Category: English

Source: The Conversation – USA (2) – By Jack Barkenbus, Visiting Scholar, Vanderbilt University

In 2025, 1 in 4 new automotive vehicle sales globally are expected to be an electric vehicle – either fully electric or a plug-in hybrid.

That is a significant rise from just five years ago, when EV sales amounted to fewer than 1 in 20 new car sales, according to the International Energy Agency, an intergovernmental organization examining energy use around the world.

In the U.S., however, EV sales have lagged, only reaching 1 in 10 in 2024. By contrast, in China, the world’s largest car market, more than half of all new vehicle sales are electric.

The International Energy Agency has reported that two-thirds of fully electric cars in China are now cheaper to buy than their gasoline equivalents. With operating and maintenance costs already cheaper than gasoline models, EVs are attractive purchases.

Most EVs purchased in China are made there as well, by a range of different companies. NIO, Xpeng, Xiaomi, Zeekr, Geely, Chery, Great Wall Motor, Leapmotor and especially BYD are household names in China. As someone who has followed and published on the topic of EVs for over 15 years, I expect they will soon become as widely known in the rest of the world.

What kinds of EVs is China producing?

China’s automakers are producing a full range of electric vehicles, from the subcompact, like the BYD Seagull, to full-size SUVs, like the Xpeng G9, and luxury cars, like the Zeekr 009.

Recent European crash-test evaluations have given top safety ratings to Chinese EVs, and many of them cost less than similar models made by other companies in other countries.

What’s behind Chinese EV success?

There are several factors behind Chinese companies’ success in producing and selling EVs. To be sure, relatively low labor costs are part of the explanation. So are generous government subsidies, as EVs were one of several advanced technologies selected by the Chinese government to propel the nation’s global technological profile.

But Chinese EV makers are also making other advances. They make significant use of industrial robotics, even to the point of building so-called “dark factories” that can operate with minimal human intervention. For passengers, they have reimagined vehicles’ interiors, with large touchscreens for information and entertainment, and even added a refrigerator, bed or karaoke system.

Competition among Chinese EV makers is fierce, which drives additional innovation. BYD is the largest seller of EVs, both domestically and globally. Yet the company says it employs over 100,000 scientists and engineers seeking continual improvement.

From initial concept models to actual rollout of factory-made cars, BYD takes 18 months – half as long as U.S. and other global automakers take for their product development processes, Reuters reported.

BYD is also the world’s second-largest EV battery seller and has developed a new battery that can recharge in just five minutes, roughly the same time it takes to fill a gas-powered car’s tank.

Exports

The real test of how well Chinese vehicles appeal to consumers will come from export sales. Chinese EV manufacturers are eager to sell abroad because their factories can produce far more than the 25 million vehicles they can sell within China each year – perhaps twice as much.

China already exports more cars than any other nation, though primarily gas-powered ones at the moment. Export markets for Chinese EVs are developing in Western Europe, Southeast Asia, Latin America, Australia and elsewhere.

The largest market where Chinese vehicles, whether gasoline or electric, are not being sold is North America. Both the U.S. and Canadian governments have created what some have called a “tariff fortress” protecting their domestic automakers, by imposing tariffs of 100% on the import of Chinese EVs – literally doubling their cost to consumers.

Customers’ budgets matter too. The average price of a new electric vehicle in the U.S. is approximately $55,000. Less expensive vehicles make up part of this average, but without tax credits, which the Trump administration is eliminating after September 2025, nothing gets close to $25,000. By contrast, Chinese companies produce several sub-$25,000 EVs, including the Xpeng M03, the BYD Dolphin and the MG4 without tax credits. If sold in America, however, the 100% tariffs would remove the price advantage.

Tesla, Ford and General Motors all claim they are working on inexpensive EVs. More expensive vehicles, however, generate higher profits, and with the protection of the “tariff fortress,” their incentive to develop cheaper EVs is not as high as it might be.

In the 1970s and 1980s, there was considerable U.S. opposition to importing Japanese vehicles. But ultimately, a combination of consumer sentiment and the willingness of Japanese companies to open factories in the U.S. overcame that opposition, and Japanese brands like Toyota, Honda and Nissan are common on North American roads. The same process may play out for Chinese automakers, though it’s not clear how long that might take.

Jack Barkenbus does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. China’s electric vehicle influence expands nearly everywhere – except the US and Canada – https://theconversation.com/chinas-electric-vehicle-influence-expands-nearly-everywhere-except-the-us-and-canada-262459

Source: The Conversation – USA (2) – By Nadir Jeevanjee, Research Physical Scientist, National Oceanic and Atmospheric Administration

Climate models are complex, just like the world they mirror. They simultaneously simulate the interacting, chaotic flow of Earth’s atmosphere and oceans, and they run on the world’s largest supercomputers.

Critiques of climate science, such as the report written for the Department of Energy by a panel in 2025, often point to this complexity to argue that these models are too uncertain to help us understand present-day warming or tell us anything useful about the future.

But the history of climate science tells a different story.

The earliest climate models made specific forecasts about global warming decades before those forecasts could be proved or disproved. And when the observations came in, the models were right. The forecasts weren’t just predictions of global average warming – they also predicted geographical patterns of warming that we see today.

These early predictions starting in the 1960s emanated largely out of a single, somewhat obscure government laboratory outside Princeton, New Jersey: the Geophysical Fluid Dynamics Laboratory. And many of the discoveries bear the fingerprints of one particularly prescient and persistent climate modeler, Syukuro Manabe, who was awarded the 2021 Nobel Prize in physics for his work.

Manabe’s models, based in the physics of the atmosphere and ocean, forecast the world we now see while also drawing a blueprint for today’s climate models and their ability to simulate our large-scale climate. While models have limitations, it is this track record of success that gives us confidence in interpreting the changes we’re seeing now, as well as predicting changes to come.

Forecast No. 1: Global warming from CO2

Manabe’s first assignment in the 1960s at the U.S. Weather Bureau, in a lab that would become the Geophysical Fluid Dynamics Laboratory, was to accurately model the greenhouse effect – to show how greenhouse gases trap radiant heat in Earth’s atmosphere. Since the oceans would freeze over without the greenhouse effect, this was a key first step in building any kind of credible climate model.

To test his calculations, Manabe created a very simple climate model. It represented the global atmosphere as a single column of air and included key components of climate, such as incoming sunlight, convection from thunderstorms, and his greenhouse effect model.

Despite its simplicity, the model reproduced Earth’s overall climate quite well. Moreover, it showed that doubling carbon dioxide concentrations in the atmosphere would cause the planet to warm by about 5.4 degrees Fahrenheit (3 degrees Celsius).

This estimate of Earth’s climate sensitivity, published in 1967, has remained essentially unchanged in the many decades since and captures the overall magnitude of observed global warming. Right now the world is about halfway to doubling atmospheric carbon dioxide, and the global temperature has warmed by about 2.2 F (1.2 C) – right in the ballpark of what Manabe predicted.

Other greenhouses gases such as methane, as well as the ocean’s delayed response to global warming, also affect temperature rise, but the overall conclusion is unchanged: Manabe got Earth’s climate sensitivity about right.

Forecast No. 2: Stratospheric cooling

The surface and lower atmosphere in Manabe’s single-column model warmed as carbon dioxide concentrations rose, but in what was a surprise at the time, the model’s stratosphere actually cooled.

Temperatures in this upper region of the atmosphere, between roughly 7.5 and 31 miles (12 and 50 km) in altitude, are governed by a delicate balance between the absorption of ultraviolet sunlight by ozone and release of radiant heat by carbon dioxide. Increase the carbon dioxide, and the atmosphere traps more radiant heat near the surface but actually releases more radiant heat from the stratosphere, causing it to cool.

This cooling of the stratosphere has been detected over decades of satellite measurements and is a distinctive fingerprint of carbon dioxide-driven warming, as warming from other causes such as changes in sunlight or El Niño cycles do not yield stratospheric cooling.

Forecast No. 3: Arctic amplification

Manabe used his single-column model as the basis for a prototype quasi-global model, which simulated only a fraction of the globe. It also simulated only the upper 100 meters or so of the ocean and neglected the effects of ocean currents.

In 1975, Manabe published global warming simulations with this quasi-global model and again found stratospheric cooling. But he also made a new discovery – that the Arctic warms significantly more than the rest of the globe, by a factor of two to three times.

This “Arctic amplification” turns out to be a robust feature of global warming, occurring in present-day observations and subsequent simulations. A warming Arctic furthermore means a decline in Arctic sea ice, which has become one of the most visible and dramatic indicators of a changing climate.

Forecast No. 4: Land-ocean contrast

In the early 1970s, Manabe was also working to couple his atmospheric model to a first-of-its-kind dynamical model of the full world ocean built by oceanographer Kirk Bryan.

Around 1990, Manabe and Bryan used this coupled atmosphere-ocean model to simulate global warming over realistic continental geography, including the effects of the full ocean circulation. This led to a slew of insights, including the observation that land generally warms more than ocean, by a factor of about 1.5.

As with Arctic amplification, this land-ocean contrast can be seen in observed warming. It can also be explained from basic scientific principles and is roughly analogous to the way a dry surface, such as pavement, warms more than a moist surface, such as soil, on a hot, sunny day.

The contrast has consequences for land-dwellers like ourselves, as every degree of global warming will be amplified over land.

Forecast No. 5: Delayed Southern Ocean warming

Perhaps the biggest surprise from Manabe’s models came from a region most of us rarely think about: the Southern Ocean.

This vast, remote body of water encircles Antarctica and has strong eastward winds whipping across it unimpeded, due to the absence of land masses in the southern midlatitudes. These winds continually draw up deep ocean waters to the surface.

Manabe and colleagues found that the Southern Ocean warmed very slowly when atmospheric carbon dioxide concentrations increased because the surface waters were continually being replenished by these upwelling abyssal waters, which hadn’t yet warmed.

This delayed Southern Ocean warming is also visible in the temperature observations.

What does all this add up to?

Looking back on Manabe’s work more than half a century later, it’s clear that even early climate models captured the broad strokes of global warming.

Manabe’s models simulated these patterns decades before they were observed: Arctic Amplification was simulated in 1975 but only observed with confidence in 2009, while stratospheric cooling was simulated in 1967 but definitively observed only recently.

Climate models have their limitations, of course. For instance, they cannot predict regional climate change as well as people would like. But the fact that climate science, like any field, has significant unknowns should not blind us to what we do know.

Nadir Jeevanjee works for NOAA’s Geophysical Fluid Dynamics Laboratory, which is discussed in this article. The views expressed herein are in no sense official positions of the Geophysical Fluid Dynamics Laboratory, the National Oceanic and Atmospheric Administration, or the Department of Commerce.

– ref. 5 forecasts early climate models got right – the evidence is all around you – https://theconversation.com/5-forecasts-early-climate-models-got-right-the-evidence-is-all-around-you-263248

Source: The Conversation – USA (2) – By Divya Mahajan, Assistant Professor of Computer Engineering, Georgia Institute of Technology

Artificial intelligence is growing fast, and so are the number of computers that power it. Behind the scenes, this rapid growth is putting a huge strain on the data centers that run AI models. These facilities are using more energy than ever.

AI models are getting larger and more complex. Today’s most advanced systems have billions of parameters, the numerical values derived from training data, and run across thousands of computer chips. To keep up, companies have responded by adding more hardware, more chips, more memory and more powerful networks. This brute force approach has helped AI make big leaps, but it’s also created a new challenge: Data centers are becoming energy-hungry giants.

Some tech companies are responding by looking to power data centers on their own with fossil fuel and nuclear power plants. AI energy demand has also spurred efforts to make more efficient computer chips.

I’m a computer engineer and a professor at Georgia Tech who specializes in high-performance computing. I see another path to curbing AI’s energy appetite: Make data centers more resource aware and efficient.

Energy and heat

Modern AI data centers can use as much electricity as a small city. And it’s not just the computing that eats up power. Memory and cooling systems are major contributors, too. As AI models grow, they need more storage and faster access to data, which generates more heat. Also, as the chips become more powerful, removing heat becomes a central challenge.

Cooling isn’t just a technical detail; it’s a major part of the energy bill. Traditional cooling is done with specialized air conditioning systems that remove heat from server racks. New methods like liquid cooling are helping, but they also require careful planning and water management. Without smarter solutions, the energy requirements and costs of AI could become unsustainable.

Even with all this advanced equipment, many data centers aren’t running efficiently. That’s because different parts of the system don’t always talk to each other. For example, scheduling software might not know that a chip is overheating or that a network connection is clogged. As a result, some servers sit idle while others struggle to keep up. This lack of coordination can lead to wasted energy and underused resources.

A smarter way forward

Addressing this challenge requires rethinking how to design and manage the systems that support AI. That means moving away from brute-force scaling and toward smarter, more specialized infrastructure.

Here are three key ideas:

Address variability in hardware. Not all chips are the same. Even within the same generation, chips vary in how fast they operate and how much heat they can tolerate, leading to heterogeneity in both performance and energy efficiency. Computer systems in data centers should recognize differences among chips in performance, heat tolerance and energy use, and adjust accordingly.

Adapt to changing conditions. AI workloads vary over time. For instance, thermal hotspots on chips can trigger the chips to slow down, fluctuating grid supply can cap the peak power that centers can draw, and bursts of data between chips can create congestion in the network that connects them. Systems should be designed to respond in real time to things like temperature, power availability and data traffic.

Break down silos. Engineers who design chips, software and data centers should work together. When these teams collaborate, they can find new ways to save energy and improve performance. To that end, my colleagues, students and I at Georgia Tech’s AI Makerspace, a high-performance AI data center, are exploring these challenges hands-on. We’re working across disciplines, from hardware to software to energy systems, to build and test AI systems that are efficient, scalable and sustainable.

Scaling with intelligence

AI has the potential to transform science, medicine, education and more, but risks hitting limits on performance, energy and cost. The future of AI depends not only on better models, but also on better infrastructure.

To keep AI growing in a way that benefits society, I believe it’s important to shift from scaling by force to scaling with intelligence.

Divya Mahajan owns shares in Google, AMD, Microsoft, and Nvidia. She receives funding from Google and AMD.

– ref. AI’s ballooning energy consumption puts spotlight on data center efficiency – https://theconversation.com/ais-ballooning-energy-consumption-puts-spotlight-on-data-center-efficiency-254192

Source: The Conversation – USA (2) – By Paul Winters, Professor of Sustainable Development, University of Notre Dame

For farmers, every planting decision carries risks, and many of those risks are increasing with climate change. One of the most consequential is weather, which can damage crop yields and livelihoods. A delayed monsoon, for example, can force a rice farmer in South Asia to replant or switch crops altogether, losing both time and income.

Access to reliable, timely weather forecasts can help farmers prepare for the weeks ahead, find the best time to plant or determine how much fertilizer will be needed, resulting in better crop yields and lower costs.

Yet, in many low- and middle-income countries, accurate weather forecasts remain out of reach, limited by the high technology costs and infrastructure demands of traditional forecasting models.

A new wave of AI-powered weather forecasting models has the potential to change that.

By using artificial intelligence, these models can deliver accurate, localized predictions at a fraction of the computational cost of conventional physics-based models. This makes it possible for national meteorological agencies in developing countries to provide farmers with the timely, localized information about changing rainfall patterns that the farmers need.

The challenge is getting this technology where it’s needed.

Why AI forecasting matters now

The physics-based weather prediction models used by major meteorological centers around the world are powerful but costly. They simulate atmospheric physics to forecast weather conditions ahead, but they require expensive computing infrastructure. The cost puts them out of reach for most developing countries.

Moreover, these models have mainly been developed by and optimized for northern countries. They tend to focus on temperate, high-income regions and pay less attention to the tropics, where many low- and middle-income countries are located.

A major shift in weather models began in 2022 as industry and university researchers developed deep learning models that could generate accurate short- and medium-range forecasts for locations around the globe up to two weeks ahead.

These models worked at speeds several orders of magnitude faster than physics-based models, and they could run on laptops instead of supercomputers. Newer models, such as Pangu-Weather and GraphCast, have matched or even outperformed leading physics-based systems for some predictions, such as temperature.

AI-driven models require dramatically less computing power than the traditional systems.

While physics-based systems may need thousands of CPU hours to run a single forecast cycle, modern AI models can do so using a single GPU in minutes once the model has been trained. This is because the intensive part of the AI model training, which learns relationships in the climate from data, can use those learned relationships to produce a forecast without further extensive computation – that’s a major shortcut. In contrast, the physics-based models need to calculate the physics for each variable in each place and time for every forecast produced.

While training these models from physics-based model data does require significant upfront investment, once the AI is trained, the model can generate large ensemble forecasts — sets of multiple forecast runs — at a fraction of the computational cost of physics-based models.

Even the expensive step of training an AI weather model shows considerable computational savings. One study found the early model FourCastNet could be trained in about an hour on a supercomputer. That made its time to presenting a forecast thousands of times faster than state-of-the-art, physics-based models.

The result of all these advances: high-resolution forecasts globally within seconds on a single laptop or desktop computer.

Research is also rapidly advancing to expand the use of AI for forecasts weeks to months ahead, which helps farmers in making planting choices. AI models are already being tested for improving extreme weather prediction, such as for extratropical cyclones and abnormal rainfall.

Tailoring forecasts for real-world decisions

While AI weather models offer impressive technical capabilities, they are not plug-and-play solutions. Their impact depends on how well they are calibrated to local weather, benchmarked against real-world agricultural conditions, and aligned with the actual decisions farmers need to make, such as what and when to plant, or when drought is likely.

To unlock its full potential, AI forecasting must be connected to the people whose decisions it’s meant to guide.

That’s why groups such as AIM for Scale, a collaboration we work with as researchers in public policy and sustainability, are helping governments to develop AI tools that meet real-world needs, including training users and tailoring forecasts to farmers’ needs. International development institutions and the World Meteorological Organization are also working to expand access to AI forecasting models in low- and middle-income countries.

AI forecasts can be tailored to context-specific agricultural needs, such as identifying optimal planting windows, predicting dry spells or planning pest management. Disseminating those forecasts through text messages, radio, extension agents or mobile apps can then help reach farmers who can benefit. This is especially true when the messages themselves are constantly tested and improved to ensure they meet the farmers’ needs.

A recent study in India found that when farmers there received more accurate monsoon forecasts, they made more informed decisions about what and how much to plant – or whether to plant at all – resulting in better investment outcomes and reduced risk.

A new era in climate adaptation

AI weather forecasting has reached a pivotal moment. Tools that were experimental just five years ago are now being integrated into government weather forecasting systems. But technology alone won’t change lives.

With support, low- and middle-income countries can build the capacity to generate, evaluate and act on their own forecasts, providing valuable information to farmers that has long been missing in weather services.

Paul Winters receives funding from the Gates Foundation. He is the Executive Director of AIM for Scale.

Amir Jina receives funding from AIM for Scale.

– ref. AI is transforming weather forecasting − and that could be a game changer for farmers around the world – https://theconversation.com/ai-is-transforming-weather-forecasting-and-that-could-be-a-game-changer-for-farmers-around-the-world-263030

Source: The Conversation – USA (2) – By Rebecca Corbett, Japanese Studies Librarian and Senior Lecturer in History, University of Southern California

“Matcha mania” shows no signs of slowing, with global demand pushing “supply chains to the brink,” as Australia’s ABC News reported in July 2025.

The powdered drink retains a massive following in Tokyo, where long lines of customers snake out of The Matcha Tokyo on any given Saturday. At the trendy, minimalist cafe, the staff uses a cast-iron kettle and a bamboo ladle. Both are a nod to the traditional Japanese way of preparing matcha, called “chanoyu,” which literally means “hot water for tea” but in English has been translated as “tea ceremony.”

Beyond Tokyo, matcha cafes and bars have also become a familiar sight in Western cities, from Stockholm to Melbourne to Los Angeles. Matcha has been a permanent fixture on the menu at Starbucks since 2019 and at Dunkin’ since 2020.

It’s been quite the rise for a drink long met with skepticism in the West.

World’s fairs serve as a stage

I spent part of 2024 as a Japan Foundation Fellow at Waseda University, where I researched how Westerners experienced matcha and chanoyu during the Meiji period, an era of rapid modernization and Westernization that lasted from 1868 to 1912.

Matcha is a form of green tea in which young tea leaves have been ground to a powder using a stone mill. Unlike other teas, which involve steeping leaves and removing them before drinking, matcha powder is then whisked into hot water.

Matcha actually originated in China. It was introduced to Japan around 1250 C.E., where it assumed a key role in chanoyu from the 1500s on. Portuguese Jesuit missionaries in Japan in the 1500s wrote about both matcha and chanoyu. But only in the 19th century did interest in matcha really take off outside Japan.

Beginning in the late 19th century, world’s fairs and expositions started being held in European and American cities. These events allowed countries from around the world to showcase their art, inventions and culture before huge audiences.

For emerging Japan, world’s fairs and expositions presented a tremendous opportunity. In its exhibits, Japanese representatives often gave chanoyu demonstrations, while both the Japanese government and tea industry heavily marketed all varieties of Japanese green tea, including matcha.

Initial skepticism

Though steeped Japanese green tea became popular in 19th-century America, where it was usually sipped with milk and sugar, matcha didn’t initially jibe with Western palates.

Eliza Ruhamah Scidmore, an American journalist and travel writer who spent decades living in Japan, described matcha as “a bowl of green gruel more bitter than quinine” in her 1891 book “Jinrikisha Days in Japan.” Wealthy Canadian tourist Katharine Schuyler Baxter detailed her experience at a matcha tea gathering in her 1895 book “In Beautiful Japan: A Story of Bamboo Lands.”

“The beverage is made of powdered leaves, is greenish in color, thick like pea-soup, fragrant, and not very palatable,” she wrote. In my research, I encountered “pea soup” being the most common descriptor of matcha at this time.

Descriptions of matcha and chanoyu also abound in newspaper articles from the era.

Canadian journalist Helen E. Gregory-Flesher described the Japanese tea ceremony for readers in San Francisco.

“Very few Europeans can drink it without feeling very unhappy,” she wrote of a thick preparation of matcha called “koicha.” “For in the first place the taste is not agreeable, and then it is so intensely strong that it is sure to disagree with them if they do manage to swallow it.”

For the St. Louis Globe Democrat, the Countess Anna de Montaigu reported on a tea gathering she attended at the St. Louis World’s Fair in 1904. She described matcha’s flavor as “exquisite,” but left her American readers with a warning: “Drunk without sugar or cream, this expensive tea … is not pleasant to the palate of the uninitiated.”

Embracing the ceremony

There are also records of a few Westerners studying chanoyu while living in Japan. While those records don’t include their thoughts on matcha, I have to assume they enjoyed drinking it – at least enough to continue their practice, since in all cases they studied chanoyu for several years.

Chanoyu isn’t a simple serving ceremony. It’s a practice that involves learning the range of ways to serve and receive matcha, as well as food, and it’s taught by various “lineages,” or schools.

Lessons involve students learning how to be a host and a guest through observation and practice. All of this learning is put into practice by hosting or being a guest at a formal tea gathering, called a “chaji.” This can last three to four hours and include a multicourse meal – the “kaiseki” – several rounds of sake, and the laying and replenishing of charcoal.

There are two servings of matcha; one is prepared as thick tea – koicha – the other as thinner tea known as “usucha.” Each is accompanied by sweets.

A Swedish woman named Ida Trotzig lived in Japan from 1888 to 1921, during which she took lessons in chanoyu. Upon returning to Sweden she published a book about chanoyu in 1911, “Cha-no-yu Japanernas teceremoni.” American Mary Averil also studied both chanoyu and ikebana, the art of Japanese flower arrangement.

In 1905, the Urasenke School of Tea in Kyoto welcomed three American sisters, Helen, Grace and Florence Scottfield, as students. There, they studied under the head of the school, and a photograph of all three girls wearing kimonos with their hair styled in a Japanese manner appeared in an issue of the school’s monthly magazine in 1908.

Matcha minus chanoyu

Scholars haven’t pinpointed the reasons for the recent global matcha boom. But I think it’s worth considering a few factors.

First, it’s clear that social media, particularly Instagram and TikTok, have played a big role. The bright green beverage is aesthetically pleasing. Its many purported health benefits have also allowed it to join the ranks of other viral superfoods, such as acai berries and kombucha.

Then there’s the way Westerners often mythologize Japan as a source of “ancient wisdom.” Accompanying that is a particular infatuation with traditional Japanese practices, lifestyles and foods – matcha included.

Finally, people seem drawn to the minimalist aesthetics associated with chanoyu, which have echoes in other Japanese practices such as dry rock gardening and calligraphy.

Interestingly, the vast majority of matcha drinkers today don’t experience chanoyu, even as matcha purveyors borrow from the practice’s aesthetics. In the late 19th century, you couldn’t drink matcha without first experiencing chanoyu. And the drink was always served “straight” – no milks, flavorings or sweeteners.

Sometimes I wonder what the Countess de Montaigu would order if she visited Pipers Tea and Coffee, which is rated the best matcha in St. Louis on Yelp. Would she prefer it straight? Or would she be won over by its In Bloom Latte – a vanilla matcha latte topped with cherry blossom-sakura cold foam?

Rebecca Corbett receives funding from The Japan Foundation. She is affiliated with the Urasenke School of Tea through membership in the Urasenke Tankokai Los Angeles Association.

– ref. Green gruel? Pea soup? What Westerners thought of matcha when they tried it for the first time – https://theconversation.com/green-gruel-pea-soup-what-westerners-thought-of-matcha-when-they-tried-it-for-the-first-time-263014