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Saturday, May 31, 2008

Will Corrections to Global Temperature Make it Easier to Duplicate with Natural Influences?

Ever since Scafetta and West, in their recent paper “Is Climate Sensitive to Solar Variability?”, March 2008 “Physics Today”, provided the graph of Phenomenological Solar Signal (PSS) from 1950 to 2007, I’ve wanted to see what effect adding other natural climate influences (volcanic aerosols, ENSO, AMO, PDO) would have on the curve. Refer to Figure 1.

How closely would the result match the global temperature anomaly curve and trend? I needed to get the time to duplicate the PSS curve, which I’ve now completed. Figure 2 is the reproduced Phenomenological Solar Signal (PSS) curve from 1950 to 2007.

Figure 2

Scafetta and West paper:


Estimates of the effects on climate of explosive volcanic eruptions vary greatly. For the Mount Pinatubo eruption of 1992, these temperature estimates range from 0.2 to 0.5 deg C. I’ve elected to use 0.2, which is documented in “Short-term climatic impact of the 1991 volcanic eruption of Mt. Pinatubo and effects on atmospheric tracers”, Pitari and Mancini, Natural Hazards and Earth System Sciences (2002) 2: 91–108.

I’ve also used annual SATO Index of Mean Optical Thickness data, with a scaling factor of 1.65176, to adjust the curve based on the values for the Mount Pinatubo eruption. (0.2 deg C/0.121083 Mean Optical Thickness) Figure 3 shows the effect of volcanic aerosols on PSS.

Figure 3


Trenberth et al (2000) in “The Evolution of ENSO and Global Atmospheric Temperatures” identified two impacts of ENSO on global temperatures: the direct year-to-year effect and the impact on the linear trend.

They state: “It shows that for the 1997-98 El Niño, where N34 peaked at about 2.5 C, the global mean temperature was elevated as much 0.24 C (Fig. 2) although, averaged over the year centered on March 1998, the value drops to about 0.17 C.” And: “For 1950-98, ENSO linearly accounts for 0.06 C of global warming.”

I used NINO3.4 SST data available from NCDC, which I then converted to anomaly data based on the average temperature from 1950 to 1979. The annual NINO3.4 anomaly value centered on December 1997 (assumes a 3-month lag between NINO3.4 and global temperature) is 1.823 deg C. The annual temperature change of 0.17 deg C would provide a scaling factor for ENSO of 0.093. (0.17 deg C/1.823 deg C). I also shifted the NINO3.4 data 1 year, in an attempt to account for the time delay between ENSO and global temperature. The linear trend of 0.06 deg C was divided equally over the 49 year span. I then assumed it remained flat from 2000 to 2007.

With the NINO3.4 data added to the curve, Figure 4, the modified PSS curve begins to take shape, reflecting many of the annual variations in global temperature anomaly.

Figure 4


From Knight et al (2005) "A Signature of Persistent Natural Thermohaline Circulation Cycles in Observed Climate", GEOPHYSICAL RESEARCH LETTERS, VOL. 32, L20708, doi:10.1029/2005GL024233, 2005: "The regression of simulated global and Northern Hemisphere mean decadal temperatures with the THC are 0.05 +/- 0.02 and 0.09 +/- 0.02_C Sv_1 respectively, implying potential peak-to-peak variability of 0.1 and 0.2_C." Peak to peak changes on the AMO are on the order of 0.45 deg C. With a global temperature change of 0.1 deg C and a 0.45 deg C swing in the AMO, the scaling factor would equal 0.22. (0.1 deg C/0.45 deg C)

Since the AMO oscillation splits the period being investigated, there is a time when it adds to global temperature and another when it subtracts. Refer to Figure 5.

Figure 5

From the mid-70s, when the AMO reached its minimum, to 2005, it added to global temperature. Between 1950 and 1975, its contribution was negative. And from 2005 to present, the AMO has been adding to the decline in global temperature, though minimally on an annual basis.


Figure 6 illustrates where the adjustments to the PSS curve stand now when compared to global temperature anomaly, as represented by HADCrut3GL. It’s not a bad fit. The obvious problem is in the trend line. The global temperature curve trend is significantly higher than the adjusted PSS curve. That appears to be caused for the most part by the variance prior to 1965.

Figure 6

In a “A Large Discontinuity in the Mid-Twentieth Century in Observed Global-Mean Surface Temperature” by Thompson et al, Nature 453, 646-649 (29 May 2008) doi:10.1038/ nature06982; Received 28 January 2008; Accepted 4 April 2008, the authors describe an instrument bias in SST that lowered global temperature as much as 0.3 deg C around 1945.

A preliminary “fix” by the Climate Research Unit of the University of East Anglia (CRU) is shown in Figure 7.

Is the pre-1965 variance in temperature curves illustrated in Figure 6 the result of that “instrument bias”, a.k.a. error, by the keeper of SSTs? Eye-balling Figures 6 & 7, the differences in both sets of curves appear to be close to the same magnitude. Or would another adjustment of the PSS curve be required? This leads us to…


The PDO is the only major natural climate phenomenon that hasn’t had its effect on global temperature specifically identified. I’ve been looking on and off for years. If a significant portion of the global ocean surface rises and falls naturally; by thermohaline circulation (THC), by meridional overturning circulation (MOC), by delayed response to ENSO signals, by some other unidentified source, or by a combination of all of the above; temperatures on land masses will change in response. The AMO causes Northern Hemisphere temperature changes, and ENSO causes temperature changes in both hemispheres. These responses have been identified. Why not the global or hemispheric response to the PDO?

The other mystery associated with the PDO is its actual magnitude. The data available from JISAO is standardized. By definition, a standardized value is the distance of one data point from the mean divided by the standard deviation of the distribution. Got that. For some climate data sets, the effect of standardization can multiply the data by a factor of four. For others such as ENSO, standardization might amplify the data by only 5 or 10%. Where does PDO data fall? Without downloading the entire global SST data set and extracting the Pacific Ocean data north of 20N, there’s no way for me to tell.

The annual PDO is illustrated in Figure 8. Based on the smoothed data, it would lower Northern Hemisphere and global temperatures between the mid-1940s and the late 1970s; magically the same period tropospheric aerosols are used by GCMs to lower global temperature.

Figure 8

So let’s add PDO data to the PSS curve and adjust the scaling factor until we get the slope of the PSS trend to match the trend of global temperature anomaly. The coefficient based solely on that requirement turns out to be 0.098, slightly higher than the coefficient for ENSO and less than half of the one used for the AMO. I’ve also shifted the PDO data one year, like ENSO. Figure 9 illustrates the change to the PSS curve and Figure 10 provides the comparison to global temperature anomaly. http://i26.tinypic.com/24mhwt2.jpg
Figure 9

Figure 10

Any future revisions to SSTs and global temperatures will also cause changes in the AMO, ENSO, and PDO, but the relationship between them will remain the same. It seems as though any attempt to raise the global temperature curve between 1945 and the mid-60s will simply require a smaller PDO scaling factor to make the curves fit.

UPDATE – JUNE 1, 2008


The following are duplicates of Figure 10, which compares Global Temperature Anomaly (HADCrut3GL) to the adjusted Phenomenological Solar Signal (PSS), but in these I replaced the Hadley Centre data with data from GISS (Figure 11) and NCDC (Figure 12). Due to the differences in SST data sets and in calculation methods, the Scaling Factor for the PDO needed to differ to match the trends. GISS required a much higher scaling factor (0.155), where NCDC require one significantly less (0.065).
Figure 11
Figure 12


I do understand that using undocumented PDO scaling to achieve the trend match corrupts the data. But is creating a non-existent tropospheric aerosol adjustment to tune GCMs any different?

I also understand the scaling factor used to calculate the ENSO contribution to annual global temperature may also contribute to changes in the PDO. This is possibly why adding PDO to the curve amplifies the ENSO signal. I have no means of removing the ENSO signal from PDO, to provide an independent contribution for it.

Hopefully, when all data sets are revised to correct the errors in mid-century SST data, the scaling of the PDO will not be required at present levels.


It occurred to me there might be interest in seeing all the adjustments that were made to the PSS curve illustrated on one graph. Refer to Figure 13. I’ve also included before and after linear trend lines.

Figure 13

UPDATE 2 – June 2, 2008

This morning at Prometheus, Roger Pielke Jr.’s Science Policy blog, in one of his battles with RealClimate, he posted a graph of projected changes to the global temperature anomaly data. Refer to Figure 14.

Figure 14

From the Pielke graph, I revised the global temperature anomaly data set and compared it to the adjusted PSS data. It’s important to note that in this comparison the PDO was not required to make up for any additional decline in the mid-20th century. It has been “zeroed.” Yet even without the PDO, the slope of the PSS trend exceeds the trend of the revised global temperature anomaly curve. Refer to Figure 15.

Figure 15

Thursday, May 29, 2008

Monthly Long-Term Effects of ENSO on Global Temperature

As discussed in my previous post, “Annual and Long-Term Impacts of El Nino/Southern Oscillation (ENSO)”, the global temperature anomaly curve, the backbone of the AGW movement, is simply a running total of annual variations in global temperature. Applying this same trend calculation to scaled NINO3.4 anomaly data results in a curve that is extremely similar to that of produced by global temperature anomaly data. Refer to:

The same relationship applies to MONTHLY global temperature anomaly and NINO3.4 data. Refer to Figure 1. I won’t repeat the discussion on creating the graphs. I followed the same process I used for the annual data.

Figure 1

To determine the monthly scaling factor, I divided the 0.093 used in “Annual and Long-Term Impacts of El Nino/Southern Oscillation (ENSO)” by 12 to arrive at 0.00775.

Tuesday, May 27, 2008

Annual and Long-Term Impacts of El Nino/Southern Oscillation (ENSO)

Keep in mind when reading this post that the NINO3.4 temperatures shown in many of the graphs have been scaled drastically; the changes in NINO3.4 temperature are, in fact, more than 10 times greater than illustrated. Also keep in mind that the changes in NINO3.4 temperature precede the changes in global temperature anomaly; global temperature is responding to the El Nino or La Nina, not vice versa.


Figure 1 is a typical illustration of global temperature anomaly from 1850 to 2007. I’ve used Hadley Centre global temperature data (HadCRUT3GL) for this post.

Figure 1

Annual changes in anomaly, Figure 2, are calculated by subtracting the prior year anomaly value from the current year value, then repeating the calculation over the range of 1850 to 2007. Temperatures are rising whenever the “Annual Change” values are above zero, and dropping when they are below zero.

Figure 2

Figure 3 illustrates the magnitude of these annual variations without the visual skewing of the anomaly data. For the most part, these annual changes are driven by El Nino and La Nina events, large and small. Doubt that? Read on.

Figure 3

The El Nino/Southern Oscillation (ENSO) data used are NINO3.4 from NCDC, available here:

Note that the NINO3.4 data begins in 1871, so the global temperature data prior to that year will be excluded.

NINO3.4 SST and anomaly curves are shown in Figures 4 and 5, where the base period for the anomaly data is 1950 to 1979, the same base period used by Trenberth and Stepaniak in “Indices of El Niño Evolution”, J. Climate, 14, 1697-1701. Refer to: http://www.cgd.ucar.edu/cas/catalog/climind/TNI_N34/index.html#Sec5

Figure 4

Figure 5

Working backwards in time, the significant 1997/98, 1982/83, 1939/40/41/42, and 1878/79 El Nino events clearly stand out. The 1939 to 42 El Nino appears to be the source of that bump in the global temperature anomaly, Figure 1, that’s centered around 1940. It’s then followed by the two major La Nina events in 1950/51 and 1954/55/56/57, which appear to have created or enhanced the global temperature dip in the 1950s.

To put the annual changes in global temperature and NINO3.4 into perspective, Figure 6 illustrates the raw data. Please click on the TinyPic links for the full-sized graphs. The correlation between the annual variations in NINO3.4 area SST and global temperature is obvious. Changes in NINO3.4 temperature are in many cases more than 10 times larger than the changes in global temperature anomaly.

Figure 6

In “Evolution of El Nino–Southern Oscillation and Global Atmospheric Surface Temperatures”, JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 107, NO. D8, 10.1029/2000JD000298, 2002, Trenberth et al identified the global temperature reaction to the 97/98 El Nino: “The regression coefficient based on the detrended relationship is 0.094 deg C per N3.4 and is deemed more appropriate. The N3.4 contribution is given in Figure 3. It shows that for the 1997–1998 El Nino, where N3.4 peaked at ~2.5 deg C, the global mean temperature was elevated as much as 0.24 deg C (Figure 2), although, averaged over the year centered on March 1998, the value drops to ~0.17 deg C.” Note: The figures referenced within the quotation are the figures in the Trenberth study, not this post.


I’m using annual data for this comparison, and the NINO3.4 data sources are different. The following will be used to verify the scaling factor: The Trenberth et al annual average value of 0.17 deg C global temperature and the annual NINO3.4 anomaly value centered on December 1997 (assumes a 3-month lag between NINO3.4 and global temperature) from the calculated NINO3.4 anomaly data illustrated in Figure 6, which is 1.823 deg C. The calculated annual scaling factor is 0.093 (0.17/1.823). The difference from the Trenberth et al regression coefficient is insignificant.

Figure 7 illustrates the correlation of annual changes in global temperature and NINO3.4 data, where the NINO3.4 data has been adjusted by the scaling factor of 0.093. Again, please use the TinyPic link to view the full-sized graph. And again, keep in mind that changes in NINO3.4 temperature precede global temperature and that the NINO3.4 data in Figure 7 has been reduced in scale by a factor of more than 10. It’s easy to see that ANNUAL changes in NINO3.4 temperature drive ANNUAL global temperature variations.

Figure 7

Why was the NINO3.4 data scaled with the value of 0.093 in Figure 7, when a larger value would have provided a better comparison? The scaling factor of 0.093 has a role in the long-term comparison.


Recall: To create the graphs of the annual changes in global temperature that were used in Figures 2, 3, 6, and 7, the anomaly from the prior year was subtracted from the anomaly of the year being calculated, and the process was repeated over the term of the data. To return the data to its original long-term state, the annual values would need to be added to each other, from year to year, using a running total. The data will shift because the base period for the anomaly data was eliminated in the first calculations, but the shape and magnitude of the curve will remain the same.

This also allows a side-by-side comparison of the long-term effects of the scaled NINO3.4 on global temperature. A running total can also be applied to that data. Refer to Figure 8.

Figure 8

The correlation between the running totals infers that ENSO drives long-term change in global temperature, in addition to the annual variations. From the early 20th Century on, temperatures rise in parallel until the 1950s, when they dip until the mid-1970s, then rise again, all a function of El Nino/Southern Oscillation. Using the NINO3.4 data, there was no need to magically apply a non-existent tropospheric aerosol forcing in the 1950s through 1970s in order to slow the warming.


I created the following graph, Figure 9, to show that the magnitude and frequency of ENSO events rose with the increase in the annual TSI changes. These annual changes in TSI were calculated in the same way used to calculate the annual changes in temperature; that is, the prior year TSI value was subtracted from the year being calculated, with the process repeating each year for the term of the data. With a few exceptions, the graph does infer that ENSO event frequency and magnitude do increase with rising variations in solar irradiance.
Figure 9

Then I noticed that a few of the data points appeared to correlate but were offset by a few years, so I shifted the TSI data four years. Refer to Figure 10. It looks nice, but it still only illustrates an implied relationship between the amplitude of TSI and the frequency and magnitude of ENSO events.

Figure 10


NINO3.4 and global temperature anomaly data correlate annually and over a long term. This provides a stronger case for cause and effect than if they had correlated in only one way.

Tuesday, May 20, 2008

GISTEMP Smoothing Radius Comparison - 1200km vs 250km

Figure 1 provides a comparison of GISTemp smoothing radius data for January, 1978 through April, 2008: the blue curve with 250km radius smoothing, the red with 1200km.

Figure 1: GISTEMP Smoothing Radius Comparison – 250km (Blue) vs 1200km (Red) – January, 1978 to April 2008 with Linear Trends

The 1200km radius curve is based on the standard data distributed through the GISS website. Source here:

The data for the 250km radius curve was created using the GISS map page, the only way I could find to acquire the data. (Do not attempt this at home if you have something worthwhile to do.)

Data Sources: Land: GISS analysis & Ocean: Hadl/Rey 2
Map Type: Anomalies
Mean Period: Each month
Time Interval: Begin: Each year & End: Same year
Base Period: Begin: 1951 & End: 1980
Smoothing Radius: 250km
Projection Type: Regular

In the upper right-hand corner of each map is the monthly anomaly data. I input those monthly values into a spreadsheet by hand. (Again, a tedious task.) Sample Anomaly Map (Mercator and Polar Projections) and Zonal Means Plot outputs follow as Figures 2a, 2b, and 2c.

Figure 2a: GISS Global Temperature Anomaly Map - April 2008 – 250km Radius Smoothing – Mercator Projection

That thick red line across the bottom across the bottom of Figure 2a is not a lower border. It is the anomaly data at the South Pole stretched by the Mercator projection. Refer to Figure 2b.

Figure 2b: GISS Global Temperature Anomaly Map - April 2008 – 250km Radius Smoothing – Polar Projections
Figure 2c: GISS Global Temperature Zonal Means Plot - April 2008 – 250km Radius Smoothing
Don’t let the 3 deg C anomaly at the South Pole concern you. The temperature forecast this week for the Amundsen-Scott South Pole Station is in the range of -40 to -60 deg C.

Back to the comparison of radius smoothing: Figure 3 illustrates the same data as Figure 1, but it’s been smoothed with a 7-month filter. Eliminating the month-to-month noise helps illustrate the amplification inherent in the 1200km smoothing.

Figure 3: GISTEMP Smoothing Radius Comparison – 250km (Blue) vs 1200km (Red) – January, 1978 to April 2008 with Linear Trends – Smoothed With 7-Month Filter

Subtracting the 250km radius data from the 1200km provides another view of the ever increasing difference between the two data sets. Refer to Figure 4.

Figure 4: GISTEMP Smoothing Radius Comparison – 1200km Radius MINUS 250km Radius – January, 1978 to April 2008 with Polynomial Trend – Smoothed With 7-Month Filter

Based on the polynomial curve, it appears the divergence of the 1200km radius data began about 1989-1990. This is near the time that the number of reporting climate stations began to decrease, reducing the land area coverage. Refer to the gray areas in Figures 2a and 2b. Does this indicate the amplification by the 1200km radius smoothing is more a function of the reduced land area coverage than of polar amplification? Or did the high frequency of El Ninos from 1990 to 2007 cause Polar amplification and excessive high-latitude warming in the Northern Hemisphere that hasn’t been suppressed yet? Or both?


Figure 5 provides a comparison of the GISTemp data (250km Radius Smoothing) with the Hadley Centre global temperature anomaly data, HADCrut3GL. They look like twins.

Figure 5: Global Temperature Anomaly Data Comparison – GISTemp With 250km Radius Smoothing versus HADCrut3GL – January 1978 to April 2008

The difference between HADCrut3GL and GISTemp with the reduced 250km smoothing, Figure 6, appears to be declining, but if the step change synched with the 97/98 El Nino was removed, the difference between the two would be relatively flat.

Figure 6: Global Temperature Anomaly Data Comparison - GISTemp With 250km Radius Smoothing MINUS HADCrut3GL – January 1978 to April 2008

The step change in Figure 6 is consistent with the step change that results when Smith and Reynolds SST data (ERSST.v2) is subtracted from Hadley Centre data (HADSST2GL). Refer to Figure 7. The “Hadl/Rey 2” data employed by GISS must be comprised of Hadley Centre data prior to a given transition date, Smith and Reynolds after, where the transition date is prior to 1997.

Figure 7: Step Change Evident in HADSST2GL Minus ERSST.v2 - January 1950 to November 2007

Saturday, May 17, 2008

Long-Term Monthly NINO 3.4 SST Data

The majority of ENSO SST-based data available online is anomaly data that has been normalized, standardized, smoothed, filtered, or combined with other variables. I went searching for and found what I believe to be unadulterated NINO 3.4 SST data. It’s available from NCDC.

Main Time Series Page:

NINO 3.4 Time Series Page:

NINO 3.4 SST Data from 1871 to Present:

There is nothing in their description to indicate anything other than raw data based on the reconstructions or composites noted.

Figure 1a illustrates the raw monthly NINO3.4 SST data for a little more than 137 years. There’s a minor increase in temperature, using the linear trend line as reference. Replacing the linear with a 6th order polynomial trend, NINO3.4 temperature has a slow underlying oscillation and it peaked in the early 1990s. Refer to Figure 1b. Only a 3rd order polynomial trend shows a minor upswing toward the end of the data series, not illustrated.

Figure 1a: NINO3.4 SST - Monthly - January 1871 to March 2008 – Linear Trend

Figure 1b: NINO3.4 SST - Monthly - January 1871 to March 2008 – Polynomial Trend

What also stands out in the monthly NINO3.4 SST data is the increase in the range of extremes in later years, the higher amplitude. But if NINO3.4 reflects changes in solar irradiance, doesn’t that seem to agree with the increasing amplitude in the solar cycle. Refer to Figure 2.

Figure 2: Solar Irradiance – Lean + ACRIM - 1880 to 2007

For those who don’t know where the NINO 3.4 region is located, I’ve provided a map with it highlighted in Figure 3. I’ve always found it remarkable that a 1 to 2 degree temperature swing in the SST of that relatively small section of the planet, and in the other adjoining NINO areas, can create havoc with global temperature and precipitation.

Figure 3: NINO 3.4 Region

The annual maximums, minimums, and average NINO3.4 data for each year from 1871 to 2007 are shown in Figure 4.

Figure 4: NINO3.4 SST Annual Maximum-Minimum-Average 1871 to 2007

Based on the linear trend in Figure 5, annual average NINO3.4 SST rose less than 0.3 deg C over 136 years. Other than that the average temperature graph appears similar to most representations of NINO3.4 temperature.

Figure 5: NINO3.4 SST Annual Average 1871 to 2007

Again using the trend lines as reference, annual maximum NINO3.4 SST, Figure 6, rose while annual minimum NINO3.4 SST, Figure 7, was flat.

Figure 6: NINO3.4 SST Annual Maximum 1871 to 2007

Figure 7: NINO3.4 SST Annual Minimum 1871 to 2007

This of course creates an increase in the difference between annual temperature extremes, as illustrated in Figure 8.

Figure 8: NINO3.4 SST Maximum minus Minimum 1871 to 2007

I hesitated before posting the next graph, because correlation might be in the eye of the beholder. In other words, I may be reading too much into what appears to be a possible influence of solar irradiance on Minimum NINO3.4 temperature. Figure 9. To scale the TSI, I used the following equation.

Figure 9 TSI Scale = (TSI-1351.8)*1.8

Someone with filtering capabilities may be able to extract a better visual.

Figure 9: NINO3.4 SST Minimum vs TSI Scaled – Lean + ACRIM 1871 to 2007

Wednesday, May 14, 2008

A Fresk Look At NCDC Absolute - Data Source Notes

Update: I was advised yesterday, May 16th, that JunkScience corrected the errors described in the following.

In the text of the first part of this series, I wrote that I originally downloaded the .csv files from JunkScience. I have not noted that I had subsequently downloaded the NCDC monthly anomaly data found here:

Then followed the NCDC directions for creating the absolute data:

There are no differences in the global land and sea and the global sea data if you use the JunkScience spreadsheets or create your own, but there are insignificant differences in the January and February mean land temperatures. This is apparently caused by an NCDC change in base monthly mean temperature data for January and February (two of those twelve monthly numbers). Since these differences in the monthly mean land temperatures effect the data for each year, and since they are so small in comparison to the annual variations in mean land temperatures, there is no problem using the JunkScience spreadsheets for simple blog evaluations such as these. The graph resolution can’t pick up the differences. However, if you’re going to process the data using the multitude of available filters, you would be wise to follow the NCDC directions and create your own absolute global temperature data.

That way you won’t have to go back and update your work halfway through your analysis or, worse, after you’ve created all the graphs and uploaded them!

This morning I notified JunkScience of their need to update the Land data. I was later notified they will post a note on the webpage and correct the problem shortly.

A Fresh Look at NCDC Absolute Part 3

Please refer to the notes on data source prior to downloading the JunkScience .csv files.


In my view, I saved the best for last.

Figure 3.1 illustrates the large span of annual global Land Surface Temperature (LST) and the significant changes in maximum and minimum values.

Figure 3.1: NCDC Absolute Global Temperature – Land – Jan 1900 to Mar 2008

Figure 3.2 shows the maximum, minimum, and average readings of each calendar year from 1880 to 2007, with all the interconnecting data removed. The difference between the minimum and maximum values is so great it hides many of the highlights in those variables.

Figure 3.2: NCDC Absolute Annual Global LST – Maximum, Minimum, Average – 1880 to 2007

The average absolute annual global LST, Figure 3.3, differs little from the standard anomaly representations of LST.

Figure 3.3: NCDC Absolute Annual Global LST – Average – 1880 to 2007

Like Figure 1.8 in the first part of this series, the curve of the annual minimum LST data, Figure 3.4, is much more linear; that is, the multiple positive and negative trends are much less pronounced, and they may have shifted. Again, using the linear trend as reference, it’s relatively straight from 1890 to 2007, with a dip between 1960 and 1980.

Figure 3.4: NCDC Absolute Annual Global LST – Minimum – 1880 to 2007

Again, that dip in Annual Minimum Global LST seems to correlate with the 1960 to 1980 drop in TSI associated with Solar Cycle 20. Refer to Figure 3.5.

Figure 3.5: TSI and Maximum-to Maximum Trends

The Maximum Global LST data holds such a surprise that I first want to illustrate the data from 1880 to 1997. Refer to Figure 3.6a and Figure 3.6b. Nothing out of the ordinary in either graph.

Figure 3.6a: NCDC Absolute Annual Global LST – Maximum – 1880 to 1997

Figure 3.6b: NCDC Absolute Annual Global LST – Maximum with Polynomial Trend – 1880 to 1997

Now, let’s add the 1998 through 2007 data. Refer to Figure 3.6c and Figure 3.6d. Look at that step change. I got a little carried away with the red trend lines in Figure 3.6c, but the transition in 1998 is visible in Figure 3.6d. I’ve marked 1997 with a red dot. Based on the linear trend lines, maximum annual global LST jumped approximately 0.6 deg C after the 1997/98 El Nino and stayed there. Hopefully, the current La Nina will draw that back down. Time will tell. Refer also to “Let’s Not Forget The Other Influences” that follows, prior to the closing.

Figure 3.6c: NCDC Absolute Annual Global LST – Maximum – 1880 to 2007

Figure 3.6d: NCDC Absolute Annual Global LST – Maximum with Polynomial Trend – 1880 to 2007

The long-term effects of the 1997/98 El Nino are also visible in Figure 3.7.

Figure 3.7: UAH MSU Temperature Anomalies 12/78 to 2/08 – Global, Northern Hemisphere, North Pole

The difference between global LST annual extremes (maximum minus minimum) decreased over time. Refer to Figure 3.8. This is no surprise.

Figure 3.8: NCDC Absolute Annual Global LST – Maximum minus Minimum – 1880 to 2007

Unexpected: the greatest rate of change occurred from 1890 to 1920, where minimum temperature rose considerably faster than maximum, and that from 1980 to 2000, the rates of change were almost identical. To create that graph, I shifted both maximum and minimum data until their polynomial trends nearly overlapped in the late part of the 20th Century. Refer to Figure 3.9.

Figure 3.9: NCDC Absolute Annual Global LST Annual Maximums and Minimums Shifted for Comparison of Polynomial Trends – 1880 to 2007


Figures 3.10a through 3.10d further illustrate the long-term effects on LST of the 1997/98 El Nino. Figure 3.10a compares average LST of the 10 years before, after, and including 1997/98. Figure 3.10b illustrates the difference in the LST average of those two decades. Since the 1997/98 El Nino years could impact the averages, Figures 3.10c and 3.10d repeat the illustrations with the years 1997 and 98 removed.

Figure 3.10a: Average Monthly LST of 1988-97 and 1998-07 – Includes El Nino Years of 1997/98
Figure 3.10b: Difference Between Monthly LST Averages of 1988-97 and 1998-07

Figure 3.10c: Average Monthly LST of 1988-96 and 1999-07 – Excludes El Nino Years of 1997/98

Figure 3.10d: Difference Between Monthly LST Averages of 1988-96 and 1999-07

In Figures 3.11a through 11d, I replaced LST with SST to see if there was an oceanic step response to the 97/98 El Nino. It’s there, and if we assume there are no other heat sources for sea surface temperature, big assumption (Refer to “Let’s Not Forget The Other Influences” that follows), it’s on the order of 0.16 deg C.

Figure 3.11a: Average Monthly SST of 1988-97 and 1998-07 – Includes El Nino Years of 1997/98

Figure 3.11b: Difference Between Monthly SST Averages of 1988-97 and 1998-07

Figure 3.11c: Average Monthly SST of 1988-96 and 1999-07 – Excludes El Nino Years of 1997/98

Figure 3.11d: Difference Between Monthly SST Averages of 1988-96 and 1999-07


Scaffeta and West in their March 2008 opinion titled “Is Climate Sensitive to Solar Variablility?”, published in “Physics Today”, produced Figure 3.12. This illustrates that more than a 0.1 deg C portion of the rise from 1997 to 2002 could result from solar irradiance. However, solar peaked in 2002, while maximum LST have remained high.

Figure 3.12: Global Surface Temperature Anomaly and Phenomenological Solar Signatures – ACRIM (Red) vs PMOD (Blue) –Scaffeta and West (2008)

The Atlantic Multidecadal Oscillation rose from the mid-70s to 2005, but it too has been dropping since. The Pacific Decadal Oscillation (PDO) and its basin-wide kin the Interdecadal Pacific Oscillation (IPO) have been decreasing spasmodically since 1997.

In addition to the 1997/98 El Nino, there have also been three El Nino episodes since: 02/03, 04/05, 06/07. These were originally countered by the La Nina episodes 98/99, 99/00, 00/01 prior to the significant La Nina we are presently experiencing.

Regardless, these variables cannot explain that large step change in maximum LST without the 97/98 el Nino.


The preceding three-part evaluation of NCDC Absolute Global Temperature Data exposed relationships and natural effects hidden by anomaly data. I hope, and I’m sure, this will lead to other finds that further detail the major impacts of natural causes of climate change. I’ve limited this investigation to graphics available with my very basic working knowledge of EXCEL. Those with more advanced statistical devices, such as Hodrick-Prescott (HP) filters and filtering processes available through WoodforTrees http://www.woodfortrees.org/ , should be able to prompt more.


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