global warming essay in 200 words

Essay on Global Warming in English (100,150, 200, 250, 300, 500 Words)

Global warming means the Earth is getting hotter. This happens because of things like pollution from cars and factories. These pollutants are called greenhouse gases, and they make the Earth's air trap more heat from the sun. One major greenhouse gas is carbon dioxide, which comes from burning fossil fuels like coal, oil, and gas. When trees are cut down and forests disappear, that's bad too, because trees help clean the air.

Because of global warming, some big problems are happening. Ice at the North and South Poles is melting, which causes the sea levels to rise. When the sea level goes up, it can flood coastal areas, making it hard for people to live there. Also, the Earth's weather is getting crazy. There are more hurricanes, droughts, and floods. These extreme weather events can be dangerous and damage homes and farms.

But don't worry! People all over the world are working to stop global warming. They use clean and renewable energy sources like the sun and wind instead of burning fossil fuels. They also make agreements like the Paris Agreement and the Kyoto Protocol to help reduce pollution. These agreements are like promises between countries to protect the planet.

Understanding global warming is important because it harms the environment and makes life harder for everyone. We need to take care of our planet by using clean energy and protecting forests. It's like being a good friend to the Earth. So, let's all work together to keep the Earth cool and safe for the future.

Why is the Essay on Global Warming Important for Your Exams? 

The essay on global warming is important for your exams for several reasons. First, it helps you learn about a critical issue that affects our planet. Global warming is the increase in Earth's temperature due to human activities like burning fossil fuels and deforestation. Knowing about this topic is essential because it's a significant environmental problem that impacts all of us.

Second, writing an essay on global warming can improve your research and writing skills. It teaches you how to gather information, organize your thoughts, and present them in a clear and structured way. These skills are valuable for your education and future career.

Third, understanding global warming is relevant to many subjects. It connects to science, as it involves the Earth's climate and ecosystems. It's also essential for geography, as it affects landscapes and weather patterns. In addition, it's a crucial aspect of social studies, as global warming has economic and political implications.

Fourth, addressing global warming is a global concern. International agreements like the Paris Agreement involve many countries working together to combat climate change. Knowing about these agreements and the actions taken by different nations can help you understand how the world is coming together to solve a shared problem.

Fifth, discussing global warming in your exams can demonstrate your awareness and concern for the environment. It shows that you're informed about the challenges our planet faces and that you're engaged in finding solutions.

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Essay on Effects of Global Warming for Students and Children

500+ words essay on effects of global warming.

Global warming refers to climate change that causes an increase in the average of Earth’s temperature. Natural events and human influences are believed to be top contributions towards the increase in average temperatures. Global warming is a rise in the surface and atmospheric temperature of the earth that has changed various life forms on the earth. The issues that ascertain global warming are divided into two broad categories – “natural” and “human influences” of global warming.

Natural Causes of Global Warming

The climate has been continuously changing for centuries. One natural cause of global warming is greenhouse gases. Greenhouse gases are carbon monoxide and sulphur dioxide . It traps the solar rays and prevents them from escaping the surface of the earth.

This causes an increase in the temperature of the earth. Volcanic eruptions are another reason for global warming. A single volcanic eruption can release a great amount of carbon dioxide and ash to the atmosphere. Increased carbon dioxide leads to a rise in the temperature of the earth.

Also, methane gas is another contributor to global warming. Methane is also a greenhouse gas. Methane is twenty times more effective in trapping heat in the atmosphere than carbon dioxide. Usually, methane gas is released from many areas like animal waste, landfill, natural gas, and others.

Get the huge list of more than 500 Essay Topics and Ideas

Human Influences on Global Warming

Human influence has been a very serious issue now as it is contributing more than natural causes of global warming. Since human evolution, the earth has been changing for many years until now and it is still changing because of our modern lifestyle. Human activities include industrial production, burning fossil fuel, mining of minerals, cattle rearing and deforestation.

Industries, transportation such as cars, buses, trucks burn fuel to power machines, which eventually releases carbon dioxide and monoxide from the exhaust, leading to an increase in a temperature rise of Earth’s atmosphere.

Another contributor is mining. During the process of mining, the methane gas trapped below the earth escapes. Rearing cattle also causes the release of methane from manure. Another cause is the most common but most dangerous – deforestation.

Deforestation is a human influence because human have been cutting down trees to produce paper, wood, build houses and more. Trees can absorb carbon dioxide from the atmosphere and their absence can lead to the concentration of such gases.

The Effect of Global Warming

The impact that global warming is causing on earth is extremely serious. There are many hazardous effects that will happen in the future if global warming continues. It includes melting of polar ice caps, leading to an increase in sea level drowning coastlines and slowly submerging continents.

Recent studies by National Snow and Ice Datacenter “if the ice melted today the seas would rise about 230 feet”. Another effect is climate change leading to the extinction of various species. More hurricanes, cyclonic storms, heat waves, drought, and extreme rainfalls will occur causing disaster to humankind.

The solution to Stop Global Warming

We humans need to work together towards the prevention of global warming. To reduce global warming we can contribute by reducing the production and concentration of greenhouse gases in the atmosphere. We need to curb usage of gasoline, electricity and other activities including mining and industrialization that cause global warming.

Another way to reduce global warming is through recycling. Recycling can help reduce open burning of garbage by reusing plastic bags, bottles, papers or glass. We need to stop open burning dry leaves or burning garbage. It contributes to releasing carbon dioxide and toxins. Besides, we should reduce deforestation and start planting more trees. Trees will help improve the temperature on earth and prevent drastic climatic change.

From today’s scenario, we can derive that our earth is “sick” and we humans need to “heal” it. Global Warming has already caused many problems for human and we need to prevent disasters of the future. Our generation needs to take care of the earth with immediate effect to safeguard future generations or they will suffer the consequences of global warming.

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A problem built into our relationship with energy itself. Photo by Ferdinando Scianna/Magnum

Deep warming

Even if we ‘solve’ global warming, we face an older, slower problem. waste heat could radically alter earth’s future.

by Mark Buchanan   + BIO

The world will be transformed. By 2050, we will be driving electric cars and flying in aircraft running on synthetic fuels produced through solar and wind energy. New energy-efficient technologies, most likely harnessing artificial intelligence, will dominate nearly all human activities from farming to heavy industry. The fossil fuel industry will be in the final stages of a terminal decline. Nuclear fusion and other new energy sources may have become widespread. Perhaps our planet will even be orbited by massive solar arrays capturing cosmic energy from sunlight and generating seemingly endless energy for all our needs.

That is one possible future for humanity. It’s an optimistic view of how radical changes to energy production might help us slow or avoid the worst outcomes of global warming. In a report from 1965, scientists from the US government warned that our ongoing use of fossil fuels would cause global warming with potentially disastrous consequences for Earth’s climate. The report, one of the first government-produced documents to predict a major crisis caused by humanity’s large-scale activities, noted that the likely consequences would include higher global temperatures, the melting of the ice caps and rising sea levels. ‘Through his worldwide industrial civilisation,’ the report concluded, ‘Man is unwittingly conducting a vast geophysical experiment’ – an experiment with a highly uncertain outcome, but clear and important risks for life on Earth.

Since then, we’ve dithered and doubted and argued about what to do, but still have not managed to take serious action to reduce greenhouse gas emissions, which continue to rise. Governments around the planet have promised to phase out emissions in the coming decades and transition to ‘green energy’. But global temperatures may be rising faster than we expected: some climate scientists worry that rapid rises could create new problems and positive feedback loops that may accelerate climate destabilisation and make parts of the world uninhabitable long before a hoped-for transition is possible.

Despite this bleak vision of the future, there are reasons for optimists to hope due to progress on cleaner sources of renewable energy, especially solar power. Around 2010, solar energy generation accounted for less than 1 per cent of the electricity generated by humanity. But experts believe that, by 2027, due to falling costs, better technology and exponential growth in new installations, solar power will become the largest global energy source for producing electricity. If progress on renewables continues, we might find a way to resolve the warming problem linked to greenhouse gas emissions. By 2050, large-scale societal and ecological changes might have helped us avoid the worst consequences of our extensive use of fossil fuels.

It’s a momentous challenge. And it won’t be easy. But this story of transformation only hints at the true depth of the future problems humanity will confront in managing our energy use and its influence over our climate.

As scientists are gradually learning, even if we solve the immediate warming problem linked to the greenhouse effect, there’s another warming problem steadily growing beneath it. Let’s call it the ‘deep warming’ problem. This deeper problem also raises Earth’s surface temperature but, unlike global warming, it has nothing to do with greenhouse gases and our use of fossil fuels. It stems directly from our use of energy in all forms and our tendency to use more energy over time – a problem created by the inevitable waste heat that is generated whenever we use energy to do something. Yes, the world may well be transformed by 2050. Carbon dioxide levels may stabilise or fall thanks to advanced AI-assisted technologies that run on energy harvested from the sun and wind. And the fossil fuel industry may be taking its last breaths. But we will still face a deeper problem. That’s because ‘deep warming’ is not created by the release of greenhouse gases into the atmosphere. It’s a problem built into our relationship with energy itself.

F inding new ways to harness more energy has been a constant theme of human development. The evolution of humanity – from early modes of hunter-gathering to farming and industry – has involved large systematic increases in our per-capita energy use. The British historian and archaeologist Ian Morris estimates, in his book Foragers, Farmers, and Fossil Fuels: How Human Values Evolve (2015), that early human hunter-gatherers, living more than 10,000 years ago, ‘captured’ around 5,000 kcal per person per day by consuming food, burning fuel, making clothing, building shelter, or through other activities. Later, after we turned to farming and enlisted the energies of domesticated animals, we were able to harness as much as 30,000 kcal per day. In the late 17th century , the exploitation of coal and steam power marked another leap: by 1970, the use of fossil fuels allowed humans to consume some 230,000 kcal per person per day. (When we think about humanity writ large as ‘humans’, it’s important to acknowledge that the average person in the wealthiest nations consumes up to 100 times more energy than the average person in the poorest nations.) As the global population has risen and people have invented new energy-dependent technologies, our global energy use has continued to climb.

In many respects, this is great. We can now do more with less effort and achieve things that were unimaginable to the 17th-century inventors of steam engines, let alone to our hominin ancestors. We’ve made powerful mining machines, superfast trains, lasers for use in telecommunications and brain-imaging equipment. But these creations, while helping us, are also subtly heating the planet.

All the energy we humans use – to heat our homes, run our factories, propel our automobiles and aircraft, or to run our electronics – eventually ends up as heat in the environment. In the shorter term, most of the energy we use flows directly into the environment. It gets there through hot exhaust gases, friction between tires and roads, the noises generated by powerful engines, which spread out, dissipate, and eventually end up as heat. However, a small portion of the energy we use gets stored in physical changes, such as in new steel, plastic or concrete. It’s stored in our cities and technologies. In the longer term, as these materials break down, the energy stored inside also finds its way into the environment as heat. This is a direct consequence of the well-tested principles of thermodynamics.

Waste heat will pose a problem that is every bit as serious as global warming from greenhouse gases

In the early decades of the 21st century , this heat created by simply using energy, known as ‘waste heat’, is not so serious. It’s equivalent to roughly 2 per cent of the planetary heating imbalance caused by greenhouse gases – for now. But, with the passing of time, the problem is likely to get much more serious. That’s because humans have a historical tendency to consistently discover and produce things, creating entirely new technologies and industries in the process: domesticated animals for farming; railways and automobiles; global air travel and shipping; personal computers, the internet and mobile phones. The result of such activities is that we end up using more and more energy, despite improved energy efficiency in nearly every area of technology.

During the past two centuries at least (and likely for much longer), our yearly energy use has doubled roughly every 30 to 50 years . Our energy use seems to be growing exponentially, a trend that shows every sign of continuing. We keep finding new things to do and almost everything we invent requires more and more energy: consider the enormous energy demands of cryptocurrency mining or the accelerating energy requirements of AI.

If this historical trend continues, scientists estimate waste heat will pose a problem in roughly 150-200 years that is every bit as serious as the current problem of global warming from greenhouse gases. However, deep heating will be more pernicious as we won’t be able to avoid it by merely shifting from one kind energy to another. A profound problem will loom before us: can we set strict limits on all the energy we use? Can we reign in the seemingly inexorable expansion of our activities to avoid destroying our own environment?

Deep warming is a problem hiding beneath global warming, but one that will become prominent if and when we manage to solve the more pressing issue of greenhouse gases. It remains just out of sight, which might explain why scientists only became concerned about the ‘waste heat’ problem around 15 years ago.

O ne of the first people to describe the problem is the Harvard astrophysicist Eric Chaisson, who discussed the issue of waste heat in a paper titled ‘Long-Term Global Heating from Energy Usage’ (2008). He concluded that our technological society may be facing a fundamental limit to growth due to ‘unavoidable global heating … dictated solely by the second law of thermodynamics, a biogeophysical effect often ignored when estimating future planetary warming scenarios’. When I emailed Chaisson to learn more, he told me the history of his thinking on the problem:

It was on a night flight, Paris-Boston [circa] 2006, after a UNESCO meeting on the environment when it dawned on me that the IPCC were overlooking something. While others on the plane slept, I crunched some numbers literally on the back of an envelope … and then hoped I was wrong, that is, hoped that I was incorrect in thinking that the very act of using energy heats the air, however slightly now.

The transformation of energy into heat is among the most ubiquitous processes of physics

Chaisson drafted the idea up as a paper and sent it to an academic journal. Two anonymous reviewers were eager for it to be published. ‘A third tried his damnedest to kill it,’ Chaisson said, the reviewer claiming the findings were ‘irrelevant and distracting’. After it was finally published, the paper got some traction when it was covered by a journalist and ran as a feature story on the front page of The Boston Globe . The numbers Chaisson crunched, predictions of our mounting waste heat, were even run on a supercomputer at the US National Center for Atmospheric Research, by Mark Flanner, a professor of earth system science. Flanner, Chaisson suspected at the time, was likely ‘out to prove it wrong’. But, ‘after his machine crunched for many hours’, he saw the same results that Chaisson had written on the back of an envelope that night in the plane.

Around the same time, also in 2008, two engineers, Nick Cowern and Chihak Ahn, wrote a research paper entirely independent of Chaisson’s work, but with similar conclusions. This was how I first came across the problem. Cowern and Ahn’s study estimated the total amount of waste heat we’re currently releasing to the environment, and found that it is, right now, quite small. But, like Chaisson, they acknowledged that the problem would eventually become serious unless steps were taken to avoid it.

That’s some of the early history of thinking in this area. But these two papers, and a few other analyses since, point to the same unsettling conclusion: what I am calling ‘deep warming’ will be a big problem for humanity at some point in the not-too-distant future. The precise date is far from certain. It might be 150 years , or 400, or 800, but it’s in the relatively near future, not the distant future of, say, thousands or millions of years. This is our future.

T he transformation of energy into heat is among the most ubiquitous processes of physics. As cars drive down roads, trains roar along railways, planes cross the skies and industrial plants turn raw materials into refined products, energy gets turned into heat, which is the scientific word for energy stored in the disorganised motions of molecules at the microscopic level. As a plane flies from Paris to Boston, it burns fuel and thrusts hot gases into the air, generates lots of sound and stirs up contrails. These swirls of air give rise to swirls on smaller scales which in turn make smaller ones until the energy ultimately ends up lost in heat – the air is a little warmer than before, the molecules making it up moving about a little more vigorously. A similar process takes place when energy is used by the tiny electrical currents inside the microchips of computers, silently carrying out computations. Energy used always ends up as heat. Decades ago, research by the IBM physicist Rolf Landauer showed that a computation involving even a single computing bit will release a certain minimum amount of heat to the environment.

How this happens is described by the laws of thermodynamics, which were described in the mid-19th century by scientists including Sadi Carnot in France and Rudolf Clausius in Germany. Two key ‘laws’ summarise its main principles.

The first law of thermodynamics simply states that the total quantity of energy never changes but is conserved. Energy, in other words, never disappears, but only changes form. The energy initially stored in an aircraft’s fuel, for example, can be changed into the energetic motion of the plane. Turn on an electric heater, and energy initially held in electric currents gets turned into heat, which spreads into the air, walls and fabric of your house. The total energy remains the same, but it markedly changes form.

We’re generating waste heat all the time with everything we do

The second law of thermodynamics, equally important, is more subtle and states that, in natural processes, the transformation of energy always moves from more organised and useful forms to less organised and less useful forms. For an aircraft, the energy initially concentrated in jet fuel ends up dissipated in stirred-up winds, sounds and heat spread over vast areas of the atmosphere in a largely invisible way. It’s the same with the electric heater: the organised useful energy in the electric currents gets dissipated and spread into the low-grade warmth of the walls, then leaks into the outside air. Although the amount of energy remains the same, it gradually turns into less organised, less usable forms. The end point of the energy process produces waste heat. And we’re generating it all the time with everything we do.

Data on world energy consumption shows that, collectively, all humans on Earth are currently using about 170,000 terawatt-hours (TWh), which is a lot of energy in absolute terms – a terawatt-hour is the total energy consumed in one hour by any process using energy at a rate of 1 trillion watts. This huge number isn’t surprising, as it represents all the energy being used every day by the billions of cars and homes around the world, as well as by industry, farming, construction, air traffic and so on. But, in the early 21st century , the warming from this energy is still much less than the planetary heating due to greenhouse gases.

Concentrations of greenhouse gases such as CO 2 and methane are quite small, and only make a fractional difference to how much of the Sun’s energy gets trapped in the atmosphere, rather than making it back out to space. Even so, this fractional difference has a huge effect because the stream of energy arriving from the Sun to Earth is so large. Current estimates of this greenhouse energy imbalance come to around 0.87 W per square meter, which translates into a total energy figure about 50 times larger than our waste heat. That’s reassuring. But as Cowern and Ahn wrote in their 2008 paper, things aren’t likely to stay this way over time because our energy usage keeps rising. Unless, that is, we can find some radical way to break the trend of using ever more energy.

O ne common objection to the idea of the deep warming is to claim that the problem won’t really arise. ‘Don’t worry,’ someone might say, ‘with efficient technology, we’re going to find ways to stop using more energy; though we’ll end up doing more things in the future, we’ll use less energy.’ This may sound plausible at first, because we are indeed getting more efficient at using energy in most areas of technology. Our cars, appliances and laptops are all doing more with less energy. If efficiency keeps improving, perhaps we can learn to run these things with almost no energy at all? Not likely, because there are limits to energy efficiency.

Over the past few decades, the efficiency of heating in homes – including oil and gas furnaces, and boilers used to heat water – has increased from less than 50 per cent to well above 90 per cent of what is theoretically possible. That’s good news, but there’s not much more efficiency to be realised in basic heating. The efficiency of lighting has also vastly improved, with modern LED lighting turning something like 70 per cent of the applied electrical energy into light. We will gain some efficiencies as older lighting gets completely replaced by LEDs, but there’s not a lot of room left for future efficiency improvements. Similar efficiency limits arise in the growing or cooking of food; in the manufacturing of cars, bikes and electronic devices; in transportation, as we’re taken from place to place; in the running of search engines, translation software, GPT-4 or other large-language models.

Even if we made significant improvements in the efficiencies of these technologies, we will only have bought a little time. These changes won’t delay by much the date when deep warming becomes a problem we must reckon with.

Optimising efficiencies is just a temporary reprieve, not a radical change in our human future

As a thought experiment, suppose we could immediately improve the energy efficiency of everything we do by a factor of 10 – a fantastically optimistic proposal. That is, imagine the energy output of humans on Earth has been reduced 10 times , from 170,000 TWh to 17,000 TWh . If our energy use keeps expanding, doubling every 30-50 years or so (as it has for centuries), then a 10-fold increase in waste heat will happen in just over three doubling times, which is about 130 years : 17,000 TWh doubles to 34,000 TWh , which doubles to 68,000 TWh , which doubles to 136,000 TWh , and so on. All those improvements in energy efficiency would quickly evaporate. The date when deep warming hits would recede by 130 years or so, but not much more. Optimising efficiencies is just a temporary reprieve, not a radical change in our human future.

Improvements in energy efficiency can also have an inverse effect on our overall energy use. It’s easy to think that if we make a technology more efficient, we’ll then use less energy through the technology. But economists are deeply aware of a paradoxical effect known as ‘rebound’, whereby improved energy efficiency, by making the use of a technology cheaper, actually leads to more widespread use of that technology – and more energy use too. The classic example, as noted by the British economist William Stanley Jevons in his book The Coal Question (1865), is the invention of the steam engine. This new technology could extract energy from burning coal more efficiently, but it also made possible so many new applications that the use of coal increased. A recent study by economists suggests that, across the economy, such rebound effects might easily swallow at least 50 per cent of any efficiency gains in energy use. Something similar has already happened with LED lights, for which people have found thousands of new uses.

If gains in efficiency won’t buy us lots of time, how about other factors, such as a reduction of the global population? Scientists generally believe that the current human population of more than 8 billion people is well beyond the limits of our finite planet, especially if a large fraction of this population aspires to the resource-intensive lifestyles of wealthy nations. Some estimates suggest that a more sustainable population might be more like 2 billion , which could reduce energy use significantly, potentially by a factor of three or four. However, this isn’t a real solution: again, as with the example of improved energy efficiency, a one-time reduction of our energy consumption by a factor of three will quickly be swallowed up by an inexorable rise in energy use. If Earth’s population were suddenly reduced to 2 billion – about a quarter of the current population – our energy gains would initially be enormous. But those gains would be erased in two doubling times, or roughly 60-100 years , as our energy demands would grow fourfold.

S o, why aren’t more people talking about this? The deep warming problem is starting to get more attention. It was recently mentioned on Twitter by the German climate scientist Stefan Rahmstorf, who cautioned that nuclear fusion, despite excitement over recent advances, won’t arrive in time to save us from our waste heat, and might make the problem worse. By providing another cheap source of energy, fusion energy could accelerate both the growth of our energy use and the reckoning of deep warming. A student of Rahmstorf’s, Peter Steiglechner, wrote his master’s thesis on the problem in 2018. Recognition of deep warming and its long-term implications for humanity is spreading. But what can we do about the problem?

Avoiding or delaying deep warming will involve slowing the rise of our waste heat, which means restricting the amount of energy we use and also choosing energy sources that exacerbate the problem as little as possible. Unlike the energy from fossil fuels or nuclear power, which add to our waste energy burden, renewable energy sources intercept energy that is already on its way to Earth, rather than producing additional waste heat. In this sense, the deep warming problem is another reason to pursue renewable energy sources such as solar or wind rather than alternatives such as nuclear fusion, fission or even geothermal power. If we derive energy from any of these sources, we’re unleashing new flows of energy into the Earth system without making a compensating reduction. As a result, all such sources will add to the waste heat problem. However, if renewable sources of energy are deployed correctly, they need not add to our deposition of waste heat in the environment. By using this energy, we produce no more waste heat than would have been created by sunlight in the first place.

Take the example of wind energy. Sunlight first stirs winds into motion by heating parts of the planet unequally, causing vast cells of convection. As wind churns through the atmosphere, blows through trees and over mountains and waves, most of its energy gets turned into heat, ending up in the microscopic motions of molecules. If we harvest some of this wind energy through turbines, it will also be turned into heat in the form of stored energy. But, crucially, no more heat is generated than if there had been no turbines to capture the wind.

The same can hold true for solar energy. In an array of solar cells, if each cell only collects the sunlight falling on it – which would ordinarily have been absorbed by Earth’s surface – then the cells don’t alter how much waste heat gets produced as they generate energy. The light that would have warmed Earth’s surface instead goes into the solar cells, gets used by people for some purpose, and then later ends up as heat. In this way we reduce the amount of heat being absorbed by Earth by precisely the same amount as the energy we are extracting for human use. We are not adding to overall planetary heating. This keeps the waste energy burden unchanged, at least in the relatively near future, even if we go on extracting and using ever larger amounts of energy.

Covering deserts in dark panels would absorb a lot more energy than the desert floor

Chaisson summarised the problem quite clearly in 2008:

I’m now of the opinion … that any energy that’s dug up on Earth – including all fossil fuels of course, but also nuclear and ground-sourced geothermal – will inevitably produce waste heat as a byproduct of humankind’s use of energy. The only exception to that is energy arriving from beyond Earth, this is energy here and now and not dug up, namely the many solar energies (plural) caused by the Sun’s rays landing here daily … The need to avoid waste heat is indeed the single, strongest, scientific argument to embrace solar energies of all types.

But not just any method of gathering solar energy will avoid the deep warming problem. Doing so requires careful engineering. For example, covering deserts with solar panels would add to planetary heating because deserts reflect a lot of incident light back out to space, so it is never absorbed by Earth (and therefore doesn’t produce waste heat). Covering deserts in dark panels would absorb a lot more energy than the desert floor and would heat the planet further.

We’ll also face serious problems in the long run if our energy appetite keeps increasing. Futurists dream of technologies deployed in space where huge panels would absorb sunlight that would otherwise have passed by Earth and never entered our atmosphere. Ultimately, they believe, this energy could be beamed down to Earth. Like nuclear energy, such technologies would add an additional energy source to the planet without any compensating removal of heating from the sunlight currently striking our planet’s surface. Any effort to produce more energy than is normally available from sunlight at Earth’s surface will only make our heating problems worse.

D eep warming is simply a consequence of the laws of physics and our inquisitive nature. It seems to be in our nature to constantly learn and develop new things, changing our environment in the process. For thousands of years, we have harvested and exploited ever greater quantities of energy in this pursuit, and we appear poised to continue along this path with the rapidly expanding use of renewable energy sources – and perhaps even more novel sources such as nuclear fusion. But this path cannot proceed indefinitely without consequences.

The logic that more energy equals more warming sets up a profound dilemma for our future. The laws of physics and the habits ingrained in us from our long evolutionary history are steering us toward trouble. We may have a technological fix for greenhouse gas warming – just shift from fossil fuels to cleaner energy sources – but there is no technical trick to get us out of the deep warming problem. That won’t stop some scientists from trying.

Perhaps, believing that humanity is incapable of reducing its energy usage, we’ll adopt a fantastic scheme to cool the planet, such as planetary-scale refrigeration or using artificially engineered tornadoes to transport heat from Earth’s surface to the upper atmosphere where it can be radiated away to space. As far-fetched as such approaches sound, scientists have given some serious thought to these and other equally bizarre ideas, which seem wholly in the realm of science fiction. They’re schemes that will likely make the problem worse not better.

We will need to transform the human story. It must become a story of doing less, not more

I see several possibilities for how we might ultimately respond. As with greenhouse gas warming, there will probably be an initial period of disbelief, denial and inaction, as we continue with unconstrained technological advance and growing energy use. Our planet will continue warming. Sooner or later, however, such warming will lead to serious disruptions of the Earth environment and its ecosystems. We won’t be able to ignore this for long, and it may provide a natural counterbalance to our energy use, as our technical and social capacity to generate and use ever more energy will be eroded. We may eventually come to some uncomfortable balance in which we just scrabble out a life on a hot, compromised planet because we lack the moral and organisational ability to restrict our energy use enough to maintain a sound environment.

An alternative would require a radical break with our past: using less energy. Finding a way to use less energy would represent a truly fundamental rupture with all of human history, something entirely novel. A rupture of this magnitude won’t come easily. However, if we could learn to view restrictions on our energy use as a non-negotiable element of life on Earth, we may still be able to do many of the things that make us essentially human: learning, discovering, inventing, creating. In this scenario, any helpful new technology that comes into use and begins using lots of energy would require a balancing reduction in energy use elsewhere. In such a way, we might go on with the future being perpetually new, and possibly better.

None of this is easily achieved and will likely mirror our current struggles to come to agreements on greenhouse gas heating. There will be vicious squabbles, arguments and profound polarisation, quite possibly major wars. Humanity will never have faced a challenge of this magnitude, and we won’t face up to it quickly or easily, I expect. But we must. Planetary heating is in our future – the very near future and further out as well. Many people will find this conclusion surprisingly hard to swallow, perhaps because it implies fundamental restrictions on our future here on Earth: we can’t go on forever using more and more energy, and, at the same time, expecting the planet’s climate to remain stable.

The world will likely be transformed by 2050. And, sometime after that, we will need to transform the human story. The narrative arc of humanity must become a tale of continuing innovation and learning, but also one of careful management. It must become a story, in energy terms, of doing less, not more. There’s no technology for entirely escaping waste heat, only techniques.

This is important to remember as we face up to the extremely urgent challenge of heating linked to fossil-fuel use and greenhouse gases. Global warming is just the beginning of our problems. It’s a testing ground to see if we can manage an intelligent and coordinated response. If we can handle this challenge, we might be better prepared, more capable and resilient as a species to tackle an even harder one.

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Essay on Global Warming

The last few decades have been monumental when it comes to technological development. Humans have developed systems and machines that make our lives easier. Especially during the early modern period from the early 16th century to as far as the late 18the century, also commonly referred to as “The Scientific Revolution” or “The Enlightenment”, modern technology leapt ahead in development in such a short time frame compared to all of history.

However, with the development of society, there has been a severe detriment to the quality of Earth’s environment. One of the most massive threats to the condition of the planet is climate change. Inadequate research and reckless misuse of natural resources are some of the core reasons for the deteriorating condition of the planet.

To understand the concept of Global Warming and its causes and effects, we need to take an in-depth look into many factors that affect the temperature of the planet and what that means for the future of the world. Here is an objective look at the topic of Global Warming and other important related topics.

What is Climate Change?

Ever since the industrial and scientific revolution, Earth is slowly being used up for its resources. Moreover, the onset of the exponential increase in the world’s population is also very taxing on the environment. 

Simply put, as the need for consumption of the population increases, both the utilisation of natural resources and the waste generated from the use of said resources have also increased massively. 

One of the main results of this over the many years has become climate change. Climate change is not just the rise or fall of temperature of different areas of the world; it is also a change in the rain cycles, wind patterns, cyclone frequencies, sea levels, etc. It affects all major life groups on the planet in some way or the other.  

What is Global Warming?

Global Warming is often considered an effect of Climate change. Global Warming is the rapid increase in the temperature of the Earth’s environment that is causing many life-threatening issues to arise.

Global Warming is a dangerous effect on our environment that we are facing these days. Rapid industrialization, increase in the population growth and pollution are causing a rise in Global Warming. Global Warming refers to the increase in the average temperature of the earth's surface during the last century. One of the reasons why Global Warming is dangerous is because it disturbs the overall ecology of the planet. This results in floods, famine, cyclones and other issues. There are many causes and results of this warming and is a danger for the existence of life on earth.

The sign of Global Warming is already visible with many natural phenomena happening around globally, affecting each living species.

Here is some data that can help to give a more precise understanding of the reality of Global Warming in the last few years:

On average, the world’s temperature is about 1.5°C higher than during the start of the industrial revolution in the late 1700s. That may not seem a lot to you, but that is an average estimate. This number is only increasing. Many parts of the world face far more severe changes in temperature that affect the planet’s overall health.

In 1950, the world’s CO 2 emissions were at 6 billion tonnes which had quadrupled in volume until 1990, just 40 years later to 22 billion tonnes. Not only that, unchecked CO 2 emissions today have reached a whopping 35 billion tonnes.

The most evident causes of Global Warming are industrialization, urbanization, deforestation, and sophisticated human activities. These human activities have led to an increase in the emission of Greenhouse Gases, including CO₂, Nitrous Oxide, Methane, and others.

Causes of Global Warming

A variety of reasons causes Global Warming. Some of which can be controlled personally by individuals but others are only expected to be solved by communities and the world leaders and activists at the global level.

Many scientists believe the main four reasons for Global Warming, according to recent studies, are:

Greenhouse gases

Deforestation

Per capita carbon emissions

Global Warming is certainly an alarming situation, which is causing a significant impact on life existence. Extreme Global Warming is resulting in natural calamities, which is quite evident happening around. One of the reasons behind Global Warming is the extreme release of greenhouse gases stuck on the earth surface, resulting in the temperature increase.

Similarly, volcanoes are also leading to Global Warming because they spew too much CO₂ in the air. One of the significant causes behind Global Warming is the increase in the population. This increase in the population also results in air pollution. Automobiles release a lot of CO₂, which remains stuck in the earth.

This increase in the population is also leading to deforestation, which further results in Global Warming. More and more trees are being cut, increasing the concentration of CO₂.

The greenhouse is the natural process where the sunlight passes through the area, thus warming the earth's surface. The earth surface releases energy in the form of heat in the atmosphere maintaining the balance with the incoming energy. Global Warming depletes the ozone layer leading to the doom's day.

There is a clear indication that the increase in Global Warming will lead to the complete extinction of life from the earth surface.

Solution for Global Warming

Global Warming can not be blamed on individuals; however, it can be tackled and maintained from worsening starting at the individual level. Of course, industries and multinational conglomerates have higher carbon emissions levels than an average citizen. Still, activism and community effort are the only feasible ways to control the worsening state of Global Warming.

Additionally, at the state or government level, world leaders need to create concrete plans and step programmes to ensure that no further harm is being caused to the environment in general. 

Although we are almost late in slowing down the Global Warming rate, it is crucial to find the right solution. From individuals to governments, everyone has to work upon a solution for Global Warming. Controlling pollution, population and use of natural resources are some of the factors to consider. Switching over to the electric and hybrid car is the best way to bring down the carbon dioxide.

As a citizen, it is best to switch over to the hybrid car and to use public transport. This will reduce pollution and congestion. Another significant contribution you can make is to minimize the use of plastic. Plastic is the primary cause of Global Warming taking years to recycle.

Deforestation is another thing to consider that will help in controlling Global Warming. Planting of more trees should be encouraged to make the environment go green.

Industrialization should be under certain norms. The building of industries should be banned in green zones affecting plants and species. Hefty penalties should be levied on such sectors contributing towards Global Warming.

Effects of Global Warming

Global Warming is a real problem that many want to prove as a hoax for their political benefit. However, as aware citizens of the world, we must make sure only the truth is presented in the media.

Various parts of the environment, both flora and fauna, are directly adversely affected by the damages caused by Global Warming. Wildlife being in danger is ultimately a serious threat to the survival of humanity as we know it and its future.

The effect of Global Warming is widely seen in this decade. Glacier retreat and arctic shrinkage are the two common phenomena seen. Glaciers are melting in a fast way. These are pure examples of climate change.

Rise in sea level is another significant effect of Global Warming. This sea-level rise is leading to floods in low-lying areas. Extreme weather conditions are witnessed in many countries. Unseasonal rainfall, extreme heat and cold, wildfires and others are common every year. The number of these cases is increasing. This will indeed imbalance the ecosystem bringing the result of the extinction of species.

Similarly, marine life is also widely getting affected due to the increase in Global Warming. This is resulting in the death of marine species and other issues. Moreover, changes are expected in coral reefs, which are going to face the end in coming years.

These effects will take a steep rise in coming years, bringing the expansion of species to a halt. Moreover, humans too will witness the negative impact of Global Warming in the end.

FAQs on Global Warming Essay

1. What Global Warming will Cause?

Global warming will have a massive impact on our earth in the end. Flood, extreme weather conditions, famine, wildfire and many more will be the result. There will be hotter days, which will also increase the wildfire and famine. In the past years, many meteorological bureaus have added purple and magenta to the forecast.

Another impact of global warming will be rising sea levels. Increased ocean temperatures will lead to the melting of glaciers and ice caps. Increase in the sea level will lead to floods in many low-lying areas.

The overall ecosystem of nature will be an imbalance. This will affect nature in the long-term.

2. Why Does Global Warming Happen?

There are many reasons for the cause of global warming. There are certain gases in the atmosphere called greenhouse gases. The energy then radiates from the surface; the greenhouse gases trap longwave radiation. We humans have added to the atmospheric blanket of greenhouse affecting the living species. Warming of air, oceans, and land is how global warming happens.

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Essay on Climate Change: Check Samples in 100, 250 Words

• Updated on  
• Sep 21, 2023

Writing an essay on climate change is crucial to raise awareness and advocate for action. The world is facing environmental challenges, so in a situation like this such essay topics can serve as s platform to discuss the causes, effects, and solutions to this pressing issue. They offer an opportunity to engage readers in understanding the urgency of mitigating climate change for the sake of our planet’s future.

Must Read: Essay On Environment  

Table of Contents

• 1 What Is Climate Change?
• 2 What are the Causes of Climate Change?
• 3 What are the effects of Climate Change?
• 4 How to fight climate change?
• 5 Essay On Climate Change in 100 Words
• 6 Climate Change Sample Essay 250 Words

What Is Climate Change?

Climate change is the significant variation of average weather conditions becoming, for example, warmer, wetter, or drier—over several decades or longer. It may be natural or anthropogenic. However, in recent times, it’s been in the top headlines due to escalations caused by human interference.

What are the Causes of Climate Change?

Obama at the First Session of COP21 rightly quoted “We are the first generation to feel the impact of climate change, and the last generation that can do something about it.”.Identifying the causes of climate change is the first step to take in our fight against climate change. Below stated are some of the causes of climate change:

• Greenhouse Gas Emissions: Mainly from burning fossil fuels (coal, oil, and natural gas) for energy and transportation.
• Deforestation: The cutting down of trees reduces the planet’s capacity to absorb carbon dioxide.
• Industrial Processes: Certain manufacturing activities release potent greenhouse gases.
• Agriculture: Livestock and rice cultivation emit methane, a potent greenhouse gas.

What are the effects of Climate Change?

Climate change poses a huge risk to almost all life forms on Earth. The effects of climate change are listed below:

• Global Warming: Increased temperatures due to trapped heat from greenhouse gases.
• Melting Ice and Rising Sea Levels: Ice caps and glaciers melt, causing oceans to rise.
• Extreme Weather Events: More frequent and severe hurricanes, droughts, and wildfires.
• Ocean Acidification: Oceans absorb excess CO2, leading to more acidic waters harming marine life.
• Disrupted Ecosystems: Shifting climate patterns disrupt habitats and threaten biodiversity.
• Food and Water Scarcity: Altered weather affects crop yields and strains water resources.
• Human Health Risks: Heat-related illnesses and the spread of diseases.
• Economic Impact: Damage to infrastructure and increased disaster-related costs.
• Migration and Conflict: Climate-induced displacement and resource competition.

How to fight climate change?

‘Climate change is a terrible problem, and it absolutely needs to be solved. It deserves to be a huge priority,’ says Bill Gates. The below points highlight key actions to combat climate change effectively.

• Energy Efficiency: Improve energy efficiency in all sectors.
• Protect Forests: Stop deforestation and promote reforestation.
• Sustainable Agriculture: Adopt eco-friendly farming practices.
• Advocacy: Raise awareness and advocate for climate-friendly policies.
• Innovation: Invest in green technologies and research.
• Government Policies: Enforce climate-friendly regulations and targets.
• Corporate Responsibility: Encourage sustainable business practices.
• Individual Action: Reduce personal carbon footprint and inspire others.

Essay On Climate Change in 100 Words

Climate change refers to long-term alterations in Earth’s climate patterns, primarily driven by human activities, such as burning fossil fuels and deforestation, which release greenhouse gases into the atmosphere. These gases trap heat, leading to global warming. The consequences of climate change are widespread and devastating. Rising temperatures cause polar ice caps to melt, contributing to sea level rise and threatening coastal communities. Extreme weather events, like hurricanes and wildfires, become more frequent and severe, endangering lives and livelihoods. Additionally, shifts in weather patterns can disrupt agriculture, leading to food shortages. To combat climate change, global cooperation, renewable energy adoption, and sustainable practices are crucial for a more sustainable future.

Must Read: Essay On Global Warming

Climate Change Sample Essay 250 Words

Climate change represents a pressing global challenge that demands immediate attention and concerted efforts. Human activities, primarily the burning of fossil fuels and deforestation, have significantly increased the concentration of greenhouse gases in the atmosphere. This results in a greenhouse effect, trapping heat and leading to a rise in global temperatures, commonly referred to as global warming.

The consequences of climate change are far-reaching and profound. Rising sea levels threaten coastal communities, displacing millions and endangering vital infrastructure. Extreme weather events, such as hurricanes, droughts, and wildfires, have become more frequent and severe, causing devastating economic and human losses. Disrupted ecosystems affect biodiversity and the availability of vital resources, from clean water to agricultural yields.

Moreover, climate change has serious implications for food and water security. Changing weather patterns disrupt traditional farming practices and strain freshwater resources, potentially leading to conflicts over access to essential commodities.

Addressing climate change necessitates a multifaceted approach. First, countries must reduce their greenhouse gas emissions through the transition to renewable energy sources, increased energy efficiency, and reforestation efforts. International cooperation is crucial to set emission reduction targets and hold nations accountable for meeting them.

In conclusion, climate change is a global crisis with profound and immediate consequences. Urgent action is needed to mitigate its impacts and secure a sustainable future for our planet. By reducing emissions and implementing adaptation strategies, we can protect vulnerable communities, preserve ecosystems, and ensure a livable planet for future generations. The time to act is now.

Climate change refers to long-term shifts in Earth’s climate patterns, primarily driven by human activities like burning fossil fuels and deforestation.

Five key causes of climate change include excessive greenhouse gas emissions from human activities, notably burning fossil fuels and deforestation. 

We hope this blog gave you an idea about how to write and present an essay on climate change that puts forth your opinions. The skill of writing an essay comes in handy when appearing for standardized language tests. Thinking of taking one soon? Leverage Edu provides the best online test prep for the same via Leverage Live . Register today to know more!

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Human activity affects global surface temperatures by changing Earth ’s radiative balance—the “give and take” between what comes in during the day and what Earth emits at night. Increases in greenhouse gases —i.e., trace gases such as carbon dioxide and methane that absorb heat energy emitted from Earth’s surface and reradiate it back—generated by industry and transportation cause the atmosphere to retain more heat, which increases temperatures and alters precipitation patterns.

Global warming, the phenomenon of increasing average air temperatures near Earth’s surface over the past one to two centuries, happens mostly in the troposphere , the lowest level of the atmosphere, which extends from Earth’s surface up to a height of 6–11 miles. This layer contains most of Earth’s clouds and is where living things and their habitats and weather primarily occur.

Continued global warming is expected to impact everything from energy use to water availability to crop productivity throughout the world. Poor countries and communities with limited abilities to adapt to these changes are expected to suffer disproportionately. Global warming is already being associated with increases in the incidence of severe and extreme weather, heavy flooding , and wildfires —phenomena that threaten homes, dams, transportation networks, and other facets of human infrastructure. Learn more about how the IPCC’s Sixth Assessment Report, released in 2021, describes the social impacts of global warming.

Polar bears live in the Arctic , where they use the region’s ice floes as they hunt seals and other marine mammals . Temperature increases related to global warming have been the most pronounced at the poles, where they often make the difference between frozen and melted ice. Polar bears rely on small gaps in the ice to hunt their prey. As these gaps widen because of continued melting, prey capture has become more challenging for these animals.

Recent News

global warming , the phenomenon of increasing average air temperatures near the surface of Earth over the past one to two centuries. Climate scientists have since the mid-20th century gathered detailed observations of various weather phenomena (such as temperatures, precipitation , and storms) and of related influences on climate (such as ocean currents and the atmosphere’s chemical composition). These data indicate that Earth’s climate has changed over almost every conceivable timescale since the beginning of geologic time and that human activities since at least the beginning of the Industrial Revolution have a growing influence over the pace and extent of present-day climate change .

Giving voice to a growing conviction of most of the scientific community , the Intergovernmental Panel on Climate Change (IPCC) was formed in 1988 by the World Meteorological Organization (WMO) and the United Nations Environment Program (UNEP). The IPCC’s Sixth Assessment Report (AR6), published in 2021, noted that the best estimate of the increase in global average surface temperature between 1850 and 2019 was 1.07 °C (1.9 °F). An IPCC special report produced in 2018 noted that human beings and their activities have been responsible for a worldwide average temperature increase between 0.8 and 1.2 °C (1.4 and 2.2 °F) since preindustrial times, and most of the warming over the second half of the 20th century could be attributed to human activities.

AR6 produced a series of global climate predictions based on modeling five greenhouse gas emission scenarios that accounted for future emissions, mitigation (severity reduction) measures, and uncertainties in the model projections. Some of the main uncertainties include the precise role of feedback processes and the impacts of industrial pollutants known as aerosols , which may offset some warming. The lowest-emissions scenario, which assumed steep cuts in greenhouse gas emissions beginning in 2015, predicted that the global mean surface temperature would increase between 1.0 and 1.8 °C (1.8 and 3.2 °F) by 2100 relative to the 1850–1900 average. This range stood in stark contrast to the highest-emissions scenario, which predicted that the mean surface temperature would rise between 3.3 and 5.7 °C (5.9 and 10.2 °F) by 2100 based on the assumption that greenhouse gas emissions would continue to increase throughout the 21st century. The intermediate-emissions scenario, which assumed that emissions would stabilize by 2050 before declining gradually, projected an increase of between 2.1 and 3.5 °C (3.8 and 6.3 °F) by 2100.

Many climate scientists agree that significant societal, economic, and ecological damage would result if the global average temperature rose by more than 2 °C (3.6 °F) in such a short time. Such damage would include increased extinction of many plant and animal species, shifts in patterns of agriculture , and rising sea levels. By 2015 all but a few national governments had begun the process of instituting carbon reduction plans as part of the Paris Agreement , a treaty designed to help countries keep global warming to 1.5 °C (2.7 °F) above preindustrial levels in order to avoid the worst of the predicted effects. Whereas authors of the 2018 special report noted that should carbon emissions continue at their present rate, the increase in average near-surface air temperature would reach 1.5 °C sometime between 2030 and 2052, authors of the AR6 report suggested that this threshold would be reached by 2041 at the latest.

The AR6 report also noted that the global average sea level had risen by some 20 cm (7.9 inches) between 1901 and 2018 and that sea level rose faster in the second half of the 20th century than in the first half. It also predicted, again depending on a wide range of scenarios, that the global average sea level would rise by different amounts by 2100 relative to the 1995–2014 average. Under the report’s lowest-emission scenario, sea level would rise by 28–55 cm (11–21.7 inches), whereas, under the intermediate emissions scenario, sea level would rise by 44–76 cm (17.3–29.9 inches). The highest-emissions scenario suggested that sea level would rise by 63–101 cm (24.8–39.8 inches) by 2100.

The scenarios referred to above depend mainly on future concentrations of certain trace gases, called greenhouse gases , that have been injected into the lower atmosphere in increasing amounts through the burning of fossil fuels for industry, transportation , and residential uses. Modern global warming is the result of an increase in magnitude of the so-called greenhouse effect , a warming of Earth’s surface and lower atmosphere caused by the presence of water vapour , carbon dioxide , methane , nitrous oxides , and other greenhouse gases. In 2014 the IPCC first reported that concentrations of carbon dioxide, methane, and nitrous oxides in the atmosphere surpassed those found in ice cores dating back 800,000 years.

Of all these gases, carbon dioxide is the most important, both for its role in the greenhouse effect and for its role in the human economy. It has been estimated that, at the beginning of the industrial age in the mid-18th century, carbon dioxide concentrations in the atmosphere were roughly 280 parts per million (ppm). By the end of 2022 they had risen to 419 ppm, and, if fossil fuels continue to be burned at current rates, they are projected to reach 550 ppm by the mid-21st century—essentially, a doubling of carbon dioxide concentrations in 300 years.

A vigorous debate is in progress over the extent and seriousness of rising surface temperatures, the effects of past and future warming on human life, and the need for action to reduce future warming and deal with its consequences. This article provides an overview of the scientific background related to the subject of global warming. It considers the causes of rising near-surface air temperatures, the influencing factors, the process of climate research and forecasting, and the possible ecological and social impacts of rising temperatures. For an overview of the public policy developments related to global warming occurring since the mid-20th century, see global warming policy . For a detailed description of Earth’s climate, its processes, and the responses of living things to its changing nature, see climate . For additional background on how Earth’s climate has changed throughout geologic time , see climatic variation and change . For a full description of Earth’s gaseous envelope, within which climate change and global warming occur, see atmosphere .

• Biology Article
• Essay on Global Warming

Essay On Global Warming

Essay on global warming is an important topic for students to understand. The essay brings to light the plight of the environment and the repercussion of anthropogenic activities. Continue reading to discover tips and tricks for writing an engaging and interesting essay on global warming.

Essay On Global Warming in 300 Words

Global warming is a phenomenon where the earth’s average temperature rises due to increased amounts of greenhouse gases. Greenhouse gases such as carbon dioxide, methane and ozone trap the incoming radiation from the sun. This effect creates a natural “blanket”, which prevents the heat from escaping back into the atmosphere. This effect is called the greenhouse effect.

Contrary to popular belief, greenhouse gases are not inherently bad. In fact, the greenhouse effect is quite important for life on earth. Without this effect, the sun’s radiation would be reflected back into the atmosphere, freezing the surface and making life impossible. However, when greenhouse gases in excess amounts get trapped, serious repercussions begin to appear. The polar ice caps begin to melt, leading to a rise in sea levels. Furthermore, the greenhouse effect is accelerated when polar ice caps and sea ice melts. This is due to the fact the ice reflects 50% to 70% of the sun’s rays back into space, but without ice, the solar radiation gets absorbed. Seawater reflects only 6% of the sun’s radiation back into space. What’s more frightening is the fact that the poles contain large amounts of carbon dioxide trapped within the ice. If this ice melts, it will significantly contribute to global warming. 

A related scenario when this phenomenon goes out of control is the runaway-greenhouse effect. This scenario is essentially similar to an apocalypse, but it is all too real. Though this has never happened in the earth’s entire history, it is speculated to have occurred on Venus. Millions of years ago, Venus was thought to have an atmosphere similar to that of the earth. But due to the runaway greenhouse effect, surface temperatures around the planet began rising. 

If this occurs on the earth, the runaway greenhouse effect will lead to many unpleasant scenarios – temperatures will rise hot enough for oceans to evaporate. Once the oceans evaporate, the rocks will start to sublimate under heat. In order to prevent such a scenario, proper measures have to be taken to stop climate change.

More to Read: Learn How Greenhouse Effect works

Tips To Writing the Perfect Essay

Consider adopting the following strategies when writing an essay. These are proven methods of securing more marks in an exam or assignment.

• Begin the essay with an introductory paragraph detailing the history or origin of the given topic.
• Try to reduce the use of jargons. Use sparingly if the topic requires it.
• Ensure that the content is presented in bulleted points wherever appropriate.
• Insert and highlight factual data, such as dates, names and places.
• Remember to break up the content into smaller paragraphs. 100-120 words per paragraph should suffice.
• Always conclude the essay with a closing paragraph.

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ENCYCLOPEDIC ENTRY

Global warming.

The causes, effects, and complexities of global warming are important to understand so that we can fight for the health of our planet.

Earth Science, Climatology

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Global warming is the long-term warming of the planet’s overall temperature. Though this warming trend has been going on for a long time, its pace has significantly increased in the last hundred years due to the burning of fossil fuels . As the human population has increased, so has the volume of fossil fuels burned. Fossil fuels include coal, oil, and natural gas, and burning them causes what is known as the “greenhouse effect” in Earth’s atmosphere.

The greenhouse effect is when the sun’s rays penetrate the atmosphere, but when that heat is reflected off the surface cannot escape back into space. Gases produced by the burning of fossil fuels prevent the heat from leaving the atmosphere. These greenhouse gasses are carbon dioxide , chlorofluorocarbons, water vapor , methane , and nitrous oxide . The excess heat in the atmosphere has caused the average global temperature to rise overtime, otherwise known as global warming.

Global warming has presented another issue called climate change. Sometimes these phrases are used interchangeably, however, they are different. Climate change refers to changes in weather patterns and growing seasons around the world. It also refers to sea level rise caused by the expansion of warmer seas and melting ice sheets and glaciers . Global warming causes climate change, which poses a serious threat to life on Earth in the forms of widespread flooding and extreme weather. Scientists continue to study global warming and its impact on Earth.

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Climate Explained: Introductory Essays About Climate Change Topics

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Our Future Is Now - A Climate Change Essay by Francesca Minicozzi, '21

Francesca Minicozzi (class of 2021) is a Writing/Biology major who plans to study medicine after graduation. She wrote this essay on climate change for WR 355/Travel Writing, which she took while studying abroad in Newcastle in spring 2020. Although the coronavirus pandemic curtailed Francesca’s time abroad, her months in Newcastle prompted her to learn more about climate change. Terre Ryan Associate Professor, Writing Department

Our Future Is Now

By Francesca Minicozzi, '21 Writing and Biology Major

 “If you don’t mind me asking, how is the United States preparing for climate change?” my flat mate, Zac, asked me back in March, when we were both still in Newcastle. He and I were accustomed to asking each other about the differences between our home countries; he came from Cambridge, while I originated in Long Island, New York. This was one of our numerous conversations about issues that impact our generation, which we usually discussed while cooking dinner in our communal kitchen. In the moment of our conversation, I did not have as strong an answer for him as I would have liked. Instead, I informed him of the few changes I had witnessed within my home state of New York.

Zac’s response was consistent with his normal, diplomatic self. “I have been following the BBC news in terms of the climate crisis for the past few years. The U.K. has been working hard to transition to renewable energy sources. Similar to the United States, here in the United Kingdom we have converted over to solar panels too. My home does not have solar panels, but a lot of our neighbors have switched to solar energy in the past few years.”

“Our two countries are similar, yet so different,” I thought. Our conversation continued as we prepared our meals, with topics ranging from climate change to the upcoming presidential election to Britain’s exit from the European Union. However, I could not shake the fact that I knew so little about a topic so crucial to my generation.

After I abruptly returned home from the United Kingdom because of the global pandemic, my conversation with my flat mate lingered in my mind. Before the coronavirus surpassed climate change headlines, I had seen the number of internet postings regarding protests to protect the planet dramatically increase. Yet the idea of our planet becoming barren and unlivable in a not-so-distant future had previously upset me to the point where a part of me refused to deal with it. After I returned from studying abroad, I decided to educate myself on the climate crisis.

My quest for climate change knowledge required a thorough understanding of the difference between “climate change” and “global warming.” Climate change is defined as “a pattern of change affecting global or regional climate,” based on “average temperature and rainfall measurements” as well as the frequency of extreme weather events. 1   These varied temperature and weather events link back to both natural incidents and human activity. 2   Likewise, the term global warming was coined “to describe climate change caused by humans.” 3   Not only that, but global warming is most recently attributed to an increase in “global average temperature,” mainly due to greenhouse gas emissions produced by humans. 4

I next questioned why the term “climate change” seemed to take over the term “global warming” in the United States. According to Frank Luntz, a leading Republican consultant, the term “global warming” functions as a rather intimidating phrase. During George W. Bush’s first presidential term, Luntz argued in favor of using the less daunting phrase “climate change” in an attempt to overcome the environmental battle amongst Democrats and Republicans. 5   Since President Bush’s term, Luntz remains just one political consultant out of many politicians who has recognized the need to address climate change. In an article from 2019, Luntz proclaimed that political parties aside, the climate crisis affects everyone. Luntz argued that politicians should steer clear of trying to communicate “the complicated science of climate change,” and instead engage voters by explaining how climate change personally impacts citizens with natural disasters such as hurricanes, tornadoes, and forest fires. 6   He even suggested that a shift away from words like “sustainability” would gear Americans towards what they really want: a “cleaner, safer, healthier” environment. 7

The idea of a cleaner and heathier environment remains easier said than done. The Paris Climate Agreement, introduced in 2015, began the United Nations’ “effort to combat global climate change.” 8   This agreement marked a global initiative to “limit global temperature increase in this century to 2 degrees Celsius above preindustrial levels,” while simultaneously “pursuing means to limit the increase to 1.5 degrees.” 9    Every country on earth has joined together in this agreement for the common purpose of saving our planet. 10   So, what could go wrong here? As much as this sounds like a compelling step in the right direction for climate change, President Donald Trump thought otherwise. In June 2017, President Trump announced the withdrawal of the United States from the Paris Agreement with his proclamation of climate change as a “’hoax’ perpetrated by China.” 11   President Trump continued to question the scientific facts behind climate change, remaining an advocate for the expansion of domestic fossil fuel production. 12   He reversed environmental policies implemented by former President Barack Obama to reduce fossil fuel use. 13

Trump’s actions against the Paris Agreement, however, fail to represent the beliefs of Americans as a whole. The majority of American citizens feel passionate about the fight against climate change. To demonstrate their support, some have gone as far as creating initiatives including America’s Pledge and We Are Still In. 14   Although the United States officially exited the Paris Agreement on November 4, 2020, this withdrawal may not survive permanently. 15   According to experts, our new president “could rejoin in as short as a month’s time.” 16   This offers a glimmer of hope.

The Paris Agreement declares that the United States will reduce greenhouse gas emission levels by 26 to 28 percent by the year 2025. 17   As a leader in greenhouse gas emissions, the United States needs to accept the climate crisis for the serious challenge that it presents and work together with other nations. The concept of working coherently with all nations remains rather tricky; however, I remain optimistic. I think we can learn from how other countries have adapted to the increased heating of our planet. During my recent study abroad experience in the United Kingdom, I was struck by Great Britain’s commitment to combating climate change.

Since the United Kingdom joined the Paris Agreement, the country targets a “net-zero” greenhouse gas emission for 2050. 18   This substantial alteration would mark an 80% reduction of greenhouse gases from 1990, if “clear, stable, and well-designed policies are implemented without interruption.” 19   In order to stay on top of reducing emissions, the United Kingdom tracks electricity and car emissions, “size of onshore and offshore wind farms,” amount of homes and “walls insulated, and boilers upgraded,” as well as the development of government policies, including grants for electric vehicles. 20   A strong grip on this data allows the United Kingdom to target necessary modifications that keep the country on track for 2050. In my brief semester in Newcastle, I took note of these significant changes. The city of Newcastle is small enough that many students and faculty are able to walk or bike to campus and nearby essential shops. However, when driving is unavoidable, the majority of the vehicles used are electric, and many British citizens place a strong emphasis on carpooling to further reduce emissions. The United Kingdom’s determination to severely reduce greenhouse emissions is ambitious and particularly admirable, especially as the United States struggles to shy away from its dependence on fossil fuels.

So how can we, as Americans, stand together to combat global climate change? Here are five adjustments Americans can make to their homes and daily routines that can dramatically make a difference:

• Stay cautious of food waste. Studies demonstrate that “Americans throw away up to 40 percent of the food they buy.” 21   By being more mindful of the foods we purchase, opting for leftovers, composting wastes, and donating surplus food to those in need, we can make an individual difference that impacts the greater good. 22   
• Insulate your home. Insulation functions as a “cost-effective and accessible” method to combat climate change. 23   Homes with modern insulation reduce energy required to heat them, leading to a reduction of emissions and an overall savings; in comparison, older homes can “lose up to 35 percent of heat through their walls.” 24   
• Switch to LED Lighting. LED stands for “light-emitting diodes,” which use “90 percent less energy than incandescent bulbs and half as much as compact fluorescents.” 25   LED lights create light without producing heat, and therefore do not waste energy. Additionally, these lights have a longer duration than other bulbs, which means they offer a continuing savings. 26  
• Choose transportation wisely. Choose to walk or bike whenever the option presents itself. If walking or biking is not an option, use an electric or hybrid vehicle which emits less harmful gases. Furthermore, reduce the number of car trips taken, and carpool with others when applicable. 
• Finally, make your voice heard. The future of our planet remains in our hands, so we might as well use our voices to our advantage. Social media serves as a great platform for this. Moreover, using social media to share helpful hints to combat climate change within your community or to promote an upcoming protest proves beneficial in the long run. If we collectively put our voices to good use, together we can advocate for change.

As many of us are stuck at home due to the COVID-19 pandemic, these suggestions are slightly easier to put into place. With numerous “stay-at-home” orders in effect, Americans have the opportunity to make significant achievements for climate change. Personally, I have taken more precautions towards the amount of food consumed within my household during this pandemic. I have been more aware of food waste, opting for leftovers when too much food remains. Additionally, I have realized how powerful my voice is as a young college student. Now is the opportunity for Americans to share how they feel about climate change. During this unprecedented time, our voice is needed now more than ever in order to make a difference.

However, on a much larger scale, the coronavirus outbreak has shed light on reducing global energy consumption. Reductions in travel, both on the roads and in the air, have triggered a drop in emission rates. In fact, the International Energy Agency predicts a 6 percent decrease in energy consumption around the globe for this year alone. 27   This drop is “equivalent to losing the entire energy demand of India.” 28   Complete lockdowns have lowered the global demand for electricity and slashed CO2 emissions. However, in New York City, the shutdown has only decreased carbon dioxide emissions by 10 percent. 29   This proves that a shift in personal behavior is simply not enough to “fix the carbon emission problem.” 30   Climate policies aimed to reduce fossil fuel production and promote clean technology will be crucial steppingstones to ameliorating climate change effects. Our current reduction of greenhouse gas emissions serves as “the sort of reduction we need every year until net-zero emissions are reached around 2050.” 31   From the start of the coronavirus pandemic, politicians came together for the common good of protecting humanity; this demonstrates that when necessary, global leaders are capable of putting humankind above the economy. 32

After researching statistics comparing the coronavirus to climate change, I thought back to the moment the virus reached pandemic status. I knew that a greater reason underlay all of this global turmoil. Our globe is in dire need of help, and the coronavirus reminds the world of what it means to work together. This pandemic marks a turning point in global efforts to slow down climate change. The methods we enact towards not only stopping the spread of the virus, but slowing down climate change, will ultimately depict how humanity will arise once this pandemic is suppressed. The future of our home planet lies in how we treat it right now. 

• “Climate Change: What Do All the Terms Mean?,” BBC News (BBC, May 1, 2019), https://www.bbc.com/news/science-environment-48057733 )
• Ibid. 
• Kate Yoder, “Frank Luntz, the GOP's Message Master, Calls for Climate Action,” Grist (Grist, July 26, 2019), https://grist.org/article/the-gops-most-famous-messaging-strategist-calls-for-climate-action
• Melissa Denchak, “Paris Climate Agreement: Everything You Need to Know,” NRDC, April 29, 2020, https://www.nrdc.org/stories/paris-climate-agreement-everything-you-need-know)
• “Donald J. Trump's Foreign Policy Positions,” Council on Foreign Relations (Council on Foreign Relations), accessed May 7, 2020, https://www.cfr.org/election2020/candidate-tracker/donald-j.-trump?gclid=CjwKCAjw4871BRAjEiwAbxXi21cneTRft_doA5if60euC6QCL7sr-Jwwv76IkgWaUTuyJNx9EzZzRBoCdjsQAvD_BwE#climate and energy )
• David Doniger, “Paris Climate Agreement Explained: Does Congress Need to Sign Off?,” NRDC, December 15, 2016, https://www.nrdc.org/experts/david-doniger/paris-climate-agreement-explained-does-congress-need-sign )
• “How the UK Is Progressing,” Committee on Climate Change, March 9, 2020, https://www.theccc.org.uk/what-is-climate-change/reducing-carbon-emissions/how-the-uk-is-progressing/)
• Ibid.  
• “Top 10 Ways You Can Fight Climate Change,” Green America, accessed May 7, 2020, https://www.greenamerica.org/your-green-life/10-ways-you-can-fight-climate-change )
• Matt McGrath, “Climate Change and Coronavirus: Five Charts about the Biggest Carbon Crash,” BBC News (BBC, May 5, 2020), https://www.bbc.com/news/amp/science-environment-52485712 )

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• Published: 22 July 2024

Neural general circulation models for weather and climate

• Dmitrii Kochkov   ORCID: orcid.org/0000-0003-3846-4911 1   na1 ,
• Janni Yuval   ORCID: orcid.org/0000-0001-7519-0118 1   na1 ,
• Ian Langmore 1   na1 ,
• Peter Norgaard 1   na1 ,
• Jamie Smith 1   na1 ,
• Griffin Mooers 1 ,
• Milan Klöwer 2 ,
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• Stephan Rasp 1 ,
• Peter Düben   ORCID: orcid.org/0000-0002-4610-3326 3 ,
• Sam Hatfield 3 ,
• Peter Battaglia 4 ,
• Alvaro Sanchez-Gonzalez 4 ,
• Matthew Willson   ORCID: orcid.org/0000-0002-8730-1927 4 ,
• Michael P. Brenner 1 , 5 &
• Stephan Hoyer   ORCID: orcid.org/0000-0002-5207-0380 1   na1  

Nature volume  632 ,  pages 1060–1066 ( 2024 ) Cite this article

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• Atmospheric dynamics
• Climate and Earth system modelling
• Computational science

General circulation models (GCMs) are the foundation of weather and climate prediction 1 , 2 . GCMs are physics-based simulators that combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. Recently, machine-learning models trained on reanalysis data have achieved comparable or better skill than GCMs for deterministic weather forecasting 3 , 4 . However, these models have not demonstrated improved ensemble forecasts, or shown sufficient stability for long-term weather and climate simulations. Here we present a GCM that combines a differentiable solver for atmospheric dynamics with machine-learning components and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best machine-learning and physics-based methods. NeuralGCM is competitive with machine-learning models for one- to ten-day forecasts, and with the European Centre for Medium-Range Weather Forecasts ensemble prediction for one- to fifteen-day forecasts. With prescribed sea surface temperature, NeuralGCM can accurately track climate metrics for multiple decades, and climate forecasts with 140-kilometre resolution show emergent phenomena such as realistic frequency and trajectories of tropical cyclones. For both weather and climate, our approach offers orders of magnitude computational savings over conventional GCMs, although our model does not extrapolate to substantially different future climates. Our results show that end-to-end deep learning is compatible with tasks performed by conventional GCMs and can enhance the large-scale physical simulations that are essential for understanding and predicting the Earth system.

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Solving the equations for Earth’s atmosphere with general circulation models (GCMs) is the basis of weather and climate prediction 1 , 2 . Over the past 70 years, GCMs have been steadily improved with better numerical methods and more detailed physical models, while exploiting faster computers to run at higher resolution. Inside GCMs, the unresolved physical processes such as clouds, radiation and precipitation are represented by semi-empirical parameterizations. Tuning GCMs to match historical data remains a manual process 5 , and GCMs retain many persistent errors and biases 6 , 7 , 8 . The difficulty of reducing uncertainty in long-term climate projections 9 and estimating distributions of extreme weather events 10 presents major challenges for climate mitigation and adaptation 11 .

Recent advances in machine learning have presented an alternative for weather forecasting 3 , 4 , 12 , 13 . These models rely solely on machine-learning techniques, using roughly 40 years of historical data from the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis v5 (ERA5) 14 for model training and forecast initialization. Machine-learning methods have been remarkably successful, demonstrating state-of-the-art deterministic forecasts for 1- to 10-day weather prediction at a fraction of the computational cost of traditional models 3 , 4 . Machine-learning atmospheric models also require considerably less code, for example GraphCast 3 has 5,417 lines versus 376,578 lines for the National Oceanic and Atmospheric Administration’s FV3 atmospheric model 15 (see Supplementary Information section  A for details).

Nevertheless, machine-learning approaches have noteworthy limitations compared with GCMs. Existing machine-learning models have focused on deterministic prediction, and surpass deterministic numerical weather prediction in terms of the aggregate metrics for which they are trained 3 , 4 . However, they do not produce calibrated uncertainty estimates 4 , which is essential for useful weather forecasts 1 . Deterministic machine-learning models using a mean-squared-error loss are rewarded for averaging over uncertainty, producing unrealistically blurry predictions when optimized for multi-day forecasts 3 , 13 . Unlike physical models, machine-learning models misrepresent derived (diagnostic) variables such as geostrophic wind 16 . Furthermore, although there has been some success in using machine-learning approaches on longer timescales 17 , 18 , these models have not demonstrated the ability to outperform existing GCMs.

Hybrid models that combine GCMs with machine learning are appealing because they build on the interpretability, extensibility and successful track record of traditional atmospheric models 19 , 20 . In the hybrid model approach, a machine-learning component replaces or corrects the traditional physical parameterizations of a GCM. Until now, the machine-learning component in such models has been trained ‘offline’, by learning parameterizations independently of their interaction with dynamics. These components are then inserted into an existing GCM. The lack of coupling between machine-learning components and the governing equations during training potentially causes serious problems, such as instability and climate drift 21 . So far, hybrid models have mostly been limited to idealized scenarios such as aquaplanets 22 , 23 . Under realistic conditions, machine-learning corrections have reduced some biases of very coarse GCMs 24 , 25 , 26 , but performance remains considerably worse than state-of-the-art models.

Here we present NeuralGCM, a fully differentiable hybrid GCM of Earth’s atmosphere. NeuralGCM is trained on forecasting up to 5-day weather trajectories sampled from ERA5. Differentiability enables end-to-end ‘online training’ 27 , with machine-learning components optimized in the context of interactions with the governing equations for large-scale dynamics, which we find enables accurate and stable forecasts. NeuralGCM produces physically consistent forecasts with accuracy comparable to best-in-class models across a range of timescales, from 1- to 15-day weather to decadal climate prediction.

Neural GCMs

A schematic of NeuralGCM is shown in Fig. 1 . The two key components of NeuralGCM are a differentiable dynamical core for solving the discretized governing dynamical equations and a learned physics module that parameterizes physical processes with a neural network, described in full detail in Methods , Supplementary Information sections  B and C , and Supplementary Table 1 . The dynamical core simulates large-scale fluid motion and thermodynamics under the influence of gravity and the Coriolis force. The learned physics module (Supplementary Fig. 1 ) predicts the effect of unresolved processes, such as cloud formation, radiative transport, precipitation and subgrid-scale dynamics, on the simulated fields using a neural network.

a , Overall model structure, showing how forcings F t , noise z t (for stochastic models) and inputs y t are encoded into the model state x t . The model state is fed into the dynamical core, and alongside forcings and noise into the learned physics module. This produces tendencies (rates of change) used by an implicit–explicit ordinary differential equation (ODE) solver to advance the state in time. The new model state x t +1 can then be fed back into another time step, or decoded into model predictions. b , The learned physics module, which feeds data for individual columns of the atmosphere into a neural network used to produce physics tendencies in that vertical column.

The differentiable dynamical core in NeuralGCM allows an end-to-end training approach, whereby we advance the model multiple time steps before employing stochastic gradient descent to minimize discrepancies between model predictions and reanalysis (Supplementary Information section  G.2 ). We gradually increase the rollout length from 6 hours to 5 days (Supplementary Information section  G and Supplementary Table 5 ), which we found to be critical because our models are not accurate for multi-day prediction or stable for long rollouts early in training (Supplementary Information section  H.6.2 and Supplementary Fig. 23 ). The extended back-propagation through hundreds of simulation steps enables our neural networks to take into account interactions between the learned physics and the dynamical core. We train deterministic and stochastic NeuralGCM models, each of which uses a distinct training protocol, described in full detail in Methods and Supplementary Table 4 .

We train a range of NeuralGCM models at horizontal resolutions with grid spacing of 2.8°, 1.4° and 0.7° (Supplementary Fig. 7 ). We evaluate the performance of NeuralGCM at a range of timescales appropriate for weather forecasting and climate simulation. For weather, we compare against the best-in-class conventional physics-based weather models, ECMWF’s high-resolution model (ECMWF-HRES) and ensemble prediction system (ECMWF-ENS), and two of the recent machine-learning-based approaches, GraphCast 3 and Pangu 4 . For climate, we compare against a global cloud-resolving model and Atmospheric Model Intercomparison Project (AMIP) runs.

Medium-range weather forecasting

Our evaluation set-up focuses on quantifying accuracy and physical consistency, following WeatherBench2 12 . We regrid all forecasts to a 1.5° grid using conservative regridding, and average over all 732 forecasts made at noon and midnight UTC in the year 2020, which was held-out from training data for all machine-learning models. NeuralGCM, GraphCast and Pangu compare with ERA5 as the ground truth, whereas ECMWF-ENS and ECMWF-HRES compare with the ECMWF operational analysis (that is, HRES at 0-hour lead time), to avoid penalizing the operational forecasts for different biases than ERA5.

Model accuracy

We use ECMWF’s ensemble (ENS) model as a reference baseline as it achieves the best performance across the majority of lead times 12 . We assess accuracy using (1) root-mean-squared error (RMSE), (2) root-mean-squared bias (RMSB), (3) continuous ranked probability score (CRPS) and (4) spread-skill ratio, with the results shown in Fig. 2 . We provide more in-depth evaluations including scorecards, metrics for additional variables and levels and maps in Extended Data Figs. 1 and 2 , Supplementary Information section  H and Supplementary Figs. 9 – 22 .

a , c , RMSE ( a ) and RMSB ( c ) for ECMWF-ENS, ECMWF-HRES, NeuralGCM-0.7°, NeuralGCM-ENS, GraphCast 3 and Pangu 4 on headline WeatherBench2 variables, as a percentage of the error of ECMWF-ENS. Deterministic and stochastic models are shown in solid and dashed lines respectively. e , g , CRPS relative to ECMWF-ENS ( e ) and spread-skill ratio for the ENS and NeuralGCM-ENS models ( g ). b , d , f , h , Spatial distributions of RMSE ( b ), bias ( d ), CRPS ( f ) and spread-skill ratio ( h ) for NeuralGCM-ENS and ECMWF-ENS models for 10-day forecasts of specific humidity at 700 hPa. Spatial plots of RMSE and CRPS show skill relative to a probabilistic climatology 12 with an ensemble member for each of the years 1990–2019. The grey areas indicate regions where climatological surface pressure on average is below 700 hPa.

Deterministic models that produce a single weather forecast for given initial conditions can be compared effectively using RMSE skill at short lead times. For the first 1–3 days, depending on the atmospheric variable, RMSE is minimized by forecasts that accurately track the evolution of weather patterns. At this timescale we find that NeuralGCM-0.7° and GraphCast achieve best results, with slight variations across different variables (Fig. 2a ). At longer lead times, RMSE rapidly increases owing to chaotic divergence of nearby weather trajectories, making RMSE less informative for deterministic models. RMSB calculates persistent errors over time, which provides an indication of how models would perform at much longer lead times. Here NeuralGCM models also compare favourably against previous approaches (Fig. 2c ), with notably much less bias for specific humidity in the tropics (Fig. 2d ).

Ensembles are essential for capturing intrinsic uncertainty of weather forecasts, especially at longer lead times. Beyond about 7 days, the ensemble means of ECMWF-ENS and NeuralGCM-ENS forecasts have considerably lower RMSE than the deterministic models, indicating that these models better capture the average of possible weather. A better metric for ensemble models is CRPS, which is a proper scoring rule that is sensitive to full marginal probability distributions 28 . Our stochastic model (NeuralGCM-ENS) running at 1.4° resolution has lower error compared with ECMWF-ENS across almost all variables, lead times and vertical levels for ensemble-mean RMSE, RSMB and CRPS (Fig. 2a,c,e and Supplementary Information section  H ), with similar spatial patterns of skill (Fig. 2b,f ). Like ECMWF-ENS, NeuralGCM-ENS has a spread-skill ratio of approximately one (Fig. 2d ), which is a necessary condition for calibrated forecasts 29 .

An important characteristic of forecasts is their resemblance to realistic weather patterns. Figure 3 shows a case study that illustrates the performance of NeuralGCM on three types of important weather phenomenon: tropical cyclones, atmospheric rivers and the Intertropical Convergence Zone. Figure 3a shows that all the machine-learning models make significantly blurrier forecasts than the source data ERA5 and physics-based ECMWF-HRES forecast, but NeuralCGM-0.7° outperforms the pure machine-learning models, despite its coarser resolution (0.7° versus 0.25° for GraphCast and Pangu). Blurry forecasts correspond to physically inconsistent atmospheric conditions and misrepresent extreme weather. Similar trends hold for other derived variables of meteorological interest (Supplementary Information section  H.2 ). Ensemble-mean predictions, from both NeuralGCM and ECMWF, are closer to ERA5 in an average sense, and thus are inherently smooth at long lead times. In contrast, as shown in Fig. 3 and in Supplementary Information section  H.3 , individual realizations from the ECMWF and NeuralGCM ensembles remain sharp, even at long lead times. Like ECMWF-ENS, NeuralGCM-ENS produces a statistically representative range of future weather scenarios for each weather phenomenon, despite its eight-times-coarser resolution.

All forecasts are initialized at 2020-08-22T12z, chosen to highlight Hurricane Laura, the most damaging Atlantic hurricane of 2020. a , Specific humidity at 700 hPa for 1-day, 5-day and 10-day forecasts over North America and the Northeast Pacific Ocean from ERA5 14 , ECMWF-HRES, NeuralGCM-0.7°, ECMWF-ENS (mean), NeuralGCM-ENS (mean), GraphCast 3 and Pangu 4 . b , Forecasts from individual ensemble members from ECMWF-ENS and NeuralGCM-ENS over regions of interest, including predicted tracks of Hurricane Laura from each of the 50 ensemble members (Supplementary Information section  I.2 ). The track from ERA5 is plotted in black.

We can quantify the blurriness of different forecast models via their power spectra. Supplementary Figs. 17 and 18 show that the power spectra of NeuralCGM-0.7° is consistently closer to ERA5 than the other machine-learning forecast methods, but is still blurrier than ECMWF’s physical forecasts. The spectra of NeuralGCM forecasts is also roughly constant over the forecast period, in stark contrast to GraphCast, which worsens with lead time. The spectrum of NeuralGCM becomes more accurate with increased resolution (Supplementary Fig. 22 ), which suggests the potential for further improvements of NeuralGCM models trained at higher resolutions.

Water budget

In NeuralGCM, advection is handled by the dynamical core, while the machine-learning parameterization models local processes within vertical columns of the atmosphere. Thus, unlike pure machine-learning methods, local sources and sinks can be isolated from tendencies owing to horizontal transport and other resolved dynamics (Supplementary Fig. 3 ). This makes our results more interpretable and facilitates the diagnosis of the water budget. Specifically, we diagnose precipitation minus evaporation (Supplementary Information section  H.5 ) rather than directly predicting these as in machine-learning-based approaches 3 . For short weather forecasts, the mean of precipitation minus evaporation has a realistic spatial distribution that is very close to ERA5 data (Extended Data Fig. 4c–e ). The precipitation-minus-evaporation rate distribution of NeuralGCM-0.7° closely matches the ERA5 distribution in the extratropics (Extended Data Fig. 4b ), although it underestimates extreme events in the tropics (Extended Data Fig. 4a ). It is noted that the current version of NeuralGCM directly predicts tendencies for an atmospheric column, and thus cannot distinguish between precipitation and evaporation.

Geostrophic wind balance

We examined the extent to which NeuralGCM, GraphCast and ECMWF-HRES capture the geostrophic wind balance, the near-equilibrium between the dominant forces that drive large-scale dynamics in the mid-latitudes 30 . A recent study 16 highlighted that Pangu misrepresents the vertical structure of the geostrophic and ageostrophic winds and noted a deterioration at longer lead times. Similarly, we observe that GraphCast shows an error that worsens with lead time. In contrast, NeuralGCM more accurately depicts the vertical structure of the geostrophic and ageostrophic winds, as well as their ratio, compared with GraphCast across various rollouts, when compared against ERA5 data (Extended Data Fig. 3 ). However, ECMWF-HRES still shows a slightly closer alignment to ERA5 data than NeuralGCM does. Within NeuralGCM, the representation of the geostrophic wind’s vertical structure only slightly degrades in the initial few days, showing no noticeable changes thereafter, particularly beyond day 5.

Generalizing to unseen data

Physically consistent weather models should still perform well for weather conditions for which they were not trained. We expect that NeuralGCM may generalize better than machine-learning-only atmospheric models, because NeuralGCM employs neural networks that act locally in space, on individual vertical columns of the atmosphere. To explore this hypothesis, we compare versions of NeuralCGM-0.7° and GraphCast trained to 2017 on 5 years of weather forecasts beyond the training period (2018–2022) in Supplementary Fig. 36 . Unlike GraphCast, NeuralGCM does not show a clear trend of increasing error when initialized further into the future from the training data. To extend this test beyond 5 years, we trained a NeuralGCM-2.8° model using only data before 2000, and tested its skill for over 21 unseen years (Supplementary Fig. 35 ).

Climate simulations

Although our deterministic NeuralGCM models are trained to predict weather up to 3 days ahead, they are generally capable of simulating the atmosphere far beyond medium-range weather timescales. For extended climate simulations, we prescribe historical sea surface temperature (SST) and sea-ice concentration. These simulations feature many emergent phenomena of the atmosphere on timescales from months to decades.

For climate simulations with NeuralGCM, we use 2.8° and 1.4° deterministic models, which are relatively inexpensive to train (Supplementary Information section  G.7 ) and allow us to explore a larger parameter space to find stable models. Previous studies found that running extended simulations with hybrid models is challenging due to numerical instabilities and climate drift 21 . To quantify stability in our selected models, we run multiple initial conditions and report how many of them finish without instability.

Seasonal cycle and emergent phenomena

To assess the capability of NeuralGCM to simulate various aspects of the seasonal cycle, we run 2-year simulations with NeuralGCM-1.4°. for 37 different initial conditions spaced every 10 days for the year 2019. Out of these 37 initial conditions, 35 successfully complete the full 2 years without instability; for case studies of instability, see Supplementary Information section  H.7 , and Supplementary Figs. 26 and 27 . We compare results from NeuralGCM-1.4° for 2020 with ERA5 data and with outputs from the X-SHiELD global cloud-resolving model, which is coupled to an ocean model nudged towards reanalysis 31 . This X-SHiELD run has been used as a target for training machine-learning climate models 24 . For comparison, we evaluate models after regridding predictions to 1.4° resolution. This comparison slightly favours NeuralGCM because NeuralGCM was tuned to match ERA5, but the discrepancy between ERA5 and the actual atmosphere is small relative to model error.

Figure 4a shows the temporal variation of the global mean temperature to 2020, as captured by 35 simulations from NeuralGCM, in comparison with the ERA5 reanalysis and standard climatology benchmarks. The seasonality and variability of the global mean temperature from NeuralGCM are quantitatively similar to those observed in ERA5. The ensemble-mean temperature RMSE for NeuralGCM stands at 0.16 K when benchmarked against ERA5, which is a significant improvement over the climatology’s RMSE of 0.45 K. We find that NeuralGCM accurately simulates the seasonal cycle, as evidenced by metrics such as the annual cycle of the global precipitable water (Supplementary Fig. 30a ) and global total kinetic energy (Supplementary Fig. 30b ). Furthermore, the model captures essential atmospheric dynamics, including the Hadley circulation and the zonal-mean zonal wind (Supplementary Fig. 28 ), as well as the spatial patterns of eddy kinetic energy in different seasons (Supplementary Fig. 31 ), and the distinctive seasonal behaviours of monsoon circulation (Supplementary Fig. 29 ; additional details are provided in Supplementary Information section  I.1 ).

a , Global mean temperature for ERA5 14 (orange), 1990–2019 climatology (black) and NeuralGCM-1.4° (blue) for 2020 using 35 simulations initialized every 10 days during 2019 (thick line, ensemble mean; thin lines, different initial conditions). b , Yearly global mean temperature for ERA5 (orange), mean over 22 CMIP6 AMIP experiments 34 (violet; model details are in Supplementary Information section  I.3 ) and NeuralGCM-2.8° for 22 AMIP-like simulations with prescribed SST initialized every 10 days during 1980 (thick line, ensemble mean; thin lines, different initial conditions). c , The RMSB of the 850-hPa temperature averaged between 1981 and 2014 for 22 NeuralGCM-2.8° AMIP runs (labelled NGCM), 22 CMIP6 AMIP experiments (labelled AMIP) and debiased 22 CMIP6 AMIP experiments (labelled AMIP*; bias was removed by removing the 850-hPa global temperature bias). In the box plots, the red line represents the median. The box delineates the first to third quartiles; the whiskers extend to 1.5 times the interquartile range (Q1 − 1.5IQR and Q3 + 1.5IQR), and outliers are shown as individual dots. d , Vertical profiles of tropical (20° S–20° N) temperature trends for 1981–2014. Orange, ERA5; black dots, Radiosonde Observation Correction using Reanalyses (RAOBCORE) 41 ; blue dots, mean trends for NeuralGCM; purple dots, mean trends from CMIP6 AMIP runs (grey and black whiskers, 25th and 75th percentiles for NeuralGCM and CMIP6 AMIP runs, respectively). e – g , Tropical cyclone tracks for ERA5 ( e ), NeuralGCM-1.4° ( f ) and X-SHiELD 31 ( g ). h – k , Mean precipitable water for ERA5 ( h ) and the precipitable water bias in NeuralGCM-1.4° ( i ), initialized 90 days before mid-January 2020 similarly to X-SHiELD, X-SHiELD ( j ) and climatology ( k ; averaged between 1990 and 2019). In d – i , quantities are calculated between mid-January 2020 and mid-January 2021 and all models were regridded to a 256 × 128 Gaussian grid before computation and tracking.

Next, we compare the annual biases of a single NeuralGCM realization with a single realization of X-SHiELD (the only one available), both initiated in mid-October 2019. We consider 19 January 2020 to 17 January 2021, the time frame for which X-SHiELD data are available. Global cloud-resolving models, such as X-SHiELD, are considered state of the art, especially for simulating the hydrological cycle, owing to their resolution being capable of resolving deep convection 32 . The annual bias in precipitable water for NeuralGCM (RMSE of 1.09 mm) is substantially smaller than the biases of both X-SHiELD (RMSE of 1.74 mm) and climatology (RMSE of 1.36 mm; Fig. 4i–k ). Moreover, NeuralGCM shows a lower temperature bias in the upper and lower troposphere than X-SHiELD (Extended Data Fig. 6 ). We also indirectly compare precipitation bias in X-SHiELD with precipitation-minus-evaporation bias in NeuralGCM-1.4°, which shows slightly larger bias and grid-scale artefacts for NeuralGCM (Extended Data Fig. 5 ).

Finally, to assess the capability of NeuralGCM to generate tropical cyclones in an annual model integration, we use the tropical cyclone tracker TempestExtremes 33 , as described in Supplementary Information section   I.2 , Supplementary Fig. 34 and Supplementary Table 6 . Figure 4e–g shows that NeuralGCM, even at a coarse resolution of 1.4°, produces realistic trajectories and counts of tropical cyclone (83 versus 86 in ERA5 for the corresponding period), whereas X-SHiELD, when regridded to 1.4° resolution, substantially underestimates the tropical cyclone count (40). Additional statistical analyses of tropical cyclones can be found in Extended Data Figs. 7 and 8 .

Decadal simulations

To assess the capability of NeuralGCM to simulate historical temperature trends, we conduct AMIP-like simulations over a duration of 40 years with NeuralGCM-2.8°. Out of 37 different runs with initial conditions spaced every 10 days during the year 1980, 22 simulations were stable for the entire 40-year period, and our analysis focuses on these results. We compare with 22 simulations run with prescribed SST from the Coupled Model Intercomparison Project Phase 6 (CMIP6) 34 , listed in Supplementary Information section  I.3 .

We find that all 40-year simulations of NeuralGCM, as well as the mean of the 22 AMIP runs, accurately capture the global warming trends observed in ERA5 data (Fig. 4b ). There is a strong correlation in the year-to-year temperature trends with ERA5 data, suggesting that NeuralGCM effectively captures the impact of SST forcing on climate. When comparing spatial biases averaged over 1981–2014, we find that all 22 NeuralGCM-2.8° runs have smaller bias than the CMIP6 AMIP runs, and this result remains even when removing the global temperature bias in CMIP6 AMIP runs (Fig. 4c and Supplementary Figs. 32 and 33 ).

Next, we investigated the vertical structure of tropical warming trends, which climate models tend to overestimate in the upper troposphere 35 . As shown in Fig. 4d , the trends, calculated by linear regression, of NeuralGCM are closer to ERA5 than those of AMIP runs. In particular, the bias in the upper troposphere is reduced. However, NeuralGCM does show a wider spread in its predictions than the AMIP runs, even at levels near the surface where temperatures are typically more constrained by prescribed SST.

Lastly, we evaluated NeuralGCM’s capability to generalize to unseen warmer climates by conducting AMIP simulations with increased SST (Supplementary Information section  I.4.2 ). We find that NeuralGCM shows some of the robust features of climate warming response to modest SST increases (+1 K and +2 K); however, for more substantial SST increases (+4 K), NeuralGCM’s response diverges from expectations (Supplementary Fig. 37 ). In addition, AMIP simulations with increased SST show climate drift, underscoring NeuralGCM’s limitations in this context (Supplementary Fig. 38 ).

NeuralGCM is a differentiable hybrid atmospheric model that combines the strengths of traditional GCMs with machine learning for weather forecasting and climate simulation. To our knowledge, NeuralGCM is the first machine-learning-based model to make accurate ensemble weather forecasts, with better CRPS than state-of-the-art physics-based models. It is also, to our knowledge, the first hybrid model that achieves comparable spatial bias to global cloud-resolving models, can simulate realistic tropical cyclone tracks and can run AMIP-like simulations with realistic historical temperature trends. Overall, NeuralGCM demonstrates that incorporating machine learning is a viable alternative to building increasingly detailed physical models 32 for improving GCMs.

Compared with traditional GCMs with similar skill, NeuralGCM is computationally efficient and low complexity. NeuralGCM runs at 8- to 40-times-coarser horizontal resolution than ECMWF’s Integrated Forecasting System and global cloud-resolving models, which enables 3 to 5 orders of magnitude savings in computational resources. For example, NeuralGCM-1.4° simulates 70,000 simulation days in 24 hours using a single tensor-processing-unit versus 19 simulated days on 13,824 central-processing-unit cores with X-SHiELD (Extended Data Table 1 ). This can be leveraged for previously impractical tasks such as large ensemble forecasting. NeuralGCM’s dynamical core uses global spectral methods 36 , and learned physics is parameterized with fully connected neural networks acting on single vertical columns. Substantial headroom exists to pursue higher accuracy using advanced numerical methods and machine-learning architectures.

Our results provide strong evidence for the disputed hypothesis 37 , 38 , 39 that learning to predict short-term weather is an effective way to tune parameterizations for climate. NeuralGCM models trained on 72-hour forecasts are capable of realistic multi-year simulation. When provided with historical SSTs, they capture essential atmospheric dynamics such as seasonal circulation, monsoons and tropical cyclones. However, we will probably need alternative training strategies 38 , 39 to learn important processes for climate with subtle impacts on weather timescales, such as a cloud feedback.

The NeuralGCM approach is compatible with incorporating either more physics or more machine learning, as required for operational weather forecasts and climate simulations. For weather forecasting, we expect that end-to-end learning 40 with observational data will allow for better and more relevant predictions, including key variables such as precipitation. Such models could include neural networks acting as corrections to traditional data assimilation and model diagnostics. For climate projection, NeuralGCM will need to be reformulated to enable coupling with other Earth-system components (for example, ocean and land), and integrating data on the atmospheric chemical composition (for example, greenhouse gases and aerosols). There are also research challenges common to current machine-learning-based climate models 19 , including the capability to simulate unprecedented climates (that is, generalization), adhering to physical constraints, and resolving numerical instabilities and climate drift. NeuralGCM’s flexibility to incorporate physics-based models (for example, radiation) offers a promising avenue to address these challenges.

Models based on physical laws and empirical relationships are ubiquitous in science. We believe the differentiable hybrid modelling approach of NeuralGCM has the potential to transform simulation for a wide range of applications, such as materials discovery, protein folding and multiphysics engineering design.

Differentiable atmospheric model

NeuralGCM combines components of the numerical solver and flexible neural network parameterizations. Simulation in time is carried out in a coordinate system suitable for solving the dynamical equations of the atmosphere, describing large-scale fluid motion and thermodynamics under the influence of gravity and the Coriolis force.

Our differentiable dynamical core is implemented in JAX, a library for high-performance code in Python that supports automatic differentiation 42 . The dynamical core solves the hydrostatic primitive equations with moisture, using a horizontal pseudo-spectral discretization and vertical sigma coordinates 36 , 43 . We evolve seven prognostic variables: vorticity and divergence of horizontal wind, temperature, surface pressure, and three water species (specific humidity, and specific ice and liquid cloud water content).

Our learned physics module uses the single-column approach of GCMs 2 , whereby information from only a single atmospheric column is used to predict the impact of unresolved processes occurring within that column. These effects are predicted using a fully connected neural network with residual connections, with weights shared across all atmospheric columns (Supplementary Information section  C.4 ).

The inputs to the neural network include the prognostic variables in the atmospheric column, total incident solar radiation, sea-ice concentration and SST (Supplementary Information section  C.1 ). We also provide horizontal gradients of the prognostic variables, which we found improves performance 44 . All inputs are standardized to have zero mean and unit variance using statistics precomputed during model initialization. The outputs are the prognostic variable tendencies scaled by the fixed unconditional standard deviation of the target field (Supplementary Information section  C.5 ).

To interface between ERA5 14 data stored in pressure coordinates and the sigma coordinate system of our dynamical core, we introduce encoder and decoder components (Supplementary Information section  D ). These components perform linear interpolation between pressure levels and sigma coordinate levels. We additionally introduce learned corrections to both encoder and decoder steps (Supplementary Figs. 4–6 ), using the same column-based neural network architecture as the learned physics module. Importantly, the encoder enables us to eliminate the gravity waves from initialization shock 45 , which otherwise contaminate forecasts.

Figure 1a shows the sequence of steps that NeuralGCM takes to make a forecast. First, it encodes ERA5 data at t  =  t 0 on pressure levels to initial conditions on sigma coordinates. To perform a time step, the dynamical core and learned physics (Fig. 1b ) then compute tendencies, which are integrated in time using an implicit–explicit ordinary differential equation solver 46 (Supplementary Information section  E and Supplementary Table 2 ). This is repeated to advance the model from t  =  t 0 to t  =  t final . Finally, the decoder converts predictions back to pressure levels.

The time-step size of the ODE solver (Supplementary Table 3 ) is limited by the Courant–Friedrichs–Lewy condition on dynamics, and can be small relative to the timescale of atmospheric change. Evaluating learned physics is approximately 1.5 times as expensive as a time step of the dynamical core. Accordingly, following the typical practice for GCMs, we hold learned physics tendencies constant for multiple ODE time steps to reduce computational expense, typically corresponding to 30 minutes of simulation time.

Deterministic and stochastic models

We train deterministic NeuralGCM models using a combination of three loss functions (Supplementary Information section  G.4 ) to encourage accuracy and sharpness while penalizing bias. During the main training phase, all losses are defined in a spherical harmonics basis. We use a standard mean squared error loss for prompting accuracy, modified to progressively filter out contributions from higher total wavenumbers at longer lead times (Supplementary Fig. 8 ). This filtering approach tackles the ‘double penalty problem’ 47 as it prevents the model from being penalized for predicting high-wavenumber features in incorrect locations at later times, especially beyond the predictability horizon. A second loss term encourages the spectrum to match the training data using squared loss on the total wavenumber spectrum of prognostic variables. These first two losses are evaluated on both sigma and pressure levels. Finally, a third loss term discourages bias by adding mean squared error on the batch-averaged mean amplitude of each spherical harmonic coefficient. For analysis of the impact that various loss functions have, refer to Supplementary Information section  H.6.1 , and Supplementary Figs. 23 and 24 . The combined action of the three training losses allow the resulting models trained on 3-day rollouts to remain stable during years-to-decades-long climate simulations. Before final evaluations, we perform additional fine-tuning of just the decoder component on short rollouts of 24 hours (Supplementary Information section  G.5 ).

Stochastic NeuralGCM models incorporate inherent randomness in the form of additional random fields passed as inputs to neural network components. Our stochastic loss is based on the CRPS 28 , 48 , 49 . CRPS consists of mean absolute error that encourages accuracy, balanced by a similar term that encourages ensemble spread. For each variable we use a sum of CRPS in grid space and CRPS in the spherical harmonic basis below a maximum cut-off wavenumber (Supplementary Information section  G.6 ). We compute CRPS on rollout lengths from 6 hours to 5 days. As illustrated in Fig. 1 , we inject noise to the learned encoder and the learned physics module by sampling from Gaussian random fields with learned spatial and temporal correlation (Supplementary Information section  C.2 and Supplementary Fig. 2 ). For training, we generate two ensemble members per forecast, which suffices for an unbiased estimate of CRPS.

Data availability

For training and evaluating the NeuralGCM models, we used the publicly available ERA5 dataset 14 , originally downloaded from https://cds.climate.copernicus.eu/ and available via Google Cloud Storage in Zarr format at gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3. To compare NeuralGCM with operational and data-driven weather models, we used forecast datasets distributed as part of WeatherBench2 12 at https://weatherbench2.readthedocs.io/en/latest/data-guide.html , to which we have added NeuralGCM forecasts for 2020. To compare NeuralGCM with atmospheric models in climate settings, we used CMIP6 data available at https://catalog.pangeo.io/browse/master/climate/ , as well as X-SHiELD 24 outputs available on Google Cloud storage in a ‘requester pays’ bucket at gs://ai2cm-public-requester-pays/C3072-to-C384-res-diagnostics. The Radiosonde Observation Correction using Reanalyses (RAOBCORE) V1.9 that was used as reference tropical temperature trends was downloaded from https://webdata.wolke.img.univie.ac.at/haimberger/v1.9/ . Base maps use freely available data from https://www.naturalearthdata.com/downloads/ .

Code availability

The NeuralGCM code base is separated into two open source projects: Dinosaur and NeuralGCM, both publicly available on GitHub at https://github.com/google-research/dinosaur (ref. 50 ) and https://github.com/google-research/neuralgcm (ref. 51 ). The Dinosaur package implements a differentiable dynamical core used by NeuralGCM, whereas the NeuralGCM package provides machine-learning models and checkpoints of trained models. Evaluation code for NeuralGCM weather forecasts is included in WeatherBench2 12 , available at https://github.com/google-research/weatherbench2 (ref. 52 ).

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Acknowledgements

We thank A. Kwa, A. Merose and K. Shah for assistance with data acquisition and handling; L. Zepeda-Núñez for feedback on the paper; and J. Anderson, C. Van Arsdale, R. Chemke, G. Dresdner, J. Gilmer, J. Hickey, N. Lutsko, G. Nearing, A. Paszke, J. Platt, S. Ponda, M. Pritchard, D. Rothenberg, F. Sha, T. Schneider and O. Voicu for discussions.

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These authors contributed equally: Dmitrii Kochkov, Janni Yuval, Ian Langmore, Peter Norgaard, Jamie Smith, Stephan Hoyer

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Google Research, Mountain View, CA, USA

Dmitrii Kochkov, Janni Yuval, Ian Langmore, Peter Norgaard, Jamie Smith, Griffin Mooers, James Lottes, Stephan Rasp, Michael P. Brenner & Stephan Hoyer

Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

Milan Klöwer

European Centre for Medium-Range Weather Forecasts, Reading, UK

Peter Düben & Sam Hatfield

Google DeepMind, London, UK

Peter Battaglia, Alvaro Sanchez-Gonzalez & Matthew Willson

School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

Michael P. Brenner

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Contributions

D.K., J.Y., I.L., P.N., J.S. and S. Hoyer contributed equally to this work. D.K., J.Y., I.L., P.N., J.S., G.M., J.L. and S. Hoyer wrote the code. D.K., J.Y., I.L., P.N., G.M. and S. Hoyer trained models and analysed the data. M.P.B. and S. Hoyer managed and oversaw the research project. M.K., S.R., P.D., S. Hatfield, P.B. and M.P.B. contributed technical advice and ideas. M.W. ran experiments with GraphCast for comparison with NeuralGCM. A.S.-G. assisted with data preparation. D.K., J.Y., I.L., P.N. and S. Hoyer wrote the paper. All authors gave feedback and contributed to editing the paper.

Corresponding authors

Correspondence to Dmitrii Kochkov , Janni Yuval or Stephan Hoyer .

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Competing interests.

D.K., J.Y., I.L., P.N., J.S., J.L., S.R., P.B., A.S.-G., M.W., M.P.B. and S. Hoyer are employees of Google. S. Hoyer, D.K., I.L., J.Y., G.M., P.N., J.S. and M.B. have filed international patent application PCT/US2023/035420 in the name of Google LLC, currently pending, relating to neural general circulation models.

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Extended data figures and tables

Extended data fig. 1 maps of bias for neuralgcm-ens and ecmwf-ens forecasts..

Bias is averaged over all forecasts initialized in 2020.

Extended Data Fig. 2 Maps of spread-skill ratio for NeuralGCM-ENS and ECMWF-ENS forecasts.

Spread-skill ratio is averaged over all forecasts initialized in 2020.

Extended Data Fig. 3 Geostrophic balance in NeuralGCM, GraphCast 3 and ECMWF-HRES.

Vertical profiles of the extratropical intensity (averaged between latitude 30°–70° in both hemispheres) and over all forecasts initialized in 2020 of (a,d,g) geostrophic wind, (b,e,h) ageostrophic wind and (c,f,i) the ratio of the intensity of ageostrophic wind over geostrophic wind for ERA5 (black continuous line in all panels), (a,b,c) NeuralGCM-0.7°, (d,e,f) GraphCast and (g,h,i) ECMWF-HRES at lead times of 1 day, 5 days and 10 days.

Extended Data Fig. 4 Precipitation minus evaporation calculated from the third day of weather forecasts.

(a) Tropical (latitudes −20° to 20°) precipitation minus evaporation (P minus E) rate distribution, (b) Extratropical (latitudes 30° to 70° in both hemispheres) P minus E, (c) mean P minus E for 2020 ERA5 14 and (d) NeuralGCM-0.7° (calculated from the third day of forecasts and averaged over all forecasts initialized in 2020), (e) the bias between NeuralGCM-0.7° and ERA5, (f-g) Snapshot of daily precipitation minus evaporation for 2020-01-04 for (f) NeuralGCM-0.7° (forecast initialized on 2020-01-02) and (g) ERA5.

Extended Data Fig. 5 Indirect comparison between precipitation bias in X-SHiELD and precipitation minus evaporation bias in NeuralGCM-1.4°.

Mean precipitation calculated between 2020-01-19 and 2021-01-17 for (a) ERA5 14 (c) X-SHiELD 31 and the biases in (e) X-SHiELD and (g) climatology (ERA5 data averaged over 1990-2019). Mean precipitation minus evaporation calculated between 2020-01-19 and 2021-01-17 for (b) ERA5 (d) NeuralGCM-1.4° (initialized in October 18th 2019) and the biases in (f) NeuralGCM-1.4° and (h) climatology (data averaged over 1990–2019).

Extended Data Fig. 6 Yearly temperature bias for NeuralGCM and X-SHiELD 31 .

Mean temperature between 2020-01-19 to 2020-01-17 for (a) ERA5 at 200hPa and (b) 850hPa. (c,d) the bias in the temperature for NeuralGCM-1.4°, (e,f) the bias in X-SHiELD and (g,h) the bias in climatology (calculated from 1990–2019). NeuralGCM-1.4° was initialized in 18th of October (similar to X-SHiELD).

Extended Data Fig. 7 Tropical Cyclone densities and annual regional counts.

(a) Tropical Cyclone (TC) density from ERA5 14 data spanning 1987–2020. (b) TC density from NeuralGCM-1.4° for 2020, generated using 34 different initial conditions all initialized in 2019. (c) Box plot depicting the annual number of TCs across different regions, based on ERA5 data (1987–2020), NeuralGCM-1.4° for 2020 (34 initial conditions), and orange markers show ERA5 for 2020. In the box plots, the red line represents the median; the box delineates the first to third quartiles; the whiskers extend to 1.5 times the interquartile range (Q1 − 1.5IQR and Q3 + 1.5IQR), and outliers are shown as individual dots. Each year is defined from January 19th to January 17th of the following year, aligning with data availability from X-SHiELD. For NeuralGCM simulations, the 3 initial conditions starting in January 2019 exclude data for January 17th, 2021, as these runs spanned only two years.

Extended Data Fig. 8 Tropical Cyclone maximum wind distribution in NeuralGCM vs. ERA5 14 .

Number of Tropical Cyclones (TCs) as a function of maximum wind speed at 850hPa across different regions, based on ERA5 data (1987–2020; in orange), and NeuralGCM-1.4° for 2020 (34 initial conditions; in blue). Each year is defined from January 19th to January 17th of the following year, aligning with data availability from X-SHiELD. For NeuralGCM simulations, the 3 initial conditions starting in January 2019 exclude data for January 17th, 2021, as these runs spanned only two years.

Supplementary information

Supplementary information.

Supplementary Information (38 figures, 6 tables): (A) Lines of code in atmospheric models; (B) Dynamical core of NeuralGCM; (C) Learned physics of NeuralGCM; (D) Encoder and decoder of NeuralGCM; (E) Time integration; (F) Evaluation metrics; (G) Training; (H) Additional weather evaluations; (I) Additional climate evaluations.

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Kochkov, D., Yuval, J., Langmore, I. et al. Neural general circulation models for weather and climate. Nature 632 , 1060–1066 (2024). https://doi.org/10.1038/s41586-024-07744-y

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Received : 13 November 2023

Accepted : 15 June 2024

Published : 22 July 2024

Issue Date : 29 August 2024

DOI : https://doi.org/10.1038/s41586-024-07744-y

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