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Friday, January 28, 2011

Removing The Effects of Natural Variables - Multiple Linear Regression-Based or “Eyeballed” Scaling Factors

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This is the second of a series of follow-up posts to Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases?. The first follow-up was Notes On Polar Amplification.

This post discusses the impacts on global temperatures of a natural mode of Sea Surface Temperature variability: the El Niño-Southern Oscillation (ENSO). If this subject is new to you, refer to the post An Introduction To ENSO, AMO, and PDO – Part 1.

INTRODUCTION
My post Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases? was cross posted at Watts Up With That? under the same title (Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases?) In a step-by-step process, that post illustrated how I was removing the linear effects of El Niño and La Niña events and of volcanic eruptions from the global temperature record during the satellite era. I then went on to explain and provide links to more detailed explanations of the secondary effects of the ENSO process and how they impact the multidecadal trend.

There were a few comments on the WUWT thread about my use of “eyeballed” scaling factors. They wondered what I meant by “eyeballed”, expressed concern about a scaling factor that was not based on statistics, and suggested using EXCEL to determine the scaling factors.

“EYEBALLED” SCALING FACTORS
The scaling and lag I used in comparisons of the global temperatures and the ENSO and Volcano proxies were established by the visual appearance of the two variables, using the larger(est) event (the 1997/98 El Nino, and the 1991 Mount Pinatubo eruption) as reference. “Eyeballed” simply referred to the visual comparison of the impacted variable (global temperature) and the impacting variables (ENSO and Volcanic eruptions). Refer to Figure 1, which is also Figure 1 from Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases?. Note how the NINO3.4 data has been scaled (multiplied by a factor of 0.16) and lagged (moved back in time 3 months) so that the rises of the two datasets are about the same during the evolution of the 1997/98 El Niño. Notice also how the response of the global temperature data to the lesser ENSO events after 2000 doesn’t always align with the NINO3.4 SST anomalies.

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Figure 1

The data indicates that the larger events (such as the 1997/98 El Nino) are strong enough to overcome the noise that can mask the global response to lesser events. In other words, I used the larger 1997/98 ENSO event as reference because the response to it was clearest. Statistical methods such as linear (or multiple linear) regression rely on the relationship between two (or more) datasets over the term of the data. Any additional noise in the data during the smaller ENSO events (and during the lesser volcanic eruptions) may bias the results.

If we could determine the cause or causes of that additional noise, then adding those variables to a multiple linear regression analysis would be helpful.

MULTIPLE LINEAR REGRESSION

Analyse-it for Excel is a statistical add-on package for EXCEL. (It has a 30-day free trial period). One of its features is multiple linear regression. Their Multiple linear regression webpage provides a general description of this feature: “Linear regression, or Multiple Linear regression when more than one predictor is used, determines the linear relationship between a response (Y/dependent) variable and one or more predictor (X/independent) variables. The least-squares method is used to minimize the vertical distance between the response and the fitted linear line.”

The response variable discussed in this post is global temperature (represented by GISS LOTI from 60S-60N) and the predictor variables are ENSO (represented by NINO3.4 SST anomalies) and volcanic eruptions (represented by GISS Stratospheric Aerosol Optical Thickness data: ASCII data). Figure 2 shows the differences in the variability of those three datasets.
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Figure 2

According to the EXCEL multiple regression software, the scaling factor to best fit the wiggles of the ENSO data to those in the global temperature data is 0.07262, and for the Aerosol Optical Thickness data it’s -3.191.

Note: The scaling factors determined by the regression software in this post are based on the “raw” data. I’ve used a 13-month running-average filter in the graphs after the fact to reduce the visual effects of seasonal variations and other noise.

In Figure 3, I’ve illustrated the multiple linear regression-estimated relationships between the GISS LOTI data, the NINO3.4 SST anomalies (ENSO), and the GISS Stratospheric Optical Thickness (Volcano) data. The scaling for the Volcano proxy data appears too large, while the scaling for the ENSO proxy appears too small. The global temperature anomaly (60S-60N) response to the 1997/98 El Niño almost doubled the rise in the scaled NINO3.4 SST anomalies.
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Figure 3

Figure 4 illustrates the result when we subtract scaled ENSO and Volcano proxy data from the Global Temperature data. That dataset is supposed to represent global temperature data that has been adjusted for ENSO and volcanic eruptions. I’ve also included the scaled NINO3.4 SST anomalies to show that much of the ENSO signal remains in the data. Note also how the response in global temperature lags the ENSO proxy.
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Figure 4

Multiple linear regression does not appear to do a good job of removing the impacts of ENSO.

THREE-MONTH LAG

Referring back to Figure 1, we can see that the global temperature response during the 1997/98 El Niño lagged the NINO3.4 SST anomalies by 3 months. Let’s also look at the Volcano proxy. Figure 5 shows it and the ENSO-adjusted GISS Land-Ocean Temperature Index (LOTI) data from Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases?. (Figure 5 was Figure 2 in that post.) In order to align the leading edge of the global temperature response to the 1991 eruption of Mount Pinatubo (the larger and clearer response), I had to lag the Volcano proxy 3 months.
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Figure 5

If we shift the NINO3.4 SST anomalies and Aerosol Optical Thickness data three months, the EXCEL Analyse-It software, of course, calculates different scaling factors: 0.08739 for the ENSO proxy and -3.495 for the Volcano proxy. The ENSO- and Volcano-adjusted data using these updated scaling factors (based on a 3-month lag) is shown in Figure 6. Again, much of the ENSO signal remains.
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Figure 6

Once again, multiple linear regression appears to have done a poor job of removing the effects of ENSO.

MODEL-PREPARED SCALING FACTORS

In a 2009 paper, Thompson et al created models of global temperature responses to ENSO and Volcanic eruptions. The paper was “Identifying Signatures of Natural Climate Variability in Time Series of Global-Mean Surface Temperature: Methodology and Insights”. Link : http://www.atmos.colostate.edu/ao/ThompsonPapers/ThompsonWallaceJonesKennedy_JClimate2009.pdf

On page 2 they provided an overview of their methods: “The impacts of ENSO and volcanic eruptions on global-mean temperature are estimated using a simple thermodynamic model of the global atmospheric-oceanic mixed layer response to anomalous heating. In the case of ENSO, the heating is assumed to be proportional to the sea surface temperature anomalies over the eastern Pacific; in the case of volcanic eruptions, the heating is assumed to be proportional to the stratospheric aerosol loading.”
The same method was used in its companion paper Fyfe et al (2010), “Comparing Variability and Trends in Observed and Modelled Global-Mean SurfaceTemperature.”
http://www.atmos.colostate.edu/ao/ThompsonPapers/FyfeGillettThompson_GRL2010.pdf

Thompson et al provided a link to their “Global Mean”, “ENSO fit”, and “Volcano fit” data. Link to Data. Figure 7 shows the three Thompson et al datasets. Note: Thompson et al examined the data from 1900 to March 2009. Since we’re only looking at the change in temperature during the satellite era (dictated by the second, more recent of the two SST datasets used by GISS) I had to shift their global mean data down 0.25 degrees C to align it with the other datasets.
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Figure 7

And Figure 8 shows what Thompson et al refer to as the “ENSO/Volcano Residual Global Mean” temperature anomalies after the ENSO and Volcano proxy signals have been removed. And, again, I’ve included the “ENSO fit” data to show how poorly it approximated the “Signatures of Natural Climate Variability in Time Series of Global-Mean Surface Temperature”. The response of their adjusted Global Mean Temperature to the 1997/98 El Niño is greater than their “ESNO fit” data, and this is after the effects of ENSO have supposedly been removed.
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Figure 8

In summary, linear regression and models prepared for climate studies appear to do a poor job of removing the linear effects of ENSO. If the secondary effects of ENSO were also included in the multiple linear regression, would the results be better?

These secondary effects are easy to see if we look at…

THE RESULTS USING THE EYEBALLED METHOD

As noted earlier, for the “eyeballed” method, I keyed the scaling of the ENSO and Volcano proxies visually off the leading edges of the 1997/98 El Niño and the 1991 eruption of Mount Pinatubo. Refer to Figure 9. In this example, I’ve reduced the scaling on the Volcano proxy data, so that its impact is approximately 0.2 deg C for the Mount Pinatubo eruption.
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Figure 9

The result when the ENSO and volcano signals are removed is shown in Figure 10. Also shown is the scaled ENSO proxy as a reference. The rises that occur after the 1986/87/88 and the 1997/98 El Niño events make it appear as though there is another lagged ENSO-related signal.
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Figure 10

And to see this signal, we invert the scaled NINO3.4 SST anomalies. Refer to Figure 11. That is, we multiply the NINO3.4 SST anomalies by a negative number (-0.1 scaling factor). The inverted ENSO data has not been lagged. But it has been shifted down 0.05 deg C to align it with the 1987/88 upward shift in the ENSO- and Volcano-adjusted global data. The two datasets diverge slightly at times between 1989 and 1996, but the adjusted global temperatures follow the inverted NINO3.4 SST anomalies reasonably well. Then there is a significant divergence during the evolution of the 1997/98 El Niño. The adjusted global temperature anomalies do not drop at that time proportionately to the El Niño. (And there is no reason global temperatures should drop a large amount. The majority of the warm water that fuels an El Niño comes from below the surface of the Pacific Warm Pool.)
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Figure 11

If we shift the NINO3.4 SST anomalies up 0.21 deg C, Figure 12, we can see that the adjusted global temperatures rise in response to the transition from El Niño to La Niña in 1998 and then, once again, they follow the general variations in the inverted NINO3.4 SST anomalies, but running in and out of synch with it.
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Figure 12

In other words, the vast majority of the rise in ENSO- and Volcano-Adjusted GISS Land-Ocean Temperature Index data could be explained by one or more detrended Sea Surface Temperature dataset(s) that mimicked inverted NINO3.4 SST anomalies with upward shifts, similar to what’s illustrated in Figure 13.
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Figure 13

I qualified the above statement with “detrended”. At a few alarmist blogs, I received a few negative comments about my Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases? post because I had used the trends in the SST subsets to explain the trend in global temperature. I had explained in the post why the upward trends were associated with the process of ENSO, but I received the complaints regardless. Those bloggers, of course, failed to read the explanation (It starts under the heading of “La Niña Events Are Not The Opposite Of El Niño Events”) or had elected to misrepresent my post. But in order to overcome this objection in this post, I’ll use detrended SST anomalies for the following illustrations. The same and other bloggers also complained about the minimal sizes of the Kuroshio-Oyashio Extension and South Pacific Convergence Zone (SPCZ) Extension SST subsets I used in the “Can Most” post. Those areas were used because they had the strongest warming signals during a La Niña. But since the objections exist, I’ll use SST datasets that represent larger portions of the global oceans.

There are two detrended sea surface temperature subsets covering significant portions of the global oceans that have the same upward changes in temperature during those two El Niño to La Niña transitions shown in Figure 13. But if we had relied on the scaling factors suggested by the multiple linear regression, or if we had used a model similar to the one created by Thompson et al, would we have noticed the relationship?

EAST INDIAN-WEST PACIFIC SST ANOMALIES
The first of the SST subsets is the East Indian-West Pacific Ocean. The coordinates of this dataset are 60S-60N, 80E-180E. I’ve highlighted those coordinates in the following map. Figure 14 is a correlation map of annual (January to December) East Indian-West Pacific SST anomalies and annual GISS LOTI data for 1982 to 2010. Much of the Northern Hemisphere land surface temperature anomalies vary with the East Indian-West Pacific SST anomalies. That is, when the East Indian-West Pacific SST anomalies rise in those upward ENSO-induced steps, so do those areas highly correlated with it.
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Figure 14

I have discussed the East Indian-West Pacific dataset in many posts over the past two years, so I do not intend to repeat the discussion here. Those posts started with Can El Niño Events Explain All of the Global Warming Since 1976? – Part 1 and Can El Niño Events Explain All of the Global Warming Since 1976? – Part 2. The most detailed explanation of why the East Indian-West Pacific SST anomalies shift upwards as a response to those two ENSO events is provided in my series of posts:
More Detail On The Multiyear Aftereffects Of ENSO – Part 1 – El Nino Events Warm The Oceans
And:More Detail On The Multiyear Aftereffects Of ENSO - Part 2 – La Nina Events Recharge The Heat Released By El Nino Events AND...During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents.
And:
More Detail On The Multiyear Aftereffects Of ENSO - Part 3 – East Indian & West Pacific Oceans Can Warm In Response To Both El Nino & La Nina Events

And for those who like visual aids, refer to the two videos included in La Niña Is Not The Opposite Of El Niño – The Videos.

Again, to overcome one of the complaints, I need to detrend the East Indian-West Pacific data. Detrending is said to eliminate the “global warming signal”. So I employed the same method used for the Atlantic Multidecadal Oscillation data. HADISST is the long-term Sea Surface Temperature anomaly dataset used by GISS in their LOTI product. The long-term HADISST (1870-2010) East Indian-West Pacific SST anomalies had a linear trend of 0.44 deg C per Century, and that’s slightly higher than the global SST anomaly trend of 0.39 deg C per century. To detrend it, I subtracted the linear trend values for each month from the East Indian-West Pacific SST data.

And as shown in Figure 15, the detrended East Indian-West Pacific SST anomalies could easily explain much of the rise in the ENSO- and Volcano-Adjusted GISS LOTI data since 1982. The similarities between the adjusted global temperature data and the East Indian-West Pacific SST anomaly data are remarkable.
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Figure 15

As discussed and illustrated in the linked posts, the upward shifts in the East Indian-West Pacific SST anomalies are secondary effects of the warm water that was leftover from the 1986/87/88 and 1997/98 El Niño events and the results of the La Niña process itself. Basically, the ENSO processes cause the SST for this part of globe to rise in response to both El Niño and La Niña events. And the effects are cumulative if a La Niña follows an El Niño. The cumulative effect can be seen in the following animation. It shows a series of maps of 12-month average global SST anomalies, and it runs from the start of the 1997/98 El Niño to the end of 1998/99/00/01 La Niña. To its right is a graph of scaled NINO3.4 SST anomalies and the SST anomalies of the East Indian-West Pacific Oceans. The data in the graph have been smoothed with a 12-month running-average filter to match the maps.
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Animation 1

And for reference, Animation 2 includes the Sea Surface Temperature Anomalies of the rest of the oceans (East Pacific, Atlantic, and West Indian), and this dataset includes the North Atlantic with its Atlantic Multidecadal Oscillation.
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Animation 2

The next variable is widely known, but it is often overlooked.

THE ATLANTIC MULTIDECADAL OSCILLATION (AMO)
The second dataset that matches the upward steps in the adjusted GISS LOTI data is the Atlantic Multidecadal Oscillation or AMO data. For those new to the AMO, refer to the post An Introduction To ENSO, AMO, and PDO -- Part 2.

The AMO data used here is detrended North Atlantic SST anomaly data. Again, as noted in the Wikipedia Atlantic Multidecadal Oscillation webpage, “detrending is intended to remove the influence of greenhouse gas-induced global warming from the analysis.” The data is available through the NOAA Earth System Research Laboratory (ESRL) Atlantic Multidecadal Oscillation webpage (the AMO unsmooth, long: Standard PSD Format data).

Figure 16 is a correlation map of annual (January to December) Atlantic Multidecadal Oscillation (AMO) data and annual GISS LOTI data for 1982 to 2010. Like the East Indian-West Pacific SST anomalies, much of the northern hemisphere varies in temperature with the AMO. Again, this means that much of the northern hemisphere surface temperatures rise with the upward steps in the AMO data.
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Figure 16

And for those interested, Figure 17 is a GISS LOTI trend map created at the GISS Global Mapswebpage. It illustrates the rise in surface temperature anomalies from 1982 to 2010. Note the similarities between it and the correlation maps in Figures 14 and 16. For the most part, the regions where the AMO and the East Indian-West Pacific SST anomalies correlate with the Global GISS LOTI data are also where most of the rises in surface temperature occurred. There are a few areas with differences, but the maps agree quite well.
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Figure 17

The AMO data is compared to the adjusted GISS LOTI data in Figure 18. Again, note the agreement between the AMO data and the adjusted GISS LOTI data.
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Figure 18

And if we compare the ENSO- and Volcano-adjusted GISS LOTI data to both detrended SST-based datasets, Figure 19, we can see that it wouldn’t require a lot of effort to explain most of the global warming from 1982 to 2010 using the AMO and detrended East Indian-West Pacific SST anomaly datasets.
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Figure 19

Let’s add the AMO and the detrended East Indian-West Pacific SST anomalies to the multiple regression analysis and see how that impacts the results.

MULTIPLE LINEAR REGRESSION WITH ENSO, VOLCANO, AMO, AND EAST INDIAN-WEST PACIFIC SST DATA

The NINO3.4 SST anomalies, the mean optical thickness data, the AMO data, and the detrended East Indian-West Pacific (EIWP) SST anomalies were all entered into the EXCEL multiple linear regression software as predictors with the GISS LOTI data as the response variable. EXCEL determined the scaling factors listed in parentheses in Figure 20. The GISS LOTI data illustrated has been adjusted by those four scaled variables. I’ve also included the scaled NINO3.4 SST anomalies as reference. The low frequency variations in the adjusted GISS data mimic the ENSO proxy before the 1997/98 El Niño. They show little relationship from 1998 to 2007, and then they appear to mimic one another again.
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Figure 20

Figure 21 compares the unadjusted GISS LOTI data (60S-60N) to the data that has been adjusted by the four factors. The trend of the adjusted data is approximately 27% of the unadjusted GISS LOTI data. In other words, approximately 73% of the rise in global (60S-60N) surface temperature could be natural.
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Figure 21

And for the sake of discussion, I had EXCEL perform the regression analysis again, but I used “raw” East Indian-West Pacific SST anomalies instead of the detrended data. As one would expect, the multiple regression software created different scaling factors, which are listed in Figure 22. It compares the resulting adjusted global (60S-60N) temperature anomalies to the unadjusted GISS LOTI data. In this instance, the trend of the adjusted data is approximately 18% of the unadjusted data, which is similar to the result I reached using the “eyeball” method and different natural factors in Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases?.
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Figure 22

Would the eyeball method with the AMO and East Indian-West Pacific adjustments reduce the global temperature trend by similar amounts? We’ll have to look at that in another post. This one is long enough.

THANKS TO…
…Michael D Smith for suggesting EXCEL to weed out lags, scaling factors, etc. Without his suggestion, I would not have looked for the Analyse-it for Excel. I think I’m going to buy it when the trial period is done.

SOURCES
The GISS LOTI, Reynolds OI.v2 SST (for the NINO3.4 SST anomalies), and HADISST (for the detrended East Indian-West Pacific SST anomalies) data used in this post are available through the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

The Stratospheric Aerosol Optical Thickness data is available from GISS:
http://data.giss.nasa.gov/modelforce/strataer/tau_line.txt

And the Atlantic Multidecadal Oscillation data is available from NOAA ESRL:
http://www.esrl.noaa.gov/psd/data/correlation/amon.us.long.data

Tuesday, January 25, 2011

Mid-January 2011 SST Anomaly Update

I’ve moved to WordPress.  This post can now be found at Mid-January 2011 SST Anomaly Update
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This mid-month update only includes the shorter-term NINO3.4 and global SST anomaly graphs; that is, the ones from January 2004 to present. Both the NINO3.4 and Global SST anomalies have dropped.

As noted in the November 2010 SST Anomaly Update, the global SST anomalies do not appear as though they will drop to the level they had reached during the 2007/08 La Niña, even if one were to account for the differences in NINO3.4 SST anomalies. This of course will be raised by alarmists as additional proof of anthropogenic global warming.

But the reason the global SST anomalies have warmed in that time is due primarily to the fact that the East Indian and West Pacific Oceans (about 25% of the surface area of the global oceans) can warm in response to both El Niño and La Niña events. Refer to Can El Niño Events Explain All of the Global Warming Since 1976? – Part 1 and Can El Niño Events Explain All of the Global Warming Since 1976? – Part 2, and the video included in La Niña Is Not The Opposite Of El Niño – The Videos.

Keep in mind, the warm water released from below the surface of the Pacific Warm Pool doesn’t simply vanish at the end of the El Niño.

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NINO3.4
NINO3.4 SST anomalies for the week centered on January 12, 2011 show that central equatorial Pacific SST anomalies have dropped in the past two weeks. They cycled back down to near their earlier low for this La Niña season. They’re at approximately -1.8 deg C.


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NINO3.4 SST Anomalies - Short-Term

GLOBAL
Weekly Global SST anomalies have dropped to a new seasonal low, but they are far from the low reached during the 2007/08 La Niña. They are presently at +0.04 deg C.
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Global SST Anomalies - Short-Term

SOURCE
OI.v2 SST anomaly data is available through the NOAA NOMADS system:
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh?lite

Saturday, January 15, 2011

Notes On Polar Amplification

I’ve moved to WordPress.  This post can now be found at Notes On Polar Amplification
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This post shows, based on GISS LOTI data, there is nothing unusual about the Polar Amplification taking place during the current warming period and also shows Polar Amplification exaggerates the cooling during periods when global temperatures decline.

This post is a follow-up to my post Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases?

BACKGROUND
I used GISS Land-Ocean Temperature Index (LOTI) data for the latitudes of 60S-60N in my post Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases? Even though I explained why I had excluded the polar data, the fact that I had deleted it was not well received by some around the blogosphere. Why did I exclude the polar data?

First, GISS Deletes Arctic And Southern Ocean Sea Surface Temperature Data. GISS then extends land surface data out over the Arctic and Southern Oceans. Since land surface temperatures warm more than sea surface temperatures during warming periods (and cool more during cooling periods), replacing sea surface temperature data with land-based data as GISS does creates a bias during seasons of sea ice melt.

Second: As I wrote in that post, Keep in mind that the Arctic is amplifying the effects of the rise in temperature at lower latitudes. This is the basis of the concept of polar amplification. If the vast majority of the change in temperature at the lower latitudes is natural, the same would hold true for the Arctic.

My simple explanation of Polar Amplification received complaints as well, but it does not differ significantly from other definitions.

For example, the Wikipedia definition of Polar amplification : “Polar amplification is defined by International Arctic Science Committee on page 23 of the Arctic Climate Impact Assessment ‘Polar amplification (greater temperature increases in the Arctic compared to the earth as a whole) is a result of the collective effect of these feedbacks and other processes.’ It does not apply to the Antarctic, because the Southern Ocean acts as a heat sink. It is common to see it stated that ‘Climate models generally predict amplified warming in polar regions’, e.g. Doran et al. However, climate models predict amplified warming for the Arctic but only modest warming for Antarctica.”

So let’s take a look at the GISS data and see if my description of Polar Amplification is confirmed or contradicted by it.

THE GISS LOTI DATA INDICATES POLAR AMPLIFICATION EXAGGERATES TEMPERATURE INCREASES AND DECREASES
Figure 1 shows Annual (January to December) GISS LOTI data (1880 to 2010) that has been detrended. (Source: Global-mean monthly, seasonal, and annual means webpage.) Figure 1 serves as a reference for the multidecadal periods of warming and cooling used in this post. Based on the detrended annual GISS LOTI data, the recent warming period began when global temperature anomalies rose from their minimum in 1976. The cooling era runs from its maximum in 1944 to the 1976 minimum. And the early 20th Century warming period starts at the minimum in 1917 and ends in 1944. (Later, I explain why I used detrended data in Figure 1.)

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Figure 1

The GISS website allows users to create two different types of maps at their Global Maps webpage:(1) global temperature anomalies, and (2) “trends,” which are the changes in surface temperature over user-defined periods (based on local linear trends).

GISS also creates “Zone Mean” data in their Surface Temperature Analysis output page. If you were to scroll down to the plot below the GISS map, you’d find a graph with “Latitude” as the x-axis and “Zonal Mean” as the y-axis. When “trend” maps are created using the GISS map-making feature, GISS calculates the average surface temperature change for every 2-degree latitude band from pole to pole, where the temperature change data is based on the local linear trends for the period selected. The data for those graphs is also available toward the bottom of the page.

Figure 2 shows the average Change in Annual Surface Temperature Anomalies per latitude band for the periods of 1944 to 1976 (the 20th Century cooling period) and 1976 to 2010 (the current warming period), according to the GISS data. Arctic temperatures dropped more than the rest of the hemisphere or globe during the cooling period. The reverse holds true during the current warming period. Figure 2 illustrates that, since the early 1940s, Polar Amplification exaggerates the multidecadal change in global temperature when temperatures rise and when they cool.
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Figure 2

THE DATA FOR THE EARLIER WARMING PERIOD REVEALS THE CURRENT ARCTIC AMPIFICATION IS NOT UNUSUAL
Referring to Figure 1 again, Global Temperatures warmed from 1917 to 2010. In Figure 3, I’ve added the Zonal Mean data for the period of 1917 to 1944 to the graph. The change in Arctic surface temperature anomalies during the early warming period is comparable to the current warming period. One could conclude that the Polar Amplification during the current period is not unusual. It’s a natural response to warming.
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Figure 3

ON THE USE OF DETRENDED GISS LOTI DATA IN FIGURE 1
Figure 4 shows the GISS LOTI data without the detrending. I used the detrended GISS LOTI data because it was difficult to determine when the early warming period started in the “raw” data, Figure 4. Was it 1907 or 1917? But based on the detrended data, the minimum occurred in 1917.
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Figure 4

If we were to use 1907 as the start year for the early 20th Century warming period, it would make a significant difference in the Arctic warming. The GISS Zonal Mean data with that start year indicates the Arctic warmed much more during the earlier warming period than the current warming period. Refer to Figure 5. (Wouldn’t want someone to accuse me of cherry-picking the start year, would I? So I used the 1917 start date).
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Figure 5

CLOSING NOTES
This post illustrated that Polar Amplification not only causes the Arctic to exaggerate a rise in global surface temperature, it also causes the Arctic to amplify a decrease in global surface temperature. It additionally showed the current warming of the Arctic is not unusual.

Someone might try to argue the Arctic data is very sparse during the early warming period, and the lack of data prevents a true comparison of the two warming periods. Realistically, one needs to consider the fact that most of the Arctic data presented by GISS in all periods is make-believe data. During seasons with sea ice, GISS uses 1200km radius smoothing to infill the vast majority of the Arctic data. That part of the GISS 1200km radius smoothing process for the Arctic is logical.

BUT (big but) sea ice melts every year, some years more than others. During seasons of sea ice melt, the fact that GISS deletes the SST data makes a significant difference, because SST varies less and has a lower trend than land surface data. This biases the Arctic (and Southern Ocean) data. GISS also deletes the SST data so they can extend the land data over open waters; that is, if they were to include the sea surface temperature data when it’s available, GISS could not extend the land data over the open ocean to reach the pole.

The Arctic amplifies the lower latitude trends when the globe is warming and when it is cooling. The Arctic should amplify any multidecadal warming or cooling signal regardless of source. If a major portion of the current warming period is due to natural variability, as suggested in the post Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases?, then I believe my statement in that post was correct. And that statement was, If the vast majority of the change in temperature at the lower latitudes is natural, the same would hold true for the Arctic.

Wednesday, January 12, 2011

hidethedecline.eu Hides A Post

I’ve moved to WordPress.  This post can now be found at hidethedecline.eu Hides A Post
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UPDATE (January 21, 2011): On the HideTheDecline post Where should we expect UHI in temperature data 1979-2009?, I have added a request of Frank for his future posts:

Title: Hopefully My Final Reply To Frank

Frank, with respect to your ongoing efforts along this line research, please do you and me a favor.

Please do not refer to or link my posts, please do not refer to me by name, and please do not link to or use my graphs in your posts. If you adhere to my request, I will have no need to return to your website and find error with what you've written and presented.

XXXXXXXXXXXXXXXXX

UPDATE (January 16, 2011): Frank Lansner has again hidden the second of his posts in which he attempts to use TLT anomalies to identify Urban Heat Island (UHI) effect. The link…
http://hidethedecline.eu/pages/posts/discussion-of-the-article-uah-reveals-urban-heat-210.php
…in my January 13, 2011 update (below) brings you to a page with “Sorry, no active content to display”.

It appears that Frank Lansner of HideTheDecline has now merged the follow-up post with the original UAH reveals UrbAn Heat post. And the comment thread from the second post, which held my criticisms of the posts and his replies, has now been turned into a linked Word file…
http://hidethedecline.eu/media/UAHUHI/bobsdiscussion.doc
…with fonts so small the comments are difficult to read without changing their size or your screen zoom. The returns and spacing between paragraphs have disappeared so that they are hard to follow.

Frank has now written a third post in the series called Where should we expect UHI in temperature data 1979-2009? He continues to subscribe to the error-filled line of thought that the difference between TLT and surface temperatures can be used to identify areas of UHI effect, Figure 2. He attempts to confirm this by using population growth maps, Figure 6. But he misses the point once again. UHI exists in areas that he has identified as “No UHI”, but there is limited to no UHI effect in other areas that he identifies as “UHI”.

My final comment to Frank (refer to above linked Word file) was titled "End Of Discussion - Goodbye Frank".

The comment reads:

Frank replied, "So basically, your result says that the UAH-Grounbased difference is from oceans..."

Wrong. Read what I wrote again. I wrote that the trend in TLT anomalies over the North Atlantic was higher than the SST trend. I did not say “the UAH-Grounbased difference is from oceans.” You misrepresent what I write repeatedly. Every chance you get, you redirect the discussion, stating that I have said one thing, when, in fact, I have said the opposite and presented data to confirm it.

What about my comment about inland Africa, Frank? There, I showed you that the GISS trend was considerably higher than the TLT trend:
http://i43.tinypic.com/if1oh5.png

I used that as an example that UHI effect is not the only reason for the difference between land surface data and TLT. There’s not a lot of UHI in the Sahara desert, Frank. At WUWT I provided you with a link to the post that graph came from. Here it is again:
http://bobtisdale.blogspot.com/2009/06/part-2-of-comparison-of-gistemp-and-uah.html

In that post, there are many graphs of many areas of the globe that disagree with your initial premise that UHI is responsible for the difference between TLT anomalies and surface temperature anomalies.

In my latest reply to you at my blog, I wrote, All you are doing is rehashing discussions we've already had on the thread at WUWT. I've already presented to you in multiple ways why what you had written there was wrong. My rewriting it another way apparently doesn't help because you become defensive and you argue.

And what are you doing in your latest reply on 14th January, 2011 at 14:44:54?

You being defensive and you are arguing.

And in my last comment at my blog, I suggested, Reread what I discussed with you on that thread. Attempt to understand what people who disagreed with you were saying to you. Don't become defensive. Don't become argumentative. Steven Mosher suggested ways for you redo all of your graphs. Have you tried it to see if it presents different results?

Have you tried that? Apparently not.

In your latest reply here, you wrote, “You get the opposite result compared to me, so it’s a little early to conclude anything much for now.”

Any of your readers, those who are interested in determining whether you or I are right, can reproduce the graphs I had presented to you, Frank. They can see for themselves I have presented reality and that your original post was flawed.

Since you, Frank, are still arguing and still misrepresenting what I have written, I cannot continue this discussion.

Your readers will understand why I have chosen to end this conversation. You might try to spin it, but your readers will understand.

Goodbye, Frank.
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UPDATE January 13, 2011: The post at HideTheDecline has reappeared.
http://hidethedecline.eu/pages/posts/discussion-of-the-article-uah-reveals-urban-heat-210.php

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Bloggers have comments deleted all the time. Most don’t like it because of the time and effort that goes into writing it. I just had something happen that’s a little bit different. Not only has my comment disappeared, so has the entire post. The title of the blog post was “List of claimed ‘errors’ mostly received from Bob Tisdale in the comments on WUWT”, and for some reason it caught my eye. It ran at the blog HideTheDecline.

Someone at HideTheDecline must have been displeased with me.

I didn’t mind having my name in the title of the post. I didn’t mind having my arguments misrepresented. But I did not like having my comment on that HideTheDecline thread deleted.

BACKGROUND
I’ve prepared a number of posts that compare TLT and surface temperature data, and if I find a blog post or comment that contradict what I’ve found, I leave a reply, explaining where I disagree. I disagreed with much of the UAH and UHI post by Frank Lansner that was cross-posted at WUWT. Apparently Frank was not satisfied with the lack of progress of his arguments at WUWT UAH and UHI, so on December 26, 2010, he prepared a post with my name in the title. Some sort of payback? Who knows?

I discovered Frank’s post at hidethedecline on January 8, 2011, and replied in a comment that was name- and date-stamped with “By Unknown on 8th January, 2011 at 01:35:38”. My comment remained on that hidethedecline thread for a couple of days without reply from Frank. When I went back to check for a reply today, January 12, 2011, I discovered not only was my comment gone, but so was the entire post. It vanished. No explanation. Someone had deleted that entire HideTheDecline post.

THE POST REMAINS IN GOOGLE CACHE
The google cache version of Frank’s post (without comments) is here: Cached. And here’s a screen cap of the cache page:


http://i54.tinypic.com/15z2y6h.jpg

MY COMMENT
The title of my comment was “If You Had Performed a Reasonable Analysis...” and it read:

If YOU had performed a reasonable analysis, Frank, you would have discovered that the vast majority of the difference between TLT and surface data exists in only one area of the globe. All areas would be impacted by UHI if it had a significant effect, but they are not. And that fact alone contradicts your claims that UHI is the primary reason for the difference between surface and TLT data.

If you had researched it a little more, you would have discovered that much of the difference between TLT and surface data occurs over the North Atlantic. One wouldn't think that there is a lot of UHI effect on the North Atlantic, Frank.
http://bobtisdale.blogspot.com/2010/10/on-differences-between-surface-and-tlt.html

If you had performed a reasonable analysis, Frank, you would have discovered that GISS land surface data for much of the United States has a lower trend than the TLT data:
http://i40.tinypic.com/nget8k.png

If you had performed a reasonable analysis, Frank, you would have discovered that there is little difference between GISS land surface data and TLT data for much of Europe:
http://i40.tinypic.com/k6ija.png

If UHI was such a dominant factor, why are they so similar, Frank?

Everyone knows the major difference between GISS and TLT data is how GISS treats the Arctic data.
http://bobtisdale.blogspot.com/2010/05/giss-deletes-arctic-and-southern-ocean.html

THE COMMENT STILL RESIDES IN GOOGLE
Since the Google cache of the post does not contain any comments, someone might think I’ve fabricated it. So I Googled the opening of my comment in quotes “If YOU had performed a reasonable analysis, Frank, you would have discovered” and Google returned with the link shown in the following screen cap.
http://i51.tinypic.com/2vdqlbo.jpg
I clicked on the link, but my comment did not exist.

I JUST SEEMS A LITTLE ODD
It’s odd that a blogger would take the time to write a post, then delete the entire post because he didn’t like a comment on the thread, but still leave the original erroneous post UAH reveals UrbAn Heat.

I’m not sure what to make of it.

Please don’t delete another of my comments, Frank Lansner.

Tuesday, January 11, 2011

December 2010 SST Anomaly Update

I’ve moved to WordPress.  This post can now be found at December 2010 SST Anomaly Update
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MONTHLY SST ANOMALY MAPThe map of Global OI.v2 SST anomalies for December 2010 downloaded from the NOMADS website is shown below. There was a mix of variations in ocean basin SST anomalies this month. Some rose, some fell. The result was a very minor rise in global SST anomalies (+0.002 deg C). They are presently at +0.099 dg C.
http://i53.tinypic.com/2r6yufb.jpg
December 2010 SST Anomalies Map (Global SST Anomaly = +0.099 deg C)

MONTHLY OVERVIEW

Monthly NINO3.4 SST anomalies continue to vary at or near the seasonal low for this La Niña. The Monthly NINO3.4 SST Anomaly is -1.53 deg C.

The drop in Northern Hemisphere this month (-0. 019 deg C) was countered by an equal increase in the Southern Hemisphere, so globally, as noted before, rose slightly (+0.002 deg C).
http://i54.tinypic.com/23mv9sp.jpg
Global
Monthly Change = +0.002 deg C
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http://i51.tinypic.com/2ivn3n8.jpg
NINO3.4 SST Anomaly
Monthly Change = -0.070 deg C

EAST INDIAN-WEST PACIFIC

The SST anomalies in the East Indian and West Pacific cycled back up this month.

I’ve added this dataset in an attempt to draw attention to what appears to be the upward steps in response to significant El Niño events that are followed by La Niña events.
http://i55.tinypic.com/2udz7yh.jpg
East Indian-West Pacific (60S-65N, 80E-180)
Monthly Change = +0.060 deg C

Further information on the upward “step changes” that result from strong El Niño events, refer to my posts from a year ago Can El Niño Events Explain All of the Global Warming Since 1976? – Part 1 and Can El Niño Events Explain All of the Global Warming Since 1976? – Part 2

And for the discussions of the processes that cause the rise, refer to More Detail On The Multiyear Aftereffects Of ENSO - Part 2 – La Niña Events Recharge The Heat Released By El Niño Events AND...During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents -AND- More Detail On The Multiyear Aftereffects Of ENSO - Part 3 – East Indian & West Pacific Oceans Can Warm In Response To Both El Niño & La Niña Events

The animations included in post La Niña Is Not The Opposite Of El Niño – The Videos further help explain the reasons why East Indian and West Pacific SST anomalies can rise in response to both El Niño and La Niña events.

NOTE ABOUT THE DATA
The MONTHLY graphs illustrate raw monthly OI.v2 SST anomaly data from December 1981 to December 2010.

MONTHLY INDIVIDUAL OCEAN AND HEMISPHERIC SST UPDATES http://i52.tinypic.com/2ymfwok.jpg
Northern Hemisphere
Monthly Change = -0.019 deg C
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http://i54.tinypic.com/f3xddu.jpg
Southern Hemisphere
Monthly Change = +0.019 deg C
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http://i55.tinypic.com/199vlc.jpg
North Atlantic (0 to 75N, 78W to 10E)
Monthly Change = -0.123 deg C
#####
http://i56.tinypic.com/sbkhw8.jpg
South Atlantic (0 to 60S, 70W to 20E)
Monthly Change = +0.057 deg C

Note: I discussed the upward shift in the South Atlantic SST anomalies in the post The 2009/10 Warming Of The South Atlantic. Will the South Atlantic return to the level it was at before that surge or will it remain at a new plateau?

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http://i52.tinypic.com/wvv706.jpg
North Pacific (0 to 65N, 100 to 270E, where 270E=90W)
Monthly Change = +0.106 Deg C
#####
http://i53.tinypic.com/nxq06p.jpg
South Pacific (0 to 60S, 145 to 290E, where 290E=70W)
Monthly Change = +0.067 deg C
#####
http://i56.tinypic.com/55hyzt.jpg
Indian Ocean (30N to 60S, 20 to 145E)
Monthly Change = -0.025 deg C
#####
http://i54.tinypic.com/rrlbom.jpg
Arctic Ocean (65 to 90N)
Monthly Change = -0.139 deg C
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http://i56.tinypic.com/amwg8k.jpg
Southern Ocean (60 to 90S)
Monthly Change = -0.080 deg C

WEEKLY SST ANOMALIES
The weekly NINO3.4 SST anomaly data portray OI.v2 data centered on Wednesdays. The latest weekly NINO3.4 SST anomalies are -1.48 deg C.
http://i55.tinypic.com/6r1xxu.jpg
Weekly NINO3.4 (5S-5N, 170W-120W)

The weekly global SST anomalies are at +0.082 deg C.
http://i53.tinypic.com/2lwu3is.jpg
Weekly Global

A NOTE ABOUT THE YEAR-TO-YEAR CHANGES
The following is a repeat of a discussion from earlier updates.

As noted in the November 2010 SST Anomaly Update, the global SST anomalies do not appear as though they will drop to the level they had reached during the 2007/08 La Niña, even if one were to account for the differences in NINO3.4 SST anomalies. This of course will be raised by alarmists as additional proof of anthropogenic global warming.

But the reason the global SST anomalies have warmed in that time is due primarily to the fact that the East Indian and West Pacific Oceans (about 25% of the surface area of the global oceans) can warm in response to both El Niño and La Niña events. Refer to Can El Niño Events Explain All of the Global Warming Since 1976? – Part 1 and Can El Niño Events Explain All of the Global Warming Since 1976? – Part 2, and the video included in La Niña Is Not The Opposite Of El Niño – The Videos. In addition, the North Atlantic also remains at elevated levels during La Niña events in response to the ENSO-related warming of the Kuroshio-Oyashio Extension. This was discussed and illustrated in the recent post The ENSO-Related Variations In Kuroshio-Oyashio Extension (KOE) SST Anomalies And Their Impact On Northern Hemisphere Temperatures.

Keep in mind, the warm water released from below the surface of the Pacific Warm Pool doesn’t simply vanish at the end of the El Niño.

SOURCE
The Optimally Interpolated Sea Surface Temperature Data (OISST) are available through the NOAA National Operational Model Archive & Distribution System (NOMADS).
http://nomad1.ncep.noaa.gov/cgi-bin/pdisp_sst.sh
or
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh

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Comment Policy, SST Posts, and Notes

Comments that are political in nature or that have nothing to do with the post will be deleted.
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The Smith and Reynolds SST Posts DOES NOT LIST ALL SST POSTS. I stopped using ERSST.v2 data for SST when NOAA deleted it from NOMADS early in 2009.

Please use the search feature in the upper left-hand corner of the page for posts on specific subjects.
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NOTE: I’ve discovered that some of the links to older posts provide blank pages. While it’s possible to access that post by scrolling through the history, that’s time consuming. There’s a quick fix for the problem, so if you run into an absent post, please advise me. Thanks.
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If you use the graphs, please cite or link to the address of the blog post or this website.