial intelligence (or machine learning) methods, paired with targeted climate feedback analyses and state-of-the-art climate model simulations run on supercomputers. Specifically, our project will:

1. Use machine learning to derive cloud-controlling relationships from large climate model datasets and from space-based observations. These relationships will provide improved estimates of the cloud response and significantly reduced uncertainty in physical climate change projections. They will further provide new insights into the relative importance of distinct physical mechanisms behind the cloud response. Cloud-controlling relationships learned from observations will also be helpful to inform future climate model development, e.g. of the new UK Earth System Model (UK-ESM).
2. Improve our understanding of the role of clouds in modulating the main modes of climate variability. Next to its importance for extreme weather, climate variability is superimposed on long-term trends due to man-made climate change. A better understanding of the role of clouds in climate variability will therefore enhance our ability to detect and attribute historical climate change, and to predict future changes in climate and its extremes.