IELTS Academic Reading Practice Test 77

Modern weather forecasting is often taken for granted, but its reliability rests on a quiet scientific revolution. This change took more than a century and is still not complete. Every day, forecasts guide choices about transport, farming, energy and safety. Yet because the atmosphere is a chaotic fluid, prediction is never straightforward. A forecast is a statement of chance based on huge amounts of data and on mathematical models of the atmosphere, ocean and land. This is why weather services now operate around the clock.

For most of history, people read the clouds, winds and changing pressure with only the naked eye. The thermometer, barometer and hygrometer made readings better, but information moved no faster than a horse. These tools recorded readings in a steady way, but they did not solve the speed problem. In the nineteenth century the electric telegraph made it possible to gather weather observations from many places at once, and in the 1860s the British naval officer Robert FitzRoy used coastal telegraph reports to issue the first public weather forecasts. His warnings were sometimes ridiculed, yet they showed that early notice could save lives. FitzRoy also set up the first storm warning service for ships, a step that later became routine.

Modern scientific forecasting began with the equations of physics. Around 1900, Vilhelm Bjerknes, a Norwegian meteorologist, formulated a set of equations that govern the behaviour of the atmosphere. Bjerknes argued that forecasting was a problem in mathematical physics, but the computational demands were enormous. In 1922 the British scientist Lewis Fry Richardson tried to calculate a weather forecast by hand from such equations. The work took him weeks and produced a wildly unrealistic result, largely because errors in the initial data distorted the calculations. Richardson still imagined a 'forecast factory' in which thousands of human computers would keep pace with the weather.

After the Second World War, digital computers changed the situation. The stored-program computer made automatic calculation possible for the first time. In 1950 a team led by Jule Charney used the ENIAC computer to integrate the atmospheric equations forward in time step by step. The result was the first successful numerical weather prediction. It showed that Richardson's method was sound, but that only a machine could carry out the enormous number of calculations required. The calculation was later done again at higher detail as machines improved.

The next advance came from an unexpected source. In the 1960s, Edward Lorenz was experimenting with a simple computer model when he discovered that tiny differences in starting values could produce completely different weather patterns. This sensitivity, often called the butterfly effect, means that small errors in observations grow rapidly. It sets a basic limit on how far ahead useful forecasts can reach. Even with perfect data, the limit would remain, but better data can extend the useful range.

Modern forecasters rely on a large global observing network. Surface stations, ships, buoys and aircraft report temperature, pressure, humidity and wind. Balloon-borne radiosondes sample the upper air, while satellites and radar provide continuous coverage of clouds, rain and storms. Radiosondes are sent up twice a day from sites around the world, giving vertical profiles of the air. The first weather satellite, TIROS-1, was launched in 1960, and satellite data have been part of routine forecasting ever since. Today, geostationary and polar-orbiting satellites watch the globe around the clock.

All these observations must be joined with a model. Data assimilation blends the latest observations with a short-range model forecast to produce the best guess of the current state of the air. The model then splits the air into a grid of millions of boxes and solves the motion equations for each box. Supercomputers do trillions of operations a second to move the weather forward in time. The grid spacing of such models has fallen from hundreds of kilometres in the 1960s to just a few kilometres today.

Because the air is chaotic, a single forecast is not reliable far ahead. Ensemble forecasting deals with this by running the model many times, each with slightly different starting conditions or with slightly different model settings. If most members agree a storm will form, forecasters can warn with high confidence. If the members disagree, the outlook remains uncertain. The spread among members shows how unsure the forecast is.

This mix of better data, faster computers and ensemble methods has brought steady gains. A five-day forecast from a modern centre is now about as accurate as a three-day forecast was a generation ago. Skill in predicting tropical storm tracks has increased, while forecasts of storm intensity have improved more slowly. Nevertheless, certain phenomena remain difficult. Local thunderstorms, fog and the rapid deepening of tropical cyclones can still surprise even the most advanced systems. Because such features are small and short-lived, they push the limits of both observation and model detail.

Research is now turning to machine learning, which may speed up some calculations and improve the use of past data. Some new systems have shown skill comparable to conventional models for short-range prediction, but they are still being tested in operational use. Yet no algorithm can remove the butterfly effect. The air will always contain uncertainty, and forecasts will therefore always be, in essence, statements of chance. Human judgement remains important, especially when automated guidance is unclear or conflicting.

De-extinction is the attempt to bring back species that have disappeared, or at least to create organisms that resemble them. The term is sometimes used loosely, but most scientists distinguish between true resurrection and the creation of a proxy. The idea has moved from science fiction to laboratory proposals as genetic tools have advanced. Its supporters argue that it could restore lost ecological functions and make up for past human damage; its critics reply that it is expensive, uncertain and a distraction from protecting living species. Behind the headlines lie a range of techniques and a series of difficult case studies.

Scientists generally divide the proposed methods into three broad groups. Not all extinct species are equally suited to these techniques, and the choice of method depends on what genetic material survives. Back-breeding selects living animals that already show traits of an extinct ancestor and breeds them together, gradually producing a population with those ancestral characteristics. Cloning removes the nucleus from an egg cell of a related species, inserts genetic material from the extinct animal, and then implants the resulting embryo into a surrogate mother. Genetic engineering edits the DNA of a close living relative so that it carries selected genes from the extinct species.

The first, and so far only, birth of an extinct mammal was the Pyrenean ibex, or bucardo, in 2003. The subspecies had disappeared when the last individual, a female called Celia, died in 2000, but cells had been frozen. Scientists inserted her DNA into goat eggs, and one of the resulting embryos was carried by a domestic goat. The kid was born alive, but it died within minutes because of severe lung defects. The experiment demonstrated that cloning an extinct mammal is biologically possible, though far from solved. Repeated attempts have since been made, but none has produced a surviving animal.

Much public attention has focused on the woolly mammoth. Researchers have compared the genomes of mammoths preserved in permafrost with those of Asian elephants, their closest living relatives. Several teams in the United States and elsewhere have reported progress in editing elephant cells in the laboratory. By using CRISPR and other editing tools, they hope to introduce mammoth genes for cold tolerance, such as thick hair and blood that remains functional at low temperatures, into elephant cells. The aim is to produce an embryo that would be carried by an Asian elephant, creating a cold-adapted elephant rather than a true mammoth. Critics note that such an animal would still have unknown needs. The slow reproductive cycle of elephants adds another layer of difficulty.

The passenger pigeon, once numbering in the billions, was driven to extinction by hunting and habitat loss; the last known bird, Martha, died in 1914. Its enormous flocks darkened the skies of eastern North America, but no amount of abundance protected it from commercial slaughter. A separate project is exploring whether genome editing could produce a bird with passenger-pigeon traits. Since no intact passenger pigeon cell survives, scientists begin with the band-tailed pigeon and plan to edit its genome in cultured cells, then convert those cells into eggs and sperm. A major obstacle is that bird reproduction differs greatly from mammalian reproduction, making cloning more difficult. Researchers must therefore develop entirely new reproductive technologies for birds.

Amphibians have also been the subject of attempts. The loss of amphibian species has been particularly severe in recent decades, giving such work urgency. In Australia, the gastric brooding frog vanished in the 1980s, but frozen tissue remained. Using a technique called somatic cell nuclear transfer, researchers created embryos that survived only a few days. For the northern white rhinoceros, of which only two females remain, scientists are working to produce stem cells that can develop into sperm and eggs, which would then be used with a southern white rhinoceros surrogate. These examples show that each project faces different technical barriers. Some researchers are also exploring genome banks as a way to preserve genetic material for future attempts.

Proponents argue that de-extinction could bring real ecological benefits. A resurrected grazer might restore grassland ecosystems, a large carnivore might control prey populations, and the publicity generated by a famous project could draw attention and money to conservation generally. Techniques developed for one species could be applied to others, including those still alive but in small numbers. They also point to a moral duty: where human actions caused extinction, we have some responsibility to try to reverse the harm. In this view, perfecting the techniques now may also help save species that are currently critically endangered. Advocates often compare de-extinction to habitat restoration, arguing that both attempt to repair past damage.

Sceptics counter that the obstacles are not only technical. DNA from extinct species is often fragmented and damaged, and edited genes may not function as expected in a living animal. Even if an animal is produced, it may not survive outside a laboratory, and the habitat that originally supported the species may have changed or disappeared. Resources spent on de-extinction cannot be spent on protecting existing species. For these reasons, many conservation biologists argue that the greatest priority should be to prevent extinctions in the first place. The funding required for a single de-extinction project could protect thousands of hectares of existing habitat. Some also question whether a proxy animal would be accepted by the public as a genuine restoration, or whether it would simply become a curiosity.

Every few years, a government announces cuts to its arts budget, and the familiar debate begins. The arguments on both sides have changed little over the decades, though the examples shift. Opponents of public funding argue that art should be supported by those who enjoy it, like any other commodity; supporters insist that the arts are different. In my view, public money for the arts is justified, but only when the reasons are clear and the arrangements protect artistic independence. The strongest case is not economic, though economics is part of it.

The economic argument is the one most often heard. Cultural tourism is frequently cited as a driver of regeneration, and some studies have found significant local effects. Museums, theatres and festivals attract visitors, who spend money in hotels and restaurants; creative industries employ large numbers of people and can revitalise neglected districts. There is truth in this, but it is a weak foundation for subsidy. If the goal is simply to generate income and jobs, public money could be directed to many other sectors with more reliable returns. The economic benefits of the arts are real, but they are, at best, a by-product. The difficulty is that such benefits are hard to measure precisely and often accrue to a few areas.

A more persuasive argument concerns public goods. Many cultural goods are non-rival and non-excludable: one person's enjoyment of a public sculpture does not reduce another's, and it is hard to charge every beneficiary. A historic building improves the streetscape for everyone, whether or not they buy a ticket. Public libraries and free-to-air broadcasts provide similar benefits. When the market cannot capture these benefits, it will under-produce them. Public funding can correct that failure, just as it does for basic science or public broadcasting. The case is not that all art is a public good, but that some important art has this character.

The deeper justification, however, is that art has intrinsic value. This is not merely a matter of taste; it is a claim about the kind of society we wish to inhabit. A society that loses its orchestras, galleries and literature loses part of its capacity to reflect on itself. This value is not easily measured, but that does not make it less real. Many privately beneficial goods are publicly funded because we regard them as merit goods, things that people ought to have access to regardless of their ability to pay. The arts arguably belong in this category. Education and healthcare are often defended on the same grounds, although the arts rarely receive the same level of consensus.

Access and equity strengthen the case. Without subsidy, high-quality theatre, opera and exhibitions would become luxuries available mainly to the wealthy. Subsidised ticket schemes, free museums and public art in ordinary places widen participation. They also support work that reflects the experiences of communities whose stories might otherwise go untold. Without such support, cultural expression would become the privilege of those who can pay. Public funding is therefore a tool for cultural democracy, not simply for the preservation of elite institutions. This does not mean that every institution should be funded, but that access should be a central goal.

Public money also encourages risk and innovation. Commercial producers must please audiences night after night, so they tend to favour familiar names and safe formats. A commercial gallery may be reluctant to exhibit an unknown artist, but a public grant can allow that artist to develop. Public funders can support new writers, experimental composers and emerging visual artists whose work may be difficult, challenging or slow to build an audience. Some of the most influential art of the past century began with public subsidy, because no private investor would take the chance. The history of modern theatre, music and visual art contains many such examples.

Nevertheless, government support brings dangers. State patronage has sometimes been used as an instrument of propaganda or social control. Politicians may be tempted to reward loyal supporters, penalise critics, or impose their own tastes. Public funding can become bureaucratic and prescriptive, stifling the very creativity it is meant to encourage. For this reason, many countries channel subsidies through arm's-length bodies, where decisions are made by specialists rather than ministers, and funding is awarded on the basis of peer review rather than political approval. The arm's-length principle was pioneered in Britain after the Second World War, when the Arts Council of Great Britain was established to distribute money without direct ministerial control.

Critics also worry that public money may crowd out private giving. If donors believe the state will pay, they may give less. Evidence on this is mixed, but it is plausible that heavy reliance on government support can weaken the habit of philanthropy. Some studies suggest that public funding can legitimise an organisation and actually attract private donors, while others find a substitution effect. A healthy arts system probably needs a mixture of earned income, private donation and public subsidy, with no single source dominating. Different countries adopt different balances: the United States relies heavily on private philanthropy, while many European countries depend more on the state.

My conclusion is that public funding of the arts should be maintained, but not as an entitlement for established institutions. It should be modest, transparent and directed mainly at widening access, supporting new work and protecting cultural assets that would otherwise be lost. The question is not whether the state should support art, but how it can do so without controlling it. The arts should be neither wholly market-driven nor wholly dependent on the state; the best systems combine accountability with freedom.