Analysis: NASA ‘has massively altered (temperature data) over the past decade’
https://stevengoddard.wordpress.com/2015/01/17/eric-steig-gets-27-million-pinocchios/…
Scientist calls warmist claim ‘preposterous’: Rips claim that there is only a ‘1-in-27 Million Chance That Earth’s Record Hot Streak Is Natural’
That “1-in-27 Million Chance That Earth’s Record Hot Streak Is Natural” Is Preposterous
http://wmbriggs.com/blog/?p=15264
How long will this last? We met a lot of bad statistics over the years, but this one wins the Blue Ribbon With Gold Lace, Free-Beer-For-Life Award of Statistical Putrescence. It is not only not true that the “1-in-27 Million Chance That Earth’s Record Hot Streak Is Natural” probability is correct, as quoted by Mashable and others who ought to know better, but that it’s so far from True that if it were to travel at a thousand miles per hour for two thousand years it would still be just as far from True as the day it started. And this is so even if you are a leftist progressive Marxist politically correct environmentalist feminist hater of Fox News Elizabeth-Warren-For-President-button-wearing Democrat who would like nothing better than if the Temperatures Of Doom were just around the corner and all agreed that you—yes, you—should put in charge of the World’s Affairs, so saving us all. It is a mark, and an important one, of how political climate “science” has become that statistics like this are quoted and accepted, and, yes, even rejected, all because people desire man-made apocalyptic the-time-to-act-is-now global warming be true or false. The reader will pardon my exasperation. Misunderstandings of probability and bad statistics now account for 83.71% (p < 0.001) of all bad science. I’ve reached the snapping point. We’ve met this statistic, or its cousin, before, when we discussed why the then touted “More On The 1 in 1.6 Million Heat Wave Chance“, created by somebody at the NCDC was ridiculous. Don’t be lazy. Read that article. Here is why all these numbers are you-ought-to-have-known-better stupid. Do you accept that some thing or things caused each month’s or year’s temperature? If not, you’re not familiar with science and so are excused. Well, some thing or things did cause the temperature (however it is operationally defined). Measuring it does not determine those causes, it only records the observations. Suppose it is true that these causes are analogous to reaching inside a bag of temperatures, pulling one out, and then blanketing the earth with it. Some of these temperatures are high, some low; a mixed bag, as it were. Now let Mother Nature pull out the temperatures and we observe them. What are the chances we see what we see? 100% is the only acceptable answer. Don’t see that? If it really is true, based on our accepted model, that the chance this year’s (or month’s or whatever) temperature is just as likely to be higher or lower than last year’s, then the probability we see the record we see is 100%. Think of a coin flip. The chance, given the obvious premises about the coin, we see any string of Hs and Ts is the same (HHHHH has the same chance as HTTHT, etc.); thus the chance we see what we see is 100% no matter what we see. Of course, we may be interested in Hs or High Months more than Ts or Low Months. The chance, accepting our model, of so-many Hs or High Months in the record can also be calculated and will be some number. Imagine some string of High Months then calculate the chance of seeing this many in a row. Make the string as long as you like, which makes the probability weer than wee, vanishingly small. Make the probability smaller than 1 in 27 million. Make it 1 in a billion! Nay, two billion! Does that mean our model of causation is false? No! No no no no no. No. We accepted the model as true! It is therefore, for these calculations, true. As in True. That means the probability is also true, given this model. But this probability doesn’t say anything about the model, it is a consequence of the model. If you don’t like this, then you shouldn’t have accepted this model as true. What about that? Why did you accept this preposterous model as true? Who in the wide word of human nuttiness ever claimed that a forgetful Mother Nature regularly reached into a bag of temperatures and cast it over the surface of the deep? I’ll tell you. Nobody. But that’s just the model everybody who is foisting these silly 1-in-27 Million Chance-like statistics on us believes. Or claims to believe, else they never would have quoted these numbers. But we know they don’t believe the model! So why do they quote these numbers? Because they want to scare you into believing, without evidence, their alternate model, apocalyptic man-made global warming is true. Some thing or things caused the temperatures to take the values we observed. If we knew what these causes were, i.e. what the model was, we would state them, yes? That chance of seeing what we saw, given this model, would not be 1 in 27 million, right? Since we would know the causes, the chance of seeing what we project would be 100%. Take an apple and drop it. Given the causal model of gravity, what is the chance apple meets earth? 100%.1 Do we have a full causal model for temperature? We do not. If we did, then meteorologists and climatologists would not make mistaken forecasts. Because they often do (especially climatologists), it must be that their models are incomplete. We do not know all the causes of temperature. But because we do not, it does not—it absolutely does not with liberty bells on—mean that we do know the cause is man-made global warming. This is the sense that the 1 in 27 million is wrong. It’s the right answer to a question nobody asked based on a model no sane person believes. Its answer is useless utterly in discovering whether global warming is true or false. If this is not now obvious to you, you are lost, lost. ————————————————————————————— 1You are not being clever but obtuse by suggesting that, say, something interferes with the apple’s (and earth’s) path. That interference changes the model, it is an additional premise. The model is no longer gravity, but gravity-with-interference. -- gReader Pro …
Why Satellite Records Cannot Be Ignored – ‘Between 1979 and 2012, the satellite and surface data followed each other closely’
Why Satellite Records Cannot Be Ignored
https://notalotofpeopleknowthat.wordpress.com/2015/01/17/why-satellite-records-cannot-be-ignored/
By Paul Homewood As we all know by now, satellite data has failed to support claims made by the surface datasets of “hottest year evah”. This has led to many now claiming that the satellite datasets should be ignored, as they don’t measure the same thing. This argument, however, ignores the fact that between 1979 and 2012, the satellite and surface data followed each other closely, albeit not always on a month by month basis. The Woodfortrees graph below compares RSS with GISS between those two years. There is a slight divergence, with GISS showing a slightly higher trend, but it is relatively small, and the direction of travel is the same. http://www.woodfortrees.org/plot/gistemp/from:1979/to:2012/plot/rss/from:1979/to:2012/plot/gistemp/from:1979/to:2012/trend/plot/rss/from:1979/to:2012/trend But now contrast this with the last two years. http://www.woodfortrees.org/plot/gistemp/from:2013/plot/rss/from:2013/plot/gistemp/from:2013/trend/plot/rss/from:2013/trend/plot/uah/from:2013/plot/uah/from:2013/trend Both satellite sets, UAH and RSS are essentially flat, yet GISS has shot up by nearly 0.2C. This divergence appears to be even more inexplicable, because, in his 1987 paper “Global Trends of Measured Surface Air Temperature” James Hansen attempted to verify his surface datasets with atmospheric temperature trends, as measured by radiosondes. http://pubs.giss.nasa.gov/docs/1987/1987_Hansen_Lebedeff_1.pdf NOTE – “These results suggest that most of the difference between the two temperature records is due to the incomplete spatial coverage of stations”. The implication is that the surface and atmospheric temperature changes should correlate over a period of time. Of course, satellite coverage is now nearly universal, with the exception of a small area around the poles, so in this respect satellite data can be regarded as more accurate than radiosondes. Until the difference between satellite and surface datasets can be fully explained, it is no more than political rhetoric to claim that 2014 was the warmest year on record. It is time that NOAA, NASA and the rest took the satellite records seriously, instead of just sweeping them under the carpet.
— gReader Pro…
Flashback 2005: NASA’s James Hansen: No Agreement On What Is ‘Surface Air Temperature’ …Few Observed Data Filled In With ‘Guesses’
2005 James Hansen: No Agreement On What Is “Surface Air Temperature”…Few Observed Data Filled In With “Guesses”
NASA has an interview with James Hansen (still) up at its site here. Here we see that “surface air temperature” (0 to 50 feet) is not even yet defined, let alone can it be determined. This does not only present lots of uncertainty in its determination, but also plenty of opportunity for measurement and interpretation mischief. Hat/tip: Reader Dennis. Here The NASA interview (my emphases added): ================================== GISS Surface Temperature Analysis The Elusive Absolute Surface Air Temperature (SAT) The GISTEMP analysis concerns only temperature anomalies, not absolute temperature. Temperature anomalies are computed relative to the base period 1951-1980. The reason to work with anomalies, rather than absolute temperature is that absolute temperature varies markedly in short distances, while monthly or annual temperature anomalies are representative of a much larger region. Indeed, we have shown (Hansen and Lebedeff, 1987) that temperature anomalies are strongly correlated out to distances of the order of 1000 km. Q. What exactly do we mean by SAT ? A. I doubt that there is a general agreement how to answer this question. Even at the same location, the temperature near the ground may be very different from the temperature 5 ft above the ground and different again from 10 ft or 50 ft above the ground. Particularly in the presence of vegetation (say in a rain forest), the temperature above the vegetation may be very different from the temperature below the top of the vegetation. A reasonable suggestion might be to use the average temperature of the first 50 ft of air either above ground or above the top of the vegetation. To measure SAT we have to agree on what it is and, as far as I know, no such standard has been suggested or generally adopted. Even if the 50 ft standard were adopted, I cannot imagine that a weather station would build a 50 ft stack of thermometers to be able to find the true SAT at its location. Q. What do we mean by daily mean SAT ? A. Again, there is no universally accepted correct answer. Should we note the temperature every 6 hours and report the mean, should we do it every 2 hours, hourly, have a machine record it every second, or simply take the average of the highest and lowest …