Update: The last word in the original title was “Forcings”, but ENSO is not a forcing; it’s a process. I’ve replaced Forcings with Variables.
>It Only Takes NINO3.4 SST Anomaly, Sunspot Number, and Volcanic Aerosols Data and A Different Mindset
In this post, only natural variables are used to simulate the Global Temperature Anomaly curve. The correlation is closer than any other attempt of this kind that I’ve seen to date, with or without anthropogenic forcings and with or without General Circulation Models (GCMs). Consider two things: First, most GCMs that government entities employ to make projections about future global climate do NOT model El Nino-Southern Oscillation (ENSO) events, even though ENSO is a dominant natural climate phenomenon. Second, those few GCMs that attempt to model ENSO events do NOT model their processes or the climate responses before, during, or after the events with any accuracy, though they are improving.
In the following, I provide a recipe so that anyone familiar with a spreadsheet and who’s capable of downloading data from the links could reproduce the results with little effort.
It’s also important to note that I am not attempting to disprove the hypothesis of Anthropogenic Global Warming (though I am skeptical of the proejections). However, I am very clearly illustrating that more effort needs to be expended to isolate natural and anthropogenic causes for the warming over the past 130+ years, especially the long-term effects of ENSO. This should be done before attempting to project the anthropogenic causes of warming into the future by 100 years or more. GCM simulations of ENSO must be improved to accomplish that.
In a series of posts titled “Can El Nino Events Explain All of the Global Warming Since 1976?”, I illustrated a number of the processes that take place during and after an El Nino event, and also showed how the response of the East Indian and West Pacific Oceans to El Nino events created step changes in global Sea Surface Temperature (SST). Please review those posts (links follow) if you’re not familiar with them.
Can El Nino Events Explain All of the Global Warming Since 1976? – Part 1
Can El Nino Events Explain All of the Global Warming Since 1976? – Part 2
Supplement To “Can El Nino Events Explain All Of The Warming Since 1976?”
Supplement 2 To “Can El Nino Events Explain All Of The Warming Since 1976?”
Anthony Watts also posted them at Watts Up With That:
The questions, answers, comments and responses at Watts Up With That that remained on-topic provide additional insight.
UNDERLYING STEP CHANGES IN THE GLOBAL TEMPERATURE ANOMALY RECORD
One of the primary points discussed in the “Can El Nino Events Explain All of the Global Warming Since 1976?” series was how El Nino events redistribute warm water from beneath the surface of the Pacific Warm Pool to the surface of the East equatorial Pacific, in the NINO regions. Those warm waters are then driven to the East Indian and West Pacific Oceans by Pacific equatorial currents and by trade winds. In the East Indian and West Pacific Oceans, when not suppressed by volcanic eruptions, those redistributed warm waters cause a noticeable rise (step change) in SST anomalies, which then blend over time with the balance of the global oceans. I used a running total of NINO3.4 SST anomalies in Figure 21 of Can El Nino Events Explain All of the Global Warming Since 1976? – Part 2 to illustrate the phenomenon over the term of the data. As noted in the discussion of Figure 21 of that post, if a coefficient is applied to the NINO3.4 SST anomalies before the running total is calculated, the resulting curve will mimic the global temperature anomalies. Refer to Figure 1, below. This step in reproducing Global Temperature Anomalies creates a curve that simulates the slopes of the rises during the early and late 20th Century, and the slope of the decline in global temperature from the mid-1940s to the late-1970s.
The underlying curve of the Reproduction data was driven by ENSO events alone.
Note 1: The calculation of a running total is explained and illustrated well in Figure A of the following link:
Note 2: The NINO3.4 SST anomalies used in this post are from the Trenberth and Stepaniak NINO3.4 SST anomaly and TNI datasets found here:
http://www.cgd.ucar.edu/cas/papers/jclim2001b/ENflavorsr.htmlhttp://www.cgd.ucar.edu/cas/catalog/climind/TNI_N34/index.html#Sec5Specifically, it’s the NINO3.4 SST anomaly data found by following the links to:
Note 3: The Trenberth and Stepaniak NINO3.4 SST anomaly dataset is a standardized and filtered version of the HADSST NINO 3.4 SST (not anomaly) data here:
At the bottom of the last link is a notation that identifies HADSST as the source. The data runs from January 1871 to December 2007, which is the period of the graphs in this post.
Note 4: As illustrated later in this post, the same effect will occur if the Trenberth and Stepaniak NINO3.4 SST anomaly data is replaced with the HADSST version of NINO3.4 SST anomaly data. The anomalies, though, must be created with the period of 1950 to 1979 as the base years.
Note 5: The coefficient used as a scaling factor in Figure 1 is 0.0052. In the paper “Evolution of El Nino–Southern Oscillation and global atmospheric surface temperatures” (2000), Trenberth et al state on page 4, “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)[Their Figure 2], although, averaged over the year centered on March 1998, the value drops to ~0.17deg C.” Dividing 0.17 deg C by 2.5 deg C leaves a yearly scaling factor of ~0.068 or a monthly scaling factor of ~0.00567 (0.068/12). I used 0.0052.
Note 6: The January 1871 data is also offset by -0.34 deg C to align it with the HADCRUT data.
Note 7: The accuracy of NINO3.4 SST anomaly data decreases before the opening of the Panama Canal in 1914. There was less ship traffic in the NINO3.4 area before that year. This could explain the divergence in the early part of the data.
Note 8: The Global Temperature Anomaly data (the blue curves in the following graphs) is HADCRUT3GL data. It is available through the Hadley Centre website:
This was as far as I originally intended to carry this post. I was going to use it as a supplement to the “Can El Nino Events Explain All of the Global Warming Since 1976?” series, but then the smoothness of the Global Temperature Anomaly Reproduction caught my eye. That curve would make a good basis from which to illustrate the minimal effects of the variations in global temperature due to changes in solar irradiance–those caused by the solar cycles. It could also highlight the short-term impacts of volcanic eruptions.
MINIMAL EFFECTS OF VARIATIONS IN SOLAR IRRADIANCE
Figure 2 is a graph of monthly sunspot numbers that were scaled to illustrate the presently accepted impact on global temperature anomalies of variations in solar irradiance caused by solar cycles. Sunspot data was used as a proxy for Total Solar Irradiance because there are no TSI datasets that provide monthly data available–at least none that I’ve found. The data was shifted by the average sunspot number for the period of January 1871 to December 2007, creating “anomalies” from the average. The scaling factor used is 0.0006.
Note 9: Monthly Sunspot data from January 1749 to October 2008 is available through NASA’s Marshall Space Flight Center.http://solarscience.msfc.nasa.gov/greenwch/spot_num.txt
Adding that scaled Sunspot Number Anomaly data to the Global Temperature Anomaly Reproduction data shows the minimal effects of the solar-cycle-caused variations in solar irradiance on global temperature. Refer to Figure 3.
The differences between Figure 1 and Figure 3 are so small that it’s difficult to see them without a blink comparator or gif animation, Figure 4. Note how, due to the timing of the solar cycles, the La Nina trough at 1975/76 was lowered and El Nino peak in 1997/98 was increased, adding to the rise in global temperature over that period. With that said, it must also be noted that all La Ninas do not occur during solar minimums and all El Ninos do not occur during solar maximums.
THE SHORT TERM EFFECTS OF VOLCANIC ERUPTIONS
The Sato Index of Stratospheric Mean Optical Thickness is available through GISS. In Figure 5, the Sato Index data has been inverted and scaled by a factor of -2.4 to show the impacts on Global Temperature anomalies of explosive volcanic eruptions. The scaling factor was selected to approximate the middle of the range of 0.2 to 0.5 deg C (the range of temperature drops associated with the Mount Pinatubo eruption of 1991).
Adding the scaled and inverted Sato Index data to the Global Temperature Anomaly Reproduction data, the effects of volcanic aerosols are illustrated. Refer to Figures 6.
Figure 7 is a gif animation that highlights the differences between the Global Temperature Anomaly Reproduction data that’s been adjusted for Solar Forcings and the data that’s also been adjusted for volcanic aerosols. Note how global temperature anomalies were depressed by volcanic aerosols from the early-1880s to the mid-1910s and from 1960 to the late-1990s. Excluding the two minor eruptions, the period between the mid-1910s and 1960 was quiet.
EL NINO NOISE
Figure 1 illustrated the slow, smooth variations in global temperature anomalies caused by the underlying small step changes in global temperature anomalies resulting from ENSO events. But ENSO events are also reflected in the global temperature record as positive “spikes” for El Nino events and negative “spikes” for La Ninas. Typical lag periods between the peak of an El Nino and the corresponding peak in global temperature anomaly are approximately 3 to 6 months. To simulate those effects (termed ENSO Noise in this post), the NINO3.4 SST anomalies are shifted 3 months and scaled by a factor of 0.09. Refer to Figure 8.
Adding the ENSO Noise to the Global Temperature Anomaly Reproduction data helps to amplify the peaks and valleys of the smoother data that was created with the running total. Refer to the gif animation, Figure 9. Note how it exaggerates the ENSO events and alters the impacts of the 1982 El Chichon and 1991 Mount Pinatubo volcanic eruptions.
This can be better illustrated in the short-term (January 1978 to December 2007) graph of the HADCRUT Global Temperature Anomaly data and that Global Temperature Anomaly Reproduction data that have been adjusted for Solar, Volcanic Aerosols, and ENSO Noise, Figure 10. There are some minor differences.
ADDITIONAL SHORT-TERM VIEWS
Figure 11 is a comparative graph of the HADCRUT Global Temperature Anomaly data and the Global Temperature Anomaly Reproduction data from January 1940 to December 1979. There are periods when the datasets diverge, but considering the recently found discontinuity in SST data in mid-1940s, the agreement is reasonable.
In Figure 12, the comparison of HADCRUT Global Temperature Anomaly data and the Reproduction of Global Temperature Anomaly data has been shifted forward in time to January 1900 to December 1944. From 1903 to 1944, the Reproduction mimics the HADCRUT Global Temperature anomaly date remarkably well, especially when the accuracy of the data during that period is considered.
The comparison from January 1871 to December 1909 is shown in Figure 13. Note how they diverge in 1903/1904.
In Figure 14, the Global Temperature Anomaly Reproduction data have been shifted up 0.3 deg C. The Reproduction data now mimics the HADCRUT data from 1871 to 1903. It appears there may have been a step change in the NINO3.4 anomaly or the HADCRUT Global Temperature Anomaly datasets around 1903/04.
COMPARISONS TO GISS AND NCDC GLOBAL TEMPERATURE ANOMALY DATA
Figures 15 and 16 compare the Reproduction of Global Temperature Anomalies (Solar plus Volcanic Aerosols plus ENSO Noise) to GISS and NCDC Global Temperature Anomaly data. A different scaling factor (0.0045) was needed for the running totals and the ranges were shifted to accommodate the differences in base years.
USE OF HADSST NINO3.4 SST ANOMALY DATA
In Figure 17, the Trenberth and Stepaniak NINO3.4 SST anomaly data was replaced with raw HADSST NINO3.4 SST anomaly data (Base years = 1950 to 1979). The Trenberth and Stepaniak data has been smoothed and standardized, but the HADSST data has not. Therefore, the scaling factor for the running total was changed (to 0.0073) and the starting point was shifted to account for these differences. This graph was provided to show that the Reproduction of Global Temperature Anomalies was not solely a function of the Trenberth and Stepaniak NINO3.4 SST anomaly data or the smoothing and standardizing they used.
Note: This was originally a two-part post. I discovered an error in the spreadsheet that was used to create the graphs for the second part. When corrected, the additional adjustment did little to enhance the reproduction so I’ve deleted Part 2 and the supplement to it.
There will be those who will note that I provided no scientific basis for the use of a running total to simulate the long-term effects of ENSO. I have, however, in this post and the posts I’ve linked, illustrated the processes and their effects and explained why the running total reflects the underlying step changes caused by ENSO.
There will be those who will say that the reproducing the Global Temperature Anomaly curve with a running total of the NINO3.4 data is to be expected. I disagree. If that were so, why are there no studies noting the effect?