Will 2016 Set Satellite Records? (Now Includes June and July Data)

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

The above bar graph shows the RSS bar heights for 2016 in blue and the corresponding heights for 1998 in red. For January, February and March, the blue bar is higher than the red bar indicating that for these three months, 2016 was warmer than 1998. However since April, the blue bar is lower than the red bar indicating that for these months, 2016 was colder than 1998. Note that the July 2016 anomaly is already lower than that of several later months in 1998. In this respect, RSS is the same as UAH6.0beta5 which, among other data sets, can also be viewed on Nick Stokes’ site.

In order to answer the question in the title, we need to know what the cumulative lengths of the blue bars above the red bars will be for all 12 months. If blue is higher, 2016 sets a new record. If red is higher, the 1998 record stays. Before going on, I just want to be clear I am talking about a calendar year record. For both satellite data sets, the monthly record high anomaly prior to 2016 was in April of 1998. As well, for both sets, this was beaten in February of 2016. There are many similarities between 1998 and 2016 on both data sets. Both were influenced by an extremely strong El Nino which caused records to be set in the beginning of the year. Then there was a drop in 1998 and so far, there is a similar drop in 2016.

However there are important difference between 1998 and 2016. In 1998, the highest anomaly was in April of 1998 and therefore not surprisingly, the second quarter of 1998 was the quarter with the highest anomaly. In contrast, the highest anomaly in 2016 was in February making the first quarter of 2016 the one with the highest anomaly.

The difference between quarters 2 and 3 for 1998 for UAH6.0beta5 was 0.165. The difference between quarters 1 and 2 in 2016 was 0.169. This is very close!

The difference between quarters 2 and 3 for 1998 for RSS was 0.140. The difference between quarters 1 and 2 in 2016 was 0.245. While this is not as close as for UAH6.0beta5, I will make similar calculations.

There are several different approaches one can use to arrive at the best guess as to whether or not 2016 will set a record. I have decided to give a table with the averages for RSS and UAH6.0beta5 by giving the averages for each of the four quarters in 1998 and the first quarter of 1999 as well as the four quarters of 2016. The first quarter of 1998 will be called 98(1), and so on.

Obviously, I can only give the first two quarters of 2016 and I will have to estimate the last two. Feel free to comment on whether you think my methods are good or whether you think they are out to lunch. For each of the two satellite data sets, I took the difference between the following quarters: 4 and 3 of 1998, and 1 of 1999 versus 4 of 1998. Then I applied those differences to quarters 3 and 4 of 2016 and put those numbers in ( ) on the table for 16(3) and 16(4).

Then I calculated the averages for 2016 based on those numbers and compared those to the 1998 and 2010 averages which are ranked first and second at this time on both sets. You can see how the projected 2016 anomalies compare to the first and second place years in each case. Below is the table described above.

According to my calculations, 2016 should come in at second place in both satellite data sets. However for RSS, it may even come in at third place.

For some anomalous reason, the anomalies for both RSS and UAH rose in July as compared to June. However that does not affect my conclusions going forward. It should be noted that both July anomalies are still way below the April, May and June averages in both cases. It should also be noted that the present July anomalies are still lower than at least the August and September 1998 anomalies for both data sets.

In the sections below, we will present you with the latest facts. The information will be presented in two sections and an appendix. The first section will show for how long there has been no statistically significant warming on several data sets. The second section will show how 2016 so far compares with 2015 and the warmest years and months on record so far. For three of the data sets, 2015 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. The two satellite data sets go to July and the others go to June.

Section 1

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 0 and 23 years according to Nick’s criteria. Cl stands for the confidence limits at the 95% level.

The details for several sets are below.

For UAH6.0: Since June 1993: Cl from -0.016 to 1.784

This is 23 years and 1 month.

For RSS: Since December 1993: Cl from -0.025 to 1.740 This is 22 years and 8 months.

For Hadcrut4.4: The warming is statistically significant for all periods above three years.

For Hadsst3: Since October 1996: Cl from -0.002 to 2.130 This is 19 years and 9 months.

For GISS: The warming is statistically significant for all periods above three years.

Section 2

This section shows data about 2016 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. 15ra: This is the final ranking for 2015 on each data set.

2. 15a: Here I give the average anomaly for 2015.

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 2015 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 prior to 2016. 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. 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.

8. sy/m: This is the years and months for row 7.

9. Jan: This is the January 2016 anomaly for that particular data set.

10. Feb: This is the February 2016 anomaly for that particular data set, etc.

16. ave: This is the average anomaly of all months to date.

17. rnk: This is the rank that each particular data set would have for 2016 without regards to error bars and assuming no changes to the current average anomaly. However as this post makes clear, I do expect changes. Think of it as an update 30 minutes into a game.

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

For UAH, version 6.0beta5 was used. Note that WFT uses version 5.6. So to verify the length of the pause on version 6.0, you need to use Nick’s program.

http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tlt/tltglhmam_6.0beta5.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.4.4.0.0.monthly_ns_avg.txt

For Hadsst3, see: https://crudata.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 2015 in the form of a graph, see the WFT graph below. Note that UAH version 5.6 is shown. WFT does not show version 6.0 yet. Also note that Hadcrut4.3 is shown and not Hadcrut4.4, which is why many months are missing for Hadcrut.

As you can see, all lines have been offset so they all start at the same place in January 2015. This makes it easy to compare January 2015 with the latest anomaly.

Appendix

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

UAH6.0beta5

For UAH: There is no statistically significant warming since June 1993: Cl from -0.016 to 1.784. (This is using version 6.0 according to Nick’s program.)

The UAH average anomaly so far for 2016 is 0.585. This would set a record if it stayed this way. 1998 was the warmest at 0.484. Prior to 2016, the highest ever monthly anomaly was in April of 1998 when it reached 0.743. The average anomaly in 2015 was 0.262 and it was ranked 3rd.

RSS

For RSS: There is no statistically significant warming since December 1993: Cl from -0.025 to 1.740.

The RSS average anomaly so far for 2016 is 0.672. This would set a record if it stayed this way. 1998 was the warmest at 0.550. Prior to 2016, the highest ever monthly anomaly was in April of 1998 when it reached 0.857. The average anomaly in 2015 was 0.358 and it was ranked 3rd.

Hadcrut4.4

For Hadcrut4: The warming is significant for all periods above three years.

The Hadcrut4 average anomaly so far is 0.896. This would set a record if it stayed this way. Prior to 2016, the highest ever monthly anomaly was in December of 2015 when it reached 1.010. The average anomaly in 2015 was 0.746 and this set a new record.

Hadsst3

For Hadsst3: There is no statistically significant warming since October 1996: Cl from -0.002 to 2.130.

The Hadsst3 average anomaly so far for 2016 is 0.648. This would set a record if it stayed this way. Prior to 2016, the highest ever monthly anomaly was in September of 2015 when it reached 0.725. The average anomaly in 2015 was 0.592 and this set a new record.

GISS

For GISS: The warming is significant for all periods above three years.

The GISS average anomaly so far for 2016 is 1.10. This would set a record if it stayed this way. Prior to 2016, the highest ever monthly anomaly was in December of 2015 when it reached 1.10. The average anomaly in 2015 was 0.87 and it set a new record.

Conclusion

In my opinion, only two things can prevent both RSS and UAH6.0beta5 from retaining their 1998 records. One is that the present downturn in ENSO numbers suddenly reverses to develop into an El Nino again. The other is that a new and revised data set will come out from RSS. And should that happen, what are the chances that 1998 gets hotter and 2016 gets colder?