It’s too hard to make business decisions in the face of climate uncertainty – here’s how ‘storylines’ could help

What will our climate look like in the future? It is hard to overstate not only the importance of answering this question, but also the challenges involved in doing so.

We know the climate is changing rapidly. But without information about where we are headed, planning – at personal, organisational and societal levels – becomes tricky, to put it mildly.

Because climate risks are also understood as financial risks, many countries around the world – including Australia – are moving to make climate risk reporting mandatory^[1]. Accordingly, this need for a plan can no longer be ignored.

But the way we currently communicate climate risk has some serious limitations.

Recent research^[2] led by Tanya Fiedler explores these limitations, and proposes that a new approach – incorporating the power of narratives – would be more useful and practical for organisations.

We all struggle with uncertainty

Why is painting a picture of our future climate to help us make decisions so difficult? Part of the answer lies in the way individuals make decisions under uncertainty^[3].

People tend to find it difficult to cope with uncertainty and ambiguity, often struggling when presented with probabilities. This can impact our decisions, leading to undesirable outcomes.

Research^[4] has also shown we find it difficult to respond to warnings that fall outside our lived experience.

The other part of the answer lies in the inherent complexity – and uncertainties^[5] – involved in developing a useful picture of the future.

The most common way to explore our future climate is to use global or regional climate models – complex mathematical simulations of our climate system. These have proven extraordinarily valuable to simulate how our climate will change due to increases in greenhouse gases.

They can project how temperature, rainfall, winds, fire risk and even hail risk are likely to change in future.

But projections are by definition uncertain, and using different models can provide different visions of the future.

The problem with zooming in

This uncertainty tends to increase both as you zoom in to particular locations and become interested in extremes^[7].

For example, how average winter rainfall is expected to change over southwest Western Australia could be relatively clear, but how extreme rainfall (capable of causing major floods) will change is far less clear.

When looking at the level of a postcode or single address we might not even know whether extreme rainfall will increase or decrease^[8].

That’s a problem for organisations trying to work out how to manage and prepare for such risks, often at the scale of an individual building. Modelling is precise, but not necessarily accurate enough for that sort of localised information.

This doesn’t mean climate models aren’t useful or don’t provide valuable information. It just means organisations may need to enhance the value of that information by combining it with other evidence.

Introducing ‘storylines’

Fortunately, there is a way to address both the behavioural and modelling issues that capitalises on how we most intuitively make sense of the world. This is through “storylines”.

Storylines^[10] were developed in the climate sciences to describe uncertain physical climate futures. They do this by employing expert judgement to prioritise an understanding of the “causal networks” that drive changes and extremes.

The valuable information held in climate model projections is combined with other types of evidence relevant to a location, to develop a plausible (and useful) story about what the future might entail.

Flood risk, for example, depends on a wide range of factors. These can include:

• the amount and intensity of rain
• whether heavy rain fell in the recent past
• changes to the catchment such as vegetation, soils and the nature of any upstream developments, including new roads or buildings.

A business only using changes in rainfall drawn from a climate or national-scale flood model for its risk assessment might “hard-wire” a future scenario that turns out to be unreliable at the scale they need^[11].

Under an alternative “storylines” approach, their best course of action to understand flood risk would be to work with experts to develop a narrative that describes changes in rainfall in addition to all other locally relevant factors.

This narrative can then be tested using traditional flood modelling methods to provide more robust and useful insights into how the local catchment will be impacted under conditions of changing rainfall.

The quantitative disciplines like finance, economics and accounting may struggle with the idea that a narrative might provide more decision-useful information than a number. Yet, research^[12] has shown that narratives can make an uncertain future more tangible than numbers, and thereby better aid with planning and decision-making.

We need a new toolkit

Answering the question “what will our future climate look like?” challenges us to think differently and to look for solutions outside the toolbox of established financial tools and techniques.

It challenges us to work – through interdisciplinary dialogue - with experts, disciplines and knowledge we might feel uncomfortable with.

Storylines could transform the way organisations understand and report their exposure to climate risk. This is unlikely to be easy, and we recognise taking quantitative information from a commercial provider may seem simpler. But it is a more honest and rigorous way of planning for the future climate.

References

1. ^{^} mandatory (asic.gov.au)
2. ^{^} research (onlinelibrary.wiley.com)
3. ^{^} make decisions under uncertainty (www.routledge.com)
4. ^{^} Research (link.springer.com)
5. ^{^} uncertainties (climateextremes.org.au)
6. ^{^} Chay_Tee/Shutterstock (www.shutterstock.com)
7. ^{^} extremes (www.ipcc.ch)
8. ^{^} increase or decrease (iopscience.iop.org)
9. ^{^} Detail from Matt Palmer/Unsplash (unsplash.com)
10. ^{^} Storylines (link.springer.com)
11. ^{^} unreliable at the scale they need (agupubs.onlinelibrary.wiley.com)
12. ^{^} research (www.sciencedirect.com)