Warmest Ten Years on Record (Now Includes all December Data)

Guest Post by Werner Brozek, Edited by Just The Facts

The table below ranks the warmest ten years according to the five data sets I cover. For each of these ten years, the year is given followed by the average anomaly for that year. In all cases, 2016 set a new record. The year 1998 appears on all five data sets as one of the top ten, but 2008 does not appear on any.

Source UAH RSS Had4 Sst3 GISS
1year 2016 2016 2016 2016 2016
1ano 0.505 0.573 0.774 0.614 0.99
2year 1998 1998 2015 2015 2015
2ano 0.484 0.550 0.760 0.592 0.87
diff 0.021 0.023 0.014 0.022 0.12
3year 2010 2010 2014 2014 2014
3ano 0.335 0.475 0.575 0.477 0.74
4year 2015 2015 2010 1998 2010
4ano 0.260 0.383 0.556 0.416 0.71
Source UAH RSS Had4 Sst3 GISS
5year 2002 2005 2005 2010 2005
5ano 0.217 0.334 0.544 0.406 0.69
6year 2005 2003 1998 2009 2007
6ano 0.199 0.319 0.536 0.395 0.66
7year 2003 2002 2013 2003 2013
7ano 0.186 0.315 0.512 0.393 0.65
8year 2014 2014 2003 2005 2009
8ano 0.178 0.273 0.508 0.389 0.64
9year 2007 2007 2009 2013 1998
9ano 0.160 0.252 0.506 0.376 0.64
10year 2013 2001 2006 2002 2012
10ano 0.132 0.247 0.505 0.368 0.63
Source UAH RSS Had4 Sst3 GISS

Below the second year of data, I give the difference between the record setting year and the second warmest year for that data set. For the satellite data sets, 1998 is the second warmest year. The others have 2015 as the second warmest year.

The margin of error for the average yearly anomaly is about 0.1. This means that to be statistically significant, the difference must be at least 0.1. As can be seen, only the GISS record is statistically significant meaning there is a greater than 95% that 2016 is the real record. For UAH, RSS, HadCRUT4.5 and Hadsst3, we have a statistical tie between the 2016 record and the second place year. However there is still a greater than 50% chance that 2016 is indeed a record. It is something like 57% for HadCRUT4.5 and about 60% for the other three.

The year 2016 started very warm but cooled off at the end. You may find it interesting where the December anomaly would rank if the December anomaly were to be the 2017 average anomaly. Of course, that will not be the case, but just for the fun of it, here are the rankings using the December anomaly on the later table in conjunction with the table above. UAH would be ranked 5th; RSS would be ranked 13th; HadCRUT4.5 would be ranked 3rd; Hadsst3 would be ranked 4th; and GISS would be ranked 3rd.

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 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.

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 November 1993: Cl from -0.009 to 1.784

This is 23 years and 2 months.

For RSS: Since July 1994: Cl from -0.005 to 1.768 This is 22 years and 6 months.

For Hadcrut4.5: The warming is statistically significant for all periods above four years.

For Hadsst3: Since March 1997: Cl from -0.003 to 2.102 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.

21. ave: This is the average anomaly for all twelve months.

22. rnk: This is the final 2016 rank for each particular data set. All five data sets set a record in 2016.

Source UAH RSS Had4 Sst3 GISS
1.15ra 3rd 3rd 1st 1st 1st
2.15a 0.261 0.381 0.760 0.592 0.87
3.year 1998 1998 2015 2015 2015
4.ano 0.484 0.550 0.760 0.592 0.87
5.mon Apr98 Apr98 Dec15 Sep15 Dec15
6.ano 0.743 0.857 1.024 0.725 1.11
7.sig Nov93 Jul94 Mar97
8.sy/m 23/2 22/6 19/9
Source UAH RSS Had4 Sst3 GISS
9.Jan 0.539 0.681 0.906 0.732 1.17
10.Feb 0.831 0.994 1.070 0.611 1.35
11.Mar 0.732 0.871 1.069 0.690 1.30
12.Apr 0.713 0.784 0.915 0.654 1.09
13.May 0.544 0.542 0.688 0.595 0.93
14.Jun 0.337 0.485 0.731 0.622 0.76
15.Jul 0.388 0.491 0.728 0.670 0.83
16.Aug 0.434 0.471 0.770 0.654 0.98
17.Sep 0.440 0.581 0.711 0.606 0.87
18.Oct 0.407 0.355 0.584 0.601 0.89
19.Nov 0.452 0.391 0.526 0.490 0.93
20.Dec 0.243 0.229 0.592 0.447 0.81
21.ave 0.505 0.573 0.774 0.614 0.99
22.rnk 1st 1st 1st 1st 1st
Source UAH RSS Had4 Sst3 GISS

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

For UAH, version 6.0beta5 was used.

http://www.nsstc.uah.edu/data/msu/v6.0/tlt/tltglhmam_6.0.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.4.5.0.0.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 2016 in the form of a graph, see the WFT graph below.

WoodForTrees.org – Paul Clark – Click the pic to view at source

As you can see, all lines have been offset so they all start at the same place in January 2016. This makes it easy to compare January 2016 with the latest anomaly.

The thick double line is the WTI which shows the average of RSS, UAH, HadCRUT4.5 and GISS.

Appendix

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

UAH6.0beta5

For UAH: There is no statistically significant warming since November 1993: Cl from -0.009 to 1.784. (This is using version 6.0 according to Nick’s program.)

The UAH average anomaly for 2016 is 0.505. This sets a new record. 1998 was previously 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 0.261 and it was ranked third but will now be in fourth place.

RSS

Presently, for RSS: There is no statistically significant warming since July 1994: Cl from -0.005 to 1.768.

The RSS average anomaly for 2016 is 0.573. This sets a new record. 1998 was previously 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 0.381 and it was ranked third but will now be in fourth place.

Hadcrut4.5

For Hadcrut4.5: The warming is significant for all periods above four years.

The Hadcrut4.5 average anomaly for 2016 is 0.774. This sets a new record. Prior to 2016, the highest ever monthly anomaly was in December of 2015 when it reached 1.024. The average anomaly in 2015 was 0.760 and this set a new record at that time.

Hadsst3

For Hadsst3: There is no statistically significant warming since March 1997: Cl from -0.003 to 2.102.

The Hadsst3 average anomaly for 2016 is 0.614. This sets a new record. 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 at that time.

GISS

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

The GISS average anomaly for 2016 is 0.99. This sets a new record. Prior to 2016, the highest ever monthly anomaly was in December of 2015 when it reached 1.11. The average anomaly in 2015 was 0.87 and it set a new record at that time.

Conclusion

The three hottest years for HadCRUT4.5, HadSST3 and GISS are 2016, 2015 and 2014 in that order. So when people talk about the last three years being the hottest, they are NOT talking about the satellite data. According to the satellite data, 2016 sets a new record with 1998 dropping to second place. However the difference is so small that we could say that 2016 and 1998 are in a statistical tie.

As I indicated in my last post, I had expected HadCRUT4.5 to be very close. As it turned out, HadCRUT4.5 is the only data set of the five that I cover that went up from November to December. The other four went down from November to December. In terms of where December would rank if its anomaly were to hold for 2017, RSS is the odd man out. How long do you think that this will be the case?

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135 Comments
myNym
January 27, 2017 1:50 am

Much ado about nothing.
The data under investigation ignores the greater picture.
A small part of the greater picture would address the last 2.6 million years, at least.

richard verney
Reply to  myNym
January 28, 2017 1:21 am

One of the big problems is the composition of our data sets.
The satellite data has a number of issues (eg orbital drift, sensor degradation, how it converts microwave measurements to temperature) with its main issue being its short duration, and in particular that it does not extend back to cover a known warming period 1920 to 1940 and then a known cooling period 1940 to mid 1970.
the land based thermometer record as compiled is a joke due to the lack of spatial coverage and fact that the precise stations used to assess temperature do not remain constant throughout the time series. They continually change, new stations are added, old stations drop out, there has been a significant change in general composition with more rural stations dropping out, and stations in high latitudes dropping out, and becoming more skewed towards stations which may be impacted by UHI, and in particular airport station data.
It is worth having a look at the collection of data, see the Peterson & Vose paper which shows extant stations going back to 1900:comment image
Look at how little spatial coverage exists in the station siting, and how heavily it is skewed towards the US. Given the skewing towards the US, it is not unreasonable to look at only US data, and when this is done the warmest 10 year period on record is the 1930s/1940 period.

richard verney
Reply to  richard verney
January 28, 2017 2:46 am

I ought also to have posted a plot showing how the number of stations has fluctuated over the years. The composition of GISS is continually changing.comment image
One cannot construct a meaningful time series, as GISS attempts to do so, when the input station data is altering every year. One is never making a like for like comparison from one year to the next.

Johann Wundersamer
January 27, 2017 2:10 am

v’

Coach Sprnger
January 27, 2017 5:36 am

Way more warmth than change. And not that much warmth.

January 27, 2017 6:35 am

Or 60 years ago, for that matter. I went through this exercise for Ottawa, where I live, and made a graph from the airport weather station that showed largely a straight line from the 1920s until the 1998 El Nino, then it was about half a degree warmer after that. I haven’t checked the result of the 2015/2016 El Nino yet. So there is definitely no AGW in Ottawa. (El Nino is not anthropogenic.)
As far as I can tell, these adjusted graphs that Nick and GISS come up with do not correspond to any location on our actual planet. Maybe they are from Mars or something.

mib8
January 27, 2017 9:02 am

Sooo, refresh us, again on exactly what equations are used/abused to get from many thousands of point temperature readings are homogenized, extrapolated, interpolated, and digested into these “one true global aggregate” temperatures and temperature “anomalies” (and how the bases were chosen for those anomalies)?
This is what keeps getting forgotten and neglected in the blizzard of daily news…by me, at least (and the same goes for the economic indicators; what is the weighted mean tariff charged on goids and services from the USA, from UK, from EU…? What of “non-tariff barriers”? By how much does the Fed dilute the value of the dollar each day?…).

Reply to  mib8
January 27, 2017 10:36 am

Sooo, refresh us, again on exactly what equations are used/abused to get from many thousands of point temperature readings are homogenized, extrapolated, interpolated, ……

Bob Tisdale may not answer all of your questions, but go here for a good start:
https://wattsupwiththat.com/2017/01/23/december-2016-global-surface-landocean-and-lower-troposphere-temperature-anomaly-update-with-a-look-at-the-year-end-annual-results/

mib8
Reply to  Werner Brozek
February 2, 2017 8:30 am

Thanks, but that appears to discuss only the results after the data have passed through most of the massive digestion process, and what happens when you dink with it a bit more, if you get my drift.

Reply to  Werner Brozek
February 2, 2017 12:57 pm

Thanks

I can only do so much in one post. However below the last post, there is a
Search WUWT:
Search
Punch in whatever you want, such as “GISS base years”. Then press the enter button and see what comes up.

Bindidon
January 27, 2017 6:12 pm

Menicholas on January 27, 2017 at 1:43 pm / Gunga Din on January 26, 2017 at 3:35 pm
This is the graph as displayed by the Real Climate „Science“:comment image
This the same graph produced by Wood For Trees, after having computed their correct offsets wrt UAH’s climatology for 1981-2010 (RSS land: -0.139; GISS land: -0.533)
http://fs5.directupload.net/images/170128/utg7z352.png
Caution has to be applied here: WFT mentions for GISS land: „extrapolated“, as you can see here on the data source for „Series 3“:
http://www.woodfortrees.org/plot/uah6-land/from:1995/mean:60/plot/rss-land/from:1995/mean:60/offset:-0.139/plot/gistemp-dts/from:1995/mean:60/offset:-0.533
And this is the graph I obtain from the ORIGINAL sources below, without any modification else than shifting the RSS and GISS anomalies by the correct climatology offsets:
http://fs5.directupload.net/images/170128/e83p2lic.jpg
This is not only science, Menicholas: this is what everybody can reproduce by downloading the sources and managing to get them into Excel.
Sources (downloaded January 28, 2017 at 02:30 am MET)
1. RSS land only (column 1: 70S-82.5N):
http://data.remss.com/msu/monthly_time_series/RSS_Monthly_MSU_AMSU_Channel_TLT_Anomalies_Land_v03_3.txt
2. UAH6.0 land only (column 2: Global land):
http://www.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt
3. GISS land only:
https://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts.txt
Menicholas: it is not the first time I see graphs originating from that „Science“ site. They were ALL wrong.
Wether it is intention or incompetence I don‘t know.
Gunga Din: in your reply to Menicholas (January 27, 2017 at 2:34 pm), you referred to GISS land+ocean; my answer would have been the same if Menicholas‘ „Science“ graph had reproduced that data instead of the land-only variant.

Reply to  Bindidon
January 28, 2017 6:19 am

Guess I wasn’t clear.
Here’s some of TheWayBackMachine’s archived GISS land only.
http://web.archive.org/web/20120601000000*/https://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts.txt
The oldest is Feb, 2012. Just a quick glance shows the value for January, 1880 is -54.
Following the link you provide the value is -90.
Now, maybe I don’t understand how to read the table (very possible), but to this layman it sure looks odd.
What happened to change the value?
How would the 1930’s line up with the “warmest 10 years” using the values from the older tables?
PS If there is something I don’t understand about reading the tables, please tell me. I do not wish to deceive myself.

Bindidon
Reply to  Gunga Din
January 28, 2017 2:28 pm

Gunga Din on January 28, 2017 at 6:19 am
PS If there is something I don’t understand about reading the tables, please tell me. I do not wish to deceive myself.
Gunga Din, you were perfectly clear and understood. And you read and understood the stuff correctly as well.
I would not like to repeat here the investigation undertaken by Nick Stokes whose results you see here (they concern lan+ocean instead of land only but that doesn’t matter):comment image
Let me at least tell you that it makes few sense to always exclusively concentrate on anomalies having been lower / higher at a given period: you must build the sequence of their differences over the entire record and inspect the sequence as a whole.
Below is a chart comparing a plot of the archive dataset with that out of the actual one:
http://fs5.directupload.net/images/170128/4zytokw7.jpg
To the blue plot (archive) and the red one (2016) I added a green one (2016 bit with the archive’s baseline offset). The difference between red and green is due to those corrections which were applied between 1951 and 1980 and thus had as consequence a shift over the entire dataset, as this period is the dataset’s baseline (also proudly named „climatology“, hmmmmh).
I know: the preferred opinion of many skeptics is here that „GISS made the past lower to get the present higher“.
Yes, of course. I’m sooo sure they are all right.

Reply to  Gunga Din
January 29, 2017 12:19 pm

Thank you, Bindidon.
I can’t help but wonder what the numbers were before Hansen got his hands on a keyboard.
And how anyone could think that, if the original numbers aren’t to be trusted, the output from a keyboard would be more trustworthy.
If the original numbers (probably written in pencil) can’t be trusted, why not just admit, “We honestly don’t know.”?

Bindidon
January 27, 2017 6:19 pm

Site stats: 300,279,356 views
Oops, it’s now really time to go to bed here in Europe, but before shutting down I see a little detail!
Congratulations to Mr Watts / Anthony for having silently bypassed the 300,000,000.