Do the Adjustments to the Global Land+Ocean Surface Temperature Data Always Decrease the Reported Global Warming Rate?

If you’ve read the first two posts in this series you might already believe you know the answer to the title question.  Those two posts were:

In this post, we’ll compare “raw” global land+ocean surface temperature data and the end products available from Berkeley Earth, Cowtan and Way, NASA GISS, NOAA NCEI and UK Met Office.

END PRODUCTS

Berkeley Earth – This land+ocean dataset is made up of the infilled land surface air temperature data created by the Berkeley Earth team and their infilled version of the HADSST3 sea surface temperature product from the UK Met Office (UKMO). For their merged land+ocean product, Berkeley Earth also infills data missing from the polar oceans, anywhere sea ice exists.  They accomplish this infilling two ways, creating separate datasets:  First, using sea surface temperature data from adjacent ice-free oceans.  Second, using land surface air temperature data from adjacent high-latitude land masses.  For this post, we’re using the data with the land-based infilling of the polar oceans to agree with the Cowtan and Way and the GISS Land-Ocean Temperature Index, both of which rely on land-surface temperature data for infilling.  The annual Berkeley Earth Land+Ocean data can be found here.

Cowtan and Way – The land+ocean surface temperature data from Cowtan and Way is an infilled version of the UKMO HADCRUT4 data. (Infilled by kriging.) As noted above, Cowtan and Way also infill areas of the polar oceans containing sea ice using land-based surface air temperature data. The annual Cowtan and Way data are here.

NASA GISS – The Land-Ocean Temperature Index (LOTI) from the Goddard Institute of Space Studies (GISS) is made up of GISS-adjusted GHCN data from NOAA for land surfaces and NOAA’s ERSST.v4 “pause buster” sea surface temperature data for the oceans, the latter of which has already been infilled by NOAA.  GISS infills missing data for land surfaces by extending data up to 1200km.   GISS also masks sea surface temperature data in the polar oceans (anywhere sea ice has existed) and extends land surface air temperature data out over the polar oceans.  The GISS LOTI data are here.

NOTES – For summaries of the oddities found in the new NOAA ERSST.v4 “pause-buster” sea surface temperature data see the posts:

Even though the changes to the ERSST reconstruction since 1998 cannot be justified by the night marine air temperature product that was used as a reference for bias adjustments (See comparison graph here), and even though NOAA appears to have manipulated the parameters (tuning knobs) in their sea surface temperature model to produce high warming rates (See the post here), GISS also switched to the new “pause-buster” NCEI ERSST.v4 sea surface temperature reconstruction with their July 2015 update. [End notes.]

NOAA NCEI – The NOAA Global (Land and Ocean) Surface Temperature Anomaly reconstruction is the product of the National Centers for Environmental Information (NCEI).  NCEI merges their new “pause buster” Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4) (see notes above) with the new Global Historical Climatology Network-Monthly (GHCN-M) version 3.3.0 for land surface air temperatures. The ERSST.v4 sea surface temperature reconstruction infills grids without temperature samples in a given month.  NCEI also infills land surface grids using statistical methods, but they do not infill over the polar oceans when sea ice exists.  When sea ice exists, NCEI leave a polar ocean grid blank. The source of the NCEI values is here.  Click on the link to Anomalies and Index Data.

UK Met Office – The UK Met Office HADCRUT4 reconstruction merges CRUTEM4 land-surface air temperature product and the HadSST3 sea-surface temperature (SST) reconstruction.  CRUTEM4 is the product of the combined efforts of the Met Office Hadley Centre and the Climatic Research Unit at the University of East Anglia. And HadSST3 is a product of the Hadley Centre.  Unlike the other reconstructions, grids without temperature samples for a given month are not infilled in the HADCRUT4 product.  That is, if a 5-deg latitude by 5-deg longitude grid does not have a temperature anomaly value in a given month, it is left blank. Blank grids are indirectly assigned the average values for their respective hemispheres before the hemispheric values are merged. The annual HADCRUT4 data are here, per the format here.

“RAW” DATA

For the “raw” global land+ocean surface temperature data, we’re using a weighted average of the global (90S-90N) ICOADS sea surface temperature data (71%) and the global “unadjusted” GHCN data from Zeke Hausfather (29%). (To confirm the percentage of Earth’s ocean area, see the NOAA webpage here.)

ICOADS – This is the source sea surface temperature data used by NOAA and UKMO for their sea surface temperature reconstructions. The source of the ICOADS data is the KNMI Climate Explorer.  Also see the post Do the Adjustments to Sea Surface Temperature Data Lower the Global Warming Rate?

For the unadjusted land surface air temperature data, Zeke Hausfather (a member of the Berkeley Earth team) has graciously updated his monthly unadjusted global GHCN land surface temperature data through March 2016, using the current version of the GHCN data. (Thank you, Zeke.) See Zeke’s comment here on the cross post at WattsUpWithThat of the original land surface air temperature post. The link to that current version of the “raw” data is here.  Also see the post UPDATED: Do the Adjustments to Land Surface Temperature Data Increase the Reported Global Warming Rate?

GENERAL NOTES

The WMO-preferred base years of 1981-2010 are used for anomalies for the ten comparison graphs.

We excluded the polar oceans in the post Do the Adjustments to Sea Surface Temperature Data Lower the Global Warming Rate?.  That is, we limited the latitudes to 60S-60N because the sea surface temperature reconstructions account for sea ice differently. We’re taking a different tack in this post:  because the suppliers of the end products handle sea ice differently (Some manufacturers infill data when and where sea ice exists, others don’t infill. Infilling is another form of adjustment.), I’m including the polar oceans in the “raw” sea surface temperature product, including the data for the latitudes of 90S-90N and comparing that to the global end products.

For the “raw” data from ICOADS, if a 2.5-deg latitude by 2.5-deg longitude grid does not contain observations-based data, it is left blank. This means there are no temperature measurements in the polar oceans when sea ice exists…like the UKMO HADCRUT4 data and the NOAA/NCEI data.

In past posts I have mentioned one of the problems with infilling the temperature data for the polar oceans by extending land surface air temperature data out over sea ice. That problem: the method fails to consider that polar sea ice during the summer likely has a different albedo than surface station locations where snow has melted and exposed underlying land surfaces. That is, sea ice will tend to reflect sunlight while exposed land surfaces would absorb it. That problem is compounded in the Arctic Ocean when ice-free ocean exists between land and sea ice. The ice-free ocean has yet another albedo, which is not the same as the ice surface or the snow-free land mass.  Those problems do not exist in winter when snow covers both sea ice and land surfaces and when the sea ice covers the Arctic Ocean to the shoreline, so the problem is seasonal.

Regardless of the season, any polar temperature data created by infilling is make-believe data.

Let’s start with the long-term data.

LONG-TERM TREND COMPARISON

This comparison starts in 1880 because the GISS and NOAA/NCEI data begin then. See Figure 1.  Because the adjustments to the sea surface temperature data reduce the amount of warming during the early 20th Century, the “raw” data have the highest long-term warming rate.  Or phrased differently, the adjustments have reduced the reported global warming since 1880.

Figure 1

Figure 1

Note:  The excessive warming of the “raw” surface temperature data from the early 1900s to the mid-1940s (due to the sea surface temperature component) presented a problem for climate models. Most of the warming then was not caused by man-made greenhouse gases (according to the models), but the warming trend of the “raw” data from the early 1900s to the mid-1940s was much higher than their recent warming rates.  For confirmation, see the graph of 30-year running trends here.  The bias corrections for the data prior to 1940 reduced those problems for the models, but did not eliminate them.  That is, the models still cannot explain the initial cooling of global sea surface temperatures from 1880 to about 1910, and, as a result, the models cannot explain the warming from about 1910 to the mid-1940s.   [End note.]

TREND COMPARISONS FOR 1950 TO 2015

1950 was one of the mid-20th Century start points used by NOAA in their study Karl et al (2015) Possible artifacts of data biases in the recent global surface warming hiatus…the “pause buster” paper. As shown in Figure 2, for the period of 1950 to 2015, the GISS and NCEI data have noticeably higher warming rates that the other datasets.  As you’ll recall, both GISS and NCEI use NOAA’s ERSST.v4 “pause-buster” sea surface temperature data, which have not been corrected for the 1945 discontinuity and trailing biases presented in Thompson et al. (2008)  A large discontinuity in the mid-twentieth century in observed global-mean surface temperature.  On the other hand, the other datasets (Berkeley Earth, Cowtan and Way, and HADCRUT4) use the UKMO HADSST3 data, which have been corrected for those mid-20th Century biases.

Figure 2Figure 2

For a more-detailed discussion of NOAA’s failure to account for those biases with their ERSST.v4 “pause-buster” data, see the post Busting (or not) the mid-20th century global-warming hiatus, which was also cross posted at Judith Curry’s ClimateEtc here and at WattsUpWithThat here.

TREND COMPARISONS FOR 1975 TO 2015

1975 is a commonly used breakpoint for the transition from the mid-20th Century cooling or slowdown (depends on the dataset) and the recent warming period. Figure 3 compares the “raw” and “adjusted” global warming rates from 1975 to 2015.  There is a 0.019 deg C/decade spread in the trends, with the Cowtan and Way data having the highest warming rate and the NOAA/NCEI data having the lowest…even lower than the “raw” data.

Figure 3

Figure 3

TREND COMPARISONS FOR 1998 TO 2015

Figure 4 compares the “raw” and “adjusted” global surface temperature anomalies starting in 1998, which is often used as the start year of the slowdown in global warming.  The GISS LOTI and NOAA/NCEI data have the highest warming rate, a result of NOAA’s excessive tweaking of the parameters (tuning knobs) in the model that manufactures NOAA’s “pause buster” ERSST.v4 sea surface temperature data, creating a trend near the high end of the parametric uncertainty range.  At the other end of the spectrum, the UKMO HADCRUT4 data has a trend that’s very similar to the “raw” data.  Keep in mind that the HADSST3 sea surface temperature data (used in the HADCRUT4 combined land+ocean data) have been adjusted for ship-buoy biases.

Figure 4

Figure 4

Both the Berkeley Earth and the Cowtan and Way land+ocean data rely on and infill HADSST3 data for the ocean portion, yet the Cowtan and Way data have noticeably higher warming rate than the Berkeley Earth data during this period. (Maybe Kevin Cowtan or Robert Way will stop by and explain that for us.)

As a reference, based on the model-mean of the climate models stored in the CMIP5 archive, which represents the consensus of the modeling groups, the expected warming rate during the period of 1998 to 2015 is 0.233 deg C/decade with the worst-case RCP8.5 scenario, and that’s about 0.1 deg C/decade to 0.13 deg C/decade higher than observed…thus my use of the term “slowdown”.

WAS THERE A “HIATUS” IN GLOBAL WARMING?  

For this heading, I’m going to borrow and update the text from the post Do the Adjustments to Sea Surface Temperature Data Lower the Global Warming Rate?

Of course there was a hiatus, but the extent of the slowdown depends on the global land+ocean temperature dataset and the period to which the slowdown is compared.  Figure 5 includes the “raw” and “adjusted” global sea surface temperature anomalies for the period of 1998 to 2013.  We ended the data in 2013, because:

  • 2013 was an ENSO neutral year…that is there no El Niño or La Niña.  (See NOAA’s Oceanic NINO Index here.)
  • The Blob and the weak El Niño conditions were the primary causes of the naturally occurring uptick in global surface temperatures in 2014 and,
  • The continuation of The Blob and the strong El Niño conditions were the primary causes of the naturally occurring uptick in global surface temperatures in 2015.

Figure 5

Figure 5

Note 1: To confirm the second and third bullet points, we discussed and illustrated the natural causes of the 2014 “record high” surface temperatures in General Discussion 2 of my free ebook On Global Warming and the Illusion of Control (25 MB).  And we discussed the naturally caused reasons for the record highs in 2015 in General Discussion 3.

Note 2: Some may claim the start year of 1998 is cherry-picked because it’s an El Niño decay year. That’s easily countered by noting that the 1997/98 El Niño was followed by the 1998 to 2001 La Niña. (Once again, see NOAA’s Oceanic NINO Index here.) Also, 1998 was used as a start year by Karl et al. (2015) and the period of 1998 to 2013 is also one year longer than the period of 1998 to 2012 used by NOAA in that paper.

[End notes.]

Karl et al. (2015) also used a sleight of hand in their trend comparisons by using 1950 as the start year of the recent warming period. The IPCC did the same thing in their analyses of it in Chapter 9 of their Fifth Assessment Report (See their Box 9.2).  Both groups referenced the hiatus warming rates to periods starting in 1950 or 1951.  Why does that indicate they were using smoke and mirrors?  The trends from those 1950 or 1951 start dates include the slowdown or cooling of global surfaces that occurred from the mid-1940s to about 1975. So let’s present the trends from the start of the recent warming period (1975) to the end of the 20th Century (1999). See Figure 6.  As you’ll recall, the year 1999 was used by NOAA in Karl et al. (2015). (Again refer to Figure 1 from Karl et al. (2015).)

Figure 6

Figure 6

Only the UKMO’s HADSST3 data have a higher warming rate than the “raw” data during this period. Some readers might believe the other data suppliers have reduced the reported global warming during the period of 1975 to 1999 to suppress the extent of the slowdown that followed.

Now let’s compare the trends for the periods of 1975 to 1999 (Figure 6) and 1998 to 2013 (Figure 5).  The slowdowns (1975-1999 trends minus 1998-2013 trends) are:

  • HADCRUT4 slowdown = 0.137 deg C/decade (compared to +0.190 deg C/decade for 1975-1999)
  • NOAA/NCEI slowdown = 0.087 deg C/decade (compared to +0.173 deg C/decade for 1975-1999)
  • Berkeley Earth = 0.086 deg C/decade (compared to +0.178 deg C/decade for 1975-1999)
  • GISS LOTI = 0.080 deg C/decade (compared to +0.178 deg C/decade for 1975-1999)
  • Cowtan and Way = 0.079 deg C/decade (compared to +0.182 deg C/decade for 1975-1999)

Not too surprisingly, the “adjusted” dataset with the no infilling (HADCRUT4) shows the greatest slowdown.

The “raw” data show a slowdown of about 0.148 deg C (compared to +0.183 deg C/decade for 1975-1999), a slightly greater slowdown than the HADCRUT4 data.

And for those of you wondering about climate models, the model mean (the model consensus) of the CMIP5-archived models (with historic and RCP8.5 forcings) shows a higher warming rate (+0.223 deg C/decade) during 1998 to 2013 than during 1975 to 1999 (+0.154 deg C/decade). That is, according to the models, global warming should have accelerated in 1998 to 2013 compared to the period of 1975 to 1999.  Instead, the data show a deceleration of global warming.

But other start years have been used for the recent “hiatus” in global warming.  NCAR’s Kevin Trenberth used 2001 in his 2013 article Has Global Warming Stalled? for the Royal Meteorological Society.  (My comments on Trenberth’s article are here.)  Figure 7 compares the trends for the “raw” global land+ocean surface temperature data and the end products for the period of 2001 to 2013. The “raw” data show a slight cooling over this short time period.  The trend of the UKMO HADCRUT4 data is basically flat at 0.001 deg C/decade. At the high end are the GISS LOTI and NOAA/NCEI data, which should result from NOAA’s excessive parameter tweaking.

Figure 7

Figure 7

The slowdowns (1975-1999 trends minus 2001-2013 trends) are:

  • HADCRUT4 slowdown = 0.189 deg C/decade (compared to +0.190 deg C/decade for 1975-1999)
  • Berkeley Earth = 0.146 deg C/decade (compared to +0.178 deg C/decade for 1975-1999)
  • Cowtan and Way = 0.138 deg C/decade (compared to +0.182 deg C/decade for 1975-1999)
  • GISS LOTI = 0.125 deg C/decade (compared to +0.178 deg C/decade for 1975-1999)
  • NOAA/NCEI slowdown = 0.121 deg C/decade (compared to +0.173 deg C/decade for 1975-1999)

Because the “raw” data trend is negative over this timeframe, the slowdown is greater than the trend for 1975 to 1999.

And once again, the climate models shows a higher warming rate (+0.184 deg C/decade) from 2001 to 2013 than for the period of 1975 to 1999 (+0.154 deg C/decade).

LET’S LOOK AT THE TRENDS FOR THE EARLY COOLING PERIOD, THE EARLY 20TH-CENTURY WARMING PERIOD AND THE MID 20TH-CENTURY SLOWDOWN/COOLING PERIOD   

In past posts and in my book Climate Models Fail, I used the breakpoints of 1914, 1945 and 1975 when dividing the data prior to the recent warming period.  The years 1914 and 1945 were determined through breakpoint analysis by Dr. Leif Svalgaard of a former GISS LOTI dataset (the version that used ERSST.v3b data).  See his April 20, 2013 at 2:20 pm and April 20, 2013 at 4:21 pm comments on a WattsUpWithThat post here.  And for 1975, I referred to the breakpoint analysis performed by statistician Tamino (a.k.a. Grant Foster).  With the inclusion of the NOAA ERSST.v4 “pause-buster” sea surface temperature data in the GISS LOTI data, I suspect there may be new breakpoints for that dataset (and that the breakpoints may be slightly different for the other datasets), but I’ll continue to use 1914, 1945 and 1975 for consistency with past posts and that book.

Specifically, 1880 to 1914 is used for the early cooling period (Figure 8), 1914 to 1945 is used for the early 20th-Century warming period (Figure 9), and the mid-20th Century slowdown/cooling period is captured in the years of 1945 to 1975 (Figure 10).

During the early cooling period of 1880 to 1914, Figure 8, most of the end products have cooling trends that are lesser negative value than the cooling trend of the “raw” data. Curiously, the trend of the NOAA/NCEI data is the same as the “raw” data. The spread in the cooling rates of the end products is about 0.04 deg C/decade.  As a reference, the model mean of the CMIP5-archived model (historic/RCP8.5) show a slight warming trend (+0.032 deg C/decade) for this cooling period.  Models wrong again.

Figure 8

Figure 8

In Figure 9, we can see that the “raw” data have the highest warming rate for the early 20th Century warming period of 1914 to 1945. The adjustments during this time period are primarily to the sea surface temperature data…an effort to account for biases resulting from the transition in temperature-sampling methods, from buckets to ship inlets.  Regardless, the spread in the warming rates is about 0.02 deg C/decade for the end products.

Figure 9

Figure 9

Of course, since the models do not simulate the cooling from 1880 to 1914, they fail to properly simulate the warming from 1914 to 1945.  The model consensus only shows a simulated warming rate of +0.057 deg C/decade during this period.  Because the models can’t explain the extent of the warming that took place in the early part of the 20th Century, apparently natural variability is capable of warming Earth’s surfaces at a rate of 0.07 to 0.09 Deg C/decade above that hindcast by the models. That of course makes one wonder how much of the recent warming was caused naturally.

The mid-20th-Century slowdown/cooling period of 1945 to 1975 is last, Figure 10.  The “raw” data and those datasets that are based on the HADSST3 data sea surface temperature data (Berkeley Earth, Cowtan and Way, UKMO HADCRUT4) show slight cooling trends during this period. Once again, they have been adjusted for the 1945 discontinuity and trailing biases that were determined in Thompson et al. (2008).  On the other hand, the two datasets that rely on NOAA’s very odd ERSST.v4 “pause buster” sea surface temperature data (GISS LOTI and NOAA/NCEI) show a slight warming trend for 1945 to 1975.  And once again, the reason those two differ from the others is that the ERSST.v4 “pause-buster” data were not corrected for the 1945 discontinuity and trailing biases.

Figure 10

Figure 10

How awkward is NOAA’s failure to correct for the 1945 discontinuity and trailing biases? Even the consensus of the climate models (CMIP5 model mean with historic and RCP8.5 forcings) shows a cooling trend (-0.014 deg C/decade, slightly more than observed) during the mid-20th-Century period of 1945 to 1975.

THE SPREADS BETWEEN ANNUAL AND MONTHLY GLOBAL LAND+OCEAN SURFACE TEMPERATURE END PRODUCTS      

Since we’re discussing global temperature products, I thought I’d illustrate something else…the extents of the disagreements between annual and monthly global surface temperature anomalies.

Figure 11 (annual) and Figure 12 (monthly) show the spreads between the 5 global land+ocean surface temperature end-products. (Please note the differences in the scales of the y-axis.) The anomalies are all referenced to the full term of the data (1880 to 2015) so not to bias the results. The minimum and maximum values for the 5 datasets were first determined.  Then the spread was calculated by subtracting the minimums from the maximums.

Figure 11

Figure 11

# # #

Figure 12

Figure 12

Curiously, referring to the annual data because it’s easier to see, the spread in the early 1900s is less than the spread for much of the mid-20th Century. Again, the anomalies are all referenced to the full term of the data (1880 to 2015) so not to bias the results.

CLOSING

We often hear people state that the adjustments to global land+ocean surface temperature data have decreased the global warming rate.  That’s very true for the long-term data (1880 to 2015) but not necessarily true for the periods after the mid-1940s.

For the post-1998 or post-2001 slowdown in global warming, the adjustments have increased the global warming rates in all datasets, with the UKMO HADCRUT4 adjustments having the least impacts.

NOAA’s failure to correct for the 1945-discontinuity and trailing biases causes the GISS LOTI and NOAA/NCEI to have relatively high warming rates for the period of 1950 to 2015.  That failure on NOAA’s part also shows up during the mid-20th-Century period of 1945 to 1975…the GISS LOTI and NOAA/NCEI show a light warming during this period, while the datasets that have been corrected for the 1945-discontinuity and trailing biases show a slight cooling.

Some persons believe the adjustments to the global temperature record are unjustified, while others believe the seemingly continuous changes are not only justified, they’re signs of advances in our understanding.  What are your thoughts?

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About Bob Tisdale

Research interest: the long-term aftereffects of El Niño and La Nina events on global sea surface temperature and ocean heat content. Author of the ebook Who Turned on the Heat? and regular contributor at WattsUpWithThat.
This entry was posted in Global Temperature Update, LOST Update. Bookmark the permalink.

25 Responses to Do the Adjustments to the Global Land+Ocean Surface Temperature Data Always Decrease the Reported Global Warming Rate?

  1. Before and after 1914 is interesting. But I was just reading a paper on the AMO thàt identified the most relevant warming periods as 1910-1940 & 1970-2000, with 1940-1970 a cool phase.
    ( Kravtsov, et al. 2014)

    It seems that another post featuring these 30-year periods might reveal the influence of the AMO.

    The question that keeps recurring to me is whether or not these fluctuations in temperature may be mistaken for secular (long term) climate change when in fact they are merely manifestations of chaotic teleconnections.

    I noticed that Professor Anastasios Tsonis has just joined the GWPF’s Academic Advisory Council and dug out some of his papers. I cite one in which he joined with others to discuss the AMO, which has been my preferred candidate as the Joker in the data-stack.

    But he has authored over 100 other papers, some of which explore topics such as chaotic behaviour of climate components, attractors, coherence, stationarity and Granger causality.

    If climate is as complex as Tsonis would have us believe, we are nowhere near knowing what has happened, what is going on now, or what to expect in future.

    Hubert Lamb was right, the best policy is watchful waiting combined with diligent study.

    References:
    Kravtsov,S., Wyatt, M. G., Curry, J. A., & Tsonis, A. A. (2014). Two contrasting views of multidecadal climate variability in the 20th century. Geophysical Research Letters, 2014

    https://pantherfile.uwm.edu/kravtsov/www/downloads/KWCT2014/DOC_AND_PDF/KWCT2014_PREVIOUS_VERSIONS_AND_REVIEWS/KWCT2014_main_FINAL1.pdf

  2. Norman Page says:

    Bob. Your work shows the importance of picking start and end dates when analyzing data. These should be picked to illustrate an empirically based working hypothesis as seen e.g. in Fig 1 at
    http://climatesense-norpag.blogspot.com/2016/03/the-imminent-collapse-of-cagw-delusion.html

    Figure 1 above compares the IPCC forecast with the Akasofu paper forecast and with the simple but most economic working hypothesis of this post (green line) that the peak at about 2003 is the most recent peak in the millennial cycle so obvious in the temperature data. The data also shows that the well documented 60 year temperature cycle coincidentally peaks at about the same time.
    The RSS and Hadrut 4 trends are shown below


    The RSS data and the Hadcrut 4 data show a small difference in the timing of the millennial peak between the data sets. The trends are truncated to avoid the temporary effect of the current powerful El Nino on illustrating the hypothesis

  3. Bob Tisdale says:

    Thanks, Norman. In another decade or so, we’ll be able to tell how well those breakpoints hold up.

  4. Bob Tisdale says:

    Thanks for the link to Kravtsov et al., Frederick.

  5. Frank says:

    Very educational work. Thanks for your work. You asked:

    “Some persons believe the adjustments to the global temperature record are unjustified, while others believe the seemingly continuous changes are not only justified, they’re signs of advances in our understanding. What are your thoughts?”

    The differences in the temperature records you show demonstrate the amount of UNCERTAINTY in the temperature record. I’m skeptical about whether they represent a real improvement that brings us closer to the “right” answer. Our technology is inadequate for the task of reliably detecting a changes within +/-0.05 degC (and possibly more) in any one decade.

    If you hadn’t already done so much work, I would have preferred to see you deal separately with land and ocean records. The issues are so different. And perhaps the difference between NH and SH hemispheres, since they vary so much in ocean coverage.

    One way you could make less work for yourself and avoid the questions about cherry-picking starting and stopping dates would be to plot warming trends, not temperatures themselves. Suppose you had plots of N-year warming trends for the raw and revised data above, with a datapoint for each year (Y) covering the period Y-0.5N to Y+0.5N. I think I would start with N=60 or 65, to look at trends across the same phase of the AMO. Then I might look at 30-year and shorter trends that would illustrate disagreements more clearly. At some point you will illustrate the folly of looking at periods that are too short: The trend jumps around irregularly and the disagreements become a significant fraction of the trend.

    On such plots, the disagreements about 1910-1940 warming, 1945-1975 cooling, 1975-1998 warming, and the 1998-2013 hiatus would be apparent on one graph. Disagreements over the handling of the 1945 ERSST discontinuity, for example, should be readily apparent.

    Finally a plot of processed trends – raw trend, which would show how important corrections were during various periods of time and how much disagreement exists about these corrections.

    Thanks again for all you already have done.

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