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?
Adam says:
January 25, 2014 at 7:41 pm
The babies are now crying that because you include the 1998 spike
It turns out that from December 1999 the slope is flat, so it is 14 years and 1 month on RSS. So it would probably be 18 years of no significant warming and this totally ignores the 1998 spike. That is well over the 15 years that NOAA mentions.
http://www.woodfortrees.org/plot/rss/from:1999.9/plot/rss/from:1999.9/trend
So the falsification criteria is 15 years to 17 years. That is why we start at the present and count backwards. Once we hit 17 years The Goose is Cooked. Unfortunately the Goose seems to be a zombie and keeps rising from the dead.
+++++++++++++++++++++++++++++++++++
They keep moving the goal……….
But if CO2 rises after temperature rises, all that proves is that a warming ocean emits CO2.
It’s more likely, according to what I’ve read here (some quoting from “papers”) that the lost ice in the north is for only a short portion of the year, and is at such a high latitude (= low angle of incidence), that the amount of reflected sunlight is small, and is less that the amount of heat that is released by water no longer insulated by an icy cap.
They may be “qualified” scientists in the sense that they have the credentials and know the jargon, but there are qualified scientists who disagree. JoNova’s Skeptics’ Handbook lists a score of them, and there are lots more. (E.g., see The D*ni*rs.)
The word “qualified” has a connotation of “unbiased,” which I don’t think applies to the IPCC’s authors and lead authors. They are chosen by its coordinating lead authors, who are chosen by the IPCC, who are chosen by the UNDER originally, and thereafter by themselves. And the manner in which the coordinating lead authors are chosen is the utmost in murkiness, according to Donna Laframboise’s The Delinquent Teenager …”
Further , those persons who become climate scientists are about as unbiased as those persons who chose to become sociologists. Sociologists enter the profession with the aim of making an impact on society (and of making a career in academia and of preening and flattering themselves about their progressivism); 85% to 90% vote for the Democratic presidential candidate. Climatologists (in the current environment) enter the profession with the aim of making an impact on the climate (and of making a career in academia and of preening and flattering themselves about their progressivism). I suspect that at least 80% vote for the Democratic presidential candidate.
Or, if they have a less activist mentality, they are go-along / get-along types who haven’t really read and considered the skeptics’ case, preferring instead to assume that the rebuttals they’ve read are unanswerable.
The persons qualified to judge their claims are scientists in related disciplines who have no dog in the fight. Two surveys by George Mason U. in the 21st century of scientist-members of the AMS & AGU found that under 40% of them were very worried about AGW. The last survey was six or seven years ago. (I imagine the percentage would be lower today if the survey were done again — as it ought to be.)
OOps–change “UNDER” above to “UN”–Word’s autocorrect expanded it for me.
Gail combs says:
If the Super Bowl gets rescheduled due to cold weather, that’ll be a stick with which we can beat the devil.
rsc;
Thanks for that. Copied and saved, will include attribution if I paste it here and there. 🙂
Nick Stokes, are you able to respond to my question about how the test for statistical significance is performed so I can replicate it? Is it just a Student T test? What do you assume for n? Just the count of samples (eg months) or is their some correction for temporal dependency in the time series?
Thanks!
Nick Stokes says: @ur momisugly January 25, 2014 at 5:17 pm
zootcadillac says: January 25, 2014 at 4:32 pm
“The data can’t lie if you treat them correctly.”
Well, that means looking at all the data. That’s where the possibility of cherry picking comes in….
>>>>>>>>>>>>>>>>>>>>>>
And that is just it. The temperature data sets contain ‘cherry picking’ and adjustments.
There is the station dropout problem – GRAPH
Followed by E.M Smith’s “Thermometer Zombie Walk”
E.M. Smith found When the GHCN data set is reduced to the 3000 thermometers with the longest records (cut off at about 64 years worth of data for the station), the “global warming” signal is not present.
And then you get into the adjustments where you run into the “A goat ate my homework” excuse book. and all the other dodging and weaving. Such as Dr. Phil Jones of the UEA CRU reply to Warwick Hughes “Why should I make the data available to you, when your aim is to try and find something wrong with it?”.
Now where did I put that garlic and steak…. Drat I ate it…
ThinkingScientist says: January 26, 2014 at 12:15 am
“Nick Stokes, are you able to respond to my question about how the test for statistical significance is performed so I can replicate it? Is it just a Student T test? “
I’ve written two blog posts, here and here. The simplest way to replicate is using the R arima function; the format is:
h=arima(T,k,xreg=time(T)/100)
The structure h will contain t-statistics etc .
James Abbot:
I am in a rush because I am fitting this in between duties. But I promised to address your answer to my question this morning so I am giving you a priority.
I remind that you raised the issue of ‘committed warming’ and claimed it poses a future threat. I answered that at January 25, 2014 at 4:41 pm here and concluded by asking you
You have not replied. After all the time trouble and effort several people have taken to answer your many points, but you have run away on the first time a point was put to you WHY?
You did reply to an earlier response I had provided to you. I had explained the Null hypothesis and its significance to assessment of anthropogenic climate change. That explanation is at January 25, 2014 at 4:17 pm and is here. Your reply to that is at January 25, 2014 at 4:54 pm and is here
Your reply to that consists of
(a)
a claim that you “have a science degree”,
(b)
the logical fallacy of Appeal to Authority saying “the scientific conclusion – by qualified scientists –“ (which ignores that I am a “qualified scientist” who had given you my conclusion),
(c)
deliberate idiocy which evades my clear statement about net forcings by talking about individual forcings.
(d)
rejection of empirical data about the current climate system on the basis that the system was different in an ice age
and (e)
assertion that there was an ice age when CO2 was lower but ignoring that there was also an ice age when CO2 was much higher.
And you having taken time, trouble and effort to provide all that nonsense,
YOU STILL HAVE NOT ANSWERED MY QUESTION!
As I said,
Richard
Konrad says, January 25, 2014 at 5:04 pm:
“Ultimately there is no way forward that involves claiming ManBearPig is not real while claiming ManBearPigglet is.”
Truest words of this thread,
Robert Wykoff says: @ur momisugly January 25, 2014 at 1:12 pm
…. I will be interested to see the generational backfire when they finally figure out they have been manipulated their entire lives.
>>>>>>>>>>>>>>>>>>>>>>>>
It will be more interesting to see the reaction when they get handed the bill and are told by the likes of Al Gore, Maurice Strong and friends with their mansions and jets and limos that they can not have the life style of their parents. My generation (Baby-boomers) was the last generation to do better than their parents. All the wealth accumulated by the middle class has been siphoned off or is to be siphoned off.
As of today,26 Jan 2014, each US baby is born with a debt of $54,383.85. You can tack on another $3,000 for state debt. If they go to school beyond high school and if they want a decent job they have to, you can add another $29,400 per borrower.
That is a heck of a lot of debt to be carrying when you start out in life. To top it off The International Monetary Fund (IMF) quietly dropped a bomb in its October (2013) Fiscal Monitor Report. Titled “Taxing Times,”
Think about that for a moment. The US unemployment is ~23% and still rising. The financial assets of the middle class are a house (now devalued) and a retirement account and little else. link The US government has already been floating the idea of confiscating retirement accounts. The Obama administration has just solicited public comment on their proposal to take money from Americans’ private 401(k) retirement accounts and convert it into government-backed annuities. Unlike an IRA any ‘excess’ money in the annuity would revert to the state instead of your heirs when you die. As we saw with Social Security, once the government gets their hands on an asset it then becomes fair game for plundering by politicians.
So what will be left for the middle class to give to the government to pay for the 10% equity in their home? This will hit the elderly the worst because many have been forced into early retirement but have accumulated equity in their homes so are ‘Rich’
I am sure the present generation graduating to no jobs, high debt and bankrupt parents is going to be really really happy about all that debt piled up on them from bailing out AIG (banks) and funding the pie in the sky windmills and solar panel corporations that trash the environment while producing nothing.
Remember they also get stuck with cleaning up the mess in ten to twenty years if not sooner. Someone will get stuck removing all the wind and solar farms and replanting the areas and it will not be the defunct corporations whose owners took the money and ran.
Unfortunately no one can replace the birds driven to extinction.
Is there still place for more nails in that coffin ?
***
Just The Facts says:
January 25, 2014 at 2:20 pm
“The Bull Shoals Reservoir is a rather narrow elongated body of water located in northern Arkansas. The orientation of the reservoir lies along a 290-300 deg radial from the city of Mountain Home (Fig. 1). Since the reservoir resulted from the intentional flooding of a natural valley, there are no terrain obstructions to winds blowing along the radial from the west-northwest. Most of the snow that fell occurred in and around the community of Lakeview.”
***
I once witnessed a coal power-plant snow-effect driving past a 2000 MW plant in WV. All the steam from cooling towers & steam vents had frozen, snowed out & covered the local area w/an inch or two of snow under frigid but cloudless conditions.
I can’t help but notice that we went through a similar flat period 1870-1890 for global temperature anomalies and also NH SST
http://www.woodfortrees.org/plot/hadcrut3gl/from:1870/to:1890/plot/hadcrut3gl/from:1870/to:1890/trend
http://www.woodfortrees.org/plot/hadsst2nh/from:1870/to:1890/plot/hadsst2nh/from:1870/to:1890/trend
but this was followed by increasing colder global temperatures as the NH and global oceans cooled
http://www.woodfortrees.org/data/hadcrut3gl/from:1890/to:1910/plot/hadcrut3gl/from:1890/to:1910/trend
http://www.woodfortrees.org/plot/hadcrut3gl/from:1898/to:1910/plot/hadcrut3gl/from:1898/to:1910/trend
To me the answer is in the ocean cycles .
herkimer says:
January 26, 2014 at 6:45 am
I can’t help but notice that we went through a similar flat period 1870-1890 for global temperature anomalies and also NH SST
While we experienced warming and cooling and no change in the past, presumably things are supposed to be different now with all of the CO2 we are putting into the air at all times. However since many things are not different, perhaps CO2 is just not the big driver that many people think it is.
P.S. Thank you to all who have commented so far and to all who will still comment.
James Abbot,
“So how do you explain the planet being 8C colder when CO2 was half the current level ? ”
And how do you explain the late Ordovician glaciation that occurred in the face of rising CO2 levels – or are you not aware of that event?
Nick Stokes: thanks for the links on the statistical significance calculations.
TS
I’d like to add to Michael D’s assertion that you don’t quite understand certainty and significant digits in a calculation.
You said:
” 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.”
Michael D said:
” it sounds like you have no scientific training in the reporting of uncertainty, which undermines the ethos of this article.”
You said:
” 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.”
Michael D resonded by using whole numbers for both games and goals scored to help you begin to get a handle on how you use significant digits to decide when to “round off” and when to not round off.
I would like to add. He is right. Your fictional example is a strawman that in no way can be equated to the MEASUREMENT of temperature and energy in the atmosphere. Let me see if I can help you understand why. When someone scores a goal, How was the scoring of that goal measured? How certain are you that the goal was actually scored? To 1 significant digit? 2? 10? A million? The measurement of whether a goal has been actually scored is 1 with NO degree of uncertainty. So whenever a goal is added to the list of goals scored it is added as 1.0000000000 . . . . . with as many significant digits as necessary to fully count the goal as 100% certain for future calculations. That is not in any way similar to the measurement and recording of a temperature reading. The measurement instrument used to measure temperature HAS uncertainty and this MUST be included in the reporting, storing and subsequent math calculations that are done on that data. Just as soon as you ADD certainty into the caculation by adding digits to the calculation that are “uncertain”, you are fooling yourself and beginning to add the perception of certainty where there is none. Extra significant digits added by calculation can be included, but they have to be included by using the original data along with it’s +/- uncertainty range to illustrate the overall “range” that the final answer could fall within. Uncertainty creeps in when the measurement has uncertainty or the possibility of error. The degree to which we are able to measure uncertainty or error is handled every day by well trained engineers as part of how they process data in the real world. Very, very few measurements are taken that have more than about 4 or 5 significant digits. 1 part in a million is very very hard to resolve with certainty. Especially when normal daily variation of that measurement is greater than 10-20. So for example CO2 concentrations that are reported with 1 PPM resolution are a farce! So too with temp measurement with accuracy greater than +/-.03 degrees C for an expensive platinum RTD. The best a good thermocouple can do is +/- 1 degree C, The best an average thermistor can do is +/- 0.1 degree C.
This is one of the biggest beefs I have about reporting temperature “anomalies”. How certain can anyone be that the difference in temp over a year or a decade is being reported when daily drift of thousands of sensors (none of which have been matched) can be as much as +/- 1 – 2 degrees C, (same with linearity) on a signal that is continuously varying by many degrees daily and even more annually! IMHO the uncertainty in the measurements of temperature vastly exceed the “anomalies” reported, especially as the “anomaly” calculated gets close to zero.
And most important of all. Time averaged (rather than time integrated) measurement of temperature is practically meaningless without simultaneous time integrated measurement of relative humidity, since water vapor in the atmosphere (where the temperature guages are located) actually contains most of the heat energy over the vast majority of the planet.
I would prefer that Werner Brozek avoid terms such as Pause, which carries the implicit assumption that global warming will in fact resume any moment. This is playing into the hands of the alarmists/warmists. Use instead some such term as “the late warming trend”. Thank you.
DonV says:
January 26, 2014 at 9:30 pm
Thank you for your post. As for the goals, you are correct and I admitted it to someone else here:
wbrozek says:
January 25, 2014 at 1:28 pm
I agree I should have picked a measured quantity to illustrate the point.
Climate science seems to follow its own rules in more ways than one. I give the numbers and realize the numbers may be +/-0.1. Do you have any issues with how Nick Stokes comes up with his numbers?
As for the humidity aspect, I agree that is a big one since the biggest increases are in the poles, and in winter, so very cold and dry air can easily get heated a lot. What we have is by no means perfect, but we have to use what we have until improvements are made.
B…b…b…b…but …. Worrrrrrrrrrst drought in CA in 500 yearrrrrrrrrrs! And as everyone knows, droughts are caused by Global warming! /sarc
One thing that does run as a common trait throughout the AGW dogma is the very (un)scientific “clutching at straws” (shades of Monty Python’s ‘Search for the holy grail’ – “it’s a sign!”).
And for those that disagree, humour is a common trait – that’s is why the Motley Fool was the only one at court who dared tell the lord of the manor the painful truth. Tim@68
I think it is rather sweet how the scientifically literate regulars on this site have attempted to engage the trolling James Abbott in an actual (though one sided) discussion. I applaud your patience and good humor.
mpainter says: January 27, 2014 at 9:47 am
I would prefer that Werner Brozek avoid terms such as Pause, which carries the implicit assumption that global warming will in fact resume any moment. This is playing into the hands of the alarmists/warmists. Use instead some such term as “the late warming trend”. Thank you.
I disagree that the Pause “is playing into the hands of the alarmists/warmists”. I think it is one of the effect memes we’ve established to date. Take a look at some excerpts from this recent Mother Jones (aka Warmist Mouthpiece) article, “Global-Warming Denial Hits a 6-Year High”:
“The new study, from the Yale and George Mason University research teams on climate change communication, shows a 7-percentage-point increase in the proportion of Americans who say they do not believe that global warming is happening. And that’s just since the spring of 2013. The number is now 23 percent; back at the start of last year, it was 16 percent.”
“The percentage of Americans who believe global warming is human-caused has also declined, and now stands at 47 percent, a decrease of 7 percent since 2012.”
“According to both Anthony Leiserowitz of Yale and Ed Maibach of George Mason, the leaders of the two research teams, the answer may well lie in the so-called global warming “pause”—the misleading idea that global warming has slowed down or stopped over the the past 15 years or so. This claim was used by climate skeptics, to great effect, in their quest to undermine the release of the UN Intergovernmental Panel on Climate Change’s Fifth Assessment Report in September 2013—precisely during the time period that is in question in the latest study.
As we have reported before, the notion of a global warming “pause” is, at best, the result of statistical cherry-picking. It relies on starting with a very hot year (1998) and then examining a relatively short time period (say, 15 years), to suggest that global warming has slowed down or stopped during this particular stretch of time. But put these numbers back into a broader context and the overall warming trend remains clear. Moreover, following the IPCC report, new research emerged suggesting that the semblance of a “pause” may be the result of incomplete temperature data due to the lack of adequate weather stations in the Arctic, where the most dramatic global warming is occurring.
Nonetheless, widely publicized “pause” claims may well have shaped public opinion. “Beginning in September, and lasting several months, coincident with the release of the IPCC report, there was considerable media attention to the concept of the ‘global warming pause,'” observes Maibach. “It is possible that this simple—albeit erroneous—idea helped to convince many people who were previously undecided to conclude that the climate really isn’t changing.”
‘Even more likely, however,’ Maibach adds, ‘is that media coverage of the ‘pause’ reinforced the beliefs of people who had previously concluded that global warming is not happening, making them more certain of their beliefs.'”
“Journalists take heed: Your coverage has consequences. All those media outlets who trumpeted the global warming “pause” may now be partly responsible for a documented decrease in Americans’ scientific understanding.”
http://www.motherjones.com/blue-marble/2014/01/global-warming-pause-climate-denial-public-opinion
That to me says that the Pause has been successful in informing people that Earth isn’t warming rapidly as has been the narrative for much of the last decade:
http://www.pbs.org/newshour/bb/climate-change/july-dec13/climate1_09-27.html
Most skeptical people are already skeptics, we are now reaching across the aisle and drawing in former believers. Thus a simple sticky lexicon is what we need, and the Pause fits the bill. The next time you find yourself in a conversation with someone who believes in CAGW, but isn’t a hard core supporter, ask them if they’ve heard of The Pause. If not , explain to them that Earth’s temperature hasn’t increased for between 9 – 17 years and recommend they look it up. You may just have created a budding skeptic.