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Is Weather Getting Worse?

   Weather seems getting worse and wilder since "Mother Nature is full of surprises these days". Global warming, a heated topic of today, is often taken for granted to be responsible for the harsh weather. However, scientists, like Kevin E. Trenberth, are cautious in making their judgment. Please read the following article and find out what role El Niño and La Niňa play.

   As you read this, flip your eyes over to the window. The sky is clear, the wind light, and the sun brilliant. Or maybe not—Mother Nature is full of surprises these days. The calendar says it's spring, but there could just as easily be a winter blizzard, a summer swelter, or an autumn cold snap on the other side of that glass pane. Almost in an instant, it seems, the weather shifts from one season to another. And wherever it swings, it seems increasingly likely to be extreme.

   Consider what Mother Nature slung our way last year in May, typically the second worst month for tornadoes. In less than 24 hours, more than 70 hellholes of wind rampaged through Oklahoma and Kansas, killing 49 and causing more than $1 billion in damages. In June, it was heat, as the Northeast began roasting through weeks of the worst drought since the 1960s; 256 people died. This year in January, blizzards pounded the U.S. from Kansas to the Atlantic Ocean. In April, 25 inches of snow fell on parts of New England.

   Why has our weather gone wild? It's the question everyone's asking, but a very tough one to answer. Although many scientists still aren't convinced that it has gone wild, some have begun saying —cautiously, hesitantly—that extreme weather events are occurring with more frequency than at any time in this century, events consistent with the profile of a warming world. "Global warming is real," says Kevin E. Trenberth, head of the Climate Analysis Section of the Center for Atmospheric Research in Boulder, Colorado. "The mean temperatures are going up. The key question is: What will it do locally? I think we're going to start feeling its effects in the changes on extremes."

   That doesn't mean you can indict weird weather in your neck of the woods as proof. Mother Nature knows how to hide her tracks. She hurls a torrential downpour today and a drought tomorrow followed by gentle rain the next week. To understand a pattern in natural variability, you can't look into the sky; you have got to study data. And for a host of reasons, that isn't easy.

   But tallying up the damage is. In the last 20 years, this country has been whacked by $I70 billion worth of weather-related disasters—hurricanes, droughts, floods, and tornadoes. Thirty-eight severe weather events occurred in a single decade, between 1988 and 1999; seven events occurred in 1998 alone—the most for any year on record.

   Globally, insurance companies are calling it a "catastrophe trend". In a report issued last December, Munich Re, the world's largest reinsurer, or insurer of insurance companies, noted that the number of natural disasters has increased more than fourfold since the 1950s. Earthquakes, which are not weather-related, caused nearly half the deaths in those catastrophes; storms, floods, and other weather woes killed the other half. In 1999, the number of catastrophes worldwide hit 755, surpassing the record of 702 set only the year before.

   In its five-point list of causes for increased damage claims, Munich Re blamed population growth first, climate change fifth. Critics may well seize upon this to diminish claims that the weather is getting worse, but taken together, it's a more frightening picture. Thanks to swelling populations in cities and along coastal areas, more of Earth's passengers are living in the wrong place at the wrong time.

   Still, the statistics meteorologists have collected on extreme weather events aren't enough to prove that the weather is getting worse. By their very definition, extreme events happen infrequently, and no one has been collecting scientifically sound data long enough to know how common they are. For example, a storm that happens once a century might require two millennia's worth of storm data to draw conclusions. To top it off, the computer models scientists use to study climate crunch numbers on a scale of centuries at a time. "Ideally, you'd like data sets that go back several hundred years," says Philip Arkin, deputy director of the International Research Institute for Climate Prediction at the Lamont-Doherty Earth Observatory in New York. "But they just don't exist. The U.S. data go back 50 years. Before World War II, it's very difficult to come up with good numbers. We have some data on heavy rain events before 1900, but there's nothing useful."

   Even if scientists could find good numbers, computer resolution is still too coarse to be able to forecast how something as simple as warming might affect climate in specific spots on the globe. The smallest amount of space on land, sea, ice, and air that scientists can study is about the size of Virginia. If they crank up the resolution by 50 percent to focus on an area half that size, they pay for it in computing time—a calculation that took 10 days to perform might now need three months.

   Keith Dixon, a research meteorologist at the National Oceanic and Atmospheric Administration's Geophysical Fluid Dynamics Laboratory in New Jersey, recalls once he was being asked precisely what global warming would mean for state ski resorts. More snow? (Good.) Or more rain? (Bad.) "I can understand why businesspeople or politicians ask. If you want to cut fuel, spend money, and make decisions, you need to know why you should be doing this." Adds his colleague, Tom Knutson: "I can certainly sympathize with them. But we can't answer it."

   Since 1995, the literature has suggested that there could be fewer frosts, more heat waves, more droughts, more intense rainfalls, tropical cyclones, and hurricanes in the 21st century when and if CO₂ levels double. But these projections rank low on the confidence scale because scientists cannot say definitively if and how the events might occur.

   All of which doesn't do the average citizen much good. He doesn't worry about 30-to-100-year shifts in the climate. What gets him is day-to-day weather: "This heat's killing me." "Crops have failed here five years in a row." "There have been three bad tornadoes in as many weeks." We live in a society uniquely privileged to learn about weather events—and to fear them. The Center for Media and Public Affairs, a watchdog group based in Washington, D.C., reports that media coverage of weather disasters more than doubled from 1997 to 1998 alone.

   Probably as a result, people are starting to blame harsh weather on global warming. Politicians are too. Jerry Mahlman, director of the GFDL, advises the White House on climate change. He remembers sitting in a conference with Vice President Gore, who asked: "Can we say that storms will be more extreme in the greenhouse-enhanced earth?" The scientist didn't flinch as he replied, "No." Gore's shoulders seemed to crumple.

   Globally, the 1990s stood as the warmest decade for which we have records. Scientists already predict that by 2100, Earth could warm up another 1.8 to 6.3 degrees Fahrenheit. Most of us think heat when we think global warming. Scientists think ice. They're worried about what will happen when all that extra heat hits the ice at Earth's poles. A dominant hypothesis says that the water cycle will speed up: Heat will hasten ocean evaporation, and because hot air can hold more moisture, it could all be whisked away to rain more upon our heads.

   Five years ago the Intergovernmental Panel on Climate Change, an international collaboration of 2 000 scientists, theorized as much in a well-publicized 56-page report. That same year, a team of scientists led by Tom Karl, now director of the National Climatic Data Center (NCDC), studied 80 years of U.S. data and confirmed an increase in extreme precipitation events, altered patterns of rain and drought, and rising temperatures since 1970. But the scientists cautioned that the study analyzed only 80 years of data, confined itself to the United States—which occupies a mere 2 percent of the globe—and found nothing out of the realm of pure chance.

   Within months came another, stronger piece of real-world data, nailed down by one of the men caught in that January snowstorm. Sifting through historical data, Trenberth had found that more, longer, and stronger El Niños have occurred during the last 20 years than in the previous 120 years. That was unusual, a chance of 1 in 2 000. El Niño, the periodic warming of the equatorial Pacific that induces storms and other climatic events, historically occurs once every three to seven years and lasts for up to two years. But even as Trenberth presented his findings at a conference in Melbourne, Australia, the Pacific was experiencing an odd, double El Niño: The first had lasted from 1991 to 1993, a weaker one from 1994 to 1995. Trenberth floated an ideal past the audience in his native New Zealand accent: Could this be due to global warming?

   The idea, Trenberth modestly recalls, caused something of a stir in the audience. Scientists found themselves wondering: What would happen if one of nature's storm machines—not completely understood but still adhering to rhythms as regular as the seasons—were pressed into service by global warming?

   Archaeological evidence suggests El Niño has been around for thousands, possibly millions of years. A known instigator of storms, floods, droughts, and secondary effects like fires, the El Niño-Southern Oscillation could go a long way toward explaining many weather extremes. Under normal circumstances, sea surface temperatures rise in the tropical Pacific, fueling strong thunderstorms. Like a vast climatic mailbag, El Niño-enhanced activity hand-delivers heat and moisture to parts of the globe where they would not normally go. Contrasting cool ocean currents in the Pacific can usher in the opposite phase, La Nina, which tends to dry out the southwestern and South Central states. La Niňa also makes weather conditions worse but rarely bullies as harshly as El Niño.

   "The Americas are greatly affected by El Niño," Trenberth says. "Europe is much less affected. If things do become more El Niño-like, then it does have implications for different parts of the country. It means we're more likely to have storms coming into southern California and going across the south, at least in the winter-time. If 1998 was any indication, you have to really watch out for the seasonal change, where it can go from wet conditions to quite dry conditions when the storm tracks move farther north."

   In the early 1990s, El Niño helped dry Indonesia and other tropical Pacific climate and blister southern Africa, but it drenched California. Together Niño and Niňa did a number on the Americas. From 1992 to 1993: winter floods in California. In 1993: flooding in the Mississippi Basin, drought in the Carolinas. From 1994 to 1995: more floods in California. In 1996: drought in the South Central states, flooding in the Midwest.

   The strongest El Niño on record, from 1997 to 1998, registered water temperatures as high as nine degrees above normal. "A normal, run-of-the-mill El Niño is about two or three degrees Fahrenheit above normal," says Trenberth. "This one was nine, so it was a real granddaddy in that respect." That was the year Hurricane Mitch left at least 11 000 dead in Central America. The NCDC calls Mitch the deadliest Atlantic hurricane since 1780.

   Today Trenberth's hypothesis is high on the agenda in such climate labs as the Geophysical Fluid Dynamics Laboratory in Princeton, the Max Planck Institute in Hamburg, Germany, and others in England, Australia, Canada, and Japan. Says Lamont-Doherty's Arkin, "It would be hard to talk about extreme weather without considering his work." But Mahlman says: "It's a good hypothesis; there's a shred of truth to it. But it still seems like a coin flip." Reviewing results in his lab in the foothills of the Rockies, Trenberth is the first to poke holes in his own work. "Part of the problem is that all the models tend to give different answers to this question," he says, "But a lot of these models don't reproduce El Niño very well in the first place. So the confidence in what they're telling you is undermined."

   Still, Trenberth believes we are likely in the coming century to see ever longer El Niños fluctuating with shorter La Niňas. Weather, including bad weather, might therefore appear to be more fixed. "That's the main thing El Niño or La Niňa does for you," he says, "It locks the patterns in. So once you get into a dry regime, you stay in a dry regime. If you get into a wet regime, you stay in a wet regime. And so you tend to get these extremes—you get battered by one storm after another. Or else you get dry spells time after time."

   Baltimore residents may recall that they sweltered in last summer's heat wave and drought only to be soaked by Hurricane Floyd whisking through in September. Scientifically, one cannot directly blame that mess on El Niño or global warming. "It was very regional," Trenberth says, "There are a number of factors that go into that, part of which was La Niňa, part of which was what's going on in lots of other places around the world—except that if it happens more and more in different places around the world, the evidence mounts that something is pushing you in that direction. The global perspective is important with regard to the global warming issue. Just watching things go by locally can help to create the overall picture, but it doesn't confirm it at all."

   So, is the weather getting worse and wilder? Maybe. Perhaps the best line on this topic was penned by the director of the Geophysical Fluid Dynamics Laboratory. In an article published last year, Mahalman wrote: "For me, the new data...indicate that we appear to be nudging noticeably closer to the ‘smoking gun' demanded by people who require very high levels of proof."

   Trenberth regards extreme weather as an analogue, a dry run to the future. And it isn't pretty. Droughts rob us of sustenance and leave us vulnerable to fire. In wet, warm conditions, insects thrive. The United Nation's World Health Organization already reports that mosquitoes carrying malaria and dengue fever have hit new highs in Latin America, Africa, and Asia. In the United States, cycles of rain and drought seven years ago permitted a deadly form of pulmonary hantavirus, carried by mice, to flourish in the Southwest.

   Handed a dress rehearsal, perhaps we should use it. We can develop strategies to cope. We can cultivate an interest in the weather outside of our commutes. And we can shake the habit of sampling locally and extrapolating globally.

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课文一

天气正越变越糟？

    因为“这些日子大自然母亲充满着意想不到的事情”，看起来天气在越变越糟，越来越狂野。全球变暖是现在的热门话题，常常理所当然地被看成恶劣气候的罪魁祸首。但是，凯文·E·川伯斯这样的科学家在下定论时态度谨慎。请阅读以下文章，看一看厄尔尼诺和拉尼娜现象所起的作用。

    当你阅读本文时，请把目光轻快地投向窗外。天空澄净，微风轻拂，日光明媚。也许并不是这样——这些日子，大自然母亲充满着意想不到的事情。日历上说现在是春天，但窗外完全可能是一场冬季的暴风雪，一阵夏日的酷暑或一股秋天的寒流。几乎是在一瞬之间，气候就好像从一个季节转到了另一个季节。而且不管天气转向何方，它看起来变得越来越极端。

    想想看去年五月大自然母亲都给我们带来了些什么，那是第二个遭受龙卷风袭击最严重的月份。在不到二十四小时之内，70多个“地狱”风暴在俄克拉何马和堪萨斯州横行肆虐，造成49人死亡以及十多亿美元的损失。六月，天气酷热，东北部连续几周在热浪下炙烤，遭受了自六十年代以来最严重的旱情，256人丧生。今年一月，暴风雪横扫美国，从堪萨斯州直至大西洋沿岸。四月，新英格兰部分地区下的雪厚达25英吋。

    为什么我们的天气变得如此狂野？这是每个人都在问的问题，但是很难回答。虽然许多科学家仍然不相信天气已经失控，但有些人——虽然态度谨慎，仍存疑虑——已经开始说，与本世纪任何时段相比，极端性天气出现的频率愈加频繁，这与全球变暖的量变曲线是吻合的。凯文·E·川伯斯是设在科罗拉多州博耳德市的大气研究中心气候分析部的主任，他说：“全球变暖是事实。”“平均气温在上升。但关键问题是，它会给具体地区带来什么？我认为我们已开始感受到它的影响，天气的变化趋于极端。”

    这并不意味着你可以用某一有限区域的怪异天气作为控诉的证据。大自然母亲知道该如何隐藏她的行踪。她今天猛地来一场倾盆大雨，明天是干旱，紧接着是一周柔柔细雨。你不能靠察看天像来了解自然变化的模式，你必须得研究数据。但由于种种原因，这并不容易。

    但是计算一下灾害损失却是容易的。过去的20年里，我们这个国家与气候相关的灾难——飓风、干旱、洪水及龙卷风——造成了高达1700亿美元的损失。仅1988年至1999年这十年间就发生了38起严重的气候事件，仅1998年就有7起气候事件，这是有史以来受灾最多的一年。

    从全球范围来看，保险公司把天气变暖称为“灾难趋势”。世界最大的再保险商，即为保险公司提供保险的幕尼黑再保险公司在去年12月份发行的一份报告中指出，自20世纪50年代以来，自然灾害的数量已经增长了四倍有余。在这些灾难中，和气候无关的地震造成了近一半人的死亡；风暴、洪灾以及其他气候性灾难夺去了另一半人的生命。1999年，全世界的灾难数目达到755起，超过前一年才创下的702起的记录。

    幕尼黑再保险公司在其列出的索赔要求日益增加的五点原因，认为人口增长首当其冲，天气变化则列在第五。一些批评家也许会紧抓住这一点来轻视天气正越变越糟的观点，但综合起来考虑，这是幅更可怕的画面。幸亏人口增长主要在城市和沿海地区，更多的“地球过客们”是在不适当的时间住在了不适当的地方。

    尽管这样，气象学家收集的有关极端性气候事件的统计数据，还不足以证实天气正越变越糟。根据他们的定义，极端性气候事件并不经常发生，而没有人收集的可靠的科学数据，在时间上长到足以弄清极端性气候事件怎样的频率才算正常的问题。例如，一场百年一遇的风暴也许需要两千年间的风暴数据，才能得出结论。为了得出结论，科学家研究气候所用的计算机模型每一次都需要以世纪为级别来处理数据。纽约拉蒙特-多尔蒂地球观测站国际天气预报研究所副主任菲利普·阿尔金说，“理想的状况是，你最好拥有数百年的数据。但根本就没有这样的数据。美国有50年前的数据。二战以前则很难弄到理想的数据。我们有一些1900年以前的暴雨灾害的数据，但派不上用场。”

    即使科学家们能够找到可用的数据，但由于计算机演算解析率仍然太粗糙，无法预测像天气变暖这样一个如此简单的变化会对地球上某个特定地点的气候产生怎样的影响。科学家们在陆上、海上、冰层及大气中能够研究的最小的范围，相当于弗吉尼亚州的大小。如果他们想研究上述一半大小的区域，提高50%的解析率，那么代价就是计算时间。先前十天的计算现在也许要三个月。

    新泽西国家海洋和大气局大气物理流体动力学实验室气象学研究人员基思·迪克森回忆说，有一次有人问全球变暖对各州的滑雪胜地具体意味着什么。会下更多的雪？（好。）或者雨量会增加？（不好。）“我明白为什么商人或政界人士要问这些问题。如果你要减少燃料、投资以及做决策，你得知道你为什么要做这些。”他的同事汤姆·克努特森补充说：“当然我很同情他们，但是我们无法回答这个问题。”

    1995年以来，文献显示，二氧化碳的含量翻番时或如果翻番，那么21世纪则意味着霜冻减少，更多的热浪、旱灾，更多的降水、热带气旋和飓风。但是这些预测都底气不足，因为科学家不能肯定这些会不会发生以及如何发生。

    这一切不能给普通公民带来什么实惠。他并不关心30-100年跨度的气候变化，他只在意每天的天气：“我要热死了。”“连续五年这儿的庄稼收成都不好。”“三周内刮了三次糟糕的龙卷风。”我们生活在这样一个社会中，有独一无二的特权来知悉天气事件——以及畏惧它们。设在华盛顿特区的一个监察组织媒体与公共事务中心报道说，仅1997至1998年一年间，媒体关于灾难性天气的报道就增加了一倍多。

    也许结果就是，人们开始把恶劣天气怪罪于全球变暖。政界人士同样如此。大气物理流体动力学实验室主任杰里·马尔曼就天气变化为白宫提供咨询。他记得有一次参加会议时，副总统戈尔问：“我们能说在地球温室效应越发严重的情况下，风暴就会变得更厉害吗？”他毫不畏缩地回答：“不能。”戈尔的肩膀似乎耷拉了下来。

    从全球范围来看，20世纪90年代是我们记录在案的最暖和的十年。科学家们已经预测，到2100年，地球的温度还要再提高1.8°F-6.3°F。我们中的大多数人一提到全球变暖就会想到热浪。科学家们想到的则是冰。他们担心，一旦多余的热量袭击地球两极的冰，会发生什么情况。一种主导的假设是，水循环将加快：热量将加快海水的蒸发，因为热空气可以含更多的湿气，就会朝我们的头上降更多的雨。

    五年前，一个由两千名科学家组成的国际联合组织，跨政府的气候变迁专家小组，在一份长达56页、引起公众广泛注意的报告中也提出了这一理论。同年，现任国家气象数据中心（NCDC）主任的汤姆·卡尔领导一个科学家小组,对80年的美国数据进行了研究，证实自1970年以来，极端性降水事件有所增加，旱涝模式改变，气温升高。但科学家提醒说，该研究只分析了80年的数据，且仅限于占全球2%面积的美国境内，没有从纯粹偶然王国中得出任何东西。

    几个月之后又得到一份来自现实世界的、更有力的数据，由一位经历了那场一月暴雪的人精确地提供。对历史数据进行筛选后，川伯斯发现过去的20年间，与这之前的120年相比，厄尔尼诺现象发生的次数更多，时间更长，强度更大。这可是不同寻常的1/2000的概率。厄尔尼诺是赤道附近的太平洋周期性变暖现象，引发风暴和其他气候事件，历史上每三至七年发生一次，持续时间可长达两年。但正当川伯斯在澳大利亚墨尔本的一次会议上提交他的发现时，太平洋正经历着一次反常的双厄尔尼诺现象：第一次从1991年持续到1993年，还有一次威力小一些，从1994年持续到1995年。川伯斯带着他那新西兰家乡口音，向听众提出这样一个观点：这能否归因于地球变暖？

    川伯斯谦逊地回忆说，这个观点在听众中引起了一层波澜。科学家们不禁问自己：如果这一自然界中的风暴机器（虽然我们还不完全了解它，但它毕竟像季节一样有规律地变动）为全球变暖的影响所左右，那么会发生什么？

    考古学方面的证据表明，厄尔尼诺已存在了数千年，或许数百万年。已知它激起风暴、洪水、干旱还有如火灾这样的副效应。厄尔尼诺向南摆动的现象对解释许多极端天气现象很有帮助。在通常情况下，赤道地区的太平洋洋面温度升高，孕育强烈的雷雨天气。厄尔尼诺增强了这一活动，它像一个巨大的气候邮包，亲手把热量和湿气带到平常并不光临的地球部分地区。形成对照的太平洋中的寒流，则会迎来一种相反的状况，拉尼娜现象。它倾向于给西南和中南部各州带来干旱。拉尼娜现象同样会使天气状况变糟，但很少像厄尔尼诺现象那样横行霸道。

    川伯斯说：“南北美洲受厄尔尼诺现象影响很严重，欧洲受的影响则要小得多。如果情况确实朝着厄尔尼诺方向发展，那么美国的不同地区都会受到影响。这意味着风暴更可能直抵南加利福尼亚州、穿越美国南部，至少在冬天会是这样。如果1998年只是初露端倪，那么现在你真得当心季节的变化，风暴路径向北移动更远时，气候状况会从湿润变得相当干旱。”

    在90年代早期，厄尔尼诺现象助长了印度尼西亚和其他一些太平洋赤道气候区的干燥天气和发生在南部非洲的庖病，但却给加利福尼亚带来滂沱大雨。厄尔尼诺和拉尼娜联手给美国造成了一系列麻烦。1992年到1993年：冬天加利福尼亚洪水泛滥。1993年：密西西比盆地受淹，南北卡罗来纳大旱。1994年至1995年：加利福尼亚涝灾比以往严重。1996年：中南部各州遭受旱灾，中西部遭受洪灾。

  记录在案的势力最强的厄尔尼诺发生在1997-1998年，水温比正常时期高了整整九度。川伯斯说：“普通的厄尔尼诺比正常时期高二至三华氏度。这次是九度，在这点上，它可真是爷字辈的。”这年，飓风“米奇”在中美洲造成至少11,000人丧生。国家天气数据中心（NCDC）称“米奇”是1780年以来杀伤力最大的大西洋飓风。

    现在，川伯斯的假说已被诸如普林斯顿大气物理流体动力学实验室、德国汉堡马克斯·普朗克研究所以及其他英国、澳大利亚、加拿大、日本的大气研究试验室列为重要议程。拉蒙特-多赫提研究所的阿尔金说：“不研究他所做的工作，就很难对极端性天气进行探讨。”而马尔曼则认为：“这是个不错的假说，里面有这么一点儿理。但看起来仍像抛硬币一样不确定。”川伯斯评论位于落基山脚下的试验室结果时，第一个指出了自己研究工作中的漏洞。他说：“问题的一部分是，所有的模式都倾向于对这一论题提出了不同的解释，但是许多模式首先不能很好地再现厄尔尼诺现象。所以你就对这些模式所告诉你的一切信心不足。”

    然而，川伯斯相信，在即将到来的下个世纪里，更可能的情形是，我们会看到持续时间更长的厄尔尼诺与时间变短的拉尼娜波动发展的状况。天气形态，包括坏天气，因而可能会变得更稳定。他说：“这就是厄尔尼诺或拉尼娜给你带来的主要影响。”“它把气候形态固定下来。于是，一旦干燥天气占了主导地位，那就一直会是干燥气候，如果受潮湿天气控制，那就一直是湿润天气。所以往往就会出现极端天气——接踵而至的风暴。或者你会一再受干旱侵袭。”

    巴尔的摩的居民也许还记得，去年夏天遭遇热浪和干旱，没想到整个九月却被“弗洛依德”飓风浸泡。科学地说，我们不能把这些直接归咎于厄尔尼诺或全球变暖。川伯斯说：“这完全是区域性的。这里面有很多因素，部分因素是拉尼娜，部分因素来自世界上其他许多地方发生的事情。除非在世界范围内的不同地方，它发生的频率越来越高，积累的证据才会促使你往那个方向考虑。对于地球变暖这个话题，全球范围的视角是重要的。对地域性事件进行观测有助于勾勒出一幅全景图画，但是却并不能证明这图画。”

    所以，天气是不是变得越来越糟，越来越狂野？或许吧。也许对这一话题做出最好概述的，是大气物理流体动力学实验室（GFDL）主任马尔曼。在去年发表的一篇文章中，他写道：“就我看来，这些新数据表明……我们离那些要求很高层次证据的人所要求的‘确凿证据’，明显地已经越来越近。”

    川伯斯认为，极端性天气是未来气候的类比和预演。前景并不美妙。干旱抢走了我们赖以生存的东西，使我们容易受火灾袭击。在湿润、温暖的情况下，昆虫会大量孳生。联合国世界卫生组织已经报告说，携带疟疾和登革热的蚊子数目在拉丁美洲、非洲和亚洲又达新高。在美国，七年一次的旱涝周期循环，使一种由老鼠传播的肺坦川病毒在西南部猖獗起来。

    给了我们一次彩排机会，或许我们应该利用它。我们可以制订应对的战略计划。我们可以培养对常规研究范围外的天气状况的兴趣。我们还可以摆脱用区域性取样调查的结论来进行全球推导的习惯。

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Text 2

Basic Instinct

   Three centuries ago, Gottfried Wilhelm Leibnitz had a dream. The German philosopher and mathematician believed that all rational thinking could be described with a formula. He imagined that by inventing an alphabet of human thought—a system of characters for irreducible concepts—and then combining these in a calculus of reasoning, mathematicians would be able to solve all scientific and moral matters. He proclaimed that "a few selected persons might be able to do the whole thing in five years".

   Leibnitz's grand scheme may seem naive, even ludicrous, but shades of it live on. From philosophers to economists, many modern researchers believe that rationality is objective and open to mathematical analysis. In their studies of reasoning and models of markets, they see rational decision-makers as supernatural beings with the logic of a supercomputer, boundless knowledge and all eternity in which to make a decision. Most of them readily accept that this view is unrealistic but, they argue, if there were no limits to our rationality we would be able to make the best possible choices. We may not think in this way, but we should.

   This view is now being challenged. In an attempt to inject some realism into the study of rationality, Gerd Gigerenzer and his team at the Max Planck Institute for Human Development in Berlin are investigating the idea that evolution has endowed us with a set of mental shortcuts—tools for making quick decisions. Sure, the human mind can perform long-winded calculations and amazing feats of memory, they say, but in everyday situations we tend to use the shortcuts in our "adaptive toolbox". Gigerenzer and his colleagues have not only identified some of these mental shortcuts but also put them to the test. It may look like sloppy thinking when we jump to a conclusion or follow a gut feeling, but our mental shortcuts turn out to be astonishingly successful.

   In the real world, a good decision is less about finding the best alternative than about finding one that works. Herbert Simon of Carnegie Mellon University in Pittsburgh was one of the first to recognise this in the 1950s when he coined the term "bounded rationality". He pointed out that the way any animal thinks depends on its cognitive limitations and the environment in which it lives. So a creature such as a field mouse, whose food is randomly distributed, needn't evolve complex foraging strategies, whereas one such as a lion, whose food sources are indicated by clues in the environment, will have an advantage if it can use sophisticated mental abilities such as planning.   

   Simon's ideas have become fashionable in recent years, and the Berlin researchers are leading the renaissance. They point out that our minds, like our bodies, have been shaped by evolution: we have inherited ways of thinking from those of our ancestors whose mental tools were best adapted for survival and reproduction. No time then for careful calculations—a cogitating ancestor would have risked losing dinner, a mate or even its life. Instead, our mental tools are fast and frugal. They allow us to make decisions based on very little information and using simple rules. Each tool, or heuristic, is designed to resolve a certain type of dilemma under certain circumstances. "There is no general tool," says Gigerenzer, "Our simple rules are problem-dependent."

   Although they apply to different sorts of problems, heuristics have a common structure, which arises from the way humans make decisions. First, we search the environment for information, or cues, upon which to base a choice. A heuristic contains rules that direct the search. Next, we must stop searching. It's pointless trying to find out everything there is to know about a nut or berry if we starve in the process. Heuristics contain a stopping rule, often ending the search after only a few cures have been considered. Finally, we must make the choice—eat, run, mate, attack. But all the survival benefits of speed are lost if we make the wrong decision.

   Perhaps the fastest and most frugal rule of thumb is the Recognition heuristic. Peter Todd, an evolutionary psychologist and cofounder of the research group, points out that, given Dr Seuss's famous menu of green eggs and ham, most people would opt for the ham. By choosing "the familiar" as the only cue worth considering, you get your calories without wasting time trying to discover whether green eggs are edible. Brown rats follow the same strategy, preferring to eat foods that they have smelt on the breath of other rats. But the recognition heuristic doesn't work only with food. Imagine you are a Stone Age man choosing a hunting party or a computer-age woman looking for business partners. Chances are you'll pick people you know, or have heard are good. The benefits are obvious.

   In many situations, simply choosing what you recognise will work better than choosing at random. When Gigerenzer and his colleague Daniel Goldstein showed volunteers pairs of cities and asked them to identify the largest of each pair, people tended to choose the city whose name they recognised. When Americans were asked to distinguish between pairs of German cities, this strategy gave a 73 per cent success rate. Random guessing would have produced around 50 percent. What's more, the success rate fell to 71 per cent when the Americans were asked to do the same for cities in the US. This "more-is-less" effect happens because the Recognition heuristic does not work as well when you know too much. "There is wisdom in ignorance," says Gigerenzer. Though many choices will not succumb to such a simple approach, the researchers have identified several other shortcuts that make a decision based on a single reason. Heuristics called Minimalist and Take The Best, for example, search through a sequence of cues until they find one that distinguishes between the alternative courses of action. Minimalist is perhaps the natural progression from the Recognition heuristic. Forced to make a choice between two cities that you recognise but know very little about, you might consider a cue such as "Do the cities have an airport?" If only one does, then you assume that it is the larger city. If both or neither do then you consider another cue at random.

   Take The Best, on the other hand, works well in situations where experience leads us to believe that we know which cues are most important. In choosing a mate, for example, many animals (including humans) have distinct priorities. Take The Best uses the cues in order of importance, stopping the search as soon as one cue distinguishes between the possible choices.

   To see whether a single reason really can form a good basis for making decision, Goldstein, working with Max Planck researchers Jean Czerlinski and Laura Martignon compared Minimalist and Take The Best with two conventional analytical tools that use all available information—multiple regression and a simplified regression known as Dawes's rule. The researchers used all four algorithms to make predictions in 20 test areas. These included the dropout rates in various Chicago high schools, given such cues as ethnic composition and class size, and the incomes of academics, given cues such as gender, rank and years since graduation.

   "The two fast and frugal heuristics always came close to, and often exceeded, the performance of the traditional algorithms," says Todd, "even though they only looked through a third of the cues on average." One reason for this success might be that in natural environments cues tend to be linked, so an exhaustive search may not provide much more useful information than a fast and frugal search.

   In nature, one-reason heuristics seem to be used by parents to decide which of their offspring to invest in. Some birds, for example, always feed the largest chick in the nest, while others chose the hungriest or feed chicks at random. In Berlin, Todd and his colleague Jennifer Davis used computer simulations to show which single reason works best under various environmental conditions. They found that when food is scarce, feeding the largest offspring gives parents the greatest chance of getting their genes passed on. In times of plenty, however, the more egalitarian approach of choosing the hungriest or feeding at random is most successful. In the wild, most bird species do seem to follow such behavior patterns. Some, such as pied flycatchers and sparrow hawks, even change tack as the availability of food changes.

   Davis and Todd also point out that human parents divide land between their children using similar reasoning. In cultures where resources are scarce, the eldest son tends to inherit the land, but where land is plentiful, it is divided more fairly among all the children.

   Such single-reason heuristics may be very useful, but they do not work in every situation. Our adaptive toolbox has more complex equipment. Humans, like many animals, use body language to distinguish friend from foe. The way people move can tell you about their intentions—whether they want to fight, play or court, for example. "Some of the most obvious cues for intention can be assessed at a distance," says Todd. But it takes more than a one-reason heuristic to decide which intention the motion cues are pointing to.

   Todd and Philip Blythe, another member of the Max Planck team, showed people virtual bugs on a computer screen that were programmed to suggest various intentions by exhibiting different cues ─  such as their speed and whether they moved in a straight line or meandered—to test a heuristic called Categorization By Elimination. This uses a succession of cues to whittle away the alternatives until only one remains. With just half the available cues, Categorization By Elimination correctly predicted two-thirds of the intentions —similar to the success rate of a trained human observer.

  This match between the performance of a real person and a heuristic is common, and the researchers view it as evidence that we do indeed think in this way. But mental short cuts are not always the best option. The team has found that people tend to use more calculated reasoning when they can take their time, while heuristics come into their own when people are forced to think on their feet.

   Even so, heuristics work in a broad range of situations. Not only do they allow us to choose between alternative courses of action, they also work when a choice doesn't come with all the options up front. If you're looking for a new frock, a new home or a new girlfriend you must search for the options—the available frocks, houses and women—as well as the cues with which to distinguish between them. How do you know when to stop looking and make a choice?

   The answer is something that Simon calls "satisficing"—perhaps best thought of as a cross between satisfying and sufficing. He says that in these situations we set ourselves aspiration levels—which may alter over time—and stop looking only once these have been achieved. Todd and Geoffrey Miller from University College London, have used computer modeling to investigate Satisficing heuristics in mate selection. The most successful strategy for individuals seems to be to learn their own rank in the mating hierarchy by looking at the quality of the partners who accept or reject them. They aspire only to those potential partners who match or exceed their assessment of themselves. Todd and Miller now aim to test how well this model matches the way people really search for partners.

   But what about love? Our emotions, it turns out, can help us to make decisions too. Romantic love acts as a potent force, stopping the search for new partners. Love also highlights the importance of some cues above others. Indeed, all emotions seem to work in this way, so helping us to make decisions that effect our survival. Fear, for example, may narrow our options to just one: flight. Parental love leads us to care for our children regardless of the personal cost. And disgust keeps us from eating rotting food.

   Survival and reproduction are the two cornerstones of evolution. And as evolution has shaped our adaptive toolbox, it is not surprising that it is chock-full of tools to solve problems such as finding food, avoiding predators, finding a mate and caring for offspring. You can imagine each tool labeled with a different sort of choice in a different environment. Gigenrenzer and his team are only beginning to reveal spanners and hammers, wrenches and saws.

   What is clear, however, is that we are not born with a full set of shiny tools just waiting to be used. Instead, we seem to get a starter kit upon which to build, adapting tools and adding new ones as we learn about the world we live in. So, for example, different cultures conform to different social norms. By learning the rules, priorities and expectations of our culture, we may be able to take advantage of generations of acquired wisdom without really understanding why. We also learn that in some social situations unpredictability can be an advantage—giving you the edge over a competitor, for example. In such cases it is rational to be inconsistent, and heuristics can be adapted to allow for that.

   Open the lid of the adaptive toolbox and you start to see that rationality is impulsive, emotional, flexible and inconsistent. It's a long way from the idealised decision-maker. As for a calculus of rational thought: dream on Leibnitz.

(2239 words)  TOP

课文二

本能

    三个世纪前，戈特弗里德·威廉·莱布尼兹曾有过一个梦想。这位德国哲学家和数学家相信，一切理性思考都可以用一个公式来描述。他设想，如果数学家们能够发明一套人类思维的符号，即一套代表无法简化的概念的符号系统，然后将这些符号用推理计算的方式结合起来，他们就能够解决一切科学的和道德的问题。他宣称，“五年以后，几个挑选出来的精英分子也许就可以承担这一切了。”

    莱布尼兹的伟大计划似乎显得天真，甚至可笑，但是，这一想法的影响却一直存在。从哲学家到经济学家，许多现代研究者都相信，理性是客观的，可以对其进行数学分析。在研究推理和市场模型时，他们将理性的决策者看作超自然的人物，认为他们决策时拥有超型计算机的逻辑思维，无限的知识以及不朽的永恒性。虽然，他们大多数人都愿意承认这一观点是不现实的，但他们又说，如果我们的理性没有限度，我们就能做出最好的选择。我们也许不这么想，但我们应该这样想。

    现在，这一观点受到了挑战。为了在理性研究中加入一些现实的成分，柏林马克斯·普朗克人类发展研究所的格德·吉杰伦泽和他的小组，目前正在研究这一观点，即进化赋予我们一套思维捷径，也就是迅速做出决定的工具。他们说，虽然人类思维确实可以进行冗长的计算并具有出色的记忆功能，但在日常生活情景中，我们往往会使用“适应工具箱”中的捷径。吉杰伦泽和他的同事们不仅找到了某些捷径，而且还对这些捷径进行了实验。我们很快做出决定或跟着直觉走时，看上去很草率，但是思维捷径结果却惊人地成功。

    在现实世界里，好的决定与其说是寻找最好的选择，不如说是寻找最实用的选择。匹兹堡卡内基梅隆大学的赫伯特·西蒙是最早认识到这一点的人之一，早在二十世纪五十年代，就造出了“有限理性”这一术语。他指出，任何动物的思考方式都依赖其认知限度和生存环境。所以，田鼠这样的生物就不需要发展复杂的捕食战略，因为它们的食物分布是随意的。而狮子类的动物，其食物来源是由环境中的线索表明的，因而，如果它们能运用复杂的思维能力如计划等，就会占有优势。

    近些年来，西蒙的观点开始流行起来，而柏林的研究者们正领导着这一复兴潮流。他们指出，我们的思维跟我们的身体一样，是在进化过程中发展起来的：我们继承了祖先的最适应生存和繁衍的思维方式。没有时间仔细考虑——一个耽于思考的祖先，就会是冒着失去食物，配偶，甚至自己的生命的危险。相反，我们的思维工具迅速而省事。它们允许我们在极少信息的基础上运用简单原则就做出决定。设计任何一种工具或者启发方式，都是为了解决某种情况下某类困境。吉杰伦泽说：“综合工具是不存在的，我们的简单原则由具体问题决定。”

    启发法虽然适用于不同的问题，但它们有一个共同结构，它产生于人类决策的方式。首先，我们在环境中搜索作为一种选择基础的信息或线索。启发包含引导搜索的原则。然后，我们必须停止搜索。如果我们在寻找的过程中挨饿，那么，试图发现一切有关坚果和浆果的知识就毫无意义。启发法包含一个终止原则，常常在只考虑几条线索后就终止搜索。最后，我们必须做出决定——进食、逃跑、结偶、进攻。但是，如果我们做出了错误的决定，快速决策得到救生益处就会消失。

   也许最迅速最便捷的制胜原则是识别启发。研究小组的合作创办人、进化心理学家彼特·托德指出，面对苏瑟博士的著名菜单 ——绿色鸡蛋和火腿，大多数人都会选择火腿。通过选择“熟悉的东西”，把这当作唯一值得考虑的线索，你就可以得到卡路里，而不必浪费时间去努力发现绿色鸡蛋能否食用。灰老鼠采用的就是这一战略，它们闻到其他老鼠呼吸中散发出的食物气味，往往更愿意食用这些食物。但是识别启发不仅仅适用于食物。假设你是一个石器时代的人，要选择狩猎伙伴，或是一个计算机时代的妇女，要寻找商业伙伴。你很有可能选择那些你认识的人，或者你听到过别人称赞的人。好处是显而易见的。

    在很多情况下，选择你所认识的对象比随机选择更好。吉杰伦泽和他的同事丹尼尔·哥尔德斯坦让志愿者看多组城市，并要他们在每组中找出最大的城市，人们倾向于选择他们能认得名字的城市。如果让美国人区分几组德国城市，这一方法的成功率为73%。要是使用随机猜测的方法，成功率大约是50%。但是，让美国人区分美国城市的时候，成功率便降到了71%。出现这种“多就是少”的结果，是因为当你知道太多的时候，识别启发就不那么有效。“无知中也有智慧，”吉杰伦泽说。尽管许多选择并不遵循这一简单的方法，研究者们还是发现了其他几条捷径，使我们根据单单一条理由就做出决定。例如“最低限度”和“最佳选择”这两条启发法，会在一个序列线索中搜寻，直到它们找到在供选择的种种行动方向中突出的那条线索。“最低限度”法或许是识别启发法的自然延伸。如果你被迫在两座你虽然知道但了解不多的城市中做出选择，你可能会考虑诸如“这些城市有机场吗？”这样的线索。如果只有一座城市有，你就会猜想它是大一点的城市。如果两座城市都有或都没有机场，那么你就会考虑另外一条随机的线索。

    另一方面，在经验使我们相信自己知道哪些线索最重要的情况下，“最佳选择”法就更为有效。例如，在选择伴侣的时候，很多动物（包括人类）都有明确地优先考虑条件本能的喜好。“最佳选择”法按照重要程度依次使用线索，一旦某条线索在诸多可能的选择中比较突出，搜寻便会立即终止。

    为了发现一条单一的理由能否真的成为决策的合理基础，哥尔德斯坦与马克斯·普朗克研究所研究员吉恩·泽林斯基和劳拉玛蒂格南将“最低限度”法和“最佳选择”法同两种传统的分析工具做了比较，这两种工具使用一切可能的信息。一种叫做多重回归法，另一种是称作道威斯法则的简单回归法。研究者们使用这四种法则在20个测试区内进行预测，预测范围包括芝加哥各中学的辍学率和教员收入，给予前者的线索有种族构成和班级大小等；给予后者的线索有性别、级别、毕业后年限。

    “‘最低限度’法和‘最佳选择’法这两种简单而便捷的启发法总是接近、甚至常常超过另两种传统法则的预测效果，”托德说，“尽管这两种快捷简单的启发法只考察了平均三分之一的线索。”这一成功的一个原因可能是，在自然环境中各线索常常互相关联，因而全面细致的搜寻所能提供的有用信息不一定多于简单便捷的搜寻。

    在自然界中，父母似乎是运用单一原因启发法来决定喂养哪些子女。例如，一些鸟类总是喂养鸟巢中最大的幼鸟，而其他一些鸟类则选择最饥饿的幼鸟，或者随机选择。在柏林，托德和他的同事詹尼弗·戴维斯用电脑模拟的方法来显示，在各种环境条件下，哪一条单一原因最为有效。他们发现，食物匮乏的时候，喂养最大的子女使父母获得最大的成功繁衍机会。但是，在食物充足的时候，平等主义的方法则更加成功，如选择最饥饿的或者随机喂养等。自然界中的大多数鸟类似乎确实遵循着这些行为模式。像斑纹京燕鸟和食雀鹰这样的鸟类，甚至根据食物的多少而改变喂养策略。

    戴维斯和托德还指出，人类社会中父母也用类似的推理方法将土地分给他们的子女。在一些资源稀少的文化中，长子常常继承土地，而在土地丰富的地方，土地则在各子女中更加公平地分配。

    这些单一原因的启发法可能十分有用，但并非在所有情况下都有效。我们的“适应工具箱”有着更加复杂的设备。像很多动物一样，人类使用肢体语言来区分朋友和敌人。你能从人们移动的方式看出他们的企图——例如，他们是要打斗、玩耍还是求爱。托德说，“推测用意的一些最明显的线索在一定距离之外就可以推测到。”但是，要判断动作线索指向哪一个目的，就需要不止一条单一原因启发法。

    托德和马克斯·普朗克研究所小组另一名成员菲利普·布莱兹给人们展示电脑屏幕上经过编程的虚拟昆虫，通过表现种不同的线索说明各种目的——线索如它们的速度，是直线移动还是曲线移动——以测试一种名为排除归类法的启发法的有效性。这一方法使用一连串线索来逐渐减少排除选项，直到最后只剩下一个选项。排除归类法只使用所提供线索的一半，却正确地预测出了三分之二的目的——预测的成功率与一个训练有素的观察者相仿。

    一个真实的人的表现和一条启发法相吻合，这是常见的。研究者认为，这证明我们确实是用这种方法来思维。但思维捷径并不总是最好的选择。该小组发现，在时间充裕的时候，人们更倾向于进行较多的计算推理，而当人们被迫立即做出反应的时候，启发法才会起作用。

    尽管如此，启发法在多种情况下都行之有效。它们不但让我们能在供选择的行动路线之间做出选择，而且，当我们无法从面前的各选项中做出选择的时候，也对我们有所帮助。如果你在寻找一件新衣服、一座新房子或一个新的女友，你必须寻找选项——可供选择的衣服、房子和女人——以及用来区分各选项的线索。你如何知道什么时候该停止观望、做出选择呢？

    答案就是西蒙称作“满足”的东西——这是介于满意和足够之间的交叉地带。他说，在寻找中，我们给自己定下了期望值——这可能会随着时间的推移而改变——而期望值一旦达到，我们就会停止观望。托德和和伦敦大学学院的杰弗里·米勒曾使用电脑模拟来调查伴侣选择中的满足启发法。通过观察那些曾经接受或拒绝过自己的伴侣的情况，人们就可以了解自己在求爱等级社会中的位置。而这似乎是最成功的方法。人们只追求那些达到或超过他们的自我评价的潜在对象。托德和米勒现在的目标是，检测这一模式和人们实际追求伴侣的方法吻合的程度。

    关于爱又是怎么样呢？结果表明，我们的情感也能帮助我们做出决定。浪漫的爱情作为一种有效的力量使我们停止搜寻新的伴侣。爱同时突出一些线索相对于另外一些线索的重要性。确实，所有的情感似乎都有这样的作用，从而帮助我们做出使我们得以生存的决定。例如，恐惧可能会将我们的各项选择压缩成一个：逃跑。父母之爱使我们不计个人代价地去抚养我们的子女。而厌恶则阻止我们去吃腐败的食物。

    生存和繁衍是进化的两大基石。正如进化影响、形成了我们的“适应工具箱”，为了解决诸如寻找食物、躲避捕食者、求偶、照顾子女等问题，我们的工具箱里就塞满了各种各样的工具，也就不足为怪了。你可以设想每一件不同的环境下贴上了不同种类选择标签的工具。吉杰伦泽和他的小组只是刚刚开始发现扳手、锤子、钳子和锯子而已。

    然而，清楚的是，我们并不是一生下来就有一套闪亮的工具在等着我们用。相反，我们似乎有一套需要扩展的入门工具，随着我们逐渐了解了我们居住的世界，改造工具并且添加新工具。比如，不同的文化和不同的社会规范相一致。通过学习我们的文化中的规则、喜好和期望，我们就可以利用一代代积累起来的智慧，而不需要真的理解其中的原因。我们还懂得，在一些社会情形中，不可预测性也能成为优势——比如让你超过某个竞争对手。在这样的情况下，一反常态也是合乎理性的，而启发法也可以通过调整以支持这种做法。

    打开“适应工具箱”的盖子，你就会开始发现，理性是冲动的、情绪化的、灵活的，也是反常的。这同理想化的决策者相去甚远。而对于推理思维，就继续莱布尼兹的梦想吧。

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