0:00:02 > 0:00:05THUNDER RUMBLES It's variable, it's hard to predict,
0:00:05 > 0:00:09it has a massive impact every hour of every day.
0:00:09 > 0:00:11It is, of course, the weather.
0:00:13 > 0:00:16I'm Alok Jha,
0:00:16 > 0:00:18and I'm a science journalist.
0:00:20 > 0:00:23I want to investigate how, through history,
0:00:23 > 0:00:26people have tried to predict what the weather will do.
0:00:28 > 0:00:30That's what this series is about.
0:00:30 > 0:00:33The story of the extraordinary characters who took on
0:00:33 > 0:00:36one of the hardest problems in science -
0:00:36 > 0:00:37how to forecast the weather.
0:00:39 > 0:00:41In this episode, the rise of the machines...
0:00:43 > 0:00:47..how cutting-edge technology has helped some extraordinary men
0:00:47 > 0:00:49and women transform meteorology.
0:00:53 > 0:00:58The music lover who created the first computerised weather forecast.
0:00:59 > 0:01:03The technocrat who took the UK Met Office to a brave new world.
0:01:04 > 0:01:08And the mild-mannered mathematician who discovered chaos theory.
0:01:11 > 0:01:13Since the Second World War, meteorology has become
0:01:13 > 0:01:18one of the most highly-advanced scientific undertakings in history.
0:01:18 > 0:01:22To me, this is one of the great achievements of modern science.
0:01:22 > 0:01:25I compare it with mapping the human genome.
0:01:25 > 0:01:29Today we see weather and climate as a giant global system.
0:01:29 > 0:01:32Understanding what it has in store for us
0:01:32 > 0:01:35is one of the biggest challenges humans face.
0:01:37 > 0:01:40I'd like to know how much further we still have to go
0:01:40 > 0:01:43before the Earth's weather and climate systems
0:01:43 > 0:01:45reveal their deepest secrets.
0:01:56 > 0:01:57Over the last 70 years,
0:01:57 > 0:02:01weather forecasting has changed beyond recognition, becoming
0:02:01 > 0:02:06one of the largest scientific and technological endeavours on Earth.
0:02:08 > 0:02:12The story starts in the early 1950s, when the Met Office,
0:02:12 > 0:02:17a small government department based in Dunstable, was the only
0:02:17 > 0:02:21organisation charged with protecting the British public from weather.
0:02:23 > 0:02:27Its staff used techniques refined during the Second World War,
0:02:27 > 0:02:32gathering data from around 500 UK weather stations.
0:02:32 > 0:02:35They drew charts of low pressure systems and weather fronts.
0:02:35 > 0:02:38And then used their judgment to make a forecast.
0:02:43 > 0:02:48In the 1950s, meteorologists were still using methods
0:02:48 > 0:02:50that were really decades old.
0:02:50 > 0:02:51So, science was being used,
0:02:51 > 0:02:55they were capturing their ideas in terms of synoptic charts,
0:02:55 > 0:02:58and these were very useful for forecasting the weather, say,
0:02:58 > 0:03:0324 hours ahead, but it was still very much a hit and miss affair.
0:03:03 > 0:03:07Things may have stayed like this for years. But, then...
0:03:07 > 0:03:09LIGHTNING STRIKES
0:03:11 > 0:03:14..in the most sudden and savage way possible,
0:03:14 > 0:03:16the weather itself intervened.
0:03:20 > 0:03:23I'm in the North Sea, off the coast of Essex.
0:03:23 > 0:03:28It's calm here today, but on the night of January 31st, 1953,
0:03:28 > 0:03:33a huge storm surge tore through this area, causing one of the worst
0:03:33 > 0:03:36natural disasters in recent British history.
0:03:39 > 0:03:41It was a perfect storm.
0:03:43 > 0:03:45A low developed over the Atlantic,
0:03:45 > 0:03:47which swept around the top of Scotland.
0:03:49 > 0:03:53The air pressure dropped so much, the sea level rose by half a metre.
0:03:55 > 0:03:59This coincided with an unusually high spring tide.
0:04:00 > 0:04:05Then, vicious northerly winds tore down the east coast at 60 knots,
0:04:05 > 0:04:07nearly 70mph.
0:04:08 > 0:04:10That combination was deadly.
0:04:10 > 0:04:14In some places, the waves were up to 20 feet high.
0:04:14 > 0:04:17They battered this coastline, and overwhelmed sea defences.
0:04:18 > 0:04:21The result was utter devastation.
0:04:23 > 0:04:26Canvey Island in Essex was inundated.
0:04:29 > 0:04:33Local councillor Ray Howard was 11 at the time.
0:04:35 > 0:04:39We all went to bed early, being it was such a foul night.
0:04:39 > 0:04:44But not realising that, soon after going to bed,
0:04:44 > 0:04:48awoken by my sister, who come rushing into the room,
0:04:48 > 0:04:51saying that there was water gushing down the street.
0:04:53 > 0:04:57From Kent up to Lincolnshire, families like Ray's were stranded.
0:04:59 > 0:05:02The water was getting deeper and deeper,
0:05:02 > 0:05:05and then we saw boats coming down the street,
0:05:05 > 0:05:09with Army personnel in, and, obviously,
0:05:09 > 0:05:13they were shouting to say that they were going to start evacuating.
0:05:15 > 0:05:17Across England, Scotland and the Netherlands,
0:05:17 > 0:05:2132,000 people had to be evacuated.
0:05:22 > 0:05:24And more than 2,000 died.
0:05:27 > 0:05:29Communities would take years to recover.
0:05:32 > 0:05:36We came back onto the island, and it was a different place.
0:05:36 > 0:05:40I mean, the devastation those floods caused was unbelievable.
0:05:42 > 0:05:44The thing that will always stick in my mind is
0:05:44 > 0:05:49when I returned back to my school, and to see that boys and girls
0:05:49 > 0:05:52that was in your class were not going to return.
0:05:55 > 0:05:59So, how did the forecasters get it so wrong?
0:05:59 > 0:06:02There would have been some indication. The observations
0:06:02 > 0:06:05we collect would have allowed forecasters to see the fact
0:06:05 > 0:06:07there was a depression, that it was moving, and, roughly,
0:06:07 > 0:06:11what its track would be. And some sense that could result in floods.
0:06:11 > 0:06:14However, there wasn't a warning system.
0:06:14 > 0:06:16This is the big difference now.
0:06:16 > 0:06:20There wasn't a way of communicating that risk to the public,
0:06:20 > 0:06:23or to the police, or the fire folk
0:06:23 > 0:06:25in a way that they could do something about it.
0:06:29 > 0:06:30Action was urgently needed.
0:06:32 > 0:06:36The government immediately began to rebuild Britain's flood defences.
0:06:39 > 0:06:45And, for weather forecasters, the storm was a transformative moment.
0:06:45 > 0:06:49The storm of 1953 shocked the Met Office into action.
0:06:49 > 0:06:51The first thing they did was to institute
0:06:51 > 0:06:53a nationwide warning service for storm tides.
0:06:53 > 0:06:55But they knew they had to go further.
0:06:58 > 0:07:01The first priority was to improve how to inform
0:07:01 > 0:07:02the public about the weather.
0:07:07 > 0:07:11Fortunately, the early 1950s saw the widespread adoption of television.
0:07:12 > 0:07:16By 1953, more than three million Britons owned TVs.
0:07:19 > 0:07:22To exploit this, the Met Office and the BBC set about making
0:07:22 > 0:07:27forecasts more watchable by introducing on-screen presenters.
0:07:28 > 0:07:30Well, as most of you will have realised,
0:07:30 > 0:07:32it's been a very much better day today.
0:07:32 > 0:07:35In the south of England, it has been for quite some time...
0:07:35 > 0:07:37This is their first attempt at this new approach.
0:07:39 > 0:07:42A trial, filmed in the autumn of 1953
0:07:42 > 0:07:44with the Met Office's Jack Armstrong.
0:07:44 > 0:07:46..however, it's not going to last very long.
0:07:46 > 0:07:49As usual, there's another depression out in the Atlantic...
0:07:49 > 0:07:54BBC television began regular broadcasts like this in early 1954.
0:07:54 > 0:07:57By tomorrow, it is expected to be through into
0:07:57 > 0:08:01the extreme north-east corner of Scotland by midday.
0:08:01 > 0:08:04But the problem went beyond communication.
0:08:06 > 0:08:09Forecasters in the 1950s could reliably predict
0:08:09 > 0:08:12weather up to 24 hours ahead.
0:08:13 > 0:08:17But the 1953 storm showed that this wasn't enough time
0:08:17 > 0:08:19when warning large areas of the country.
0:08:20 > 0:08:24Forecasters had to find a new way of doing things.
0:08:26 > 0:08:30What they couldn't do was to really exploit the mathematical
0:08:30 > 0:08:33and physical underpinnings of the subject.
0:08:33 > 0:08:34So, this is it.
0:08:34 > 0:08:37If you're going to break that barrier, go into two days,
0:08:37 > 0:08:40three days, then you've got to be able to use the underpinning
0:08:40 > 0:08:43mathematics in a more powerful way.
0:08:45 > 0:08:47It had been known since the 19th century
0:08:47 > 0:08:52that the weather's behaviour obeys just seven mathematical equations.
0:08:53 > 0:08:55For a long time,
0:08:55 > 0:08:59scientists had theorised they could be used to improve forecasts.
0:09:00 > 0:09:04These seven equations tell us how wind speed and direction,
0:09:04 > 0:09:06pressure, air temperature,
0:09:06 > 0:09:09humidity, and density all interact with one another.
0:09:09 > 0:09:13So, it's something you can write down on one side of A4,
0:09:13 > 0:09:17and that is the basis of your model. It's as simple as that.
0:09:17 > 0:09:19The difficulty is in then solving it.
0:09:22 > 0:09:26In 1916, an English mathematician, Lewis Fry Richardson,
0:09:26 > 0:09:29had tried to solve the equations of the weather.
0:09:30 > 0:09:33He'd attempted this in his spare time between shifts
0:09:33 > 0:09:35as an ambulance driver in World War I.
0:09:38 > 0:09:40In doing so, he showed, in principle,
0:09:40 > 0:09:43that mathematics could be used to predict the weather.
0:09:45 > 0:09:49It was called numerical weather prediction, a technique that might
0:09:49 > 0:09:52finally take the guesswork out of weather forecasting.
0:09:54 > 0:09:56But the problem was the actual equations
0:09:56 > 0:09:59are mind-bogglingly complicated.
0:10:00 > 0:10:05Richardson laboured for two years, just to make a six-hour forecast.
0:10:06 > 0:10:10So, the technique was theoretically sound but far from practical.
0:10:15 > 0:10:19Then, some 30 years later, in the 1940s,
0:10:19 > 0:10:22a new invention changed everything.
0:10:29 > 0:10:32These are some of the first electronic computers.
0:10:34 > 0:10:36They soon found many applications.
0:10:36 > 0:10:40Everything from ballistics to accounting,
0:10:40 > 0:10:41even airline reservations.
0:10:44 > 0:10:47But the question was would they be powerful enough to produce
0:10:47 > 0:10:49useful weather forecasts?
0:10:53 > 0:10:56Very soon, meteorologists had hit a massive problem.
0:10:56 > 0:10:59The weather is a complex and ever-changing interaction
0:10:59 > 0:11:03between air temperature, pressure, humidity and cloud cover.
0:11:03 > 0:11:07The computers of the 1940s just couldn't handle that much data.
0:11:07 > 0:11:12Somebody had to make the equations of weather simple enough
0:11:12 > 0:11:14to run on these early computers.
0:11:17 > 0:11:20MUSIC: "Moonlight Sonata" by Ludwig van Beethoven
0:11:21 > 0:11:25The person who did this was an extraordinary character.
0:11:27 > 0:11:30An American mathematician called Jule Charney,
0:11:30 > 0:11:33and his inspiration was music.
0:11:44 > 0:11:48Charney once wrote to a friend to say that nature was a musician
0:11:48 > 0:11:50more like Beethoven than Chopin.
0:11:50 > 0:11:54What he meant was fewer higher-pitched, fiddly notes,
0:11:54 > 0:11:58and more chords, bass lines and grand expressions.
0:12:07 > 0:12:10Imagine the weather is like a musical instrument.
0:12:10 > 0:12:12The high notes ...
0:12:12 > 0:12:14HE PLAYS HIGH NOTES
0:12:14 > 0:12:17..represent the ripples and disturbances that barely register.
0:12:17 > 0:12:18Whereas the low notes...
0:12:18 > 0:12:20HE PLAYS LOW NOTES
0:12:20 > 0:12:22These are the things that really drive the weather system forward.
0:12:22 > 0:12:26What Charney did was to come up with a mathematical framework
0:12:26 > 0:12:28that allowed meteorologists to ignore and iron out
0:12:28 > 0:12:31these little ripples and disturbances,
0:12:31 > 0:12:33and focus instead on the things that really mattered.
0:12:35 > 0:12:36It was a crucial step.
0:12:37 > 0:12:41Charney had given computers a way to predict the movement of larger
0:12:41 > 0:12:45weather systems without getting bogged down in details,
0:12:45 > 0:12:47like small, local showers.
0:12:47 > 0:12:49Or at least that was the theory.
0:12:50 > 0:12:53Charney now had to make it work for real.
0:12:59 > 0:13:02On Sunday, 5th March, 1950,
0:13:02 > 0:13:05Charney kicked off the world's first numerical forecast.
0:13:09 > 0:13:12He used a machine not too different from this one.
0:13:12 > 0:13:15This is the Colossus at Bletchley Park, and it was designed
0:13:15 > 0:13:19and built to crack German codes in the Second World War.
0:13:20 > 0:13:24The machine Charney had was even bigger - the size of a room -
0:13:24 > 0:13:26and it took an entire team of people,
0:13:26 > 0:13:28working night and day, to keep it running.
0:13:31 > 0:13:33To test Charney's ideas,
0:13:33 > 0:13:36the team programmed their computer to recreate
0:13:36 > 0:13:42the weather across North America on four separate days in early 1949.
0:13:45 > 0:13:49They then compared the machine's predictions with the actual
0:13:49 > 0:13:51weather on those days.
0:13:51 > 0:13:52So, how did it do?
0:13:54 > 0:13:59Well, the computer did correctly predict a low over America,
0:13:59 > 0:14:01but it got some major details wrong.
0:14:02 > 0:14:04The real low moved much faster
0:14:04 > 0:14:08and covered a different area to the simulation.
0:14:08 > 0:14:10As it happened, their first forecast,
0:14:10 > 0:14:13and I quote, was "uniformly inaccurate".
0:14:13 > 0:14:18But, by looking at that, they began to understand how to do computer
0:14:18 > 0:14:21modelling, what the sensitivities were, how to develop them.
0:14:21 > 0:14:25And it set the starting point, I think, for what has now
0:14:25 > 0:14:29become the way that weather is forecast right around the world.
0:14:38 > 0:14:41Charney's experiment ensured that modelling the weather
0:14:41 > 0:14:43mathematically was the way forward.
0:14:49 > 0:14:52Looking back, you have to admire these early
0:14:52 > 0:14:55pioneers for their scientific ambition.
0:15:00 > 0:15:04I find it remarkable that those people who began to programme
0:15:04 > 0:15:08these equations into computers and have the bravery, almost,
0:15:08 > 0:15:12to try and model something as, as messy as the weather
0:15:12 > 0:15:15in computers, using equations.
0:15:15 > 0:15:18The fact that they tried, I think is incredible.
0:15:22 > 0:15:25The pioneers of computerised weather forecasting
0:15:25 > 0:15:28would need all the bravery and determination they could muster.
0:15:32 > 0:15:36In Britain, the story of how computers became central
0:15:36 > 0:15:39to our meteorological science was as much down to
0:15:39 > 0:15:43individual personalities as it was to raw technical innovation.
0:15:45 > 0:15:48To tell it, I've come to one of the world's most advanced
0:15:48 > 0:15:51weather measurement facilities, and it's in Hampshire.
0:15:51 > 0:15:54This is the Chilbolton Observatory.
0:15:54 > 0:15:57It's the biggest steerable radar antenna in the world
0:15:57 > 0:15:59being used for weather forecasting.
0:15:59 > 0:16:02It's gathering information about clouds.
0:16:02 > 0:16:04It's helping to steer meteorological flights,
0:16:04 > 0:16:08and it's even tracking weather satellites.
0:16:08 > 0:16:10If there's a symbol for how technological
0:16:10 > 0:16:13weather forecasting's become, this is it.
0:16:16 > 0:16:20It seems obvious now that something as complex as the weather
0:16:20 > 0:16:23would need equipment on this scale in order to keep track of it.
0:16:23 > 0:16:25But it wasn't always the case.
0:16:25 > 0:16:28When this kind of technology was being developed
0:16:28 > 0:16:30in the 1950s and 60s,
0:16:30 > 0:16:32many meteorologists were unconvinced
0:16:32 > 0:16:34that it would ever be useful to them.
0:16:36 > 0:16:41In those decades, a real tension developed between man and machine.
0:16:41 > 0:16:46Many were unconvinced that the raw calculating skills of computers
0:16:46 > 0:16:49would ever replace good old-fashioned human judgment.
0:16:50 > 0:16:54There was actually a great deal of scepticism in the community
0:16:54 > 0:16:57that even with modern electronic computers,
0:16:57 > 0:17:01they could actually do the calculations in a reliable way.
0:17:01 > 0:17:04In other words, they could see that there were plenty of pitfalls,
0:17:04 > 0:17:08plenty of opportunities for calculations to go wrong.
0:17:08 > 0:17:11And, therefore, they didn't really have the confidence
0:17:11 > 0:17:15that numerical techniques would actually be superior
0:17:15 > 0:17:16to a human forecaster.
0:17:19 > 0:17:22Overcoming this attitude would need a strong character.
0:17:22 > 0:17:26Someone who was both ambitious and a top-flight scientist.
0:17:28 > 0:17:33This is John Mason, a physicist with a long fascination for weather.
0:17:34 > 0:17:38He was appointed director-general of the Met Office in 1965,
0:17:38 > 0:17:40aged just 42.
0:17:41 > 0:17:46Dr Stan Cornford was there when Mason first took over.
0:17:46 > 0:17:51He didn't seem an attractive person at that stage.
0:17:51 > 0:17:54But, in fact, he was a very able man
0:17:54 > 0:17:56and he didn't underestimate his abilities.
0:17:58 > 0:18:00Mason knew the world was changing,
0:18:00 > 0:18:02the economy booming.
0:18:03 > 0:18:05Growing industries like aviation
0:18:05 > 0:18:10demanded ever more accurate and detailed forecasts.
0:18:10 > 0:18:14And he was convinced that computers were the best way to provide them.
0:18:15 > 0:18:22He ensured that we could use the best available scientific tools, I suppose.
0:18:24 > 0:18:27And, in particular, always making sure that we had the biggest
0:18:27 > 0:18:29and best computer that was possible.
0:18:29 > 0:18:33Mason was a very firm part of that
0:18:33 > 0:18:35and his leadership helped make it possible.
0:18:38 > 0:18:40Mason was a man in a hurry.
0:18:41 > 0:18:44With older and wiser colleagues telling him
0:18:44 > 0:18:47that computers weren't really up to the task of weather forecasting,
0:18:47 > 0:18:49he decided to force the issue.
0:18:52 > 0:18:54In November 1965,
0:18:54 > 0:18:59he organised the Met Office's first ever press conference.
0:18:59 > 0:19:01There, he made an announcement
0:19:01 > 0:19:03from which there could be no turning back.
0:19:05 > 0:19:06At the press conference,
0:19:06 > 0:19:09John Mason took the biggest gamble of his career.
0:19:09 > 0:19:12He said that from then on, all Met Office predictions
0:19:12 > 0:19:14would be based on computer calculations,
0:19:14 > 0:19:17rather than just human skill and experience.
0:19:17 > 0:19:21Brimming with confidence that many of his colleagues didn't share,
0:19:21 > 0:19:24Mason promised the assembled press something incredible.
0:19:24 > 0:19:27Two computer-based weather forecasts every day,
0:19:27 > 0:19:31that he said would be more accurate than anything they'd seen before.
0:19:34 > 0:19:35Further raising the stakes,
0:19:35 > 0:19:40Mason handed out copies to each of the journalists present.
0:19:40 > 0:19:41And here it is.
0:19:41 > 0:19:45The very first operational weather chart made by a computer.
0:19:45 > 0:19:47It might not look like much,
0:19:47 > 0:19:50but this is a landmark in modern weather forecasting.
0:19:52 > 0:19:56The computer-generated chart predicted calm and settled weather
0:19:56 > 0:19:58for the next day, for most of the country.
0:20:00 > 0:20:03It was vital for both Mason's reputation,
0:20:03 > 0:20:06and the Met Office's future as a hi-tech organisation,
0:20:06 > 0:20:09that the actual weather matched this forecast.
0:20:15 > 0:20:18Fortunately, the Met office computer got it right.
0:20:18 > 0:20:21The forecast that it made for November 3, 1965
0:20:21 > 0:20:25was an excellent match for what actually happened in the real world.
0:20:25 > 0:20:29Journalists, even politicians were impressed.
0:20:29 > 0:20:32That success allowed Mason to secure the funding he needed
0:20:32 > 0:20:36to remake the Met Office in his hi-tech vision.
0:20:37 > 0:20:39Mason's timing was impeccable.
0:20:39 > 0:20:42He put his faith in computers at a time
0:20:42 > 0:20:46when another spectacular breakthrough was about to take
0:20:46 > 0:20:49their ability to forecast the weather to new heights.
0:21:00 > 0:21:04Tiros, meaning Television And Infrared Observation Satellite,
0:21:04 > 0:21:07is more than just a dream.
0:21:07 > 0:21:10This experimental weather satellite means that man's vision
0:21:10 > 0:21:14is no longer limited to looking up at the clouds gathering above him.
0:21:17 > 0:21:21That was really the game changing moment.
0:21:21 > 0:21:23That was when scientists realised the potential,
0:21:23 > 0:21:28that if we could make measurements from space in a controlled way,
0:21:28 > 0:21:31ie with a satellite in a controlled orbit of the Earth,
0:21:31 > 0:21:35then that really would give us this all-encompassing global picture
0:21:35 > 0:21:37of what the atmosphere's doing now.
0:21:39 > 0:21:43These are some of the first images of our planet from space,
0:21:43 > 0:21:45showing the global weather system.
0:21:49 > 0:21:53This one, taken of Hurricane Esther in 1961,
0:21:53 > 0:21:56was the first direct evidence for how such storms develop.
0:21:59 > 0:22:03Satellites greatly improved computer-based forecasting,
0:22:03 > 0:22:08which needs the best information about how the weather is behaving now
0:22:08 > 0:22:10in order to calculate what it'll do next.
0:22:12 > 0:22:13Your computer models,
0:22:13 > 0:22:16you have to feed them with what we call the initial conditions.
0:22:16 > 0:22:20So, the conditions of the atmosphere now,
0:22:20 > 0:22:22you need to know that perfectly over the whole globe.
0:22:22 > 0:22:25Frankly, when the satellites came,
0:22:25 > 0:22:26that was really a revolution
0:22:26 > 0:22:29because that's what gave you these global pictures
0:22:29 > 0:22:32that we were waiting for.
0:22:32 > 0:22:34From the mid-1960s,
0:22:34 > 0:22:37satellites and computers have given us a powerful way
0:22:37 > 0:22:41to calculate how weather systems evolve
0:22:41 > 0:22:43from their initial atmospheric conditions.
0:22:50 > 0:22:52It's perhaps the biggest reason
0:22:52 > 0:22:56why we can have such a close relationship with the weather today.
0:22:58 > 0:22:59From what we should wear,
0:22:59 > 0:23:02to whether we should put the heating on.
0:23:03 > 0:23:05From what we choose to eat and drink,
0:23:05 > 0:23:08to when we leave the house.
0:23:08 > 0:23:10Even how we choose to get around.
0:23:12 > 0:23:16Nowadays, it's amazing how accessible and how accurate
0:23:16 > 0:23:19the short-term weather forecast has become.
0:23:19 > 0:23:21RADIO: Northwest Fitzroy...
0:23:21 > 0:23:25But despite this, the last 50 years have not been smooth sailing.
0:23:25 > 0:23:28..occasional rain, moderate or good.
0:23:28 > 0:23:29Occasionally poor.
0:23:29 > 0:23:31..Irish Sea.
0:23:31 > 0:23:33Southwest, five to...
0:23:34 > 0:23:37We all take weather forecasts for granted.
0:23:37 > 0:23:39But they're by no means perfect.
0:23:39 > 0:23:44In fact, there are very recent examples of when they've gone drastically wrong.
0:23:44 > 0:23:47Good afternoon to you. Earlier on today,
0:23:47 > 0:23:50apparently a woman rang the BBC and said she heard there was a hurricane on the way.
0:23:50 > 0:23:53If you're watching, don't worry, there isn't. But having said that...
0:23:53 > 0:23:56This is probably the most infamous weather forecast
0:23:56 > 0:23:58ever broadcast in the UK.
0:23:58 > 0:24:01On 15 October 1987,
0:24:01 > 0:24:05the BBC's Michael Fish joked with viewers about a hurricane
0:24:05 > 0:24:07that was rumoured to be heading towards Britain.
0:24:07 > 0:24:09He was dismissive of the idea.
0:24:09 > 0:24:11Most of the strong winds, incidentally,
0:24:11 > 0:24:14will be down over Spain and across into France as well.
0:24:14 > 0:24:15But there's a vicious-looking area
0:24:15 > 0:24:17of low pressure on our doorstep nevertheless,
0:24:17 > 0:24:19around about the Brittany area.
0:24:19 > 0:24:22And that is going to head across the southeastern corner of the country
0:24:22 > 0:24:24bringing, if nothing else, a lot of rain with it.
0:24:24 > 0:24:26But what happened next?
0:24:26 > 0:24:28Well, we all know what happened next.
0:24:33 > 0:24:34Good afternoon.
0:24:34 > 0:24:37The worst storms for hundreds of years hit the south of England
0:24:37 > 0:24:39earlier this morning, killing a dozen people
0:24:39 > 0:24:42and bringing the whole south-east to a halt.
0:24:42 > 0:24:45Gales of over 100mph smashed buildings
0:24:45 > 0:24:48and caused millions of pounds worth of damage.
0:24:48 > 0:24:49There was no warning.
0:24:49 > 0:24:53The weathermen were caught with their forecasts down.
0:24:53 > 0:24:55In the early hours of the 16th of October,
0:24:55 > 0:25:00the great storm of 1987 smashed into England as she slept.
0:25:01 > 0:25:07Winds of 122mph ripped across the south-east of the country.
0:25:07 > 0:25:11Roofs were torn from homes, power lines were down,
0:25:11 > 0:25:13road and rail networks were blocked,
0:25:13 > 0:25:17and in all, 18 people lost their lives.
0:25:21 > 0:25:23Most of the damage was done by falling trees,
0:25:23 > 0:25:25even huge mature ones like this
0:25:25 > 0:25:28were uprooted by the hurricane force winds.
0:25:28 > 0:25:32Here on Hampstead Heath, many of them still lie
0:25:32 > 0:25:35exactly as they fell on that stormy morning.
0:25:37 > 0:25:40Not surprisingly, people were furious.
0:25:43 > 0:25:45Well, joining me now from the London Weather Centre,
0:25:45 > 0:25:48is the BBC's weather forecaster Ian McCaskill.
0:25:48 > 0:25:50Well, Ian, you chaps were a fat lot of good last night.
0:25:50 > 0:25:52Well, we have been forecasting
0:25:52 > 0:25:56high winds and gales relentlessly since Sunday. We admit...
0:25:56 > 0:25:59I admit we weren't forecasting hurricane force winds
0:25:59 > 0:26:03and that's what we got and that's what we will get
0:26:03 > 0:26:06maybe once every 50 years, maybe once in a lifetime.
0:26:07 > 0:26:10By this time, meteorologists understood
0:26:10 > 0:26:12how a storm of this size develops.
0:26:14 > 0:26:18Fish and his colleagues had even forecast strong winds, as they
0:26:18 > 0:26:22recognised a massive low-pressure system moving in from the Atlantic.
0:26:24 > 0:26:27But the crucial moment when it suddenly intensified just
0:26:27 > 0:26:30hours before making land, happened in a narrow
0:26:30 > 0:26:35band of the Atlantic between any observation ships or weather buoys.
0:26:38 > 0:26:41It's impossible to know whether any lives could have been saved
0:26:41 > 0:26:45or damage avoided if the forecast had been any different.
0:26:45 > 0:26:48But what we do know is the whole episode shook
0:26:48 > 0:26:50confidence in the Met Office.
0:26:50 > 0:26:53This is the organisation that was meant to protect us,
0:26:53 > 0:26:56to warn us against the worst effects of the weather.
0:26:59 > 0:27:01Just as in 1953,
0:27:01 > 0:27:06this very public disaster was a wake-up call for forecasters.
0:27:07 > 0:27:10Was that a bad moment for the Met Office?
0:27:10 > 0:27:12Well, actually, on the day, probably yes.
0:27:12 > 0:27:14But in the longer term, no, it wasn't.
0:27:14 > 0:27:18It was a really good thing to have happened for us, because it
0:27:18 > 0:27:23made us think very seriously about how we can do this better.
0:27:23 > 0:27:25What science do we need to invest in?
0:27:25 > 0:27:28How do we transform our forecasting system
0:27:28 > 0:27:30so that this doesn't happen again?
0:27:31 > 0:27:34As a first step, an internal Met Office
0:27:34 > 0:27:37inquiry called for an increase in the quality
0:27:37 > 0:27:41and quantity of weather observations off the south-west coast of England.
0:27:46 > 0:27:47But that's not all.
0:27:47 > 0:27:51The Michael Fish broadcast was based on a computer model
0:27:51 > 0:27:54and it forced the Met Office to look again at how
0:27:54 > 0:27:56they used computers to make forecasts.
0:27:58 > 0:28:01Computer models, based on the strict rules of maths and physics,
0:28:01 > 0:28:05were supposed to take the guesswork out of forecasting.
0:28:05 > 0:28:08How could they possibly have failed so dramatically?
0:28:11 > 0:28:14The answer lay in the fact that the fundamental mathematical
0:28:14 > 0:28:18equations of the weather contained a truth about nature that
0:28:18 > 0:28:19no-one wanted to confront.
0:28:25 > 0:28:30Our weather forecasting model is based on some mathematics
0:28:30 > 0:28:33that has a rather special property to it,
0:28:33 > 0:28:36and it's a property that makes weather very interesting,
0:28:36 > 0:28:41and now and again, makes weather forecasting very difficult.
0:28:41 > 0:28:44What they had to face up to, after the '87 storm, was that there was
0:28:44 > 0:28:47something else there which just couldn't be left
0:28:47 > 0:28:50out of the forecasting picture any longer.
0:28:54 > 0:28:58In fact, the way scientists understood the world had to
0:28:58 > 0:29:00be completely revolutionised.
0:29:01 > 0:29:03By the start of the 20th century,
0:29:03 > 0:29:06scientists saw the world like a clockwork toy.
0:29:06 > 0:29:08If you understood the parts of the system,
0:29:08 > 0:29:11and how those parts interacted with each other,
0:29:11 > 0:29:14you could predict everything about that system's future,
0:29:14 > 0:29:17whether it's the orbit of the planets around the Sun or
0:29:17 > 0:29:19a ball flying through the air.
0:29:19 > 0:29:22And weather prediction was going along similar lines.
0:29:22 > 0:29:24If you could understand how the air moved,
0:29:24 > 0:29:26then you could predict the weather.
0:29:26 > 0:29:30Unfortunately, a discovery in the mid-20th century shattered
0:29:30 > 0:29:32those dreams of absolute predictability.
0:29:32 > 0:29:35And it's something you've probably heard of.
0:29:35 > 0:29:36Chaos theory.
0:29:38 > 0:29:42This told scientists that however perfectly they understood a system,
0:29:42 > 0:29:47making accurate predictions about its future were almost impossible.
0:29:47 > 0:29:50This completely changed how they saw the world, and for weather
0:29:50 > 0:29:55forecasters, it meant that uncertainty and unpredictability
0:29:55 > 0:29:58had to be brought right to the heart of what they were doing.
0:29:59 > 0:30:04It all started with these unremarkable looking squiggles.
0:30:04 > 0:30:05But make no mistake,
0:30:05 > 0:30:10this is one of the most significant images in the history of science.
0:30:11 > 0:30:14The first-ever proof of chaos theory.
0:30:19 > 0:30:25To tell the story, we have to go back in time to 1965, to the USA
0:30:25 > 0:30:28and the Massachusetts Institute of Technology.
0:30:31 > 0:30:34Here, a mild-mannered meteorology professor called
0:30:34 > 0:30:38Edward Lorenz worked with early computers, generating weather
0:30:38 > 0:30:43models as a way to combine his two great loves - maths and weather.
0:30:45 > 0:30:48Edward Lorenz had a set-up a bit like this one.
0:30:48 > 0:30:51He'd input data here about the weather - things like temperature
0:30:51 > 0:30:55or air pressure - the computer would take those numbers, run them
0:30:55 > 0:30:59forward in time and then print out its results here as a graph.
0:30:59 > 0:31:02That, in essence, was the weather forecast.
0:31:02 > 0:31:06Lorenz's model was incredibly basic.
0:31:06 > 0:31:09Using just three mathematical equations to simulate
0:31:09 > 0:31:15how his initial conditions might change in a real weather system.
0:31:15 > 0:31:19But from his early results, it looked as though it was doing a good
0:31:19 > 0:31:25job, outputting weather patterns that seemed to mimic the real thing.
0:31:26 > 0:31:31So far, so good, but Lorenz wanted to check the simulation,
0:31:31 > 0:31:36so he decided to run the whole thing again, using the same starting data.
0:31:36 > 0:31:40All he wanted to do was repeat the results from his first run
0:31:40 > 0:31:44to prove that his model was accurate and reliable.
0:31:44 > 0:31:46But what he saw shocked him.
0:31:49 > 0:31:53Lorenz expected that both his runs would produce identical graphs,
0:31:53 > 0:31:56and at the start, that's exactly what they did.
0:31:56 > 0:31:59But very soon, the curves began to diverge,
0:31:59 > 0:32:01and by the time you get over here,
0:32:01 > 0:32:05they are two separate curves altogether, and that made no sense.
0:32:05 > 0:32:09Why would the computer plot two different curves from the same
0:32:09 > 0:32:11starting data?
0:32:11 > 0:32:14It was a mystery.
0:32:14 > 0:32:18The model itself hadn't changed, and as far as he knew, Lorenz
0:32:18 > 0:32:23had put in his data exactly as he had recorded it from the first run.
0:32:23 > 0:32:27Why on earth was the second curve so different to the first?
0:32:32 > 0:32:34The answer turned out to be remarkably simple.
0:32:34 > 0:32:39Lorenz's computer stored its data to six decimal places,
0:32:39 > 0:32:41but to save time, in a second run,
0:32:41 > 0:32:44Lorenz had only input the data to three decimal places.
0:32:44 > 0:32:47It was a small change, a fraction of a percent,
0:32:47 > 0:32:49but it made all the difference
0:32:49 > 0:32:53and it accounted for those wildly different graphs that he saw.
0:32:55 > 0:32:58This is the key idea in chaos theory -
0:32:58 > 0:33:03an immeasurably small difference at the start of a process can
0:33:03 > 0:33:06have huge consequences to how it ends up,
0:33:06 > 0:33:10and this effectively makes that process unpredictable.
0:33:13 > 0:33:17One of the hardest ideas to get your head around in science
0:33:17 > 0:33:21is that while some systems are inherently predictable...
0:33:22 > 0:33:26..others seem inherently unpredictable.
0:33:32 > 0:33:36These unlikely visual aids represent the key
0:33:36 > 0:33:39to explaining what Lorenz saw in his results.
0:33:41 > 0:33:43And it's all to do with their shape.
0:33:48 > 0:33:51This football is a sphere, and when I drop it on the ground,
0:33:51 > 0:33:54I know where it's going to end up.
0:33:54 > 0:33:56Even if I change the starting conditions and drop
0:33:56 > 0:34:00it from higher or at a different angle, I can predict its path.
0:34:01 > 0:34:05Things are very different for this rugby ball.
0:34:05 > 0:34:09When I drop this, I honestly don't know where it will end up.
0:34:17 > 0:34:19Now, every time I drop this ball,
0:34:19 > 0:34:23it went in a completely different direction, which makes no sense,
0:34:23 > 0:34:27because both of these balls operate under the same laws of physics.
0:34:27 > 0:34:29The only difference really is the shape,
0:34:29 > 0:34:32but that difference turns out to be crucial.
0:34:32 > 0:34:35Every time I drop this ball, even the very slight
0:34:35 > 0:34:38differences in the starting conditions, accidental ones
0:34:38 > 0:34:43really, will make huge differences in how the ball bounces around.
0:34:43 > 0:34:45And this is how the weather works.
0:34:45 > 0:34:48When Lorenz was making his computer models, he thought
0:34:48 > 0:34:51that weather operated a bit like this football - predictably.
0:34:51 > 0:34:54In fact, weather is much more unpredictable,
0:34:54 > 0:34:56like this rugby ball.
0:34:59 > 0:35:02At its core, this is the very essence of chaos,
0:35:02 > 0:35:06where a tiny error at the outset doesn't make for a tiny difference
0:35:06 > 0:35:10in the outcome, it makes for a different outcome entirely.
0:35:13 > 0:35:16In the case of the 1987 storm,
0:35:16 > 0:35:19the tiny error came from missed observations of the atmospheric
0:35:19 > 0:35:23conditions in a small part of the North Atlantic.
0:35:27 > 0:35:31So what this reveals is that actually just missing one
0:35:31 > 0:35:35bit of crucial information can, now and again,
0:35:35 > 0:35:38and it should be stressed, now and again, make a big difference.
0:35:38 > 0:35:42And that is the hallmark of chaotic behaviour in the atmosphere.
0:35:45 > 0:35:48The lesson from the 1987 storm was clear -
0:35:48 > 0:35:52computerised weather prediction had to be reformed,
0:35:52 > 0:35:57so it could deal with the chaos inherent in weather.
0:35:57 > 0:36:00This is the European Centre for Medium-Range Weather Forecasts
0:36:00 > 0:36:03near Reading.
0:36:05 > 0:36:09In the 1980s, Tim Palmer worked with a team here to come up with
0:36:09 > 0:36:12a new style of forecasting to combat chaos.
0:36:12 > 0:36:15- Hello, Tim.- Hi, Alok, how are you doing?- This looks incredible.
0:36:15 > 0:36:17Welcome to the European Weather Centre.
0:36:17 > 0:36:19So just tell me what we're looking at here.
0:36:19 > 0:36:21We're looking at an ensemble forecast,
0:36:21 > 0:36:24this is a modern way of doing weather forecasting.
0:36:24 > 0:36:28An ensemble isn't just one forecast, but 50.
0:36:28 > 0:36:32Forecasters give their computer a range of starting conditions,
0:36:32 > 0:36:35rather than a single best guess, like they did before.
0:36:35 > 0:36:38What we're seeing here are the 50 individual weather forecasts
0:36:38 > 0:36:40that make up an ensemble.
0:36:40 > 0:36:42They are run from almost but not quite identical starting
0:36:42 > 0:36:48conditions and then we move these 50 models forward in time to next week,
0:36:48 > 0:36:51and then we look to see, do all these 50 forecasts stick together?
0:36:51 > 0:36:54In which case, one can be quite confident about what the future
0:36:54 > 0:36:58weather is going to be, or do they all diverge and do their own thing?
0:36:58 > 0:37:01This is kind of shown more clearly here, where we've zoomed in on just
0:37:01 > 0:37:05a few of these individual members, if you like, forecasts.
0:37:05 > 0:37:09So for example, this one would be a rather unpleasant day
0:37:09 > 0:37:13across much of the UK - blustery, cold, very windy type of day,
0:37:13 > 0:37:16not the day to have a picnic, by any means.
0:37:16 > 0:37:18But just move one member over, so 23 to 24,
0:37:18 > 0:37:21and this actually is meteorologically quite different.
0:37:21 > 0:37:24There's very little pressure gradient across the UK.
0:37:24 > 0:37:26It's sort of a reasonably nice day.
0:37:26 > 0:37:30There might be a shower or something, but it could well be a very pleasant day.
0:37:30 > 0:37:33So in each of these predictions, you're getting a spread
0:37:33 > 0:37:37of how the weather might change over the next 12 hours or seven days?
0:37:37 > 0:37:41That's right. We're getting basically 50 plausible,
0:37:41 > 0:37:45equally plausible, equally likely, a priori estimates of the weather.
0:37:45 > 0:37:48The clever thing about this ensemble method is that
0:37:48 > 0:37:50if the 50 forecasts are all similar,
0:37:50 > 0:37:54then we can be confident about what the weather will do.
0:37:54 > 0:37:55But if they are very different,
0:37:55 > 0:37:59we have to accept that we can't be sure what will happen.
0:37:59 > 0:38:03So this tool allows us to estimate that sort of degree of confidence
0:38:03 > 0:38:06or degree of predictability ahead of time,
0:38:06 > 0:38:09so the forecasters these days on the TV
0:38:09 > 0:38:13will often talk, actually, about uncertainty.
0:38:13 > 0:38:16They know it's uncertain, because they've looked at these 50 forecasts
0:38:16 > 0:38:17and they can see, in half of them
0:38:17 > 0:38:20the low-pressure tracks up towards Scotland,
0:38:20 > 0:38:24but in the other half, it tracks across southern England.
0:38:24 > 0:38:27And built into this system is a way of preventing mistakes,
0:38:27 > 0:38:30as with the storm of 1987.
0:38:30 > 0:38:34Suppose there are just three or four of the forecasts which have an
0:38:34 > 0:38:37incredibly violent and intense storm,
0:38:37 > 0:38:40you may not want to ignore that,
0:38:40 > 0:38:43and if those are situations where people's lives are risks,
0:38:43 > 0:38:46for example, then that is a dangerous situation to be in.
0:38:46 > 0:38:48So pretty much all around the world now,
0:38:48 > 0:38:49this is the technique that is used,
0:38:49 > 0:38:52because it provides not only the most likely situation,
0:38:52 > 0:38:55but it provides probabilities of extreme weather
0:38:55 > 0:38:56and it provides estimates of confidence.
0:39:02 > 0:39:05Ensemble forecasting really came into its own
0:39:05 > 0:39:11when the St Jude's Day storm struck on October 28th 2013.
0:39:12 > 0:39:14It produced violent winds that were
0:39:14 > 0:39:18only slightly less powerful than the 1987 storm,
0:39:18 > 0:39:22and it took a similar path across the south of England.
0:39:25 > 0:39:29But the big difference was that in 2013, we were ready for it.
0:39:31 > 0:39:34Warnings about today's storm were first given a week ago,
0:39:34 > 0:39:37giving people and the emergency services time to prepare,
0:39:37 > 0:39:39unlike the great storm of 1987,
0:39:39 > 0:39:42which took weather forecasters by surprise.
0:39:43 > 0:39:45A week's warning for the St Jude's Day storm
0:39:45 > 0:39:48showed that satellites, computer models
0:39:48 > 0:39:52- and ensemble forecasting- have - made a difference.
0:39:52 > 0:39:55Let's go across and take a look at today's chart.
0:39:57 > 0:39:59Compared to how they were in the 1950s,
0:39:59 > 0:40:03our forecasts have improved enormously.
0:40:03 > 0:40:06Our five-day forecasts today
0:40:06 > 0:40:10are as good as the one-day forecasts back then.
0:40:10 > 0:40:12Well, that's the forecast. Bye-bye for now.
0:40:12 > 0:40:15But what if we wanted to look further ahead?
0:40:15 > 0:40:18How good are we at predicting what the coming season will be like?
0:40:19 > 0:40:21Hello again. Let's begin with what
0:40:21 > 0:40:23should be good news for most of us.
0:40:23 > 0:40:26The Met Office have issued a long-range forecast for the summer.
0:40:26 > 0:40:27Here's the headline -
0:40:27 > 0:40:30we're more likely to need a barbecue than a brolly.
0:40:30 > 0:40:33In April 2009, the Met Office prediction
0:40:33 > 0:40:35of a barbecue summer went badly wrong...
0:40:35 > 0:40:36There will be some rain at times,
0:40:36 > 0:40:39but we're not expecting a wash-out, by any means.
0:40:41 > 0:40:45..As what actually happened was one of the wettest summers on record.
0:40:49 > 0:40:51And then, in the same year,
0:40:51 > 0:40:57the Met Office said the chances of a cold winter in the UK were only 20%.
0:40:57 > 0:41:01In reality, the entire island was frozen solid.
0:41:05 > 0:41:09Since these events, the Met Office has stopped issuing seasonal
0:41:09 > 0:41:12forecasts, replacing them with monthly outlooks.
0:41:14 > 0:41:18The demand and interest for forecast weeks and months
0:41:18 > 0:41:19and years ahead is huge.
0:41:19 > 0:41:24The science of that, arguably, is very much in its infancy.
0:41:24 > 0:41:28We are beginning to understand the processes that affect
0:41:28 > 0:41:31weather in those weeks and months ahead.
0:41:31 > 0:41:33Long-term forecasts are hard,
0:41:33 > 0:41:37because the effects of chaos increase with time.
0:41:37 > 0:41:41A ten-day forecast isn't simply twice as hard as a five day one,
0:41:41 > 0:41:44it can be orders of magnitude more difficult.
0:41:45 > 0:41:50To see why, I want to show you a simple experiment with a twist.
0:41:50 > 0:41:52This is a simple pendulum.
0:41:52 > 0:41:54You'll have seen these in grandfather clocks or even
0:41:54 > 0:41:56demos in science lessons at school.
0:41:56 > 0:41:58And they're very straightforward.
0:41:58 > 0:42:01Even I could write you the mathematical equations
0:42:01 > 0:42:04that govern how this works, and even tell you when it stops.
0:42:04 > 0:42:07But it doesn't take much to turn this very simple system
0:42:07 > 0:42:10into something completely unpredictable.
0:42:11 > 0:42:13This is a double pendulum.
0:42:13 > 0:42:15It doesn't look hugely different to what we had before,
0:42:15 > 0:42:18but watch what happens when I swing it.
0:42:18 > 0:42:21At first, it's all very normal, but very soon,
0:42:21 > 0:42:23it starts to behave completely erratically.
0:42:23 > 0:42:26Now, physicists are terrified of the double pendulum,
0:42:26 > 0:42:30because you can write the maths for this, but you can't solve anything.
0:42:30 > 0:42:32You can't predict what's going to happen.
0:42:32 > 0:42:34And you can see here, this completely erratic motion.
0:42:34 > 0:42:38And it's because there's two parts to this system now.
0:42:38 > 0:42:41The top pendulum affects the motion of the bottom one,
0:42:41 > 0:42:44which in turn, affects the motion of the top one.
0:42:44 > 0:42:47And this feedback causes all sorts of headaches
0:42:47 > 0:42:49if you want to know what's going to happen next.
0:42:52 > 0:42:55Weather is full of these feedback loops.
0:42:55 > 0:42:57Factors like humidity, temperature
0:42:57 > 0:43:01and air pressure all interact with each other, as the trillions
0:43:01 > 0:43:05of atoms that make up our atmosphere collide on a global scale.
0:43:07 > 0:43:11So a small change in one variable can be multiplied across the others.
0:43:17 > 0:43:19This means the longer a system runs,
0:43:19 > 0:43:21the more unpredictable it can become.
0:43:25 > 0:43:28This chaotic behaviour is the reason why however good our
0:43:28 > 0:43:33observational technology is, however sophisticated our computer models,
0:43:33 > 0:43:36precise weather forecasts beyond a few weeks
0:43:36 > 0:43:38are still out of our grasp.
0:43:40 > 0:43:46Chaos has always been there, but we no longer ignore it.
0:43:46 > 0:43:48- Meteorologists don't speak about what- will- happen,
0:43:48 > 0:43:50- but what- might- happen.
0:43:52 > 0:43:54And for businesses, who have to calculate
0:43:54 > 0:43:56risks in the long-term, knowing
0:43:56 > 0:44:00the probability of what the weather might do is crucial information.
0:44:02 > 0:44:07From the energy sector to supermarkets to civil aviation,
0:44:07 > 0:44:09forecasting is vital to the economy.
0:44:12 > 0:44:16And weather prediction itself is big business today,
0:44:16 > 0:44:17with more private companies
0:44:17 > 0:44:21than ever before providing thousands of forecasts every day.
0:44:26 > 0:44:27All this is possible
0:44:27 > 0:44:32because of our understanding of the chaotic nature of our atmosphere.
0:44:32 > 0:44:35But there's another frontier to atmospheric science
0:44:35 > 0:44:37beyond just predicting the weather.
0:44:39 > 0:44:42And it's become one of the most important subjects
0:44:42 > 0:44:45in modern science.
0:44:45 > 0:44:47Predicting the climate.
0:44:50 > 0:44:54In simple terms, climate is just the average of how the weather
0:44:54 > 0:44:58behaves over the very long time, so whereas in a weather prediction,
0:44:58 > 0:45:01we might be looking a few days or weeks ahead,
0:45:01 > 0:45:02in a climate prediction,
0:45:02 > 0:45:05you're looking decades, even centuries ahead.
0:45:05 > 0:45:09It's not easy to do and there are a lot of uncertainties involved,
0:45:09 > 0:45:12but it's incredibly important,
0:45:12 > 0:45:15and we've been trying to do it for much longer than you might think.
0:45:19 > 0:45:21Climate science hinges on understanding not just
0:45:21 > 0:45:25how our atmosphere moves around the planet, but what it's made of
0:45:25 > 0:45:28and how it interacts with the oceans and the land below.
0:45:29 > 0:45:33Any changes on this scale need decades to take effect.
0:45:35 > 0:45:38But we have been able to make predictions about their impact.
0:45:41 > 0:45:45The first breakthrough was the realisation that carbon dioxide,
0:45:45 > 0:45:50released by burning fossil fuels, might cause the planet to warm up.
0:45:51 > 0:45:55In the '70s, I wrote one of the first papers
0:45:55 > 0:45:57on what the effects of doubling
0:45:57 > 0:46:01the carbon dioxide concentration in the atmosphere might be.
0:46:01 > 0:46:05At that time, I thought of this as a bit of an academic exercise,
0:46:05 > 0:46:07but of course, now, here we are,
0:46:07 > 0:46:11it being one of the defining problems of the 21st century.
0:46:16 > 0:46:20This effect has been on the world's agenda for some time now.
0:46:21 > 0:46:24But Julia was not the first person to discover it.
0:46:24 > 0:46:27She was picking up on work done much earlier.
0:46:29 > 0:46:32Our knowledge of man-made climate change dates back to
0:46:32 > 0:46:34an experiment done in the mid-19th century.
0:46:36 > 0:46:40I've asked chemist Professor Andrea Sella to recreate it for me.
0:46:42 > 0:46:45- Andrea, hello. How are you? - Hey, Alok, good to see you.
0:46:45 > 0:46:50What is this remarkable set-up you've got here? It's incredible.
0:46:50 > 0:46:54Well, this is a recreation of one of the great demonstrations that a man
0:46:54 > 0:46:59called John Tyndall did at the Royal Institution in the mid-19th century.
0:46:59 > 0:47:02So I can see lenses, there is a light source.
0:47:02 > 0:47:05What does this show me about anything?
0:47:05 > 0:47:08Well, what Tyndall was trying to understand really was what
0:47:08 > 0:47:13was it in the atmosphere that absorbed different types of light?
0:47:13 > 0:47:16So what we've got is a very, very intense light source,
0:47:16 > 0:47:18and it's giving out a combination of two things.
0:47:18 > 0:47:23One is that it's giving out loads of visible light, but the other thing
0:47:23 > 0:47:26is it's also producing what was then called radiant heat.
0:47:26 > 0:47:31In other words, you could feel the warmth coming from that lamp.
0:47:31 > 0:47:35This light represents two things - the Sun and the warmth that's
0:47:35 > 0:47:39radiated back into the atmosphere from the Earth's surface.
0:47:39 > 0:47:44So what Tyndall showed was the fact that it was actually heat
0:47:44 > 0:47:49radiation, if you will, which could actually be focused down.
0:47:49 > 0:47:53And I'm going to show you that, with a little piece of guncotton.
0:47:53 > 0:47:54What I'm going to do is I'm going
0:47:54 > 0:47:58to put it actually into the beam here and we're going to see.
0:47:58 > 0:47:59What am I looking for?
0:47:59 > 0:48:04Well, something might actually happen if the whole thing gets hot.
0:48:04 > 0:48:06Let's just see if I can get it in the right spot.
0:48:06 > 0:48:08- It's incredibly bright right now. - It's quite tricky.
0:48:08 > 0:48:09- Ooh!- Ooh!
0:48:09 > 0:48:11- OK.- OK. That was obvious.
0:48:11 > 0:48:16So clearly, this stuff has gotten hot enough to actually ignite.
0:48:16 > 0:48:19Now... Yeah, it does smell a little bit.
0:48:19 > 0:48:24So what Tyndall did then was to actually systematically go
0:48:24 > 0:48:28through the various components of the atmosphere and of course
0:48:28 > 0:48:33those included nitrogen and oxygen and water vapour and so on.
0:48:33 > 0:48:37And then he actually put carbon dioxide in between.
0:48:37 > 0:48:39So in here, you've got carbon dioxide.
0:48:39 > 0:48:42I've taken a little bit of carbon dioxide in the form of dry ice.
0:48:42 > 0:48:45I've left it inside so that it evaporates.
0:48:45 > 0:48:48So that tank is completely full.
0:48:48 > 0:48:51And what I'm going to do is I'm actually going to put a little
0:48:51 > 0:48:55bit of the same cotton here and I'm going to place it at the focus.
0:48:55 > 0:48:59- Are we expecting something similar? - Well, let's...let's see.
0:48:59 > 0:49:00Let's see what happens.
0:49:00 > 0:49:02So, I've got it at the focus,
0:49:02 > 0:49:05- you can see the way it has brightened up.- Still bright.
0:49:05 > 0:49:06It's still bright.
0:49:06 > 0:49:09OK, this is less exciting, Andrea.
0:49:09 > 0:49:12Well...exactly.
0:49:12 > 0:49:15The incredible thing is that you could leave this here
0:49:15 > 0:49:19for a very long time, and it just doesn't catch fire.
0:49:19 > 0:49:23The reason is, because in fact, this tank of CO2
0:49:23 > 0:49:26is acting as a kind of filter along the way.
0:49:26 > 0:49:29So this is absorbing the heat that would normally have passed
0:49:29 > 0:49:32- straight through. - It's absorbing that energy.
0:49:32 > 0:49:35And so the result is that that is going to get just a little
0:49:35 > 0:49:41bit warmer, but it prevents anything happening at the far end.
0:49:41 > 0:49:44'What's happening on this desktop demonstrates the warming
0:49:44 > 0:49:46'effect of carbon dioxide in our atmosphere.'
0:49:46 > 0:49:50But if you have more carbon dioxide in the atmosphere,
0:49:50 > 0:49:54the inevitable consequence is that the Earth's temperature will get
0:49:54 > 0:49:56higher and higher slowly.
0:49:56 > 0:50:02Exactly, and way back in 1855, John Tyndall suggested that
0:50:02 > 0:50:04if we continued to burn coal,
0:50:04 > 0:50:07which was of course the fuel of the Industrial Revolution,
0:50:07 > 0:50:09that the world would become warmer as a result.
0:50:09 > 0:50:12He suggested, essentially, what we know of now as global warming.
0:50:12 > 0:50:16The extraordinary thing is that we've known about this stuff
0:50:16 > 0:50:19for something like 170 years.
0:50:21 > 0:50:26The implications of this experiment are still being wrestled with.
0:50:26 > 0:50:30In fact, understanding how our climate is changing
0:50:30 > 0:50:33and what the impacts might be is the biggest challenge
0:50:33 > 0:50:35faced by meteorologists today,
0:50:35 > 0:50:38shaping a new approach to the whole field.
0:50:46 > 0:50:49So, whereas I started my career having to understand
0:50:49 > 0:50:54meteorology and atmospheric motions and atmospheric physics,
0:50:54 > 0:50:58I now need to understand about ocean physics,
0:50:58 > 0:51:04ocean biogeochemistry, how plants grow, how sea ice,
0:51:04 > 0:51:08particularly in the Arctic, how that forms and melts.
0:51:08 > 0:51:10All those things we need to understand now,
0:51:10 > 0:51:14if we're going to say useful things, robust things,
0:51:14 > 0:51:19about how our climate will evolve as we go into a warming world.
0:51:21 > 0:51:25In the UK, many institutions share this huge task.
0:51:27 > 0:51:29Collaborating on projects
0:51:29 > 0:51:32like the Facility for Airborne Atmospheric Measurements,
0:51:32 > 0:51:37which operates dozens of flights a year to study our climate.
0:51:39 > 0:51:42Today, scientists on board will test an instrument that will
0:51:42 > 0:51:47measure aspects of our planet in an entirely new way.
0:51:48 > 0:51:51It's really exciting to be on these flights.
0:51:51 > 0:51:55It's a new instrument and, you know, we've been working on it
0:51:55 > 0:51:57for a very long time, so the chance now to see
0:51:57 > 0:52:01it flying and measuring real data is really exciting to me, personally.
0:52:03 > 0:52:07This brand-new technology is the latest tool to carry on Tyndall's
0:52:07 > 0:52:11early experiments into how our atmosphere absorbs light and heat.
0:52:12 > 0:52:16It's designed to measure a key factor in how much radiation
0:52:16 > 0:52:19gets trapped in the different layers of our atmosphere.
0:52:21 > 0:52:24Each of these channels is looking at a part
0:52:24 > 0:52:27of the spectrum where the atmosphere is different transparency,
0:52:27 > 0:52:30so some of them see quite well right through the atmosphere
0:52:30 > 0:52:32and some of them only see the bit fairly close
0:52:32 > 0:52:34to where you are at the moment, and that's why
0:52:34 > 0:52:37we've got this spread in the amount that's coming down.
0:52:37 > 0:52:39So this one here isn't seeing very far away from us,
0:52:39 > 0:52:42so it's getting quite high amounts of thermal radiation.
0:52:42 > 0:52:44What we're looking at is quite warm,
0:52:44 > 0:52:47whereas this one here is basically seeing almost all the way
0:52:47 > 0:52:48through to space and space is very cold,
0:52:48 > 0:52:53so this is giving us temperatures of about -250 Celsius,
0:52:53 > 0:52:56so not much radiation coming down there at all.
0:52:57 > 0:53:00Of particular interest to Stuart is the effect of clouds.
0:53:00 > 0:53:03Some contain a high amount of ice,
0:53:03 > 0:53:06and these reflect the Sun's rays back into space.
0:53:08 > 0:53:11This instrument will measure a new thing,
0:53:11 > 0:53:12it's going to measure cloud ice.
0:53:12 > 0:53:15So once it's up on the satellite, it will be giving us
0:53:15 > 0:53:18daily global measurements of cloud ice content, which is
0:53:18 > 0:53:21a really important thing to get right in our climate models,
0:53:21 > 0:53:24because it's got a big impact on how much of the solar radiation
0:53:24 > 0:53:26gets down to the surface and how much of it
0:53:26 > 0:53:28gets reflected back up into space.
0:53:29 > 0:53:31Once tested on the plane,
0:53:31 > 0:53:35Stuart's sensor will be launched into space on a satellite,
0:53:35 > 0:53:38adding to the vast network of weather observation systems
0:53:38 > 0:53:41that circle our planet.
0:53:42 > 0:53:45When the very first satellite was launched,
0:53:45 > 0:53:48the models were pretty highly developed, we knew quite
0:53:48 > 0:53:51a lot about the atmosphere, but we didn't have many observations.
0:53:52 > 0:53:55We're now in the situation where one could argue
0:53:55 > 0:53:59we have more observations than we really know what to do with.
0:54:00 > 0:54:04In the years immediately after the Second World War, Met Office
0:54:04 > 0:54:08forecasters received a few hundred observations per day.
0:54:10 > 0:54:13Now, thanks to everything from aircraft
0:54:13 > 0:54:18to a global network of satellites, they receive a staggering
0:54:18 > 0:54:22106 million weather observations every single day.
0:54:22 > 0:54:26Bringing those observations together, bringing those together
0:54:26 > 0:54:30in a coherent way, that's the challenge for science at the moment.
0:54:33 > 0:54:37Making sense of all this data, to figure out what it can tell us
0:54:37 > 0:54:39about our future, is an enormous task.
0:54:41 > 0:54:44And it's one that the Met Office have to take on
0:54:44 > 0:54:46in order to uphold their commitment
0:54:46 > 0:54:49to protecting the British public from danger.
0:54:53 > 0:54:54That means building this,
0:54:54 > 0:54:58one of the two biggest weather computers in the world.
0:54:59 > 0:55:04It's the only one that can simulate both short-term weather
0:55:04 > 0:55:07and long-term climate simultaneously.
0:55:12 > 0:55:15On every scale, this computer is enormous.
0:55:15 > 0:55:18It takes up two rooms, each one the size of a football pitch.
0:55:18 > 0:55:21It weighs the same as 11 double-decker buses,
0:55:21 > 0:55:27and it can compute 23,000 trillion calculations per second,
0:55:27 > 0:55:31but that's what you need if you want to simulate the entire Earth
0:55:31 > 0:55:33and all its weather systems.
0:55:36 > 0:55:39The computer code running in this machine
0:55:39 > 0:55:42has taken 100 scientists ten years to write.
0:55:44 > 0:55:48It's a virtual planet Earth, complete with simulated weather
0:55:48 > 0:55:52that churns about the atmosphere with astonishing accuracy.
0:55:54 > 0:55:59This single tool is both a magnifying glass and crystal ball.
0:56:04 > 0:56:07Up to a week in advance, it can show us what the weather
0:56:07 > 0:56:12will do in Britain down to resolution of one square kilometre.
0:56:15 > 0:56:19And looking decades into the future, it can give predictions for
0:56:19 > 0:56:22how our climate will behave across the globe.
0:56:26 > 0:56:30When we think about what our models enable us to do,
0:56:30 > 0:56:34that we can actually simulate what we see going on outside,
0:56:34 > 0:56:37see our weather being simulated, to me,
0:56:37 > 0:56:41this is one of the greatest achievements of modern science.
0:56:41 > 0:56:42It's probably one of the unsung
0:56:42 > 0:56:44great achievements of modern science.
0:56:44 > 0:56:49Just because it's a computer code and therefore not visible,
0:56:49 > 0:56:53doesn't mean it isn't something that is a huge success.
0:56:53 > 0:56:57I compare it a bit with the human genome, mapping the human genome.
0:56:57 > 0:57:00It's a similar achievement.
0:57:00 > 0:57:03This is a remarkable coming together of science
0:57:03 > 0:57:06and encapsulating all our understanding of that
0:57:06 > 0:57:12science in these codes and producing something, a simulation that
0:57:12 > 0:57:17looks so remarkably like reality, it's an extraordinary achievement.
0:57:20 > 0:57:23This giant machine encapsulates everything meteorologists
0:57:23 > 0:57:27have learnt throughout the history of their science.
0:57:29 > 0:57:34Hard-earned insights gleaned from tragedies and triumphs alike.
0:57:37 > 0:57:40The early pioneers wanted to see what the weather
0:57:40 > 0:57:43had in store for us just over the horizon.
0:57:45 > 0:57:47And now, thanks to their legacy,
0:57:47 > 0:57:50we have the tools to see a whole planet at a glance...
0:57:52 > 0:57:55..and look hundreds of years into the future.
0:57:57 > 0:58:00We've been studying the weather for more than 150 years,
0:58:00 > 0:58:03and in that time, we have learned how to understand
0:58:03 > 0:58:07and predict it, but if the weather has taught us anything at all,
0:58:07 > 0:58:12it's that this is a mysterious and intriguing force of nature,
0:58:12 > 0:58:14always slightly out of our grasp.
0:58:14 > 0:58:18We need to ask deeper questions, probe even further,
0:58:18 > 0:58:22if we're going to keep up to pace with what it has in store for us.