Bridging theory to reality in projections of the Asian and West African monsoons (Bridge)

In Asia and West Africa the majority of rain falls during the summer monsoon season. Monsoon rain is vital for agriculture, and a late or weak monsoon can mean disaster for crops, to the point where the Indian finance minister once described the monsoon as the country's 'real finance minister'. However, while we have a strong conceptual understanding of climatic changes controlled by thermodynamics (e.g. temperature, sea level), changes controlled by wind patterns (e.g. regional precipitation) are far less intuitive. State-of-the-art models struggle to correctly simulate patterns of monsoon rainfall in the present day, and predict a range of future changes. Without basic understanding of the wind circulations controlling the monsoons it is impossible to judge which predictions we can trust, both seasonally and under global warming.

Recently, major advances have been made in our understanding of the mechanisms controlling the monsoons by using very abstract model configurations: aquaplanets (planets covered only in water) and simulations including simple continents. By stripping back the complexity of the real world, these models have at last given us basic theories for the controls on when and where zonal-mean tropical rain falls. However, these successful theories have in general not yet been adapted to the regional scale, and this presents an enormous opportunity for a step-change in our fundamental understanding of regional monsoons, their variability and response to climate change. To address the challenge of connecting theory to reality, we have identified a novel approach combining machine learning methods with a hierarchy of model simulations and data.

The model hierarchy will allow us to study how monsoon circulations behave and theory performs as complexity increases. In particular, we will make use of a new, highly-configurable idealised climate model, Isca, which allows us to run simulations ranging from very simple aquaplanets up to a simplified model of Earth within a single, consistent framework. A major challenge in understanding regional monsoons is that the mathematics underpinning theory becomes highly complex at a local scale. However, machine learning has recently been applied to similar problems in oceanic science to identify regions governed by different key processes. We will use these techniques to simplify the mathematics and develop regional theories for monsoon rainfall.
Theories appropriate to each region will be used to interpret the behaviour of the latest state-of-the-art climate models. We will use both simulations of past and future climate, and more idealised simulations targeted at identifying differences between models in simulating processes contributing to climate change (e.g. sea ice, plant physiology). This should help in understanding biases in simulations of historical climate and constraining intermodel differences in projections of climate under global warming. By identifying which models can be trusted to simulate the monsoons and the drivers of future changes, it should be possible to produce more robust and useable projections for these key regions.
The final phase of the project will test whether different theories are needed to understand monsoon behaviour on different timescales. Do theories for climatological rainfall also explain rainfall variations week-to-week, or decade-to-decade? Do these processes have a lead time which could provide information for subseasonal-to-seasonal or decadal forecasting? Can theoretical insight help us untangle how decadal variations in sea surface temperature modulate the processes governing interannual variability in monsoon rain?

By approaching these complex problems from a new perspective, Bridge aims to at last build the same level of confidence in our predictions of circulation-governed monsoon rainfall as we have in thermodynamically controlled climate features.