Land Surface Temperature Contribution To Non-Polar Temperatures
Guest Post by Bob Tisdale
INTRODUCTION
Frank Lansner’s post Did GISS discover 30% more land in the Northern Hemisphere? at Jo Nova’s blog created a recent stir. Watts Up With That ran a similar post by Frank, Tipping point at GISS? Land and sea weight out of balance. Both posts spawned rebuttals/explanations, including Zeke Hausfather’s post The GISTemp Land Fraction at Lucia’s The Blackboard and my post Notes On The GISTEMP Ratio Of Land To Sea Surface Temperature Data, which Anthony Watts co-posted as GISS land and sea ratios revisited, on the same day that he ran Frank Lansner’s post.
Basically, Frank Lansner’s post contends that GISS has increased the ratio of land to sea surface data with time, from zero percent early in the 20th century to near 70% in recent years. With a close examination of the graph that was being presented as a reference, Frank’s land surface data looked unusual, and I believe Frank’s observations are skewed by his choice of base years, and possibly by his smoothing method. I discussed this with him in a detailed comment that I posted at WattsUpWithThat and Jo Nova’s website. Refer to:
OVERVIEW OF THIS POST
There are lingering beliefs that there’s something unusual about the way GISS handles land surface data. In an effort to dispel those misunderstandings, the land surface data contribution to combined land and sea surface temperature data will be illustrated in this post, using a very simple method. Sea surface temperature data will be subtracted from GISS, Hadley Centre, and NCDC combined (land and sea) surface temperature products. The remainders, which are the contributions of the land surface temperature data to the combined products, will be compared. Personally, I was surprised with the results. But first, we need to eliminate the effects of known differences between the GISS and the other two global temperature datasets.
GISS treats the polar regions differently than the Hadley Centre and NCDC. GISS has better land surface temperature data coverage than the Hadley Centre and NCDC in the Arctic and Antarctic. And the Hadley Centre and NCDC include Arctic and Southern Ocean sea surface temperature data as seasonal sea ice melts, while GISS deletes sea surface temperature from areas where there is seasonal sea ice. The treatment of polar data by GISS was discussed in GISS Deletes Arctic And Southern Ocean Sea Surface Temperature Data, which was also co-posted at WattsUpWithThat, GISS Deletes Arctic And Southern Ocean Sea Surface Temperature Data. So, due to those differences, this post will only examine the global temperature data between the latitudes of 60S and 60N. These latitudes represent approximately 85% of the surface area of the globe.
Note: It is also known there is little to no Antarctic land surface temperature data prior to the 1950s. But this can’t explain the results Frank Lansner was reporting.
DATA SOURCES
GISS and Hadley Centre combined (land and sea) surface temperature anomaly data were downloaded from the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere
The KNMI Climate Explorer was also the source of Hadley Centre sea surface temperature (SST) anomaly data (HADSST2) used in its combined product. It also served as the source of the two SST components of the GISS combined product, HADISST from January 1880 to November 1981 and Reynolds (OI.v2) from December 1981 to present. The method employed to merge the two SST datasets used in the GISS product is discussed under Step 4 of the GISS current analysis webpage. The base years (1982 to 1992) used for splicing are different than those presented by the KNMI Climate Explorer for the GISS combined product, so I shifted the merged SST anomaly data to account for this.
I wanted to compare NCDC data in this post also, but it is not available through the KNMI Climate Explorer, and since the NCDC does not break down its standard combined surface temperature product into the desired latitude band (60S-60N) on its Global Surface Temperature Anomalies page, I used a second source. The NCDC also has SST, LST, and combined temperature anomaly data available through its ERSST Version 3/3b webpage, and it is available in multiple latitude bands, including 60S-60N. Scroll down to their link ftp://eclipse.ncdc.noaa.gov/pub/ersstv3b/pdo under the heading of “ASCII Time series Tables”.
Figure 1 compares the combined global (land and sea) surface temperature anomalies of the standard NCDC product and the data available through the ERSST.v3b webpage. The trends are identical at 0.057 deg C/decade. The difference appears to be caused by the use of different base years. The standard NCDC product uses 1901 to 2000 for base years while the data available through the ERSST.v3b webpage appears to be based on the NCDC climatology of 1971 to 2000. So this post uses the NCDC SST and combined (LST and SST) data that’s available through the ERSST.v3b webpage:
ftp://eclipse.ncdc.noaa.gov/pub/ersstv3b/pdo
http://i28.tinypic.com/vrsikk.jpg
Figure 1
Links to the data presented in Figure 1:
“Standard” NCDC Global combined product:
ftp://ftp.ncdc.noaa.gov/pub/data/anomalies/monthly.land_ocean.90S.90N.df_1901-2000mean.dat
NCDC Global combined product through ERSST.v3b webpage:
ftp://eclipse.ncdc.noaa.gov/pub/ersstv3b/pdo/aravg.mon.land_ocean.90S.90N.asc
Note: There are two different series of data available through the ERSST.v3b webpage. Those ending with .gv3.asc are recent additions, and since they have a slightly different trend and I have not found any mention of them in any other webpage, I have not used them in this post.
NON-POLAR SST AND COMBINED (LST & SST) SURFACE TEMPERATURE ANOMALY COMPARSONS
Figures 2 through 4 are comparison graphs of GISS, Hadley Centre, and NCDC SST and Combined (SST&LST) datasets for the latitudes of 60S-60N. All data has been smoothed with a 13-month running-average filter. They are being provided as references.
http://i26.tinypic.com/dmd2k1.jpg
Figure 2
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http://i32.tinypic.com/2gvitja.jpg
Figure 3
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http://i25.tinypic.com/258bcs6.jpg
Figure 4
CONTRIBUTION OF LAND SURFACE TEMPERATURE ANOMALY DATA TO NON-POLAR SURFACE TEMPERATURES
As discussed in the overview, to determine the contribution of land surface temperature anomaly data, the SST anomaly data was subtracted from the combined data. Before subtraction, the SST data was scaled by a factor of 0.755 to represent the ratio of ocean to land between the latitudes of 60S-60N. The remainders for each dataset are shown in Figures 5 through 7. Note that these graphs represent the remainder of subtraction, not the actual land surface temperature anomalies. To convert them back to land surface anomalies, they would have to be scaled.
http://i32.tinypic.com/14izupt.jpg
Figure 5
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http://i29.tinypic.com/2qak75i.jpg
Figure 6
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http://i30.tinypic.com/2ymc2vk.jpg
Figure 7
COMPARISON OF LAND SURFACE TEMPERATURE CONTRIBUTIONS
Figure 8 compares the remainders resulting from the subtraction of the scaled SST data from the combined (LST&SST) GISS, Hadley Centre, and NCDC products. As shown in Figures 5 through 7, the land surface residuals are noisy, so for this comparison, the data was smoothed with a 37-month running-average filter. While there are slight differences in the yearly and decadal variations in Figure 8, the linear trends for the three datasets are basically the same, differing only 0.001 deg C/decade.
http://i31.tinypic.com/33cp74w.jpg
Figure 8
This suggests, for the latitudes of 60S-60N, the differences between the combined products from GISS, Hadley Centre, and NCDC result from differences between the SST data they employ. Refer to An Overview Of Sea Surface Temperature Datasets Used In Global Temperature Products. And the most significant differences in SST anomalies occur before 1940. From 1880 to 1940, the SST anomaly data used by the Hadley Centre and NCDC have significant dips and rebounds, as shown in Figure 3 and 4, while the dip and rebound is much less pronounced in the SST data used by GISS, Figure 2.
SO HOW DOES GISS LAND SURFACE DATA DIFFER FROM THE HADLEY CENTRE AND NCDC?
The Hadley Centre and NCDC land surface temperature anomaly datasets represent continental land masses only, and on a global basis, both of those datasets exclude Antarctica. The land surface data presented by GISS, on the other hand, includes continental land mass data plus much more. First, looking at Figure 9, continental land mass data is extended out over the oceans in the GISS land surface temperature product with 1200km radius smoothing. (Figure 9 is a trend map available through the GISS Global Maps webpage, and it shows the regional changes in temperature anomaly from 1880 to 2009. I’ve cropped the map to show the latitudes, 60S-60N, used in this post.)
http://i31.tinypic.com/4kd9ns.jpg
Figure 9
Second, GISS also includes data from island surface stations and from “Ship stations,” and these values are also extended out over the oceans. This GISS dataset is a carryover from the methods developed by GISS back in the 1980s, when SST datasets were incomplete. They were attempting to simulate global temperature anomalies without using SST data. This is explained further in a WUWT comment from Zeke Hausfather to Frank Lansner in which Zeke quotes from a correspondence from Dr. Reto Ruedy of GISS:
In part it reads, “The curve NCDC and most likely you are computing shows the mean temperature over the land area (which covers about 1/3 of the globe, a large part of it located in the Northern hemisphere).
“None of our graphs represents that quantity. We could obtain it by creating a series of maps, then averaging just over the land areas (similar to what we do to get the US graph).”
It continues, “Since our interest is in the total energy contained in the atmosphere which correlates well with the global mean surface temperature, all our graphs display estimates for the global mean, the ones based on station data only as well as the ones based on a combination of station and ship and satellite data. Obviously, the latter is the more realistic estimate and we keep the first one mostly for the following historical reason:
“When we started out in the 1980s analyzing available temperature data, historic ocean temperature data were not yet available and we did the best we could with station data. As soon as ocean data compilations became available, we used them to refine our estimates (calling it LOTI). But we kept the earlier estimates also, mostly for sentimental reasons; they are rarely if ever mentioned in our discussions (see also the ‘note’ in the ‘Table’ section of our main web site).”
And continuing this post, the “‘note’ in the ‘Table’ section of [the GISTEMP] main web site” reads, “Note: LOTI provides a more realistic representation of the global mean trends than dTs below; it slightly underestimates warming or cooling trends, since the much larger heat capacity of water compared to air causes a slower and diminished reaction to changes; dTs on the other hand overestimates trends, since it disregards most of the dampening effects of the oceans that cover about two thirds of the earth’s surface.
And again, LOTI represents the GISTEMP combined land and sea surface data and the dTs represents the land surface data.
CLOSING REMARKS
The GISS Global-mean monthly, seasonal, and annual means dataset does not represent continental land mass temperature anomalies as many believe. It, therefore, cannot be employed in analyses like the one Frank Lansner is attempting to perform.
Dr. Ruedy’s statement that GISS could create a continental land temperature anomaly dataset similar to NCDC “by creating a series of maps, then averaging just over the land areas,” is another way of saying they could create it by masking the areas where the land surface data extends out over the oceans. And this was noted in the post Notes On The GISTEMP Ratio Of Land To Sea Surface Temperature Data.
Then there’s the similarity in the linear trends of the land surface contributions for the three combined datasets, Figure 8. It confirms the findings of the independent researchers who are creating land surface temperature anomaly datasets: the results are pretty much the same as the GISS, Hadley Centre, and NCDC data.
Niels, here is a tool where you combine the HADISST/reynoldsV2 SST in the areas NOT filled out with the GHCN land temperature station based GISS land fraction to get full values (and maps) globally:
http://data.giss.nasa.gov/gistemp/maps/
Thats how it works, you combine the GHCN temp station area data with the remaining SST area. And thats why its so odd that the SST use just explodes from 1980 – 1995 while the GISS land coverage actually is reduced 4% 1980 – 1995.
So what temperature series is better to represent the GISS land than the updated
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt
?
Do you think that an alternative version of this will change anything? Tell me what dataset you believe is to be used then, and i will test it. Honestly i would be a little surpriced if an alternative GISS land component would change anything.
K.R. Frank
Correction, it shold say:
And thats why its so odd that the GISS land component used just explodes from 1980 – 1995 (from 40 to73%) while the actual GISS land coverage actually is reduced 4% 1980 – 1995.
Niels: Can you give ANY good reason for this? Just move on, nothing to see?
– I think its a sceptic vertue to explore such curiosities.
K.R. Frank
Grank Lansner wrote: “The GISS LST is the temperature for the area (both land and some ocean) covered as illustrated here in 1980 and in 1995:
http://hidethedecline.eu/media/GISSglobal/fig7.jpg”
This dataset…
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt
..is NOT the dataset that GISS uses in its combined kand and sea surface temperature anomaly data:
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt
Therefore your analysis is wrong!
Frank Lansner: Sorry about spelling your name wrong above.
You replied to Niels A Nielsen: You want me to pretend that the GLB.Ts is NOT used in the GLB.Ts+dSST ?????
Where does this idea come from? ”
Because it is not the data used in the GLB.Ts+dSST data. That is what everyone has been trying to telling you. Here’s a link to my first post:
http://bobtisdale.blogspot.com/2010/07/notes-on-gistemp-ratio-of-land-to-sea.html
In it I wrote, “So in order to properly create a weighted average of GISTEMP land and sea surface temperature data with 1200km radius smoothing, the land surface data where it extends out over the oceans would first need to be masked.”
And that means that YOU, Frank Lansner, would have to take the GISS land surface dataset that has a global coverage that looks like this…
http://i29.tinypic.com/1264utz.jpg
…and convert it to one that looks like this:
http://i25.tinypic.com/345c6bk.jpg
After you have done that, then you have other problems to contend with before you can perform your analysis.
Frank Lansner:You wrote, “Thats how it works, you combine the GHCN temp station area data with the remaining SST area.”
Wrong. That is not how GISS creates its combined land plus sea surface temperature data,
Frank Lansner: To confirm what I wrote to you above…
” Wrong. That is not how GISS creates its combined land plus sea surface temperature data”…
refer to page 1 and 2 of Hansen et al (1996):
http://pubs.giss.nasa.gov/docs/1996/1996_Hansen_etal_1.pdf
In it they describe the early SST dataset they used, “Smith et al. (1996) developed an interpolation method using spatial patterns from empirical orthogonal functions (EOFs) to improve SST analyses for 1950-81, when satellite data were not available. The method produces spatial EOFs from OI analyses for 1982-1993. The dominant EOF modes are used as basis functions and fit, in least squares sense, to the in situ data to determine the time dependence of each mode. The SST field reconstructed from these spatial and temporal modes is confined to 59°N-45°S because of limited in situ data at higher latitudes.”
That was an early version of the Reynolds SST data. The current version covers the oceans globally.
Then Hansen et al (1996) describe the method they use to add the SST data. They write, “We have combined EOF SSTs for 1950-1981 and OI for 1982-1995 with meteorological station surface air temperature data to produce a surface temperature index for 1950-1995. The analysis method, as in HL87, divides the world into 8000 equal-area boxes. A box over land receives diminishing influence from stations up to 1200 km distant. A box over ocean between 59°N and 45°S uses the local SST, because of the complete coverage. A coastal box uses a meteorological station if one is located within 100 km of the box center.”
Refer also to the post at Lucia’s:
http://rankexploits.com/musings/2010/the-gistemp-land-fraction/
Specifically, refer to Nick Barnes (Comment#49280) July 19th, 2010 at 10:24 am. In it he describes how GISS merges the land and sea surface data. He writes, “For each grid cell, if there are fewer than 240 valid monthly data points in the ocean series, or if the nearest land station is less than 100km from the centre of the cell, then the land series is used. Otherwise the ocean series is used for that grid cell.”
And since both SST datasets are complete on a monthly basis, the portion of his description, “…if there are fewer than 240 valid monthly data points in the ocean series…” would no longer be a factor.
Bob, you write:
“This dataset…
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt
..is NOT the dataset that GISS uses in its combined kand and sea surface temperature anomaly data:
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt”
But this sounds odd…
Assuming you are correct (?) And we cant use http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt
– GISS has not made available the dataset they then use? Why?
And they only use parts of their http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt data in the combined? Which parts?
You dont have to answer all this, of course, it just sounds strange, and I will dig into this.
K.R. Frank, and yes as niels says: you are a patient man, thankyou. I just have to know whats behind rather than just “be told” 🙂
Frank
“But this sounds odd…
Assuming you are correct (?) And we cant use http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt
– GISS has not made available the dataset they then use? Why?
And they only use parts of their http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt data in the combined? Which parts?
You dont have to answer all this, of course, it just sounds strange, and I will dig into this.”
get the code and read how it works. Its about 10K lines. doesnt take very long to figure it out. sheesh.
Is not
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt
the temperatures for the entire area covered by the GHCN stations used bhy GISS, land + ocean as illustrated here:
http://hidethedecline.eu/media/GISSglobal/fig7.jpg
(Yes it is!!)
And so, how can I find the exact SST fraction that GISS uses? Without going through 10 K lines of code?
Answer: You can just for each year isolate the SST part from
http://data.giss.nasa.gov/gistemp/maps/
?
K.R. Frank
Hmm, but that wont do the final trick sonce SST anomali from one area need not equal the full SST…
Anyway: How do you guys explain problem 2 and 3 i mentioned:
http://hidethedecline.eu/pages/posts/follow-up-on-giss-rdquolandrdquo-temperatures-187.php
K.R. Frank
Frank Lansner wrote, “Anyway: How do you guys explain problem 2 and 3 i mentioned:
http://hidethedecline.eu/pages/posts/follow-up-on-giss-rdquolandrdquo-temperatures-187.php”
Your assumed problems 2 and 3 also appear to be based on your use of the wrong land surface dataset.
Frank Lansner: Sorry, my July 26, 2010 at 12:33 am reply above was wrong. It should have read, Your assumed problem 3 also appears to be based on your use of the wrong land surface dataset.
With respect to “problem 2” in your post, the marine air temperature data is different from the other datasets in a number of ways. Marine air temperature datasets are typically known as NMAT and the “N” stands for night or nighttime, because the NMAT datasets avoid “solar” biases by using only readings taken at night. On the other hand, SST and the island station and “ship station” used by GISS include daytime and nighttime readings.
The coverage of NMAT datasets are also very sparse. There is no data in the Northern Hemisphere north of 60N, and very little data in the Southern Hemisphere:
http://i27.tinypic.com/29408pl.jpg
So to compare the NMAT and SST datasets correctly, you would have to match the latitudes of the SST data so that they are the same as the NMAT data.
UAH publish 60N-60S SST satellite data, but only from 2002 I think. Would that be of any use or is it too short a series?
I know what to do now 🙂 – I will post an update on the subject within 2-3days.
Thanks for valuable input all, its highly appreciated.
K.R. Frank
Bob i would very much if you could anser my quetion above:
***
Is not
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt
the temperatures for the entire area covered by the GHCN stations used bhy GISS, land + ocean as illustrated here:
http://hidethedecline.eu/media/GISSglobal/fig7.jpg
***
I think yes, but if NO: What is then this
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt
?
And Bob, I think you are overlooking a severe problem mentioned in my “problem 2”, that has nothing to do with missing masking from my part or the like, but let it be, i will explain illustrated soon, i wont take more of yours or the moderators time.
K.R. Frank
Bob, a bit O/T but as this seems to be drawing to a close.
Do you have a graph of global SST with the PDO and AMO removed?
tallbloke says: “Bob, a bit O/T but as this seems to be drawing to a close.
Do you have a graph of global SST with the PDO and AMO removed?”
It’s impossible the remove the PDO, as far as I know, based on the methods used to calculate it. But I did write a post in which I removed the North Atlantic SST anomalies:
http://bobtisdale.blogspot.com/2009/03/has-global-warming-accelerated.html
Frank Lansner: You asked, “Is not
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt
the temperatures for the entire area covered by the GHCN stations used bhy GISS, land + ocean as illustrated here:
http://hidethedecline.eu/media/GISSglobal/fig7.jpg”
GISS uses only parts of it. The GISS land+ocean data uses SST over the oceans and land surface data over land. They will extend land surface data over the oceans by 100km if a surface station is along the coast.
But, of course, the Arctic and the Southern Ocean around Antarctica are different. In these oceans, SST data is deleted where it is permanent sea ice or seasonal sea ice. GISS then extends land surface data out over the oceans. Refer to:
http://bobtisdale.blogspot.com/2010/05/giss-deletes-arctic-and-southern-ocean.html
You asked, “What is then this
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt?”
The “GISS GLB.Ts” dataset is an updated version of the data used in Hansen and Lebedeff (1987):
http://pubs.giss.nasa.gov/docs/1987/1987_Hansen_Lebedeff.pdf
You wrote, “And Bob, I think you are overlooking a severe problem mentioned in my ‘problem 2’, that has nothing to do with missing masking from my part or the like…”
I discussed your “problem 2” in my July 26, 2010 at 1:31 am reply.
Regards
Frank Lansner: Here is something to consider: GISS released the source code for GISTEMP in September 2007. Refer to:
http://climateaudit.org/2007/09/08/hansen-frees-the-code/
Steve McIntyre and many other persons who understand programming have thoroughly investigated the methods GISS uses since then. If there had been an unusual change in the ratio of land to sea surface temperatures in GISTEMP, I believe it would have been noticed already.
While I don’t believe you should stop investigating data, always keep in mind that there are other people who have looked at it before you. And if they haven’t found what you believe to be an error, maybe it’s not an error.
Bob this is really really fine information, thankyou very much.
As I said we are working on a follow up where the GISS temperatures are tested from another angle, and all you write will be taken into account.
But I have to say some of the info i receive from here and there is not perfectly matching 🙂 , So perhaps Joanne nova or me will ask you a few questions in the coming time, perhaps on mail.
K.R. Frank
Bob Tisdale says:
July 26, 2010 at 3:57 am (Edit)
I did write a post in which I removed the North Atlantic SST anomalies:
http://bobtisdale.blogspot.com/2009/03/has-global-warming-accelerated.html
Thanks Bob, that’s useful. I’m interested in the 1910 and 1940 down and up spikes. These coincide with generally negative and positive phases of the PDO, and I was wondering how much they are a feature of all basin temps of that era.
tallbloke: You wrote, “I’m interested in the 1910 and 1940 down and up spikes. These coincide with generally negative and positive phases of the PDO, and I was wondering how much they are a feature of all basin temps of that era.”
Sounds like an idea for a future post. As noted in the post above, the severity of the dip from 1880 to 1910 depends on the SST dataset. It’s strong in the ERSST.v3b and HADSST2 and weak in the HADISST.
You have to keep in mind how sparse the data is back then. A while back I investigated the period of 1870 to 1940 by comparing ERSST.v3b, HADISST, and HADSST2 to the source ICOADS data. I limited my investigations to shipping lanes in the individual ocean basins since that’s where the majority of the data was located. The differences between the ICOADS data and the other datasets varied between ocean basins for single dataset and they varied from one dataset to the next. There were no constants. For example, I could not find what anyone might consider a constant “Folland adjustment”, even in the HADSST2, which receives the least amount of tinkering. And since I could not identify or explain what was going on, I shelved the post. My conclusion was, the data is so sparse in the early 20th century and the ERSST.v3b and HADISST depend so strongly on the infilling using optimum interpolation, EOF, and EOT (stands for empirical orthogonal teleconnections and it’s used in ERSST) that I was wasting my time trying to identify and explain the differences.
In other words, was there a major dip and rebound from 1880 to 1940? Maybe.
Frank Lansner: You wrote, “So perhaps Joanne nova or me will ask you a few questions in the coming time, perhaps on mail.”
Or leave me a link to your new post.
Regards
Hi Bob,
Yes, how much of a dip and rebound is the question. The characteristic of the SST’s relationship to the solar cycles on your 37 month smoothed plots is revealing. During the modern period, from 1960 the cycles are clearly visible, but much less so in the 1880-1940 period. No doubt a lot of that is down to poor data collation and calibration across datasets, but if my thinking is right, then some of it (1880-1900) is due to biggish el nino’s firing off as the sea lost heat while solar activity declined. Some of it is due to the sst’s not following the sun closely during the very low solar cycles (1900-1920) and then the steep climb to 1940 would have to be down to low cloud albedo in the roaring 20’s and the depression 30’s. I wonder how best to mine 20’s/30’s weather reports for clues without having to sit in library stacks….