<p>Gordon Stanger's reply is the correct answer to this question, but I wanted to point out something else. Saying that there "hasn't been a global warming trend in 20 years" is a tricky statement to begin with. There are a number of papers discussing it, but I think this one is a good summary:<br>
<a href="http://onlinelibrary.wiley.com/doi/10.1029/2009GL037810/abstract;jsessionid=0D260FA124707D1AD055454F9C2FBAB4.f01t03" rel="nofollow" title="Is the climate warming or cooling?">Is the climate warming or cooling?</a><br>
Essentially, the point is that climate is variable enough that you can get decades of no trend or even slight global cooling; but the longer-term trends <em>always</em> show warming. Where you choose to start and end your regression line to calculate a trend is an easy way to get whatever answer you want. To quote their abstract:</p>

<blockquote>
  <p>Numerous websites, blogs and articles in the media have claimed that the climate is no longer warming, and is now cooling. Here we show that periods of no trend or even cooling of the globally averaged surface air temperature are found in the last 34 years of the observed record, and in climate model simulations of the 20th and 21st century forced with increasing greenhouse gases. We show that the climate over the 21st century can and likely will produce periods of a decade or two where the globally averaged surface air temperature shows no trend or even slight cooling in the presence of longer-term warming.</p>
</blockquote>

CC BY-SA 3.0
2016-06-10T20:29:53.887Z
2016-06-10T20:29:53.887Z
Jareth Holt
<p>Most estimates of global surface temperature "anomaly" come from NASA GISS network database <a href="http://data.giss.nasa.gov/gistemp/station_data/" rel="nofollow">http://data.giss.nasa.gov/gistemp/station_data/</a> .
There are some stations that show cooling trend over 100-year-long history of observations, as shown here:
<a href="https://stevemosher.wordpress.com/2010/09/29/needs-chec/" rel="nofollow">https://stevemosher.wordpress.com/2010/09/29/needs-chec/</a> 
This is considered as a normal "bell-curve" distribution.</p>

<p>However, as it was noted in old discussions,  there are several examples where the OPPOSITE trend happens on ADJACENT stations that are only 50 km apart. This means that the spatial distribution of surface stations is insufficient to reconstruct characteristics of temperature field (averages, etc.), because the spatial sampling does not satisfy criteria for Nyquist-Shannon Sampling Theorem.</p>

<p>To get the sampling right, the station site locations must be at least half of 50km, or about 25 km apart around the globe. With Earth surface having 5×10<sup>8</sup> km<sup>2</sup>, the 25×25 km grid should have about 800,000 stations. Given 30% dry land, there should be about 240,000 surface stations to have any scientifically reasonable estimation of trends. This is the most conservative estimations for necessary amount of data to collect.The GISS network has less than 6,000 stations world-wide, which means that the data are undersampled by a factor of 40.  </p>

<p>If one has a field so vastly undersampled, selective treatment of datasets could result in anything, which explains contradictions in various estimates.</p>

CC BY-SA 3.0
2016-09-14T01:46:51.517Z
That's why the scientist invented satellites
arkaia
CC BY-SA 3.0
2016-09-14T05:42:33.473Z
Yep. Then you probably can explain all algorithms and criteria for discarding data due to cloud conditions, how the standard TLT profile is related to local atmospheric column at the moment of taking data, how robust and objective is inter-calibration, how well satellite drift and orbital decay is corrected, and specifically how deterioration of thermal insulation and optics is factored in.
Ale..chenski
CC BY-SA 3.0
2016-09-14T12:56:28.597Z
That is why we have a field of science called "remote sensing of environment" in which we TRY to answer those and many other questions. You can question pretty much all the research in: http://www.sciencedirect.com/science/journal/00344257 and http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?reload=true&punumber=36 Good luck in your crusade!
arkaia
CC BY-SA 3.0
2016-09-14T15:37:13.907Z
You may have a field of science called whatever, but do you have the answers?
Ale..chenski
CC BY-SA 3.0
2016-09-14T15:54:16.137Z
My former supervisor on remote sensing used to cite the Bible: Proverbs 23:9: "Do not speak in the hearing of a fool, For he will despise the wisdom of your words."
arkaia
CC BY-SA 3.0
2016-09-14T16:22:19.947Z
So far  you didn't provide any "words" yet, so it is difficult to judge their wisdom. I repeat the first question: what is the basis of algorithms to determine if the shot has cloudy or non-cloudy condition?
Ale..chenski
CC BY-SA 3.0
2016-09-14T17:07:59.860Z
http://dx.doi.org/10.1175/1520-0426(1992)009<0067:EDAFSI>2.0.CO;2 http://dx.doi.org/10.1016/0034-4257(95)00188-3 http://dx.doi.org/10.1016/0034-4257(95)00036-Z http://dx.doi.org/10.1016/0034-4257(95)00199-9 http://dx.doi.org/10.1175/2008JTECHA1052.1 https://www.google.com/patents/US5612901 and so many more... They are not my words, rather the words of the experts in the field. Have a conversation with them if you want to learn more. Thanks for the entertaining conversation.
arkaia
CC BY-SA 3.0
2016-09-14T17:33:45.307Z
Thanks. Six-bit measurement resolution sounds very impressive. Sorting of pixels exercise looks fairly scientific too, if you discard hazing effects. Accuracy of "scene-specific emissivity" must be also impressive, especially if you ignore seasonal land changes. Accuracy of all this pile of assumptions must exceed the accuracy of Bible, true.
Ale..chenski
CC BY-SA 3.0
2016-09-14T17:47:28.543Z
My last comment: As somebody that has worked with satellites for a number of year (~15), you demonstrate your ignorance with pretty much all your comments. Claiming that the work of hundreds of experts over more than 60 years of image processing is bogus without any evidence is at least rash, and likely reckless.
arkaia
CC BY-SA 3.0
2016-09-14T19:30:55.430Z
I am not claiming that all work is bogus. The efforts are commendable. I am claiming that the quality and accuracy of obtained data are inadequate for the purpose of justification of surface temperature trends.
Ale..chenski
CC BY-SA 3.0
2016-09-10T22:29:09.350Z
2016-09-14T03:54:15.893Z
2016-09-14T03:54:15.893Z
Ale..chenski
<p>There are several climate datasets around the world, two in America - NASA's GISTEMP, and NOAA's MLOST, and one in the UK - HadCRUT. These are the main datasets but there are others around the world, China, Japan etc.</p>

<p>The datasets differ slightly in the starting date, number of met stations included, degree of interpolation, and the way they get round the problem of lack of met stations in oceanic areas. </p>

<p>As to being 'tampered with', this is a subtle malicious implication that the data has been fudged. It is also an insult to the scientific integrity of the thousands of climate scientists involved, whose aim, as in all of science, is to get at the truth. Of course, the data have been 'processed'. This is an essential part of data quality control to interpolate missing data, and to ensure consistency of the data set. But no data has been 'tampered with' in the sense of trying to lead to false conclusions.
One of the striking features of all the global datasets is that, despite minor differences, they all show the same basic characteristics of an accelerating global temperature - the 'hockey stick graph'. </p>

<p>There is much dishonesty about, but not with the climate scientists. It is the climate skeptics who are being dishonest (not to mention scientifically incompetent). </p>

CC BY-SA 3.0
2016-09-10T05:40:46.277Z
Could you please elaborate on the meaning of "data processing " to "ensure consistency of the data set"? What criteria were employed to ensure the consistency?
Ale..chenski
CC BY-SA 3.0
2016-09-11T09:55:49.300Z
Most raw climatic data is fill of gliches: e.g  typos, mixed numeric and alphabetic data entry, confusion between zero and 'no data', transcription errors, discontinuities, systematic errors (for as host of reasons). All of this has to be corrected and kicked into a consistent usable digital format - not so much of a problem in advanced western countries, but very much of a problem everywhere else. Then there is inter-station correlation to check out anomalies. In overseas work I find that more time is spent on all this data pre-processing than on the actual analysis.
Gordon Stanger
CC BY-SA 3.0
2016-09-12T19:00:06.783Z
I understand about consistent digital formats. But the question was, what are the objective criteria by which researchers judge that some data point(s) needs correction? The records are made at least daily, which must follow local weather. What is the radius of "inter-station" correlation, and how it was established?
Ale..chenski
CC BY-SA 3.0
2016-06-09T05:53:21.320Z
2016-06-09T05:53:21.320Z
Gordon Stanger
<p><a href="http://climate.nasa.gov/vital-signs/global-temperature/" rel="nofollow">Nasa's temperature record</a> is showing temperature increase. When I confronted a climate change skeptic with it, he replied that the data has been tempered with, and that there are different data sets which do not show temperature increase.</p>

<p>The exact reply was:</p>

<blockquote>
  <p>According to RSS, UAH and radiosonde global temp anomaly datasets,
  there hasn't been a global warming trend in 20 years;</p>
  
  <p><a href="http://www.woodfortrees.org/plot/rss/from:1996.6/to:2015.7/plot/rss/from:1996.6/to:2015.7/trend/plot/esrl-co2/from:1996.6/to:2015.7/normalise/trend/plot/esrl-co2/from:1996.6/to:2015.7/normalise" rel="nofollow">http://www.woodfortrees.org/plot/rss/from:1996.6/to:2015.7/plot/rss/from:1996.6/to:2015.7/trend/plot/esrl-co2/from:1996.6/to:2015.7/normalise/trend/plot/esrl-co2/from:1996.6/to:2015.7/normalise</a></p>
  
  <p>GISS and HADCRUT4 datasets have added heat to raw temperature data to
  artificially keep the warming trend going as NOAA freely admits:</p>
  
  <p><a href="https://www.ncdc.noaa.gov/img/climate/research/ushcn/ts.ushcn_anom25_diffs_urb-raw_pg.gif" rel="nofollow">https://www.ncdc.noaa.gov/img/climate/research/ushcn/ts.ushcn_anom25_diffs_urb-raw_pg.gif</a></p>
</blockquote>

<p>This sounds reasonable to me.</p>

<p>Can anyone familiar with climate science explain why there are several data-sets, and why they are saying different things, and why NASA's set is the correct one?</p>

<climate><climate-change><climate-models>
Need different temperature change data sets explained
Arsen Zahray
# Need different temperature change data sets explained

(Asked by: username_0 on 2016-06-08)

[Nasa's temperature record](http://climate.nasa.gov/vital-signs/global-temperature/) is showing temperature increase. When I confronted a climate change skeptic with it, he replied that the data has been tempered with, and that there are different data sets which do not show temperature increase.

The exact reply was:

> According to RSS, UAH and radiosonde global temp anomaly datasets,
> there hasn't been a global warming trend in 20 years;
>
> <http://www.woodfortrees.org/plot/rss/from:1996.6/to:2015.7/plot/rss/from:1996.6/to:2015.7/trend/plot/esrl-co2/from:1996.6/to:2015.7/normalise/trend/plot/esrl-co2/from:1996.6/to:2015.7/normalise>
>
> GISS and HADCRUT4 datasets have added heat to raw temperature data to
> artificially keep the warming trend going as NOAA freely admits:
>
> <https://www.ncdc.noaa.gov/img/climate/research/ushcn/ts.ushcn_anom25_diffs_urb-raw_pg.gif>

This sounds reasonable to me.

Can anyone familiar with climate science explain why there are several data-sets, and why they are saying different things, and why NASA's set is the correct one?

**Tags:** climate, climate-change, climate-models


---

## 3 answers

### Answer by username_4 on 2016-06-09. Score: 6 | Accepted answer

There are several climate datasets around the world, two in America - NASA's GISTEMP, and NOAA's MLOST, and one in the UK - HadCRUT. These are the main datasets but there are others around the world, China, Japan etc.

The datasets differ slightly in the starting date, number of met stations included, degree of interpolation, and the way they get round the problem of lack of met stations in oceanic areas.

As to being 'tampered with', this is a subtle malicious implication that the data has been fudged. It is also an insult to the scientific integrity of the thousands of climate scientists involved, whose aim, as in all of science, is to get at the truth. Of course, the data have been 'processed'. This is an essential part of data quality control to interpolate missing data, and to ensure consistency of the data set. But no data has been 'tampered with' in the sense of trying to lead to false conclusions.
One of the striking features of all the global datasets is that, despite minor differences, they all show the same basic characteristics of an accelerating global temperature - the 'hockey stick graph'.

There is much dishonesty about, but not with the climate scientists. It is the climate skeptics who are being dishonest (not to mention scientifically incompetent).

> **Comments:**
> **username_2**: Could you please elaborate on the meaning of "data processing " to "ensure consistency of the data set"? What criteria were employed to ensure the consistency?  
> **username_4**: Most raw climatic data is fill of gliches: e.g  typos, mixed numeric and alphabetic data entry, confusion between zero and 'no data', transcription errors, discontinuities, systematic errors (for as host of reasons). All of this has to be corrected and kicked into a consistent usable digital format - not so much of a problem in advanced western countries, but very much of a problem everywhere else. Then there is inter-station correlation to check out anomalies. In overseas work I find that more time is spent on all this data pre-processing than on the actual analysis.  
> **username_2**: I understand about consistent digital formats. But the question was, what are the objective criteria by which researchers judge that some data point(s) needs correction? The records are made at least daily, which must follow local weather. What is the radius of "inter-station" correlation, and how it was established?  

---

### Answer by username_1 on 2016-06-10. Score: 2

username_4 username_4's reply is the correct answer to this question, but I wanted to point out something else. Saying that there "hasn't been a global warming trend in 20 years" is a tricky statement to begin with. There are a number of papers discussing it, but I think this one is a good summary:  
[Is the climate warming or cooling?](http://onlinelibrary.wiley.com/doi/10.1029/2009GL037810/abstract;jsessionid=0D260FA124707D1AD055454F9C2FBAB4.f01t03 "Is the climate warming or cooling?")  
Essentially, the point is that climate is variable enough that you can get decades of no trend or even slight global cooling; but the longer-term trends *always* show warming. Where you choose to start and end your regression line to calculate a trend is an easy way to get whatever answer you want. To quote their abstract:

> Numerous websites, blogs and articles in the media have claimed that the climate is no longer warming, and is now cooling. Here we show that periods of no trend or even cooling of the globally averaged surface air temperature are found in the last 34 years of the observed record, and in climate model simulations of the 20th and 21st century forced with increasing greenhouse gases. We show that the climate over the 21st century can and likely will produce periods of a decade or two where the globally averaged surface air temperature shows no trend or even slight cooling in the presence of longer-term warming.


---

### Answer by username_2 on 2016-09-10. Score: -3

Most estimates of global surface temperature "anomaly" come from NASA GISS network database <http://data.giss.nasa.gov/gistemp/station_data/> .
There are some stations that show cooling trend over 100-year-long history of observations, as shown here:
<https://stevemosher.wordpress.com/2010/09/29/needs-chec/>
This is considered as a normal "bell-curve" distribution.

However, as it was noted in old discussions, there are several examples where the OPPOSITE trend happens on ADJACENT stations that are only 50 km apart. This means that the spatial distribution of surface stations is insufficient to reconstruct characteristics of temperature field (averages, etc.), because the spatial sampling does not satisfy criteria for Nyquist-Shannon Sampling Theorem.

To get the sampling right, the station site locations must be at least half of 50km, or about 25 km apart around the globe. With Earth surface having 5×108 km2, the 25×25 km grid should have about 800,000 stations. Given 30% dry land, there should be about 240,000 surface stations to have any scientifically reasonable estimation of trends. This is the most conservative estimations for necessary amount of data to collect.The GISS network has less than 6,000 stations world-wide, which means that the data are undersampled by a factor of 40.

If one has a field so vastly undersampled, selective treatment of datasets could result in anything, which explains contradictions in various estimates.

> **Comments:**
> **username_3**: That's why the scientist invented satellites  
> **username_2**: Yep. Then you probably can explain all algorithms and criteria for discarding data due to cloud conditions, how the standard TLT profile is related to local atmospheric column at the moment of taking data, how robust and objective is inter-calibration, how well satellite drift and orbital decay is corrected, and specifically how deterioration of thermal insulation and optics is factored in.  
> **username_3**: That is why we have a field of science called "remote sensing of environment" in which we TRY to answer those and many other questions. You can question pretty much all the research in: http://www.sciencedirect.com/science/journal/00344257 and http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?reload=true&punumber=36 Good luck in your crusade!  
> **username_2**: You may have a field of science called whatever, but do you have the answers?  
> **username_3**: My former supervisor on remote sensing used to cite the Bible: Proverbs 23:9: "Do not speak in the hearing of a fool, For he will despise the wisdom of your words."  
> **username_2**: So far  you didn't provide any "words" yet, so it is difficult to judge their wisdom. I repeat the first question: what is the basis of algorithms to determine if the shot has cloudy or non-cloudy condition?  
> **username_3**: http://dx.doi.org/10.1175/1520-0426(1992)009<0067:EDAFSI>2.0.CO;2 http://dx.doi.org/10.1016/0034-4257(95)00188-3 http://dx.doi.org/10.1016/0034-4257(95)00036-Z http://dx.doi.org/10.1016/0034-4257(95)00199-9 http://dx.doi.org/10.1175/2008JTECHA1052.1 https://www.google.com/patents/US5612901 and so many more... They are not my words, rather the words of the experts in the field. Have a conversation with them if you want to learn more. Thanks for the entertaining conversation.  
> **username_2**: Thanks. Six-bit measurement resolution sounds very impressive. Sorting of pixels exercise looks fairly scientific too, if you discard hazing effects. Accuracy of "scene-specific emissivity" must be also impressive, especially if you ignore seasonal land changes. Accuracy of all this pile of assumptions must exceed the accuracy of Bible, true.  
> **username_3**: My last comment: As somebody that has worked with satellites for a number of year (~15), you demonstrate your ignorance with pretty much all your comments. Claiming that the work of hundreds of experts over more than 60 years of image processing is bogus without any evidence is at least rash, and likely reckless.  
> **username_2**: I am not claiming that all work is bogus. The efforts are commendable. I am claiming that the quality and accuracy of obtained data are inadequate for the purpose of justification of surface temperature trends.  

---

