How Would A Super El Niño Affect RSS in 2014? (Now Includes March Data)

Image Credit: WoodForTrees.org

Guest Post By Werner Brozek, Edited By Just The Facts

I will attempt to answer the question in the title from two different perspectives. First of all, can a super El Niño cause the present 1998 record in RSS to be broken in 2014? The next question is whether or not the slope of 0 will go under Santer’s 17 years. To answer the first part, we need to note that the average anomaly in 1998 was 0.550. The average anomaly for the first three months this year so far is 0.213. So a simple equation can be set up as follows to see what average would be required for the remaining 9 months to set a record. 12(0.550) = 3(0.213) + 9x. Solving for x gives 0.66. Naturally this is above 0.55, but more importantly is how this compares to the highest 9 month average during the 1998 super El Niño. According to the above plot of RSS with a mean of 9 months, that number is 0.63.

Since 0.66 is required, it may initially appear as if we need an El Niño that is stronger than the one in 1998. However the 9 month average before the 1998 El Niño started was around 0, whereas it is around 0.2 now. So the climb to potentially set a record is not as high. So it is possible for an El Niño that is almost as strong as the 1998 El Niño to set a record, however things have to move fast. The April anomaly for RSS does not necessarily have to be 0.66, but as a guess, I would say it should jump to at least 0.4 from the 0.214 March value and then it must make good jumps in the next months. According to the graph above, when the December number for RSS is in, the new 9 month height must be just above the 1998 nine month height in order for a new record to be set.

I would be very surprised if 2014 broke the 1998 record. In 1997, the El Niño started in May 1997 and the peak did not come until about March 1998. Right now, we are not above 0.5, so in my opinion, there is just not enough time to break the 1998 mark this year. As well, quoting Bob Tisdale:

“[T]he time lag between the major changes in the sea surface temperatures of the equatorial Pacific (NINO3.4 region) and the response in global surface temperatures is a few (3 to 4) months. For lower troposphere temperature anomalies, it’s about 5 to 6 months.”

Moving on to Santer’s 17 years, if we assume it takes a while for an El Niño to form and for it to affect RSS temperatures, I predict that at least to the end of 2014, RSS will still have over 17 years of pause. To verify this for yourself, note the area BELOW the green line in the top graph of this post between August 1996 and December 1997. If temperatures do spike, the August 1996 date has a bit of room to be moved forward until December 1997 is hit. Then, the new area ABOVE the green line at the far right needs to be more or less equal to the present area below and to the left of the 1997 spike. In light of what was just said in terms of how long it takes for temperatures to change, there just does not seem to be enough time for much to happen. I will concede that November and December could have very high anomalies, however it would not be for a long enough period to cause a huge area above the green line. Keep in mind that I am just talking about the case to the end of 2014. Anything can happen in 2015.

In the parts below, as in the 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 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 2014 to date compares with 2013 and the warmest years and months on record so far.

The appendix will illustrate sections 1 and 2 in a different way. Graphs and a table will be used to illustrate the data.

(P.S. As of May 1, the Hadcrut3 data was not out. Since the March anomaly for Hadcrut4 was 0.034 above the January anomaly, I made the assumption that the March anomaly for Hadcrut3 would also be 0.034 above its January anomaly. Since March showed a huge spike from February in Hadcrut4, I thought it would be better to estimate the March value in Hadcrut3 rather than just leaving things as they were at the end of February.)

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 7 months to 17 years and 8 months.

1. For GISS, the slope is flat since September 2001 or 12 years, 7 months. (goes to March)

2. For Hadcrut3, the slope is flat since June 1997 or 16 years, 10 months. (goes to March)

(This was estimated.)

3. For a combination of GISS, Hadcrut3, UAH and RSS, the slope is flat since December 2000 or 13 years, 4 months. (goes to March)

(This was estimated.)

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

5. For Hadsst3, the slope is flat since November 2000 or 13 years, 5 months. (goes to March)

6. For UAH, the slope is flat since September 2004 or 9 years, 7 months. (goes to March using version 5.5)

7. For RSS, the slope is flat since August 1996 or 17 years, 8 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 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 all slopes are essentially zero. As well, I have offset them so they are evenly spaced. 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. 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 -0.044 to 2.366

For RSS: Since November 1992: CI from -0.023 to 1.882

For Hadcrut4: Since August 1996: CI from -0.005 to 1.308

For Hadsst3: Since January 1993: CI from -0.016 to 1.812

For GISS: Since July 1997: CI from -0.004 to 1.246

Section 3

This section shows data about 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 and 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 are followed by the last two numbers of the year.

9. Jan: This is the January 2014 anomaly for that particular data set.

10.Feb: This is the February 2014 anomaly for that particular data set, etc.

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

13.rnk: This is the rank that each particular data set would have if the anomaly above were to remain that way for the rest of the year. It will not, but think of it as an update 15 minutes into a game. Due to different base periods, the rank is more meaningful than the average anomaly.

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

For RSS, see: http://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 2013. This makes it easy to compare January 2013 with the latest anomaly.

Appendix

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

RSS

The slope is flat since August 1996 or 17 years, 8 months. (goes to March)

For RSS: There is no statistically significant warming since November 1992: CI from -0.023 to 1.882.

The RSS average anomaly so far for 2014 is 0.213. This would rank it as 11th 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.

UAH

The slope is flat since September 2004 or 9 years, 7 months. (goes to March using version 5.5)

For UAH: There is no statistically significant warming since February 1996: CI from -0.044 to 2.366.

The UAH average anomaly so far for 2014 is 0.167. This would rank it as 10th 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 December 2000 or 13 years, 4 months. (goes to March)

For Hadcrut4: There is no statistically significant warming since August 1996: CI from -0.005 to 1.308.

The Hadcrut4 average anomaly so far for 2014 is 0.450. This would rank it as 10th 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

(Since March was not out as of May 1, the numbers below assume Hadcrut3 made the same jump in March from January as Hadcrut4 did.)

The slope is flat since June 1997 or 16 years, 10 months. (goes to March)

The Hadcrut3 average anomaly so far for 2014 is 0.414. This would rank it as 10th 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 November 2000 or 13 years and 5 months. (goes to March).

For Hadsst3: There is no statistically significant warming since January 1993: CI from -0.016 to 1.812.

The Hadsst3 average anomaly so far for 2014 is 0.333. This would rank it as 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 September 2001 or 12 years, 7 months. (goes to March)

For GISS: There is no statistically significant warming since July 1997: CI from -0.004 to 1.246.

The GISS average anomaly so far for 2014 is 0.613. This would rank it as 6th 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

We do not know if an El Niño will form in 2014, nor do we know how strong it will be if it does form. However, RSS is unlikely to set a new record or fall below Santer’s 17 years in 2014. As for other data sets, it is hard to say what will happen. However GISS has the unique distinction of having its January (0.69) and March (0.70) anomaly above its average record of 2010 (0.66). It could even set a record without an El Niño. Would that be what the doctor ordered? WUWT? ☺