Latest news with #JagadishShukla
Time of India
27-07-2025
- Science
- Time of India
A Billion Butterflies: Insights from Dr Jagadish Shukla
Dr Jagadish Shukla talks about his journey in climate science AI-generated audio summary; not verified by the edit team. Excerpts: Q. This book beautifully details your achievements in weather and climate science. How did it come about? A. This book owes its origin to three main points—one personal and two professional. That's why the book is a combination of both. Personal: The book is dedicated to my three granddaughters. I wanted them to know that their grandfather didn't just talk about climate—he wanted to act. They are the ones who will face the most serious consequences of climate change, especially as we continue to ignore recommendations to reduce greenhouse gases. Professional: First, I wanted to explain to a general audience that weather prediction, seasonal forecasting, and climate change are three distinct areas—each with different methods and mathematics. While weather prediction is familiar and climate change is long-term, my work focused on seasonal climate prediction. At the time, the dominant belief was that the butterfly effect made predictions beyond 10 days impossible. But having grown up in India, where I experienced monsoon droughts and floods lasting an entire season, I couldn't accept that such patterns were purely random. I believed other factors must be involved. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Free P2,000 GCash eGift UnionBank Credit Card Apply Now Undo When I went to MIT, I discovered that the very champion of the butterfly effect was a professor there. So challenging that paradigm was daunting. But my professors encouraged me to pursue the problem. That's where the title 'A Billion Butterflies' comes in. The butterfly effect suggests small changes can lead to big outcomes—like a butterfly's flutter affecting the weather. I asked: what if billions of butterflies fluttered? That's equivalent to a major shift in initial conditions. I found that ocean temperature was far more influential than countless small perturbations. This interest in seasonal prediction, especially for the monsoon, shaped my research. I showed that if we know ocean temperatures, we can predict monsoons. Finally, I come from a poor village with no electricity, drinking water, or proper school—I studied under a banyan tree using kerosene lamps. I walked barefoot three miles to school. Despite that, I believe we all have the inherent ability to achieve something if we don't give up. I wanted to share my journey so children, especially in underdeveloped regions, wouldn't lose hope. Q. You opened up an entire vista for the layperson through your generous sharing and collaboration. How did that approach shape your work? A. Thank you. I went out of my way to ensure people don't feel it's all very complicated. It's actually simple—we just need the right kind of education and explanation. On the first page of my memoir, I give an example from my class at George Mason University. I asked my students why nights are colder than days. They all answered, 'Because there's no sun.' I told them that answer would get a B-minus. They were surprised. The real reason is that the Earth is constantly losing energy to space, 24 hours a day. At night, without sunlight to offset that loss, we feel the cooling. In the day, the sun compensates for that loss, so we don't feel cold. Understanding climate is about energy balance: the sun gives energy, and the Earth loses it. Over a year, they roughly balance out—that's why the climate has been stable for thousands of years. But now, with increasing CO₂, that balance is shifting. CO₂ acts like a blanket, trapping energy and preventing it from escaping to space. That's global warming . So the explanation for why nights are cold and why the climate is changing is deeply connected—and actually very simple. Q. You talk in greater detail about global warming and how you were a skeptic before joining the IPCC , but later changed your views. Could you talk about that shift? A. I wasn't sceptical about the basic physics—that increasing CO₂ warms the planet. My scepticism was about whether we could already say it was happening, because by 1988, the observed warming wasn't significant enough. My concern was that making bold claims too early could damage the credibility of science. By 2007, nearly 20 years later, our IPCC panel received the Nobel Peace Prize. That was the first time scientists globally agreed that humans were indeed changing the climate. It took years of data, modelling, and debate to reach that level of confidence. Some conservatives dismissed global warming as a political agenda, but as you might've seen from the book, we debated thoroughly before making any statement. In 2007, we finally had enough evidence to say: yes, human activity is affecting the planet's climate—an enormous statement considering the scale of Earth's systems. The 2015 Paris Climate Conference was one of the best I've attended. I was part of the Indian team, and thanks to leaders like PM Modi, former US President Obama, and the Chinese presidency, it was a historic moment. For the first time, nearly 200 countries agreed that humans are driving climate change and committed to action. Of course, not all countries are doing enough—especially the United States, which has been among the worst performers. And with recent political changes, we're uncertain about the future. If action is not taken, the consequences could be severe. But right now, politics has taken over. Q. You spent decades working on the Indian summer monsoon. What were the key innovations and discoveries you made? And are there still aspects you would love to explore further? A. That's a very good question—I could write another memoir just to answer it. As far as weather forecasting is concerned, I didn't introduce any technical innovation. But I did play a key role in pushing the Indian govt to acquire a supercomputer and advanced models. When former Prime Minister Rajiv Gandhi met President Reagan, and Reagan agreed to provide a supercomputer, I was asked to help set up a weather forecasting centre in New Delhi. I'm proud to say that this centre, which uses a global model, now produces 10-day forecasts that are comparable to many international agencies. The real innovation I brought was recognising that butterflies—used metaphorically for small disturbances—can't affect seasonal averages like the monsoon. They can influence weather over a few days, but not an entire season. The weather tomorrow depends entirely on the weather today, and since we never know the exact state of the atmosphere everywhere, we can't predict the weather perfectly beyond a point. But even if we can't predict daily weather beyond 10 days, I argued that we could predict the seasonal average. That was the basis for our paper on Monsoon Predictability, which laid the groundwork for the science of seasonal forecasting. My focus began with the monsoon, but the idea extended far beyond that. Climate change, of course, is a different challenge altogether. And here's an important point: we have more confidence in predicting climate 100 years from now than weather 100 days from now. That's because short-term weather is shaped by initial conditions and chaotic variations, while long-term climate depends mainly on external factors—especially how much carbon dioxide we release and how much deforestation takes place. Over the last century, solar changes and volcanic eruptions have had only minimal impact on global temperature. The main drivers of recent climate change are human: deforestation and greenhouse gas emissions. Deforestation accounts for about 20 to 25 percent of the impact, while carbon dioxide and other greenhouse gases make up about 75 to 80 percent. That's why climate change is such a complex problem—it's driven by activities that are essential to modern life, like transport and heating, yet they also shape the future of the planet. Q. You mention the relationship between war, weather, and technology, and also note that accurate models and predictions are a matter of national security. Could you elaborate on that connection? A. The reason India wanted its own supercomputer and weather forecasting model—and why I supported it fully—was because it's a matter of both national security and building local expertise. Some might argue, why not just rely on the European Centre for Medium-Range Weather Forecasts, which is among the best in the world? But that's not enough. National security becomes critical here. For example, during the Falklands War, when Britain attacked Argentina, the Europeans reportedly blocked Argentina's access to weather forecasts. Some say this severely impacted Argentina's response—one of their ships was hit by a British torpedo while high-ranking officers were on deck, unaware of the weather conditions. There are many such examples that highlight why having your own weather prediction system is crucial—especially in wartime. It's also essential for disaster management. If a cyclone is about to hit, you need precise, hour-by-hour forecasts to prepare and minimise damage. So, accurate and independent weather models are vital not just for science, but for national preparedness and security. Q. Your book emphasises the importance of accurate data, and that forecasts are only reliable up to around 10 days. In that context, do you think weather apps are actually reliable? A. You're absolutely right to raise that question. All these weather apps ultimately depend on the output generated by supercomputers running billions of mathematical equations—something I emphasise throughout my book. These models solve equations every few minutes to produce accurate weather forecasts. Once that data is generated, television channels and apps take it and present it with visuals, animations, and stories—but the core forecast still comes from those scientific models. Until about two years ago, those supercomputer-based models were the only source of detailed and reliable forecasts. This approach—refining mathematical models over decades—has been one of the great scientific achievements of the 20th century. We've steadily improved weather prediction, hour by hour and even minute by minute, thanks to this method. But two years ago, things changed. Companies like Google and Microsoft introduced artificial intelligence–based methods. These AI systems don't use physical models at all. Instead, they feed decades of forecast data—generated by traditional models—into AI systems and try to find patterns to make predictions. In a way, it's like going back to what we were doing 30 or 40 years ago: relying purely on past data, without physical understanding. That said, AI has produced surprisingly good results. The European Centre for Medium-Range Weather Forecasts—widely considered the best in the world—recently acknowledged that some AI forecasts are now as good as theirs. And AI developers are confident they can surpass traditional models. This is a completely different kind of revolution. Unlike our approach, which is grounded in physical laws and deep understanding, AI focuses purely on prediction. And for many people—those selling umbrellas or deciding what to plant in a field—understanding doesn't matter as much as having a usable forecast. That's why AI-based apps are exploding in popularity. I mentioned in my book that there are already around 10,000 weather-related apps, and that number is rapidly growing.
The Hindu
24-06-2025
- Science
- The Hindu
Interview with Jagadish Shukla, author of A Billion Butterflies: A Life in Climate and Chaos Theory
Eminent climate scientist Dr. Jagadish Shukla has devoted a lifetime to improving seasonal weather predictions, and especially monsoonal predictions for India. He grew up in rural Uttar Pradesh and seeing how people's lives depended on the monsoon and information around it, made it his mission to forecast seasonal weather events. In doing so, he has changed the course of modern weather prediction. He tells the story in his new book, A Billion Butterflies: A Life in Climate and Chaos Theory, a personal memoir as well as a log about the course weather and climate science has taken. Edited excerpts from an interview. One of the things that makes your book fascinating is that it deals with a topic that people talk of daily, but has a limited understanding of. A fascinating line says, 'Climate is what you expect, weather is what you get.' What does that mean? All that it means is that long-term average weather is climate. Typically, a 30-year average of values is considered as climate. So what you expect to happen on a certain date is based on this, and what actually happens – weather -- is over and above that. The reason it is important to understand this is that we tend to think climate is fixed, but it is not. It is changing every day and changing in a well defined manner and it is also different over different places. The title of the book and your area of study refer to the chaos theory and thereby the butterfly effect. When applied to climate science, does it really mean that we are looking at the variables that go into the forecast models? First of all, the equations that define weather and climate are the same; just that weather does not consider some big factors like chemistry, aerosols etc. The butterfly effect is all about weather. Predictions are based on what happens today and the equations chosen. However, these predictions, hold good only for a few days. Even with improvements in computing and satellite observations, accuracy begins to get tricky after 10 days. This is because the equations which do the prediction are non-linear and small errors on the first day can lead to very large variations a few days ahead. And that's the origin of the word 'butterfly effect' as defined by one of my advisers, Professor Edward Lorenz from MIT. What is even more interesting is when he first spoke of this effect on forecasts, he used the analogy of a seagull flapping its wings over an ocean. The butterfly terminology came much later because the actual graphical result of his paper resembles a flapping butterfly! My motivation when studying the monsoon was to find exceptions to the butterfly effect and I found it eventually -- it was the ocean temperatures. Science is not just about experiments and ideas; it is also about communicating those ideas. My work showed that once ocean temperatures are included as a factor, even a billion butterflies flapping their wings could not affect it significantly. It is evident from your work that meteorology and forecasting has improved dramatically, including in India. How are we placed in terms of how we look at climate change? Tthe very first supercomputer that came to India in 1989 was for weather. While we have kept pace since and our weather forecasts are comparable to what is happening globally, our monsoon forecasts still need work. In terms of climate, it is disappointing that developed countries like the U.S. has shown great reluctance to accept the reality of climate change. India requires a national effort towards climate assessment and adaption for buy-in and action from policy makers and effective governance. You were the lead author of the IPCC assessment report that shared the Nobel Peace Prize along with Al Gore in 2007. Do you think it was a kind of a global turning point in terms of climate change discourse? I think so. And it had one good effect as well as a very bad one. The good part was that this was the first time scientists could conclusively state and prove that human activities are negatively affecting global climate. Eight years later at the Paris climate change conference (COP21), nearly 200 countries agreed to a legally binding international treaty to make efforts to limit global warming and temperature rise. The bad news came from the U.S. and perhaps elsewhere. This was the point where the fossil fuel industry stepped up their attacks on actively trying to disprove climate science through both overt and covert means. It really is the worst combination of politics and profit motives undermining one of society's greatest challenges. It almost seems as if your life is driven forward by destiny. And you keep referring to the monsoon. How much of a critical part was it in your early life and in shaping your career? As far as my personal life was concerned, especially early on, it just felt like things were happening on their own; with many things being beyond my control. It was much later that I started making my own decisions. So far as the monsoon is concerned, that certainly has been the central part of my journey. In my village Mirdha, monsoons or its failure, had a profound effect on life, including food on your plate. And so, I went to MIT with a very clear aim – to be able to predict the monsoon. Because that was the way I felt I could help my village, my country, the agricultural community. Twice in my life I was very close to shifting to other spheres of work, but my interest and efforts remained focused on the monsoon. What does a life dedicated to scientific rigour mean? Does it take a toll on your personal life? Oh certainly, it does. When you are excited about what you are doing and you think you are making progress, you tend to ignore some aspects of your personal life. I often feel that perhaps my children did not have enough time to be with me and know me better. There was a point where my daughter asked what her dad looks like. That said I am indebted to the complete support and trust of my wife. You have gone back to your village and helped set up a woman's college and contributed otherwise nationally as well. So would you say that your life has sort of come full circle? I wouldn't call it a full circle; rather life has been like that all along. I was always involved with family, Mirdha, India and science – to the extent that some people believed that I was doing all of this to eventually run for a political office! We have seen that climate change affects certain strata of society more than others. How well do you think we are prepared to adapt to these changes? People say that climate change is the biggest problem facing us. For me, it is only one of the two biggest problems. The other being inequality and lack of social justice. In India for example, we go to international forums and say that our per capita income is relatively small and so we should be exempt from taking serious climate action. But when you look closely, it is less than 10% of the population that is responsible for most of the actual emissions. While it is the remaining 90% that will bear the brunt of the impacts of climate change. As far as I am concerned, climate action in the end is a sort of a fight against the injustices that exists in this world. What really stands out from the book is how you are driven by a great belief in your own understanding of life. Even if this has meant standing contrary to existing view points. Yes, I have conviction. But I have also been open to being proven wrong. In modern society, especially democracies like the U.S., there is always a lot of talk about liberty and freedom; but not so much about happiness. Thanks to my mother, right from my childhood, I have understood that giving to others and society is one of the best ways to attain this. Billion Butterflies: A Life in Climate & Chaos Theory Jagadish Shukla Macmillan ₹699 The interviewer is a birder and writer based in Chennai.
Scroll.in
03-06-2025
- Science
- Scroll.in
From the memoir: The story of climate scientist Jagadish Shukla, who modernised monsoon prediction
Stories written by An excerpt from 'A Billion Butterflies: A Life in Climate and Chaos Theory'. Jagadish Shukla
Hindustan Times
26-05-2025
- Science
- Hindustan Times
Dr Jagadish Shukla, India's top meteorologist, on climate, forecasting, and India's failures
The arc of Dr. Jagadish Shukla's life is an extraordinary one, shaped in equal parts by curiosity, resilience, and good fortune. Born in the drought-prone village of Mirdha in Uttar Pradesh, with no electricity and barely a functioning school, Shukla rose to become one of the world's most respected meteorologists. At the Massachusetts Institute of Technology (MIT), he was mentored by pioneering climate scientist Jule Charney, turned down an offer from the legendary Pakistani theoretical physicist and Nobel laureate Abdus Salam, and went on to challenge the butterfly effect theory, showing that slowly evolving ocean boundary conditions could offer more predictive skill than chaotic initial states. Until just four decades ago, the scientific consensus held that weather could not be predicted beyond ten days; Shukla's work helped overturn that idea, laying the foundation for modern seasonal forecasting. In the 1980s, he played a key role in bringing India its first Cray supercomputer for weather forecasting. Today, Shukla, 81, is a Distinguished University Professor at George Mason University, where he founded the Department of Atmospheric, Oceanic, and Earth Sciences, as well as the Center for Ocean-Land-Atmosphere Studies. His recently released book, A Billion Butterflies: A Life in Climate and Chaos Theory, blends reminiscences, science history, and urgent critique. In this interview with the Hindustan Times, Dr. Shukla reflects on his childhood, scientific breakthroughs, and the state of Indian meteorology. One of the defining experiences of my childhood was going through major droughts. In 1972, while I was a graduate student at MIT, I returned to my village for a few days, and there was no food. It was a terrible drought, but at the time, nobody, not in India, not even at MIT, could explain why. It was only ten or fourteen years later that we discovered the connection to ocean temperatures in the Pacific. That stayed with me. From a young age, I kept wondering: what could I do for my village? That question drove my work. Rain, too, was both worshipped and feared. When it came for the first time, it brought excitement and joy. But then it would just keep coming, and soon there was flooding. For people who were not well off, it was devastating. Most villagers didn't have proper houses, so rooms would leak or flood. I saw both sides of it very early, the hope and the hardship. When I came to MIT, I was already interested in predicting the monsoon. But the big gurus were saying, 'You can't predict beyond ten days.' So I had to change strategy. I worked on why monsoon depressions form. That's how I got my PhD. But I knew that wouldn't help people directly. I wanted to go further: to seasonal prediction. And that's where the idea came in: the boundary conditions like ocean temperature, they change slowly. If we understand them, we can predict beyond ten days. That's what I focused on. So let me just say that I'm not happy with it because the skill of seasonal predictions is not as good as I had hoped. We're still learning. Everyone talks about how AI and machine learning are changing everything, but for seasonal or climate prediction, they haven't helped. AI is only now becoming comparable to physics-based weather forecasting for short-term predictions, up to ten days, using our dynamical models. These models are built on the laws of physics and not just on historical data. AI models need vast amounts of past data to train on, much of which comes from our physics-based systems in the first place. What they do have is very fast machines. They take 70 years of data, every six hours, globally, and fit it to predict day one, then day two, and so on. But again, that's only for short-term forecasting. In my biased opinion, AI alone will never match physics-based models. Some centres, like one in Europe, have already started combining the two — and that shows promise. India has made some progress, especially in short-term forecasting and disaster response. But when it comes to advanced systems like those used by the European Centre, we're not there yet. Our seasonal forecasts aren't as reliable, and part of the problem is our inability to assimilate global data effectively. We also lack a consolidated national centre with the technical talent and computing power needed to build and refine models. Forecasting here is fragmented. Research happens in one place, operations in another. Right now, climate science sits under the Ministry of Earth Sciences, mainly institutions like NCMRWF (National Centre for Medium Range Weather Forecasting) and IMD (Indian Meteorological Department), where I've worked. Policy, on the other hand, is handled by the Ministry of Environment, Forest and Climate Change. The ministry was renamed in 2014 to the Ministry of Environment, Forest and Climate Change (MoEFCC). But ironically, since the name change, 'climate' often gets left out. Many climate scientists aren't even invited to meetings anymore. So yes, the structure exists. But the coordination doesn't. That's the problem. That's the system. The structure is there. But nothing happens. Urban climate resilience will require major planning reforms. There is no doubt climate change is real — we're already seeing extreme rain and heat. But most of our cities aren't built for it. We need adaptation, not just mitigation. That means managing water, heat, health, and infrastructure in a coordinated way. That's why I've long called for a National Climate Assessment. In the US, the White House does it — every sector, every state. In India, ministries work in silos. It has to come from the top, from the Prime Minister's office. When Manmohan Singh launched the National Action Plan on Climate Change, he understood this. He was very progressive, always able to grasp the science quickly. That kind of leadership is what made a difference then. And for rural areas and farmers, the problem is the same. Communication. Forecasts are improving, but the farmer in my village still doesn't know what's coming in the next five days. Seasonal forecasts can help a lot, and we now have good models to do that. But again, we need systems that can communicate this information in a local language, through trusted channels. AI can help here, not with the forecast itself, but in getting the right information to the right people at the right time. Indian Ocean temperature plays a very important role in the monsoon. For a long time, we used to think predictions were mainly based on the Pacific Ocean and El Niño. But this is where the future lies. The second big factor after the ocean is land. Can we predict how wet the land will be for the whole season? My early work was on land, not just how wet it is, but also the snow cover over the entire Eurasian continent. That affects the monsoon too. The area where progress is being made now is in predicting these boundary conditions. Because it's no longer just about atmospheric models. You need a very good land model that can calculate the interaction between the atmosphere and snow.
