Can the public make sense of uncertainty in weather and climate prediction?
- 11 Oct 2012, 14:00
- Roz Pidcock
Despite it being a good idea to carry sunglasses, an umbrella
and snow provisions at all times in the UK to be truly prepared,
people generally want to be told something certain about the
weather. But forecasting how earth's climate system will evolve in
the next few hours, days, weeks and months is far from an exact
Following our blog
last week from the
Royal Society meeting on uncertainty in weather and climate
prediction, we take a look at what uncertainty in forecasting means
to the general public.
In forecasts we see online,
climate scientists express some of the uncertainty by giving
probabilities rather than a precise prediction. For example, the
Met Office predict a 80 per cent chance of rain in London this
afternoon. But when the Met Office started to do this last year,
complaints in some parts of the media revealed some of the
issues scientists face.
As Liz Stephens from the University of Bristol explained to the
Royal Society meeting on Friday, a
Daily Mail piece complained that the extra information was
confusing and that it could be used as "a way to deflect blame"
when people found themselves caught in unexpected downpours.
But people have a fundamental understanding of uncertainty and
use it everyday to make decisions. As Professor Peter Webster from
the Georgia Institute of Technology explained:
"Ask people how they cross the busiest
road in their area, they assess risk all the time".
We tend to accept uncertainty when doctors talk about medical
treatments in terms of the chance of success. So why do people
trust climate scientists less when they talk about uncertainty in
weather and climate forecasts?
Weighing up the
Scientists use their expert knowledge and thousands of daily
observations from across the globe to design computer models that
mimic the climate system. They run slightly different versions of
the model to determine the most and least likely outcomes for how
the weather could change over time. This is called an
Even the best models are approximations of reality because the
real climate system has levels of complexity that we cannot fully
capture this way, or even yet comprehend. As Professor Tim Palmer,
organiser of the meeting, explained in an interview with Carbon
"Inevitably there are errors and
uncertainties in these observations and the observations don't
cover the entire globe so the initial state will be slightly
Michael Fish's infamous BBC forecast
of a storm that hit south east England in 1987. The models
predicted England would escape most of the damage, with the storm
passing over the channel into Europe.
Ensemble forecasting is also the way scientists work out
seasonal and long-term forecasts. But uncertainties amplify over
time, which means that scientists' ability to accurately mimicking
the real climate system decreases for longer forecasts. Palmer
famous Michael Fish storm of 1987 was a classic case where tiny
uncertainties in the position of a low pressure system over the
Atlantic made an enormous impact on whether it amplified or
From a scientific perspective, quantifying uncertainty with
probabilities is a good thing that leads to more reliable
forecasts. But it also presents a challenge. As the Met Office's
Ken Mylne explained to the meeting:
"We can't get away from the uncertainty
of the climate system, so we have to find ways to communicate
Many techniques exist for presenting weather forecasts, ranging
from a simple 'deterministic' prediction of what the weather will
be like - for example, 'overcast', 'sunny intervals' or 'light
rain' - to a complex array of probability information and graphics.
For example, the UK Met Office website
offers users a forecast which shows the maximum and minimum
forecasted temperatures as well as the most likely temperature.
One way the Met Office show
uncertainty in weather forecasts is by showing the full range of
forecast temperatures, not just the most likely.
To explore whether some ways of displaying uncertainty are more
effective than others, the Met Office launched a six-week public
survey last year called
"The Weather Game".
Preliminary analysis of the results from the 8,000 participants
suggests that adding uncertainty information to a forecast did not
confuse people. Interestingly, adding an image such as an umbrella
or a shaded bar to the likelihood of rain, expressed as a
percentage, did not improve people's ability to interpret the
forecast. The fact that cold hard numbers are particularly
effective is perhaps a surprising result, but as Mylne explained in
an interview with Carbon Brief today:
"People might think that they are
confused by a number but it's unambiguous, whereas people might
interpret images or words like 'medium risk' in different
It's important to realise that people will have different
preferences and that different methods might work for different
media, however. Stephens, the Weather Game project's leader, told
Carbon Brief yesterday:
"It might be that if the probability of
precipitation was presented in a different setting, like on the
television, we'd get different results".
The Met Office isn't the only organisation experimenting with
uncertainty information. The BBC commissioned some research into
the best ways to communicate probability, the results of which
should be released soon.
Where do we go from
The message from the research seems to be that, despite concern
in the media, complexity is not a bad thing when it comes helping
people understand uncertainty in weather forecasting.
For longer-term climate forecasts, scientists do not have the
luxury of comparing forecasts to the actual weather every few weeks
to continually improve their models. Instead, the uncertainty needs
to be gradually reduced through more long-term observations and
improving scientists' knowledge of the climate system through
As Mylne told us, the key to improving understanding of short
term weather forecasts, extreme weather forecasts and longer term
climate forecasts is familiarity.
"We need to start getting these
[probability forecasts] out there more...then it will be so much
easier for people to understand things like seasonal forecasts and
long-range forecasts that they don't see everyday".
But he agrees that this shift is likely to be more of a marathon
than a sprint. If just a small proportion of people understand
today why uncertainty in weather and climate prediction is not a
dirty word, more of the next generation will, and so on until
probability forecasting becomes the norm.