Image Credit: WoodForTrees.org
Guest Post By Werner Brozek, Edited By Just The Facts
You may have seen the following recent articles (1, 2 and 3) by Walter Dnes on his January Leading Indicator. The same idea can also be shown in a different way. Namely, we can compare this decade to the last decade and see how things are turning out. We have all read that the 2001 to 2010 decade was the hottest in recorded history. But what has happened since then? How does 2001 compare with 2011, and 2002 with 2012, and 2003 with 2013? And what will happen in 2014?
See the above graph that shows January 2001 to January 2004 and then from January 2011 to January 2014 for RSS. Compare the left red point with the left green point. Note that the left red point is higher than the left green point indicating that January 2001 was warmer than January 2011. As it turns out, 2001 was warmer than 2011. Now compare the right red point with the right green point. Note that the right red point is higher than the right green point indicating that January 2004 was warmer than January 2014. What logical predictions can be made here?
I am now going to provide 8 pairs of numbers, however the final number in the last pair will be missing until next January. The format of the numbers is as follows:
(Jan 2001, Jan 2011); (2001 anomaly, 2011 anomaly);
(Jan 2002, Jan 2012); (2002 anomaly, 2012 anomaly);
(Jan 2003, Jan 2013); (2003 anomaly, 2013 anomaly);
(Jan 2004, Jan 2014); (2004 anomaly, 2014 anomaly?).
For the RSS data, note that in every case, the second number is lower than the first. What do you predict for *?
(0.101, 0.080), (0.246, 0.143);
(0.359, -0.064), (0.315, 0.187);
(0.440, 0.439), (0.320, 0.218);
(0.311, 0.262), (0.202, *).
Information on the prior Januaries for the other 5 data sets can be found in Section 3 below.
Are there physical reasons to explain Walter Dnes January Leading Indicator? I can think of several. Perhaps you can add to this list.
1. By the laws of averages, half of all Januaries should be above the yearly average and half should be below. So with a number of high Januaries, the final anomalies would be higher than for a number of low Januaries.
2. Related to the above, if the January anomaly went from 0.4 to 0.3, and if we assume the previous year also had an average anomaly of 0.4, and with the chances being 50% for an anomaly of less than 0.3 for the new year, the odds are greater than 50% for an anomaly of less than 0.4.
3. The number in January may be so much higher or lower that it takes 11 months of normal values to partially negate the effect of the high or low January value. To use a sports analogy, two teams may be very equal, but one team has the jitters for the first 5 minutes and is down by 3 goals in this time. It is quite possible that the rest of the game is not long enough for this deficit to be overcome. Walter’s method is analogous to being allowed to predict the outcome of a game after watching the first 5 minutes.
4. According to Bob Tisdale, effects of El Nino or La Nina often show themselves in January so in those cases, it would be obvious why the rest of the year follows.
5. Any other cycle such as a sun that getting quieter every year would automatically be reflected in the anomalies for January and the rest of the year as well.
6. Can you think of others?
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 January of 2014 compares with 2013 and the warmest years and months on record so far. In addition to what I have presented previously, I will compare the anomalies for January 2013 with those of 2014 as well as January 2004 with those of 2014
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 1 month to 17 years and 5 months.
1. For GISS, the slope is flat since November 2001 or 12 years, 3 months. (goes to January)
2. For Hadcrut3, the slope is flat since August 1997 or 16 years, 6 months. (goes to January)
3. For a combination of GISS, Hadcrut3, UAH and RSS, the slope is flat since January 2001 or 13 years, 1 month. (goes to January)
4. For Hadcrut4, the slope is flat since January 2001 or 13 years, 1 month. (goes to January)
5. For Hadsst3, the slope is flat since December 2000 or 13 years, 2 months. (goes to January)
6. For UAH, the slope is flat since January 2005 or 9 years, 1 month. (goes to January using version 5.5)
7. For RSS, the slope is flat since September 1996 or 17 years, 5 months (goes to January). So RSS has passed Ben Santer’s 17 years.
(P.S. The anomaly for February for RSS has come in and the time is now 17 years and 6 months going from September 1996 to February 2014.)
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 page. 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 February 1996: CI from -0.042 to 2.415
For RSS: Since November 1992: CI from -0.022 to 1.900
For Hadcrut4: Since October 1996: CI from -0.027 to 1.234
For Hadsst3: Since January 1993: CI from -0.016 to 1.812
For GISS: Since September 1997: CI from -0.014 to 1.299
Section 3
This section shows data about January 2014 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, GISS. Down the column, are the following:
1. 13ra: This is the final ranking for 2013 on each data set.
2. 13a: Here I give the average anomaly for 2013.
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.
8. sig: This the first month for which warming is not statistically significant according to Nick’s criteria. The first three letters of the month is followed by the last two numbers of the year.
9. Jan14: This is the January 2014 anomaly for that particular data set.
10.Jan13: This is the January 2013 anomaly for that particular data set.
11.diff: Here I simply indicate if the difference between the January 2014 number is negative or positive with respect to the January 2013 number. A negative difference indicates 2014 will be forecast to be cooler than 2013 and vice versa. See Row 1 for the 2013 rank.
12.Jan14: This is a repeat of the January 2014 anomaly for that particular data set. I am repeating row 9 for clarity as row 12 will now be compared to row 13.
13.Jan04: This is the January 2004 anomaly for that particular data set.
14.diff: Here I simply indicate if the difference between the January 2014 number is negative or positive with respect to the 2004 number. A negative difference indicates 2014 will be forecast to be cooler than 2004 and vice versa.
15.04rk: Here I give the rank in 2004 for each particular data set.
16.rnk: This is the rank that each particular data set would have if the anomaly for January 2014 were to remain that way for the rest of the year. Of course it will not, but think of it as an update 5 minutes into a game. Due to different base periods, the rank is more meaningful than the average anomaly.
| Source | UAH | RSS | Had4 | Had3 | Sst3 | GISS |
|---|---|---|---|---|---|---|
| 1. 13ra | 7th | 10th | 8th | 6th | 6th | 6th |
| 2. 13a | 0.197 | 0.218 | 0.486 | 0.459 | 0.376 | 0.60 |
| 3. year | 1998 | 1998 | 2010 | 1998 | 1998 | 2010 |
| 4. ano | 0.419 | 0.55 | 0.547 | 0.548 | 0.416 | 0.66 |
| 5. mon | Apr98 | Apr98 | Jan07 | Feb98 | Jul98 | Jan07 |
| 6. ano | 0.662 | 0.857 | 0.829 | 0.756 | 0.526 | 0.93 |
| 7. y/m | 9/1 | 17/5 | 13/1 | 16/6 | 13/2 | 12/3 |
| 8. sig | Feb96 | Nov92 | Oct96 | Jan93 | Sep97 | |
| Source | UAH | RSS | Had4 | Had3 | Sst3 | GISS |
| 9.Jan14 | 0.235 | 0.262 | 0.506 | 0.472 | 0.341 | 0.70 |
| 10.Jan13 | 0.504 | 0.439 | 0.450 | 0.392 | 0.292 | 0.63 |
| 11.diff | neg | neg | pos | pos | pos | pos |
| 12.Jan14 | 0.235 | 0.262 | 0.506 | 0.472 | 0.341 | 0.70 |
| 13.Jan04 | 0.184 | 0.311 | 0.516 | 0.504 | 0.359 | 0.56 |
| 14.diff | pos | neg | neg | neg | neg | pos |
| 15.04rk | 12th | 11th | 11th | 7th | 9th | 13th |
| Source | UAH | RSS | Had4 | Had3 | Sst3 | GISS |
| 16.rnk | 4th | 6th | 4th | 5th | 11th | 1st |
(P.S. The RSS anomaly for February is in and it has a value of 0.162. When averaged with the January anomaly of 0.262, it comes to 0.212 and this would make 2014 rank 11th if it stayed this way.)
What can we conclude from the two sets of differences above? Below, I will assume that Walter Dnes’ qualitative prediction holds true and give the results.
For UAH, the final rank would be colder than 7th but warmer than 12th.
For RSS, the final rank would be colder than 11th.
For Hadcrut4, the final rank would be colder than 11th but warmer than 8th.*
For Hadcrut3, the final rank would be colder than 7th but warmer than 6th.*
For Hadsst3, the final rank would be colder than 9th but warmer than 6th.*
For GISS, the final rank would be warmer than 6th.
*Obviously we cannot have any contradictions. As Walter explained, there is a lot of noise in these numbers. To find the true prediction, we need to find the place that January 2014 fits on the best fit line. The January 2004 numbers were closer to the line on Walter’s graphs, so they would give the most reliable estimate in my opinion. Note that my numbers are for version 5.5 for UAH and I have numbers for Hadsst3 that Walter does not have, so I cannot comment on those. However it seems as if the odds favour cooling according to Walter.
See the following graph:
All graphs have been offset so they start at 0.4 for January 2004. So to see if 2014 is expected to be colder or warmer than 2004, see if the final point, namely January 2014, is above or below 0.4. But keep in mind this is only a qualitative estimate.
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. This makes it easy to compare last January 2013 with January 2014.
Appendix
In this part, we are summarizing data for each set separately.
RSS
The slope is flat since September 1996 or 17 years, 5 months. (goes to January) So RSS has passed Ben Santer’s 17 years.
For RSS: There is no statistically significant warming since November 1992: CI from -0.022 to 1.900.
The RSS anomaly for January is 0.262. This would rank it in 6th place if it stayed this way. 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 2013 was 0.218 and it is ranked 10th.
(P.S. The anomaly for February for RSS has come in and the time is now 17 years and 6 months going from September 1996 to February 2014. Also, when the February anomaly of 0.162 is averaged with the January anomaly of 0.262, it comes to 0.212 and this would make 2014 rank 11th if it stayed this way.)
UAH
The slope is flat since January 2005 or 9 years, 1 month. (goes to January using version 5.5)
For UAH: There is no statistically significant warming since February 1996: CI from -0.048 to 2.415.
The UAH anomaly for January is 0.235. This would rank it in 4th place if it stayed this way. 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 2013 was 0.197 and it is ranked 7th.
Hadcrut4
The slope is flat since January 2001 or 13 years and 1 month. (goes to January)
For Hadcrut4: There is no statistically significant warming since October 1996: CI from -0.027 to 1.234.
The Hadcrut4 anomaly for January is 0.506. This would rank it in 4th place if it stayed this way. 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 2013 was 0.486 and it is ranked 8th.
Hadcrut3
The slope is flat since August 1997 or 16 years, 6 months. (goes to January)
The Hadcrut3 anomaly for January is 0.472. This would rank it in 5th place if it stayed this way. 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 2013 was 0.459 and it is ranked 6th.
Hadsst3
For Hadsst3, the slope is flat since December 2000 or 13 years and 2 months. (goes to January).
For Hadsst3: There is no statistically significant warming since January 1993: CI from -0.016 to 1.812.
The Hadsst3 anomaly for January is 0.341. This would rank it in 11th place if it stayed this way. 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 2013 was 0.376 and it is ranked 6th.
GISS
The slope is flat since November 2001 or 12 years, 3 months. (goes to January)
For GISS: There is no statistically significant warming since September 1997: CI from -0.014 to 1.299.
The GISS anomaly for January is 0.70. This would rank it as 1st place if it stayed this way. 2010 was the warmest at 0.66. The highest ever monthly anomaly was in January of 2007 when it reached 0.93. The anomaly in 2013 was 0.60 and it is ranked 6th.
Conclusion
According to Walter’s criteria, the message seems to be rather mixed as to whether we will have cooling or warming this year. At times, the qualitative indication is opposite to the quantitative indication. This shows that there is quite a bit of noise in the data. Overall, there seems to be a greater indication of cooling. My inclination is to trust the quantitative number in these cases to give the best indication as to where we are headed.
I would say that unless a very strong El Nino develops fairly quickly, there will be little change this year and the length of the period of no warming will continue to increase. As well, the period of no statistically significant warming will also increase this year unless we have an El Nino. Do you agree?
Your pedantic insistence on starting decades on the “1” year is a fatal distraction to the whole article. When people say “nineties” or “noughties”, they mean the decade starting on the “0” year. Stick to popular convention or lose the attention of your audience from the very outset.
NZ Willy says:
March 9, 2014 at 12:08 pm
Your pedantic insistence on starting decades on the “1″ year is a fatal distraction to the whole article.
You raise a good point, but see:
http://wattsupwiththat.com/2014/03/07/jli-final-forecasts-for-2014/#comment-1585645
A few quotes:
“Can anyone explain why the Met Office refers its prediction on a long term (1961-1990) average? Why not (1951-1980) or (1971-1990) or the most recent (1981-2010)? Is it because the anomaly may look higher when using 1961-1990 as the base?”
Climate science seems to follow its own rules with regards to the start and end of decades.
NZ Willy: though I certainly agree with you in general, and I celebrated the new millennium at the start of 2000 not 2001, unfortunately meteorological organizations all use xxx1 to yyy0, silly though it may be.
Rich.
I’ve been hearing a distant grinding sound for some time now. Guessing that would be nicky’s teeth….
The word “decade” is ambiguous. It can refer to a ten year period ending with a zero, or a 10 year period ending with a 9, or any ten year period. For the purposes of science, the latter definition is what we should look at IMHO. Nature doesn’t care what sort of number our calendar assigns to a given year.
If we take “decade” to mean any 10 year period, then the most recent decade is 2004 – 2013. And, this decade is not the warmest decade on record.
So far, interannual temperature variation is on par with the variability over a period of ~150 years. If the interannual variability represents noise and this is compared to the signal of the long-term trend, the signal to noise is insignificant.
NZ Willy says:
March 9, 2014 at 12:08 pm
“Your pedantic insistence on starting decades on the “1″ year is a fatal distraction to the whole article. When people say “nineties” or “noughties”, they mean the decade starting on the “0″ year. Stick to popular convention or lose the attention of your audience from the very outset.”
Pedantic!
Are you one of those people who count to ten starting at zero and ending at nine?
In year terms all centuries and decades start at one. The first decade AD was not 0 to 9 AD , it was 1 to 10 AD. No such year as 0 AD mate. All other decades have followed on from this one.
Alan
One reason for having such a long flat period (and having it increase in width backwards as time goes by) would be if we are over some local peak and headed downwards for a time into the near future.
David in Cal says:
March 9, 2014 at 12:25 pm
If we take “decade” to mean any 10 year period, then the most recent decade is 2004 – 2013. And, this decade is not the warmest decade on record.
True, but one needs to be careful how this is interpreted. 2004 to 2013 is warmer than 1994 to 2003.
However check out the following. The green line for the 2001 to 2011 decade is certainly higher than the blue line which is the line for the most recent 120 months.
http://www.woodfortrees.org/plot/rss/from:2001/to/plot/rss/from:2001/to:2011/trend/plot/rss/last:120/trend
NZ Willy says:
March 9, 2014 at 12:08 pm
“nineties” or “noughties”
Say what? You just made those up. Don’t make stuff up.
The post is about Januaries. Start with link #1 above. There you can see the original idea:
“. . . the rule goes like so…
If this year’s January anomaly is warmer than last year’s January anomaly, then this year’s annual anomaly will likely be warmer than last year’s annual anomaly.
If this year’s January anomaly is colder than last year’s January anomaly, then this year’s annual anomaly will likely be colder than last year’s annual anomaly.”
Even if there is a strong el-Nino, unless it is stronger than 1998-99 we should not expect a significant change in the long term slope. What goes up, comes down and usually more rapidly.
Farmer wise, any change in more than an average of four degrees in annual, monthly and weekly can be a disaster to farming. Now that we are in a sunspot minimum and new data is pouring in we really don’t know where the bottom is.
Now let’s take Joseph D’Aleo’s work..the minimum will last until 2030. One s identity points out there is a climate lag for 5 years. So an equal Medieval Global Warming period will not resume until 2036.
All the data here is greatly appreciated an should be stored for later analysis. We don’t have much in the way of solar minimum data other than historical items reflecting cold and drought in the early 1700s and 1800s.
I greatly appreciate this document, Dr. Watts.
Paul
January 2000 had an anomaly of -0.055 and the average for 2000 was 0.092. January 2010 had an anomaly of 0.593 and the average for 2010 was 0.472.
So the relationship certainly applied then.
As well, RSS shows cooling from January 2000, but not as steep as from 2001. See:
http://www.woodfortrees.org/plot/rss/from:2000/to:2014.05/plot/rss/from:2000/to:2014.05/trend
Most misleading of all to most folks is the phrase “in recorded history.” Most folks probably
assume that is the period in which written human records exist, not since weather data has
been systematically recorded. Big difference.
fhhaynie says:
March 9, 2014 at 12:51 pm
Even if there is a strong el-Nino, unless it is stronger than 1998-99 we should not expect a significant change in the long term slope.
That is a good point. In the following, check out the area below the green line from September 1996 to December 1997. Then check out the area above the green line from December 1997 to December 1998.
http://www.woodfortrees.org/plot/rss/from:1996.65/plot/rss/from:1996.65/trend
The positive area is larger than the negative area so a 1998 El Nino would push the flat slope start time into the 2000s. However if there is a delay before a strong El Nino manifests itself, then it may be possible that the start of the flat slope line will not go past December 1997.
So which ten year period was the warmest, according to official figures?
How high up the list of ten year periods was the most recent such period (2004-2013?
You’re getting a mention on several climate blogs …
http://www.climate-resistance.org/2014/03/the-gwpf-crok-lewis-and-positioning-sceptics.html#comment-301039
For the public record, the blogs on which the above linked comments (or similar) have been deleted are ..
Skeptical Science
Watts Up With That
Stoat-Connelly
Science of Doom
Lucia’s Blackboard
The Air Vent
Australian Climate Madness
[Request “you” (the Visiting Physicist) identify who the “you” is in the first sentence above. Mod]
REPLY: The reason for your multiple comment removal is simple, Doug Cotton: you’re a spamming idiotic troll that won’t take “go away” for an answer. Look forward to a post here all about your antics. – Anthony
“1. By the laws of averages, half of all Januaries should be above the yearly average and half should be below.”
Really? What’s the average of 1,1,1 and 3? By the “laws of averages” half should be above and half should be below if and only if half are above and half below. It’s risky to generalize about the distribution given only the mean — the half above and half below conclusion assumes facts not in evidence.
AndyL says:
March 9, 2014 at 1:12 pm
So which ten year period was the warmest, according to official figures?
It would depend on your data set. See the following where I plotted RSS from the beginning and then took the mean of 120 months or 10 years. The peak is at October 2002. So the highest 10 year period would be 5 years before this to 5 years after this. In other words from October 1997 to September 2007.
http://www.woodfortrees.org/plot/rss/from:1979/mean:120
According to that other global satellite dataset, UAH, there is no ongoing cooling trend. There is an ongoing warming trend:
http://woodfortrees.org/plot/uah/from:2001/to:2004.08/plot/uah/from:2011/plot/uah/from:2001/trend
Col Mosby says:
March 9, 2014 at 1:09 pm
absolutely! – and a pet peeve of mine that most layfolk don’t or cannot be bothered to know the difference. However, it is even worse for geologists (like myself) – who would generally consider the whole existence of human life (and all and any records thereof – including cave drawings, etc) to be but a drop in the proverbial ocean in terms of the [geological] ‘record’ !
After the first third, OMG, what a mess. Makes me pine for Willis.
Kate Forney says:
March 9, 2014 at 1:14 pm
“1. By the laws of averages, half of all Januaries should be above the yearly average and half should be below.”
Really? What’s the average of 1,1,1 and 3?
I was not thinking of only 4 numbers when I made that statement. If you have a 160 year record for Hadcrut4 for example, you can say that half of all Januaries and half of all Februaries, etc would be above the annual average and half would be below. Naturally, if you have 34 years, then you would expect 17 to be above and 17 below. In real life, for a short period, you would likely have a range such as 14 to 20 being above or below.
It is something like flipping a coin. Wide differences can happen with 10 flips, but with 500 flips, you would get very close to 250 heads and 250 tails.
“1. By the laws of averages, half of all Januaries should be above the yearly average and half should be below.”
I think you mean “median,” not “average.”
Kristian says:
March 9, 2014 at 1:28 pm
According to that other global satellite dataset, UAH, there is no ongoing cooling trend. There is an ongoing warming trend:
True enough. On the other hand, if we use the WTI which combines Hadcrut3, GISS, RSS and UAHversion5.5, then the slope is very slightly negative but it is certainly not statistically significant.
http://www.woodfortrees.org/plot/wti/from:2001/plot/wti/from:2001/trend
Werner
Thanks for your reply about the hottest ten year period.
I was thinking about something that would be easier to communicate. By thinking about whole calendar years, would it be possible to state something like “the hottest ten years were from Jan 1997 to Dec 2006. Jan 04 to Dec 13 was the eighth hottest set of ten years”.
not according to our latest, published, aussie, daily study:
CAGW wipes out BODACIOUS TUBES:
9 Mar: New Scientist: Michael Slezak: Bummer. Climate change will shrink gnarly Aussie waves
Bodacious tubes on Australia’s east coast are being wiped out by global warming…
Andrew Dowdy and his colleagues from the Australian Bureau of Meteorology in Melbourne ran 18 climate models forwards and backwards from the present day to try to spot how the changing climate might influence big waves…
Where there might have been waves taller than 6 metres on 36 days a year in the 1950s, now it happens on only about 34 days a year…
The results can’t be generalised to other parts of the world, though, Hemer says. The weather conditions that drive waves in this region may not be the same elsewhere so these studies need to be repeated for each location.
Journal reference: Nature Climate Change: DOI: 10.1038/nclimate2142
http://www.newscientist.com/article/dn25189-bummer-climate-change-will-shrink-gnarly-aussie-waves.html
Col Mosby says:
March 9, 2014 at 1:09 pm
Most misleading of all to most folks is the phrase “in recorded history.”
Sorry! I meant since man recorded temperatures.
the CAGW crowd pick their dates willy nilly:
10 Mar: BusinessInsider: Chris Pash: Sorry Surfies, Climate Change Means Fewer Gnarly Waves On East Coast Australia
Dr Andrew Dowdy of the bureau’s Centre for Australian Weather and Climate Research and colleagues used data from 1992 to 2010 to analyse storm days…
http://www.businessinsider.com.au/sorry-surfies-climate-change-means-fewer-gnarly-waves-on-east-coast-australia-2014-3
Kristian says:
March 9, 2014 at 1:28 pm
“According to that other global satellite dataset, UAH, there is no ongoing cooling trend. There is an ongoing warming trend:”
This says you are wrong in that regard. We have reached a local maximum, for how long is the question now.
http://climatedatablog.wordpress.com/uah/
NZ Willy says:
March 9, 2014 at 12:08 pm
Your pedantic insistence on starting decades on the “1″ year is a fatal distraction to the whole article. When people say “nineties” or “noughties”, they mean the decade starting on the “0″ year. Stick to popular convention or lose the attention of your audience from the very outset.
=============================================================================
This probably goes back to the fact that there is no year zero in the Gregorian calendar. Thus the first Century is the century ending in the year 100. Because of this, technically the 20th century was the century that ended in 2000.
In common parlance, people refer to the years 1990 to 1999 as the nineties, but the technical reality of our calendar system is that the years 1991 to 2000 were the tenth decade of the 20th century and the years 2001 to 2010 were the first decade of the 21st century which will be the years 2001 to 2100.
hmmm!
VIDEO: 17 SECONDS: 9 Mar: ITV Wales: Changes in the Sun’s activity may have led to natural climate change
Welsh researchers have found changes in the Sun’s activity over the last thousand years may have led to marked natural climate change.
Scientists at Cardiff University studied the seabed to determine how the temperature of the North Atlantic had altered, with the results showing that changes in the sun’s activity can have a considerable impact on the dynamics of the ocean, with potential effects on regional climate.
They say the study will allow them to better predict regional climate change.
Professor Ian Hall, from the University says this could lead to colder winters.
http://www.itv.com/news/wales/2014-03-09/changes-in-the-suns-activity-may-have-led-to-natural-climate-change/
Alan,
The “first decade AD” is a retroactive anachronism since not only did NO ONE, even the disciples, use phrases like, “in the year of our Lord,” at anything like the timeframe you’re discussing, and certainly wouldn’t have until they because disciples around the time we now refer to as 30AD, anyways.
Now, as far as a very traditional CHURCH calendar goes – you are right that there was a year 1AD and immediately before it was a 1BC with not “zero” year in between those, but again the CHURCH calendar did NOT exist until centuries later. And since precise knowledge of the birth date of Jesus doesn’t allow us to know if he was actually born in the year 1AD (probably wasn’t) then that’s all a bit of hand waving in any case, isn’t is?
That all having been said, because of the obvious MATHEMATICAL problem associated with the absence of the number zero for sciences that actually using precise dates that go back that far all of those sciences ASSUME a year 0, which, for obvious reasons then is by definition as “AD” year as opposed to a “BC” year, so yes, the SCIENTISTS who use AD and BC years (though they sometimes say CE or BCE instead) DO count the first decade of the 1st Century AD rom 0 through 9.
An example of such a science is astronomy. Here, for example, is the lunar eclipse data from NASA for the year that you would call 1BC. Note the year number in the data:
TD of Phase Greatest
Cat Calendar Greatest Luna Saros Ecl. Pen. Um. —- Durations —- in Zenith
Num Date Eclipse ΔT Num Num Type QSE Gamma Mag. Mag. Pen. Par. Total Lat. Lng.
s m m m
04821 0000 Jan 10 02:04:40 10534 -24737 63 T- p- -0.0445 2.7699 1.7825 327.2 213.8 98.8 22N 16E
04822 0000 Jul 05 11:33:06 10529 -24731 68 T+ pp 0.2117 2.5037 1.4360 369.9 230.1 93.9 23S 129W
04823 0000 Dec 29 17:27:44 10524 -24725 73 P a- -0.7104 1.5392 0.5695 283.9 151.2 –
Sorry I don’t have time to format that nicely…
Is that RSS downtrend from 1/1/01 statistically significant yet?
AndyL says:
March 9, 2014 at 1:50 pm
would it be possible to state something like “the hottest ten years were from Jan 1997 to Dec 2006. Jan 04 to Dec 13 was the eighth hottest set of ten years
I can give you the rankings of the hottest years on the 6 data sets I am discussing. However I do not have the the information that you want on hand. It is easy enough to figure out via WFT though. First of all, you need to know which data set you wish the information for. Then do it by trial and error. For example. Punch in 1995 to 2005. (This gives from January 1995 to December 31 2004.) The click the “raw data”. You should see the “number of samples”, ie the number of months, which should be 120. Then you see the “mean” or average for those 120 months.
Then do 1996 to 2006, etc. Then rank all means.
And can we please drop that “1AD” rubbish going on? The starting with a “1” reflects nothing more than the meme “hottest decade evah!” which was (on HadCRUT4) 2001-2010. The reason for that meme is that 2002-11 was cooler, 2003-12 cooler still.
Doesn’t work for the less adjusted RSS of course.
Mike McMillan says:
March 9, 2014 at 1:44 pm
No. Median is the middle number of an ordered set of values. Average is the sum of all the values divided by the number of values, also called the mean
Just read a article by Mocton saying that RSS is flat for 17 years 6 months. You say above 17 years 5 months. What is the discrepancy?
Andrew says:
March 9, 2014 at 2:10 pm
Is that RSS downtrend from 1/1/01 statistically significant yet?
No, it is not. See:
http://moyhu.blogspot.com.au/p/temperature-trend-viewer.html?Xxdat=%5B0,1,4,48,92%5D
This gives:
Rate: -0.612°C/Century;
CI from -1.893 to 0.669;
So in other words, there is a 95% chance the real slope is between +0.669 C/century to -1.893 C/century.
Here’s a sharp image for a coffee mug or button:
Image—A hockey stick with its shaft slanting upwards & to the right and its blade flat.
Caption—Who’s in Denial Now?
rogerknights says:
March 9, 2014 at 2:27 pm
Very good!
NZ Willy says:
Your pedantic insistence on starting decades on the “1″ year is a fatal
distraction to the whole article.
==================================
If you wish, you could create your own calendar. It’s been the convention
for centuries (yes, centuries!) to count years from 1. There is no year 0 AD
nor 0 BC. It’s 1 BC and 1 AD. You might have also noticed all years start
with month 1 and proceed through to month 12 not month 0 proceeding to
month 11. All days in a month start at day 1 and go to day 28, 29, 30 or 31
for whatever month requires it.
By creating your own calendar, you would then be absolutely free to start with
year 0 and month 0 with day 0 and go through to month 11 and day 30. I would
point out however, you would be out of step with everybody else, not only in NZ
but the rest of the world.
NEXT button-image, if a cooling trend begins:
Image—Hockey stick’s shaft is angled less steeply upward and the blade pointing down, although at a less steep angle than the shaft is rising.
Caption—The Warm is Turning
And here’s one for Josh, after Mann loses his suit:
Image—Mann in a goalie’s uniform tied in knots like Nick Stokes and backhandedly slapping the puck into his own net
Caption—Own Goal
Scott Scarborough says:
March 9, 2014 at 2:25 pm
Just read a article by Monckton saying that RSS is flat for 17 years 6 months. You say above 17 years 5 months. What is the discrepancy?
My title said: “Now Includes January Data”. I did not want to mix things up by giving everything else for January but only RSS for February in the data. However I did say in the Appendix:
(P.S. The anomaly for February for RSS has come in and the time is now 17 years and 6 months going from September 1996 to February 2014. Also, when the February anomaly of 0.162 is averaged with the January anomaly of 0.262, it comes to 0.212 and this would make 2014 rank 11th if it stayed this way.)
Since I am on this topic, UAHversion5.6 has also come in. However version5.5 that WFT uses has not come in yet. It would be reasonable to assume that version 5.5 also would drop by 0.12 in February. Should this happen, then the average for January and February is 0.235 + 0.115)/2 = 0.175. This would then rank 10th. As well, the time for a slope of 0 would be at least 9 years and 5 months. But until the data comes in, I am only guessing.
You should never write a post like this without first making it clear that a warming trend has been “adjusted” into the raw data. Never give in on the fact that years in the 1930s were warmer than anything we’ve seen since. This can not be re-stated too often.
wbrozek says:
March 9, 2014 at 1:38 pm
“I was not thinking of only 4 numbers when I made that statement. If you have a 160 year record for Hadcrut4 for example, you can say that half of all Januaries and half of all Februaries, etc would be above the annual average and half would be below. Naturally, if you have 34 years, then you would expect 17 to be above and 17 below. In real life, for a short period, you would likely have a range such as 14 to 20 being above or below.”
Ok, what’s the average of a series of 159 1’s and 1 3?
You are assuming that the distribution is symmetrical about the mean. Where have you established that as true? Have you actually looked to see that the mean and the median are the same with respect to Hadcrut4? I don’t see where you show that.
“It is something like flipping a coin. Wide differences can happen with 10 flips, but with 500 flips, you would get very close to 250 heads and 250 tails.”
Unless, of course, the coin is biased, then you might end up with a 350/150 split, right?
Thanks! I think it’s grade “A+” too. I urge some artist or cartoonist who’s reading this to whip up those images and post them on a thread devoted to cartoon/button/slogan ideas here. Hopefully he will include also this long-time suggestion of mine:
Image–A pair of upraised, chain-shackled hands decisively snapping a hockey stick (with its blade at the right).
A large caption (perhaps around the perimeter): “Gore Resisters’ League.”
Kate Forney says:
March 9, 2014 at 2:42 pm
I don’t see where you show that.
Someone actually brought out that point relating to an earlier post of mine:
http://wattsupwiththat.com/2013/12/22/hadcrut4-is-from-venus-giss-is-from-mars-now-includes-november-data/
The numbers really looked very different for Hadcrut4 versus GISS and it was suggested that different base periods could account for it. For example, in one base period of 30 years, there could have been many more warmer July anomalies than in another base period. I actually did a count and found no evidence over the 30 year base period that there were biases. So I am applying some analysis with common sense. If you can prove that this assumption is totally out to lunch, be my guest.
The calendar year is a very confusing unit because it is a unit that has duration. Unlike the pound, for example, which has no weight.
How do years work? You use them for counting. When you celebrate your twenty-first birthday, you are celebrating your birth twenty-one years after its occurrence. When saying, “I am twenty-one today,” you are in your 22nd year and counting.
The beginning of the Second Millenium was January 1, 2000. On that day we were in the year 2001 and celebrating the passing of 2,000 years.
I’ve noticed a few posts declaring that the sunspot cycle is at or near a minimum. Let me assure you that this is not the case. We’ve had a double-humped cycle and today’s daily number at spaceweather.com is 138. It’s been stronger in the 2nd hump and I wouldn’t be surprised if it went higher, but it’s years away from zero.
Mike McMillan says:
March 9, 2014 at 1:44 pm
“1. By the laws of averages, half of all Januaries should be above the yearly average and half should be below.”
I think you mean “median,” not “average.”
+++++++++++++++++++++++++++++++++++++++++++++++++++++++
Mathematical point of order:
MEDIAN is the center point of an ordered (ascending or descending) set of discrete data;
MODE is the most frequently appearing value in a set of discrete data;
MEAN is the “average” of a set of discrete data;
Statements about data distribution (e.g.: half January values above & half below) must either be based on actual data analysis (e.g.: by observation, half the January data points were above the MEAN…) or assumptions about the statistical distribution of data. The statement that “…half the January data is above & below the MEAN and/or MEDIAN…” assumes data is normally distributed (“normal distribution” is one of several specific statistical distributions, and requires MEAN = MEDIAN = MODE), which seems to be inaccurate for the limited data set under discussion.
I apologize for having hijacked this thread about the decades. Was not my intention! Did not realize my comment would be the first. Sorry! Thanks also to those who pointed out that meteorologists typically go “1” to “0”.
“You’re getting a mention on several climate blogs …”
Cotton is spamming that blog too? Unreal.
1. 4 years is not a decade.
2. I do not believe any such analysis can predict what the trend will be this year.
wbrozek says:
March 9, 2014 at 2:55 pm
. If you can prove that this assumption is totally out to lunch, be my guest.
I don’t have a dog in the hunt, and was only trying to be helpful. With the little I know about temperatures, I can see no reason, a priori, why one would necessarily believe that there are as many Januaries above the average as below. In fact, I would find such a discovery rather remarkable, given that the temperature time-series seems to be widely known to be non-stationary.
Since you seem to rely on that particular observation, I think it would improve your article if you could make explicit your reason for believing it so.
Chip Javert says:
March 9, 2014 at 3:14 pm
Mike McMillan says:
March 9, 2014 at 1:44 pm
“1. By the laws of averages, half of all Januaries should be above the yearly average and half should be below.”
I think you mean “median,” not “average.”
+++++++++++++++++++++++++++++++++++++++++++++++++++++++
Mathematical point of order: …
Chip beat me to the point. Assuming that half the Januaries will be above average is only valid if the data are normally distributed. Are they? You must show that first in order to make the statement. In many northern places, such as Alberta, monthly temperatures in January follow a bi-modal distribution. Are the monthly anomalies normally distributed- off hand I do not know.
Anyone who deals in statistics regularly cringes at this so called “law of averages”. This from Wikipedia is a good comment on the “law”:
“The law of averages is a layman’s term used to express a belief that outcomes of a random event will “even out” within a small sample.
As invoked in everyday life, the “law” usually reflects bad statistics or wishful thinking rather than any mathematical principle. “
Chip Javert says:
March 9, 2014 at 3:14 pm
What I was talking about has nothing to do with “Mean” or “Mode” or “Median”. I was not comparing 100 different Januaries with each other. I was comparing Januaries with the average anomaly for the year. So if we have 100 different anomalies for 100 different years, I am assuming that for 50 of those years, the January anomaly will be above the yearly anomaly. And 50 Januaries will be below the yearly anomaly.
NZ Willy says:
March 9, 2014 at 3:22 pm
I apologize for having hijacked this thread about the decades.
No problem! I anticipated it at some point which is why I had my response virtually ready right away.
PS: My image would also be great on billboards. Hop to it, Heartland and CFACT! (Or, better yet–Big Oil; and don’t forget to grease my palm!) Here’s the idea again:
Image—A hockey stick with its shaft slanting upwards & to the right and its blade flat.
Caption—Who’s in Denial Now?
Poptech says:
March 9, 2014 at 3:30 pm
I do not believe any such analysis can predict what the trend will be this year.
According to Walter Dnes’ first article, there is a 90% success rate when RSS shows a warmer January. That is a lot better than the MET office! Of course the MET office does not set the bar very high. Or they give such a large range that it is almost impossible to miss.
“The global average temperature in 2014 is expected to be between 0.43 C and 0.71 C above the long-term (1961-1990) average of 14.0 C”
PPS–For a billboard, the hockey stick would be overlaid by a temperature graph and a chart-grid with anomaly numbers on it. And there’d be a footnote giving the source of the data.
….was going to mention ‘visitin’ fizz-assist’ has been identified as Doug Cotton on several other sites, but Poptech beat me to it.
Are there physical reasons to explain Walter Dnes January Leading Indicator? I can think of several. Perhaps you can add to this list.
1. By the laws of averages, half of all Januaries should be above the yearly average and half should be below
Not only is this a non-physical reason, it isn’t true for all distributions.
rogerknights says:
Here’s a sharp image for a coffee mug or button:
Image—A hockey stick with its shaft slanting upwards & to the right and its blade flat.
Caption—Who’s in Denial Now?
Sort of like this?
Kudos to Merrick (March 9, 2014 at 2:10 pm) for his most interesting post which shows that THERE WAS A YEAR ZERO !!!!! Hoo boy, what a riposte to the pedants. Thanks, Merrick! :-))
DAV says:
March 9, 2014 at 4:52 pm
Not only is this a non-physical reason, it isn’t true for all distributions.
I apologize to every one for not being clearer. What I was trying to get at is related to the saying: “An apple does not fall far from the tree.”
To paraphrase Walter Dnes’ post, if the future January is warmer than the one you are comparing it with, then the anomaly of that future year will also be warmer than the one you are starting with.
So I will start by taking 31 cold years on Hadcrut4: 1900 to 1930.
I found that 16 Januaries were higher than the yearly average and 13 were lower with 2 virtual ties.
Now I will take 30 warm years: 1984 to 2013.
I found that 14 Januaries were higher than the yearly average and 15 were lower with 1 virtual tie.
Now here is, in effect,what I was trying to say. If you take any January from 1900 to 1930 and compare it to any January from 1984 to 2013, the Januaries from 1984-2013 will all be higher than the Januaries from 1900-1930. According to my paraphrase of Walter, every year from 1984-2013 should then be warmer than any year from 1900-1930. And indeed that is the case. So besides saying what I did, I should also have added that the January anomalies are never far from the annual average. Sorry about the confusion!
Strange we had an argument about what signals a new year or century in an Ancient History graduate unit. For example 5th Century AD is actually the years falling in the 400s. Like 20th Century is the 1900s. According to our lecturer, the 21st century started on January 1st 2000. As AD work backwards and BC go forward. Does it really matter, if the year is specifically mentioned?
Wow—that looks too good to be true! (Thanks for the find.) Here’s its full URL: http://stevengoddard.files.wordpress.com/2013/03/screenhunter_256-mar-02-06-55.jpg
Here are the changes I recommend if the image and caption I suggested are used as our side’s badge & billboard:
1. The chart’s lines should be an average of the five data sets, to avoid accusations of cherry-picking.
2. The spiky ups-and-down lines should be shown in a faint color in the background.
3. There should be a more prominent running mean line.
4. The hockey stick’s blade and shaft should be wider (even though unrealistic compared to a real hockey stick), in order to cover more of the swings of the running mean.
5. Most important, the blade should be flat (horizintal). We mustn’t over-reach—we mustn’t even SEEM to over-reach. We mustn’t give the other side a comeback.
In subsequent years, if Ma Nature cooperates with global cooling, the blade can be rotated downwards.
I suggest that a large-scale (1000+) but short-run (two weeks?) billboard campaign be run every year in mid-February, after the GASTA numbers for the previous year are published. At that point the sub-title, “(The Warm Is Turning)” can be added.
All of this nit picking about what defines a decade and whether or not miniscule trends are statistically significant provide a totally pointless distraction.
The significant question is: Did, or did not, models predict the future accurately?
This defines the level of understanding climate science has about the climate. We need no other measure.
If people would stop arguing about a 2C increase in global temps, why don’t they start worrying more about a drop of 2C. Seems the climate commission in Australia are relaying that Australia had a 150 records of highest temps last year. As we haven’t kept temperatures since 1788 hardly a surprise. The original Aborigines just got on with life and changed locations so they could provide food and water, etc.
I effectively ignored those articles. This analogy of yours clearly explains why I believe this analysis to be meaningless for prediction,
“Walter’s method is analogous to being allowed to predict the outcome of a game after watching the first 5 minutes.”
I attribute it more to meteorological voodoo than a valid prediction method. As for the reason you see differences in the temperature record trend dates, is my belief that RSS is the least manipulated and biased record as of right now. That is until the RSS team decides that they have to get rid of the current cooling trend.
Yes, global cooling will continue for the next 3 years. Here is something I have spent the last two days working on. It is a connection between solar cycles and the Multivariate ENSO Index. For the solar cycles I used Dr Svalgaard,s chart. Below is the relevant part of a comment I made about an hour ago.
Late Friday evening, as I finished the reading for the day at WUWT, I had the thought to straighten up a few folders where I save stuff. As I was in the process of doing that, once again I found myself comparing several charts to refresh my thoughts. I took the chart of the Multivariate ENSO Index and set it on the desktop. Then I put a solar cycle chart from pics into the preview so that I could then compare the two. I could not find the copy of Dr Svalgaard,s great high resolution chart at the time. The other solar charts which I had were of a coarser image. I went online and saved a recent solar chart from Dr Hathaway, which had a better resolution and current data. As I perused the combination of the two charts and puzzled over where to start to find a first puzzle piece connection, the first connection came into view. My thought had been to use the grand max of 1959 as the first piece. That should have been the easiest one to fit into some other piece on the MEI. And then I saw a fit. The grand max of 1959 fit with the El Nino of 1990, which began right at the end of 1989. The connection was a spacing of 30 years +/-1. The reason why no ones connect the Sun with the warming is that the warming from the Sun enters into the oceans and then comes out of the oceans 30 years later. Then I started examining the MEI for further connections, and there they were. I started with El Ninos and solar maxs. Every one was there, solar max…El Nino starts. I quickly glanced at a few of the minimums and sure enough, solar minimum…La Nina starts. I started writing down the sequences and improving my approach to the exercise. Then I noticed that there were a few events that did not readily connect with the La Nina. All of the major El Ninos were looking good though. I knew that I had found something. Inspiration grew! Then I thought that I should look once more for Dr Svalgaard,s higher res chart. I had a little trepidation with that thought as his chart had refuted a previous ‘connect the dots’ idea that I had. Plus I had already left my cryptic message up above saying that ‘I found something’. Yet, I knew full well that I had to use Dr Svalgaard,s work, or I would be deceiving myself. I found his chart and went to work, and BINGO. It went way beyond my expectations. Every move and tweak on the MEI had the right 30 year phase offset pattern, and I do mean every little move. Connections that I could not make with Dr Hathaway,s chart were completely verified with Dr Svalgaard,s work. Next step, here is the data connections. I use the prefixes ‘pre’ and ‘post’ to denote a shift which occurs before or after the top of a max or the bottom of a min.
Also note that, Note that the use of Nino and Nina only implies the changes in the MEI and not that the conditions for Nino or Nina were actually fulfilled.
SSN pre Min-1919/20 Nina-1949/50
SSN Min -1924/25 Nina-1954/55
SSN Max -1927/29 Nino-1957/58
SSN pre Min-1929/30 Nina-1959/60
spike-up-1933 Nino-1964
SSN min -1934/35 Nina-1964/65
SSN pre Max-1935/36 Nino-1965/66
SSN postMin-1936/37 Nina-1967/68
SSN Max -1938/39 Nino-1968/69
SSN pre Min-1940/41 Nina-1970/71
spike-up-1942 Nino-1972
SSN Min -1943/44 Nina-1973/74
SSN Max -1947/48 Nino-1977/78
1948-spike-down Nina-1978
SSN postMax-1948/49 Nino-1978/79
1950/51-spike down Nina-1981
spike up-1951 Nino-1981
1952-spike down Nina1982
spike up-1952 Nino-1982
1952.1/2-spike down Nina-1982.1/2
SSN post spike 1951/52 Nino-1982/83
1954-spike down Nina-1984
spike up-1954 Nino-1984
SSN Min-1954/55 Nina-1984/85
SSN pre Max-1957 Nino-1986/87
1958/59 spike down Nina-1988/89
SSN Grand Max-1959/60 Nino+ -1990/95
SSN postMin-1966/67 Nina-1996/97
SSN Max-1967/68 Nino-1997/98 El Grande
1968 spike down Nina-1998/99
spike up-1970 Nino-2000
1970-spike down Nina-200/01
SSN Max end-1971 Nino-2001
SSN pre Min-1972 Nina-2002
SSN post Max-1972/73 with continued up spikes Nino-2002/03/04/05
1974 spike down Nina-2004
SSN Min-1976 Nina-2006
SSN pre Max-1977 Nino-2007
SSN post Min-1977 Nina-2007
SSN Max 1978-itty bitty Nino-2008-itty bitty
1978 spike down Nina-2008
SSN Max-1979 Nino-2009/10
SSN pre Min-1981 Nina-2010/11
SSN Max-1982 Nino-2012
SSN pre Min-1983 early Nina-2013
spike up-1983 Nino-2013
SSN pre Min-1983 Nina-2013 late
spike up-1983 Nino-2013 late
SSN Min-1984 Nina-2014
and that is all she wrote for now, as the saying goes. That is every twist and turn of the MEI as correlated with Dr Svalgaard,s great work in his high res solar cycle chart.
Further, as I consider this to be accurate that means that I should now be able to make a prediction for future El Nino and La Nina. Here it is. It looks like a definite la Nina for now. That is an easy prediction, See I am already spot on with that prediction. The first swing back towards an El Nino will be early next year, but that should be an El Nado and short. After that it should be a strong La Nina all the way till late 2016 and then another short small El Nado. Late 2016 should be the beginning of a true El Nino that will go through 2018, and then back to La Nina. The winter of 2016/17 is very probable for a very heavy rain for the Pacific Northwest. I will leave my prediction there for now. I am tired, and my eyes are bugging out from trying to follow the year by year chart by Dr Svalgaard, which has no larger indicators to show where one might be such as 1970, 1980, 1990, etc etc.
This should allow for anyone to predict future MEI conditions, and also hindcast MEI to ssn and vice versa.
“Filby became pensive. ‘Clearly,’ the Time Traveller proceeded,’ any real body must have extension in four directions: it must have Length, Breath, Thickness, and – Duration. But through a natural infirmity of the flesh, which I will explain to you in a moment, we incline to overlook this fact”
H. G. Wells – The Time Machine.
The ‘Duration of Time, a Cycle or a Period’ will always be just so. 😉
Kate Forney says:
March 9, 2014 at 1:14 pm
yeah, it always a blast when people confuse mean for median…and its not like the information is hard to find…..
Ode to The Pause
(inspired by the children’s poem “There was an old woman who swallowed a fly”)
Some climate alarmists predicted no pause,
We all know the cause that demanded no pause,
The end times they saw.
Some climate alarmists have blamed some volcanoes,
That’s quite a trick with no change in albedos!
They blamed the volcanoes to post-hoc the pause
But we all know the cause that demanded no pause,
The end times they saw.
Some climate alarmists now blame the sun,
The same sun they said didn’t matter- what fun!
They blamed the dim sun for lack of volcanoes,
They blamed the volcanoes to post-hoc the pause,
But we all know the cause that demanded no pause,
The end times they saw.
Some climate alarmists are blaming the vortex,
That cools down our winters enough to wear Gore-Tex!
They blamed the cold vortex to pass on the sun,
They blamed the dim sun for lack of volcanoes,
They blamed the volcanoes to post-hoc the pause,
But we all know the cause that demanded no pause,
The end times they saw.
Some climate alarmists are blaming La Nina,
That mischievous foe of death-trains prima donnas,
They blamed the La Nina, with vortices sparse,
They blamed the cold vortex to pass on the sun,
They blamed the dim sun for lack of volcanoes,
They blamed the volcanoes to post-hoc the pause,
But we all know the cause that demanded no pause,
The end times they saw.
Some climate alarmists now blame the past,
And cool the old data, how tardily dast!
They twiddle the past to ignore the La Nina,
Ignorable ‘til it becomes an El Nino!
They blamed the La Nina, with vortices sparse,
They blamed the cold vortex to pass on the sun,
They blamed the dim sun for lack of volcanoes,
They blamed the volcanoes to post-hoc the pause,
But we all know the cause that demanded no pause,
The end times they saw.
Some climate alarmists are infilling where,
The ‘best-measured’ data is not really there!
They infill the Arctic and twiddle the past,
They twiddle the past to ignore the La Nina,
Ignorable ‘til it becomes an El Nino!
They blamed the La Nina, with vortices sparse,
They blamed the cold vortex to pass on the sun,
They blamed the dim sun for lack of volcanoes,
They blamed the volcanoes to post-hoc the pause,
But we all know the cause that demanded no pause,
The end times they saw.
Some climate alarmists keep averaging farther,
To smear out the pause that’s become such a bother.
The widening window avoids filling-in,
They infill the Arctic and twiddle the past,
They twiddle the past to ignore the La Nina,
Ignorable ‘til it becomes an El Nino!
They blamed the La Nina, with vortices sparse,
They blamed the cold vortex to pass on the sun,
They blamed the dim sun for lack of volcanoes,
They blamed the volcanoes to post-hoc the pause,
But we all know the cause that demanded no pause,
The end times they saw.
Some regular folks plot the data and see,
The cycles and monsters of uncertainty.
Destroyed they must be!
[! Mod]
Yes, if nothing erupts or something hits us from space, ENSO will tell the future of global temperatures, I think.
Thank you, Herr Brozek. for sharing ALL THAT EXCELLENT DATA and for your long hours of devoted and MIGHTY effort to promote the cause of TRUTH.
Janice
Chris Y — APPLAUSE! APPLAUSE! Well done (at 8:45pm).
***********************************************************
Hi, Mr. Valencia! #(:)) — still grateful.
Janice Moore says:
March 9, 2014 at 9:40 pm
You are welcome!
Herr Brozek! — smile.
Dear bush bunny,
Please enlighten your lecturer.
There was no year 0 and we generally use CE – common era, and BCE -before the common era – these days. You can use BC or AD if you wish though. And note that it’s BCs that go backwards , not Ads.
Anyway, if anybody had been using the current western system of dates at the time the first year would have been Year 1 [or Year 0001], from January 1 to December 31. The first century therefore was from the start of Year 0001 to the end of Year 0100. The fifth century was from the start of Year 0401 to the end of Year 0500. The 20th century was from the start of Year 1901 to the end of 2000. I believe there was a bit of discussion about this 14 years ago.
And to more pertinent topics….
The Australian weather bureau has a neutral outlook for La Nina – El Nino for 2014
http://www.bom.gov.au/climate/enso/
While suggesting that conditions slightly, possibly, perhaps, maybe favour the development of an El Nino.
AndyL says:
:large
March 9, 2014 at 1:12 pm
So which ten year period was the warmest, according to official figures?
How high up the list of ten year periods was the most recent such period (2004-2013?)
Coming out of the LIA, which was the coldest period in 5,000 years, you would expect the current year to be the warmest, since you are on an uptrend. In fact, as you move forward in time since 1880, 8 of 14 or 57% of ALL decades are ranked #1. 36% are ranked as #2. Likewise, 62.9% of ALL years are in the top 10, and 14.4% of all years since 1880 are ranked #1. 18 of the last 18 years have been in the top 10 (that is a long stretch, mostly because temperatures were rising, and then just went flat, so almost everything in the last 18 years is among the warmest).
Here is the decade chart:
Here is the individual rank and run chart: http://naturalclimate.wordpress.com/2012/01/27/268/ If you look at that one for a few minutes, you’ll see how those conclusions are drawn.
So, to answer your question, almost all of them are the warmest (or were).
But how long can that last? Now we’re starting to reach the downtrend created by the peaks of the HCO and MWP. So, if we are really in a long term downtrend since the HCO, and if I draw lines across the peaks, these represent the peak natural variations one would expect to still remain on the same trend. Meaning I don’t think we will exceed those peaks, although with some warming, we may actually do just that and stabilize the downtrend… If you look at this chart, you can see what I mean. Do you think we’ll exceed upward past the trend? I say, not much if at all, because the average temp is usually (almost always) below the peaks.
[Clarify what the term HCO means please. Mod]
NZ Willy says:
March 9, 2014 at 5:14 pm
Kudos to Merrick (March 9, 2014 at 2:10 pm) for his most interesting post which shows that THERE WAS A YEAR ZERO !!!!! Hoo boy, what a riposte to the pedants. Thanks, Merrick! :-))
=====================================
I accept that Pol Pot had a Year Zero; I’m not quite so sure about the rest of the world.
goldminor says:
March 9, 2014 at 7:28 pm
That appears to be good work.
If the lag time for ocean retention of solar input variations can be pinned down to 30 years + or – one year or so then maybe we can get some predictive skill sufficient to rebut or verify the proposition.
The solar input variations to the oceans would, in the first place, be a result of solar changes affecting global cloudiness as per my New Climate Model.
Col Mosby says: @ March 9, 2014 at 1:09 pm
Most misleading of all to most folks is the phrase “in recorded history.” Most folks probably
assume that is the period in which written human records exist, not since weather data has
been systematically recorded. Big difference.
>>>>>>>>>>>>>
And the misleading was done on purpose. An actual data could easily have been used and would be more in keeping with science.
So much for “Communicating Science” it is more about revitalizing the propaganda.
AndyL says:
March 9, 2014 at 1:12 pm
So which ten year period was the warmest, according to official figures?
How high up the list of ten year periods was the most recent such period (2004-2013?
>>>>>>>>>>>>>>>>
Around 1935 to 1945 before they got rid of the “Blip”
When mathematicians do sums, sometimes the results must be precisely accurate & correct & not just an estimate. For example, landing men on the Moon. Those same NASA people decided to see by how much the global temperature would increase, if the total quantity of recoverable oil, gas and coal on the planet were burned.
We know that since since August 1997 until January 2014 or 16 years, 6 months the rise in global temperature was 0.2°C, just one fifth of a degree C, so far. So, how hot could it get & will that heat stave of the coming glacial period in 10 years?
http://www.breitbart.com/Breitbart-London/2014/03/08/Earth-is-safe-from-global-warming-say-the-men-who-put-man-on-the-moon
So, we’re going to freeze!
From a global perspective, 2014 will continue to see warming, albeit statistically insignificant warming.
MOD: “HCO” = Holocene Climate Optimum, IMO
Stephen Wilde says:
March 10, 2014 at 5:24 am
————————————
Thanks Stephen. This could be the Rosetta Stone of climate. It makes sense in that it was the Sun all along. Every little shift in the MEI can be pinned to 30 years earlier in the solar cycle chart, when using Dr Svalgaard,s chart. If you use Dr Hathaway,s current chart only a portion can be seen as connecting. Yet even then it still connects with all of the major MEI events. The small events do not show.
PPPS:
Here’s another subtle but pointed billboard-campaign idea. It should be run after a lengthy or substantial period of global cooling that alarmists and the media have spun as being consistent with—if not actually proof of—global warming:
Image: Big Brother (the well-known guy with the scowl and the crew cut)
Caption: Cool Is Warm!
Sub-caption: And Don’t You Forget It!
I think a serious mistake in reasoning (and basic math) has been made here. Mr. Brozek states, ” By the laws of averages, half of all Januaries should be above the yearly average and half should be below”.
Unless I’m missing something, this statement is incorrect. It is a description of the mathematical median, not the mean (average). For example, consider 100 samples where 99 of them are 0, and 1 is 10000. The average in this case will be 10000/100 = 100. Obviously 99 of the samples fall below the average, and 1 is above it.
Given this mistake, I’m not sure how to interpret the author’s first point.
PPPPS:
Or maybe, “Cool Is the new Warm”
davideisenstadt says:
March 9, 2014 at 8:40 pm
Kate Forney says:
March 9, 2014 at 1:14 pm
yeah, it always a blast when people confuse mean for median…and its not like the information is hard to find…..
David Waller says:
March 10, 2014 at 11:37 am
I think a serious mistake in reasoning (and basic math) has been made here. Mr. Brozek states, ” By the laws of averages, half of all Januaries should be above the yearly average and half should be below”.
Unless I’m missing something, this statement is incorrect. It is a description of the mathematical median, not the mean (average). For example, consider 100 samples where 99 of them are 0, and 1 is 10000. The average in this case will be 10000/100 = 100. Obviously 99 of the samples fall below the average, and 1 is above it.
Given this mistake, I’m not sure how to interpret the author’s first point.
You may have missed what I wrote here:
wbrozek says:
March 9, 2014 at 6:42 pm
I will repeat my reply again in case you missed it. Please let me know how I could have rephrased it clearer.
I apologize to every one for not being clearer. What I was trying to get at is related to the saying: “An apple does not fall far from the tree.”
To paraphrase Walter Dnes’ post, if the future January is warmer than the one you are comparing it with, then the anomaly of that future year will also be warmer than the one you are starting with.
So I will start by taking 31 cold years on Hadcrut4: 1900 to 1930.
I found that 16 Januaries were higher than the yearly average and 13 were lower with 2 virtual ties.
Now I will take 30 warm years: 1984 to 2013.
I found that 14 Januaries were higher than the yearly average and 15 were lower with 1 virtual tie.
Now here is, in effect,what I was trying to say. If you take any January from 1900 to 1930 and compare it to any January from 1984 to 2013, the Januaries from 1984-2013 will all be higher than the Januaries from 1900-1930. According to my paraphrase of Walter, every year from 1984-2013 should then be warmer than any year from 1900-1930. And indeed that is the case. So besides saying what I did, I should also have added that the January anomalies are never far from the annual average. Sorry about the confusion!
Just to repeat. I was NOT talking about the possibility that one January may have been 100 C above the yearly anomaly and with possibly 99 Januaries being 1 C below the yearly anomaly. I was strictly talking about the NUMBER of January anomalies that are either HIGHER or LOWER than the YEARLY average for each respective year.
In other words, if you take the 160 yearly anomalies from the Hadcrut4 record and compare them to the January for that year, about 80 Januaries will be above the yearly average and 80 Januaries will be below the yearly average.
Lots of discussions above, many of them valuable.
But …. (You knew there was a “but” coming didn’t you?) 8<) … why not "test" a Dec+ Jan or Nov + Dec + Jan prerequisite?
If the El Nino (or La Nada or La Nina) condition is coming from any particular specific earth condition (or any not-usual combination of conditions) at any one latitude or area in the Pacific, then would not a longer precursor be more likely to produce a valid post-cursor?
Too long a pre-condition? No valid results, because there is no "if then, that occurs" series of events. Reasonable restriction.
Too short a pre-condition? The "result" is already underway, so there is no prediction possible. Reasonable restriction.
It is like looking for the relationship between snowfall or extreme cold conditions and a "cold front" or (in Texas) a "blue norther" … If you wait until the cold, very clear days after the cold front, you already "know" a cold front has passed, and you "know" that very dry air and very cold air has arrived. If you wait before that cold air arrives, you may miss the cold fronts that don't get so far south that they hit your city.
Well this says that Global cooling is likely to continue. No more flat line, now downhill!
http://climatedatablog.files.wordpress.com/2014/02/uah-global.png
and CET fell off a cliff and hasn’t reached bottom yet.
http://climatedatablog.wordpress.com/cet/
David in Cal says:
March 9, 2014 at 12:25 pm
If we take “decade” to mean any 10 year period, then the most recent decade is 2004 – 2013. And, this decade is not the warmest decade on record.
There’s no need to stick to calendar decades, or even whole years. The monthly figures are available for all these data-sets, so we can just look for the hottest 120-month period in each of them. As you might expect, they’re all different, though with the exception of RSS, only slightly so.
Here they are: The anomalies aren’t the same, because they use various different periods for the zero anomaly, so I have included the most recent 120-month figure for comparison (up to Feb 2014, except for Hadcrut4 which is to January}
GISS Jan 2002 to Dec 2011, anomaly 0.59, latest 120-month anomaly 0.58
RSS Oct 1997 to Sep 2007, anomaly 0.27 latest 120-month anomaly 0.23
UAH Oct 2001 to Sep 2011, anomaly 0.19, latest 120-month anomaly 0.187
HadCr Jan 2001 to Dec 2010 anomaly 0.48, latest 120-month anomaly 0.46
And if you define “decade” as being the most recent 120-month period, in all cases it is the warmest on record, when compared with other “decades” which ended exactly 10 years, 20 years, 30 years ago, etc
Incidentally, the 2 land-based data sets, which go back into the 19th century, both show the 1930’s as being about 0.6 to 0.7 deg C cooler than today. I have absolutely no idea how accurate that may be.
chris y on March 9, 2014 at 8:45 pm
Great poem!
I haven’t read all the comments; some one might have already noticed this… The early naughts that you are using contain the 2002-2003 El Niño where as most of the early 10s are more in La Niña or neutral conditions. Is that what you meant to compare? ENSO?
Actually, I am surprised they are as similar in over magnitude as they are.
trafamadore says:
March 10, 2014 at 5:27 pm
Is that what you meant to compare? ENSO?
My intention this time was to indicate what has been happening to temperatures in the recent past and what may be happening in the future based on the anomalies for January. As for why things are happening, I will leave that for others to ponder. Have you seen:
http://wattsupwiththat.com/2014/02/10/the-reason-for-the-pause-in-global-warming-excuse-37-in-a-series-trade-winds/
Thank you to Richard for Richard Barraclough says:
March 10, 2014 at 4:06 pm
I also wish to thank all others who have made valuable contributions to this thread and to those who will still make valuable contributions. Be assured that everything you write is read by many people, even if no one specifically makes a comment on your comment. Granted, there may be the odd exception where people go way off topic and usually have extremely long comments to boot. But we won’t mention any names.
[Clarify what the term HCO means please. Mod]
Mod: HCO=Holocene Climate Optimum
[Thank you. Many readers will not recognize every acronym that you feel is very common. Mod]
UAH Update
The February anomaly for UAHversion5.5 has just come in. It was down by 0.109 from January at 0.127. When averaged with January’s 0.236, it comes to 0.182. Should the anomaly stay this way, UAHversion5.5 would come in 10th. The time for a 0 slope increases to 9 years and 5 months from October 2004 to February 2014. Things could change of course, but so far, Walter’s method looks very good for both RSS and UAH where we have February values.
goldminor says:
March 10, 2014 at 10:57 am
This could be the Rosetta Stone of climate.
———————————————————-
I meant to say ‘the Risotto Stone’ of climate. What was I thinking? Did I mention that I was tired?
I realize that my first comment from earlier yesterday is a bit wild. Still I think there is something of value in the thought. I have spent this morning taking a third look at what I think I am seeing. I would now say that the offset is at 31 years between the solar cycles and the MEI. I am going to redo my prediction for ENSO a little bit and then restate that later. I believe that it can be predicted.
goldminor,
I wonder if Anthony or one of his helpers could assist you to display your graphics here for us all to see.
To be able to demonstrate, publicly, a good match for ENSO with Leif’s high resolution solar data would be very helpful.
Richard Barraclough says: @ March 10, 2014 at 4:06 pm
……..Incidentally, the 2 land-based data sets, which go back into the 19th century, both show the 1930′s as being about 0.6 to 0.7 deg C cooler than today. I have absolutely no idea how accurate that may be.
>>>>>>>>>>>>>>>
Not accurate at all. Hansen removed that inconvenient spike in temperatures around 1930 to 1940.
The old 1999 Hansen (US) graph shows 1997 and 1998 COOLER than the spike. The total adjustment is ~ 0.5 – 0.6 degrees.
Here are Hansen (Global) GISS graphs for 1980, 1987 and 2007 showing the progressive adjusting of temperatures.
Meanwhile:
Trend after 2001 identical as before 2001:
http://www.woodfortrees.org/plot/gistemp/from:1975/trend/plot/gistemp/from:1975/plot/gistemp/from:1975/to:2001/trend
If pathological science’s methods used anything could be ‘proved’.
Stephen Wilde says:
March 11, 2014 at 1:52 pm
goldminor,
———————-
The graphics are simple, the current MEI from 1950 to present and Dr Svalgaard,s solar chart. Along with that, the presentation of how I am looking at the two and the dates of the connections as I see them. Let me show a small sample of what I call key little points. These are small blips on the MEI that are either blue or red and sit in front or in the middle of a larger sequence of a La Nina or El Nino. I will start with 3 comparisons from the 2000s of small Ninos from the MEI.
1st…a small uptick at 2008 + 4 months on the MEI. Svalgaard shows at 1977 +4/6/mo an uptick coming off of the minimum into cycle 21. This is a moderate upward move, bit it does not have much influence as the La Nina on either side is the main action.
2nd…a small uptick at 2001 +3/mo on MEI. Svalgaard shows at 1970 +3/4/mo a strong upward move, which turns into the second largest peak of cycle 20. A possible reason for seeing only a small uptick on this 2nd peak is likely due to cycle 20 being a moderate peak. Also, much of the ssn count around the peaks of 20 sit in a close grouping a 1/4 of the way below the peaks. The La Nina that this small peak sits in continues to dominate.
3rd…a small uptick at 2000 +2/3/mo on MEI. Svalgaard shows at 1969 +1/2/mo a moderate uptick, which becomes the 3rd highest peak of cycle 20. There is a sharp down tick afterwards. The further explanation in the ‘2nd’ example applies to why the influence is small and produces only a small uptick.
That these small shifts all lead back to a corresponding move 31 years prior is what really strikes me. I can read the entire MEI in this fashion. There is no point that I have seen where the fit is not made between the two charts. here is 3 examples from the 3 small Ninas in the early 1980s.
1st…1980 +10/mo……1949 +10/mo shows a down tick. This is a point after the peaks of cycle 18.
2nd…1981 +4/mo……1950 + 4/mo. This is 2/3rds into a severe drop, but there is a small peak as Nino dominates.
3rd…1981 +9/10/mo……1950 +10/mo. This is the end of the severe drop. Yet Nino is still king.
I can also place the 2 Nino small upticks that sit between these 3 Ninas.
There are other very interesting examples which appear to show that these 31 year moves can lead to a reinforcing or not of the real time period, which they link to. This makes me wonder if this is the explanation for the warm trend, when a pulse from a warm spike from 31 years prior fits in with an ongoing current strong upward move in the ssn count, as I see examples that link in that fashion. This works in reverse also in that the above examples show how a strong move up or down can be muted if the main forcing is opposite direction.
Splice says:
March 12, 2014 at 5:56 am
————————————
We all know that there was a warming in that period. What are you trying to show?
Thanks, goldminor.
I hope someone with a bit of ‘weight’ picks up on all that.
@goldminor
I’ve shown that the trend after 2001 was identical as in 25 preceding years.
Of course I could show anythng I want ’cause no one here understands how existance/non-existance of trends must be proved (having flat trend line proves nothing – I could “prove” 15-years stop that lasted form half of 1979 until half of 1994:
http://www.woodfortrees.org/plot/rss/from:1979.5/to:1994.5/trend/plot/rss/to:1994.5 )
I’ve simply using pathological science’s method, that’s why I’m able to ‘prove’ anything I want – and as everyone here knows only pathological science they are unable to detect what I’m doing wrong in the examples above.
@ splice
if you want to prove something
you have to try and bring in more variables
here are 4
http://www.woodfortrees.org/plot/hadcrut4gl/from:1987/to:2015/plot/hadcrut4gl/from:2002/to:2015/trend/plot/hadcrut3gl/from:1987/to:2015/plot/hadcrut3gl/from:2002/to:2015/trend/plot/rss/from:1987/to:2015/plot/rss/from:2002/to:2015/trend/plot/hadsst2gl/from:1987/to:2015/plot/hadsst2gl/from:2002/to:2015/trend/plot/hadcrut4gl/from:1987/to:2002/trend/plot/hadcrut3gl/from:1987/to:2002/trend/plot/hadsst2gl/from:1987/to:2002/trend/plot/rss/from:1987/to:2002/trend
here are another three (my own)
http://blogs.24.com/henryp/2013/02/21/henrys-pool-tables-on-global-warmingcooling/
ergo:
it is globally cooling
from around 2000
live with it
Splice says:
March 12, 2014 at 5:56 am
A man can grow in height until he is 20 and then stop growing for the next 10 years. How do you prove he stopped growing from 20 to 30? You plot a graph of height versus years from 20 to 30. But by plotting from 0 to 30, you seem to try to “prove” the man is still growing from 20 to 30. This is especially true if there was a huge growth spurt between 18 and 20.
@HenryP
Nope. Before the moment you are talking there was istensive The turn of the century global coolling observed:
http://www.woodfortrees.org/plot/hadcrut4gl/from:1987/to:2015/plot/hadcrut4gl/from:2002/to:2015/trend/plot/hadcrut3gl/from:1987/to:2015/plot/hadcrut3gl/from:2002/to:2015/trend/plot/rss/from:1987/to:2015/plot/rss/from:2002/to:2015/trend/plot/hadsst2gl/from:1987/to:2015/plot/hadsst2gl/from:2002/to:2015/trend/plot/hadcrut4gl/from:1997.3/to:2002/trend/plot/hadcrut3gl/from:1997.3/to:2002/trend/plot/hadsst2gl/from:1997.3/to:2002/trend/plot/rss/from:1997.3/to:2002/trend
And before The turn of the century global coolling temperatures were pretty flat:
http://www.woodfortrees.org/plot/hadcrut4gl/from:1987/to:2015/plot/hadcrut4gl/from:1987/to:1997.3/trend/plot/hadcrut3gl/from:1987/to:2015/plot/hadcrut3gl/from:1987/to:1997.3/trend/plot/rss/from:1987/to:2015/plot/rss/from:1987/to:1997.30/trend/plot/hadsst2gl/from:1987/to:2015/plot/hadsst2gl/from:1987/to:1997.3/trend/plot/hadcrut4gl/from:1997.3/to:2002/trend/plot/hadcrut3gl/from:1997.3/to:2002/trend/plot/hadsst2gl/from:1997.3/to:2002/trend/plot/rss/from:1997.3/to:2002/trend
So we never exerienced any warming.
This is how pathological science works.
@wbrozek
Of course we don’t have access to the person’s raw height – we are observing person’s height together with their shoes and hat, which changes between measurements.
That’s why we are able (by using pathological science’s methods) to prove anything we want – even that the person was NEVER GROWING (look my previous post).
Of course it is possible to find out if the peron is growing or not even when measured together with shoes and hat, but you have tu use science’s methods instead of pathological science’s methods. But everyone hehe konws only the latter.
wbrozek says:
March 12, 2014 at 12:41 pm
——————————————-
That is a nice straight forward analogy. You made him add shoes and hat to try and muster a response. Next round the guy will be on a ladder holding an umbrella.
HenryP says:
March 12, 2014 at 11:55 am
————————————-
Henry, here is my master prediction for the Multivariate ENSO Index over the next 5 years. This thought stems from a link that I think could exist between solar cycles and the oceans. The current La Nada will move back into La Nina in the next several months. After that it will further deepen through the end of the year. Around the beginning of next year Dec/Jan, An El Nado will start which could last for 3/5 months. It might reach above +0.5 for a short time during that 3/5 month period. By May of 2015, the MEI will head back to a La Nina. That La Nina will become a strong La Nina, which will last until late spring of 2018. During that 3 year period, there may be 3 points where the La Nina can weaken, 3rd/4th/mo of 2016, 1st/2nd/mo of 2017, and the 9th/10th/mo of 2017. Under this proposed scenario the next El Nino would start around mid 2018 at the earliest.
goldminor says:
March 12, 2014 at 5:52 pm
Next round
I do not think I can handle another round. ☹
@goldminor @wbrozek
Nope. I’ve just shown how you could prove anything you want by using pathological sciene.