Another Year, Another Nail in the CAGW Coffin (Now Includes December Data)

WoodForTrees.org – Paul Clark – Click the pic to view at source

Image Credit: WoodForTrees.org

Guest Post By Werner Brozek, Edited By Just The Facts

CAGW refers to Catastrophic Anthropogenic Global Warming. Few people doubt that humans have some influence on climate, however the big debate is whether or not we are causing enough warming to have catastrophic consequences decades from now. The best evidence thus far is that climate goes in numerous different cycles and that whatever influence humans have, is minimal. Certainly, what happened, and what did not happen, in 2013, does not justify any alarm.

The above graph illustrates the change over the past year for the length of the period of no warming for RSS. At the end of 2012, the Pause was for a period of 194 months. By the end of 2013, this Pause had increased by 14 months to 208 months, namely the 12 months in 2013 and an additional 2 months further back in 1996. Of course, Santer’s 17 years was reached when 204 months of no warming was reached in October. For the year 2013, RSS ranks it as the 10th warmest year.

Since warming did not happen in 2013, what about climate change? Let us consider the polar vortex event at the beginning of January that led to the greatest cold in the United States in 20 years. According to RSS, 8 of the Decembers prior to 2013 were warmer than that of 2013. So neither a warm 2013 nor a warm December can be blamed for the polar vortex activity. Extra CO2 could potentially cause some things to happen via the mechanism of an initial warming. But if warming has not been occurring, then there is no way that man-made CO2 can be blamed.

At this time, I would like to address another topic that sometimes comes up. Occasionally, the view is expressed that the anomalies should not be given to more digits than can be justified. So if temperatures are recorded to the nearest 1/10 degree, the anomalies should also be to the nearest 1/10 degree instead of to the nearest 1/1000 degree for example. I do not consider this a big deal and I would like to illustrate it with a sports analogy. Suppose we were to compare three different soccer or hockey teams and decided that the average number of goals per game is one thing to look at. Suppose that over 1000 games, Team A made 520 goals, Team B made 1040 goals and Team C made 1460 goals. The goals per game would be 0.52, 1.04 and 1.46. So Team B scored twice as many as Team A and Team C scored almost three times as many. However a “purist” would say that since we cannot have a hundredth of a goal, but only a whole number of goals, we need to round off all numbers to the nearest whole number. In that case, 0.52 and 1.04 and 1.46 would all get rounded to 1. As a result, the information is useless. In my opinion, the decimal places are certainly something to keep in the backs of our minds, but for me to change all numbers in the table on Section 3 to the nearest 1/10 C would be a waste of time and about as useful as rearranging the deck chairs on the Titanic. Furthermore, to average 12 numbers after rounding them could give quite different results, depending on whether more numbers were rounded up or down.

Also, I use UAH version 5.5 since that is what WFT uses. Paul Clark might upgrade WTI to version 5.6 and HadCRUT4 if you drop a tip and a note in his Charity Tip Jar. In version 5.5, 2013 is ranked 7th. However version 5.6 has 2013 ranked 4th. In contrast, RSS for 2013 is ranked 10th. Let us assume that the error bars for each data set is +/- 0.1 C. The value of the anomaly for UAH version 5.6 was 0.236. What would be the range of ranks if we assumed the range in the anomaly at the 95% level was from 0.136 to 0.336? The answer is from 3rd to 10th. Now let us do the same for RSS. The RSS average anomaly for 2013 was 0.218. Numbers from 0.118 to 0.318 gives a rank range of 5th to 14th. If we only used UAH version 5.6 and RSS, it would seem that the “real” rank for the satellite data set is 7th or 8th. Do you agree?

In the six data sets I am analyzing, the ranks for 2013 range from 6th to 10th. This really is nothing for the warmists to celebrate. While it varies slightly between different data sets, a rank of about 8 means that the increase in the period of no warming plods along a month at a time. In order to really make a difference in the rankings and significantly shorten the period of no warming, the new rankings need to be 5 or less.

On the table in Section 3, I give the ranks for the six data sets for 2012 in row 1. As it turns out, the average anomaly for each set for 2013 (row 21) was warmer than for 2012 (row 2). So since 2013 was warmer than 2012 and with the year now being over, each 2012 ranking has been updated making it one higher than stated in earlier posts.

It is possible that some rankings in row 22 could still change as adjustments are made to 2013 data in future months. In particular, GISS is in 7th place by only a difference of 0.002.

In Section 2, I give the times for which there has been no statistically significant warming on 5 of the data sets. At this point, I do not want to get into a discussion about NOAA’s statement that starts with “The simulations rule out (at the 95% level) zero trends for intervals of 15 yr or more…”. But I merely wish to point out that NOAA and climate science in general feel that being 95% confident whether or not warming is occurring over a certain interval has a certain amount of significance. I have used the program by Nick Stokes available on his moyhu.blogspot.com to come up with those time periods. The time periods with no statistically significant warming varies from 16 years to 21 years on the five data sets. These times vary, but they are generally at least four years longer than the period for a slope of 0. In my last post, there were questions about the 95% significance. Nick Stokes has agreed to address all questions related to this aspect of the analysis.

In the sections below, we will present you with the latest facts. The information will be presented in three sections and an appendix. The first section will show for how long there has been no warming on several data sets. The second section will show for how long there has been no statistically significant warming on several data sets. The third section will show how 2013 compares with 2012 and the warmest years and months on record so far. The appendix will illustrate sections 1 and 2 in a different way. Graphs and a table will be used to illustrate the data.

Section 1:

This analysis uses the latest month for which data is available on WoodForTrees.com (WFT). All of the data on WFT is also available at the specific sources as outlined below. We start with the present date and go to the furthest month in the past where the slope is a least slightly negative. So if the slope from September is 4 x 10^-4 but it is – 4 x 10^-4 from October, we give the time from October so no one can accuse us of being less than honest if we say the slope is flat from a certain month.

On all data sets below, the different times for a slope that is at least very slightly negative ranges from 9 years and 3 months to 17 years and 4 months.

1. For GISS, the slope is flat since July 2001 or 12 years, 6 months. (goes to December)

2. For Hadcrut3, the slope is flat since July 1997 or 16 years, 6 months. (goes to December)

3. For a combination of GISS, Hadcrut3, UAH and RSS, the slope is flat since December 2000 or 13 years, 1 month. (goes to December)

4. For Hadcrut4, the slope is flat since December 2000 or 13 years, 1 month. (goes to December)

5. For Hadsst3, the slope is flat since December 2000 or 13 years, 1 month. (goes to December)

6. For UAH, the slope is flat since October 2004 or 9 years, 3 months. (goes to December using version 5.5)

7. For RSS, the slope is flat since September 1996 or 17 years, 4 months (goes to December). So RSS has passed Ben Santer’s 17 years.

The next graph shows just the lines to illustrate the above. Think of it as a sideways bar graph where the lengths of the lines indicate the relative times where the slope is 0. In addition, the sloped wiggly line shows how CO2 has increased over this period.

WoodForTrees.org – Paul Clark – Click the pic to view at source

When two things are plotted as I have done, the left only shows a temperature anomaly.

The actual numbers are meaningless since all slopes are essentially zero and the position of each line is merely a reflection of the base period from which anomalies are taken for each set. No numbers are given for CO2. Some have asked that the log of the concentration of CO2 be plotted. However WFT does not give this option. The upward sloping CO2 line only shows that while CO2 has been going up over the last 17 years, the temperatures have been flat for varying periods on various data sets.

The next graph shows the above, but this time, the actual plotted points are shown along with the slope lines and the CO2 is omitted:

WoodForTrees.org – Paul Clark – Click the pic to view at source

Section 2:

For this analysis, data was retrieved from Nick Stokes’ Trendviewer available on his website moyhu.blogspot.com. This analysis indicates for how long there has not been statistically significant warming according to Nick’s criteria. Data go to their latest update for each set. In every case, note that the lower error bar is negative so a slope of 0 cannot be ruled out from the month indicated.

On several different data sets, there has been no statistically significant warming for between 16 and 21 years.

The details for several sets are below.

For UAH: Since January 1996: CI from -0.008 to 2.437

For RSS: Since November 1992: CI from -0.018 to 1.936

For Hadcrut4: Since September 1996: CI from -0.003 to 1.316

For Hadsst3: Since June 1993: CI from -0.009 to 1.793

For GISS: Since June 1997: CI from -0.004 to 1.276

Section 3:

This section shows data about 2013 and other information in the form of a table. The table shows the six data sources along the top and other places so they should be visible at all times. The sources are UAH, RSS, Hadcrut4, Hadcrut3, Hadsst3, and GISS. Down the column, are the following:

1. 12ra: This is the final new ranking for 2012 on each data set after the 2013 ranking has been accounted for.

2. 12a: Here I give the average anomaly for 2012.

3. year: This indicates the warmest year on record so far for that particular data set. Note that two of the data sets have 2010 as the warmest year and four have 1998 as the warmest year.

4. ano: This is the average of the monthly anomalies of the warmest year just above.

5. mon: This is the month where that particular data set showed the highest anomaly. The months are identified by the first three letters of the month and the last two numbers of the year.

6. ano: This is the anomaly of the month just above.

7. y/m: This is the longest period of time where the slope is not positive given in years/months. So 16/2 means that for 16 years and 2 months the slope is essentially 0.

9. Jan: This is the January, 2013, anomaly for that particular data set.

10. Feb: This is the February, 2013, anomaly for that particular data set, etc.

21. ave: This is the average anomaly of all months to date taken by adding all numbers and dividing by the number of months. However if the data set itself gives that average, I may use their number. Sometimes the number in the third decimal place differs slightly, presumably due to all months not having the same number of days.

22. rnk: This is the final rank for each particular data set for 2013. In cases where two numbers are close, future adjustments may change things. For example GISS could easily end up in 6th from 7th. Due to different base periods, the rank is more meaningful than the average anomaly.

Source UAH RSS Had4 Had3 Sst3 GISS
1. 12ra 10th 12th 10th 11th 10th 10th
2. 12a 0.161 0.192 0.448 0.403 0.346 0.58
3. year 1998 1998 2010 1998 1998 2010
4. ano 0.419 0.55 0.547 0.548 0.416 0.67
5. mon Apr98 Apr98 Jan07 Feb98 Jul98 Jan07
6. ano 0.662 0.857 0.829 0.756 0.526 0.94
7. y/m 9/3 17/4 13/1 16/6 13/1 12/6
Source UAH RSS Had4 Had3 Sst3 GISS
9. Jan 0.504 0.439 0.450 0.392 0.292 0.63
10.Feb 0.175 0.192 0.479 0.436 0.309 0.52
11.Mar 0.183 0.203 0.405 0.392 0.287 0.60
12.Apr 0.103 0.217 0.427 0.404 0.364 0.48
13.May 0.077 0.138 0.498 0.480 0.382 0.57
14.Jun 0.269 0.291 0.457 0.431 0.314 0.61
15.Jul 0.118 0.221 0.520 0.483 0.479 0.53
16.Aug 0.122 0.166 0.528 0.496 0.483 0.61
17.Sep 0.294 0.256 0.532 0.517 0.457 0.74
18.Oct 0.227 0.207 0.478 0.446 0.391 0.61
19.Nov 0.111 0.131 0.593 0.576 0.424 0.78
20.Dec 0.177 0.158 0.489 0.475 0.352 0.60
Source UAH RSS Had4 Had3 Sst3 GISS
21.ave 0.197 0.218 0.486 0.461 0.376 0.61
22.rnk 7th 10th 8th 6th 6th 7th

If you wish to verify all of the latest anomalies, go to the following:

For UAH, version 5.5 was used since that is what WFT used.

http://vortex.nsstc.uah.edu/public/msu/t2lt/tltglhmam_5.5.txt

For RSS, see: ftp://ftp.ssmi.com/msu/monthly_time_series/rss_monthly_msu_amsu_channel_tlt_anomalies_land_and_ocean_v03_3.txt

For HadCRUT4, see: http://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/time_series/HadCRUT.4.2.0.0.monthly_ns_avg.txt

For HadCRUT3, see: http://www.cru.uea.ac.uk/cru/data/temperature/HadCRUT3-gl.dat

For HadSST3, see: http://www.cru.uea.ac.uk/cru/data/temperature/HadSST3-gl.dat

For GISS, see: http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt

To see all points since January 2013 in the form of a graph, see the WFT graph below:

WoodForTrees.org – Paul Clark – Click the pic to view at source

As you can see, all lines have been offset so they all start at the same place in January.

Appendix:

In this section, we are summarizing data for each set separately.

RSS

The slope is flat since September 1996 or 17 years, 4 months. (goes to December) So RSS has passed Ben Santer’s 17 years.

For RSS: There is no statistically significant warming since November 1992: CI from -0.018 to 1.936.

The RSS average anomaly for 2013 is 0.218. This would rank it in 10th place. 1998 was the warmest at 0.55. The highest ever monthly anomaly was in April of 1998 when it reached 0.857. The anomaly in 2012 was 0.192 and it is now ranked 12th.

UAH

The slope is flat since October 2004 or 9 years, 3 months. (goes to December using version 5.5)

For UAH: There is no statistically significant warming since January 1996: CI from -0.008 to 2.437.

The UAH average anomaly for 2013 is 0.197. This would rank it 7th. 1998 was the warmest at 0.419. The highest ever monthly anomaly was in April of 1998 when it reached 0.662. The anomaly in 2012 was 0.161 and it is now ranked 10th.

Hadcrut4

The slope is flat since December 2000 or 13 years and 1 month. (goes to December)

For Hadcrut4: There is no statistically significant warming since September 1996: CI from -0.003 to 1.316.

The Hadcrut4 average anomaly for 2013 is 0.486. This would rank it 8th. 2010 was the warmest at 0.547. The highest ever monthly anomaly was in January of 2007 when it reached 0.829. The anomaly in 2012 was 0.448 and it is now ranked 10th.

Hadcrut3

The slope is flat since July 1997 or 16 years, 6 months. (goes to December)

The Hadcrut3 average anomaly for 2013 is 0.461. This would rank it 6th. 1998 was the warmest at 0.548. The highest ever monthly anomaly was in February of 1998 when it reached 0.756. One has to go back to the 1940s to find the previous time that a Hadcrut3 record was not beaten in 10 years or less. The anomaly in 2012 was 0.403 and it is now ranked 11th.

Hadsst3

For Hadsst3, the slope is flat since December 2000 or 13 years and 1 month. (goes to December).

For Hadsst3: There is no statistically significant warming since June 1993: CI from -0.009 to 1.793.

The Hadsst3 average anomaly for 2013 is 0.376. This would rank it 6th. 1998 was the warmest at 0.416. The highest ever monthly anomaly was in July of 1998 when it reached 0.526. The anomaly in 2012 was 0.346 and it is now ranked 10th.

GISS

The slope is flat since July 2001 or 12 years, 6 months. (goes to December)

For GISS: There is no statistically significant warming since June 1997: CI from -0.004 to 1.276.

The GISS average anomaly for 2013 is 0.61. This would rank it as 7th. 2010 was the warmest at 0.67. The highest ever monthly anomaly was in January of 2007 when it reached 0.94. The anomaly in 2012 was 0.58 and it is now ranked 10th.

Conclusion:

Everything seemed to go wrong for the warmists this year. The temperatures did not go up; a ship got stuck in huge ice in the Antarctic during their summer; north polar ice made a big come back; and climate change happenings were not significantly different from what can be expected. Can anyone point to anything for warmists to hang their hat on, so to speak, in 2013?

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As I mentioned in the article, Nick Stokes is available to answer all questions with respect to the meaning of the numbers in Section 2 of this report since it uses his program to come up with the 95% numbers. So take advantage of this opportunity.
Here is my question: From January 1997 to the present, according to GISS, the slope is 0.741 C/century and the confidence limits are 0.125 to 1.356. So in other words, we can be 95% certain that the real slope is positive and somewhere between 0.125 C/century and 1.356 C/century. Right? However the slope for RSS from January 1997 is (negative) -0.127 C/century. Does this mean that we can be 95% certain that either GISS or RSS are out to lunch? Thank you!

Even NOAA’s surface data, which claims 4th hottest ever for 2013, is losing steam quickly. They won’t say that their own data shows the 2004-2013 decade to be only 0.1 degree Celsius warmer than the decade before it, the lowest since 1982 and a trend that’s falling fast.

John W. Garrett

Marvelous. Thank you.

RichardLH

“GISS
The slope is flat since July 2001 or 12 years, 6 months. (goes to December)
For GISS: There is no statistically significant warming since June 1997: CI from -0.004 to 1.276.”
And this graph from Nate shows that to be true. I mean (pun) just how flat can a line become?
http://snag.gy/hFsMF.jpg
Original ©Nate Drake, enhancement © RLH
“Filter on NON-detrended GISS LOTI data: … I ran a 5 pass-multipass with second order polynomials on 15 year data windows as per the Savitzky–Golay method.” Nate Drake

richardscourtney

Werner Brozek:
You ask:

Here is my question: From January 1997 to the present, according to GISS, the slope is 0.741 C/century and the confidence limits are 0.125 to 1.356. So in other words, we can be 95% certain that the real slope is positive and somewhere between 0.125 C/century and 1.356 C/century. Right? However the slope for RSS from January 1997 is (negative) -0.127 C/century. Does this mean that we can be 95% certain that either GISS or RSS are out to lunch? Thank you!

No. It is an indication that GISS and RSS are indicating different things.
There is no agreed definition of Global Average Surface Temperature Anomally (GASTA) and each team that provides time series of GASTA adopts a different method to derive it. Indeed, GISS and RSS are clearly measuring different things in different ways because GISS derives its data from weather measurement thermometers and RSS derives its data from from microwave sounding units (MSU) mounted on satellites.
For more explanation of this problem concerning ‘What is GASTA?’ please read Appendix B of this
Parliamentary Submission
Richard

ThinkingScientist

Is there a clear description/reference on how the statistical significance is computed. I am particularly interested in how you decide the effective number of samples, n.

big debate is whether or not we are causing enough warming to have catastrophic consequences decades from now.

part of the alarmists problem is they are trying to say that the Catastrophic consequences are TODAY, when everyone with a memory greater than a gold fish can see that is not the case. They should have stuck with their original meme. The problem is today is the future they were warning about 25 years ago (Manhattan is still not underwater hansen).

joshuah says:
January 25, 2014 at 12:11 pm
Not only that, but the top 10 years in GISS range from 0.58 to 0.67, which is a difference of 0.09. So if the error bar is 0.1, then we statistically have a 10 way tie for first place. Perhaps it is even larger if we can include numbers as low as 0.47, since 0.47 + 0.1 = 0.67 – 0.1.

cnxtim

“Few people doubt that humans have some influence on climate”
Then count me in that “few”.
I think it is the height of human self regard and sheer folly that anything other than that colossal nuclear energy source in the sky is the beginning and end of that is what we call climate. So much so, that any other influences (especially humanity) are minuscule beyond reason, measurement or any need for expensing a single penny of the public purse.
By all means, keep observing, reporting and predicting weather changes and patterns, but give up on finding the “hidden” cause – it is bloody obvious!

NotTheAussiePhilM

Isn’t that called ‘Cherry Picking’ where for each of 6 datasets you choose a different start point in order to give the result you want (i.e. Slope of zero)?

Leon Brozyna

The more decimal places that are displayed, the less impressed that I am.

ThinkingScientist

Try applying the same method to CET and see how long the period before present without a statistically significant trend is.

RockyRoad

The Warmistas can always thank the EPA and Prez Obama for lying about the climate and the need to “address” the issue (like they lie about everything else).
Warmistas like lies–they believe in them, repeat them, and hang their hat on them (figuratively, of course).
I’m waiting for them to outlaw these termperature datasets as being too “dangerous” in the hands of skeptics and counterproductive to “Social Progress”.
You heard it here first.

Werner Brozek

NotTheAussiePhilM says:
January 25, 2014 at 12:30 pm
Isn’t that called ‘Cherry Picking’ where for each of 6 datasets you choose a different start point in order to give the result you want (i.e. Slope of zero)?
With what I am reporting, I have no choice at all. My final month is December 2013 and the first month is where ever the data lead me. That can be September 1996 or October 2004.

Jim Cripwell

Boy, am I glad to see that the year 2013 added 14 months to the flatlining. I have always maintained that, in theory, this could happen. It is nice to see that it has actually happened.

Les Johnson

NotTheAussiePhilM:
Isn’t that called ‘Cherry Picking’ where for each of 6 datasets you choose a different start point in order to give the result you want (i.e. Slope of zero)?
No. Each dataset measures something different, or uses a different algorithim. But all have a common end point (now). Cherry picking would include picking an end point, to get the “best”results.
What one can say, is that ALL datasets show a slope of zero over the latter portion the record.
All records also show an insignificant temperature rise since June 1997.

Michael D

When you say However a “purist” would say that since we cannot have a hundredth of a goal, but only a whole number of goals, we need to round off all numbers to the nearest whole number. In that case, 0.52 and 1.04 and 1.46 would all get rounded to 1. As a result, the information is useless. it sounds like you have no scientific training in the reporting of uncertainty, which undermines the ethos of this article.

Jim Cripwell says:
January 25, 2014 at 12:38 pm
Boy, am I glad to see that the year 2013 added 14 months to the flatlining. I have always maintained that, in theory, this could happen. It is nice to see that it has actually happened.
That is only because we had no La Nina last year. Should 2014 be a La Nina year, then we could add another 24 months in a 12 month period by going back an additional year. The latest weekly ENSO number is -0.607 so you never know what might happen.

wbrozek on January 25, 2014 at 12:23 pm
Indeed. I’m still not even sure what it means to condense an entire globe to a single number with two decimal points. But if they’re going to treat such numbers as gospel truth and make absolute statements about ‘9 of 10 hottest years since 2000’ etc, I’m going to keep pointing out other (and IMO better) ways to analyze the same numbers to show that they don’t show the trends that they think they show.

Michael D says:
January 25, 2014 at 12:45 pm
it sounds like you have no scientific training in the reporting of uncertainty
Trust me, I know all about significant digits. When adding or subtracting, you round to the lowest decimal place so when adding 2.3 cm + 4.68 cm + 5.789 cm, you do the adding and round to a single decimal place since that is the lowest number of decimal places. But if these numbers were multiplied to find a volume for example, you multiply and round to 2 significant digits since that is the lowest number of significant digits. Right?
But in these cases, they give anomalies like 0.352. I know very well that this is not between 0.35150 and 0.35249. As I said, I treat 0.352 as a +/- 0.1 so the “real” number 95% of the time is assumed to be between 0.252 and 0.452.

Can anyone point to anything for warmists to hang their hat on, so to speak, in 2013?
They will be sighing with relief that the world is not going to suffer from a runaway greenhouse effect, that we can continue expanding our economies based on fossil fuels including the hundreds of years worth of shale gas, reducing poverty (both fuel and general) Taxes on fuels should come down as will the disfiguring and pointless windmills. We can continue to attempt to develop nuclear fusion reactors and Thorium reactors. The Met Ofice can provide us wit accurate weather forecasts based on reality, not computer models. Finally the billions we are pouring into climate research can be used sensibly instead of funding research conducted by deluded liars!
Anyway that is the way I see it, I don’t think the CAGW crowd will be happy, but to be honest, who cares?

Pachygrapsus

Werner, thank you for a well-written and informative post.
I would disagree with your sports analogy regarding significant figures. When you look at the number of goals a team scores there is no uncertainty. You can express it with an infinite number of 0’s to the right of the decimal and all of those are significant. When you use them to compare goals per game you’re not fudging the numbers at all. That’s not the case with global temperature. By using false precision to rank one year over another as “warmer” you open the door to all kinds of ridiculous press releases that mislead the public and policy makers.

Silver ralph

having artificially raised temperatures previously, it becomes ever harder to do the same again and again. especially with Anthony watching all the station output.

Robert Wykoff

I find it rather amusing that the first crop of high school graduates that have been steeped in global warming “science” in every class from social studies to math to history. Who has seen countless nature shows that the last 10 minutes are dedicated to how global warming will kill all of the cute cuddly creatures they just saw. The kids can probably tell you why CO2 is bad and killing everything, but couldn’t solve a simple math problem, or write a semi-coherent thought. These kids who have known nothing but global warming their entire lives, will be the first to graduate high school in a world where the global temperature hasn’t moved a 1/10th of a degree during their entire life times. I will be interested to see the generational backfire when they finally figure out they have been manipulated their entire lives.

dr.bill

The starting date for this graph is the average birthdate of the undergrads that I am teaching this semester. They tell me that they are horrified to see how much “global warming” they have had to endure during their lives, and are convinced that the “highly correlated” CO2 shown on the graph is definitely the culprit. /sarc

John Tillman

CACA trough feeders measure success based upon funding, conference attendance & media & official adulation, so for them 2013 was another banner year. Objective reality doesn’t matter to them until it can no longer be explained away.

Pachygrapsus says:
January 25, 2014 at 1:10 pm
Thank you! You raise a good point with the infinite number of significant digits. If you are not familiar with Canadian or American football, just skip to the next entry. However a fullback may rush for 59 yards in 7 carries and then they would say the average was 8.4 yards per carry even though all initial entries were written down to the nearest whole yard.
Now as for climate, if one year is higher by 0.1 C, we should probably say something like that the probability is 55 % that the one year was warmer than the other.

Let’s just rename “Polar Vortex” to “Polar Moartex!”
Moar alarmism!
Moar shrill accusations of impiety!
Moar ways to say warming is cleverly hiding and affecting weather from its clever hiding place! Cleverly!

Just The Facts
“damning waterways”
I just went outside with a thermometer, looked out over the Pacific Ocean and said “Damn thee, Pacific Ocean!” No change in temperature.
(A small joke at your expense. Sorry – could not let it go by. It was just there, calling me.)

James Abbott

Another Year, Another Nail in the CAGW Coffin (Now Includes December Data)
is yet another long-winded attempt to try and prove a pre-determined position, rather than looking at the actual data in a balanced way.
There is no doubt that the rate of warming has been slower in the last decade or so than the previous few decades. However, on both the NASA GISS and NOAA data the ten warmest years are all since (and including) 2002, except for 1998.
This flat period cannot be extrapolated to try and “prove” that dangerous climate change will not happen many decades from now.
There were 2 previous flat periods – one from the start of the 1980s and again from the start of the 1990s. They were not as long as the current one, but they did take place and warming resumed subsequently and they can be fairly confidently pinned to known forcing factors.
The length of the current flat period is unusual, but in recent years the neutral or negative ENSO and the quiet Sun are forcing factors consistent with the data we are seeing. Lets wait and see what happens when we get the next El Nino.
As to the conclusions:
“Everything seemed to go wrong for the warmists this year. The temperatures did not go up; a ship got stuck in huge ice in the Antarctic during their summer; north polar ice made a big come back; and climate change happenings were not significantly different from what can be expected. Can anyone point to anything for warmists to hang their hat on, so to speak, in 2013?”
1. Temperatures did go up, very slightly
2. Irrelevant – ships often get stuck in the ice
3. North Polar ice did not “make a big comeback”. Apart for a few days and only just, ice area has been below the mean (1979 – 2008) for 11 straight years and is currently around 800,000 sq km below the mean.
http://arctic.atmos.uiuc.edu/cryosphere/
4. What are “climate change happenings ?”
5. Given that many sceptics have been predicting a new mini-ice age round the corner, where is the cooling trend ?

richardscourtney

James Abbott:
re your post at January 25, 2014 at 1:44 pm.
Posting under more than one name is against WUWT rules.
So, I ask if you are you Brad Keyes posting under another name?
If you are not, then I congratulate you because your post gave me as good a laugh as his rants.
Your reference to “ships often get stuck in the ice” was an especially funny Turney of phrase.
Richard

Kristian

Werner Brozek says:
“Few people doubt that humans have some influence on climate (…)”
Yes, and one can really start to wonder why exactly. Seeing there is (still) no trace of such an influence anywhere … It is just an assumption without a shred of evidence to back it up.

FrankK

No. The big debate is whether we have any measurable effect at all. You can’t say there is an effect if there is no evidence for it. It’s merely a hypothesis.

wbrozek says: January 25, 2014 at 12:04 pm
“Does this mean that we can be 95% certain that either GISS or RSS are out to lunch? Thank you!”

Werner, the first thing to say is that they are measuring different things – surface vs lower troposphere.
But even if they weren’t – you can test whether the two readings could be from the same population. But it isn’t as simple as saying that one reading is outside the range of the other. If for example, the true value is 0.3 °C/cen, that’s well within the range of both. I’m pretty sure the proper test wouldn’t say that RSS and GISS were inconsistent (it they were measuring the same thing).

There are so many absurdities and uncertainties in the global surface temperature record, that it’s not worth the paper it’s printed on. For example, according to one set of figures that NOAA/NCDC continues to publish, every year from 1900 to 1997 has a global surface temperature between 16°C and 16.92°C, whereas according to the January 2014 edition of NASA’s global land-ocean temperature index, the year 2010 is the “hottest” on record, with a global surface temperature of 14.67°C. Both NASA and NOAA/NCDC play fast and loose with global surface temperatures, altering them by a degree or two as and when they see fit. Nothing they publish in this matter has any credibility.
http://gst-fiasco.blogspot.co.uk/

James Abbott says:
January 25, 2014 at 1:44 pm
Thank you for your comments. I would like to address the following:
This flat period cannot be extrapolated to try and “prove” that dangerous climate change will not happen many decades from now.
The highest rate of warming for a period of 25 or more years since 1880 was 0.18 C/decade. I do not consider this dangerous. Furthermore, temperatures seem to go in 60 year cycles and we are now in a flat or downward trend. As well, the effect of more CO2 is logarithmic so if the last 16 years have been rather flat, why should the future be dangerous?

Michael D

OK, I’ll explain further. I refer to your fictional illustration:
The goals per game would be 0.52, 1.04 and 1.46. So Team B scored twice as many as Team A and Team C scored almost three times as many. However a “purist” would say that since we cannot have a hundredth of a goal, but only a whole number of goals, we need to round off all numbers to the nearest whole number. >/em>
“Purists” would only round to whole numbers if they are also mathematically illiterate. The significant figures quoted depend on the intended purpose. As a record of what was achieved, the full significant figures should be quoted. As a prediction of what can be expected from those teams in the future, some sort of uncertainty model should be attached. Soccer is such a low-scoring game that I would be tempted to assume a Poisson distribution, in which case the
variance of goals scored over the whole 100 game season is equal to the total number of goals scored. A typical uncertainty estimate would be the square root of the variance. Thus Team C can be expected to score 1460 goals in its next 100 games plus or minus 40 (use only one significant figure in an uncertainty of this type). One could correctly say that Team C can be expected to score an average of 1.46 plus or minus 0.04 goals per game next season, so all three digits in 1.46 are significant.
In a single game, Team C can be expected to score 1.46 goals, but the expected uncertainty is (square root of 1.46) = 1.2, When the uncertainty is so large relative to the expected value, it is best to just say “Team C can be expected to score between 0 and 3 goals per game.”

richardscourtney says:
January 25, 2014 at 12:21 pm
Nick Stokes says:
January 25, 2014 at 1:59 pm
Thank you! (I will read Appendix B later.)

Kristian

richardscourtney says, January 25, 2014 at 12:21 pm:
“Indeed, GISS and RSS are clearly measuring different things in different ways because GISS derives its data from weather measurement thermometers and RSS derives its data from from microwave sounding units (MSU) mounted on satellites.”
Well, there is definitely something going on between what the two satellite teams of UAH and RSS put out also. The discrepancy is quite striking and both cannot be right, if any (and one would assume that they measure basically the same thing):
http://woodfortrees.org/plot/rss/from:1997/compress:3/plot/uah/from:1997/compress:3/offset:0.03
Something clearly happens in late 2005.

Kristian
richardscourtney

Kristian:
You quoted from part of my post and ignored the rest.
I wrote

There is no agreed definition of Global Average Surface Temperature Anomally (GASTA) and each team that provides time series of GASTA adopts a different method to derive it.

and I linked to an item which explained it.
Had you read Appendix B at my link then you would have understood the problem you question. Here is the link again Parliamentary Submission
Richard

Kristian

Here’s a simple solution to the discrepancy:
http://woodfortrees.org/plot/uah/from:1979/to:1992/compress:3/plot/uah/from:1992/to:2005.67/compress:3/offset:0.06/plot/uah/from:2005.67/compress:3/plot/rss/from:1979/to:2005.67/compress:3/offset:-0.07/plot/rss/from:2005.67/compress:3/offset:-0.04
Lift the midsection of the UAH timeseries by 0.06 degrees between 1992 and late 2005, only that. And then move the final part of the RSS timeseries (starting in late 2005) up by 0.03 degrees.
Close to perfect match.
Of course, justifying these corrections will be harder. That would be for the UAH and RSS teams to look into. I fear it won’t happen …

Kristian

Richard,
Please stop taking everything as a personal attack on you. I am not disagreeing with you at all. I’m just using your statement to illuminate another part of the puzzle. Jeez.

James Abbott

richardscourtney
That is my name. I always post under my own name.
Not sure why ships getting stuck in ice is funny. They do though – as a few minutes research on the web will show you..
wbrozek
You say
“The highest rate of warming for a period of 25 or more years since 1880 was 0.18 C/decade. I do not consider this dangerous. Furthermore, temperatures seem to go in 60 year cycles and we are now in a flat or downward trend. As well, the effect of more CO2 is logarithmic so if the last 16 years have been rather flat, why should the future be dangerous?”
You might not consider it dangerous, sitting here in 2014, but future generations might think otherwise. Even if they can cope with the changes in weather patterns and increased heat, sea level rise will pose huge challenges for them. Maybe you don’t care about future generations.
Anyway, you are selective:
In the last 4 decades, including the current flat period, the warming is about 0.6C (NASA GISS 5 year running mean) so thats 0.15C per decade, not far short of your highest rate.
Provide evidence of your 60 year cycle please. In the last century the only significant cooling period was during WW2. Otherwise its been a pattern of warming and flat spells – no decreases. Your 60 year cycle does not appear to exist within the last century.
You say “the effect of more CO2 is logarithmic”. So what is the function you are using that relates CO2 concentration with temperature please ? Presumably you must know that in order to propose that future rises in CO2 will cause no problems in terms of temperature rise ?
Fact is that CO2 is a powerful greenhouse gas. Without it the planet would be probably be completely frozen. We know that when concentrations were about half current levels during the deepest parts of the ice ages the temperature was as much as 8C lower than now. So you expect us to believe that as we go beyond 400ppm there will be no further significant warming ? Thats quite a claim without the physics to back it up.

richardscourtney

Kristian:
re your silly post at January 25, 2014 at 2:28 pm.
I did NOT take your post as a “personal attack”.
I pointed out that you had taken one sentence of my post out of context and ignored its main point.
You may be right that a “correction” between UAH and RSS is needed. But that is NOT what I was saying and I clarified the matter.
Jeez.
Richard

Kristian says:
January 25, 2014 at 2:08 pm
Something clearly happens in late 2005.
You bring up a very interesting point with far reaching implications. Do you recall the latest article on Cowtan and Way:
http://wattsupwiththat.com/2013/12/03/cowtan-way-and-signs-of-cooling/
“The divergence
Recently in the article written by Steven McIntyre he showed, that the Cowtan and Way hybrid global data start to markedly, one could say “hockeyschtickly”, diverge from the HadCRUT4 global data in about ~2005. He showed it well on this his picture:”
Apparently UAH shows Antarctica warming since 2005 and RSS does not show this. And Cowtan and Way used UAH and not RSS to fill the missing pieces for Hadcrut4. That is how they got the rise as I understand it. Had they used RSS, I do not believe they would have gotten this rise.

Just The Facts says:
January 25, 2014 at 1:01 pm
Thanks for that post. Well put. I can’t understand why people would dispute that humans don’t have an impact. As a multi-generational rancher and an engineer, I have witnessed many examples of what you commented on, and MEASURED it. You can affect local conditions with plantings, land clearing, water … And you can affect local conditions with design of facilities both on, above and below the ground and the effects are all measurable. Build a large hydro dam in a BC valley and watch the huge change in the micro climate. How far does that extend and does it affect the global climate? I don’t know if we can measure that on a global basis, but we can sure measure it locally so we know we have some impact. How much? Don’t know. Small I expect, but I would also bet it is measurable since we can watch forest fire smoke plumes cover a continent. That has an effect. How much? I don’t know, but I am pretty sure there is one.

Larry Hamlin

The UAH record looks pretty flat for the inclusive period 2002 through end 2013. Thanks for putting all these analyses together. Much appreciated.