How AI helps predict extreme weather | BBC News

Summary

In this episode of AI Decoded, experts discuss the revolutionary impact of AI on weather forecasting. Oxford University's Dr. Shuyi Chen highlights the development of a digital twin of Earth, a supercomputer model that simulates climate change scenarios. The European Center's Florence Rabier and the UK Met Office's Professor Steven Belcher emphasize AI's role in enhancing accuracy, particularly in predicting extreme events. They also touch on citizen science initiatives, encouraging public participation in data collection to combat climate change and improve forecasting.

Takeaways

• 🌐 AI is revolutionizing weather forecasting by processing vast amounts of data and creating highly detailed models of the Earth's climate system.
• 🔍 Oxford University is developing a 'digital twin of the Earth' to simulate natural phenomena and human activities, aiding in the prediction of extreme weather events.
• 🌡️ AI models are improving the accuracy of weather predictions, particularly in tracking tropical cyclones, with an increase of about 25% in accuracy.
• 🌍 The European Center for Medium-Range Weather Forecasts (ECMWF) collaborates with 35 countries to run global models that predict weather patterns worldwide.
• 💻 AI technology allows for the creation of high-resolution, physics-based models that can predict weather down to specific urban areas, such as differentiating temperatures across London.
• 🌪️ AI is particularly useful in predicting severe weather events, which are becoming more frequent and intense due to climate change.
• 🌤️ The UK Met Office is using AI to enhance weather forecasts with fine details, such as temperature variations within urban areas, by incorporating machine learning techniques.
• 🌿 Citizen scientists can contribute to climate research by submitting local weather data, which can be used to improve models and predictions.
• 🌎 AI is being used to not only predict immediate weather but also to model long-term climate change scenarios, helping to plan for the future.
• 🛠️ Despite advancements, AI models are not perfect and have areas for improvement, such as predicting the intensity of typhoons, which is currently less accurate.

Q & A

What is the significance of AI in weather forecasting?

AI is revolutionizing weather forecasting by processing vast amounts of data and making predictions more accurate. It enables the creation of digital twins, which are highly detailed models of the Earth that can simulate different climate scenarios, helping scientists predict the evolution of climate change.

How does the digital twin of the Earth aid in climate prediction?

The digital twin of the Earth is a super model that encompasses all knowledge of the physics of the atmosphere and the Earth system. It is run on supercomputers and allows for the simulation of natural phenomena and human activities, providing highly detailed, interactive data that supports decision-making around extreme weather events.

What role does cloud computing play in making AI-generated weather forecasts more accessible?

Cloud computing allows for the storage and sharing of AI-generated weather forecasts, making them readily available to governments and aid agencies. This technology enables regions with limited resources to access sophisticated weather forecasting tools, which can be run on a laptop rather than requiring a supercomputer.

How has the collaboration between Oxford University and the UN World Food Program improved weather prediction in the Horn of Africa?

Oxford University has developed an AI system in collaboration with the UN World Food Program that pulls together current and historic data to provide localized weather forecasts. These forecasts are accessible on a website and are generated using AI models that can run on local equipment, enhancing the ability of governments and aid agencies to prepare for climate disasters.

What is the impact of AI on the accuracy of predicting tropical cyclones?

AI models have shown to be approximately 25% more accurate in predicting the track of tropical cyclones, such as typhoons and hurricanes. However, they are still being improved upon, as they are about 20% less accurate in predicting the intensity of these storms.

How does the UK Met Office use AI to enhance weather forecasting?

The UK Met Office uses AI to add fine detail onto routine forecasts, providing high-resolution data on temperature and rainfall. This is achieved by combining crowdsourced data with machine learning tools, which allows for the production of very detailed forecasts that can inform anticipatory actions, especially in urban areas where heat islands can exacerbate weather conditions.

What is the 'weather on the web' initiative mentioned in the script?

The 'weather on the web' initiative is a platform where citizens can submit their local weather observations, such as temperature and rainfall, to contribute to weather forecasting. This crowdsourced data is then used by organizations like the UK Met Office to improve the accuracy and detail of their forecasts.

How has the accuracy of weather forecasts improved over the past decades?

Over the last 50 years, the accuracy of weather forecasts has improved tremendously due to advancements in satellite observations, global models, and increased computational power of supercomputers. The improvement is often measured by the increase in the forecast range, with the 4-day forecast now as accurate as the 3-day forecast was a decade ago.

What is the potential of citizen scientists in contributing to weather and climate studies?

Citizen scientists play a crucial role by contributing local data through initiatives like 'weather on the web' and other crowdsourcing platforms. This data helps scientists to better understand and predict weather patterns and climate change, ultimately aiding in the fight against these global challenges.

How does the EU's Copernicus program document and predict climate change?

The Copernicus program uses historical weather and climate data to document changes in temperature, storm frequency, and other climate indicators. It also uses these models to predict future climate scenarios based on different scenarios of greenhouse gas emissions, particularly carbon dioxide.

Outlines

00:00

🌐 Revolutionizing Weather Forecasting with AI

The segment begins with a discussion on the integration of AI in weather forecasting, emphasizing the need for accurate climate predictions amidst rapid environmental changes. AI's role in processing vast amounts of data and modeling the Earth's climate is highlighted, with references to Oxford University's work on AI and cloud computing to make forecasting more accessible. The European Center for Medium-Range Weather Forecasts and the UK Met Office's use of AI to create a 'digital twin' of Earth for detailed climate simulations is explored, showcasing how these technologies can predict climate change and assist in disaster preparedness.

05:02

🌍 Global Collaboration in Weather Modeling

This paragraph delves into the global nature of weather forecasting, noting the collaboration between European and American models. It discusses the use of both historical and real-time data in forecasting, with a case study on Typhoon G. The segment highlights the improved accuracy of AI in tracking tropical cyclones by about 25%, while also acknowledging its current shortcomings in predicting storm intensity. The discussion also touches on the personal experiences of a meteorologist growing up in Tornado Alley, emphasizing the potential of AI to enhance warning systems.

10:03

🏙️ High-Resolution Weather Modeling and Citizen Science

The focus shifts to high-resolution weather modeling, particularly in urban areas like London. The Met Office's use of AI to enhance local weather forecasts with data from citizen scientists is discussed. The segment explains how machine learning can improve temperature forecasts, especially in urban heat islands. It also addresses the challenge of matching预报 accuracy with localized weather phenomena and how citizen science can contribute to more precise weather predictions.

15:04

🌡️ AI in Climate Research and Localized Forecasting

The conversation turns to how AI complements traditional weather models, especially in predicting rainfall—a critical aspect of localized weather. Dr. Nath from Oxford University discusses the development of AI systems that can run on basic hardware, making sophisticated weather forecasting accessible to regions with limited resources. The segment also covers the application of these AI models in Kenya and Ethiopia, providing real-time examples of how they improve weather forecasting and aid in disaster preparedness.

20:05

🌌 Documenting Climate Change and Engaging Citizen Scientists

The final paragraph summarizes the program's discussion on climate change documentation and the role of citizen scientists. It covers the EU's Copernicus program's efforts to track climate changes since 1940 and the potential of AI to predict future scenarios based on greenhouse gas emissions. The segment concludes with a call to action for citizen scientists to contribute data, which can help combat climate change and biodiversity loss, highlighting initiatives like the Met Office's 'weather on the web' and other crowdsourcing projects.

Keywords

💡AI

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the context of the video, AI is used to enhance weather forecasting by processing vast amounts of data and predicting climate changes. The script mentions AI's role in creating a 'forecasting revolution' and its application in creating a digital twin of the Earth.

💡Digital Twin

A digital twin is a virtual model of a process, product, or service that uses real-time data to monitor and improve the performance of its physical counterpart. In the video, the concept is used to describe a detailed interactive model of the Earth that simulates natural phenomena and human activities to predict climate changes and extreme weather events.

💡Climate Change

Climate change refers to long-term shifts in temperatures and weather patterns. It is a significant theme in the video, where scientists discuss the use of AI and digital models to predict and prepare for the impacts of climate change on weather patterns and extreme events.

💡Weather Forecasting

Weather forecasting is the science of predicting future weather conditions based on the analysis of meteorological data. The video emphasizes the advancements in weather forecasting due to AI, which allows for more accurate predictions and the ability to model 'what if' scenarios using digital twins.

💡Supercomputers

Supercomputers are high-performance computers that can perform calculations much faster than a standard computer. In the script, supercomputers are mentioned as the backbone for running complex simulations for the digital twin of the Earth, enabling scientists to process and analyze large datasets related to climate and weather.

💡Cloud Computing

Cloud computing is the delivery of computing services, including servers, storage, databases, networking, software, analytics, and intelligence, over the Internet (the cloud) to offer faster innovation, flexible resources, and economies of scale. The video discusses how cloud computing can make AI-generated weather forecasts more accessible to governments and aid agencies.

💡Machine Learning

Machine learning is a subset of AI that enables machines to learn from data without being explicitly programmed. In the context of the video, machine learning techniques are used to refine weather forecasts by adding fine details to the existing physical models, resulting in more accurate predictions.

💡Citizen Science

Citizen science involves public participation and collaboration in scientific research. The video suggests that citizen scientists can contribute to climate and weather studies by submitting their observations, which can help improve the accuracy of weather forecasts and climate models.

💡Extreme Weather Events

Extreme weather events are severe meteorological occurrences that can have significant impacts on human life and the environment. The video highlights the importance of AI in predicting such events, like typhoons and heatwaves, to help save lives and protect livelihoods.

💡Renewables

Renewables refer to energy sources that are naturally replenished on a human timescale, such as wind, sunlight, and rain. The video mentions the role of weather forecasts in the context of renewable energy, as understanding weather patterns is crucial for harnessing these energy sources effectively.

Highlights

AI is revolutionizing weather forecasting by processing vast amounts of data and making predictions more accessible to governments worldwide.

Oxford University's team is using AI to fill gaps in weather data and making it more accessible through cloud computing.

The EU's Destination Earth initiative is creating a digital twin of the planet to predict natural phenomena and climate change evolution.

AI combined with climate science and supercomputers is used to create highly detailed, interactive data for decision-making during extreme weather events.

ECMWF runs global models to predict weather interconnectedness, which is crucial for accurate medium-range forecasts.

AI models have shown to be approximately 25% more accurate in predicting the tracks of tropical cyclones like typhoons and hurricanes.

Despite advancements, AI models still have room for improvement, particularly in predicting the intensity of weather phenomena.

Citizens can contribute to weather forecasting by submitting local data through initiatives like 'weather on the web'.

The UK Met Office is using AI to increase the precision of weather forecasts down to the level of specific landmarks.

AI allows for the generation of multiple weather forecasts to explore uncertainty, aiding in anticipatory actions for climate disasters.

Oxford University is pioneering an AI system that can run on a laptop, making sophisticated weather forecasting accessible to developing regions.

The digital twin of the Earth not only predicts climate change but also documents historical weather and climate data.

Climate change documentation is crucial for understanding historical weather patterns and informing future predictions.

Citizen scientists can play a role in climate change studies by contributing data through crowdsourcing initiatives.

The accuracy of weather forecasts has improved significantly over the past decades, with AI accelerating this progress.

The program concludes with a call for citizen science involvement in climate and biodiversity studies.

Transcripts

we are back with our weekly segment AI

[Music]

decoded welcome to the program we have

had a summer break from AI decoded but

if like me you were on the British

beaches Sheltering from the rain then

maybe you were scaning your mobile

weather app to see if the sun might ever

reappear which got us thinking what

about Ai and the weather how do you

predict climate when it is changing so

fast how do you process that incredible

amount of computerized data that is now

being generated well you model it and

that is where AI is making huge advances

there is a forecasting Revolution

underway so accurate says the guardian

and now in much more accessible format

that very soon governments around the

world will be able to save lives and

protect livelihoods before extreme

events even occur we'll hear from the

team at Oxford University who are

filling in the gaps with AI and making

it more readily available through Cloud

computer shoting or how about this from

the EU destination Earth a digital copy

of our planet on which scientists are

running complex simulations to predict

natural phenomena AI combined with

climate science powered by

supercomputers a digital twin if you

will that will help scientists predict

the evolution of climate change with me

has ever our regular commentator and

colleague uh Stephanie har is here also

in the studio the very well-known

meteorologist Florence rabier Dr rabier

director general of the European Center

for medium-range weather forecast and

joining us also on Zoom Professor Steven

Belcher who is chief of Science and

Technology at the UK Met Office welcome

to you all um fla uh we're going to

start with you um and the Earth's

digital twin that you and your team have

built in collaboration with the the AI

industry so let's get a view for the

viewers let's let's just show the

viewers what it entails and we'll talk

off the

back to create a better future we must

push the boundaries of

today simulations of our Earth system

known as digital twins will help us

understand predict and plan for a

rapidly changing

world this twin will offer highly

detailed Interactive

data that can support decision making

around extreme weather

[Music]

events this twin will show us possible

Futures it simulates different climate

change scenarios over many decades

helping us to be ready for whatever

happens wow so in simple terms you are

stimulating the natural phenomena and

the human activities on Earth putting it

all together through this supercomputer

and what putting it

onto a digital twin of the earth yes so

digital twin of the Earth because it's

this super model of everything that the

Earth is doing that we can predict

through Computing equations so it's a

model where all our knowledge of the

physics of the atmosphere and the Earth

system is encompassed in that model so

it's a computer program that we put on a

supercomputer and we run it but all our

knowledge of the physics accumulated

since Newton and all is there about

gravity condensation you know storms Etc

and it's a digital twin of the Earth

because it's very accurate and it has a

very high resolution and also it's

interactive you can play a bit with it

and simulate what if scenarios so I

imagine that in Times Gone by you would

do that at a very local level but of

course we're all interconnected the

world is is is a global environment our

our weather systems uh and activities

are all connected so so how does this

enable you to improve the forecasting

that you do yeah so at ecmwf what we do

we run these Global model so it's really

across the whole world because if you

want to know the weather in Europe now

you have to know what happened in the US

a few days ago and in the Atlantic and

even in the Pacific if you want to

predict the weather 7 Days beforehand so

it's all interconnected you're right you

have to start with a global scale and

then you can refine at the local scale

as well but you really have to know what

whatever happens on the world at any

point in in time in order to go further

in time in your predictions and that's

what we've been doing for about 50 years

in collaborations with our member states

35 countries in Europe supporting this

work so Florence is this a uniquely

European initiative or do you work with

other partners around the world so there

are several Global models in the world

and our because we are European we are

working in collaboration with 35

meteorological services in broadly

Europe broad Europe but Amer model Etc

ah okay but are you using historical

data or like live data or both probably

both I mean for the weather forecast

usually we use the current data so the

data we've seen in the last 12 hours but

the model had seen the data beforehand

and it's a sort of continuous process we

combine physics and data we go forward

combine again so the latest forecast

uses the latest data but historically

we've used the data from the last

decades and rolling like this I tell you

what we we've got a a real life example

uh that mapped the recent typhoon

typhoon G I think it was called uh in

July this year so so you see all the

lines around the two main lines so

there's the red line and the black line

which we'll talk about in a second what

are the other lines that we're looking

at so that's typically what we do as a

forecast so we we predict the weather

but in particular we concentrate on

severe events like that because this

typhoon G me was really uh had dire

circum you know consequences with 100

people dead and millions affected so

what we produce every day we produce not

just one forecast but we produce

actually several forecast together to to

depict the whole probabilities of what

the weather will do this way we don't

just simulate the track of the typhoon

but all the possible tracks that we

think the typhoon will take in the next

few days these are the gray lines so

which is the AI model

so the gray Line's our physics based

model and the black line is the real

observed track of typhoon and in the

blue what you have is our best estimate

that we had before AI of where the

typhoon would go and would hit China

he's telling me that red one is the AI

that's almost tracking exactly what

happened in real time in that case it is

and that's incredible it is incredible

but you can't judge everything on one

case of course we accumulate all the

these cases and we do statistics but it

is true that the AI models are in

general about 25% more accurate in

predicting the track of tropical

Cyclones typhoon and hurricanes which is

huge but they are not doing everything

right either so in particular in terms

of the intensity of the typhoon they are

actually about 20% worse so it's not all

perfect you've got a real interest in

this because I know that you grew up in

tornado Valley in America right so

Tornado Alley is the midwestern part of

the United States that runs from North

Dakota all the way down to Texas and

then probably for 500 to a, miles on

either side so I grew up just outside

Chicago and routinely we practice these

drills as children you get a little bit

of warning we're talking seconds and you

have to find the nearest basement and

get underground because a storm will

come through that can destroy an entire

town in seconds presum this could tell

you which street to go to well we're not

at that sort of scale especially as we

work I have big

expectations but but it's get but it's

getting better and better all the time

it is getting better all let's bring in

Professor Steven Belcher who is the

chief of Science and Technology at the

UK Met Office um welcome to you um so

far we've talked about global weather

patterns uh Stephen climate Trends

mapping evolution of weather patterns

but how much more precise is weather

prediction getting dayto day because of

this AI

technology well it's worth remember

remembering that there's always pressure

to increase the accuracy and the utility

of weather forecasts uh today's great

example we've had certainly lots of rain

here down in the southwest of England so

with climate change making extreme

events even more extreme we're demanding

that our forecasts get better to uh help

us understand what the impacts of those

might be also we've got new applications

of weather forecasts and just think

about the roll out Renewables this is

meaning that weather is now the fuel of

the future so understanding that fuel is

another application of of our weather

forecasts and so to make them more

accurate we need increased lead time so

we need warnings further ahead of when

we're getting these extreme events but

we'd also like finer detail just as

Florance was describing earlier I was

making quite big demands of Florance but

I'm going to put up an image here about

just how accurate this can get so here's

a here's an image of London that we'll

all recognize um uh you can see the

Millennium D there the bend in the River

East London tell us what we're looking

at here on the left and on the

right right so at the Met Office we

complement What's Done in Florence's

organization the European Center by

producing highresolution physic Bas

modeling of the weather over the UK so

the leftand side is showing you the grid

that we divide London into in order to

provide that weather so what what we're

seeing now is the weather on at that

kind of resolution about 1 and 1 half

kilom we we kind of increment the the

differences in the rainfall and the

temperatures what we've been able to do

in fact one of our Rising Stars Lewis

blun here at the Met Office working with

students at the University of reading

and also at the Bureau of meteorology in

Australia um has devised a machine

learning techniques to add fine detail

onto those routine for CS that gives us

detail resolutions of hundreds of meters

in the temperature so you can tell the

temperature and the Heat and the

rainfall literally over the Millennium

Dome uh the temperature at this stage

and maybe other variables in the future

but but the the temperature is what

Lewis and his colleagues uh worked on

and the reason this is important is that

we we've known for some time that uh

when we have heat waves um those heat

waves are more extreme in urban areas so

in particular those who live in cities

will have noticed that the temperatures

don't cool down so much at night and we

call that the urban heat island so what

Lewis and his team did was to take data

actually from

crowdsourced data in back Gardens and uh

citizens in London their data so of

variable quality frankly plus five

professional weather stations they Mash

that together together with machine

learning tools and augment the regular

forecast that we produce here at the Met

Office and can then produce these

temperature forecasts at these very fine

levels so it's another example of how AI

can really change what we're doing in

the in the weather World Professor beler

I've got a question for you exactly

about this um taking data from your back

Garden sometimes when I'm standing in

London I will consult the Met Office app

regularly in fact and it will say that

it's sunny and I'm being rained on why

is that happening and second how or when

will I be able to send data to you

saying no no here in hatney it's

raining so you can you can send data to

us right now so it's called weather on

the web the wow site so please do that

you can Lodge your longitude and

latitude and send in your data to us and

as I say that's the data that Lewis and

his colleagues used to produce this um I

think

in terms of weather forecast let's not

forget that over the last 50 years

through the Advent of satellite

observations and other observations

right around the world the Improvement

of those global models that Florence was

describing earlier and and the increased

scale of supercomputers that we've got

these physics-based models we've got the

weather forecast has increas improved

tremendously well and and one of the

statistics we describe is that the

weather forecast incre improved by one

day per decade so the 4-day forecast now

is as good as the three-day forecast was

10 years ago so this is often referred

to as the quiet revolution in weather

forecasting what AI is doing is really

accelerating that Revolution so it's a

loud Revolution we're going to continue

the conversation we have to get a short

break but we're going to see how this

can be applied around the world um

coming up after the break we'll bring in

the climate physicist Dr shuy Nath she

is part of the physics team at Oxford

where they just pioneered a new approach

to predicting extreme wither stay with

us welcome back we are warned repeatedly

that climate change will affect millions

of people worldwide in fact is already

affecting lives and livelihoods and

particularly so in some of the poorest

regions of the world where they don't

have access to this realtime forecasting

or the vast computer power needed to

produce it well Dr shuy na is a climate

scientist at Oxford University she's

been working with the UN world food

program to develop an AI system that is

pulling together all this data current

and historic and applying that to

localized area that information can now

be condensed and shared through cloud

computing to help the governments and

Aid agencies better prepare for climate

disasters let's bring in our guest then

Dr shudy D It's good to talk to you

thank you very much for coming on the

program

um there were some brilliant physicists

like you in the in the Oxford University

Department I I want to better understand

that what the AI is doing to speed up

the process and fill in the

gaps yeah so thank you for having me at

oxid physics we're exploring hybrid

modeling approaches so we're looking at

how AI can best complement our existing

physical weather models so as Florence

says these models have all the physical

knowledge that we've accumulated since

Newton and we're complimenting it with

AI particularly at Oxford we're looking

at rainfall since this is a high impact

very localized feature of the weather

and what we're seeing is that when we

take the best of the physical weather

forecasts um we can really use AI as a

data driven technique to correct the

structural errors that exist in these

physical forecasts um that could arise

from incomplete representation of

atmospheric processes to better deliver

actual accurate rainfall forecasts um

within the region that we work in so

that's the greater Horn of

Africa and um Dr n how do you see

Ordinary People in the regions where

you're working being able to access this

very sophisticated and high powerered

technology that you're working

with so that's a very good question and

that's actually in my opinion one of the

real strengths of AI it's a very lowcost

lightweight model of being able to

represent very complex phenomena so we

work closely with all the local

mological bodies and we work with

developing the model with them so they

actually run the AI models um inhouse

and that means that they can actually

generate weather forecasts on a laptop

mind you that's a laptop as compared to

a supercomputer which is what typically

is used to generate weather forecast can

you give us an example of where you've

used that a real a real time example of

course yeah so we use it in Kenya so

they update the forecasts every day um

in Kenya and Ethiopia and the forecasts

are also available on a website so um

the website name is sean. pac. net and

they're updated every day um from the

in-house forecast generated on you know

their uh equipment so it really is a way

of giving these people a bit more access

ible weather forecasting but presumably

that I mean the Breakthrough of all this

is that you can if you know what's

coming and and the long range

forecasting improves you can the aid

agencies can can put can sort of store

forward the aid that they're going to

need for what's coming at them so you

know so often on our programs we're

we're sort of saying well we can't get

to these inaccessible areas but now the

stuff will already be there because

we've already forecasted what's coming

exactly so we particularly actually

focus on linking research to actions so

we work with linking these forecasts to

anticipatory action and as you said we

can have these long range forecasts and

also chipping in on what Florence

mentioned about how you know weather is

quite chaotic so there's lots of

possibilities that can arise from a

given starting point so we have a lot of

uncertainty and you need to actually

generate a lot of different weather

forecasts to explore that AI allows you

to do that in a very lowcost way so you

can generate forecasts that explore the

uncertainty space in a very lowcost

manner so that you can actually properly

inform anticipatory action in these

areas we've talked a lot about weather

what we've not actually talked a lot

about is is climate change and and of

course there are climate deniers out

there Florence uh that we must uh

acknowledge I'm got to put a picture on

screen do you ever remember this uh this

was a a tornado that was coming at the

Florida Panhandle um and also was some

questions about whether it might go to

Alabama and and they got a Sharpie out

and they actually drew it on the end at

the Trump Administration which uh which

tells you that that uh you know we it

clearly is something that that people

try to play with when when we talk about

climate the way climate's changing what

weather is going to do but your your for

are so accurate Florence and presumably

with your digital twin earth You can

predict how climate is going to evolve

well into the future well exactly and we

use the same weather model to to do

climate models as well so they are just

a bit more complicated but it it's based

on the same sort of of modeling but also

we can document climate change and

that's what we're doing with the

Copernicus program from the EU going

back since you know from 1940 and really

depicting what the weather and climate

have been doing every hour from 1940 to

now we have this picture of the Earth

and we can then document how much the

temperature have increased how much the

frequency of storms have increased Etc

so it's predicting it but there is

already this reality we not we have

enough information to know what has had

happened already and then with these

models then we can do a digital twin of

of the climate as well and go forward in

the future with different scenarios of

course of what will happen in the

reduction of greenhouse gases because of

course it all depends how much we can

reduce the amount of carbon dioxide in

particular that we put in the

atmosphere and I've got a question for

all three of our distinguished scientist

only got a minute left so you're going

to have to make it quick go we keep

hearing that we're running out of data

and that this is a big problem for AI

but I wonder if that's actually true

particularly when it comes to weather

climate change and biodiversity loss we

have to fix obviously the climate change

and biodiversity problem do you feel

that there's a way for citizen

scientists to get back into action and

be submitting data to all of you

scientists so that you can help us fight

these bigger problems St Stephen pick

that up because we've just about 30

seconds left yeah it's a great shout and

as we talked about earlier we've got the

weather on the web there's another great

crowdsourcing initiative to look at um

in and early sighting of insects around

the UK which we've also connected with

climate change um here at the Met Office

along with many other partner

organizations so I think it's a great

shout for citizen science this one I

could talk plenty more as I always could

on this program every week we we never

get to the bottom of everything but

listen uh Florence Stephen Dr Nath

Stephanie thank you all very much uh for

your time really fascinating discussion

just a reminder we are putting all these

half hour programs on the BBC's Aid

decoded YouTube

site so you can find all our pass

programs there we'll do it again same

time next week thanks for watching