Unseasonal summer doesn't make climate models wrong
- 04 Jul 2012, 15:44
- Verity Payne
Everyone knows we Brits love to grumble about the weather. Now
climate skeptic commentators are exploiting our love of a good
weather-related moan to suggest projections about long-term climate
can't be right. But seasonal projections - a developing area of
climate forecasting - are quite different beasts from more mature
techniques like short term weather forecasting and long-term
climate modelling. We've taken a look.
The Met Office comes in for a lot of criticism when Britain's
suffering bad weather. In part, this is probably more of that love
of grumbling we were talking about - as Rob Varley, Met Office
operations and services director told the
Telegraph earlier this month:
"There is a link between weather that
people don't want and criticism of what we do."
Over recent weeks, skeptic commentators
Christopher Booker and
Matt Ridley, along with lobby group the
GWPF have all focused on the terrible weather in order to
return to one of their favourite arguments.
The argument goes: the Met Office's seasonal forecast - for
April, May and June - didn't predict the record rainfall we've
experienced, so this calls into question projections of climate
decades or centuries into the future.
"[W]hy should we believe long-term
'climate' predictions from global warming scaremongers like the Met
Office when they can't even get the weather forecast right from one
week to the next?"
Climate is what you expect. Weather is what you
First up, this argument confuses climate and weather. Weather is
the short-term - day to day, week to week - change in atmospheric
conditions. Climate, on the other hand, is the long-term average of
weather conditions. To work out how climate is changing scientists
tend to use records spanning at least
The website Climate
Bites has some useful analogies for the difference between
weather and climate, including:
"Drawing conclusions about climate by
looking at the weather is like saying 'I lost 2 lbs yesterday!'
Every veteran weight-watcher knows that one day means nothing. It's
the long term trend that counts."
And this video is useful:
Having said this, there is some interesting new research in this
area which suggests a different story. Physicist Shaun Lovejoy and
suggest that what we traditionally think of as climate - the
predictable average of weather - is actually 'macroweather'.
Lovejoy proposes climate fluctuations act on scales greater than
10 - 30 years, and may be controlled by less predictable drivers,
such as deep ocean currents. This is quite new stuff, but it's
worth keeping an eye on - if true, it will have implications for
uncertainty in climate projections. More from Lovejoy lower
Prediction is difficult. Especially about the
This fundamental distinction between weather and climate means
that forecasting the two are rather different propositions,
although the models used to simulate both weather and climate are
basically the same.
The main thing that determines a day to day weather forecast is
the data that gets put into the model - observations of recent
weather such as temperature, wind speed, wind direction, and how
much rain or snow there's been, from as many locations as possible.
These data are known as the 'initial values'. Computer models
predict how the weather might evolve over the following days, so to
a great degree the quality of these measurements decides the
quality of a weather forecast.
Any small errors in these observations can quickly escalate as
time moves on, so the further into the future a model forecasts,
the less accurate it becomes. (You can see for yourself how
accurate the Met Office's recent short term weather forecasts have
Forecasting the exact weather many years or decades ahead is
simply not possible, so these initial values are not so important
for climate projections. Instead, in climate modelling, the aim is
to work out long-term climate trends caused by likely future
changes to climate drivers - things like how much sunlight there'll
be, how heat moves through oceans, or greenhouse gas levels.
How well the model can simulate changes to climate due to these
climate drivers determines the quality of climate projections. By
repeating model simulations many times, scientists can get an idea
of the range of likely outcomes caused by future changes in climate
drivers. But this is quite a different thing than trying to do a
weather forecast for the next hundred years.
Seasonal projections are a work in progress
Seasonal forecasts sit somewhere between climate and weather
forecasts, and only give the likelihood of experiencing different
types of conditions compared to normal - for example how 'dry',
'near-average' and 'wet' it might be. For April, May and June the
"The [seasonal] forecast for average UK
rainfall slightly favours drier-than-average conditions for
April-May-June as a whole, and also slightly favours April being
the driest of the 3 months [...] The probability that UK
precipitation for April-May-June will fall into the driest of our
five categories is 20-25% whilst the probability that it will fall
into the wettest of our five categories is 10-15%"
The fact that the 'slightly favoured drier than average'
conditions didn't materialise doesn't really make the seasonal
forecast 'wrong'.The forecast gave a 10-15 per cent chance of there
being very wet conditions; it was indeed very wet.
Essentially, if you want to call a seasonal forecast 'right' or
'wrong', you're usually going to be able to do so. The Met Office
describes this as "the absurd situation that a single
probabilistic forecast is always 'right'. Simply, there is no way
of verifying a single probabilistic forecast." At the same time,
detractors of the weather forecasters will usually be able to find
part of the forecast that doesn't pan out.
Met Office Head of Climate Impacts Richard Betts told
his twitter followers:
"[S]easonal forecasting is very hard,
but we're giving it a go in order to learn and improve"
So the claim that because seasonal forecasts are 'wrong' we
shouldn't trust long-term climate projections is also based on a
misunderstanding of how to read the Met Office seasonal
All models are wrong, but some are useful
The uncertainties associated with long-term climate projections
- to 2100, say - are
likely to get larger. For example, Lovejoy told us that if his
new definition of climate is right;
"It means that [climate model]
projections past 10-30 years will be less reliable - the missing
mechanism could make anthropogenic effects either "better" or
"worse" depending on feedbacks. I don't think any of this
brings into question the reality of anthropogenic warming, however
it does increase the range of uncertainty associated with future
scenarios (chiefly beyond 10- 30 years)."
But does a large uncertainty range in climate model projections
mean we should just ignore the projections? Whether we chose to
accept or ignore model projections is a risk, as Professor Peter
Muller, University of Hawaii, explains:
"Not doing anything about the projected
climate change runs the risk that we will experience a catastrophic
climate change. Spending great efforts in avoiding global warming
runs the risk that we will divert precious resources to avoid a
climate change that perhaps would have never happened. People
differ in their assessment of these risks, depending on [things
like] their values. To a large extent the discussion about global
warming is about these different risk assessments rather than about
the fairly broad consensus of the scientific community."
So we can choose to accept or reject action based on climate
projections. But making the choice should probably be informed by
more than what the weather's been recently.