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Global sea levels are about eight inches higher today than they were in 1880, and they are expected to rise another two to seven feet during this century. At the same time, some 5 million people in the U.S. live in 2.6 million coastal homes situated less than 4 feet above high tide.
你知道嗎，今天的全球海平面要比1880年的時(shí)候高出8英寸，而就在本世紀(jì)內(nèi)，全球海平面預(yù)計(jì)還將上漲2到7英尺。另外，美國(guó)沿海地區(qū)有260萬(wàn)戶家庭的500余萬(wàn)人口的住宅，在海水滿潮時(shí)，只高出海平面不到4英尺。
Do the math: Climate change is a problem, whatever its cause.
毫無(wú)疑問(wèn)，氣候變化是個(gè)大問(wèn)題，不管導(dǎo)致它的原因是什么。
The problem? Actually making those complex calculations is an extremely challenging proposition. To understand the impact of climate change at the local level, you’ll need more than back-of-the-napkin mathematics.
那么如何計(jì)算氣候?qū)Νh(huán)境的影響呢？事實(shí)上，要進(jìn)行這些復(fù)雜的計(jì)算，是一個(gè)極具挑戰(zhàn)性的課題。要想了解氣候變化對(duì)一國(guó)一地的影響水平，絕對(duì)不是在一張餐巾紙上寫寫畫畫就能算得出來(lái)的。
You’ll need big data technology.
這時(shí)你就需要大數(shù)據(jù)技術(shù)了。
Surging Seas is an interactive map and tool developed by the nonprofit Climate Central that shows in graphic detail the threats from sea-level rise and storm surges to all of the 3,000-plus coastal towns, cities, counties and states in the continental United States. With detail down to neighborhood scale—search for a specific location or zoom down as necessary—the tool matches areas with flooding risk timelines and provides links to fact sheets, data downloads, action plans, embeddable widgets, and other items.
“上升的海平面”（Surging Seas）是由非盈利組織“氣候中心”（Climate Central）開(kāi)發(fā)的一款互動(dòng)式地圖工具，它用圖形的形式詳細(xì)描繪了海平面上升和風(fēng)暴潮給美國(guó)大陸沿海3000多個(gè)城市、城鎮(zhèn)和農(nóng)村造成的威脅。它的細(xì)節(jié)可以精確到每一個(gè)街區(qū)——你可以搜索一個(gè)特定的地理位置，或是按照需要繼續(xù)縮小目標(biāo)范圍。這個(gè)工具會(huì)與存在洪泛風(fēng)險(xiǎn)的地區(qū)進(jìn)行匹配，并且提供相關(guān)實(shí)時(shí)報(bào)道、數(shù)據(jù)下載、行動(dòng)計(jì)劃、內(nèi)嵌小工具和其它相關(guān)事項(xiàng)的鏈接。
It’s the kind of number-crunching that was all but impossible only a few years ago.
這種數(shù)據(jù)處理方式僅僅在幾年前還是不可能實(shí)現(xiàn)的。
‘Just as powerful, just as big’
能力有多大，困難就多大
“Our strategy is to tell people about their climate locally in ways they can understand, and the only way to do that is with big data analysis,” said Richard Wiles, vice president for strategic communications and director of research with Climate Central. “Big data allows you to say simple, clear things.”
氣候中心的戰(zhàn)略溝通副總裁兼研究主任理查德o懷爾斯表示：“我們的戰(zhàn)略是以人們能夠理解的方式告訴他們當(dāng)?shù)氐臍夂蚯闆r，唯一能實(shí)現(xiàn)這個(gè)目標(biāo)的方法就是通過(guò)大數(shù)據(jù)分析。大數(shù)據(jù)讓你能夠簡(jiǎn)單、清晰地表達(dá)。”
There are actually two types of big data in use today to help understand and deal with climate change, Wiles said. The first is relatively recently collected data that is so voluminous and complex that it couldn’t be effectively manipulated before, such as NASA images of heat over cities, Wiles said. This kind of data “l(fā)iterally was too big to handle not that long ago,” he said, “but now you can handle it on a regular computer.”
懷爾斯指出，目前主要有兩種大數(shù)據(jù)形式可以用來(lái)幫助人們了解和應(yīng)對(duì)氣候變化。第一類是某些在近期才收集到的數(shù)據(jù)，但它們往往數(shù)據(jù)量極大且非常復(fù)雜，擱在以前很難對(duì)其進(jìn)行有效分析，比如美國(guó)國(guó)家航空航天局（NASA）對(duì)各大城市的熱成像繪圖。懷爾斯表示，這種數(shù)據(jù)“一直到不久之前，還因?yàn)閿?shù)據(jù)量過(guò)大而基本上沒(méi)法處理，但是現(xiàn)在你已經(jīng)可以在一臺(tái)普通的電腦上處理它們了。”
The second type of big data is older datasets that may be less-than-reliable. This data “was always kind of there,” Wiles said, such as historic temperature trends in the United States. That kind of dataset is not overly complex, but it can be fraught with gaps and errors. “A guy in Oklahoma may have broken his thermometer back in 1936,” Wiles said, meaning that there could be no measurements at all for two months of that year.
第二類大數(shù)據(jù)是一些相對(duì)較老但可能不那么可靠的數(shù)據(jù)。懷爾斯表示，這些數(shù)據(jù)“基本上一直都在那兒”，比如美國(guó)的歷史氣溫趨勢(shì)。這種數(shù)據(jù)一般不太復(fù)雜，但有可能存在不少缺口和誤差。比如懷爾斯就指出：“1936年，俄克拉荷馬州的某個(gè)負(fù)責(zé)量氣溫的家伙有可能不小心把溫度計(jì)弄壞了。”這樣的話，當(dāng)年可能就有兩個(gè)月根本沒(méi)有氣溫記錄。
Address those issues, and existing data can be “just as powerful, just as big,” Wiles said. “It makes it possible to make the story very local.”
懷爾斯表示，要解決這些問(wèn)題，現(xiàn)有的數(shù)據(jù)可以說(shuō)“能力有多大，困難就有多大。但是大數(shù)據(jù)技術(shù)使得揭示一城一地的氣候變化成為可能。”
Climate Central imports data from historical government records to produce highly localized graphics for about 150 local TV weather forecasters across the U.S., illustrating climate change in each station’s particular area. For example, “Junes in Toledo are getting hotter,” Wiles said. “We use these data all the time to try to localize the climate change story so people can understand it.”
氣候中心從政府的歷史記錄中獲取原始數(shù)據(jù)，然后為美國(guó)各地的150余家地方電視臺(tái)的天氣預(yù)報(bào)節(jié)目制作高度本地化的氣候圖形，以闡釋該地區(qū)的氣候變化。比如懷爾斯指出：“今年六月，托雷多市變熱了。我們一直利用這些數(shù)據(jù)試圖讓當(dāng)?shù)厝肆私鈿夂蜃兓厔?shì)。”
‘One million hours of computation’
100萬(wàn)小時(shí)的計(jì)算
Though the Climate Central map is an effective tool for illustrating the problem of rising sea levels, big data technology is also helping researchers model, analyze, and predict the effects of climate change.
氣候中心的地圖是闡釋海平面上升情況的一個(gè)非常有效的工具。此外，大數(shù)據(jù)技術(shù)還能幫助研究人員模擬、分析和預(yù)測(cè)氣候變化的影響。
“Our goal is to turbo-charge the best science on massive data to create novel insights and drive action,” said Rebecca Moore, engineering manager for Google Earth Engine. Google Earth Engine aims to bring together the world’s satellite imagery—trillions of scientific measurements dating back almost 40 years—and make it available online along with tools for researchers.
谷歌地圖引擎（Google Earth Engine）的工程經(jīng)理瑞貝卡o摩爾介紹道：“我們的目標(biāo)是助力最好的大數(shù)據(jù)分析技術(shù)，以催生新穎的見(jiàn)解并且促進(jìn)行動(dòng)。”谷歌地圖旨在將全球的衛(wèi)星圖像進(jìn)行匯總，其中還包括40年來(lái)數(shù)以萬(wàn)億計(jì)的觀測(cè)數(shù)據(jù)，并將其與其它為研究人員開(kāi)發(fā)的工具一道放在網(wǎng)上。
Global deforestation, for example, “is a significant contributor to climate change, and until recently you could not find a detailed current map of the state of the world’s forests anywhere,” Moore said. That changed last November when Science magazine published the first high-resolution maps of global forest change from 2000 to 2012, powered by Google Earth Engine.
比如在全球荒漠化問(wèn)題上，摩爾表示：“全球荒漠化是氣候變化的一個(gè)重要推手，直到不久之前，還沒(méi)有一份詳細(xì)的實(shí)時(shí)地圖能夠顯示全球各地的森林情況。但現(xiàn)在情況不同了，去年11月，《科學(xué)》（Science）雜志在谷歌地圖引擎的幫助下，發(fā)布了首張2000至2012年的高分辨率全球森林變化圖。
“We ran forest-mapping algorithms developed by Professor Matt Hansen of University of Maryland on almost 700,000 Landsat satellite images—a total of 20 trillion pixels,” she said. “It required more than one million hours of computation, but because we ran the analysis on 10,000 computers in parallel, Earth Engine was able to produce the results in a matter of days.”
摩爾介紹道：“我們運(yùn)行的森林測(cè)繪算法是由馬里蘭大學(xué)（University of Maryland）的馬特o漢森教授開(kāi)發(fā)的，總共利用了70萬(wàn)張美國(guó)陸地資源衛(wèi)星的圖像，加起來(lái)大約有20萬(wàn)億個(gè)像素點(diǎn)。它需要超過(guò)100萬(wàn)小時(shí)的計(jì)算時(shí)間，但由于我們是在10,000臺(tái)計(jì)算機(jī)上并行計(jì)算的，因此谷歌地球引擎才得以在幾天內(nèi)就得出了結(jié)果。
On a single computer, that analysis would have taken more than 15 years. Anyone in the world can view the resulting interactive global map on a PC or mobile device.
如果只用一臺(tái)計(jì)算機(jī)計(jì)算的話，完成這樣一次分析大概需要超過(guò)15年的時(shí)間。但現(xiàn)在全球各地的任何人都可以在電腦或移動(dòng)設(shè)備上查看這次分析得到的這張互動(dòng)式全球地圖。
‘We have sensors everywhere’
傳感器無(wú)所不在
Rapidly propelling such developments, meanwhile, is the fact that data is being collected today on a larger scale than ever before.
在這些項(xiàng)目取得快速進(jìn)展的背后離不開(kāi)這樣一個(gè)事實(shí)：如今我們對(duì)數(shù)據(jù)的收集程度已經(jīng)遠(yuǎn)超以往任何時(shí)候。
“Big data in climate first means that we have sensors everywhere: in space, looking down via remote sensing satellites, and on the ground,” said Kirk Borne, a data scientist and professor at George Mason University. Those sensors are continually recording information about weather, land use, vegetation, oceans, ice cover, precipitation, drought, water quality, and many more variables, he said. They are also tracking correlations between datasets: biodiversity changes, invasive species, and at-risk species, for example.
喬治梅森大學(xué)的數(shù)據(jù)學(xué)家柯克o波恩教授指出：“大數(shù)據(jù)技術(shù)在氣候研究領(lǐng)域的發(fā)展，首先意味著傳感器已經(jīng)無(wú)所不在。首先是太空中的遙感衛(wèi)星，其次是地面上的傳感器。”這些傳感器時(shí)刻記錄著地球各地的天氣、土地利用、植被、海洋、冰層、降水、干旱、水質(zhì)等信息以及許多變量。同時(shí)它們也在跟蹤各種數(shù)據(jù)之間的關(guān)聯(lián)，比如生物多樣性的變化、入侵物種和瀕危物種等等。
Two large monitoring projects of this kind are NEON—the National Ecological Observatory Network—andOOI, the Ocean Observatories Initiative.
在這一類監(jiān)控項(xiàng)目中有兩個(gè)比較有代表性的大型項(xiàng)目，一個(gè)是美國(guó)國(guó)家生態(tài)觀測(cè)站網(wǎng)絡(luò)（NEON），一個(gè)是海洋觀測(cè)計(jì)劃（OOI）。
“All of these sensors also deliver a vast increase in the rate and the number of climate-related parameters that we are now measuring, monitoring, and tracking,” Borne said. “These data give us increasingly deeper and broader coverage of climate change, both temporally and geospatially.”
波恩指出：“這些傳感器令我們現(xiàn)在正在觀測(cè)和追蹤的氣候參數(shù)無(wú)論在等級(jí)還是數(shù)量上都有了極大的提高。另外無(wú)論是在時(shí)間上還是在地理空間上，這些數(shù)據(jù)對(duì)氣候變化的覆蓋都變得越來(lái)越深、越來(lái)越廣。”
Climate change is one of the largest examples of scientific modeling and simulation, Borne said. Efforts are focused not on tomorrow’s weather but on decades and centuries into the future.
波恩表示，氣候變化是科學(xué)建模仿真應(yīng)用得最廣泛的例子之一。科學(xué)家不僅利用建模仿真來(lái)預(yù)測(cè)明天的天氣，而且還用它來(lái)預(yù)測(cè)幾十年甚至幾百年后的氣候。
“Huge climate simulations are now run daily, if not more frequently,” he said. These simulations have increasingly higher horizontal spatial resolution—hundreds of kilometers, versus tens of kilometers in older simulations; higher vertical resolution, referring to the number of atmospheric layers that can be modeled; and higher temporal resolution—zeroing in on minutes or hours as opposed to days or weeks, he added.
他還表示：“大規(guī)模的氣候模擬現(xiàn)在每天都在運(yùn)行，有些甚至可能更為頻繁。”這些模擬的水平分辨率更高，達(dá)到幾百公里，而過(guò)去的模擬只能達(dá)到幾十公里。同時(shí)它們垂直分辨率也變得更高，這也就表示可以對(duì)大氣層中更多的層進(jìn)行建模。另外還有更高的瞬時(shí)分辨率，也就是說(shuō)只需要幾分鐘或幾個(gè)小時(shí)就可以進(jìn)行歸零校正，而不是幾天或幾個(gè)星期。
The output of each daily simulation amounts to petabytes of data and requires an assortment of tools for storing, processing, analyzing, visualizing, and mining.
每天的氣候模擬都會(huì)生成幾千兆字節(jié)的數(shù)據(jù)，并且需要一系列工具進(jìn)行存儲(chǔ)、處理、分析、挖掘和圖像化。
‘All models are wrong, but some are useful’
所有模型都是錯(cuò)的，但有些很有用
Interpreting climate change data may be the most challenging part.
氣候變化數(shù)據(jù)的解讀可能是最具有挑戰(zhàn)性的部分。
“When working with big data, it is easy to create a model that explains the correlations that we discover in our data,” Borne said. “But we need to remember that correlation does not imply causation, and so we need to apply systematic scientific methodology.”
波恩指出：“搞大數(shù)據(jù)時(shí)，要建立一個(gè)模型來(lái)解釋我們?cè)跀?shù)據(jù)中發(fā)現(xiàn)的某種關(guān)聯(lián)是很容易的。但我們得記住，這種關(guān)聯(lián)并不代表原因，所以我們需要應(yīng)用系統(tǒng)化的科學(xué)方法。”
It’s also important to heed the maxim that “all models are wrong, but some are useful,” Borne said, quoting statistician George Box. “This is especially critical for numerical computer simulations, where there are so many assumptions and ‘parameterizations of our ignorance.’
波恩還指出，搞大數(shù)據(jù)最好要記住統(tǒng)計(jì)學(xué)家喬治o博克斯的名言：“所有模型都是錯(cuò)的，但有些很有用。”他表示：“這對(duì)數(shù)字計(jì)算機(jī)模擬尤為重要，因?yàn)槠渲杏泻芏嗉僭O(shè)和‘代表了我們的無(wú)知的參數(shù)’”。
“What fixes that problem—and also addresses Box’s warning—is data assimilation,” Borne said, referring to the process by which “we incorporate the latest and greatest observational data into the current model of a real system in order to correct, adjust, and validate. Big data play a vital and essential role in climate prediction science by providing corrective actions through ongoing data assimilation.”
波恩表示：“要想解決這個(gè)問(wèn)題，以及解決博克斯警告我們的問(wèn)題，最重要的是做好數(shù)據(jù)同化。”也就是“把最新最好的觀測(cè)數(shù)據(jù)納入一個(gè)真實(shí)系統(tǒng)的實(shí)時(shí)模型中，以對(duì)數(shù)據(jù)進(jìn)行糾正、調(diào)整、確認(rèn)。通過(guò)以不間斷的數(shù)據(jù)同化作為校正措施，大數(shù)據(jù)在氣候預(yù)測(cè)科學(xué)中扮演了至關(guān)重要且不可或缺的角色。
‘We are in a data revolution’
我們已經(jīng)在一場(chǎng)數(shù)據(jù)革命之中
Earlier this year, the Obama administration launchedClimate.data.gov with more than 100 curated, high-quality data sets, Web services, and tools that can be used by anyone to help prepare for the effects of climate change. At the same time, NASA invited citizens to help find solutions to the coastal flooding challenge at an April mass-collaboration event.
今年早些時(shí)候，奧巴馬政府推出了官方的氣象研究網(wǎng)站Climate.data.gov，上面有100多種精心編輯的高質(zhì)量數(shù)據(jù)以及網(wǎng)頁(yè)服務(wù)和工具，任何人都可以利用這些數(shù)據(jù)與工具來(lái)研究氣候變化的影響。與此同時(shí)，NASA也在今年四月的一次大型協(xié)作活動(dòng)上，邀請(qǐng)普通民眾協(xié)助其尋找應(yīng)對(duì)沿海洪災(zāi)的解決方案。
More recently, UN Global Pulse launched a Big Data Climate Challenge to crowdsource projects that use big data to address the economic dimensions of climate change.
最近，聯(lián)合國(guó)“全球脈動(dòng)”行動(dòng)（UN Global Pulse）推出了一項(xiàng)“大數(shù)據(jù)氣候挑戰(zhàn)”項(xiàng)目，將一些用大數(shù)據(jù)研究氣候變化對(duì)經(jīng)濟(jì)的影響的項(xiàng)目通過(guò)眾包的形式進(jìn)行了發(fā)布。
“We’ve already received submissions from 20 countries in energy, smart cities, forestry and agriculture,” said Miguel Luengo-Oroz, chief scientist for Global Pulse, which focuses on relief and development efforts around the world. “We also hope to see submissions from fields such as architecture, green data centers, risk management and material sciences.”
“全球脈動(dòng)”行動(dòng)主要致力于全球各地的扶貧救災(zāi)與發(fā)展事業(yè)，該行動(dòng)的首席科學(xué)家盧恩戈o奧羅茲表示：“我們已經(jīng)收到了來(lái)自20多個(gè)國(guó)家的在能源、智能城市、林業(yè)和農(nóng)業(yè)等領(lǐng)域的意見(jiàn)書。我們也希望收到建筑、綠色數(shù)據(jù)中心、風(fēng)險(xiǎn)管理和材料科學(xué)等領(lǐng)域的意見(jiàn)書。”
Big data can allow for more efficient responses to emerging crises, distributed access to knowledge, and greater understanding of the effects personal and policy decisions have on the planet’s climate, Luengo-Oroz added.
盧恩戈o奧羅茲補(bǔ)充道，大數(shù)據(jù)還可以用于提高突發(fā)災(zāi)害的應(yīng)急工作效率，提供更廣泛地獲取知識(shí)的渠道，以及幫助我們更好地了解私人與政府的決策會(huì)對(duì)地球的氣候造成哪些影響。
“But it’s not the data that will save us,” he said. “It’s the analysis and usage of the data that can help us make better decisions for climate action. Just like with climate change, it is no longer a question of, ‘is this happening?’ We are in a data revolution.”
奧羅茲表示：“然而拯救我們的不是那些數(shù)據(jù)，而是那些讓我們能做出更好的決策來(lái)應(yīng)對(duì)氣候變化的數(shù)據(jù)分析與使用方法。這就像氣候變化本身一樣，現(xiàn)在已經(jīng)不是‘它開(kāi)始了嗎’的問(wèn)題。我們已經(jīng)在一場(chǎng)數(shù)據(jù)革命之中。”
