GHCN V4 warming

Reblogged from Clive Best:

In this post I investigate what has changed in global temperatures moving from GHCN-V3 to GHCN-V4, and in particular why V4 gives higher temperatures than V3  after 2000.

1 V4-V3-anomalies-768x452

Whenever a new temperature series is released it inevitably shows an increase in recent warming, forever edging closer to  CMIP5 models.   The Hiatus in warming as reported in AR5,  has now completely vanished following regular “updates” to the HadCRUT4 temperatures since 2012.  Simultaneously model predictions have been edging downwards through a process of “blending” them to better fit the data. Ocean surface temperature data are now joining in to play their part in this warming process. The new HADSST4 corrections produces ~0.1C more warming than HADSST3, and the main reason for this is simply a change in the definition of the measurement depth of floating buoys. No doubt a new HadCRUT5 is now in the pipeline to complete the job. Of course nature itself doesn’t care less about how we measure the global temperature,  and the climate remains what it is. It is just the ‘interpretation’ of measurement data that is changing with time and this process seems to always increase recent temperatures. The world is warming by 10ths of a degree overnight as each new iteration is published. Now I have discovered that the latest GHCN V4  station data is continuing this trend as identified in the previous post. I have looked more deeply into why.

GHCNV4 has far more stations (27410) than V3 (7280) but turns out to be a completely new independent dataset. It is not an evolution of V3 even though it is called V4. GHCNV4 is 85% based on GHCN-Daily which is an NCDC archive of daily weather station records from around the world. V4 has no direct ancestry to V3 at all. Even the station ID numbering has been radically changed from that used in V3, making it almost impossible to track down any changes in station measurement data between V3 and V4. Despite that, I decided  to dig down a bit further.

About a year ago I actually studied GHCN-Daily using a 3D icosahedral grid to integrate the daily anomalies into annual anomalies.  In the end I got almost exactly the same result as CRUTEM4 for recent years after 1950, which  also agreed with the then GCHN V3. That implies that the data were then aligned with the results of both CRU and V3C. So something else has changed when moving data from GHCN-Daily to GHCN-V4.

2 Volcanoes-768x344

So how is it possible that now V4 shows significantly more warming than V3 after 2002, when a year ago GHCN-Daily did not? Have the underlying station data been “corrected” yet again since V3C? To investigate this I used a convoluted method to identify only the V3 stations buried inside the V4 inventory by using their WMO IDs mapped through the GHCN-Daily directory. This procedure identified about half of  the 7280 versions of V3 stations, bearing in mind that V4 contains 24710 stations! The other half are not primary WMO stations. I then used my standard Spherical Triangulation algorithm to calculate annual global temperatures based only on these 3500 V4 versions of  V3 stations. If the underlying station temperatures were  the same as those in V3C then they should produce the same results as those from V3C.  Do they?

The results  are shown below.

3 V4withV3sts-768x458

Perhaps even more striking is the monthly agreement between the full V4C result and the V4 result restricted to 3500 V3 stations. The agreement is remarkably good. It should be compared to the V4 versus V3 comparison in the previous post.

4 V4V3-monthy-768x431

So the answer to the question is no they do not agree with V3.  This must mean that the V4 versions of V3 station data are indeed different to those in the original V3 station data. So it is these changes that have caused the apparent increase in warming since 2004. The graphs above  show  that they are almost identical to the full  station results from V4C. It is also not true that somehow V4  has greater coverage in the Arctic and this can explain the increased warming over V3. The reason is simply that the underlying data have somehow been changed.

You get a different result from V4 and V3 using the same station data.

Whatever happened to the Global Warming Hiatus?

Reblogged from Clive Best:

The last IPCC assessment in 2013 showed a clear pause in global warming lasting 16 years from  1998 to 2012 – the notorious hiatus. As a direct consequence of this  AR5 estimates of climate sensitivity were reduced and CMIP5 models appeared to clearly overestimate trends. Following the first release of HadCRUT4 in 2014  the ‘headline’ then was that 2005 and 2010 were marginally warmer than 1998. This was the first dent in removing the hiatus. Since then each new version of H4 has showed further incremental warming trends, such that by 2019 the hiatus has now completely vanished. Anyone mentioning it today is likely to be ridiculed by the climate science community. So how did this reversal happen within just 5 years? I decided to find out exactly why the post 1998 temperature record changed so dramatically in such a short period of time.

In what follows I always use the same algorithm as CRU for the station data and then blend that with the Hadley SST data. I have checked that I can reproduce exactly the latest HadCRUT4.6 results based on the current 7820 stations from CRU merged with  HadSST3. Back in 2012 I downloaded the original station data from CRU –  CRUTEM3. I have also downloaded the latest CRUTEM4 station data.

Figure 1 compares the latest HadCRUT4.6 results with the last version of HadCRUT3.

Fig1-768x452

I had assumed that the reason for the apparent trend change was because CRUTEM4 had added many new weather stations in the Arctic (removing some in S.America as well), while additionally the SST data had also been updated (HadSST2 moved to HADSST3). However, as I show below, my assumption simply isn’t true.

To investigate I recalculated a ‘modern’ version of HadCRUT3 by using only the original 4100 stations (used by CRUTEM3) from CRUTEM4 station data.  The list of these stations are defined here. I then merged these with  both the older HadSST2 and HADSST3 to derive annual global temperature anomalies. Figure 2 shows the result. I get almost exactly the same values as the full 7820 stations in HadCRUT4. It certainly does not reproduce HadCRUT3 !

Fig2-768x452

This result provides two conclusions.

  1. Modern CRUTEM3 stations give a different result to the original CRUTEM3 stations.
  2. SST data is not responsible  for the difference between HadCRUT4 and HadCRUT3

To confirm point 1) I used exactly the same code to regenerate HadCRUT3 temperature series using the original CRUTEM3 station data as opposed to the ‘modern’ values based on CRUTEM4.

Fig3-768x452

The original CRUTEM3 station data I had previously downloaded in 2012. These are combined with HADSST2 data. Now we see that  the agreement with the H3 annual temperatures is very good, and indeed reproduces the hiatus.

So the conclusion is very simple. The monthly temperature values in over 4000 CRUTEM3 stations have all been continuously changed, and it is these changes alone that have resulted in transforming the 16 year long hiatus in global warming into a rising temperature trend. Furthermore all these updates have only affected temperatures AFTER 1998! Temperatures before 1998 have hardly changed at all, which is the second requirement needed to eliminate the hiatus.

P.S. I am sure there are excellent arguments as to why pair-wise ‘homogenisation’ is wonderful but why then does it only affect data after 1998 ?

GHCN v3.3 vs v4 Anomaly Graphs – Europe

From Musings from the Chiefio:

[Bottom Line Up Front:  “…there is a “Tailoring” operation going on. The changes are NOT just a little fix up here and a correction there. It looks to me like it has direction and purpose. Cool the Baseline Period. Cool warm past periods. Warm the recent data UNLESS it is too high in the last 2 decades, then you cool them so the nearest data can look warmer in comparison. Stamp out cold periods in the middle. Remove cool periods recently if not already suppressed. The question that remains for me is just: “Is that an accident from ignoring the effects of Instrument Change, or a deliberate planned act?”]

The Climates of Europe

Europe ranges from the frozen to the deserts of the Middle East. Most of the countries are geographically small, but terrain can vary dramatically in short distances. Just look at the change from Switzerland to the Mediterranean coast of northern Italy. I’ll try to take things in a reasonable order that allows for better comparing one set of graphs to a nearby country. For much of Europe, water dominates, as there are coastlines on the Atlantic, North Sea, Norwegian Sea / Arctic, Black Sea, Baltic Sea, Mediterranean, etc. etc. For other bits, mountains and inland conditions dominate. But being compact, the larger external drivers tend to be the same over many neighboring countries and their graphs ought to be comparable. Ireland and the UK, or the Baltic States for example.

Here’s the Koopen Climate map from the wiki:

Euope Koppen Climaate Map

Euope Koppen Climaate Map

You will need to look at the Middle East for some of the countries included in “Europe” from the GHCN point of view, so here’s that climate map:

Middle East Koppen Climate Map

Middle East Koppen Climate Map

As GHCN v3.3 divided Russia and Kazakhstan into a European part and an Asian part, but v4 does not, I’ve moved the European part of the data into Asia for the comparison graphs, so those countries are in the Asian graph posting. For our purposes, Europe stops at the Russian Border, not the Urals.

I’ve done a general grouping of countries into bands that more or less follow the map of climate zones. I was not rigorous about it. So some countries might be more properly compared to a different set of nearby countries. Let the map be your guide for your own comparisons.

In general, I start with the Middle East / bit of Mediterranean band, then work along the Mediterranean to the Atlantic coastal countries, and back across the more inland nations, finally turning to those with coastlines on the Baltic Sea and Arctic Ocean. Moving from hotter to colder with the volatile inland areas in the middle.

So to some extent my groupings were just to make the process more orderly rather than strict climate matches. But it ought to put comparable places next to similar neighbors most of the time.

I’ve made a quick first comment on each of these countries. Of neccesity, given the number, these are at best a cursory look and some sniditude sprinkled in. This group desperately needs some “Crowd Sourced” scrutiny of the graphs. I’ve flagged a couple that are particularly dodgy, and noted The Usual “drop the baseline 1/2 C raise the present about 1/2 C” and the frequent “The Jump” about 1990-2000 (that likely correlates with MMTS rollouts, IMHO… but needs a good “Dig Here!” for each country).

One other theme is the frequent 1C to 2.5 C range of “change to history”. IF our v3.3 data were really that crappy in 2015, what evidence proves it is any less crappy now? How do you find 1/2 C of “Global Warming” from CO2 inside 2 C of “random error” and maybe another 1 C of “thermometer changed; moved near buildings for the wire”?

It just looks to me like the data are crap and being “massaged”, with each release, to fit a narrative. That’s my opinion; I hope you will look at the graphs and form your own.

With that, here’s Europe:

The Countries

Here are the countries of Europe per GHCN. You will note many of the abbreviations do not match the names. That is due to the names changing over time, different native language spelling, etc. Also note that R! and K!, the European parts of Russia and Kazakhstan in GHCN v3.3 are not graphed here, so we subtract 2 from the 57 total lines to get 55 “countries” for graphing:

MariaDB [temps]> source bin/Europe.sql
+------+-------+--------+----------------------------+
| cnum | abrev | region | cname                      |
+------+-------+--------+----------------------------+
| 601  | AL    | 6      | Albania                    |
| 602  | AM    | 6      | Armenia                    |
| 603  | AU    | 6      | Austria                    |
| 604  | AJ    | 6      | Azerbaijan                 |
| 605  | BO    | 6      | Belarus                    |
| 606  | BE    | 6      | Belgium                    |
| 607  | BK    | 6      | Bosnia and Herzegovina     |
| 608  | BU    | 6      | Bulgaria                   |
| 609  | HR    | 6      | Croatia                    |
| 610  | CY    | 6      | Cyprus                     |
| 611  | EZ    | 6      | Czech Republic             |
| 612  | DA    | 6      | Denmark                    |
| 613  | EN    | 6      | Estonia                    |
| 614  | FI    | 6      | Finland                    |
| 615  | FR    | 6      | France                     |
| 616  | GG    | 6      | Georgia                    |
| 617  | GM    | 6      | Germany                    |
| 653  | GI    | 6      | Gibraltar [United Kingdom] |
| 618  | GR    | 6      | Greece                     |
| 699  | GK    | 6      | Guernsey                   |
| 619  | HU    | 6      | Hungary                    |
| 620  | IC    | 6      | Iceland                    |
| 621  | EI    | 6      | Ireland                    |
| 698  | IM    | 6      | Isle of Man                |
| 622  | IS    | 6      | Israel                     |
| 623  | IT    | 6      | Italy                      |
| 697  | JN    | 6      | Jan Mayen [Norway]         |
| 693  | JE    | 6      | Jersey                     |
| 624  | JO    | 6      | Jordan                     |
| 625  | K!    | 6      | Kazakhstan E               |
| 626  | LG    | 6      | Latvia                     |
| 627  | LE    | 6      | Lebanon                    |
| 696  | LS    | 6      | Liechtenstein              |
| 628  | LH    | 6      | Lithuania                  |
| 629  | LU    | 6      | Luxembourg                 |
| 648  | MK    | 6      | Macedonia                  |
| 630  | MT    | 6      | Malta                      |
| 631  | MD    | 6      | Moldova                    |
| 632  | MJ    | 6      | Montenegro                 |
| 695  | MH    | 6      | Montserrat                 |
| 633  | NL    | 6      | Netherlands                |
| 634  | NO    | 6      | Norway                     |
| 635  | PL    | 6      | Poland                     |
| 636  | PO    | 6      | Portugal                   |
| 637  | RO    | 6      | Romania                    |
| 638  | R!    | 6      | Russia E                   |
| 639  | RI    | 6      | Serbia                     |
| 641  | LO    | 6      | Slovakia                   |
| 642  | SI    | 6      | Slovenia                   |
| 643  | SP    | 6      | Spain                      |
| 694  | SV    | 6      | Svalbard [Norway]          |
| 645  | SW    | 6      | Sweden                     |
| 646  | SZ    | 6      | Switzerland                |
| 647  | SY    | 6      | Syria                      |
| 649  | TU    | 6      | Turkey                     |
| 650  | UP    | 6      | Ukraine                    |
| 651  | UK    | 6      | United Kingdom             |
+------+-------+--------+----------------------------+
57 rows in set (0.01 sec)

55 is a LOT of countries and that’s 110 graphs. That’s one long posting. Due to that, I’m going to divide Europe into several parts. Middle East near the Mediterranean and including bits in the Cacasus, then across the Mediterranean coastal areas, at France & Spain we transition to countries that are also Atlantic Coastal, pick up Portugal, the UK, and others before heading back inland across the inland countries. Finally making the turn up the Baltics and into the Nordic countries.

This may not end up an equal number of nations in each grouping, but it will tend to group together those nations with similar environments.

Middle East & Black Sea:

IS Israel
JO Jordan
LE Lebanon
SY Syria
CY Cyprus
TU Turkey
AM Armenia
AJ Azerbaijan
GG Georgia

Mediterranean:

GR Greece
MK Macedonia
AL Albania
MJ Montenegro
BK Bosnia and Herzegovina
HR Croatia
SI Slovenia
IT Italy
MT Malta 
SP Spain
GI Gibraltar
PO Portugal

Atlantic: 

EI Ireland
IM Isle of Mann
GK Guernsey 
JE Jersey
UK United Kingdom
FR France
NL Netherlands
BE Belgium
LU Luxembourg

MH Montserrat

Inland:  

GM Germany
PL Poland
EZ Czech Republic
LO Slovakia
AU Austria
LS Liechtenstein
SZ Switzerland
HU Hungary
RI Serbia
BU Bulgaria
RO Romania
MD Moldova
UP Ukraine
BO Belarus

Nordic & Baltic: 

LH Lithuania
LG Latvia
EN Estonia
FI Finland
SW Sweden
DA Denmark
NO Norway
JN Jan Mayen [Norway]
SV Svalbard [Norway]
IC Iceland

Should you need to look up where one of these countries is located, here is the Political Map of Europe (click or open in a new tab to embiggen):

Political Map of Europe from mapofeurope.com

Political Map of Europe from mapofeurope.com

Do note that the political map of Europe is subject to rapid and unexpected changes and has been in such rapid flux since at least the Greeks and Persians “arguments” and well before the Roman Empire rearranged the whole thing (not to mention the Holy Roman Empire). Then there were the W.W.I and W.W.II changes, and most recently Russia trimming a bit off Ukraine. Just realize the data often dates from times when the country was a different country, different size, shape, and sometimes location. (Poland got shifted over about 300 km, at the end of W.W.II.) How these specific geographical changes are accounted for in the data is a minor “Dig Here!”. Just don’t expect this political map to be precise in 20 years. Or perhaps even next week… /snark;

The Graphs

For most countries there are two graphs. The second one typically shows the v3.3 “anomaly” average for a given country with one dot for each year of data, and the v4 anomaly average for the same country for the same years. You might expect them to mostly be the same, after all, it isn’t like we can go back to 1800s Spain and install some more thermometers. Yet they are typically different. This seems very odd to me as this is the “Un-adjusted” data set.

The GHCN documentation goes out of their way to state that the upstream national Bureau Of Meteorology might well do their own adjusting, but the changes seen across so many countries are so similar that some systematic changes must be happening after collection of the data. Is this a “Quality control” process? Is it mining historical records to collect missing data points? Is it just using more thermometers so “instrument change” artifacts show up? Is it a deliberate “Data Diddle”? Is it “homogenization” that is not being called an “adjustment”? Does it matter what is the cause?

The first graph in each set is just the difference between the v3.3 and v4 anomaly dots on the second graph. This makes it much easier to see the changes and any patterns in them. What you might expect to see would be a mostly straight line at zero as most historical data ought not change, Perhaps one or two years where a spot is off zero as some data were filled in, or an instrument was found to have errors in the reporting that were correctable (Say during an overlap of two instruments during an upgrade, and which instrument was used in the average was changed). You would especially expect that THE most recent data from our best and newest instruments would be most stable. That isn’t what you will actually see. Often, the most recent data changes the most. Odd that.

A note on the “Baseline Period”: NASA GISS uses 1950-1980 as a “baseline” for computing anomalies. Hadley uses 1960-1990. The creators of GHCN load up the data set with extra records for more instruments during that “Baseline Period” spanning about 1950 to 1990. It is my opinion that this biases the data. One simple example (that we saw in the Africa graphs) is that the more instruments you have, in more places, the harder it is to have an extreme event in the data. You might have a cold hail dump in a very small area, or an opening in cloud cover causing a warm spike, and get a couple of C of movement. But over a larger area, those small scale events tend to be averaged out. For this reason the early years of data often have a much wider range of the “anomaly” as there is only one instrument. As more records are made from more instruments, this range narrows. Now, what is truly odd, is that after the Baseline Period there is a large reduction in the number of instrument records used; yet the range continues to narrow for most countries. Something else is going on with the recent data.

To avoid those “Baseline Artifacts”, I compute these anomalies without a baseline. How? For each instrument record, for each month of the years, I add up all the data points and find the average. The instrument record is only compared with itself, and only within each individual month. Each monthly data point is then differenced with the average for that month for that instrument record to find the anomaly for that data point. For example, if the Rome Airport record in June is averaged and found to be (a hypothetical) 31 C and June of 1948 was found to be 32.4 C then the anomaly would be +1.4 C. Then all the anomalies for a given year inside a given country are averaged to make the ‘spot” on the anomaly graphs below.

In this way no “baseline” is needed. June in Rome has an average monthly temperature and it ought to be representative across all years. IF this June is warmer than the average of all past Junes, then it is a warm June. Where this “has issues” is that it will change the average as more data is added in a given instrument record. If, for example, the most recent years of v4 are all hotter, then the v4 average will be slightly hotter and then the “anomaly” of older data will be lower as the difference from a hotter average will be greater. Since v3.3 was in use through 2015, there are only 3 years additional data in v4 (out of a set that spans over 200 years) so this effect ought to be small. Should one wish to eliminate it entirely, the v4 data could be truncated in 2016. As my goal is to find and illuminate sources of change, not average them away and hide them, I prefer to emphasize what is happening in the data and make visible what otherwise might be hidden in the more traditional processes. Do note that even using a “baseline period”, in the example given, the present data would show as abnormally warm and the general distribution of the anomaly plots would be substantially the same

Part of my goal is to illuminate how just using “anomalies” does NOT fix issues of instrument change. The assertion is made that instrument changes do not matter as the data are used as anomalies. Well, here I’m using anomalies and instrument change DOES matter. It is my belief that it matters when using a baseline period as well (just harder to demonstrate).

So keep in mind that these graphs are for the purpose of discovering issues while the usual processing is for the purpose of removing issues (or covering them up). Different purposes require different approaches.

With that, we’ll start our “European” tour in the Middle East, and over toward countries of the Caucasus.

Middle East & Black Sea:

IS Israel

I’m going to describe some of the things we see in the graphs in more detail here. Later I’ll refer to them in a more shorthand way. So reading this description and a bit of study of this set of graphs is helpful for all the other comments.

Over 2 C range in the changes, the non-adjustment adjustments between v3.3 and v4. Here we see the (by now) classic pattern of cooling the past with most of the time before about 1940 dropped by 1/4 to 1/3 C, some as much as -1 C. Then the transition to warming the data at about 1980. Finally, in the 2000’s, we see something more like fine tuning. Raise a little here, take a tuck there, and you get a nice trend at the end out of otherwise more scattered data.

GHCN v3.3 vs v4  Israel Difference

Looking at the anomaly plot we see a few, by now classic, features. There’s a big “Dip” in the “baseline period” from 1950 to 1990 with very few hot years and a much narrower range of the data. The very first years range more broadly (likely due to just one instrument in the record). A line laid across at about the +1C level intersects similar anomalies in the pre-1940 period as in the post 2010 period. Part of what makes this interesting is that in the recent data for most of the GHCN there are far fewer thermometer records than in earlier years, so is the “hot now” just an artifact of returning to fewer instruments? A similar line at about -1C has most of the data riding just on top of it between about 1880 and 1990, with scattered outlier years 1/2 C to 1 C below it. Then “something happens”. Suddenly all the “low going anomaly” years never get below zero. Between 1900 and 1990, the range of the volatility of years is about 2 C (from +1c to -1C) yet after 2000 it is closer to 1 C or even less than that. Did yearly weather volatility really end? Or is some processing done to the data removing cold excursions? Were instruments in places prone to cold excursions dropped from the record?

We see that in the data for California. All four of the current stations in GHCN v3 were “on the beach”. One in San Francisco, 3 down near Los Angeles. It just is not possible to have a very cold excursion there. Gone are the data from the high cold snowy Sierra Nevada, the inland northern areas that freeze hard in a Canada Express. I suspect this happens around the world too, but have not mined the data to find out (yet). So an open “Dig Here!” is to find out just why the Israel data suddenly stops having any cold years. After all, we’ve recently had abnormal snow in the Middle East with “once in a lifetime” snowfall in some areas. You would expect that to show up as a cold year “anomaly”, yet it doesn’t. Why?

Looking at the red spots vs black we find them much colder in the deep past, typically moved above the black spots more recently. Then there is what looks like the reduction of range in v4 vs v3.3; where some spots further from the mass of the data get pulled closer. That’s a bit harder to just “eyeball” and so is an object for future statistical analysis. Is some “QA Process” being used to compress the accepted range of data?

Finally, I note one other thing I’ve seen a few times. THE most recent red dot is not far from zero. Hmmm… how can it be constantly accumulating heat when so many of the countries of the world are, right now, just about average? Yet the past got colder… Also, it is often the case that the data from the prior decade or two gets changed a LOT but the most recent year or two doesn’t. It is as though there is an attempt to reduce the “SCREAMING HOT!!!” claims of the mid-2000s so that the present data points looks hottest. We see some of that here with some of the post-2000 data points pulled down to below the +1 C line.

Overall, it just looks very “un-physical” the way the baseline period drops, then post-baseline has very narrow range and a huge “flip” upward (often narrowing to a point – I’ve taken to calling that a “Duck Tail” as it reminds me of one. Israel has a recent near zero data point that kind of mutes the effect, but it’s still visible). What would be expected is that the range of prior years ought to be preserved in the present, and the whole mass of data ought to “turn upward” if there were “Global Warming”. Essentially the parallelogram of data ought to be preserved, but bend. Instead we see a reshaping of the data from a 2 C wide “band” into a 1/2 C wide flip / spike; then the present data point stuck on the end. It just looks very very wrong and “manicured”.

Now, just for a moment, scroll down one country and look at the anomaly plot for Jordan. These two countries are almost on top of each other. At one point Israel is only about 10 miles wide, IIRC. They share a border at the Jordan River. You would expect their two anomaly graphs to be almost identical (perhaps with a bit more range for Jordan as it is a little more inland). Yet they are quite different. How does coastal Israel warm almost 2 C out of the “baseline period” while more inland Jordan only manages 1 C? Whatever is causing the changes is at odds with known geology, physics, and weather patterns. In general, comparing neighbor countries leads me to believe that it is instrument issues and siting issues “shaping the data” more than anything to do with CO2.

Why did CO2, constantly accumulating and with much more impact in the first bit of accumulation than in the latest (it has a decreasing effect from more additions as it has already done what it will do…), why did it “wait” from the onset of significant production in the 1930s all the way to about 1995 before having any effect in the Israeli data? Why is it that ONLY after the year 2000 does CO2 suddenly suppress cold years? IMHO, the answer is that “it doesn’t”. Something else is “shaping the data” and it isn’t CO2.

GHCN v3.3 vs v4  Israel Anomaly

What happened about 1990 to 2000 across all the various countries? There are a couple of highly likely suspects, IMHO. We generally converted from “Liquid in glass” thermometers to electronic MMTS instruments. These use electrical power and have a wire connecting them to a building. This means they often had to be closer to buildings than in the past. Concrete and tarmac are known to suppress cold anomalies as they soak up heat during the day and give it back at night. Furthermore, there is a strong bias toward using airport data (shown in the earlier v2 analysis) and airports changed from grass fields to minor asphalt runways to 10000 feet of wide concrete at Jet Ports over the years. Furthermore, we’ve had massive airport expansion with hectares of tarmac and concrete poured out for parking areas, taxiways, and more. Then, burning tons of kerosene per hour tends to keep the local air a bit warmer… As does removing all the snow for winter operations. That kind of thing matches the appearance of the data far better than does a decreasing effect with onset in about 1950 from accumulating CO2.

OK, this was the “deep dive” on reading a set of graphs. From here on out it will be much shorter notes. You know what to look for now and how to look for it, so I’m just going to toss in things that strike my fancy.

JO Jordan

Over a 2 C range of “changes”. Generally everything prior to 1985 cools, with the notable exception that the 1950s data go a bit nuts. Recent “best quality” data with 3/4 C range to the diddle factor.

GHCN v3.3 vs v4  Jordan Difference

Compared to Israel, Jordan looks a lot less “manicured”. Hardly any “Duck Tail” at all. Range of data stays around 2 C anomaly until after 2000. We again have the odd bit that the last dot is “near zero” and just prior data got changed.

Overall, not really seeing “warming” in Jordan.

GHCN v3.3 vs v4  Jordan Anomaly

LE Lebanon

Oh God is this one a mess. over 2 C range to the “changes”? Really? On this we base paranoid delusions about a 1/2 C of “Global Warming”?

At least the data cut off in 2000 for the comparison. (We get to just “eyeball” the v4 data in the second graph). Pretty much a mess, though, with a bit of overall warming of the deep past but between 1900 and 1980 a whole lot of “cooling the past”.

GHCN v3.3 vs v4  Lebanon Difference

It is quite possible that the recent anomaly data goes “off scale” of this graph. Generally, when data points plotted very near the upper bounds I’d replot with wider bounds and find more spots. This was enough of a mess I didn’t see the value.

We get “the usual” dip in the baseline then post about 1995 a sudden Jump Up of about 2 C, yet recent data points are nearer the zero anomaly line. So despite all that, no “Global Warming” in Lebanon, eh? So what happened to the data between 1990 and 2010? CO2 doesn’t act for only 2 decades, then stop.

GHCN v3.3 vs v4  Lebanon Anomaly

SY Syria

Pretty much bombed to rubble now, so recent data not good for much. How much did history change?

Some minor cooling of the past in the baseline period of about 1/4 C, a strange “dip” around 2000, then things go crazy with 2.5 C range of changes.

GHCN v3.3 vs v4  Syria Difference

A very sparse plot. Looks like v4 added some historical data pre-1950. We get “the usual” dip in the baseline period though more centered on 1970 – 1990. Present temperatures about zero anomaly. Lines at +1C and -1C pretty much bound the mass of the data with similar outliers over the line (modulo the drop-out in the baseline)… until 2000 when low going data are gone. We see this a lot.

GHCN v3.3 vs v4  Syria Anomaly

CY Cyprus

Island, surrounded by warm water. Ought not change much.

BUT, we get over 2 C range of changes of the past. Scattered all over the place. Then, post the dropout around 1990, all the data are warmed about 1/2 C.

GHCN v3.3 vs v4  Cyprus Difference

Looking at the anomaly plot, it is pretty much bounded by a line at +0.75C and -1C until 2000. Then the lower bound shifts up to about the zero line and we get some hot anomalies in the +1.75 range. What causes a “step function” like that? Instrument change does. CO2 not so much…

GHCN v3.3 vs v4  Cyprus Anomaly

TU Turkey

Turkey had complained that GHCN was only using the few thermometers that showed warming and ignoring the ones that showed cooling. Wonder if that “sensitized” folks to not fool with Turkey? Looks like some W.W.I data got adjusted.

This is more nearly what you would expect the difference graphs to look like. Almost all the data points at or near zero. A couple of places where some historical issue might have been found, and fixed (and that ought to be annotated in footnotes… but isn’t). I do find it odd that we’ve got an over 1 C change in the most recent data, though only one year.

GHCN v3.3 vs v4 Turkey Difference

The anomaly plot is more like I’d expect to see also. Range “about the same” over most of the graph. Lines about +1.25 C and -1.25 C contain most of the data points with similar “outlier” years over time. The only really odd bit is the way, again, cold excursions end in 2000 and we get a kind of short fat “duck tail” (maybe more like a goat tail 😉

GHCN v3.3 vs v4 Turkey Anomaly

AM Armenia

This kind of difference graph just shouts “data quality or data diddle” issues. You have a rather stochastic 1/2 C of change spread pretty much over all years. What on earth justifies that? Is it an artifact of adding in / changing what thermometers were in use? If so, how can we claim 1/2 C of “Global Warming” when it could just be instrument changes that are ignored (and happen in ALL the data)?

GHCN v3.3 vs v4  Armenia Difference

Not much to see here, really. A bit of general cooling of the past, but mostly just note that the range of “normal” is wider at about +1.75C and -2C. Recent data not outside that range significantly. Only really “odd bit” is (again) the loss of cold anomalies after 2000.

GHCN v3.3 vs v4  Armenia Anomaly

AJ Azerbaijan

Odd changes here. There’s a flat zero lead-in segment (what I’d expect to see in a lot more countries) then all the anomalies drop by 1/4C to 1/2 C. Mostly I’d suspect they added another instrument record in Azerbaijan and that induced some jitter in the yearly averages. At least, that’s the “Dig Here!” I’d look for first.

GHCN v3.3 vs v4  Azerbaijan Difference

Interesting shape to this anomaly plot. It looks like the “embarassing” pre-1880 data with a warm bit were dropped. A line at -1C has cold anomalies below it pretty much across the board. After 2000, there’s still a couple, but the space above the line has fewer between it and the zero line; so still some strong “thinning” of the cold anomalies going on. IF you include that 1875 data, a line at the +1 point shows not much changing at all until the post-2000 era when you get a few years jumping up by 2C to almost 3C of anomaly. Yet others at almost -3C anomaly. This graph likely ought to be redone with 4 / -4 range to assure there’s nothing further out.

GHCN v3.3 vs v4  Azerbaijan Anomaly

GG Georgia

How does it work that the most recent ‘best ever’ data has the most variation and change in the “unadjusted” data?

GHCN v3.3 vs v4  Georgia Difference

A line at +1C / -1C contains a pretty much rectangular mass of data with modest excursions, up until the post 2000 era. Then it pops up to +2C to +3C. Nothing below +1C. Wonder if they had an airport renovation? It certainly isn’t CO2 gradually accumulating over 1/2 century.

GHCN v3.3 vs v4  Georgia Anomaly

Mediterranean:

We now move to the Mediterranean coastline. You might argue with some of my choices about Atlantic vs Mediterranean, for places like France that are a bit of both. But the grouping of the graphs doesn’t prevent you scrolling down to look at and compare whatever you like. I’ve tried to group things that I think can be easily compared, closer to each other.

These places ought to be strongly water moderated and with very flat temperature anomaly plots. Their temperatures ought to track somewhat the local water temperatures. Cyprus, up above, could be compared here, too.

GR Greece

Interesting difference graph. before 1900 has a bit of a dip, after that a similar size rise until the 1950 start of the baseline period when it starts to dance around by about 1/2 C all over the place.

GHCN v3.3 vs v4  Greece Difference

Not much “Global Warming” visible in the anomalies for Greece. the range of 1.5 C in the early years narrows a bit by 1900 likely as more thermometers show up. It stays about between the +/-1 C lines until 1960 when we “take a dip” of about 1 C for the baseline tops, then post 2000 we get ‘the usual” loss of stuff below the -1/2 C anomaly point The most recent data point being below zero, “Global Warming Has Left the Parthenon!”… 😉 There’s a couple of high stragglers in the prior years of the 20-teens but not dramatically higher than the 1C normal range line and similar to the 1920’s data

Way to go Greece! Singlehandedly conquering “Global Warming”!

GHCN v3.3 vs v4  Greece Anomaly

MK Macedonia

Similar ‘near zero’ line of no changes in the deeper past, then 1/2 C of “jitter” in the “recent” data (up to about 1990). Wonder why v3.3 cut off in 1990?

GHCN v3.3 vs v4  Macedonia Difference

Ah, that explains it. It was cooling then…

So is that 1900s data to be believed? BOTH v3.3 and v4 keep it in, so I’d say so. It is nicely warmer than now…

A line at +1 C “takes a dip” in the 1970-1990 part of the baseline period, but both the 1930s and the recent period are about the same. No “Global Warming” here, either. Again, the latest data point is below zero, so cool in Macedonia. We do see a “thinning out” of low going anomalies after 2000, and if you removed the latest data point you would have a nice Duck Tail, but the only warming is statistical via averaging away the present cold data point into several just prior warm ones (just call it ‘weather’ and ignore it), and using the “dip” in the baseline period as, well, your baseline…

Compared in total, there is no warming. Just a cold dip in the “new little ice age” ’70s.

GHCN v3.3 vs v4  Macedonia Anomaly

AL Albania

In some ways not interesting. In others interesting in that this is how it ought to look (minus the hole of missing data…) with a very flat run of almost nothing changing up to 1980. Then there’s a little “tuck” in the baseline period of about 1/2 C but only for 1/2 decade of it. The big gap of 20 years is bit odd, though.

GHCN v3.3 vs v4  Albania Difference

So v4 adds some data prior to 1950 and puts some “in the gap”. OK. A line at about -0.8 C looks to connect the bottoms, with the most recent data right down there in the “We’re COLD not warming” end of things. A line at about +1 C connects the “new” data from 1940s with the data from about 2009 as both ride just on top of it.

Overall, it looks to me like, at best, there’s a cycle with peaks in 1945 and 2010 (so about a 65 year span) and at worst theres some data doctoring going on in the baseline period to make a bit of dip so ‘the present’ looks warm in comparison.

GHCN v3.3 vs v4  Albania Anomaly

MJ Montenegro

Oh Gawd! Cool the past by 1/2 C, then after the 1990 end of the baseline period, go nuts with 3.5 C range of “changes”? How can you possibly use that to say anything about Climate? From one “version” of “the same data” to another a 3.5 C range of random changes?

GHCN v3.3 vs v4  Montenegro Difference

The anomaly plot shows the expected “dip” in the baseline period but otherwise a line at +/- 1C bounds the bulk of the anomalies. Recent “data” have both high and low “fliers” in the 1-2 range and -1 to -2 range. Double the number above than below, so statistically it will average to “warmer”, but it isn’t. It’s just more wild and random changes in the data.

GHCN v3.3 vs v4  Montenegro Anomaly

BK Bosnia and Herzegovina

Now that’s the way to sculpt data! Drop the past by a full 1 C, and fairly consistently too! Then warm the present by up to 1 C, but with some random variation so it doesn’t look too suspicious.

GHCN v3.3 vs v4  Bosnia & Herzegovina Difference

And just look at the result! A marvelous tilted set of temperatures rising from -2 C anomaly to +2 C anomaly. Just beautiful!

But, uh, fellas, you do know that CO2 is only supposed to have caused 1/2 C of warming so far, right? That other 3.5 C of warming is kind of an embarrassment…

GHCN v3.3 vs v4  Bosnia & Herzegovina Anomaly

HR Croatia

This is what I’d expect to see from added instruments in one set vs the other. Sort of a ‘snake in a tunnel’ effect with semi-random wandering between bounds in about a 1/2 C range. Then we hit the ‘baseline period’ of 1950 and that range just compresses right out as more instrument records are loaded up in that range.

GHCN v3.3 vs v4  Croatia Difference

The expected “dip” in the baseline, but otherwise a line at +1C nicely catches most of the tops… until 1995. Similarly, a line at -1C catches most of the bottoms, though there’s a strong dip in 1940 but it looks like that is slowly being erased.

Then, just after 1995 we get a very strong turn upward in the whole mass of the data. I note that the 2000-2010 data for v3.3 look to have been cooled down so more recent data can look ‘hottest evah!”. So sculpting that v4 data into a more finely pointed Duck Tail.

Then, no cold recent data point for these folks, no sirree. A good solid +1.5 C hot anomaly for them. Ignore those other countries nearby with a very cold “now”…

Somehow I don’t think CO2 would have hung around making things cooler through 1990 and then suddenly make it warmer by 1C almost overnight…

GHCN v3.3 vs v4  Croatia Anomaly

SI Slovenia

Another difference graph that’s a real mess. From -1 C cooling in the early 1960s to +1.75 warming added in the 1990s? Just what the heck is going on with this “historical” data?

GHCN v3.3 vs v4 Slovenia Difference

Then that shape we’ve gotten far too familiar with. Nearly nothing changing until the 1990s or so. Small dip centered on 1975 in the baseline period. Then “WHACK!” it’s suddenly 2 C hotter and no low going anomalies below zero after about 2010.

So the assertion is that CO2 causes a +2 C change, in one decade. NOTHING before that. Right? Bueller? Bueller?…

GHCN v3.3 vs v4 Slovenia Anomaly

IT Italy

WOW. Deep past changes by at least +2 C (and may have gone off my graph range), then they cool most of the past by about another 1/2 C, then run up the recent data by 1/2 C. Looks like Italy is with the program of Data Diddle.

GHCN v3.3 vs v4 Italy Difference

Well, I can see why they needed that much “change of historical record”. Just look at those hot 1800s black spots. Those just had to go! (Nobody cares about the 1700s so you can ignore them. No, really. GIStemp cuts off the data in 1880 and Hadley in 1850 IIRC.) There’s a very severe “dip” in the baseline period then “the usual” flip up after 2000. Still, a line laid at +1C (use a ruler on your screen if you need to) has more spots above it in the past than recently. Any “warming” comes as a statistical artifact from the severe pruning of low going cold years after 2000. MMTS at airports anyone? Or just what ever caused that 1/2 C warming of the “historical record” in v3.3 to v4?

GHCN v3.3 vs v4 Italy Anomaly

MT Malta

Little island. Middle of the water. Not a lot of places for changing of location. So no real surprise almost all the “differences” are a straight line at about zero. Does look like a bit of effort to erase the “hot 1930s” and then a lot of nice cold added to the baseline period. Starting in 1960, so these are European centric folks as it’s only the USA that starts the baseline in 1950…

GHCN v3.3 vs v4 Malta Difference

You will note that I got bored about here and started playing with the color. Many of the graphs after this have BLUE for the v3.3 anomaly dots. It was reaching the point where seeing black vs red was getting blurry… Hey, I’ve done about 200 “countries” by now! I think the blue is a little easier to see, at least on my monitor.

So lay a line at about the +1 C point. 1860-75 warm, 1949 warm (good thing it was just outside the baseline, so it didn’t need cooling!) and about the same now. For the BLUE dots. Then the red gets cooled a tiny in the past, raised some in the very recent period, and those bothersome “very hot years” from the late 90s-2000 push for $$$ get cooled down so as not to upstage the present.

We have a very fine plunge of over 1 C in the baseline period then a sharp pivot in 1980 with a straight line shot up by 2 C to now. Couldn’t do better with a chisle.

Then the very latest data point is left alone so nobody notices. Yeah, it’s at -1 C anomaly. It’s cold. You can fix it next year… Besides, in the averages nobody will see it. Just call it weather and move on.

GHCN v3.3 vs v4 Malta Anomaly

SP Spain

OMG what a mess. 1.2 C colder deep past, baseline only 1/4 C colder. Recent data about 1/4 C of lift.

GHCN v3.3 vs v4 Spain Difference

Guess it needed all that diddle to deal with those warm temperatures in the 1800s. It is essentially flat highs at about +0.8 C right up to 1990. Then the cold bound runs about -1 C past that cold dip of 1975 and THEN runs up after about 1990.

It really looks like a big bite was taken out of the highs in the “Baseline Period” and another out of the lows after 2000.

GHCN v3.3 vs v4 Spain Anomaly

GI Gibraltar

Rock, stuck on the side of Spain. Lots of water. Some the more volatile Atlantic.

What can you do with all that stability and water…. I know, change the data!

So the past gets about a solid 3/10 C of cooling, then recent data get a good 0.5 C of warming. +0.8 C overall change of slope just from the changes of the data between v3.3 and v4. As there can’t be that many official recording stations on The Rock, so change of actual locations recorded is limited, one wonders what did change?

GHCN v3.3 vs v4 Gibraltar Difference

But desperate times call for desperate measures. Clearly with that mountain of hot in 1875 something had to be done to warm up the trend. So the past gets The Big Chill, and then post 1975 gets The Burn.

Oh, and once again I note that it is the loss of post baseline lows where all the “warming” happens, not in actually higher highs.

GHCN v3.3 vs v4 Gibraltar Anomaly

PO Portugal

Yeah, technically more Atlantic (that starts just one more down) but I left it here as it shares the peninsula with Spain and Gibraltar.

Not much changing in sleepy Portugal. 1/4 C cooling of the deep past and the baseline period. Some quasi-random jitter likely from changing what thermometers are in the set.

GHCN v3.3 vs v4 Portugal Difference

Hot mostly bounded by about a 0.8 C line with a couple of ‘fliers’ recently. Latest data points maybe 1/4 C above average. Nice stable pleasant. Bit of a dip in the baseline period. The hot 1930s-40s of elsewhere shows up here as a small loss of lows. Looks like that one cold year about 1998 gets warmed up some. Then anything “below average” or below the zero line is just gone.

I’m still wondering how CO2 causes (or even just allows) that Big Dip in the baseline (when we were all driving Muscle Cars that got 10 MPG over here) and then causes a sudden onset 1C rise in the lows after the 1990s (when it became Politically Correct to be a Warmista…)

GHCN v3.3 vs v4 Portugal Anomaly

Atlantic:

There’s a whole bunch of islands around Ireland and the UK. These all ought to look almost the same. Same water. Same climate. Lots of water to dampen temperature swings.

EI Ireland

Not much in the way of changes. Odd that the “best more recent” data have the widest range of change.

GHCN v3.3 vs v4 Ireland Difference

Doesn’t look like much Global Warming to me. Lines at +/-1 C pretty much bound the data. Last v4 data point a full 1 C up, but is that weather or is it wishful thinking? Hard to say.

This one really calls out for two trend lines on the blue and the red to see if any diddle was applied. That’s something for later.

GHCN v3.3 vs v4 Ireland Anomaly

IM Isle of Mann

No data in v3.3 so all we get is the v4 data:

MariaDB [temps]> SELECT COUNT(deg_C) FROM anom3 WHERE abrev=’IM’;
+————–+
| COUNT(deg_C) |
+————–+
| 0 |
+————–+
1 row in set (0.09 sec)

You would never think this was in the same general area surrounded by water. Almost 3 C increase from 1965 to about 2015.

IMHO that says there’s something wrong with the thermometer in Isle of Man. I do note that just 4 years back from the end we have a data point at negative anomaly, so it isn’t like the place is definitely on the Global Warming Agenda. I’d suspect more a change to MMTS and an airport improvement project when the Jet Age arrived.

GHCN v3.3 vs v4 Isle Of Man Anomaly

GK Guernsey

No data in v3.3 so all we get is the v4 data:

MariaDB [temps]> SELECT COUNT(deg_C) FROM anom3 WHERE abrev=’GK’;
+————–+
| COUNT(deg_C) |
+————–+
| 0 |
+————–+
1 row in set (0.03 sec)

Flat to 1990, then jumps up about 1 C across the board and goes flat again. Instrument or site change anyone?

That last +2 C data point is strange, especially after the 0 the prior year. Someone park a jet near the thermometer?

GHCN v3.3 vs v4 Guernsey Anomaly

JE Jersey

No data in v3.3 so all we get is the v4 data:

MariaDB [temps]> SELECT COUNT(deg_C) FROM anom3 WHERE abrev=’JE’;
+————–+
| COUNT(deg_C) |
+————–+
| 0 |
+————–+
1 row in set (0.42 sec)

No very hot last data point here. In fact, it’s a flat zero.

Other than a pronounced “dip” in the baseline period, it’s pretty much dead flat with a low bound about -0.8 C and an upper bound around +0.8C anomaly. There is also a tendency for lows to be missing after 1990 and for a few more highs near the upper range around 2000; but is that real or is that data collection artifacts? When did Jersey get an MMTS “upgrade”?

Also, are not Guernsey, Jersey, and Isle of Man close enough to each other they ought to be nearly identical?

GHCN v3.3 vs v4 Jersey Anomaly

UK United Kingdom

With all the scrutiny put on them after the Climate-gate email scandals, I’m not surprised their difference graph is nearly dead flat.

They do work in about 1/4 C cooling of the baseline and then the recent data get more juice, but only in a couple of years.

GHCN v3.3 vs v4 United Kingdom Difference

Again lay your visual line at about 1 C. Only the last couple of years bounce much above it recently, and those are almost the same as the bounce above in the late 1700s. A line along about -1 C also bounds most of the lows. There are a few more fliers in the early years with fewer instruments in the record, and it being the Little ice Age. About 1900 it’s warmer in the cold years. Then the baseline period hits, and the New Little Ice Age scare. Lows again reach below -1 C anomaly. There’s a clear “bite” out of the highs then too. Then, post 2000 is a bit odd. Generally lows are suppressed. But there is that odd string of cold years just prior to the latest batch.

In any case, I don’t see any actual warming so much as I see some loss of really cold years. I’m OK with that. Really really OK.

GHCN v3.3 vs v4 United Kingdom Anomaly

France

Looks like the French are not fooling around with their data much. Substantially flat. I wonder if the dip around 2000 was removing some fudge ’cause someone got caught? Wonder what was in their news then…

GHCN v3.3 vs v4 France Difference

Very interesting anomaly plot. Partly due to just the length of it and 1760s being warm. Then it’s quite colder in 1850 and there’s that chunk from about 1850 to 1990 where the tops are bounded at about +1/2 C anomaly, while the bottoms from about 1790 to 1990 run about -1/2 C with some jitter. There’s a modest “bite” out of the highs in the baseline period, then The Pivot happens about 1995. BIG loss of lows, a full 1 C Jump Up in highs. then it pauses. Sure looks like instrument change to me.

So no real wonder they had to cool the data from the late 1990s to make the trend more “trendy”.

GHCN v3.3 vs v4 France Anomaly

NL Netherlands

What in the world are the Dutch doing?

2 C range of changes to history. BIG cooling of the 1850 to 1900 era, then the 1700s warmed by 1 C? Is that so the “average change” is nearer to zero ’cause nobody cares about the 1700s? The baseline gets a nice 1/2 C cool spike added, but recent data also gets a tiny bit of cooling. We’ll have to see if that makes a better “trend” for v4 recent data points…

GHCN v3.3 vs v4 Netherlands Difference

And it does! How nice. just prune out those bothersome hot years that were so important for justifying the prior Big Scare! stories and the Paris and Copenhagen meetings. The work on the 1800s is going nicely too. Though with that much hot 1800s to remove, it looks like you still have some work to do.

My overall impression of the mass of data (both blue and red) is that there isn’t really much warming going on here. Lines at +1 C and -1.5 C pretty much have similar stuff inside them and similar fliers outside. Only post 2000 “goes weird” with the loss of low going anomalies. Even there it looks like they started to put back in a couple of them.

GHCN v3.3 vs v4 Netherlands Anomaly

BE Belgium

How can somewhere this small get that much thermometer data fiddle?

2.5 C range to the fiddle. LOTS of cooling of data post 1925, but more in the baseline period than outside it.

GHCN v3.3 vs v4 Belgium Difference

The actual anomalies look a mess too. Generally bounded by +1 and -1.5 C but not as smoothly as others. Big 1 C Dip in the baseline period. Post 2000 pruning of low going anomalies. Really? Only one cold year post 2000? No snow in Copenhagen during the meeting? Or did that not cross the border to Belgium? Then there’s that 2.5 C Rocket Ride up in the later couple of years. (Though I note the just preceding years have been cooled down so they don’t get in the way of the narrative…)

Frankly, that’s not looking so much like a duck tail as it is like a different kind of erection… not that I think Belgium might be full of folks looking to screw the rest of us or anything… So just tell me how CO2 does nothing until 2000, then makes it 2.5 C hotter…

GHCN v3.3 vs v4 Belgium Anomaly

LU Luxembourg

Ah, the Classics. Cool the deep past 1/2 C, grade it gently into the present just post 1990 baseline end, leaving a nice flat +1/4 C into the present. Top with a +1/2 C Cherry at the end. Someone went to art class, didn’t they?

GHCN v3.3 vs v4 Luxembourg Difference

The result? Up to 1980, a wonderfully cold past. Only one year was over +1 C and it’s been pounded back down. Nice deep cold at -2 C in some years. Then, about 1990, a gentle Pivot into a ruler straight rise of the highs by a smooth +2C, lows tagging along, and while a bit pruned, there are just enough low going years to look real.

Too bad it doesn’t look at all like the countries right next to you in the same climate areas and with the same weather. Such artistry being ruined by the neighborhood.

GHCN v3.3 vs v4 Luxembourg Anomaly

MH Montserrat

No data in v3.3 so all we get is this v4 anomaly graph. I do have to wonder why an island in the New World is showing up as “Europe” in v4, but it is, so here’s the graph.

MariaDB [temps]> SELECT COUNT(deg_C) FROM anom3 WHERE abrev=’MH’;
+————–+
| COUNT(deg_C) |
+————–+
| 0 |
+————–+
1 row in set (0.10 sec)

Really does look like an error. How could they let this data into the set? It isn’t warming at all. 1940-41 was the hottest year. Good thing they cut it off at 1970…

GHCN v3.3 vs v4 Montserrat Anomaly

Inland:

All of Europe has water not too far away, but these countries start having much more inland effects. More mountains and more opportunities for temperature extremes. Polar Express air flows less muted.

GM Germany

Nice “tuck” taken in 1950. Then recently a couple of years with a hot pop.

GHCN v3.3 vs v4 Germany Difference

A line about +1C shows the present not much different from the late 1700s. There’s a bit-O-cold in the Little Ice Age and in the Baseline Period. Similarly, a line at about -1.75 C clips the lows with an obvious rise between 1900 and 1950. Almost a square cut lack of lows.

Right on cue, post about 1995, we get the Pivot and highs rise 1 C in a straight shot up the Duck Tail formation. There’s a significant lack of lows after 2000, with just a couple of years showing below zero anomaly

Chop off the last half dozen years, would you call that warming? I’d not. At best, narrowing the range of excursions and pruning out cold years. Then given the stories of snow lately, I’d not even count the last half dozen years of the “pause” as warming either.

GHCN v3.3 vs v4 Germany Anomaly

PL Poland

Doesn’t look like much being diddled in Poland.

GHCN v3.3 vs v4 Poland Difference

Given some of the data points close to the upper edge of the graph, I likely ought to do one of these with +4 / -4 range to assure no “fliers” are off the page. I think I did that, but frankly it’s all a bit fuzzy now… 😉

A line at +1C has a consistent few fliers above it up to about 2000. There’s some dip in the 1950s part of the Baseline Period. Similarly, a line laid at -2 C is pretty much a ‘lower bound with a few fliers” up until about 1960. So is that bit that’s below -1.5 C centered on 1950 just the “new Little Ice Age” stuff, or a “baseline artifact”? Is the data below -2 C from 1700s to about 1875 just the Little Ice Age? Or the result of wide range from just one or a few thermometers. Probably doesn’t matter…

The “BIG DEAL” is really just post 2000. That’s where the cold anomaly goes to rapidly rising culminating in a +1 C lower bound, and the upper range of anomaly shoots up to at least +2 C, forming a generally broad fat rising tail. Then those two hot years in v3.3 get their blue spots whacked down to sharpen it all up in a very nice, if still a bit broad, Duck Tail.

GHCN v3.3 vs v4 Poland Anomaly

EZ Czech Republic

Given how little natural warming they have to work with, I guess it is no surprise such desperate measures would be used in the Czech data.

Deep -1/2 C cut in the deep past, don’t bother cooling the end of the Little Ice Age, pull it down again into 1900, then a wobbly run up to +1/2 C warming of the more recent data.

GHCN v3.3 vs v4 Czech Republic Difference

The result? A VERY nice and VERY sharp Duck Tail at the end. Removal of a lot of that annoying 1700s heat.

We are still left wondering how CO2 basically does nothing from 1880 to 1990, with the highs actually cooling over the period, with lows basically around -1 1/2 C, and then suddenly “turn on a dime” about the year 1995 and rocket up temperatures by 1.5 C on the tops, and 3 C on the bottoms?

GHCN v3.3 vs v4 Czech Republic Anomaly

LO Slovakia

That’s a bit of a mess. Cooling the deeper past by up to 1 C seems a bit much. Warming the last few data points 1/2 C seems unnecessary in that context. Then chopping at the hot 1940s data by a full -2C? Man that’s vicious..

GHCN v3.3 vs v4 Slovakia Difference

But the result does look like warming. Very un-physical, but hey, it was a quick hatchet job anyway, right? And Slovakia is so small it will just blend in with the averages and nobody will notice…

Hot bothersome 1800s data removed. Fill in the war years but without any hot years. (Average and homogenize much? /sarc;) then nothing much at all happening until about 1985 / 1990 when the Mother Of All Duck Tails gets made. Smooth as can be, not a single flier in sight.

So has Slovakia really been having ever less range of their weather since 1975 with lows consistently rising EVERY SINGLE YEAR by a total of almost 4 C ? REALLY?

GHCN v3.3 vs v4 Slovakia Anomaly

AU Austria

Another one for the Ham Handed Brigade. Suck the past down by a consistent 1/2 C, pivot through the baseline and then raise temps by a consistent 1/4 C. The result?

GHCN v3.3 vs v4 Austria Difference

Yet Another almost Ram’s Horn like Duck Tail.

Past cooled nicely. Just a 1/2 C at a time and eventually that whole hot history of the early 1700s can be erased. We cool into about 1990 as the top anomalies drop from nearly +2 C to just +0.75 C. On the bottoms, a line about -1.25 C pretty much is the floor under most of the data, though there are dips in about 1860 and 1950s. There’s a strange volatility range squeeze about 1975 with range narrowed to just 1 C or so. Then The Pivot happens and with lows rising faster than highs, we get a very nice Duck Tail, though with a ruffled spot about 2000.

So my usual question here: How did CO2 “Do Nothing” until the year 2000, then suddenly take action?

GHCN v3.3 vs v4 Austria Anomaly

LS Liechtenstein

No data in GHCN v3.3, so all we get is the v4 anomaly chart.

MariaDB [temps]> SELECT COUNT(deg_C) FROM anom3 WHERE abrev=’LS’;
+————–+
| COUNT(deg_C) |
+————–+
| 0 |
+————–+
1 row in set (0.32 sec)

Only starts in the late 1960s? Then runs up by a full 4 C range from the low in 1985 to the high in 2010? I don’t know what’s going on there, but CO2 it isn’t.

It looks like there was a “step change” about 1992 with the data being in two blocks, offset by 1 C between them.

Might be an easy test case for when their thermometer changed to MMTS…

GHCN v3.3 vs v4 Liechtenstein Anomaly

SZ Switzerland

More Germanic precision here Neatly pull down the past by 1/2 C go flat about 1900, then some rise at the end, but not too much, and “cherry on top’ in the last bit of data.

GHCN v3.3 vs v4 Switzerland Difference

The result? More of that bothersome hot past removed as the blue dots fall. After about 1990, the pivot into a great sharp Duck Tail.

Lay a line at +1 C and it nicely aligns with the blue dots up to about 1950. There’s bit of dip in the colder 1800s and again a bit dip in the Baseline Period (odd, though, that we didn’t have as much Ice Age weather in the 1960s New Little Ice Age Scare as we had in the real Little Ice Age) and even to about 1990 where it intersects the warming out of the baseline dip.

A line about -1.5 C runs through most of the low excursions up to about 1900. Then it rises to about -1 C. That holds to about 1990. Then it is Rocket Ride City as low anomalies rise 3 C from -1C to +2C and narrow to a fine point.

But has Switzerland really warmed by 2 C in warm years, 3 C in cold years, and lost ALL cold years? Just since 1990 / 2000?

If so, what would this have to do with CO2, since we’ve been in The Pause since 1998 and CO2 ought to have had the biggest effect between 1940 and 1990 (at least, that is what they were telling us in the 1990s…)

GHCN v3.3 vs v4 Switzerland Anomaly

HU Hungary

Another of the classic form. Cooing the past by about 1/3 C, rising to gentle warming of the recent data.

GHCN v3.3 vs v4 Hungary Difference

Volatility of about 3 C in the older data, likely from limited numbers of thermometers. Narrows to about 2 C in the late 1800s. Holds there, taking a dip in the “baseline period”, then about 1990 / 1995, The Pivot and we again get a finely sculpted Duck Tail.

Am I the only one who finds that sudden rise of 2 C and narrowing of range to a point, just a bit over the top insanely not physical?

GHCN v3.3 vs v4 Hungary Anomaly

RI Serbia

Drop the past 1/4 C, shift to warming just a bit recently.

GHCN v3.3 vs v4 Serbia Difference

That’s an interesting shape. So it was -2 C in the late 1800s, rose to average about 1950. Stayed that way until 1995, then shoots up by a sudden 2-3C? Really? An overall rise of 5 C at the extremes?

GHCN v3.3 vs v4 Serbia Anomaly

BU Bulgaria

Quite a scatter in the 1940s and earlier. Bit of cooling all the way to recent, then a touch up.

GHCN v3.3 vs v4 Bulgaria Difference

OK, I can see why they needed to knock down the 1930s hot spot, then the baseline around the 1980s gets cooled too. Then had to take some out of the highs of The Pause so the most recent data looks warm. Got it.

GHCN v3.3 vs v4 Bulgaria Anomaly

RO Romania

Don’t know what to make of this. Sort of a sag in the middle with jitter effect.

GHCN v3.3 vs v4 Romania Difference

Ah, I see. Making the baseline a bit lower, reducing the hot parts of the 1800s. Otherwise a line at 1 C would show nothing warmer really until the last data points and them only barely different from the 1800s highs. Then a line along about -1.25 C would also show not much happening (so those red dots needed a pull down) until about 2000 when low anomalies suddenly become extinct and lows rise by 2 C in under 2 decades. I also note that the highs near 2000 had to be banged down to sharpen the Duck Tail and make a better rise. Squash that Pause!

GHCN v3.3 vs v4 Romania Anomaly

MD Moldova

Oh what fresh hell is this? 2.5 C range of “changes” to history? Really? Pull most of history down 1/4 C and then the “recent and best” data goes crazy with changes?

GHCN v3.3 vs v4 Moldova Difference

Ah, yes, the familiar. Squash the warm 1800s. Tilt the whole past lower, and especially pull down the higher blue spots in the Baseline Period, then bang down the Scare Hottest Ever of the early 2000s as we need more scare now, less Pause.

Then the oh too familiar by now, Pivot about 2000 where low going anomalies just go away. A full 4 C rise in the cold edge

A bit ham handed, but hey, who looks at Moldova?

GHCN v3.3 vs v4 Moldova Anomaly

UP Ukraine

General slight cooling of the past, The Dip into the baseline period gets some help, then things go nuts in the late 90s to date.

GHCN v3.3 vs v4 Ukraine Difference

By now it’s becoming a bad joke how the Big Scare SIGN THE PARIS AGREEMENT SEND MONEY!!!! years of extra hot in the 90s and early 2000s have to be pounded back down so the recent data can be the Hottest Evah!!! But there’s no joke too old to tell again…

A line about 1 C (with the new pounded down data in the 90s / 00s) intersects the tops at the start and in about 1990. Bit of a drop out in the Baseline Period and about 1890.

A line about -1.5 C runs along with sporadic excursions below it until about 1995.

Then, The Pivot.

From 1995 or so to date, the highs rise about 1.25 C and the lows rise about 3.5 C forming a very sharp Duck tail (once you pound out the prior highs of the Big Scare For Paris era…)

GHCN v3.3 vs v4 Ukraine Anomaly

BO Belarus

Well that’s different. A bit of cooling of the deep past, but from about 1950 onward it’s all a lot of cooling.

GHCN v3.3 vs v4 Belarus Difference

Oh, I see. Making those last data points really stand out has HOTTEST EEEEVVVVAAAHHHH!!!!! Guess you did have a lot of 1990s hot air to get rid of…

Strange how the bottoms stay cold until 2000, then run up at a crazy rate with a full +2C higher anomaly (from about -1C to about +1C.

So has Belarus enjoyed a giant surplus of grain production from this rise? I mean, compared to 1941, it looks like it is 5 C warmer. That’s just GOT to mean more grain production, longer growing seasons. Folks on the lake side beaches in swimming gear into the fall, shorter skirts, men in T shirts in spring working in the garden? It’s all like being in Crimea or Turkey now, right? Right? /sarc;

GHCN v3.3 vs v4 Belarus Anomaly

Nordic & Baltic:

LH Lithuania

A bit of a puzzlement. We have “the usual” cooling of the past and added “dip” in the “baseline period”, but then the more recetent temperatures get cooled some as well. As though an algorithm did the first bits but someone was embarrassed about an inflated present and tried to fix it. That there is almost 2 C of “range of change” does not give confidence in a 1/2 C Global Warming signal hiding in that sea of changes.

GHCN v3.3 vs v4 Lithuania Difference

Then, looking at the anomaly graph, it looks a lot like nothing much is happening. Other than the most recent “flyer” of a very high reading, the historical v3.3 highs are not increasing. That would explain the need to “cool the past”. Then we also have the common artifact of post 2000 the cold gets trimmed. Almost like the bottom half of the range is being left out. So is that deliberate Data Diddle, the effect of MMTS at concrete jungle Jet Ports, or does CO2 just wait 20 years then suddenly give you wonderfully pleasant days no warmer than before, but with no cold excursions everyone (including plants and animals) hates?

GHCN v3.3 vs v4 Lithuania Anomaly

LG Latvia

About 1.5 C range of changes. A very gentle cooling of the past, then the baseline period gets a more vigorous cooling but with a lot of random jitter to try to hide it. Finally, the end gets a nice shot of warming in a couple of years, but overall cooling of the data.

GHCN v3.3 vs v4 Latvia Difference

Once again we see the latest data point left high (guess you do need to cool the “recent highs” so this year can look really high) and that “after 2000 kill the cold data” effect. The high range doesn’t really rise other than the last data point (but no worries, I’m sure it will be cooled off next time…)

GHCN v3.3 vs v4 Latvia Anomaly

EN Estonia

A 2 C range of “corrections” (or whatever these “non-adjustment adjustments” are), really? Again we get a deep cooling of the recent past, but leaving the most recent data point above it all as “Hottest Evah!” in the anomaly graph. The general cooling of the past by about 1/4 C continues too.

GHCN v3.3 vs v4 Estonia Difference

Looking mostly at the blue dots up through about 1995, there’s no warming at the top and no real loss of cold at the bottom. Then we had The Leap after 1995e data.

I’d give this one about a B- for doing a pretty good job of tailoring the data, in that it isn’t an obvious Duck Tail flip up and narrow to a point at the end; but it lacks imagination and not enough of recent data was left hot. Just one or two data points? Really? That’s not a strong trend, that’s just weather.

GHCN v3.3 vs v4 Estonia Anomaly

FI Finland

What scatter there is in the changes! This is the error band in Finland? From version to version years can move anywhere in about a 2 C range? It looks like the general thrust is to remove the natural volatility of Finland (note the range on the anomaly plot is +4 to -6 so much wider than others) and try to get a better “trend” in the mood swings.

GHCN v3.3 vs v4 Finland  Difference

The anomaly chart is fascinating too. The present highs are no higher than the hot 1930s-40s or the point in the 1800s. Any recent “warming is entirely from loss of cold years after about 2000. Has Finland really not had ANY cold year since about 1995 to 2000? When did they install MMTS equipment at their major reporting places, and how many are jet airports?

GHCN v3.3 vs v4 Finland  Anomaly

SW Sweden

Overall cooling of the past. Then there are what look like volatility reduction changes. Two spots with about 2.5 C of range between them, wiped out. Odd, given that such volatility is attested in the historical record…

GHCN v3.3 vs v4 Sweden Difference

Ah, looking at the anomaly chart makes it all clear. Can’t have those hot year blue spots or folks might start talking about how Sweden has had hot weather in the past. A line across the top of the highs doesn’t show warming. One through the bottom 1/5 or 1/10 of the graph shows a cold Little Ice Age, and then a most curious loss of cold after about 1995 with a compression of volatility (range) and a very fat tail. What is like a duck tail but rising to a rounded lump instead of a point? Maybe it’s a Lemming Tail… 😉

GHCN v3.3 vs v4 Sweden Anomaly

DA Denmark

Some patterns are so common they are boring. Again with the gentle cooling of the deep past and more vigorous cooling up through the baseline period. A full 1/2 C in many years. Guess that’s one way to find 1/2 C of “CO2 Warming”, just cool the baseline period by 1/2 C. Overall, about a 2 C range of non-adjustment adjustments in the data. Error bars anyone? Anyone?

GHCN v3.3 vs v4 Denmark Difference

The anomaly graph is interesting. You can sort of see the “pivot point’ around the 1960s where the dots are clear and sort of purple as some land on top of each other, then in the past they look like a ‘smear’ as they pull away from each other. Then recent data is more sporadically changed, but generally the ‘smear’ is to red on top instead of the bottom as in the past. Odd that they just dump the oldest warm data.

GHCN v3.3 vs v4 Denmark Anomaly

NO Norway

This one is a bit facinating. It must be viewed in the context of the next two “v4 only” graphs. So two remote places owned by Norway are added (or perhaps moved from Norway proper to their own “country”?) and Norway gets a trim of the “Duck Tail” to where it is basically flat on the anomaly graph. Is that becuase those two were split out of the prior data set, or because with them added with their big spike at the recent times, Norway could be made less obvious? Does this show that anomalies do not hide instrument selection bias, or that the Data Diddlers were working over time? What a choice…

So there’s 1.5 C range of “changes” in the current data recent years, but with a very unusual “cool the present”. IF I had to guess, I’d guess that it is an artifact of splitting out those two other places into their own “countries”. This is a minor “Dig Here!” to track where the Svalbard and Jan Mayan thermometers were “accounted for” in v3.3, if at all.

GHCN v3.3 vs v4 Norway Difference

In the anonaly plot we have a significant loss of cold spikes after about 1995 and HAD a very nice “Duck Tail” spike, until in v4 we don’t. There is still the general “cooling of the past” and the “baseline” by about 1/4 C.

GHCN v3.3 vs v4 Norway Anomaly

JN Jan Mayen [Norway]

No data in v3.3, so all we get is the v4 anomaly graph.

MariaDB [temps]> SELECT COUNT(deg_C) FROM anom3 WHERE abrev=’JN’;
+————–+
| COUNT(deg_C) |
+————–+
| 0 |
+————–+
1 row in set (0.00 sec)

We see basically nothing happening to the highs until about 2000. There’s a very pronounced “dip” in the cold ’70s when the Climate Scare Du Jour was that a “New Ice Age” was coming. Then, after 2000, things jump up about 3 C. Is CO2 supposed to “do nothing” until the year 2000 then cause 3 C in a step function? Uh, no.

GHCN v3.3 vs v4 Jan Mayen Anomaly

SV Svalbard [Norway]

No data in v3.3 so all we get is the v4 anomaly graph.

MariaDB [temps]> SELECT COUNT(deg_C) FROM anom3 WHERE abrev=’SV’;
+————–+
| COUNT(deg_C) |
+————–+
| 0 |
+————–+
1 row in set (0.34 sec)

Rather remarkably like Jan Mayen. Nothing until “The Jump” ™ a bit before 2000. Anyone want to bet some airport expansion and increased construction happened then along with a new digital thermometer?

GHCN v3.3 vs v4 Svalbard Anomaly

IC Iceland

Iceland is rather interesting. There’s a full 1 C of range of “changes” in the most recent data that’s supposedly the best there is. Why? Perhaps to reduce the very strong cyclical nature of the anomaly graph?

GHCN v3.3 vs v4 Iceland Difference

The anomaly graph has the recent hot side of the cycle roughly the same as the hot 1920s to 30s. Very strong cold dip in the “Baseline Period”, and then it looks like, about 2000, a big “step function” up. There was one year with a “normal” degree of cold after that, and the “changes” in v4 rubbed it out.

GHCN v3.3 vs v4 Iceland Anomaly

In Conclusion

This whole series has been grueling to do.

But now I’m done. Just a summary “index it all” posting to wrap around them all.

Just be glad all you have to do is look at a few graphs and read a comment or two about them 😉

My overall impression of the graphs and the data is that there is a “Tailoring” operation going on. The changes are NOT just a little fix up here and a correction there. It looks to me like it has direction and purpose. Cool the Baseline Period. Cool warm past periods. Warm the recent data UNLESS it is too high in the last 2 decades, then you cool them so the nearest data can look warmer in comparison. Stamp out cold periods in the middle. Remove cool periods recently if not already suppressed. The question that remains for me is just: “Is that an accident from ignoring the effects of Instrument Change, or a deliberate planned act?”

With that, I’m done with the v3.3 vs v4 comparisons! Yay! Over to you folks for more analysis.

What’s Wrong With The Surface Temperature Record? Guest: Dr. Roger Pielke Sr.

Reblogged from Watts Up With That:

Dr. Roger Pielke Sr. joins [Anthony Watts] on a podcast to discuss the surface temperature record, the upcoming IPCC report, and climate science moving forward.

Dr. Roger Pielke Sr. explains how the Intergovernmental Panel on Climate Change (IPCC) is incorrectly explaining climate change to the media and public. Pielke highlights how the IPCC ignores numerous drivers of climate aside from CO2, leading to numerous factual inaccuracies in the IPCC reports.

Climate monitoring station in a parking lot at University of Arizona, Tucson

We also cover what is wrong with the surface temperature record – specifically why many temperature readings are higher than the actual temperature.

Available on Amazon at a special low price – click image

Pielke is currently a Senior Research Scientist in CIRES and a Senior Research Associate at the University of Colorado-Boulder in the Department of Atmospheric and Oceanic Sciences (ATOC) at the University of Colorado in Boulder (November 2005 -present). He is also an Emeritus Professor of Atmospheric Science at Colorado State University.

BIG NEWS – Verified by NOAA – Poor Weather Station Siting Leads To Artificial Long Term Warming

Sierra Foothill Commentary

Based on the data collected for the Surface Station Project and analysis papers describing the results, my friend Anthony Watts has been saying for years that “surface temperature measurements (and long term trends) have been affected by encroachment of urbanization on the placement of weather stations used to measure surface air temperature, and track long term climate.”

When Ellen and I traveled across the country in the RV we visited weather stations in the historical weather network and took photos of the temperature measurement stations and the surrounding environments.

Now, NOAA has validated Anthony’s findings — weather station siting can influence the surface station long temperature record. Here some samples that were taken by other volunteers :

clip_image004Detroit_lakes_USHCN

Impacts of Small-Scale Urban Encroachment on Air Temperature Observations

Ronald D. Leeper, John Kochendorfer, Timothy Henderson, and Michael A. Palecki
https://journals.ametsoc.org/doi/10.1175/JAMC-D-19-0002.1

Abstract

A field experiment was performed in Oak Ridge, TN, with four…

View original post 248 more words

Fake climate science and scientists

Reblogged from Watts Up With That:

Alarmists game the system to enrich and empower themselves, and hurt everyone else

by Paul Driessen

The multi-colored placard in front of a $2-million home in North Center Chicago proudly proclaimed, “In this house we believe: No human is illegal” – and “Science is real” (plus a few other liberal mantras).

I knew right away where the owners stood on climate change, and other hot-button political issues. They would likely tolerate no dissension or debate on “settled” climate science or any of the other topics.

But they have it exactly backward on the science issue. Real science is not belief – or consensus, 97% or otherwise. Real science constantly asks questions, expresses skepticism, reexamines hypotheses and evidence. If debate, skepticism and empirical evidence are prohibited – it’s pseudo-science, at best.

Real science – and real scientists – seek to understand natural phenomena and processes. They pose hypotheses that they think best explain what they have witnessed, then test them against actual evidence, observations and experimental data. If the hypotheses (and predictions based on them) are borne out by their subsequent findings, the hypotheses become theories, rules, laws of nature – at least until someone finds new evidence that pokes holes in their assessments, or devises better explanations.

Real science does not involve simply declaring that you “believe” something, It’s not immutable doctrine. It doesn’t claim “science is real” – or demand that a particular scientific explanation be carved in stone. Earth-centric concepts gave way to a sun-centered solar system. Miasma disease beliefs surrendered to the germ theory. The certainty that continents are locked in place was replaced by plate tectonics (and the realization that you can’t stop continental drift, any more than you stop climate change).

Real scientists often employ computers to analyze data more quickly and accurately, depict or model complex natural systems, or forecast future events or conditions. But they test their models against real-world evidence. If the models, observations and predictions don’t match up, real scientists modify or discard the models, and the hypotheses behind them. They engage in robust discussion and debate.

They don’t let models or hypotheses become substitutes for real-world evidence and observations. They don’t alter or “homogenize” raw or historic data to make it look like the models actually work. They don’t hide their data and computer algorithms (AlGoreRythms?), restrict peer review to closed circles of like-minded colleagues who protect one another’s reputations and funding, claim “the debate is over,” or try to silence anyone who dares to ask inconvenient questions or find fault with their claims and models. They don’t concoct hockey stick temperature graphs that can be replicated by plugging in random numbers.

In the realm contemplated by the Chicago yard sign, we ought to be doing all we can to understand Earth’s highly complex, largely chaotic, frequently changing climate system – all we can to figure out how the sun and other powerful forces interact with each other. Only in that way can we accurately predict future climate changes, prepare for them, and not waste money and resources chasing goblins.

But instead, we have people in white lab coats masquerading as real scientists. They’re doing what I just explained true scientists don’t do. They also ignore fluctuations in solar energy output and numerous other powerful, interconnected natural forces that have driven climate change throughout Earth’s history. They look only (or 97% of the time) at carbon dioxide as the principle or sole driving force behind current and future climate changes – and blame every weather event, fire and walrus death on manmade CO2.

Even worse, they let their biases drive their research and use their pseudo-science to justify demands that we eliminate all fossil fuel use, and all carbon dioxide and methane emissions, by little more than a decade from now. Otherwise, they claim, we will bring unprecedented cataclysms to people and planet.

Not surprisingly, their bad behavior is applauded, funded and employed by politicians, environmentalists, journalists, celebrities, corporate executives, billionaires and others who have their own axes to grind, their own egos to inflate – and their intense desire to profit from climate alarmism and pseudo-science.

Worst of all, while they get rich and famous, their immoral actions impoverish billions and kill millions, by depriving them of the affordable, reliable fossil fuel energy that powers modern societies.

And still these slippery characters endlessly repeat the tired trope that they “believe in science” – and anyone who doesn’t agree to “keep fossil fuels in the ground” to stop climate change is a “science denier.”

When these folks and the yard sign crowd brandish the term “science,” political analyst Robert Tracinski suggests, it is primarily to “provide a badge of tribal identity” – while ironically demonstrating that they have no real understanding of or interest in “the guiding principles of actual science.”

Genuine climate scientist (and former chair of the School of Earth and Atmospheric Sciences at the Georgia Institute of Technology) Dr. Judith Curry echoes Tracinski. Politicians like Senator Elizabeth Warren use “science” as a way of “declaring belief in a proposition which is outside their knowledge and which they do not understand…. The purpose of the trope is to bypass any meaningful discussion of these separate questions, rolling them all into one package deal – and one political party ticket,” she explains.

The ultimate purpose of all this, of course, is to silence the dissenting voices of evidence- and reality-based climate science, block creation of a Presidential Committee on Climate Science, and ensure that the only debate is over which actions to take first to end fossil fuel use … and upend modern economies.

The last thing fake/alarmist climate scientists want is a full-throated debate with real climate scientists – a debate that forces them to defend their doomsday assertions, methodologies, data manipulation … and claims that solar and other powerful natural forces are minuscule or irrelevant compared to manmade carbon dioxide that constitutes less that 0.02% of Earth’s atmosphere (natural CO2 adds another 0.02%).

Thankfully, there are many reasons for hope. For recognizing that we do not face a climate crisis, much less threats to our very existence. For realizing there is no need to subject ourselves to punitive carbon taxes or the misery, poverty, deprivation, disease and death that banning fossil fuels would cause.

Between the peak of the great global cooling scare in 1975 until around 1998, atmospheric carbon dioxide levels and temperatures did rise in rough conjunction. But then temperatures mostly flat-lined, while CO2 levels kept climbing. Now actual average global temperatures are already 1 degree F below the Garbage In-Garbage Out computer model predictions. Other alarmist forecasts are also out of touch with reality.

Instead of fearing rising CO2, we should thank it for making crop, forest and grassland plants grow faster and better, benefitting nature and humanity – especially in conjunction with slightly warmer temperatures that extend growing seasons, expand arable land and increase crop production.

The rate of sea level rise has not changed for over a century – and much of what alarmists attribute to climate change and rising seas is actually due to land subsidence and other factors.

Weather is not becoming more extreme. In fact, Harvey was the first Category 3-5 hurricane to make US landfall in a record 12 years – and the number of violent F3 to F5 tornadoes has fallen from an average of 56 per year from 1950 to 1985 to only 34 per year since then.

Human ingenuity and adaptability have enabled humans to survive and thrive in all sorts of climates, even during our far more primitive past. Allowed to use our brains, fossil fuels and technologies, we will deal just fine with whatever climate changes might confront us in the future. (Of course, another nature-driven Pleistocene-style glacier pulling 400 feet of water out of our oceans and crushing Northern Hemisphere forests and cities under mile-high walls of ice truly would be an existential threat to life as we know it.)

So if NYC Mayor Bill De Blasio and other egotistical grand-standing politicians and fake climate scientists want to ban fossil fuels, glass-and-steel buildings, cows and even hotdogs – in the name of preventing “dangerous manmade climate change” – let them impose their schemes on themselves and their own families. The rest of us are tired of being made guinea pigs in their fake-science experiments.

Paul Driessen is senior policy advisor for the Committee For A Constructive Tomorrow (CFACT) and author of articles and books on energy, environmental and human rights issues.

GHCN v3.3 vs. v4 Anomaly Australia / Pacific Islands

Reblogged from Musings from the Chiefio:

In prior postings I did a sample of various countries around the world, and a full set of North America, South America and Antarctica. This extends that set with Australia and the Pacific Islands. Note that these are often near the equator and may be on either side of it, so seasonality may vary by graph.

I’m going to group things into Australia, New Zealand, North of Australia (Indonesia, Papua New Gunea, Philipines, etc.), then those islands scattered across the center of the Pacific. Why? Because countries in those areas ought to look a lot more like each other in terms of Anomaly than like those in other groups. The Pacific is dominated by ENSO and tropical conditions. while the countries north of Australia have Indian Ocean influences and share a current flow up the coast of Asia. To some extent New Zealand is “special” in that it is closest to The Southern Ocean so has more cold southern islands and arctic water exposure. Similarly, Singapore is on the Malay Peninsula and north of the equator so it, and Malaysia, ought to reflect some of the Asian Continent; but protruding into the mixed ocean area will also reflect climate similar to Indonesia. Finally, Australia is unique in this group as it has a large hot desert in the center.

Here’s the Koppen Climate graph for the World (from the Wiki) so you have something for comparison.

Köppen-Geiger Climate Map for 1980-2016

From that you can easily see how Australia and New Zealand differ from the tropical ocean group.

What I find fascinating in these graphs is just how much the islands temperature recordings vary (often a lot) while they are in the same climate zone as a nearby neighbor and share a large body of nearly uniform temperature water between them. I expect change between distant islands, but we often see it on neighboring islands. I think that likely is an instrument or siting change issue. Who has the large airport and tourists, and who doesn’t. But that needs an historical retrospective photo essay on each of the places like that and someone else will need to take that “Dig Here!”.

Which countries?

This bit of SQL programming gets us a table of countries in Region 5, the Australia & Pacific Islands group (remember that in Linux the command “cat” is “concatenate and print” and with just one file name prints out the contents. In this case the program named “ApacList.sql”):

chiefio@PiM3Devuan2:~/SQL/bin$ cat ApacList.sql 
SELECT cnum, abrev,region, cname 
FROM country WHERE region=5 ORDER BY cname;

So what does that give us? Here’s the result:

MariaDB [temps]> source bin/ApacList.sql
+------+-------+--------+------------------------------------------+
| cnum | abrev | region | cname                                    |
+------+-------+--------+------------------------------------------+
| 521  | AQ    | 5      | American Samoa [United States]           |
| 501  | AS    | 5      | Australia                                |
| 522  | BX    | 5      | Brunei                                   |
| 523  | KT    | 5      | Christmas Island [Australia]             |
| 524  | CK    | 5      | Cocos (Keeling) Islands [Australia]      |
| 525  | CW    | 5      | Cook Islands [New Zealand]               |
| 527  | FM    | 5      | Federated States of Micronesia           |
| 502  | FJ    | 5      | Fiji                                     |
| 528  | FP    | 5      | French Polynesia                         |
| 529  | GQ    | 5      | Guam [United States]                     |
| 503  | ID    | 5      | Indonesia                                |
| 530  | JQ    | 5      | Johnston Atoll [United States]           |
| 504  | KR    | 5      | Kiribati                                 |
| 505  | MY    | 5      | Malaysia                                 |
| 531  | RM    | 5      | Marshall Islands                         |
| 598  | MQ    | 5      | Midway Islands [United States}           |
| 506  | NR    | 5      | Nauru                                    |
| 532  | NC    | 5      | New Caledonia [France]                   |
| 507  | NZ    | 5      | New Zealand                              |
| 533  | NE    | 5      | Niue [New Zealand]                       |
| 534  | NF    | 5      | Norfolk Island [Australia]               |
| 535  | CQ    | 5      | Northern Mariana Islands [United States] |
| 536  | PS    | 5      | Palau                                    |
| 599  | LQ    | 5      | Palmyra Atoll [United States]            |
| 508  | PP    | 5      | Papua New Guinea                         |
| 509  | RP    | 5      | Philippines                              |
| 537  | PC    | 5      | Pitcairn Islands [United Kingdom]        |
| 541  | WS    | 5      | Samoa                                    |
| 511  | SN    | 5      | Singapore                                |
| 512  | BP    | 5      | Solomon Islands                          |
| 597  | TT    | 5      | Timor-Leste                              |
| 538  | TL    | 5      | Tokelau [New Zealand]                    |
| 517  | TN    | 5      | Tonga                                    |
| 518  | TV    | 5      | Tuvalu                                   |
| 520  | NH    | 5      | Vanuatu                                  |
| 539  | WQ    | 5      | Wake Island [United States]              |
| 540  | WF    | 5      | Wallis and Futuna [France]               |
+------+-------+--------+------------------------------------------+
37 rows in set (0.90 sec)

MariaDB [temps]> 

So 37 Countries. 74 total graphs. This is going to take a while…

First I’ll put up Australia and New Zealand as they are the two most different from everything else in terms of climate types.

Australia

GHCN v3.3 vs v4 Australia Difference

Interesting that the general trend is a roll off of heat. But a couple of years get a hot bump at the end.

GHCN v3.3 vs v4 Australia Anomaly

New Zealand

GHCN v3.3 vs v4 New Zealand Difference

Abut 1/2 C cooling of the deep past, but not much else.

GHCN v3.3 vs v4 New Zealand Anomaly

North Of Australia

I’m going to start this group with Indonesia, as it is the largest, then work my way around the nearby bits. These all ought to be substantially the same as they all share the same giant bath tub of water and currents.

Indonesia

GHCN v3.3 vs v4 Indonesia Difference

Either the historic Indonesia data are crap and need a lot of fixes, or they can’t decide what their temperature was in the past.. Nice warming jump added at the recent end.

GHCN v3.3 vs v4 Indonesia Anomaly

Timor-Leste

Looks like these folks are missing data in v3.3:

MariaDB [temps]> SELECT year,AVG(deg_c) FROM anom3 AS A 
INNER JOIN country AS C ON A.country=C.cnum  
WHERE C.abrev='TT' GROUP BY year;
Empty set (0.09 sec)

So the anomaly difference graph report fails:

============ RESTART: /SG500/xfs/chiefio/Py3/Aapac/a3v4deltaTT.py ============
stuffed SQL statement for TT Timor-Leste 
Executed SQL
[]
Got data
This is the exception branch
All Done
>>> 

So taking num3>0 and num4>0 of of the script (so it accepts years with no data, the result becomes:

============ RESTART: /SG500/xfs/chiefio/Py3/Aapac/a3v4deltaTT.py ============
stuffed SQL statement for TT Timor-Leste 
Executed SQL
[('1917', None), ('1918', None), ('1919', None), ('1920', None),
 ('1927', None), ('1928', None), ('1929', None), ('1930', None),
 ('1931', None), ('1932', None), ('1933', None), ('1934', None),
 ('1936', None), ('1938', None), ('1939', None), ('1940', None),
 ('1941', None), ('1951', None), ('1952', None), ('1953', None),
 ('1954', None), ('1955', None), ('1956', None), ('1957', None),
 ('1958', None), ('1959', None), ('1960', None), ('1961', None),
 ('1962', None), ('1963', None), ('1964', None), ('1965', None),
 ('1966', None), ('1967', None), ('1968', None), ('1969', None),
 ('1970', None), ('1971', None), ('1972', None), ('1973', None),
 ('1974', None), ('1975', None), ('1976', None), ('1977', None),
 ('1978', None), ('1979', None), ('1980', None), ('1981', None),
 ('1982', None), ('1983', None), ('1984', None), ('1985', None),
 ('1990', None)]
Got data
after the transpose

And we get an empty graph. All those “None” for difference data.

Printing the two sets of data has only the v4 data show up on the graph of anomalies:

GHCN v3.3 vs V4 Timor-Leste Anomaly

Where it looks like nobody has got around to molesting the data and making it toe the PC Line. We have a very hot 1930s, a cold 1960s, A return to ALMOST as hot in the 1980s, then a cold dip in the ’90s. Rather like we all experienced and rather like recorded in the history of the times. Golly.

Papua New Guinea

GHCN v3.3 vs V4 Papua New Guinea Difference

Very little change over much of the history, then about 1/4 C cooler in recent years with some ‘fliers’ of 1/2 C higher.

GHCN v3.3 vs V4 Papua New Guinea Anomalies

VERY significant range compression in the GISS/Hadley baseline years (about 1950-1990) then it widens out again with a bit of “higher highs”, then most recently it gets a cold year. Not looking at all like general warming over the years from CO2.

Malaysia

GHCN v3.3 vs V4 Malaysia Differences

Wow! Really cooling the past there in Malaysia. A full degree C colder in many cases; rising to only 1/2 C colder just before the baseline period. Then the baseline period kept at zero. (Remember this is just change between version 3.3 and version 4 for what is supposedly the SAME place and the same instruments recorded at one time in the past…) Then the recent data gets about a 1/3 C “lift” (but freezing the past has already created the slope needed…)

GHCN v3.3 vs V4 Malaysia Anomalies

Here we can see that it is no warmer now than it was in the past in the old version; but only after cooling the past a full degree C does that unfortunate fact go away.

Singapore

GHCN v3.3 vs V4 Singapore Differences

Interesting cold “adjustment” in the 1870s and then that dip in the late 1990’s is interesting, finally we end with an uptick of only about 1/4 C in the last datapoint.

GHCN v3.3 vs V4 Singapore Anomalies

Then the actual anomaly data shows a nice “dip” in the baseline period, but otherwise the actual temperature change has not been much at all over the years. Other than that one hot dot at the very end…

Brunei

Lookslike Brunei was also not in GHCN v3.3 so no “difference in anomalies” graph can be made:

MariaDB [temps]> SELECT year,AVG(deg_C) FROM anom3 AS A 
INNER JOIN country AS C ON A.country=C.cnum 
WHERE C.abrev='BX' GROUP BY year;
Empty set (0.48 sec)

So all we’ll get is the v4 anomaly data on the Anomalies graph:

GHCN v3.3 vs v4 Brunei Anomalies

Then this is too short a record to say much at all about climate. It’s about 35 years so only a tiny bit over one half the known 60 year cycle. Fitting a trend to cyclical data is a fools errand. I note in passing that recent years are about the same as the mid 1990s.

Philippines

GHCN v3.3 vs v4 Philippines Differences

Not much changed between the two data set versions. Looks like the W.W.II data changed a bit more.

GHCN v3.3 vs v4 Philippines Anomalies

So the 1800s were a bit of cold, then we see about 1-1.5 C of range in the Yr/Yr data until the “Baseline period” where the range narrows (closer to 3/4 C though near 1980 things are remarkably constant. In more recent years we have the return of some range (though it looks like minus some cold excursions) and the final temperature is very much like about 1965, 1942 or so, and around 1932. So while the slope of a fit line might well show a trend, the present temperature is not out of line with hot periods in the past. My best guess would be a bit of growth of the airport, UHI, and jet exhaust.

Palau

Oddly, Pelau right nearby the Philippines, has a different shape to their data…

GHCN v3.3 vs v4 Palau Differences

The deep past gets changed to a little cooler, then the present has a 1.5 C range to the CHANGES between version 3.3 and version 4 of what is supposedly the same place and data. Now Pelau isn’t big enough to have a whole lot of thermometers to chose between and among, so just why is the data that “mailable”? Eh?

GHCN v3.3 vs v4 Palau Differences

The actual anomaly graphs have the usual compressed “waistline” with reduced range in the “Baseline Years”, then with an otherwise almost constant spread and range of data from about 1950 to 1995, when suddenly the low ranges start to pull up. The spectacular bit, though, is the spike of roughly 2 C in the last few years. I’m sorry, but CO2 effects to not lurk for 40 years doing not much then suddenly show up in one year and stay for 3 or 4. That’s something else. Jet exhaust maybe? Isn’t that a big US Military spot?

Pacific Island Arc

This set is all those islands and atolls scattered around the Pacific Ocean toward North America (compared to the prior set). As ENSO tends to make an oscillator between the E and W sides of this basin, and some of these are N of the equator while most are S, I’m generally going to lay them out from near New Zealand over toward the Americas, but with those North of the Equator near the middle (some US owned Atolls mostly) set out separately. (Provided I can keep straight which of these rocks is classified as a what and who has had which name change and…)

Up North & Scattered

Here’s a few islands and atolls in the more northern part of the Pacific and scattered around a bit in the Big Empty.

Midway Islands

Midway also has no data in the GHCN v3.3 set:

MariaDB [temps]> SELECT year,AVG(deg_C) FROM anom3 AS A 
INNER JOIN country AS C ON A.country=C.cnum 
WHERE C.abrev='MQ' GROUP BY year;
Empty set (0.06 sec)

MariaDB [temps]>

So once again all we will get is the GHCN v4 Anomalies graph:

GHCN v3.3 vs v4 Midway Islands Anomalies

Other than a couple of “fliers” recently, the temperatures are rather like the hot points in the 1930s-50s. I note that the W.W.II years are missing. Low excursions are about the same in the 1920s-1940 and in the 1955-1975 range, then just “go away”. Rather like a 1/2 C “step function” happened in 1979. Very strange. Wonder if there was any equipment change then?

Johnston Atoll

GHCN v3.3 vs v4 Johnston Atoll Difference

Not much going on with Johnston Atoll. Then again they already have 2 C range in the anomaly (see next graph) so maybe nothing more was needed…

GHCN v3.3 vs v4 Johnston Atoll Anomaly

Once again almost nothing really happening until 1980, then a jump up; followed by another big jump up in about 1995. Odd little atoll. Wonder what was going on then… From the Wiki:

Chemical weapon demilitarization mission 1990–2000
Johnston Atoll Chemical Agent Disposal System (JACADS) building
Main article: JACADS

The Army’s Johnston Atoll Chemical Agent Disposal System (JACADS) was the first full-scale chemical weapons disposal facility. Built to incinerate chemical munitions on the island, planning started in 1981, construction began in 1985, and was completed five years later. Following completion of construction and facility characterization, JACADS began operational verification testing (OVT) in June 1990. From 1990 until 1993, the Army conducted four planned periods of Operational Verification Testing (OVT), required by Public Law 100-456. OVT was completed in March 1993, having demonstrated that the reverse assembly incineration technology was effective and that JACADS operations met all environmental parameters. The OVT process enabled the Army to gain critical insight into the factors that establish a safe and effective rate of destruction for all munitions and agent types. Only after this critical testing period did the Army proceed with full-scale disposal operations at JACADS. Transition to full-scale operations started in May 1993 but the facility did not begin full-scale operations until August 1993.

All of the chemical weapons once stored on Johnston Island were demilitarized and the agents incinerated at JACADS with the process completing in 2000 followed by the destruction of legacy hazardous waste material associated with chemical weapon storage and cleanup. JACADS was demolished by 2003 and the island was stripped of its remaining infrastructure and environmentally remediated.

Oh… So a lot of stuff shipped in, big construction, then years of running an incinerator… a BIG incinerator. I’m sure that had nothing to do with it… /sarc;

Wake Island

GHCN v3.3 vs v4 Wake Island Differences

Nothing changed much for years other than a roughly 1/4 C cooling of the past then BAM a 1.5 C range of changes in a few year and then back to not much change.

GHCN v3.3 vs v4 Wake Island Anomalies

Looks to me like another “Step function” of about 1 C in 1980 with a sight cooling trend over a cyclical spike of about 15 to 20 years. Not at all what steady increases in a warming gas would cause.

The more Southern Group

These are the islands that make an equatorial to South Pacific arc.

North Marianas Islands

GHCN v3.3 vs v4 Northern Mariana Islands Differences

Gosh, a 2.5 C range in teh anomalies just from variation in instrument chosen or processing. When you can get that much essentially “random” variation from what is supposedly the same small place and the same data / instruments, where are the error bars on that 1/2 C of “Global Warming” fantasy?

Interesting that this pushes up the “New Ice Age Comming” 1970s and pulls down the present. Just how crazy bad was this “Global Warming” chart that they needed to take 2.3 C out of it?

GHCN v3.3 vs v4 Northern Mariana Islands Anomalies

Gee from warming black dots to dead flat red dots in one “fix”. I wonder who got caught doing what and had to fix it? ;-0

Guam

GHCN v3.3 vs v4 Guam Differences

Another small island with big changes in their “historical” data. Looks like a tiny rise in the early ’80s, then a big 1 C cut around 2000.

New data looks to be about as high as what was reduced. I guess it would look bad to have a “Halt” to “Global Warming”, so need to take a tuck in that older “hot” time and preserve the warming “trend” that way… I note in passing that the 1920s to 1940s are about as hot as “whichever hot now is really now”, and only the “baseline period” is nominally cool.

Fed. Islands Of Micronesia

GHCN v3.3 vs v4 Federated States Of Micronesia Differences

Another one with a change “dip” around 2000.

GHCN v3.3 vs v4 Federated States Of Micronesia Anomalies

Oddly, even though in the same giant bathtub of warm water as Guam, these Islands have a cold 1920s to 1940s. Then essentially dead flat from 1950 to about 2000-2005 and only then a jump up (or smooth rise depending on version). Doesn’t look at all like a gentle persistent rise of 1/2 C due to CO2 and looks a whole lot more like ENSO, cyclical changes with step functions, or diddled data / bad measuring.

Marshal Islands

GHCN v3.3 vs v4 Marshal Islands Difference

Not much in the change department. Bit of a minor down tweak at the end.

GHCN v3.3 vs v4 Marshal Islands Anomalies

Other than the “dip” or “sag” in the “basline period” of about 1950 to 1990, not much in the anomalies either. OTOH, they have a nice 1 C range from bottom of the baseline to now pretty much baked in, so why change anything? Just ignore that pesky pre-baseline data and call it a warming trend.

Nauru

GHCN v3.3 vs v4 Nauru Differences

Changes all over the place and with a 2 C range. Big dropout from 1940 to 1960. Huge cooling of the hot 1930s.

GHCN v3.3 vs v4 Nauru Anomalies

Ah, that’s why. Turn a cooling down trend into a slight warming then throw away any recent data and anything newer than 1970. Can’t keep a place that’s getting cooler in the data now can we?

Kiribati

GHCN v3.3 vs v4 Kiribati Differences

Another “dogs breakfast” of changes. Almost 3 C of “fix ‘er up” done with cooling the 1920s to ’30s. Got to erase that pesky hot ’30s somehow. Then pull down the ’80s a little to erase the “pause” and make it a smoother trend.

GHCN v3.3 vs v4 Kiribati Anomalies

And “Bob’s Yer Uncle” a flat to cooling trend becomes a “warming out of the baseline period”. (Even though over all the data it isn’t warming, but no worries, nobody cares about data older than W.W.II).

Christmas Island

GHCN v3.3 vs v4 Christmas Island Differences

Again a big dropout of data in the baseline, then a nice 1/2 C of “Pop” added in 2000-2010.

GHCN v3.3 vs v4 Christmas Island Anomalies

So not erased the hot 30s & 40s here yet (which just begs the question how they could vary so much from nearby island to nearby island…) but did get rid of that annoying cold dip after 2000. Add a couple of juiced up hot years in the recent data and you too can turn a dead flat trendless Island into a Global Warming place. Just ignore that 30s & 40s data (don’t worry, it will be taken care of in v5, I’m sure… /sarc;)

Solomon Islands

GHCN v3.3 vs v4 Solomon Islands Differences

Another place with a 2 C range in the ‘fix up’ differences. Makes one wonder how bad the recent data are to need to much changing.

GHCN v3.3 vs v4 Solomon Islands Anomalies

Essentially trendless until after 2000. Even then not much (and mostly from removing low going excursions). Wonder if they moved the thermometer closer to a cement runway 😉

Tuvalu

GHCN v3.3 vs v4 Tuvalu Differences

About a 1 C range of what looks like a few semi-random changes.

GHCN v3.3 vs v4 Tuvalu Anomalies

Nothing much at all going on until the year 2000 then a sudden jump up of about 1/2 C consistent with the 1940 temperatures. This will create a false trend if you plot a trend line from the “baseline period” to the present when at best there’s a cyclical thing happening (and at worst it is an instrumentation issue).

Tokelau

Oh man is this one a challenge / amusing:

That big pop up of up to 1.5 C in 1965-70 range shows that somebody did go back and get different data, yet the result (graph below) is still just crazy time.

GHCN v3.3 vs v4 Tokelau Anomalies

A full 4 C+ of range, all over the place, with the most recent data quite cool. No trend until the late 1970s, then a massive pop up of 1 C for near a decade+, a drop of 4 C, and then it returns with mostly cooler data but still bouncing around by 2 C. This one is a real “Dig Here!” issue.

Wallis & Fortuna

GHCN v3.3 vs v4 Wallis & Fortuna Differences

One degree C of changes in the data with no clear pattern nor reason. So one full degree C of “jitter” can be in the data with no connection at all to CO2 (By Definition – since this is only the result of change in instruments or processing – and I doubt there were many instruments to change in Wallis & Fortuna).

GHCN v3.3 vs v4 Wallis & Fortuna Anomalies

Other than a “dip” in the baseline period (that rughly 1965-1985 low) it is essentially flat. Present temperatures essentially the same as around 1960.

Samoa

GHCN v3.3 vs v4 Samoa Differences

How unusual. the past is warmed in the v4 data and the present is cooled. I guess having 2 C of warming in Samoa didn’t look very CO2 physical as it was only supposed to be about 1/2 C.

GHCN v3.3 vs v4 Samoa Anomalies

We still have a nice 2 C of range, rising from -1 C in 1900 through 0 C (or equal to the average) in 1920 to 1980, then finally a bit of “lift” in the end with one year at +1 C and another at closer to +1.5 C. Yet the low years are about normal. Wonder what was in the missing years (and why “modern” data is missing but we have full data prior to 1995 or so…

American Samoa

GHCN v3.3 vs v4 American Samoa Differences

GHCN v3.3 vs v4 American Samoa Anomalies

Vanuatu

GHCN v3.3 vs v4 Vanuatu Differences

Vanuatu looks like another of those “too hot to be CO2 physical need to cool it” charts. Nothing much changes in the past, but the recent (“highest quality”) data gets cooled up to 3/4 of a degree C.

GHCN v3.3 vs v4 Vanuatu Anomalies

Basically a flat chunk from about 1950 to 1990 then a sudden jump up by about 3/4 C to 1.5 C. Anyone want to bet it became a “destination” then and the airport got bigger with more jet traffic and tarmac / concrete? But I can see where you would want to blend down that big jump into a more gentile rise. Doesn’t stand out as so odd then.

New Caledonia

GHCN v3.3 vs v4 New Caledonia Differences

A gentle cooling of the 1940s so they blend in with each side (can’t have them being about the same as now, can we?)

GHCN v3.3 vs v4 New Caledonia Anomalies

So now it looks like a steady flat period from about 1940 to 1965, then warming. Except most of the recent years data looks a lot like the 1930s.

Norfolk Island

GHCN v3.3 vs v4 Norfolk Island Anomalies

Nice little 1/2 C “POP” up in the recent years there. Wonder what that does?

GHCN v3.3 vs v4 Norfolk Island Differences

Oh, erases that cold dip… Realistically, this isn’t warming. A couple of recent years have a warm spike, but about the same as 1998 and the 19-teens, and with a (pre-erasure) cold dip in the 2010’s about like prior years too.

Fiji

Fiji is a bit of a trip. They change the recent data to about 1 C warmer and it is still cooling.

GHCN v3.3 vs v4 Fiji Differences

So about 1/2 C cooler in 1990 to 1/2 C warmer in the early 2000s. Looking at the graph below, it seems to have taken a “rolling off to cooler” in the black dots and turned it into a “continuing to warm”… Wonder if they manicure fingernails as well? /snark;

GHCN v3.3 vs v4 Fiji Anomalies

While it does look like a trend line from the “Baseline” years to the present would have a warming trend, the data overall do not. “Now” is no warmer than 1900 or 1930 or 1980. It does look like some low going excursions might be being clipped off. Airport cement anyone?

Tonga

GHCN v3.3 vs v4 Tonga Difference

Looks like about a 3/4 C range of mindless changes.

GHCN v3.3 vs v4 Tonga Anomaly

And more random coin toss than trend in the anomalies.

Niue

GHCN v3.3 vs v4 Niue Differences

Nobody changing much n Niue.

GHCN v3.3 vs v4 Niue Differences

And no “Global Warming” either… Guess that’s why the data get sparse after 1990, so it can be “re-imagined” and infilled via homogenizing from somewhere else.

French Polynesia

GHCN v3.3 vs v4 French Polynesia Difference

Again with the cooling of the baseline window… I think we’re getting a trend here… but not in the climate.

GHCN v3.3 vs v4 French Polynesia Anomaly

Pitcairn Islands

Poor Pitcairn Islands. Off near nowhere. Not important enough for anyone to diddle the data…

GHCN v3.3 vs v4 Pitcairn Islands Differences

Nearly nothing changed.

GHCN v3.3 vs v4 Pitcairn Islands Anomalies

No discernable trend to the anomalies / data… Guess “Global Warming” isn’t very global after all…

In Conclusion

IMHO the degree of change of what ought to be the same data from the same instruments between these “versions” of the “same” data indicate that any warming found is as likely to be error, or more likely to be error, than anything real.

Just looking at the anomaly profiles shows that islands located in the same body of water with nearly constant sea surface temperatures have very different profiles, or shapes of the plotted data. How do you do that when the environment is the same from island to island?

My best guess is that it is local siting issues (in particular measuring at airports with changes of size, materials, and traffic – from grass shack by a Pan Am Clipper seaport to 10,000 foot of concrete and Jet Age Vacationing), or just flat out lousy measuring.

What I do NOT see in the data is a general and steady increase in warming, year over year, across many stations; the kind of thing CO2 and radiative blocking ought to cause.

There will not be a Tech Talk in this posting as it is in the prior postings and all that changes is the letter code used to select for the countries. If you want to know more about the data base used, the codes, and the processing done, see the prior postings.

Adjusting Good Data To Make It Match Bad Data

Reblogged from RealClimateScience.com:

mwr-035-01-0007b.pdf

On election day in 2016, both satellite data sets (UAH and RSS) showed a 15 year long hiatus in global warming, and bore no resemblance to the warming trend being generated by NOAA and NASA.  I captured this image in a November 16, 2016 blog post.

Gavin Schmidt Promises To Resign | The Deplorable Climate Science Blog

This is what the same graph looks like now.

Wood for Trees: Interactive Graphs

In the next image, I overlaid the current RSS graph on the 2016 image.  You can see how RSS was adjusted to match the NASA data.

I predicted this would happen on

Look for the satellite data to be adjusted to bring it into compliance with the fully fraudulent surface temperatures. The Guardian is now working to discredit UAH, so it seems likely that RSS will soon be making big changes – to match the needs of the climate mafia. Bookmark this post.

RSSChanges

Roy Spencer at UAH made the same prediction on January 9, 2017

“I expect there will soon be a revised TLT product from RSS which shows enhanced warming, too.

Here’s what I’m predicting:

1) neither John Christy nor I will be asked to review the paper

2) it will quickly sail through peer review (our UAH V6 paper is still not in print nearly 1 year after submission)

3) it will have many authors, including climate model people and the usual model pundits (e.g. Santer), which will supposedly lend legitimacy to the new data adjustments.

Let’s see how many of my 3 predictions come true.

-Roy”

Wood for Trees: Interactive Graphs

The reason I made this prediction was because Ted Cruz used an RSS graph in a Senate hearing in March of 2015. Carl Mears at RSS then came under intense pressure to make his data match the surface temperature data.

My particular dataset (RSS tropospheric temperatures from MSU/AMSU satellites) show less warming than would be expected when compared to the surface temperatures. All datasets contain errors. In this case, I would trust the surface data a little more because the difference between the long term trends in the various surface datasets (NOAA, NASA GISS, HADCRUT, Berkeley etc) are closer to each other than the long term trends from the different satellite datasets. This suggests that the satellite datasets contain more “structural uncertainty” than the surface dataset.

Ted Cruz says satellite data show the globe isn’t warming

You can see what Mears did to bring his data into compliance. This was his web page in November 2016.

Note that after 1998, the observations are likely to be below the simulated values, indicating that the simulation as a whole are predicting too much warming.

Climate Analysis | Remote Sensing Systems

But under intense pressure,  Mears altered his own data to bring it into compliance.  The large discrepancy became a small discrepancy.

there is a small discrepancy between the model predictions and the satellite observations.

Remote Sensing Systems

The image below overlays Mears’ old graph (V3) on his new one (V4.) It is clear what he did – he  eliminated the blue error interval, and started using the high side of the interval as his temperature.

RSS V3 shows no warming since 2002.

The warming was all created by tampering with the data to eliminate the error interval.

Spreadsheet

The corruption is now complete.  NASA has announced that new satellite data matches their surface temperature data. This was done to keep the President’s Commission on Climate Security from having accurate data to work with.

All government climate data goes through the same transition in support of global warming alarm. The past keeps getting cooler, and recent years keep getting warmer.

NASA 1999   NASA 2016

Government climate agencies appear to be using Orwell’s 1984 as Standard Operating Procedure.

Cooling Down the Hysteria About Global Warming

Reblogged from Watts Up With That:

Guest essay by Rich Enthoven

Recently, NASA released its annual report on global temperatures and reported that 2018 was the fourth hottest year on record, surpassed only by three recent years. This claim was accompanied by dire predictions of climate change and for immediate action to dramatically curtail CO2 emissions around the globe. Like every concerned citizen read this report with interest. I also read it as an informed and trained climate analyst – and I can tell that there are some serious problems with the report and its conclusions.

For starters, I can assure my readers that I am not a climate change “denier.” No one doubts the climate changed when it experienced the Ice Age that ended 12,000 years ago. I have read enough scientific literature to believe the well documented view that the planet experienced the Medieval Warm Period (950 – 1250 AD) and Little Ice Age (1550 – 1850 AD) when global temperatures changed materially. I have also read enough scientific literature to understand that solar and ocean cycles affect global climate.

NASA is now reporting significant changes to the global temperature. According to NASA (and others) the entire globe experienced a persistent warming trend in the early part of the 20th century (1911 – 1940). Then, this trend reversed, and the globe cooled until the 1970’s.[1] Now, NASA is reporting that the global temperature increased .31° C in the last 10 years and that this trend is different than the .31° C increase NASA reports for the 1930’s[2]. But, a closer look at the data and methods used by NASA should make any reader skeptical of their results.

image

Land Temperatures

It turns out, that over long periods of time it is actually quite difficult to measure temperature changes from climate consistently. The problems arise from changes in measurement technology (mercury bulbs then, semiconductors now) and changes in the sites surrounding the measurement locations. A good way to think about this problem is to consider Dallas Love Field Airport where average temperatures have been reported monthly since 1940. During that time Love Field transformed from a tiny airport near a small city[3] – to large urban airport with 200 daily flights. These changes have generated massive heat at the airport. It is no wonder that the reported temperatures at Love Field have trended up by approximately 2.9 ° F since 1940. [4]

image

But, when we look at the temperatures in Centerville, TX – much less affected by land use changes – we see the opposite trend. The average reported temperature in Centerville has been on a declining trend and now averages (on trend) .3 °F less than it was in 1940.[5]

As a result of this urban heat effect, scientists around the world have been identifying (or constructing) ‘pristine’ weather monitoring stations to get a clearer look at temperature changes. These stations are located in areas where urban development has not occurred and is not expected. These locations do not show any meaningful change in reported land temperatures. The best data comes from the National Oceanic and Atmospheric Administration (NOAA) which set up 114 rural temperature monitoring stations in the US in 2002 (USCRN). When we look at these, we see no persistent increase in US temperatures.[6] In fact, 2018 was .3°F colder than the first two years measured. February and March 2019 combined to be the coldest two-month period (temperature anomaly) ever recorded by the USCRN.

MONTHLY TEMPERATURE CHANGES AT USCRN STATIONS

image

And it is not just the US rural temperatures that are stable – all around the globe, temperature growth is eliminated once land use changes are eliminated. Shown below are temperature graphs from rural areas in Netherlands, Ireland, Chile, Antarctica, Japan[7], and China[8].

image

image

Further calling into question the global land temperature data used by NASA are climate scientists themselves. Seventeen leading climate scientists (including scientists at NOAA) recently co-authored a paper calling for a new network of global weather stations in which they lamented the “imperfect measurements and ubiquitous changes in measurement networks and techniques.”[9]

Even these efforts to measure temperature change may not be enough – even the ‘pristine’ USCRN temperature measurement locations continue to biased towards warmer temperatures from land use changes. For example, a parking area and road was built next to the USCRN weather station[10] at the University of Rhode Island leading to a .34 ° C increase in measured temperatures at that location.[11][12]

image

Ocean and Satellite Temperature Measurement

The NASA global temperature estimate also relies heavily on estimates of temperatures in the ocean and air above it. Ocean temperatures have been measured over the years with highly inconsistent methods (buckets off ships; water flowing through ship engine rooms; buoys; and lately, satellites). In addition to technology changes, there are short term annual ocean cycles such as the well-publicized El Nino/La Nina and long term (multi decade) cycles such as the Pacific (and Atlantic) Decadal Oscillations which affect ocean temperatures at many depths over decades. A recent report out of UC San Diego described the problem “Determining changes in the average temperature of the entire world’s ocean has proven to be a nearly impossible task due to the distribution of different water masses.”[13]

Respected climate scientists are tackling the ocean measurement challenge and come up with results very different than the NASA report. Satellite measurements from University of Alabama show atmosphere temperatures over the ocean increasing since 1980 (end of the last cooling period per NASA) but only at .13 ° C per decade.[14] Both major satellite measurement groups report temperatures are lower now than they were in 1998, although by different amounts.[15] Harvard University oceanographer Carl Wunsch estimated the average temperature of the ocean grew by .02 degrees during 1994 – 2013.[16] Scripps Institute of Oceanography recently estimated the ocean temperature growth at .1 ° C total over the last 50 years. The science and history of measuring ocean temperatures is far from ‘settled’ and there are plenty of credible estimates that ocean temperatures are not changing rapidly or at anywhere near the rate that NASA is estimating.

Back to the NASA Temperature Estimate

To come up with their global temperature assessments, NASA faces all these problems and more. For starters, there is very little reliable global scale land data before 1940, and there are still shortages of reliable data in many parts of the world. (Africa, Middle East). Most of the historical data has been affected by land use changes and measurement technology changes. As they have tried to deal with these problems, NASA has dramatically changed the locations and methods that they use to assess temperatures over the last several decades.[17] Some observers question whether the new locations and technologies have the same pattern as the old ones would have had.

Not only have they adjusted the locations they take land measurements from, NASA adjusts the data that goes into their estimates[18]. Here are examples from the NASA website for Darwin Airport, Australia and Reykjavik, Iceland that show the liberal data changes adopted by NASA.[19]

image

image

Readers should note several problematic elements of these graphs:

1) The unadjusted data does not indicate warming at these locations over the last 80 years.

2) The unadjusted data is shown in such a faint outline that its hard to see. Why would NASA present it this way?

3) As NASA changed each data set, they made the past appear cooler – the “adjusted, cleaned” data is cooler than the “unadjusted” data – and the “homogenized” data is cooler still. A cooler past allows NASA to claim current temperatures are dramatically higher.

The NASA has “adjusted, cleaned, and homogenized” the data from these locations along with thousands of others to make up the data set that NASA uses. They then add data from satellites and use data grid methodology to come up with a final temperature change result.

Needless to say, the NASA changes have been the subject of considerable debate – within the climate scientist community, the climate “skeptic” community, and even NASA itself.[20] The “unadjusted” raw data has been adjusted meaningfully over the years as NASA recalculates.[21] The satellite measurements are very controversial according Zeke Hausfather, climate researcher at Berkley Earth – “If you don’t like adjustments, you really shouldn’t use the satellite record.”[22] A major problem is that the average adjustments between raw and final data average strongly in one direction – the adjustments tend to cool the past – which makes the present temperatures seem warmer by comparison.[23] NASA itself is apparently unhappy with their current formulas and plans to release version four of their “adjustments” soon.[24]

Other Indicators of Global Temperatures

The debate about the temperatures adjustments and estimates used by NASA can quickly get in to mathematical manipulations that are well beyond the level of this article. Scientists are arguing about changes in the global temperature that are on the order of one percent of one degree centigrade. Fortunately, we can look at a variety of other climate indicators in an effort to verify whether temperatures are changing. According to the theory endorsed by NASA, humans have been increasing carbon dioxide (CO2) in the atmosphere for more than 70 years[25] – and this increased CO2 has led to demonstrably higher global temperatures which affect major aspects of global climate.

Fortunately for the planet, there is no evidence of change in large scale climate indicators that should be changing with the temperature. Here are some notable examples:

· US Land Temperatures: In 1986, James Hansen testified to congress that rising CO2 levels would cause US temperatures to rise by three to four degrees by 2020. [26] This prediction was spectacularly wrong – US land temperatures have moved at most a fraction of that amount since 1986.[27]

image

· Sea Level Rise: NASA (and later Al Gore) have made it clear that a warmer planet would cause ice to melt and the seas to expand – rising by up to four feet in 2050[28]. An accelerating trend in sea levels would potentially inundate lower elevation cities. But, NOAA data makes it clear that there is no change in the rate of sea level increase since measurements began.[29] If the warming globe would accelerate sea level changes, and we don’t see acceleration – it seems reasonable to suggest the globe isn’t warming.

image

image

· Hurricanes and Other Adverse Weather Events: By the early 2000s climate scientists told us to expect an increase in hurricanes due to higher temperatures in the ocean. Instead, the US experienced a major hurricane drought from 2006 – 2016.[30] In fact, global hurricanes/typhoon activity have shown no up trend in frequency or severity for the last fifty years.[31] The IPCC also reported in 2013 that there was no change in frequency of other adverse events such as droughts, floods, and tornados.

image

· Glaciers: Observers often become concerned as they see glaciers melting and blame it on global warming. It is certainly true that on average glaciers in the northern hemisphere have been retreating lately. But, glaciers have been retreating since the end of the Little Ice Age (1850) and numerous studies point out that many glaciers were actually melting faster during early 1900’s than they are today.[32] Glacier Bay in Alaska is a good example of the long term melting trend.

image

· Snowfall: In 2001, the scientists at IPCC (worlds global authority on climate change) said that rising global temperatures would result in a reduction in snowfall and even the end of skiing industry.[33] However, according to both NOAA and Rutgers University, snowfall has been trending up across the northern hemisphere since 1970. If less snow is expected from higher temperatures – is more snow an indicator of lower temperatures?[34]

image

These are large scale indicators that should not be subject to much measurement debate. They are not subject to “adjustments.” They all tell me that the NASA report is hopelessly biased in favor of reporting a temperature increase that is not happening.

Motivation for NASA to Report Higher Temperatures

Why would NASA come up with results so different from those of other climate observations? Consider the history of the NASA global temperature estimates. In 1986, James Hansen broadly publicized his global warming theory in testimony before the US Senate. For the next 27 years, Mr. Hansen was the chief scientist at NASA in charge of preparing and presenting those estimates. Is it unreasonable to suggest that the “adjustments” and formulas he used after his Senate testimony were biased with an effort to make his predictions turn out to be correct? How much of the NASA estimate is a simple self-fulfilling prophesy?

It’s not just NASA that is subject to significant pressure which likely introduces bias into their results. Climate scientists may be in the same position as those in other fields (i.e. nutrition, pharmaceuticals, psychology) where the desire to produce a pre-selected result influences the inputs, methods, and findings of their science. Alarming results (“hottest ever!” “disaster predicted” “urgent action needed”) all generate headlines; speaking engagements; trips to climate conferences (IPCC); and additional funding for more research. When scientists find opposite results (“nothing is really changing” “it’s just weather” “random events as usual”) they get no publicity; no funding; and instead are attacked (“pro big oil” “anti-environment” or worst of all, a “climate change denier.”)[35] There are indeed thousands of scientific papers that are at odds with NASA, but they don’t get nearly the media coverage and they are not included in NASA’s estimates.

Summary

It is time for a much more open and fair reporting and debate about global temperatures and climate change. Every time an adverse weather event occurs, we have news media blaming it on climate change that isn’t happening. We now have people marching in the streets over a non-existent crisis. All around the globe, trillions of dollars are being spent to avert a perceived global temperature crisis that is not happening. These energies and funds could be spent on far better uses to protect our environment, educate our people, and actually help the planet. We could be spending money on keeping toxins out of our ecosystems; keeping our oceans clean and healthy; improving sustainable farming techniques; expanding and protecting our natural habitats. Its time to take real action to protect and improve our planet – and stop the misplaced worry about climate change.


[1].https://climate.nasa.gov/vital-signs/global-temperature/

[2] Temp anomalies per NASA site: 2018 +.82 ° C less 2008 +.51 ° C =+.31 ° C. 1939 -.03 ° C – 1929 -.34 ° C =+.31 ° C

[3] Dallas population 400,000. Love Field had three daily flights. Wikipedia

[4] Data per iweathernet.com. Authors trend analysis – least squares regression.

[5] Iweathernet.com Authors trend analysis – least squares regression.

[6] https://www.ncdc.noaa.gov/temp-and-precip/national-temperature-index/time-series?datasets%5B%5D=uscrn&parameter=anom-tavg&time_scale=p12&begyear=2004&endyear=2019&month=3 See also https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2015GL067640 for discussion of this data series. Trend is not significant at any reasonable level of certainty. Measurements themselves are subject to +/-.3°C at source.

[7] Temperatures from Japanese Meteorological Association.

[8] https://www.sciencedirect.com/science/article/pii/S0048969718331978

[9] Journal of Climatology 3/1/18 – https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.5458

[10] Data available at: https://www1.ncdc.noaa.gov/pub/data/uscrn/products/monthly01/CRNM0102-RI_Kingston_1_W.txt

[11] https://iowaclimate.org/2018/04/09/infrastructure

[12] Moose, Wy in Grand Teton National Park is experiencing record park visitors. Are they affecting measured temperatures at the USCRN site there?

[13] https://www.sciencedaily.com/releases/2018/01/180103160129.htm)

[14] https://www.nsstc.uah.edu/climate/2019/february2019/tlt_201902_bar.png Note this is closer to one third of the NASA estimated increase.

[15] http://www.drroyspencer.com/2014/10/why-2014-wont-be-the-warmest-year-on-record/

[16] https://www.tandfonline.com/doi/full/10.1080/16000870.2018.1471911)

[17] https://data.giss.nasa.gov/gistemp/history/

[18] https://data.giss.nasa.gov/gistemp/history/

[19] https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show.cgi?id=501941200000&dt=1&ds=5

[20] Sample paper on the debate from Journal of Geophysical Research – “There remain important inconsistencies between surface and satellite records.” https://pielkeclimatesci.files.wordpress.com/2009/11/r-345.pdf

[21] https://realclimatescience.com/2019/03/nasa-tampering-with-reykjavik-raw-temperature-data/

[22] https://www.carbonbrief.org/explainer-how-surface-and-satellite-temperature-records-compare

[23] https://data.giss.nasa.gov/gistemp/history/

[24] https://data.giss.nasa.gov/gistemp/

[25] CO2 has risen from 315 ppm to 380 ppm per Mauna Loa Observation 1960 – 2018.

[26] https://reason.com/archives/2016/06/17/climate-change-prediction-fail/print).

[27] https://www.ncdc.noaa.gov/temp-and-precip/national-temperature-index/time-series?datasets%5B%5D=uscrn&parameter=anom-tavg&time_scale=p12&begyear=2004&endyear=2019&month=2.

[28] https://www.nytimes.com/1988/06/24/us/global-warming-has-begun-expert-tells-senate.html?/pagewanted=all

[29] NOAA Tides & Currents – https://tidesandcurrents.noaa.gov/sltrends/sltrends_station.shtml?id=9414750

[30] US Hurricanes: https://journals.ametsoc.org/doi/pdf/10.1175/BAMS-D-17-0184.1

[31]Global Cyclone activity: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2011GL047711

[32] http://appinsys.com/globalwarming/gw_4ce_glaciers.htm

https://www.the-cryosphere-discuss.net/tc-2018-22/tc-2018-22.pdf https://www.researchgate.net/publication/312185500_High_sensitivity_of_North_Iceland_Trollaskagi_debris-free_glaciers_to_climatic_change_from_the_%27Little_Ice_Age%27_to_the_present

[33] https://www.theguardian.com/environment/2001/jan/23/globalwarming.climatechange)

[34] In 2019, Mother Nature is making this point emphatically with at or near record snowfall and cold temperatures across North America and Europe.

[35] Prof. Ross McKitrick http://www.rossmckitrick.com/uploads/4/8/0/8/4808045/gatekeeping_chapter.pdf and Judith Curry are well known commentators on this phenomenon.

GHCN v3.3 vs v4 Anomaly North America

Reblogged from Musings from the Chiefio:

GHCN v3.3 vs v4 Anomaly North America

In prior postings I did a sample of various countries around the world, and a full set of South America and Antarctica. This extends that set with North America.

I’m goiing to group things into North Continental (USA, Canada, Greenland), Central (Mexico and Central American countries) and Carib, those islands in the Caribbean Sea. Why? Because countries in those areas ought to look a lot more like each other in terms of Anomaly than like those in other groups. The Caribbean is dominated by water and tropical conditions. Central America and Mexico are about the same but with a bit less water influence. The USA, Canada and to some extent Greenland are large land masses prone to cold winters and significantly further north (so more summer / winter sun changes). This also lets me group making the graphs into smaller work units and it is less oppressive 😉

Here’s the Koppen Climate graph for North America so you have something for comparison.

Koppen Climate Zones for North America

From that I think you can see why I’d put Cuba into the Caribbean rather than count it in South America… like GHCN did…

The Graphs

Northern Big 3 (Canada, USA, Greenland)

Greenland

GHCN v3.3 vs v4 Greenland Difference

So about 1 C range of changes both ways. So an error band of about 1 C? Is that what this means? So we can’t know if there is 1/2 C of warming… The the actual anomalies below bounce around by a 3 C range (that might have a flier off the graph – I didn’t check). Looks to me like Greenland data is just chaotic weather. I do note that the really big DIP happens right on the “baseline period” used by GISStemp (1950-1980) or Hadley (1960-1990)…

GHCN v3.3 vs v4 Greenland Anomaly

Canada

GHCN v3.3 vs v4 Canada Difference

Interesting that it’s a roll off to colder. The anomalies (below) look a mess, but without evident warming. More like the loss of some very low going extremes recently. (One wonders if the big freeze in the last couple of years will show up in future data?)

GHCN v3.3 vs v4 Canada Anomaly

USA

GHCN v3.3 vs v4 USA Anomaly Difference

Again with recent data cooled. Wonder if they were embarrassed by all the attention over the last few years. We also again see no real warming tops, only loss of cold excursions and a general narrowing of the range. Or perhaps being early to the party, they had already “cooked” the v2 data so no more needed here. /snark;

GHCN v3.3 vs v4 USA Anomaly

The Caribbean & Bermuda

These are all in one large shallow water basin with common weather. They ought to be nearly identical.

Antigua & Barbuda

Antigua has no data in GHCN v3.3:

MariaDB [temps]> SELECT COUNT(deg_C) 
FROM anom3 AS A 
INNER JOIN country AS C on A.country=C.cnum 
WHERE C.abrev="AC";
+--------------+
| COUNT(deg_C) |
+--------------+
|            0 |
+--------------+
1 row in set (0.00 sec)

So all we get is the v4 anomaly graph:

GHCN v4 Antigua & Barbuda Anomaly

Bermuda

GHCN v3.3 vs v4 Bermuda Difference

Not a lot of change in the data. They seem to have cooled since 1980 (see graph below) and were hotter back around 1880 – 1860.

GHCN v3.3 vs v4 Bermuda Anomaly

The Bahamas

GHCN v3.3 vs v4 Bahamas Difference

About 1/2 of recent data warmed about 1/2 C, and a cool dip about 1940-1980; but now about the same as the mid 1800s. Cyclical changes?

GHCN v3.3 vs v4 Bahamas Anomaly

Barbados

GHCN v3.3 vs v4 Barbados Difference

Drop the past about 0.4 C and warm the recent about 1/4 C – Presto! a trend! Except that those pesky 1800s look about the same as now. Better use that cold snap from 1940 to 1980 as the “baseline period” and ony measure warming against it.

GHCN v3.3 vs v4 Barbados Anomaly

Cayman Islands

GHCN v3.3 vs v4 Cayman Islands Difference

Again with cooling the past a bit. But from 1990 to date is basically flat. Ought not the “warming from CO2” be more now and less in 1970?

GHCN v3.3 vs v4 Cayman Islands Anomaly

Dominica

Dominica has no data in v3.3, so all we get is the v4 anomaly graph.

MariaDB [temps]> SELECT COUNT(deg_C) 
FROM anom3 AS A 
INNER JOIN country AS C on A.country=C.cnum 
WHERE C.abrev="DO";
+--------------+
| COUNT(deg_C) |
+--------------+
|            0 |
+--------------+
1 row in set (0.06 sec)

GHCN v4 Dominica Anomaly

Dominican Republic

GHCN v3.3 vs v4 Dominican Republic Difference

So they had a crazy high 2 C “warming” rate, chop 1 C out of it and get a more respectable 1 C rate. Or is there just 1.5 C of “random” in temperature measuring on islands?

GHCN v3.3 vs v4 Dominican Republic Anomaly

Grenada

GHCN v3.3 vs v4 Grenada Difference

Again with 2 C of warming, so chop a degree C out of some of it, yet the most recent data points (see below graph) are below normal. But that’s just weather, right?

GHCN v3.3 vs v4 Grenada Anomaly

Guadaloupe

GHCN v3.3 vs v4 Guadeloupe Difference

A whopping 5 C range of temperatures, but currently about the same as 1940. Natural variability or error band, one of the other, is way higher than that 1/2 C of Project Fear Warming.

GHCN v3.3 vs v4 Guadeloupe Anomaly

Haiti

GHCN v3.3 vs v4 Haiti Difference

Looks like nobody fooled with the temperatures between versions. Guess a major hurricane will stop that kind of thing for a while. Anomaly graph below is rather flat too. Had a rise in to 1960, then was ignored for about 20 years, and now the temperatures are like “1940 all over again”.

GHCN v3.3 vs v4 Haiti Anomaly

Jamaica

GHCN v3.3 vs v4 Jamaica Difference

Push up the 40s (outside the baseline interval) a 1/2 C and drop the baseline spots about 1/2 C but with some noise in it, then lift the very recent by about 1/2 C. I think we’ve seen that before.

Per the anomaly graph below, Jamaica has warmed about 3 C. All that without setting regular record temperatures and with nearby islands not having the same rise. Airport tarmac anyone? Didn’t Jamaica have a big pop in tourism with the whole Bob Marley / Reggae thing? I know I went. Locals were complaining about added traffic, all the airplanes and hotels… I note in passing the hot late 1800s highs are about the same as now.

GHCN v3.3 vs v4 Jamaica Anomaly

Martinique

GHCN v3.3 vs v4 Martinique Difference

Differences look like about 1 C range of random. Anomaly trend looks like it was cold in the ’60s and recovered.

GHCN v3.3 vs v4 Martinique Anomaly

Netherlands Antilles

GHCN v3.3 vs v4 Netherlands Antilles Difference

A nice 1/4 C or so cooling of the past, and another 1/4 C to 1/2 C warming of the recent data, pretty soon you got yourself a trend. Except now is about the same as 1980.

GHCN v3.3 vs v4 Netherlands Antilles Anomaly

Puerto Rico

GHCN v3.3 vs v4 Puerto Rico Difference

Well that’s interesting. In v3.3 it was heating up by 1 C in Puerto Rico, so now the change is a 1 C cooling, and Puerto Rico is showing mostly flat trend in the red anomaly spots below. Almost like a decade of scrutiny and being pulled before congress might have caused some folks to fear being caught…

GHCN v3.3 vs v4 Puerto Rico Anomaly

St. Kits & Nevis

GHCN v3.3 vs v4 Saint Kits & Nevis Difference

Nothing goning on in St. Ktis & Nevis. Oddly, as all these islands are in the bathtub together, their anomaly graphs ought to all look alike. Yet they don’t…

GHCN v3.3 vs v4 Saint Kits & Nevis Anomaly

St. Lucia

Saint Lucia has no data in either version, so no graphs at all:

MariaDB [temps]> SELECT COUNT(deg_C) 
FROM anom3 AS A 
INNER JOIN country AS C on A.country=C.cnum 
WHERE C.abrev="DO";
+--------------+
| COUNT(deg_C) |
+--------------+
|            0 |
+--------------+
1 row in set (0.06 sec)


MariaDB [temps]> SELECT COUNT(deg_C) 
FROM anom4 WHERE abrev="ST";
+--------------+
| COUNT(deg_C) |
+--------------+
|            0 |
+--------------+
1 row in set (0.31 sec)

Kinda makes you wonder why it is in the inventory at all.

St. Pierre & Miquelon

GHCN v3.3 vs v4 Saint Pierre & Miquelon Difference

Nice gentle almost unnoticed 1/4 C cooling of the past, a narrow “dip” in the baseline interval. All in all, nicely done sculpting.

GHCN v3.3 vs v4 Saint Pierre & Miquelon Anomaly

ST. Vincent & The Grenadines

Saint Vincent & The Grenadines have no data in version v3.3.

MariaDB [temps]> SELECT COUNT(deg_C) 
FROM anom3 AS A 
INNER JOIN country AS C on A.country=C.cnum 
WHERE C.abrev="VC";
+--------------+
| COUNT(deg_C) |
+--------------+
|            0 |
+--------------+
1 row in set (0.01 sec)

So all we get is this v4 anomaly graph:

GHCN v4 Saint Vincent & The Grenadines Anomaly

Trinidad & Tobago

GHCN v4 Trinidad & Tobago Difference

WOW, that 2 C of change down in the “baseline interval” is about the same as the dip in the anomaly graph then… Without that 2 C of down, Trinidad & Tobago would look sort of flat; with “now” temps about the same as the 1920-1930 temps.

GHCN v4 Trinidad & Tobago Anomaly

Virgin Islands (US)

One wonders what happened to the British Virgin Islands in terms of temperatures… but moving on…

GHCN v4 U.S. Virgin islands Difference

Sure doesn’t look like warming to me. Given some islands flat, and some with 3 C of warming, I think we’re looking at land use issues, thermometer location, or error bands / data fudging.

GHCN v4 U.S. Virgin islands Anomaly

Mexico & Central America

Mexico

GHCN v3.3 vs v4 Mexico Difference

Gee, that recent rise at the tail of the anomaly data (below) seems to match in shape the rise in the difference graph (above).

GHCN v3.3 vs v4 Mexico Anomaly

Belize

GHCN v3.3 vs v4 Belize Difference

The actual anomaly data (below) isn’t warming on the top end, though we do see the loss of low going anomalies in the recent data. As though tons of concrete at the airport held heat over night… The change graph isn’t doing much either, though around 1980 got a spike. Sleepy little Belize…

GHCN v3.3 vs v4 Belize Anomaly

Guatemala

GHCN v3.3 vs v4 Guatemala Difference

Not really warming though we lose some of the low going anomalies in recent data (see graph below) and the changes between v3.3 and v4 are not much. Looks like nobody is paying attention to Guatemala.

GHCN v3.3 vs v4 Guatemala Anomaly

El Salvador

GHCN v3.3 vs v4 El Salvador Difference

Looking at the anomaly plot below, there’s a nice “dip” in the baseline interval, and a gap about 1990-2005, but then some crazy changes in the above difference graph. Looks to me like +/- 1 C of error band and nobody really knowing just what the temperature is in tenths of C.

GHCN v3.3 vs v4 El Salvador Anomaly

Honduras

GHCN v3.3 vs v4 Honduras Difference

Already has a nice “dip” in the baseline interval, but those changes in the recent time are just wild.

GHCN v3.3 vs v4 Honduras Anomaly

Nicaragua

GHCN v3.3 vs v4 Nicaragua Difference

We have 1/2 C drop added to the baseline, and 1 C of rise added in recent data. Just about the same as the “warming” found. Looks like someone found a way to get Nicaragua data in line with goals.

GHCN v3.3 vs v4 Nicaragua Anomaly

Coasta Rica

GHCN v3.3 vs v4 Costa Rica Difference

Nice dip in the baseline interval, but what’s this with the cooing at the end? Costa Rica just not getting hot? And right next to Nicaragua too. Ought to be nearly identical, but isn’t.

GHCN v3.3 vs v4 Costa Rica Anomaly

Panama

GHCN v3.3 vs v4 Panama Difference

Gee.. Looks to me like Panama is being flat to slightly cooling (see below) and not much change at all between v3.3 and v4. I wonder if this is at a US Military Base and they are grumpy if you play with their data? I wonder if that’s why it cuts off in 1980… Seems to me someone ought to know the temperature in Panama right now.

GHCN v3.3 vs v4 Panama Anomaly

Tech Talk

The specifics on the report / graph making programs is in the first “by country” posting so will not be repeated here.

This SQL produced the list of countries in North America (Region 4):

SELECT cnum, abrev,region, cname 
FROM country WHERE region=4 ORDER BY cname;

Here’s the list:

MariaDB [temps]> source bin/Namerica.sql
+------+-------+--------+------------------------------------+
| cnum | abrev | region | cname                              |
+------+-------+--------+------------------------------------+
| 426  | AC    | 4      | Antigua and Barbuda                |
| 423  | BF    | 4      | Bahamas, The                       |
| 401  | BB    | 4      | Barbados                           |
| 402  | BH    | 4      | Belize                             |
| 427  | BD    | 4      | Bermuda [United Kingdom]           |
| 403  | CA    | 4      | Canada                             |
| 429  | CJ    | 4      | Cayman Islands [United Kingdom]    |
| 405  | CS    | 4      | Costa Rica                         |
| 430  | DO    | 4      | Dominica                           |
| 407  | DR    | 4      | Dominican Republic                 |
| 408  | ES    | 4      | El Salvador                        |
| 431  | GL    | 4      | Greenland [Denmark]                |
| 409  | GJ    | 4      | Grenada                            |
| 432  | GP    | 4      | Guadeloupe [France]                |
| 410  | GT    | 4      | Guatemala                          |
| 411  | HA    | 4      | Haiti                              |
| 412  | HO    | 4      | Honduras                           |
| 413  | JM    | 4      | Jamaica                            |
| 433  | MB    | 4      | Martinique [France]                |
| 414  | MX    | 4      | Mexico                             |
| 434  | NT    | 4      | Netherlands Antilles [Netherlands] |
| 415  | NU    | 4      | Nicaragua                          |
| 416  | PM    | 4      | Panama                             |
| 435  | RQ    | 4      | Puerto Rico [United States]        |
| 417  | SC    | 4      | Saint Kitts and Nevis              |
| 436  | ST    | 4      | Saint Lucia                        |
| 438  | SB    | 4      | Saint Pierre and Miquelon [France] |
| 437  | VC    | 4      | Saint Vincent and the Grenadines   |
| 424  | TD    | 4      | Trinidad and Tobago                |
| 425  | US    | 4      | United States                      |
| 440  | VQ    | 4      | Virgin Islands [United States]     |
+------+-------+--------+------------------------------------+
31 rows in set (0.00 sec)

In Conclusion

So there you have it. A few days of “seat time” at the computer. Hopefully it is of use to someone. I think it does point out what country’s data needs more scrutiny. Then there’s also the question of why trends in some nations are different from another very nearby and in the same body of water. Furthermore, there’s a great “cross check” for this in that there is surface water temperature data from decades of hurricane tracking. On these small islands, air temperature never strays far from water temperature. I was swimming in Jamaica once when rain started. The ocean, air, and rain all at 86 F.

It does look like there was a cyclical “dip” after the hot 1930s-40s into a cool ’50s-70s and we have newspaper and magazine articles from then shouting about a New Little Ice Age (and I personally remember it – IT Happened.) So despite the folks saying that picking it for a “baseline” doesn’t matter, I think it’s just too convenient. The method I used to calculate anomaly has no baseline. A given thermometer is only compared to itself over a selected month across the years. (So, for example, the Jamaica Airport thermometer would have all of its Januaries averaged then the January anomaly computed against that. Repeat for each other month of the year for each instrument.) Then there is just the shortness of most records. Many just start in that cold period and rise out of it, not having an old 1870 hot sample to see in their past.

Finally, with that much change showing up in some very small countries, you know they didn’t have a dozen thermometers to choose from in 1890. It must be some kind of “intervention” when decades of data all move by the same amount. It just screams “Fudging the data” (though I’m sure they would call it fixing errors in the past). But when all the “warming” comes from the “fixing up” and “adjusting” (even of this “unadjusted” data) or from splicing a 1/2 cycle and calling it a trend: Just where is the room for CO2?

It took me a few days of “seat time” to make this set and post it, so don’t worry if you just want to look at a few each day over time. After the first dozen even my eyes started to glaze over 😉 But these will be here for months or years to come, for your pondering.

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