Guest Post by Werner Brozek, Edited by Just The Facts:

Lubos Motl has an excellent article entitled: “Le Chatelier’s principle and nature’s adaptation” If this topic interests you, I would highly recommend that you read it.

“Le Châtelier’s principle can be stated as: When a system at equilibrium is subjected to change in concentration, temperature, volume, or pressure, then the system readjusts itself to (partially) counteract the effect of the applied change and a new equilibrium is established.” Wikipedia

Before we apply Le Chatelier to climate, let us apply it to a glass of water and ice. If a glass is half full of water and half full of ice, and if it is in a place that is 0 C, nothing will change. However if we place a stress on this system by placing the glass in a room at 20 C, then the system will try to counteract this change. In this case, it does so by melting the ice until it is all gone. And only after this will the temperature go up.

The equation heat + ice <-> water is driven to the right due to the addition of something on the left side of the equation. As a result, ice decreases and water increases. And in the end, the temperature will not be as hot as you may expect due to the counteraction.

What happens when a “substance” on the left decreases? If the ice and water is put into a freezer that is at -5 C, then the reaction is driven left so water decreases and ice increases. In this case, it does so by freezing the water until it is all gone. And only after this will the temperature go down. And in the end, the temperature will not be as cold as you may expect due to the counteraction.

Now let us apply this to climate. Suppose we have a lake at 0 C in the spring and we have a hot spell with + 30 C for a week. Calculations might make you conclude that after a week, the lake should be at + 10 C. However due to Le Châtelier’s principle, the system will adjust to partially counteract this. In the case of a lake, more water would evaporate as it got hotter and this would take a lot of heat with it. As as result, the temperature at the end of the week is still warmer than before, but not as warm as you may have calculated.

Now suppose we have a lake at 0 C in the fall and we have a polar vortex with – 30 C for a week. You might calculate that the water would be at – 10 C at the end of the week. Of course this does not happen. The reaction above is driven left and ice forms and heat is also given off. Mind you, the heat does not really heat anything up. Rather it merely slightly slows down the rate at which the air at – 30 C cools the water and causes more ice to form. Furthermore, the ice provides some insulation to slow down the rate at which additional water freezes.

What about the tropical oceans? Suppose everything is at equilibrium and the sun suddenly loses 1 % of its energy. Then the oceans would heat up less and water would evaporate less causing fewer clouds and perhaps later clouds. As a result, the earth still cools but slower than would otherwise be the case.

Now suppose everything is at equilibrium and the sun suddenly gains 1 % more energy. Then the oceans would heat up more and water would evaporate more causing more clouds and perhaps earlier clouds which would reflect more heat away. As a result, the earth still warms but slower than would otherwise be the case.

For more on this topic, see the following articles, 1 and 2, on emergent phenomena by Willis Eschenbach.

In the sections below, as in previous posts, 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 some data sets. At the moment, only the satellite data have flat periods of longer than a year. 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 2015 so far compares with 2014 and the warmest years and months on record so far. For three of the data sets, 2014 also happens to be the warmest year. 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 on at least one calculation. 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.

1. For GISS, the slope is not flat for any period that is worth mentioning.

2. For Hadcrut4, the slope is not flat for any period that is worth mentioning. Note that WFT has not updated Hadcrut4 since July and it is only Hadcrut4.2 that is shown.

3. For Hadsst3, the slope is not flat for any period that is worth mentioning.

4. For UAH, the slope is flat since April 2009 or an even 6 years. (goes to March using version 5.6)

5. For RSS, the slope is flat since December 1996 or 18 years and 4 months. (goes to March)

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 upward sloping blue line at the top indicates that CO2 has steadily 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 the two slopes are essentially zero. 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 18 years, the temperatures have been flat for varying periods on the two sets.

Section 2

For this analysis, data was retrieved from Nick Stokes’ Trendviewer available on his website. 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 14 and 22 years according to Nick’s criteria. Cl stands for the confidence limits at the 95% level.

Dr. Ross McKitrick has also commented on these parts and has slightly different numbers for the three data sets that he analyzed. I will also give his times.

The details for several sets are below.

For UAH: Since August 1996: Cl from -0.027 to 2.198

This is 18 years and 8 months.

(Dr. McKitrick says the warming is not significant for 16 years on UAH.)

For RSS: Since January 1993: Cl from -0.014 to 1.700

This is 22 years and 3 months.

(Dr. McKitrick says the warming is not significant for 26 years on RSS.)

For Hadcrut4.3: Since April 2000: Cl from -0.014 to 1.366

This is 14 years and 11 months.

(Dr. McKitrick said the warming was not significant for 19 years on Hadcrut4.2 going to April. Hadcrut4.3 would be slightly shorter however I do not know what difference it would make to the nearest year.)

For Hadsst3: Since May 1995: Cl from -0.003 to 1.687

This is 19 years and 11 months.

For GISS: Since November 2000: Cl from -0.041 to 1.354

This is 14 years and 5 months.

Section 3

This section shows data about 2015 and other information in the form of a table. The table shows the five data sources along the top and other places so they should be visible at all times. The sources are UAH, RSS, Hadcrut4, Hadsst3, and GISS.

Down the column, are the following:

1. 14ra: This is the final ranking for 2014 on each data set.

2. 14a: Here I give the average anomaly for 2014.

3. year: This indicates the warmest year on record so far for that particular data set. Note that the satellite data sets have 1998 as the warmest year and the others have 2014 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. Periods of around a year or less are not counted and are shown as “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 are followed by the last two numbers of the year.

9. sy/m: This is the years and months for row 8. Depending on when the update was last done, the months may be off by one month.

10. McK: These are Dr. Ross McKitrick’s number of years for three of the data sets.

11. Jan: This is the January 2015 anomaly for that particular data set.

12. Feb: This is the February 2015 anomaly for that particular data set, etc.

14. ave: This is the average anomaly of all months to date taken by adding all numbers and dividing by the number of months.

15. rnk: This is the rank that each particular data set would have for 2015 without regards to error bars and assuming no changes. Think of it as an update 15 minutes into a game.

Source UAH RSS Had4 Sst3 GISS 1.14ra 3rd 6th 1st 1st 1st 2.14a 0.27 0.255 0.564 0.479 0.67 3.year 1998 1998 2014 2014 2014 4.ano 0.42 0.55 0.564 0.479 0.67 5.mon Apr98 Apr98 Jan07 Aug14 Jan07 6.ano 0.663 0.857 0.835 0.644 0.93 7.y/m 6/0 18/4 0 0 0 8.sig Aug96 Jan93 Apr00 May95 Nov00 9.sy/m 18/8 22/3 14/11 19/11 14/5 10.McK 16 26 19 Source UAH RSS Had4 Sst3 GISS 11.Jan 0.351 0.366 0.690 0.440 0.75 12.Feb 0.296 0.327 0.656 0.406 0.78 13.Mar 0.256 0.255 0.683 0.427 0.84 Source UAH RSS Had4 Sst3 GISS 14.ave 0.301 0.316 0.676 0.424 0.79 15.rnk 3rd 5th 1st 2nd 1st

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

For UAH, version 5.6 was used. Note that WFT uses version 5.5 however this version was last updated for December 2014 and it looks like it will no longer be given.

http://vortex.nsstc.uah.edu/public/msu/t2lt/tltglhmam_5.6.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.4.3.0.0.monthly_ns_avg.txt

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 2014 in the form of a graph, see the WFT graph below. Note that Hadcrut4 is the old version that has been discontinued. WFT does not show Hadcrut4.3 yet. As well, only UAH version 5.5 is shown which stopped in December. WFT does not show version 5.6 yet.

As you can see, all lines have been offset so they all start at the same place in January 2014. This makes it easy to compare January 2014 with the latest anomaly.

Appendix

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

RSS

The slope is flat since December, 1996 or 18 years, 4 months. (goes to March)

For RSS: There is no statistically significant warming since January 1993: Cl from -0.014 to 1.700.

The RSS average anomaly so far for 2015 is 0.316. This would rank it as 5th 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 2014 was 0.255 and it was ranked 6th.

UAH

The slope is flat since April 2009 or an even 6 years. (goes to March using version 5.6)

For UAH: There is no statistically significant warming since August 1996: Cl from -0.027 to 2.198. (This is using version 5.6 according to Nick’s program.)

The UAH average anomaly so far for 2015 is 0.301. This would rank it as 3rd place. 1998 was the warmest at 0.42. The highest ever monthly anomaly was in April of 1998 when it reached 0.663. The anomaly in 2014 was 0.27 and it was ranked 3rd.

Hadcrut4.3

The slope is not flat for any period that is worth mentioning.

For Hadcrut4: There is no statistically significant warming since April 2000: Cl from -0.014 to 1.366.

The Hadcrut4 average anomaly so far for 2015 is 0.676. This would set a new record if it stayed this way. The highest ever monthly anomaly was in January of 2007 when it reached 0.835. The anomaly in 2014 was 0.564 and this set a new record.

Hadsst3

For Hadsst3, the slope is not flat for any period that is worth mentioning. For Hadsst3: There is no statistically significant warming since May 1995: Cl from -0.003 to 1.687.

The Hadsst3 average anomaly so far for 2015 is 0.424. This would rank 2nd if it stayed this way. The highest ever monthly anomaly was in August of 2014 when it reached 0.644. The anomaly in 2014 was 0.479 and this set a new record.

GISS

The slope is not flat for any period that is worth mentioning.

For GISS: There is no statistically significant warming since November 2000: Cl from -0.041 to 1.354.

The GISS average anomaly so far for 2015 is 0.79. This would set a new record if it stayed this way. The highest ever monthly anomaly was in January of 2007 when it reached 0.93. The anomaly in 2014 was 0.67 and it set a new record.

Conclusion

Nature has many negative feedbacks that help prevent Earth from getting too hot or too cold. If this were not the case, then we would not be here to discuss this. James Hansen once spoke of boiling oceans, be impressed…

UAH Update:

Version 5.6 had 2014 as third warmest and after 3 months in 2015, 5.6 was on track to be 3rd warmest.

However version 6 now has 2014 to be 6th warmest and after 3 months, version 6 is on track for 2015

to be 5th warmest, exactly like RSS.

In addition, the length of the pause on version 6 is over 18 years, similar to that of RSS.

Of course, the time for statistically significant warming would also greatly increase. I do not yet have that

period, however I expect it to be over 22 years in line with RSS.

Share this: Print

Email

Twitter

Facebook

Pinterest

LinkedIn

Reddit



Like this: Like Loading...