Analysis says NOAA global temperature data ‘doesn’t constitute a “smoking gun” for global warming’

Mikhail Voloshin writes this detailed analysis of NOAA and GISTEMP climate data  processing on his Facebook page:

Random Walk analysis of NOAA global temperature anomaly data


The global temperature record doesn’t demonstrate an upward trend. It doesn’t demonstrate a lack of upward trend either. Temperature readings today are about 0.75°C higher than they were when measurement began in 1880, but you can’t always slap a trendline onto a graph and declare, “See? It’s rising!” Often what you think is a pattern is actually just Brownian motion. When the global temperature record is tested against a hypothesis of random drift, the data fails to rule out the hypothesis. This doesn’t mean that there isn’t an upward trend, but it does mean that the global temperature record can be explained by simply assuming a random walk. The standard graph of temperatures over time, despite showing higher averages in recent decades than in earlier ones, doesn’t constitute a “smoking gun” for global warming, neither natural nor anthropogenic; merely drawing a straight line from beginning to end and declaring it a trend is a grossly naive and unscientific oversimplification, and shouldn’t be used as an argument in serious discussions of environmental policy.


I find myself frequently citing and explaining my trend analysis of global mean temperature anomaly data from the National Oceanic and Atmospheric Administration. Sometimes I cite this analysis for people who are genuinely interested in understanding my findings. Usually though, I merely show it to debate opponents just to prove that, unlike them, I’ve actually looked at the numbers directly and have the know-how to understand them (and, of course, I welcome engagement with those who can say likewise). I write this paper to make these subjects accessible to laymen and non-experts.

First and foremost I’ll present a link to my math. I don’t claim that it’s elegant, but I do claim that it addresses a few specific questions about one specific set of data. The rest of this document is an explanation of what those questions are, and what results I found.

The Excel spreadsheet (.xlsx) : NCDC (NOAA) Temperature Anomaly vs. Markovian Null Hypothesis

I ran these numbers several years ago, and then repeated the run a few times since then. My study doesn’t address the quality of the underlying data, nor the “massaging” that the NOAA performs on its raw instrument records in order to produce a single numerical value every year representing the global mean temperature anomaly (more on that later). What my study does address is to ask, even given the NOAA’s own year-over-year numbers: Do those numbers actually represent an upward trend at all? To this end, I test the NOAA’s temperature records against a Random Walk Hypothesis, a principle used in technical analysis of stocks to determine whether or not a trend (either upwards or downwards) exists.


In order to assert that the global temperature anomaly is rising, the raw temperature numbers must demonstrate an upward trend. This is more complicated than it seems. A simple glance at the NOAA mean temperature record since 1880 clearly shows that temperatures are nominally higher today than they were a century ago (I say “nominally” because the seldom-depicted error bars are enormous – again, more on that later). But there’s a difference between saying that something has risen versus saying that it is rising.

Imagine you’re in Vegas and you come to a roulette wheel whose last four spins were 3, 13, 17, and 22. Would you bet that the next spin is going to be around 28 or so? Most people would – and most people would lose their shirt as a result. That’s because there’s no underlying phenomenon driving an increasing output of the roulette wheel. The evident pattern in the numbers is purely illusory. What you’re seeing isn’t a “trend”, merely a coincidence.


The human mind is exceptional at finding patterns – it is, in fact, our greatest evolutionary adaptation. But we have evolved very few safeguards against false positives. The human capacity for pattern recognition enables us to follow the tracks of fleeing prey, to determine the optimal time of the year in which to plant crops, and to derive the universal law of gravitation by observing celestial bodies. However, that same capacity also compels us to perceive images of the Virgin Mary on slices of burnt toast.

Every one of us, to one extent or another, suffers from pareidolia, the perception of patterns and correlations where none actually exist. It is from this tendency toward pareidolia that arise superstition, occultism, magical thinking, and even social ills such as racism. Of course, it’s also our correct perception of patterns and correlations in the world that enable us to discover natural laws, construct tools, develop technologies, advance medicine, and so forth. Clearly, a correctly perceived pattern is invaluable, but an incorrectly perceived one can be catastrophic.

Unfortunately, humans don’t have any innate way to know when our perceived patterns are right and when they’re wrong – obviously, if such a way existed, we would never be wrong!

It’s only been in the last 300 years or so, with the advent of the Age of Enlightenment, that we’ve developed practices such as the scientific method to help draw the line between true patterns and false ones, and it wasn’t even until the 20th century that Karl Popper’s principle of falsifiability was introduced as an integral component of the search for truth. The question, “Is the pattern I believe I see actually real?” is one that we’ve only begun asking very recently, and the vast majority of us – even scientists – still don’t find it easy. Entertaining the idea that you might be wrong is not something that comes naturally.

Inspiration from the finance industry

There is, in fact, an entire industry of people who literally put their fortunes on the line every day in an effort to differentiate illusory patterns from genuine ones. It’s the finance industry, and I happen to be a part of it, and I have some of their tools and techniques at my disposal. While these tools and techniques offer absolutely no insight whatsoever into the underlying physical properties of Earth’s climate system – thermodynamic feedback or cloud cover or convection or so on – they are exceptional at answering one simple question: Is there even a pattern here in the first place? Even before you get into all the computer models designed to figure out what’s causing the upward trend, you first have to establish that there is indeed an upward trend at all!

At issue is the question of predictive value – the question of whether observations of the past can help predict the future. Quantitative analysts in the finance industry, much more so than academic scientists, require their theories to exhibit predictive value.

Still Available: Climate Change the Facts, 2017

After all, when a scientist writes a paper that fails to correctly extrapolate future data, that scientist can merely write subsequent papers to explore why the previous hypotheses were wrong, ad infinitum. Indeed, as of this current writing, we see exactly this happening in climate science right now, as papers such as Emission budgets and pathways consistent with limiting warming to 1.5◦C and Causes of differences in model and satellite tropospheric warming rates try to explain why past climate models have predicted temperatures much higher than the ones actually measured in subsequent years.

(It’s worth noting that the latter paper, Causes of differences…, is co-authored by the prestigious activist climatologist Michael E. Mann, who developed the “Hockey Stick” historical temperature reconstruction made famous in Al Gore’s An Inconvenient Truth. It’s furthermore worth noting that, if you actually read the Causes paper, you’ll see that the analysis offered there is extremely similar to mine but in reverse. In effect, while I argue that natural fluctuation has caused temperatures to drift upward from a zero baseline (or at least that the data alone doesn’t rule out such a claim), they claim that natural fluctuation has caused temperatures to drift downward to mask at least part of what would otherwise be an already-cataclysmic anthropogenic global warming signal. I contend that we can’t rule out the possibility that our current temperatures are simply the result of natural back-to-back warm spells; they contend that current temperatures should be even higher, but are being held down by natural back-to-back cold spells. Our techniques are largely the same; and, counterintuitively, though the conclusions are mutually exclusive, the data fully supports both interpretations. Such is the nature of basing your argument on the evolution of systems of random variables – they could do the thing you predict, but they could also do a lot of other things, too. Random variability is a fickle mistress; and if you choose to dance with her, so too can your opponents.)

But when a hedge fund manager makes a prediction about the future movement of a stock or an index, and that manager is wrong, then the fund loses millions of dollars. I’ve personally witnessed this happen many times. It’s not a pretty picture.

As such, while both scientific academia and the finance industry are in the practice of drawing conclusions from numerical sequences, finance is much more strongly incentivized to ensure that those conclusions are actually correct. As in, true. As in, correspond to things that ultimately happen in the real world.

Climatology is not an experimental science. It’s slightly outside of our current technological capability to temporarily remove all clouds just to make sure we’re computing albedo correctly, or to keep all air in the atmosphere from moving for a while so that we can isolate convective effects from conductive and radiative ones. As such, in order to still be a scientific discipline at all (as opposed to merely an exercise in modern-day numerology), climatology must depend heavily on data processing techniques in order to provide the falsifiability that would otherwise be supplied by controlled experimental methodology. Climatology is not alone in being a non-experimental discipline; it shares this property with fields such as astrophysics and paleontology, for example. But these fields have succeeded largely on the merit of being extraordinarily good at making accurate specific predictions about subsequently gathered independent data – and you need to use analytic techniques to define exactly what is meant by the terms “accurate”, “specific”, and “independent” (or, for that matter, “data”). And, indeed, climatology has a much harder road to trek than both astrophysics (there are billions upon billions of observable stars, but only one observable Earth) and paleontology (a triceratops skeleton exists in the present day and is readily examinable by any researcher; the temperature of the North Atlantic in 1880, not so much). What’s even worse is that new data in climatology is slow to arrive, and is incredibly noisy and error-riddled when it’s acquired (again, more on “adjustments” below). What all of this means is that climate data needs to be handled with great tentativeness, and claims of the predictive value of resultant hypotheses need to be evaluated very thoroughly before being righteously asserted as “truth”.

Random walks

Now, when it comes to the Earth’s mean temperature, the simplest and most basic assumption, i.e. the null hypothesis, is the same as the null hypothesis for any other time series: that it behaves as a Markov process – specifically, a sub-type called a Martingale. What this means, quite simply, is that it has no “memory” outside of its immediate state – and that the single best predictor of any given year’s temperature is the temperature that came before it. That is, if the mean temperature in, say, 1980 was 14°C, then your best bet for the temperature in 1981 would be, likewise, 14°C. There will be some very small perturbation – sometimes the Earth radiates more energy than before, sometimes it receives more energy from the Sun due to solar activity, etc. – but overall you’d still bet on 14°C. Now, imagine that the perturbation was +0.05°C, so that 1981 turned out to be 14.05°C. What would be your bet for the temperature of 1982? Again, 14.05°C, plus/minus some small perturbation. Basically, whatever the temperature is in any given year, that’s what the temperature is likely to be the following year.

At this point, most people assume that, if the perturbations are unbiased, then this means that value of the Martingale stays near its initial value. If upward perturbations and downward perturbations occur at roughly the same frequency, then they figure that the upward ticks and downward ticks should generally cancel one another out over time, and that the overall value should never deviate far from its starting point.

In this regard, most people are wildly mistaken. While opposite perturbations do indeed cancel one another out, like-sided perturbations accumulate upon one another, causing the Martingale to potentially walk extremely far from its starting point. The long-term expected value of the process is indeed zero, but in practice it can deviate wildly based on pure Brownian motion.

To drive this point home, check out these sample runs of a randomly generated simulation of a temperature sequence, intended to mimic the NOAA’s annual temperature anomaly records since 1880. (In the spreadsheet, the code to generate these can be found under the tab “Random Walk sample run”.) All of these charts were created with the same simple technique: The anomaly starts at 0, and then every year a small random perturbation is added. The function that generates these perturbations is “fair”, i.e. it has no intrinsic positive or negative bias. Nonetheless, as you can see, perturbations can accumulate to cause the final value to be far from 0 indeed. By sheer coincidence, a chain of positive perturbations can arise that drive the value high, and then it tends to remain there; likewise by coincidence, alternating chains of positive and negative perturbations can arise, causing the cumulative value to swing wildly from positive to negative and back again. Mathematically, what this function is doing is taking the integral of a Gaussian random variable, and the results can often be highly unintuitive.

Consecutive runs of “Random Walk sample run”, computing the integral of a Gaussian random variable. – click to enlarge

The key takeaway is that one cannot merely look at a graph of historical data, slap a trendline on it, and then assert that there’s some underlying force that’s propelling that trend. Stock traders have a very long history of doing exactly that and winding up penniless. Scientists who have to perform trend analysis, in particular climatologists, would be wise to learn from their mistakes.


The National Oceanic and Atmospheric Administration (NOAA) is a scientific agency within the United States Department of Commerce. Within the NOAA is an organization called the National Centers for Environmental Information (NCEI), which hosts many publicly accessible archives of weather data of many different kinds. (At the time of my original analysis, this was part of a different sub-organization called the National Climatic Data Center (NCDC). The NCDC has been wrapped into the NCEI, but some of the web links still point to the NCDC). You can get extremely high-resolution data sets featuring time series collected from individual weather stations, satellites, ocean buoys, and so on – and I’ve worked with these data sets for analyses outside the scope of the ones I cover here.

But the NOAA summarizes it all into an annual year-over-year chart.

The raw data for the summary chart can be found here:…

NCDC (NOAA) Temperature Anomaly vs. Markovian Null Hypothesis

There are a few things to understand about this chart, and the data that underlies it.

Measuring temperature anomaly, not absolute temperature

The first thing you may note is that the chart’s Y axis measures an “anomaly” rather than an absolute temperature. The chart does not depict a single specific value for the Earth’s temperature in any given year. Instead, it shows the temperature difference. But the difference from what, exactly? What actually was the Earth’s average temperature in any given year?

That’s actually not very easy to answer, nor is a specific number particularly meaningful. The issue is that different measurement techniques – satellites vs. ground stations vs. ocean buoys, etc. – offer such wildly different temperature profiles that globbing them all together is considered extremely poor scientific practice.

As a result, climatologists essentially don’t talk about the global temperature at all, but rather the global temperature anomaly. That is, while different measurement techniques tend to produce wildly different readings, the change in those readings tend to be more homogeneous and universal – at least, in theory.

The analogy most climatologists cite is this: Imagine you’re measuring an infant for a fever. You put thermometers in its mouth, in its armpit, and in its butt. The three thermometers report very different absolute numbers. But if the infant’s temperature does indeed rise, then all three thermometers will show an increase in whatever their numbers may be. Therefore, while the actual values of the thermometers may be meaningless, there is nonetheless a signal evident from each thermometer’s deviation from its own respective baseline.

As such, their annual mean global anomaly number for each year is calculated roughly as follows.

  1. For each station/buoy/etc., they break up its readings into time segments normalized to a year-over-year window, such as day of the year or month of the year, depending on how often its readings were collected; and then they’ll compute a mean for them. For example, for a station whose data was collected monthly, they will take that station’s readings for all Januaries that the station was in service, readings for all Februaries, etc.; and they’ll compute that station’s mean January temperature, mean February temperature, etc. They keep these “time segments” (in this case, months) separate in order to keep all numbers relatively close to one another for precision – otherwise, you’d be mixing warm summer temperatures with cold winter ones.
  2. For each station, for each time segment, they’ll rephrase that station’s records in terms of difference from the mean. For example, if a station’s all-time mean for all Januaries was 2°C, and its reading in specifically January of 1980 was 2.5°C, then they’ll rephrase the station’s January 1980 reading as +0.5°C . If some other station, let’s say in the Bahamas, has an all-time January mean of 20°C, and its reading on January 1980 was 20.5°C, then they’ll rephrase that station’s January 1980 reading as, likewise, +0.5°C. This relative difference isn’t the station’s temperature reading; this is the station’s “temperature anomaly”.
  3. Now that all stations (buoys, etc.) have been rephrased into “temperature anomalies” from their own individual year-normalized average readings, their position on the globe is taken into account and averaged into a year-normalized reading for a “gridbox”. That is, there may be many more stations in, say, Ohio, than in Mozambique. Because of this, if you were to merely average all stations together without taking their placement into account, you would risk over-representing the local conditions of Ohio and under-representing the local conditions of Mozambique. Therefore, they break up the globe into a grid. For each gridbox, for each time window, they average all of the temperature anomalies for all of the stations in that gridbox. Thus they compute an average anomaly for, say, Ohio January 1980, Ohio February 1980, Ohio March 1980, Mozambique January 1980, Mozambique February 1980, and so on. (The astute observer will note that this creates an additional problem: overcertainty in the record of Mozambique! After all, if Ohio is sampled with a hundred of weather stations but Mozambique is sampled with only a couple, then the Mozambique records are much more likely to depict local conditions at those stations rather than a true measurement of the regional climate.)
  4. For each time window (in this example, for each month), they average all of the globe’s gridboxes together to represent the global anomaly within that time window (i.e. that month).
  5. They average together the global anomaly of all the time windows (months) in a year to compute that year’s global temperature anomaly.

This homogenization process certainly strips a great deal of detail from the raw data. This loss of detail could be represented by offering error bars, but the NOAA’s error bars only represent a small handful of the sources of uncertainty (or more specifically, the over-representation of certainty) that arise from this process.

There’s a much greater source of uncertainty, too: the fact that, even before all this averaging starts to take place, the raw data from the individual instruments is subjected to “adjustment”.

Measurements are heavily “adjusted”

The NOAA compiles an enormous amount of data from many different sources in order to produce a single final number for every year. There are literally thousands of ground stations, many of which use different measuring technologies – some new ones might use digital thermometers, for example, while older ones might still use mercury. In the over 130 years that these stations have been in use, different protocols have been developed for what time of day to read them, how to select sites for them, and so on.

What’s worse is that many stations are missing months or years of data, due to disrepair or downtime during upgrades. The missing data for these stations is often filled in artificially through a process called “imputation”, which involves replacing the values of unmeasured months with a linear interpolation (or some related model) of the temperature before and after the missing window. This process treats the imputed data with the same level of certainty as data that represents actual measurements, but of course the imputed readings are purely fictitious – a “best guess” of what the station would have reported. Because this “best guess” is made by trying to keep the backfilled data consistent with an overall trendline, imputation risks the creation of a self-fulfilling prophecy: we use the assumption of a regression model to fill missing data with assumed values, and then we use those assumed values to validate the regression model. There are other imputation techniques, but they all amount to the same thing: pretending you’ve collected data that you didn’t actually collect.

By itself, imputation isn’t inherently bad science, but imputed data needs to be supplied with relatively enormous error bars to reflect its fundamentally fictitious nature.

For example, imagine a station measured a high of 20°C on a Monday, failed to get a reading on Tuesday due to a software bug, and then measured 22°C on Wednesday. (Assume that the thermometer itself is very precise, so that these measurements are both exact for all intents and purposes, i.e. +/-0°C). What can we fill in for the temperature on Tuesday? The “best guess” would seem to be 21°C, and indeed that’s a perfectly reasonable imputation value. But we didn’t actually measure this hypothetical 21°C. The station glitched that day. It could have still been 20°C. It could have already jumped to 22°C. It could have even gone down to 19°C or up to 23°C, and then swung relatively back to 22°C on Wednesday. We weren’t there, we don’t know. We can say with a great deal of certainty that the temperature on Tuesday probably wasn’t, say, 5°C, and likewise it probably wasn’t 40°C. But we can’t just put in a value of 21°C alongside the adjacent values of 20°C and 22°C and pretend that it’s just as reliable and factual as them. At best, we have to log the temperature with corresponding error bars, such as 21°C +/- 1°C. The appropriate size of these error bars is open to debate, but what’s certain is that they have to be much bigger than the error bars of actual collected instrument readings, possibly by several orders of magnitude.

So the question is: how do the climatologists that crunch these numbers, in fact, handle the error bars? Well, not to get into a lengthy digression on the topic, but suffice it to say that I’ve examined and experimented with their data processing code. Not from the NOAA/NCDC specifically, but from NASA’s Goddard Institute for Space Studies (GISS), which compiles a surface temperature analysis called GISTEMP that is then used by organizations such as the NOAA. Feel free to download the source code. Or browse it on Github. See for yourself. How do they handle error bars? Simple: they don’t.

And that’s just the land stations. The ocean buoys and satellites each have their own problems, with corresponding mitigation approaches.

All of these variations result in a very “dirty” raw data set. The NOAA stands by this data set on the grounds that it’s the best we have, and that the sheer size of the data set helps ensure that any problems with any individual station will come out in the wash.

Problems arise, however, when these backfilling and massaging techniques themselves introduce systemic bias into the data set, or when the data acquired through such techniques is mixed with original, unadulterated data (of which the official data set contains very little at this point).

For example, one “correction” applied to many older ground stations is to try to normalize their measurements to a common time of day. Before electronic record-keeping, daily station data would be logged by a human being physically trekking to the station every day, looking at the thermometer, and writing down the reading in a journal. However, there was no official standard for what time of day the researcher should do this, and clearly measurements taken in the mornings would be colder than measurements taken in mid-afternoon. As such, the NOAA applies a “correction” by taking early-morning measurements and increasing them by some amount to try to simulate what the station would have measured if it was checked in the afternoon. (An evaluation of the time of observation bias adjustment in the U.S.)

Likewise, ocean temperatures used to be measured by having ships lower a bucket into the ocean, draw that bucket aboard the ship, and stick a thermometer into the sea water. We now use buoys and satellites. According to the NOAA, the bucket process inadvertently introduced inordinately low temperatures – specifically, “…a cold bias of between 0.18 and 0.48C …[due to] the evaporative cooling of canvas and wooden buckets. The modeled bias was affected by variables such as the marine air temperature and both ship and wind speed.” (Bias Corrections for Historical Sea Surface Temperatures Based on Marine Air Temperatures) As such, to “compare apples to apples” – that is, to compare satellite and buoy readings with old ship-based readings – the NOAA “adjusts” the older measurements upward by some amount designed to offset this cooling effect, thereby giving us a number that represents not what the bucket thermometer actually said, but what the bucket thermometer hypothetically should have said if it wasn’t for those pesky “evaporative cooling” and “ship and wind speed” issues.

As a data analyst myself, my biggest problem with these adjustments is that they introduce enormous sources of systemic uncertainty. One cannot, in my opinion, go back into a historical data set and tweak the readings to reflect what you believe the instruments “should have” said at the time. The instruments didn’t say that; they said precisely what they said, and nothing more nor less. The issue is that you can’t perform an experimental validation of your “adjustment” without a time machine. You can concoct all sorts of smart-sounding reasons for why some data point or another should be increased or decreased just so, but how can you know you’re right? After all, what separates science from armchair speculation is the scientific method, and you can’t go back in time and perform the scientific method retroactively. In the case of those bucket-based ocean measurements, for example, how can the NOAA be certain that they increased the historical data enough? How can they be certain that they didn’t increase it too much? There’s no certain way to answer that question. In my opinion, the bucket readings and the buoy readings should be considered completely separate data sets, and not attempted to be merged with one another; that way, whatever systemic biases that affect the buckets remain consistent within the bucket data, and any possible heretofore-unknown systemic biases in the buoy set likewise remain with the buoys. This creates a much more fragmented temperature record that’s much harder to work with and contains enormous error bars – but that’s precisely the point. It’s better to represent your uncertainty truthfully than to pretend to know something you don’t. But I digress.

This adjustment process is not some deep dark secret (though I believe that, if more people knew about it, they’d be as skeptical about it as I am). The NOAA freely discusses these and other adjustments in their temperature monitoring FAQ. Naturally, these adjustments are the source of much criticism and debate. Some, like myself, are frustrated by the lack of error propagation and the failure to account for the inherent uncertainty that arises whenever one performs imputation or mixes heterogeneous data sources. Others are concerned that the adjustments themselves reflect the institutional academic incentives of the researchers – i.e. that climatologists tend to actively brainstorm and publish rationalizations for adjustments that will make older records colder and newer records warmer, while intuitively dismissing the possibility of the reverse (NOAA Adjustments Correlate Exactly To Their Confirmation Bias). And still others flat-out accuse climatologists of implementing these adjustments in bad faith (Doctored Data, Not U.S. Temperatures, Set a Record This Year and Exposed: How world leaders were duped into investing billions over manipulated global warming data).

If you’d like to know more about this adjustment process, check out this somewhat technical but spectacularly detailed write-up on the blog of Dr. Judith Curry. You might also appreciate this essay: Systematic Error in Climate Measurements: The surface air temperature record. A very good and less technical (but still very specific and detailed) writeup can also be found here: Explainer: How data adjustments affect global temperature records.

Finding sources of possible historic systemic bias and adjusting them is an ongoing task at the NOAA, as well as all other climate-monitoring organizations. And that brings me to the next important thing to bear in mind about the source data: the historical record changes over time.

The historical record changes over time

If you were to download the NOAA’s temperature readings in 2012, you would see different numbers than if you were to download them in 2015, or today.

For example, in 2012, the global temperature anomaly in 1880 was -0.16°C (per my spreadsheet). Today (September 2017), the global temperature in 1880 is -0.12°C. Apparently, 1880 was colder in 2012 than it is (was?) today.

On the face of it, it would appear that the NOAA employs The Doctor as a senior climatologist, and he’s bringing back temperature data from Earths from alternate timelines.


What’s actually happening is that the NOAA changes its adjustment practices over time. For example, in 2017 the NOAA updated the techniques that it uses for reconstructing historical sea temperature records (Extended Reconstructed Sea Surface Temperature (ERSST) v5), resulting in slight differences to the final yearly averages.

Again, from a personal perspective, what this tells me is that the certainty in the entire data set is grossly overstated. Put bluntly: if you’re going to tell me that a temperature reading gathered over a century ago needs to be changed by some amount in order to be “more accurate”, and then a few years later you come tell me that that same temperature reading actually needs to be changed by some different amount for the same reason, then I’m going to seriously question whether the word “accurate” means what you think it means. The first thing I’m going to ask is: When are you going to come tell me next what an even “more accurate” adjustment should be? I’m just going to take whatever you’re telling me now, assume your next value will be as different as your previous values have been, and in my own mind I’ll recognize the existence of error bars that are implicit from merely the fact that you can’t get your story straight. In the case of the NOAA’s global temperature anomaly for 1880, at one point they said it was -0.16°C, now they’re saying it’s -0.12°C, at various times they’ve said it’s various other things, and who the hell knows what value they’ll give it next. Maybe they’ll say it was actually -0.08°C; maybe they’ll say no wait, we were right the first time, it actually is -0.16°C after all. The point is, these repeated revisions make their numbers untrustworthy; you can’t take any historical value to the bank because you don’t know what it will be after their next revision.

And it’s not just historical data, either. It includes satellite records, which have recently experienced a spate of revisions based on new calculations that allege that their readings don’t properly account for orbital decay (A Satellite-Derived Lower-Tropospheric Atmospheric Temperature Dataset Using an Optimized Adjustment for Diurnal Effects). The latest such adjustment, as of this writing, has been generating particular attention due to the fact that the post-adjustment data set shows a 140% greater temperature increase since 1998. Climate alarmists consider this latest adjustment to be a powerful vindication of their stance; but, ironically, from a data quality standpoint this dramatically worsens the case for believing the processed instrument data. Again, put bluntly: If you’re going to tell me that the numbers you’ve been reporting have been off by 140% all along because of a glitch you only discovered today, then why should I believe the numbers you tell me now? What other currently unknown glitches exist in your instrumentation that you will only discover tomorrow, and how much will they demonstrate your current numbers are off by, and in what direction? In essence, every time an alarmist screams, “My God, it’s worse than we thought!”, what a data scientist hears is, “You just admitted that you didn’t know what you were doing before, and I’m going to infer that you probably still don’t.”

The point is, no matter what other error bars the NOAA might ascribe to the measurement, in addition to those error bars, each historical measurement also has a substantial extra degree of uncertainty that arises merely from the fact that the NOAA is reporting it. This doesn’t mean, of course, that the data is wrong per se; it just means that the data is much fuzzier/blurrier than it seems, and you have to squint much harder than you think in order to see a pattern in it.

Techniques and Results

The purpose of the kind of analysis I performed in this spreadsheet is to determine the likelihood of seeing the observed numerical sequence from an unbiased Markov process (i.e. a Martingale). More specifically, the question I ask, in various ways, is: Assuming that the temperature system can be represented as a Martingale, what is the probability that, if it’s started at the observed point in 1880, it would evolve to observations at least as extreme as what we observe today? That is, if the anomaly in 1880 was -0.16°C (which is what the NOAA records currently say it was), then what’s the probability that, purely by a random walk with no directional forcing whatsoever, the anomaly might end up at +0.57°C or beyond (which is what the NOAA records say it currently is)?

If the probability is low (traditionally <5%), then that means that the underlying assumptions are implausible — i.e. it would be a strong contradiction of the hypothesis that the system is unbiased. A high probability, on the other hand, does not rule out some bias, but it does indicate that the observations can be adequately explained without the assumption of an upward or downward trend or “forcing” of any kind.

If you’re technically minded and are following along on the spreadsheet, you’ll see that I perform a few analyses of the data, each chosen to be as general and agnostic as possible – that is, making as few assumptions as I possibly can about any properties about the underlying physical system.

For what it’s worth, a very similar analysis was performed in 2012 on behalf of British Parliamentarian Lord Bernard Donoughue. His work and mine were carried out independently of one another; I didn’t know about his question when I wrote my spreadsheet, and if he knew about my spreadsheet at the time of his question then I’d at least like a commemorative fountain pen.

Number of increases vs. number of decreases

In the first analysis, “+/- Bernoulli on NCDC”, I compare the number of year-over-year upward steps against the number of year-over-year downward steps. It’s that simple: Does the temperature of any given year likely to be higher or lower than the year before it?

The NOAA’s historical data, as covered by the spreadsheet, spans 132 years. 70 of those years were hotter than the previous one. 62 of them were colder.

What does that mean? Well, on the one hand, yes, there were more temperature increases than there were decreases. On the other hand, the increases barely outnumber the decreases.

So, what are the chances that we would see this kind of distribution of hotter/colder years if there wasn’t an inherent upward bias? I.e., could we see this kind of fluctuation purely by coincidence?

The question might be confusing to laymen. After all, if there was no upward bias, then we’d see the exact same number of upticking years as downticking ones, right?

Well, no. Imagine you flip ten pennies – fair, unbiased, normal pennies, each with 50/50 odds of heads or tails. Would you always expect to get exactly five heads and exactly five tails? Of course not. Sometimes you might get six heads and four tails. Sometimes you might get seven heads and three tails. Even getting all ten heads and no tails, though highly unlikely, is still possible.

So if you were to flip 132 pennies, what are the odds you’d get at least 70 heads (and the rest tails)? Or, for that matter, vice versa – 70 tails (and the rest heads)? That is, what are the chances that, with no inherent bias whatsoever, you’d get 8 more of one than the other?

The answer is 54%. You are in fact very likely to encounter a pattern like this from unbiased random fluctuation. Sometimes that random fluctuation moves your value higher, sometimes lower – but 54% of the time, a sequence of 132 random fluctuations will move you 8 or more steps away from your starting point.

Let’s phrase it another way: Imagine you play a little game with yourself. You start with a score of 0. Then you flip 132 pennies. For every heads, you add a point. For every tails, you subtract a point. What will your final score be? Well, if you play this game many times, you will see that 54% of the time your score is either >=8 or <= -8.

In the tradition of contemporary science journals, a metric called a “p-value” indicates the strength of an experiment’s results. In layman’s terms, the p-value of a result is the probability that the result could have been produced by the null hypothesis – which typically means unbiased natural random chance. For example, if you’re a biologist who’s feeding rats some experimental new foodstuff to see if it’s carcinogenic, and several of your rats do indeed develop cancer, then the p-value tells you how likely it would have been for at least that many rats in your study to develop cancer anyway with or without your foodstuff. An experiment’s p-values need to be small in order for a null hypothesis to be rejected; traditionally, p-values need to be less than 5% for journals to even consider publishing a paper. The example of the rats makes it clear why; because it’s not impossible for rats to just spontaneously develop cancer anyway, the odds of the observed cancer rates have to be so small (i.e. the cancer rates themselves have to be so high) that it would be functionally impossible (or at least extremely unlikely) for anything other than the foodstuff to have caused the cancer. At that point, the scientist must conclude that the foodstuff caused the cancer.

So in observing 70 upticks out of 132 years, must a scientist conclude that something is causing an unusual rate of upticks? Could those upticks be caused by, for lack of a clearer term, nothing?

Well, it turns out that there’s a 54% chance that those upticks are indeed caused by “nothing” (not literally nothing, of course, but merely by a very large and very noisy combination of forces that buffet the value hither and thither, with no preference of direction). One cannot conclusively state that there is anything driving upticks to be more frequent than downticks. Sometimes the temperature goes up, sometimes it goes down. And yes, it has gone up a little bit more often than it’s gone down. But it would be erroneous to assert that it clearly exhibits an upward trend.

Magnitude of rises vs. magnitude of drops

Given that the temperature seems to rise and to fall with roughly equal frequency, perhaps the rises are bigger than the falls? After all, even if the number of steps were equal or even if it was biased in favor of downward steps, if those steps are much smaller than the upward steps then it would be reasonable to assert that something is pushing the temperature upwards.

Specifically, such a pattern would be seen if there was a natural fluctuation overlaid atop a steadily increasing undercurrent – natural fluctuation would still make there be occasional upsteps and downsteps, but the undercurrent would make the downsteps smaller and the upsteps bigger.

This hypothetical undercurrent is what climatologists seek to clarify when they discuss a “signal”, to separate it from the natural fluctuation that data processing considers as “noise”.

As such, I performed a test to see whether or not downward fluctuations and upward fluctuations were “equivalent”. Technically, what this means is: Do they appear to come from the same distribution? Another way to think about it is: Could whatever process generated the upward movements also have generated the downward movements? If yes, then the upward and downward movements are essentially interchangeable; if not, then something is driving one and/or suppressing the other.

The test I performed is called Kolmogorov-Smirnov (K-S), which I selected specifically because it permits direct testing of two empirical distributions without making any assumptions at all about any underlying generative function or hypothetical source population. It involves taking two data sets, sorting their values from highest to lowest, and comparing the gap between them. A wide gap indicates that the values were probably drawn from different populations; a narrow gap indicates that they could have been drawn from the same population.

Listing the magnitudes of temperature rises against the magnitudes of temperature drops, sorted from highest to lowest, produces the following graph. (The “Decreases” looks dashed because there are fewer decreases than increases, requiring us to introduce gaps to make the spread equivalent. These gaps are strictly visualization artifacts; the K-S calculation is fully capable of handling empirical data series of different sizes.)

Visual representation of Kolmogorov-Smirnov test of year-over-year increases against decreases in the NOAA global mean temperature anomaly data set.

Visually, it’s clear that the Increases line and the Decreases line track very closely with one another, strongly suggesting that they were produced by the same process (or equivalent processes). If they weren’t, then this graph would show one curve offset from the other, or one curve flatter than the other, or one curve rising higher than the other, or in some other way introducing a gap between the two lines.

The K-S computation reveals a p-value of 0.591. That is, there’s a 59.1% chance that the null hypothesis is true, i.e. that the two data series were drawn from the same source population. This is nowhere close to the traditional value of 0.05 that’s usually required to reject the null hypothesis.

In short, it doesn’t appear that there’s any difference in the sizes of upward and downward steps in the year-over-year temperature anomaly data. There doesn’t appear to be any kind of “forcing” that drives upward steps to be bigger or downward steps to be smaller. If there is to be any kind of “signal”, it must be purely in the number of upward steps compared to downward ones – except, of course, we already ruled that out.

Wald-Wolfowitz Runs Test

The Wald-Wolfowitz “runs test” is widely considered a “standard” test for sequence randomness. It’s presented as a basic quantitative technique in the NIST Engineering Statistics Handbook.

The runs test is similar in nature to the “# Increases vs. # Decreases” test I performed and described above. Its operating principles depend on measuring the number of “runs” in the data set – that is, the number of times that a sequence changes direction. For example, if several years in a row exhibit upticks followed by a downtick, that series of years is considered a “run”. A random data set in which each element is drawn from a uniform distribution exhibits a very easy-to-predict number of runs of various lengths, and therefore a comparison of your actual observed number of runs against the expected number of runs offers a clue that your data set consists of such uniform random variables.

Running the year-over-year sequence of upticks vs. downticks through a Wald-Wolfowitz runs test produces a p-value of 0.586. I.e. there’s a 58.6% chance that a sequence of unbiased uniform random variable iterations (e.g. flips of a penny) would produce at least as many runs as the ones observed in the NOAA data set. Again, this is far above the traditional p-value threshold of 0.05, so the null hypothesis – that the temperature data is produced by an unbiased random process – cannot be rejected.

Autocorrelation tests (out of scope)

Traditionally, when evaluating a time series for evidence of a random walk, it’s customary to perform one or more tests that compare the data set to time-shifted versions of itself. These include the Box-Pierce Test and the related Ljung-Box Test, a Variance Ratio Test, and others. The primary use of these tests is to try to find cyclic patterns within a data set, such as a low-frequency rise and fall that is much larger than the individual steps.

In the NOAA data set, the Pacific Decadal Oscillation (PDO), which drives El Nino/La Nina events, exhibits such a cyclical pattern; and indeed, most of what we currently know about the PDO is data that we gleaned empirically from performing autocorrelation tests on time-series analysis. Likewise, the roughly 11-year solar cycle is likely to show a sustained pattern of rising and falling temperatures that has been shown to correlate well with year-over-year temperature anomaly data.

However, by definition, these cyclical patterns are ones that reset at the end of every cycle. Identifying such patterns isn’t a useful exercise for the task of determining whether or not there exists an overarching directional trend within a data set. The temperature data may rise and fall in 11-year crests with the solar cycle and in 30-year crests with the PDO; but if each iteration of the PDO were a little bit warmer than the last, then we would use different tests than autocorrelation to reveal that – such as the tests we’ve performed above.

Nor are autocorrelation tests useful in discovering cycles whose duration is longer than the total duration of the data set itself. A cycle needs to repeat at least once during the observation period in order to be identifiable as a cycle at all, so autocorrelation tests on data gathered since 1880 cannot tell us, for example, whether or not we’re in the upswing period of some hypothetical 500-year-long oscillation.

For these reasons, I’ve left autocorrelation tests out of the scope of this analysis.

Overdoing it

We have a joke in the world of quantitative analytics: If you torture the data for long enough, it will eventually confess to anything you require.

I could fill this paper with a dozen more tests for randomness, and eventually I will find one that rejects the null hypothesis at a p-value level of 0.05. However, the reason for that is itself pure chance. Remember, the (informal) definition of “p-value” is the probability that “randomness” (or more technically, factors outside of the controlled parameters) caused your observed results, and it’s traditional to reject the possibility of mere “randomness” when the observed results have less than a 5% chance of being explained by randomness alone. The caveat is that, with every experiment you run or with every analysis you perform, you roll that die again – and eventually that d20 will roll a 1.

XKCD has a great illustration of such an event in action.

This phenomenon is called the Look-Elsewhere Effect. It’s also known as the Multivariate Effect, the Texas Sharpshooter Effect, and others. In data mining operations, we call it “data dredging”, and we try hard to avoid it (those of us with scruples and professional integrity, at least).

I only mention it here because I don’t believe any data science discussion targeted at laymen is complete without it. Bringing awareness of the Look-Elsewhere Effect is a bit of a personal crusade of mine. I’ve seen the Look-Elsewhere Effect wreak havoc in academia and finance alike – whether it’s in the form of technical traders trying to simultaneously buy/sell on 20 different mutually exclusive trading strategies, or epidemiologists claiming that power lines cause childhood leukemia after testing 800 different possible ailments, or a neuroscience researcher observing the effects of photographs of human faces on the brain activity of a dead salmon.

Anyway, my point is: There comes a point at which data mining becomes data dredging, a point at which further tests actually muddy your results and make them less convincing. This seems like a good place to stop before that happens.

Conclusions and Discussion

The simplest assumption one can make about any physical system, no matter how complex, is that its state in the next moment in time will be roughly the same as its state at the present one. While this is obviously not always true, the burden of proof lies with whoever claims that the system shouldn’t remain static, that there exists some force that will compel it to some state other than the one in which it’s currently found. That burden can be met by employing proof by contradiction, by demonstrating that the system evolves in a manner that would be so unlikely in a static scenario that the static scenario simply cannot be true.

The annual global temperature anomaly data provided by the NOAA consists of many layers of complexities, of which the actual global temperature anomaly, i.e. the underlying physical phenomenon being measured, is merely the beginning. The instrumentation itself, the processing of instrument records, the merging of heterogeneous instrument sets, and the collation of those records into a single annual value is fraught with byzantine methodologies that introduce uncertainties (if not outright biases) at multiple systemic levels.

Through all of this complexity, therefore, the safest and most uncontroversial assumption is that of single-timestep autocorrelation: whatever value this whole process produced for any given year, it should produce approximately the same value the following year. When this assumption is extended for many years (over 130 in the NOAA data set), it produces a pattern called a “random walk”, which can amble aimlessly away from its starting point without any force explicitly “pushing” it in one direction or another.

This assumption is qualitatively different from the assumption used by climatologists in the formulation of the very term “temperature anomaly”. Their assumption is that there exists some ideal desired “normal” value that their instruments should be measuring (a “zero anomaly” state). Whatever value they measured one year, they believe that the following year’s value should be closer to this “normal” value. When that isn’t the case, they hold that there must be some external “unnatural” force that’s driving those values away from their desired “normal” state, and this force is anthropogenic global warming.

Essentially, this is the difference between assuming that the underlying physical system behaves like a soccer ball in a valley (where it naturally lies at the center, and if you kick it in any direction, its natural tendency is to roll back to the center) versus like a soccer ball in a flat open field (where it naturally lies wherever it last got kicked, and if you kick it in any direction, it will land at some new spot and remain there as its starting point for whatever next kick might come along).

Few climatologists, indeed few physical scientists of any kind, would deny that the steady-state assumption is always at least tentatively valid; i.e. that a physical system’s state at time t, absent any other knowledge, is best predicted by its state at time t-1 – and, indeed, recent discussions of the Earth storing thermal energy in its oceans is consistent with the idea that the Earth’s temperature in any given year is typically going to be whatever it was the year before plus/minus some small variation. But likewise, few data analysts would deny that there must be some physically enforced boundaries on the terrestrial thermal system – if Earth’s temperature truly was an unrestrained random walk, then at some point in the last few billion years a series of same-direction steps would have coincidentally arisen that would have either incinerated the planet or frozen it to such a chill that it would have snowed oxygen. These two positions aren’t mutually exclusive; essentially, it’s possible for the Earth’s thermal system to function as a random walk within a certain range, but for the boundaries of that range to be rigidly enforced. Conceptually, this could be visualized as a flat soccer field at the bottom of a valley; the ball will generally land where you kick it, but you can’t kick it completely out of the field. However, this transmutes the discussion into hypotheses about just how wide this field is, how steep the walls are, etc.; and, unfortunately, this discussion is almost entirely speculation. Certainly the answers to such conjectures do not lie in the 130-year-old instrument temperature data set; and if it did, then the data needs to unambiguously reflect that.

The point of this discussion, therefore, is to emphasize that, when it comes to temperature anomaly data, Occam’s Razor suggests that the year-over-year time series is a random walk. The burden of proof is on those claiming that there is a trend to the time series, that the “walk” isn’t random. This burden can be met by showing that the data exhibits statistical properties that would be extremely unlikely for a purely random data set.

Verdict: Proof of non-randomness not found

The analysis in this paper shows that the data does not exhibit telltale markers of non-randomness. We’ve applied several techniques that would show non-randomness – techniques borrowed from the finance industry, a world extremely well-versed in the finding of true patterns in time-series data; and each technique failed to rule out the null hypothesis. One does not need to introduce the assumption of an upward forcing function in order to explain the evolution of temperatures in the post-industrial period. The data is consistent with the assertion that the temperature has evolved in the last 130 years due to nothing more than purely random sloshing.

What this means is that it is naive to merely look at the 130-year annual temperature anomaly graph and conclude that it represents a rising trend. Analytically speaking, it doesn’t clearly show anything more prominent than the path of a proverbial drunkard stumbling between the bar and his home.

A logical prerequisite to any discussion about whether or not humans are causing climate change is the establishment of an actual upward signal at all. Despite the impression one might get through visual pareidolia, the data does not exhibit such a signal, rendering all logically dependent discussions ungrounded from reality and suitable only for abstract conjecture.

Random variability is a fickle mistress

But I do need to add this caveat: The data doesn’t disprove a trend either.

The purpose of this analysis is merely to establish that it is well within reason to believe that the 130-year global temperature anomaly record is the result of a random walk, rather than a forced physical phenomenon; i.e. that a random walk can produce the temperature record as we’ve observed it. But some kind of systemic forcing, be it anthropogenic or natural, can produce this record as well.

  • The data is consistent with a random walk that has wobbled its way upward through pure coincidence.
  • The data is also consistent with natural forcings.
  • The data is also consistent with a combination of natural and man-made forcings.

In fact, per the Causes of differences… paper cited above, the data is even consistent with a very large anthropogenic signal that looks smaller than it should because it is being masked by a random walk that has wobbled its way downward!

All of these proposed physical processes and combinations thereof can produce temperature histories that match what we’ve observed from the instrument record. Yes, some of these proposed processes are more plausible than others; some involve making more underlying assumptions than others, some involve more articles of faith than others. The decision of which process best represents reality then moves away from which one could have created this data, and into topics of model plausibilities and Bayesian prior probabilities.

What one cannot do, though, is hold the data aloft as though it is a divine truth etched into tablets by an almighty being (as if it hasn’t been gathered and processed by dirty, filthy humans), and declare that it supports your model. Data doesn’t “support” any model. Data can invalidate a model, but just because you’ve produced a model that’s consistent with the data doesn’t mean that there aren’t an infinite number of competing models that are also consistent with that same data.

So if you’ve ever, in the course of a heated argument, thrown graphs in someone’s face believing that the visuals speak for themselves and that the data is on your side, know this: You’re wrong. The data isn’t on your side. The data is never on your side. At best, the data might simply be not against your side. But data by itself isn’t on anyone’s side. At best, you maybe aren’t the data’s enemy. But never believe that the data is your friend. Data has no friends.

That’s why it and I get along.


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