A FRESH LOOK AT NCDC ABSOLUTE PART 2

Please refer to the notes on data source prior to downloading the JunkScience .csv files.
http://bobtisdale.blogspot.com/2008/05/fresk-look-at-ncdc-absolute-data-source.html


GLOBAL OCEAN


In Part 1 of this series, the magnitude of the variations in Land Surface Temperature (LST) overwhelmed Sea Surface Temperature (SST), making SST appear almost flat. Figure 2.1 provides a detailed view of the annual global SST span and the changes in its maximum and minimum values.


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Figure 2.1: NCDC Absolute Global Temperature – Ocean – Jan 1900 to Mar 2008



Pulling the mass of data from the middle, Figure 2.2 illustrates the maximum, minimum, and average readings of each calendar year from 1880 to 2007. Figures 2.3 through 2.5 provide a better view of the individual data and their linear trends.



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Figure 2.2: NCDC Absolute Annual Global SST – Maximum, Minimum, Average – 1880 to 2007

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Figure 2.3: NCDC Absolute Annual Global SST – Average – 1880 to 2007



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Figure 2.4: NCDC Absolute Annual Global SST – Maximum – 1880 to 2007



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Figure 2.5: NCDC Absolute Annual Global SST – Minimum – 1880 to 2007



The next graph was a surprise to me. The difference between global SST annual extremes (maximum minus minimum) increased over time. Refer to Figure 2.6. This indicates that maximum annual global SST grew faster than minimum, which is the opposite of the combined and, logically, the LST trends. (In order for the annual global combined temperature difference between extremes to be decreasing, then that decrease, logically, has to come from LST, and that decrease in LST extremes has to be greater than the increase in SST extremes.)



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Figure 2.6: NCDC Absolute Annual Global SST – Maximum minus Minimum – 1880 to 2007




Note the 50- to 60-year oscillation in the curve compared to the linear trend line. It will be easier to see with a polynomial trend. Refer to Figure 2.6b.


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Figure 2.6b: NCDC Absolute Annual Global SST – Maximum minus Minimum – 1880 to 2007



Adding raw Atlantic Multidecadal Oscillation (AMO) data for comparison, though it has been shifted 0.5 deg C, Figure 2.7, the magnitude of the AMO oscillations (red data) suppress the global SST data (blue).


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Figure 2.7: NCDC Absolute Annual Global SST – Maximum minus Minimum vs AMO – 1880 to 2007



Assume that the surface area of the Atlantic Ocean is approximately 30% of the global ocean area and that the North Atlantic represents 50% of the Atlantic. Multiply the AMO data by 15%, then add 0.7 deg C to shift the range, and the correlation becomes apparent. See Figure 2.7b.



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Figure 2.7b: NCDC Absolute Annual Global SST – Maximum minus Minimum (Blue) vs AMO (Red) – 1880 to 2007 – AMO Scaled and Ranged





Just in case you’ve never seen it, Figure 2.8 is a comparison of global average SST with the AMO.


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Figure 2.8: NCDC Absolute Annual Global SST – Average vs AMO – 1880 to 2007

To put the relationship of AMO and Global SST into perspective: In Figure 2.9 NCDC, the AMO is extracted from the average annual global SST, using the 15% factor discussed above.

The impact on global SST is minimal, BUT…

http://i29.tinypic.com/fk15ah.jpg
Figure 2.9 NCDC: Absolute Global SST – Annual Average vs Annual Average with AMO Removed – 1880 to 2007

Now recall the effect of the AMO on Northern Hemisphere LST. From the RealClimate Glossary, “This pattern is believed to describe some of the observed early 20th century (1920s-1930s) high-latitude Northern Hemisphere warming and SOME, BUT NOT ALL (my caps), of the high-latitude warming observed in the late 20th century.”
http://www.realclimate.org/index.php?p=38

In the next post in the series, I’ll visit NCDC Absolute Land Surface Temperature.

A Fresh Look at NCDC Absolute Temperature- Part 1

Please refer to the notes on data source prior to downloading the JunkScience .csv files.
http://bobtisdale.blogspot.com/2008/05/fresk-look-at-ncdc-absolute-data-source.html

OVERVIEW

Like many people, I’ve examined temperature anomaly data and reexamined it until it seems to offer no new climate insight. NCDC Absolute data (Courtesy of JunkScience) is sure to hold correlations that aren’t visible in anomaly data.

http://www.junkscience.com/MSU_Temps/NCDCabs.csv
http://www.junkscience.com/MSU_Temps/NCDCabsLand.csv
http://www.junkscience.com/MSU_Temps/NCDCabsOcean.csv

In Figure 1.1, the first thing that stands out is the difference in the amplitude of the annual variations in land and sea surface temperatures. Next, the year-to-year variations in the minimum land surface temperature (LST) are greater that the total amplitude of sea surface temperature (SST).

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Figure 1.1: NCDC Absolute – Land vs Ocean – January 1900 to March 2007

The combined land and sea data was added to Figure 1.2. http://i29.tinypic.com/iozy2h.jpg
Figure 1.2: NCDC Absolute – Land vs Ocean vs Land + Ocean – January 1900 to March 2007

And in Figure 1.3, SST data for the NINO3.4 region, which occupies a small portion of the global surface area, Figure 1.3b, was added to provide a comparison of its order-of-magnitude to the other indices.
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Figure 1.3: NINO3.4 SST vs NCDC Absolute – Land vs Ocean vs Land + Ocean – January 1900 to March 2007

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Figure 1.3b: NINO3.4 Region

NCDC ABSOLUTE COMBINED LAND AND OCEAN DATA

For the following graphs, I employed EXCEL to extract the Annual Maximums, Minimums, and Averages of the monthly values in the Combined Land and Ocean data for each year, as illustrated in Figure 1.4, eliminating that cluster of spaghetti in the middle.

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Figure 1.4: NCDC Absolute Annual Global Land + Ocean – Maximum, Minimum, Average – 1880 to 2007

The decreasing difference between annual maximum and minimum temperature is illustrated in Figure 1.5. Minimum temperatures rose at a greater rate than maximum temperatures.
http://i27.tinypic.com/4tvcz4.jpg

Figure 1.5: NCDC Absolute Annual Global Land + Ocean – Max Minus Min – 1880 to 2007

The curve of the annual average of the monthly absolute global land plus ocean temperature values, Figure 1.6, is very similar to anomaly data graphs, with minor variations. The most notable of these is, the 2005 temperature value is higher than 1998.

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Figure 1.6: NCDC Absolute Annual Global Land + Ocean – Average – 1880 to 2007

Annual maximum values in the global combined land and ocean temperature are illustrated in Figure 1.7. Compared to the Average curve above, the changes are minimized slightly. It too provides the typical changes in trend: the decrease from 1880 to 1910, the increase from 1910 to 1944, the decrease from 1944 to 1976, and the increase from 1976 to 2007. However, when compared to the linear trend line, the curve appears to be a natural oscillation with a span of 50 to 60 years.

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Figure 1.7: NCDC Absolute Annual Global Land + Ocean – Maximum – 1880 to 2007

The curve of the annual Minimum data, Figure 1.8, is much more linear; that is, the multiple positive and negative trends are much less pronounced, and they may have shifted. In some respects, using the linear trend as reference, it’s relatively straight from 1890 to 2007, with a dip between 1960 and 1980.

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Figure 1.8: NCDC Absolute Annual Global Land + Ocean – Minimum – 1880 to 2007

That dip in Annual Minimum Global Temperature correlates with the 1960 to 1980 drop in TSI associated with Solar Cycle 20. Refer Figure 1.9.

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Figure 1.9: TSI

But how do I illustrate that correlation? Let’s try running linear trend data between the Solar Cycle maximums, in effect to connect them. Refer to Figure 1.10.http://i28.tinypic.com/zluur4.jpg
Figure 1.10: TSI and Maximum-to-Maximum Linear Trends.

Extracting the Maximum-to-Maximum Linear Trend curve and overlaying it on the Annual Minimum Land + Ocean data results in a reasonable correlation, better than I anticipated. Refer to Figure 1.11. To scale the TSI, I had to shift it by 1354.4, but note that it aligns without a multiplier, indicating a reasonably high climate sensitivity. The TSI curve ends in 2002, the last year of the maximum of Solar Cycle 23. http://i28.tinypic.com/2ept3qp.jpg
Figure 1.11: NCDC Absolute Annual Global Land + Ocean – Minimum vs Maximum-to-Maximum TSI - 1880 to 2007

The correlation of the two curves appears to infer that minimum annual global temperature is a function of maximum TSI, or a derivative thereof. Oceanic time lags and inertia could be used to explain the process.

“A Fresh Look at NCDC Absolute Part 2” will be finished soon.

Is There a Cumulative ENSO Forcing? Part 2

WHY A RUNNING TOTAL?

If the intensity and frequency of El Nino and La Nina events were equal, they would balance one another, and a running total would hover near zero.

Example:
Annual Nino3.4 Values = +2, -1.5, +1.0, -1.5
Annual Nino3.4 Running Total = +2, +0.5, +1.5, 0

But the intensity and frequency of positive and negative ENSO events are not equal and preparing the running total of the data created a curve that mimicked global temperature anomaly.

There were 8 El Ninos years, but only 3 La Ninas from 1976 to 1997. And from 1950 to 1975, there were 11 La Ninas and only 7 El Ninos. Reference:

http://www.cpc.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml

Is this one of the reasons why global temperatures fell gradually from the 50s to the late 70s, then rose so quickly afterwards?

MONTHLY NINO3.4 (RAW) DATA

In Figure 1, I ran through the same process of creating running total graphs of the raw NINO3.4 data described in the prior post, but this time using monthly data.

Figure 1

Again, in order to make it comparable to global temperature anomaly, I needed to scale it, this time with a coefficient of 0.007. I also shifted the data by -1.7 deg C to create Figure 2.

Figure 2

Both data were smoothed with a 15-month running average filter, to reduce the noise in the global temperature anomaly data in the following Figure 3.
Figure 3

Note about the absence of year-to-year perturbations in the NINO3.4 running total: Recall that the monthly running total data has been scaled by a factor of less than 1%. What I’ve done is taken a small part of a noisy natural oscillation to simulate the global temperature anomaly curve. The raw monthly NINO3.4 data (Not a running total) versus global temperature anomaly is illustrated in the Figure 4.
Figure 4

ONI and MEI data only date back to 1950. I’ll use the monthly NINO3.4 running total data from 1950 to 2007 as reference. See Figure 5.
Figure 5

OCEANIC NINO INDEX (ONI) DATA

ONI is an ENSO Index maintained by the Climate Prediction Center. It is currently calculated from ERSST.v3 data from SST anomalies in the NINO 3.4 region. Data available here:
http://www.cpc.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml

Figure 6 is a graph of monthly ONI data.
Figure 6

Applying a running total to the monthly ONI data creates a graph (Figure 7) that I originally anticipated when I used the NINO3.4 data from the Trenberth ENSO reconstruction. The negative trend from 1950 to 1976 results from the domination of La Nina episodes. The higher frequency and amplitude of El Nino episodes result in the positive trend from 1976 to 2007.
Figure 7

Now, if the anomaly base of the ONI data is shifted by 0.15139 deg C, the running total again mimics global temperature anomaly, as shown in Figure 8. And again, the scale is wrong.
Figure 8

Figure 9 illustrates the running total of monthly ONI data (scaled by a coefficient of 0.0055 and offset 1.46 deg C) compared to the monthly NINO3.4 data (scaled with a coefficient of 0.007). There is little difference between the curves.
Figure 9

MULTIVARIATE ENSO INDEX (MEI)
The MEI varies from other ENSO indices as it includes additional components, not just SST: sea level pressure, two surface wind components (zonal and meridional), surface air temperature, and cloudiness. These additional variables are said to do “a better job than other indices for the overall monitoring of the ENSO phenomenon, including, for instance, world-wide correlation with surface temperatures and rainfall. It is maintained by the NCDC.”

Discussion:
http://www.cdc.noaa.gov/people/klaus.wolter/MEI/
Data:
http://www.cdc.noaa.gov/people/klaus.wolter/MEI/table.html

Figure 10 illustrates the raw MEI data.

Figure 10

Eyeballing it, it varies slightly from the other ENSO indices, but when compared with ONI and NINO3.4, MEI has a greater linear trend. Refer to Figure 11. The reason the other two indices have different trends has to be the differences in the SST data sets.


Figure 11

Second Order Polynomial Trends (Figure 12) also reveal that the MEI data has a higher positive amplitude during mid years than the ONI and NINO3.4 indices. This changes the curve enough to significantly alter the relationship of a running total.
Figure 12

Due the selected base year for the anomaly calculations, it too creates running trend with an expected two-trend curve, as illustrated in Figure 13.

Figure 13

The additional components of the MEI change the data to the point that it has to be shifted significantly (0.28 Deg C) to recreate a curve that resembles global temperature anomaly, as illustrated in Figure 14. And once more, the scale is wrong.
Figure 14

The magnitude of this shift requires such a small coefficient (0.0025) to make it correlate with the NINO3.4 curve (Figure 15) that the year-to-year ENSO perturbations are severely decreased in magnitude.
Figure 15

WHAT HAS ALL THIS PROVEN SO FAR?

Little, other than ENSO indices can be adjusted so that their running totals mimic global temperature anomaly.

WHAT DOES IT IMPLY?

That ENSO influences global temperature, or that global temperature influences ENSO, or a combination of both. Nothing new there. For now, it’s just a new way of looking at the interrelationship between ENSO and Global Temperature anomaly.

WHAT WAS I IN SEARCH OF WHEN I BEGAN THIS EXERCISE?

I was looking for long-term trends in NINO3.4 data. Then I stumbled on the correlation with global temperature anomaly. What’s wrong with that trend or the others the individual indices wish to divulge without my tinkering? Nothing.

Figure 16 (Same as Figure 1)


Figure 17 (Same as Figure 7)



Figure 18 (Same as Figure 13)

All three indicate that a shift occurred in 1976, which is consistent with current understanding. Following 1976, they indicate a contribution to global temperature. Between 1950 and 1976, all three indicate a deduction, to varying extents. And for the long-term analysis, it indicates an ENSO contribution between 1909 and 1941. Because the authors of the long-term index question the reliability of the data prior to 1909, we’ll exclude it from this summation.

All of that is reasonably consistent with ENSO relationship with the PDO, Figure 19, though the depiction differs.

Figure 19