科学与数学bloomberg.com
究竟是什么在让世界变暖？
What's Really Warming the World?
2015 — 2017，它一次次回到首页
黑点是上首页（≥ 50 分）的那次投稿，空心点是没冲上去的投稿，点越大分数越高。
导读
这是彭博社制作的交互式数据可视化，把太阳活动、火山、气溶胶、臭氧和温室气体等可能影响气候的因素分别纳入模型，再与观测到的全球气温曲线对比，直观展示为何只有温室气体排放能解释长期升温。它面向非专业读者，用可操作的图形呈现气候归因问题。
为什么经久不衰：它把复杂的气候归因争论压缩成一张可交互的图，多年后仍是理解“为什么不是太阳或火山”的经典入门材料。
HN 评论精选
模型拟合历史数据不等于有预测能力，关键要看外推表现。
很酷的可视化。要记住，模型数据与现实吻合是应该的，因为模型本来就是靠拟合现实来校准的。真正的考验是它能否从已有数据外推未来：比如只用1990年前的数据，它能否正确给出2015年的温度，而不是像1990年代模型那样给出离谱预测；更大的问题是，用2015年数据预测2040年，和未来25年的现实能有多吻合。我们在建模方面似乎进步很大，这是好事。
jasonkester查看英文原文
Cool visualization. It's worth keeping in mind that the modeled data lines up with reality because it's supposed to. That's how you calibrate your model, by making sure it fits reality. The real trick is to see how well your model extrapolates from the data you have out into the future. As in, if you feed it data up to, say, 1990, will it correctly spit out 2015 temperatures that fit the reality of 2015, or will it spit out crazy 2015 predictions like the models that were built in 1990 did. And, the bigger question: How will its predictions for 2040 (given 2015 data) match up to the reality over the next 25 years. We seem to be getting a lot better at the modeling side. That's a good thing, since the first couple decades of watching people panicking and fighting each other over whatever scary results came out of the first generation climate models wasn't any fun to watch.
IPCC报告是高效、可靠的气候科学入门材料。
我不是气候科学家，但据我的经验，最可靠、最省时地了解气候变化的方式是读IPCC报告。具体说，时间紧就读《决策者摘要》，它是按非技术政客的阅读水平写的；他们能看懂，你也能。更长的报告对想深入科学的人也很精彩。报告由全球数百名科学家编写、数千人评审，并且非常注意概率语言。花点时间读它们，能省掉读其他地方99%内容的时间。
hackuser查看英文原文
This repeats some buried comments but I think it's worthwhile: I'm not a climate scientist, but in my experience the absolute most reliable, most time-efficient way to learn about climate change is the IPCC reports. I wonder if there is anything written in any other field that compares: http://www.ipcc.ch/ Specifically, if you are short on time, read the 'Summaries for Policymakers', written at the level and attention spans of non-technical politicians. They are quite readable and as I wrote in another post, if they can understand it, so can you. :) (The longer reports are fascinating, if you have an interest in science and want to get lost in something.) As I understand it the reports are prepared by a global team of hundreds of scientists, and reviewed by thousands more.[1] (Seriously, has anything like that existed in any other field?) They are meant to cover the breath of climate science and the reports also are meticulous about the language of probabilities. Spend a little time reading them and it will save you the time of reading 99% of what's written elsewhere, and you'll be much better informed. --- EDIT: [1] Review process: http://www.ipcc.ch/activities/activities.shtml (scroll down to "The AR5 Writing and Review Process") -- for example, one report had over 50,000 comments on two drafts from >600 experts. --- EDIT 2: Website interface help. Can you believe this needs to be written, and for HN readers? I had JavaScript off which makes the site usable (if not pretty). With JavaScript on, apparantly the UX concept is 'Easter eggs': There are 4 images arranged horizontally at the top; these are report covers (with text too small to read even if you knew they were clickable). If you click a report cover then the section beneath it changes to display a description of and links to that report. All that work making the reports accessible to the world, hamstrung by web design.
质疑历史全球数据的覆盖范围和质量是否足以支撑研究。
请不要把这当成否认气候变化，这是一个诚实的问题：科学家如何得知全球尺度上很久以前的臭氧、气溶胶、温室气体等数据点？20世纪后半叶之前的数据是不是很零散？如果是，为什么还认为它足够好，可以用在要求数据质量与正确性的科学研究中？
macinjosh查看英文原文
Please don't take this as a denial of climate change, it is an honest question. How do scientists learn the levels of ozone, aerosols, green house gases, and the other data points going so far back at a global scale? Is the data from before the latter half of the 20th century spotty? If so, why is it considered good enough to use in a context of scientific research where quality and correctness of data is paramount?
用贝叶斯视角看，CO2假说整体上远优于零散的替代解释。
强烈推荐Richard Alley博士的演讲《最大的控制旋钮：地球气候史中的二氧化碳》，它更严谨、科学，但仍然很好懂。作为贝叶斯主义者，我特别喜欢他反复强调的一点：对正在发生的事当然可能存在其他解释，但我们找不到比CO2更好地解释数据的解释。很多怀疑者指出主流人为全球变暖假说的小漏洞，但真正的贝叶斯检验是“一个假说比另一个假说更好地解释观测数据的程度有多大”。把所有大气CO2论证的片段放在一起，它对观测的解释远远好过一千个彼此不连贯的“那这个又怎么解释？”。
Homunculiheaded查看英文原文
For a more rigorous, scientific and yet still thoroughly digestible talk I strongly recommend watching Dr. Richard Alley's talk "The Biggest Control Knob: Carbon Dioxide in Earth's Climate History" [0] As a Bayesian I particularly enjoy Alley's running theme that: while there certainly could be alternative explanations for what is happening, we simply cannot find anything that explains the data better than CO2. I see many skeptics pointing out tiny holes in the main AGW hypotheses, but the real Bayesian test is "how much better does one hypothesis explain the observed data than then other?". When you put all the pieces of the atmospheric CO2 argument together it seem to explain what we're observing dramatically better than a thousand "...but what about?" that don't fit together into a coherent counter hypothesis. As an example: Suppose I come home and see my front window broken, my door open and my laptop missing. I assume I have been robbed based on this evidence. You could say "but couldn't the window have been broken by some kid throwing rocks?", "maybe you left the door unlocked and the wind blew it open", "are you absolutely sure you didn't leave your laptop at work?" While individually each of these counter hypotheses may explain a single event just as well, together they don't work: P(window broke, door open, laptop missing | robbed) x P(robbed) >> P(window broke, door open, laptop missing | neighbor threw rock, left door unlocked and left laptop at work) x P(neighbor threw rock & left door unlocked & left laptop at work). [0] https://www.youtube.com/watch?v=RffPSrRpq_g edit: forgot to add my priors in that last section
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