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Was 1934 the hottest year on record?
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1934 used to be the hottest year on record in the USA. However, the USA only comprises 2% of the globe. What about the other 98%? According to NOAA temperature records, as of 2024, the hottest years on record globally were 2016 and then 2023, the latter year's temperature smashing the record by a wide margin. |
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Climate Myth...
1934 - hottest year on record
Steve McIntyre noticed a strange discontinuity in US temperature data, occurring around January 2000. McIntyre notified NASA which acknowledged the problem as an 'oversight' that would be fixed in the next data refresh. As a result, "The warmest year on US record is now 1934. 1998 (long trumpeted by the media as record-breaking) moves to second place." (Daily Tech).
At a glance
Let's not shy away from the fact that in the contiguous United States, the year 1934 was particularly warm. It was among a cluster of years marked by the notorious droughts known as the 'Dust Bowl' years, during which huge dust-storms were frequent and did great damage to the soils of the Prairies.
But how significant is 1934 in the bigger, global picture? Let's take a look.
The background to this tale involves the NASA GISS temperature dataset. In August 2007, blogger Steven MacIntryre noticed a series of sudden temperature leaps in that dataset. They had occurred early in the year 2000, leading some to speculate that the Y2K computer bug must have been behind them.
NASA investigated. The data used for the NASA GISS record are from the National Oceanic and Atmospheric Administration (NOAA). NOAA had adjusted the data to filter out spurious excess warming. Sources of such biases are well-known. They include time of observation, non-ideal siting of weather-stations, relocation of them and urban heat island effects.
The specific error was nothing to do with Y2K. It was simply that, from January 2000, NASA were mistakenly using unadjusted data, so all those spurious anomalies were still in there and it looked warmer than it should.
Nobody's perfect and that includes scientists, but science is a self-correcting process. Errors that do occur are corrected when found. Correcting this specific error meant that some six years of temperature data had to be adjusted downwards. That meant that the order of the warmest years was also affected and after adjustment, 1934 and its Dust Bowl heat once again stood out prominently.
That's what happened back then, in a nutshell. Now to look at 1934 in context, with the added benefit of another 17 years of hindsight, of course.
Firstly, the corrected temperature record covered only the Lower 48 - the states of the USA excluding Alaska and Hawaii - where 1934 was indeed a very hot year. Zooming out of the USA - making up around 2% of the world's surface - to the whole globe, however, shows that 1934 was in fact a rather chilly year. In order to understand what's happening to global temperatures, the whole globe - the other 98% - also needs to be considered, year in year out.
Secondly, it may have been possible to attempt crudely dressing-up 1934 as another 'final nail' in the 'global warming coffin' in 2007, but no longer. If you now look at the global league-table of warmest years, the ten hottest of them have occurred since 2010, with 2023 being just the latest record-breaker.
The year 1934 was a very warm one in the United States. No-one disputes that. In fact, it's meteorologically quite interesting. The Dust Bowl years are thought to have been at least partly human-caused - by poor agricultural land-management. But the way temperatures have gone now, 1934 is merely of local, historic importance: a curio to look back at from time to time - and a warning to look after your topsoil!
Please use this form to provide feedback about this new "At a glance" section. Read a more technical version below or dig deeper via the tabs above!
Further details
In the NASA GISS temperature dataset, for the period 2000-2006, unadjusted NOAA records were erroneously included, thereby incorporating sources of bias making the record warmer than it should have been. This error was spotted by blogger Steve McIntyre in August 2007 and after investigation it was corrected by NASA. But the error immediately got people talking in certain quarters, with a particular focus on one year: 1934.
The year 1934 was a very hot year in the United States: in 2017, it still ranked sixth behind 2012, 2016, 2015, 2006, and 1998. It was in fact part of a series of hot dry years that are referred to as the time of the Dust Bowl. This severe crisis of historic proportions was caused by a combination of natural factors, especially severe drought, and human-made ones. In particular, it was the widespread failure to apply farming methods appropriate to dry elevated plains, such as ways to prevent wind erosion, that made a bad situation worse. Nature did the rest.
Natural topsoil is a precious resource indeed and they managed to lose much of it in a series of huge dust-storms. Strong winds are not uncommon over the Great and High Plains and land use has to be designed with resilience to them born in mind. The drought occurred in three main waves that took place in 1934, 1936, and 1939–1940. Some regions of the High Plains experienced drought conditions for as long as eight years.
As bad as the Dust Bowl years were, global warming takes into account temperatures over the entire planet, including the oceans. In any case, the land area of the U.S. Lower 48 accounts for only 2% of Earth's total surface area. Despite the U.S. sweltering in 1934, that year was not especially hot over the rest of the planet, as you can see on the 1934 map in fig. 1 (below). Globally, 1934 temperatures were actually cooler than average for the 20th century.
1934
2022
Figure 1. Global temperature maps for 1934 (top) and 2022 (bottom). Source NASA.
Science deniers pointing at 1934 as 'proof' that recent hot years are not that unusual are wrong, for several reasons. Apart from anything else, science does not set out to prove things: it presents evidence and develops hypotheses to explain things. That aside, the key sin here was the choosing of a single warm year (1934) in a single country (USA Lower 48) to make a talking-point about a phenomenon that is global in its nature and reach. That is an obvious example of the fallacy of 'cherry-picking' - waving around a single fact that supports a dubious claim and thereby ignoring the rest of the data (i.e. the rest of the world - fig. 2). It's essential to step back and look at the bigger picture at all times. Anyone failing to do that by cherry-picking out single years in single places is not behaving in a scientific manner.
Figure 2: Multiple independent global surface temperature products show a very coherent pattern of temperature change over the 1880-2023 period. While there is overlap in the weather station inputs and ocean data, the methods for correcting for missing data, inhomogeneities, spatial sampling etc. are independent. Graphic: Realclimate.
Regional and year-to-year temperature variations will always occur. Our climate is noisy like that. The reason we are so worried about climate change is not because of a single extreme in one place on one date. It's the long-term average trend, over the entire world. That trend shows an undeniable increase in global surface temperatures and global ocean temperatures. As of the time of writing (May 2024), the years 2023, 2016, 2020 and 2019 are the hottest on record. So far.
This rapid global heating is dramatically altering the planet we live on and we don't have a spare. If there's one thing 1934 should always remind us about, though, it is the consequences of not looking after our home. That's what the history books will recall about the 1930s on the prairie-lands.
Last updated on 9 June 2024 by John Mason. View Archives
LTO @75,
The graphics in the OP show the distribution of "global temperature" for 1934 & 2016, but not the actual "global temperature". You appear to be disputing the method by which that distribution can be extended to places that have no measured temperatures.
Assuming you are happier with 250km smoothing (as opposed to the more normal 1,200km), here are two graphics based on the same GISS data with such 250km smoothing.
The ERSST data has less gaps, even in 1934, probably because the weather stations sail around the oceans and are thus always filling in gaps. Or it may be that the methodology used in ERSST isn't impacted by the 250km smooting button at GISTEMP.
You will see in the top right corner of these graphics the resulting global temperature anomaly. Note that the 1934 global anomaly (1900-2000 base) is still negative. It would be a very brave man who suggested these graphics do not entirely support the message of the OP.
Oops!!
I've lost a graphic @76.
[DB] Try this:
Vs this:
GISS Map Graphic output is in the form of a temporary file and need to be hosted somewhere.
LTO,
Since this is a scientific blog you must support your claims with citaations to the relevant literature.
Please support your wild claim " It is an absolute nonsense to suggest we know anything about the temperature across the globe to this level of resolution". This data was hashed over in the 1070's and 1980's. In the 2010's the BEST group, specifically designed by deniers to find errors in the conclusions of GISS and HADCRU, found that the evalations of GISS and HADCRU were accurate. On what basis do you now claim errors?? You initially claimed that you had no relevant experience with this data. Now you claim that on your own authority all scientists are wrong. That is a very weak argument.
Your second wild claim " I don't believe that the 1,200 km radius is valid for temperature anomalies" was also resolved in the 1970's. GISS went with 1200 km and HADCRU with 250 km. Data accrued since then has shown that HADCRU is less accurate than GISS. On what basis do you challenge this scientific conclusion? It appears that you are again citing your experience without reviewing the data.
I look forward to your citation of peer reviewed data to suppoprt your wild claims. In the absence of peer reviewed citations you must at least read the relevant literature before you make the claim that scientists have incorrectly concluded 1200 km is accurate. Since the deniers who funded BEST were unable to find any errors I expect you will be hard pressed to find any.
Hi Michael
I’m afraid you’re confusing different things. Please try to stay on topic. Merely posting irrelevant guff in an offensive manner trying to shift the burden of proof isn’t conducive to a productive conversation. If you feel a need to caricature someone’s arguments in order to address them, it just comes across as looking like you don’t know what you’re talking about and are trying to mask this. I’m certain that isn’t the case, and if you feel like addressing my actual comments using science rather than bravado and appeals to authority I’d be really interested to hear your thoughts.
Hi MA
Could you use imgur.com or similar to post links to your graphs? I can only see a 2016 one.
I’ve had a quick look at the ERSST data set available here https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5 A sentence jumps out:
”Note that the data are more reliable after the 1940s”
Again, this goes to my point: the chart suggests we have spatial resolution to map the temperature anomaly in 1934 against an average that runs from 1900 to 1999 in areas of the world, including the antarctic, where we had virtually if not literally zero measurements within 1200 km until well after 1934. This is a nonsense.
Based on the rather rude ideological Responses I’ve had from some people, I suppose it’s necessary to state explicitly that my criticism here is specifically about that chart giving a totally misleading impression of the information available in 1934, particularly in the Southern Hemisphere. Not a general global warming criticism. If your model pops out an anomaly for a region of -2 against a heavily derived baseline for a region in which there have been zero reliable measurements, it simply fails the red face test (unless you acknowledge the error is probably something like 10, rendering those parts of the chart worthless).
[DB] Please revisit MA's comment here to see both graphics in question.
Off-topic snipped.
LTO,
I have already provided you a peer reviewed reference to support the 1200 km measurement when I cited Hansen 2006 in post 67. I provided you data from BEST showing their numbers cover 80% of the globe in 1934. It is not my problem if you do not read the citations you are given. You need to cite data to support your wild claims.
Your argument is an argument from incredulity. If you wish to stand on that basis you are welcome.
Michael, it appears you missed my responses to the references you cited at #69 and #70. They did not support your argument. If you think I misunderstood something please clarify. You're the one making wild claims, in my view. The issue may be that you're looking at things from a big picture perspective. I'm trying to actually understand the detail, which is where the devil always is. The temperature data is so incredibly derived, and yet never seems o be presented with error bars. This is an enormous issue when precision has massively increased in recent decades
MA: Thanks for thr graphs at 77. They seem really weird on two counts, but hopefully you can explain what I'm missing:
1. How is it possible that there are more blanked out areas in 1934 than 2016? The baseline is apparently being calculated as a 1900-1999 average, and so this implies there is baseline data to compare 2016 anomaly data against for which there was no actual data in 1934. But if there was no data in 1934, how could a baseline for the period 1900-1934 be calculated in the first place?
2. In the 1934 chart, I've drawn a couple of circles of radius roughly 1000km. Could you tell me how many temperature measurements there were within them in your dataset in 1934?
If I'm reading the 1934 chart correctly, it's asserting that 1934 was 0.1 C cooler than the 1900-2000 baseline, but with no data for roughly 15% of the globe. What is the error on this 0.1C figure?
Thanks!
Link to chart for Q2 above here: https://imgur.com/a/Df4UrT6
LTO @83,
(1) The usual advice on choice of anomaly period (often ignored) is to use the latest 3 complete decades as the anomaly base. This is because these data will be more accurate than that of earlier periods.
The choice of the 1900-2000 period as an anomaly base was because I set the GISS mapping-engine to that base, there having been talk up-thread of 1934 being below the 29th century average. (This indeed appears to, be tha case.)
To provide an anomaly when any data is missing requires some form of in-filling. With a 1900-2000 base period there will be a lot of in-filling as there is a lot of blank spaces for the early years of this period.
(2) Concerning the number of temperature measurements within your circles:-
The answer would be "lots." The data is from ICOADS who reckon to something like 25% ocean coverage in 1934. So there are plenty of gaps. (But do note it depends on wht you call a gap. The [almost] million 1934 measurements may provide 25% cover but they are but point-measurements in oceans which stretch 361 million sq km.) Yet the areas you circle would have been well covered having had pleanty of whaling ships traversing them in 1934.
You also ask about confidence intervals for global temperature records in years-gone-by. Perhaps the quickest way to find such data is Berkeley Earth. They put the 1934 global anomaly in the range -0.120ºC to -0.246ºC (95%CI) and 2016 +0.905ºC to +0.993ºC, this with a 1951-80 anomaly base. Subtract 0.066ºC for a 1900-2000 base.
LTO - you seem to be trying to assert that globally,1934 might have hotter than now and doing that by inferring that coverage bias is higher than calculations from comparing spatially separated stations indicate.
Would you still doubt that it is hotter now than 1934 if you looked at a temperature series that was constructed only from stations operating in 1934? (ie global temperature is being measured on the same basis).
Infilling. How could you gain confidence that infilling was doing a good job in sparse stations? How about estimating the value of a station only from those around it and comparing with actual value? This is how the error estimates are built. Does it seem a reasonable assumption that the observed variability in SSTs from modern satellite era can be used as basis for estimating the underlying variability in SST in the past as well?
Hi MA, thanks for the response.
1. Could you please elaborate on what this "in-filling" is?
2. With respect, 'lots' isn't a great answer. 25% coverage means no coverage for 75% of the ocean, and yet the 1934 chart shows anomaly readings for roughly 90% of the ocean. Are you saying that the vast majority of the ocean data in the chart is a result of this 'in-filling'. This isn't even getting into issues of reliability of data and calculation of anomalies when a given ship instrument probably never measured precisely the same place at the same time twice.
3. Yes the Berkeley earth data seem to be one of the few sources that show their errors loud and proud, and as I understand it they use a wide selection of proxy data and acrual measurements to arrive at their estimates. I haven't disputed whether or not 1934 was a cold year globally, but I'm rather saying the chart at the start of this article is massively misleadingp and something of a work of fiction, pretending we know far more about thr global spatial temperature anomalies and with more certainty than we actually do. It raises alarm bells.
You work on basis that "if you dont have thermometer there, you dont know anything", but as the methodology papers show, that is not the case. If this was so then couldnt reconstruct the temperature from just 60 stations, (massively less than 25% coverage). but as I pointed out earlier, you can.
I wasnt aware the BEST used proxy data. Can you elaborate? I dont see it in a quick scan of methodology report.
I found error estimates for other series in a few seconds of search. Which do you want to know?
The chart is only "a work of fiction" if you discount all the work on estimates of error in global temperature series. The producers of data series publish papers on the methodology which discuss the issues and errors. For you to ignore these and declare the series as "fiction" means the burden of proof does move to you. They have peer-reviewed papers justifying their series. If want to cast doubt, then you need to show us what is wrong with their analysis.
Hi Scadden
In #86 you assert that I’m trying to assert something that I never have. It’s a weird thing to do. Please just focus on my actual criticisms rather than invent new ones on my behalf.
In #87 you assert the basis I apparently work on, which is again incorrect. You either don’t understand the “60 stations” analysis (it’s not actually 60, but that’s not really the point. I address this in #75), or you don’t understand what my criticism is. Read my actual criticisms rather than leaping to conclusions about what you imagine them to be.
Re Berkeley earth, yes you’re right re proxy data. I meant to make the point that it is land only, and the discussion here is largely about the ocean anomaly ‘data’ shown in the 1934 chart at the top of This page.
ITO,
Reviewing your posts at 69 and 70 I see no links to any peer reviewed data (and no data analysis at all). I see only a rambling account of your uninformed opinion. You provide no statistical evidence (or mention of statistical calculations) to support your wild claims that the temperature record is not supported by enough data.
We need to review the rules. This is a scientific web site. You must produce data to support your wild claims. I have produced peer reviewed articles to support my claims. You have provided only your opinion of those articles. The burden is on you to provide data to support your claims. If you do not like the articles I cite it is your responsibility to find articles that detail their mistakes or provide detailed calculations that they are incorrect. Simply stating your unsupported opinion as a random guy on the internet who claimed at the start to not beingvery informed does not carry any weight.
Do your homework and read the literature. If you want to know all the details they are published on the internet. Find them yourself. It is not our responsibility to find all the obscure details that you are interested in.
You have not demonstrated that you know what a temperature anomaly is. That knowledge is crucial to our discusion. As for your unsupported claim that GISS cannot estimate the anomaly over a radius of 1200 km BEST says:"the correlation structure [of the temperature field] is
substantial out to a distance of ~1000 km, and non-trivial to ~2000 km from each site". BEST uses a different averaging technique than GISS uses. When two different groups using different mathmatical techniques agree that the data can be averaged over long distances you need to provide more than "I don't believe it". Knowing what an anomaly is would help you understand.
I suggest you restart your discussion and try to learn the basic teminology before you try to reanalyze the data. You might benifit from reading some of the old posts at Open Mind although you will have to look way back in his blog to find basic discussion of the temperature record. Realclimate also has good technical discussions of the temperature record in their library.
You have demonstrated an extraordinary knowledge of denier references to the temperature record. Please link the web site that you are using to inform yourself so that we can see the original argument. It probably has already been debunked and we can simply link to that debunk.
My apologies. I was really guessing and trying to understand what your beef was. Can perhaps we clarify that?
I am assuming your beef is with Fig 1. I understand that you are claiming it to be work of fiction because you believe ocean temperature anomaly, particularly in southern hemisphere was unknowable in 1934. (I believe NASA would assert that this is best estimate available from available data and reanalysis methods but they would also acknowledge large error bars). If I have misunderstood you, then please be clearer.
My point with the 60 station analysis, is that is demonstrates how well methods that perforce assume very large spatial correlation of anomalies work in practise.
My primary source for how SST derived is Huang et al 2016 but you have chase down the older papers (particular Smith and Reynolds contributions) for errors and validation studies. I do not believe the error estimates for SST in 1900-1941, southern regions (as high as 0.9C) invalidate the figure 1 but maybe I have again misunderstood you.
Hi Scadden
Accepted! :) My beef is that the 1934 chart gives the impression we know things about the global distribution of temp anomalies that we simply don’t. I consider it to be extremely misleading, even if the point of this article (1934 was only hot in localised areas) is true. Generally, I‘m finding there is a real lack of acknowledgement and proper treatment of uncertainties. Unfounded certainty raises red flags, and is unscientific.
If you’ve got a solid background in experimental science you’ll know that the vast majority of scientists (particularly those in softer fields) don’t really understand statistics and treatment of errors. It’s not a trivial point, nor is validation of data. As we get more And more measurement data, it becomes less and less reasonable to rely on old analyses of validation and associated errors. As I understand it Berkeley Earth was set up precisely because of myriad errors in climate data analysis (even if the eventual conclusions on land temperature changes were in line with prior analysis).
To illustrate what I mean, see the annotated image here. https://imgur.com/a/TkeiRrt There are ‘features’ in areas where there was probably no data collected in 1934, meaning that what we’re looking at in the areas I’ve highlighted is a largely imaginary baseline (I.e. using infilled data modelling for much of the 20th century) overlaid with imaginary 1934 data. If you look at the areas I’ve highlighted in the 2016 data, you can see there are real features that you’d only identify from having actual measurements. No such measurements exist in 1934, and so the model just assumes everything is cold, and makes up some features to give the impression of being realistic. Do you see my point?
Re the SST data, I address the Huang et al data set in #80 above - ERSST explicitly acknowledge on their website unreliability of data from the 1940s and earlier. I must say I’m a bit confused now about whether the chart is supposed to be sea surface temperatures or air temperatures. Obviously the two are linked, but they’re not the same thing.
Hi Michael
I don’t think you’re a serious part of the discussion here. My post at #69 specifically discusses data from Hansen’s 1987 paper, and overlays those figures against the 1934 chart at the top of this article to illustrate my point, which you haven’t addressed. Your asserting that that isn’t there makes you look like a fool. I’m relatively sure that isn’t actually the case, although the uncertainty in that belief is increasing rapidly. Your only purpose here appears to be to insult me, as opposed to explaining concepts or arguing points of view. I’m learning a huge amount about this topic and the myriad complexities from the discussions I’m having on this site, but not from you. Please either change your tone or stop harassing me.
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LTO @92/93,
Perhaps the difficulty you have with your "beef" is that it has a great similarity to that more commonly persented by folk who are in denial about AGW and who argue robustly against the very evidence itself. Given that similarity, you do need to be on the very top of your game to differentiate yourself from such contrarians. So not grasping that GISS LOTI is a combination of surface air temperature (GHCN) surface sea temperature (ERSST) is a rather profound omission. I would also add that the lack of patience with those comments challenging you is also something of a contrarian characteristic.
Picking up on your assertion that the ERSSTv5 info web page says that "explicitly acknowledge(s) on their website unreliability of data from the 1940s and earlier"; this is incorrect. The statement made is "Note that the data are more reliable after the 1940s" and this refers to both land and ocean data. What is 'reliable' depends on your purpose. Note the description following that statement. So if that is to compare the ERSST 2° × 2° grided data cell-by-cell (2° of latitude is 210km), then 'reliability' pre-1950 may be more of an issue for you.
If you have specific concerns with 'uncertainty' regarding these data, it would be good if you set them out. And by that, I mean describing them properly rather than presenting accusative statements such as "the vast majority of scientists ... don’t really understand statistics and treatment of errors. It’s not a trivial point..." or suggesting there is "imaginary 1934 data ... No such measurements exist in 1934" being presented as real. If your "beef" were to rattle out levels of accuracy in temperature records, we wouldn't expect such language.
LTO , my comment is a general one :-
Granted that our knowledge of localized climatic conditions becomes increasingly imperfect as we look backward through the past century (and indeed, past millennium) . . . is there any substantive conclusion (from that truism) which you wish to present to the readers of this thread?
You make the valid point that there should be better understanding of "statistics and treatment of errors". But (so far) you have not drawn any substantive conclusion (from that point) regarding the science of modern climate. Are you trying to show that the mainstream/consensus assessment of AGW is wrong in a major way? And if so . . . how is it wrong?
You yourself have indicated ( in #87 , though in a passive way ) that the contrarian scientists who undertook the BEST project did end up in confirming the mainstream science. Which is also confirmed by the simple physical evidence of melting ice / rising sea level / oceanic acidification / species migration / etcetera.
And as for "statistics and treament of errors" . . . none of us should forget that the methodology of statistics has the primary purpose of illuminating/assessing scientific "fact" (or more grandly, "truth"). I am sure you would like to agree that statistics ought not to be used to obfuscate, or to persuade the reader toward falsehoods & pseudo-science. Of course.
Hi MA
You may notice that ‘beef’ was first used in #91, and not by me.
I’ve set out my concerns now a number of times. I asked you some questions in #87 you haven’t answered. If you don’t know the answers, it‘s fine to admit it. My purpose here isn’t to trick anyone, just to increase my understanding of the relative uncertainties. There are indeed many profound omissions in my knowledge on this topic, which is precisely why I’m here. I’ve never pretended otherwise. I’d add with certainty that there are many profound omissions in the knowledge of people who have made up their minds that climate change will destroy the world in 12 years, as well.
There’s an irony here that a site called sceptical science treats questions about extremely complicated data manipulation with dismissive rudeness. You haven’t done this - your answers have been exceptionally informative - but the same can’t be said of some of your colleagues. It isn’t my job to ‘distinguish’ myself from different people asking different questions, although frankly I’d have thought that the ‘asking different questions’ element would do this.
Your claim that the comment about ERSST data before 1950 being unreliable (which I quoted in full in #80) is referring to both land and sea data isn’t apparent at all, and the page appears to be talking solely about The Extended Reconstructed Sea Surface Temperature (ERSST) dataset. https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5 It may be another profound omission of mine to think that this data set is just, you know, sea surface temperatures, but I suspect I’m on more solid footing here. I’m not sure what the relevance of the point is, either way, as it doesn’t move anything forward from my comments at #92
As to the point about statistical error analysis, I suspect you’re being disingenuous. If not, read this and prepare to be amazed: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1182327/
[DB] Inflammatory and off-topic snipped.
LTO @96 ,
further to my points @95 , have you come to any substantive useful conclusions about AGW and mainstream climate science?
Thus far, your posts have been remarkably discursive and deficient in critical thinking.
Validation and errors have been re-examined a number of times, as the explicitly stated in the Huang 2016 method paper and easily followed in the bibliography. As a side note BEST was set up because of perceived errors in the temperature record. They tried a new methodology (always good) but ended up discovering that that existing methodology worked after all. Perception is not reality, but BEST credit, this is science and skepticism working as it should.
What is peeving for me and other commentators is that your insistence "we know things about the global distribution of temp anomalies that we simply don’t" Uncertainty and large error bars are not the same as "dont know". Huang comments on pre-1940 data are more deeply discussed in the methods and earlier papers. 1940 is when change in measurement methodology occurred as well as issues with coverage. These all combine to uncertainties of up to 0.9C in lower southern hemisphere oceans. Double that if you like and the difference between 1934 and now is still pretty much as depicted by the figure.
Sparcity of measurement does not translate into unknowable. You can use modern satellite data to show anomalies are spatially correlated over large distance and estimate errors in unsampled areas. You dont appear to have read how the anomaly baselines are actually calculated. Try Smith and Reynolds 2003 and note especially the discussion about high and low frequency distributions. And, yes, weather models are useful. Humidity measurements made down wind constrain what temperatures upwind can be etc etc. And, no you dont have to take it on faith. Reanalysis models do extensive validation otherwise noone would trust them.
Remarkable that someone considered something in my post at #96 to be inflammatory and yet lets eclectic spew insult after insult cf #97.
Scadden: you're persisting in refusing to engage on the points I'm actually making about the 1934 chart. You say that uncertainty and large error bars aren't thr same as don't know, but this depends on what the question is, and what you're trying to do with the data. The 1934 chart doesn't present any error bars, uncertainties or indications of statistical significance at all nor acknowledge the massive spread in uncertainties across different parts of the globe. The error is not homogeneous. This, as I've been repeatedly saying, is incredibly misleading as it leads people to think we know far more than we actually do.
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LTO @96.
Firstly, my apologies for the "... and this refers to both land and ocean data" within my comment @94. This was part of a rather-too-long passage that was cut, and should have been cut in its entirety. (This cut passage concerned the "reliability" comment on the ERSST info web-page being part of the caption for a missing graphic.)
Secondly, looking at your many comments down this thread, you "beef" is that you consider it inappropriate to in-fill temperatures on an anomaly map. You term this in-fill for the 1934 map in the OP (actually a 5-year period centred on 1934) "exceptionally misleading," "massively misleading and something of a work of fiction," "fanciful" and suggest it is unjustifiable. You appear to expect coverage to be at least ten stations per ~200km square. And despite the long-long interchange since #59 up-thread, I don't see any movement from that position.
You appear to want folk here to furnish you with station-number/measurement-numbers for some remote part of the world at some time in the past; this information to demonstrate that you are justified in branding the anomay map in the OP "exceptionally misleading." But such a conclusion ignores the ability of folk to produce such anomaly maps that remain remarkably accurate with far fewer data. I think you are less after understanding (which you have said is your purpose) and more are looking for a research assistant to furnish you with ammunition for your erroneous "beef."
Thirdly, I am aware that you asked a question @87. Demanding an answer isn't a good way of getting an answer. Besides, I am not entirely sure an answer would be helpful to your understanding.
So perhaps there is need to restructure your 'enquiry' here. So far, it is producing a lot of words and with very little coherence.