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.

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:

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:

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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