On The Differences Between Surface and Satellite Datasets

Guest post by Bob Tisdale

OVERVIEW

The differences between surface temperature and lower troposphere temperature (TLT) anomalies are examined in this post. The globe is broken down into sectors in an effort to locate the area with the greatest difference between surface temperature and TLT anomalies. The responses of Sea Surface Temperature (SST) and Sea TLT to El Niño and La Niña events are also examined to see if they may cause part of the divergence between the surface temperature and TLT data after the 1997/98 El Nino. The post also presents animated maps of surface temperatures and TLT anomalies and animated maps of the correlations of NINO3.4 SST anomalies and global temperatures to further highlight the differences.

INTRODUCTION

The differences between the infilled surface-based (GISS and NCDC) temperature anomaly datasets and the Lower Troposphere Temperature (RSS and UAH) anomaly datasets create a multitude of comments from both sides of the global climate change debate whenever the datasets are plotted together.

While there may be biases created by surface station location, infilling method, Urban Heat Island (UHI) adjustments (or lack thereof), etc., let’s put the land-based discussions aside for this post and consider the possibility that Sea Surface Temperature (SST) might respond differently than the lower troposphere temperatures to El Niño and La Niña events, and that those differences in response might account for some of the divergence.

Satellite-based TLT data has been available since 1979, providing more than 3 decades of data for comparisons to surface data. Unfortunately, the surface temperatures and TLT between 1982 and 1995 are overwhelmed by the dips and rebounds caused by the volcanic eruptions of El Chichon and Mount Pinatubo. Accounting for these in the global data is relatively easy, but we’ll be dividing the globe into sectors in this post and having to account for the volcanic eruptions in each would add unnecessary complexity, so we’ll only look at the period from 1995 to present. Additionally, GISS Deletes Arctic And Southern Ocean Sea Surface Temperature Data in areas with seasonal sea ice and extends adjoining land surface temperature data (with its higher trend) out over the oceans. GISS does this to infill temperature data in the ocean areas with sea ice. The NCDC product does not take this step. To eliminate that difference, the data north of 60N and south of 60S have been excluded from this post.

So basically we’ll be examining the global land and sea surface temperature and lower troposphere temperature data minus the poles from 1995 to present. Figure 1 illustrates the average of the surface temperature datasets (GISTEMP and NCDC) and the average of the TLT datasets (RSS and UAH). We’ll use the averages initially to simplify the illustrations (and to eliminate discussions of any differences between the individual datasets). The TLT anomalies in Figure 1 have been scaled so that the rise in response to the 1997/98 El Niño is approximately the same as the surface data. (And for those wondering why the data ends in May 2010, I’ve used the KMNI Climate Explorer as the source of data for this post, and the latest RSS TLT anomaly data update there is May 2010.) Note also that the title block states that the surface data has been lagged 2 months. In other words, the surface data responded 2 months earlier to the 1997/98 El Niño than the TLT anomalies, so I’ve shifted the surface data back in time by two months. The linear trend of the surface data is more than twice that of the scaled TLT data. The surface data has a linear trend of approximately 1.1 deg C per Century but the trend of the scaled TLT data is only 0.4 deg C per Century. And much of this appears be caused by the failure of the surface data to respond as fully as the TLT data to the 1998/99/00/01 La Niña.

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Figure 1

NOTE: The TLT scaling factors and surface data lags vary per subset in the following. The scaling factors and lags are noted in the title blocks.

If we subtract the scaled TLT anomalies from the surface data, Figure 2, we can see that there appears to be an upward shift in the surface data in 1998 that is not present in the TLT data. And after 1998, the surface data rises at a rate that is faster than the TLT anomalies.

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Figure 2

LET’S DIVIDE THE GLOBE INTO THREE SECTORS

I’ve divided the globe (minus the poles) into three sectors for the comparisons that follow. The areas are split at 180, 78W, and 80E longitude. To simplify the discussion, I’ve identified them as “Far West”, “Near East and Near West,” and “Far East”, based on their proximity to zero longitude. Refer to Figure 3. The “Far West” data includes the Eastern Pacific and most of North America. The “Near East and Near West” includes the Atlantic and Western Indian Oceans and South America, Africa, Europe, and Western Asia. And the “Far East” data includes the East Indian and West Pacific Oceans and Eastern Asia and Australia.

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Figure 3

FAR WEST COMPARISON

A time-series graph of the surface and TLT anomalies for the Far West sector is shown in Figure 4. These datasets capture the Eastern Pacific and most of North America. The surface temperatures have been lagged 5 months to align the leading edges of the responses to the 1997/98 El Niño. Note also that the TLT anomalies have not been scaled, which means the variations in TLT anomalies for the East Pacific and most of North America are comparable to the changes in surface temperature anomalies. Also note that this is the only one of these three sectors where the TLT anomalies are rising faster than the surface temperatures, though the difference in the trends is relatively small. And of course, the linear trends themselves are very small, with the TLT anomalies having a linear trend of less than 0.3 deg C per Century. This means that this sector of the globe does not contribute to the disparity between surface and TLT anomalies; in fact, this area would suppress the difference.

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Figure 4

FAR EAST COMPARISON

The Far East data, Figure 5, represents the East Indian and West Pacific Oceans, along with Eastern Asia and Australia. For this part of the globe, the surface data needed to be advanced one month to align the rises of the responses to the 1997/98 El Niño. The surface data does not drop as far as the TLT data during the 1998/99/00/01 La Niña and remains elevated during the 2002/03 El Niño. The two datasets diverge again during the 2007/08 La Niña. These divergences cause the surface data to have a higher trend than the TLT anomalies. The difference in the linear trends, though, is only approximately 0.2 deg per Century. This accounts for little of the disparity in trends shown in Figure 1.

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Figure 5

And that means much of the divergence in the two global datasets comes from the Near East-Near West sector, so we’ll concentrate on that sector for the rest of the post.

COMPARISON OF NEAR EAST AND NEAR WEST

For the Near East and Near West portion of the globe, Figure 6, the linear trend for the surface temperature data is approximately 1.9 deg C per Century, while the trend for the scaled TLT data is approximately 0.47 deg C per Century. The surface data has very little drop during the 1998/99/00/01 La Niña.

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Figure 6

This is confirmed in Figure 7, which illustrates the difference between the scaled TLT and surface data for the Near East and Near West. Note the significant upward step in the latter half of 1998.

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Figure 7

Animation 1 provides maps of the surface temperature and lower troposphere temperature anomalies of the Near East and Near West datasets used in this post. The surface datasets are on the left and the TLT datasets are on the right. The base years for anomalies (1979 to 2009) are the same for all. The maps provide 12-month average temperatures from July to June for the first five years being examined in this post: the ENSO-Neutral year 1996/1997, the El Niño year 1997/1998, and the three La Niña years of 1998/1999, 1999/2000, and 2000/2001. The period of July to June was used since ENSO events typically peak in November, December and January. The animation is provided to show the similarities and differences in the responses to El Niño and La Niña events between the surface-based and TLT datasets.

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Animation 1

Figure 8 is the July 1998 to June 1999 cell from the animation. I’ve isolated it because much of the difference between the surface and TLT anomalies (Figure 7) occurs during this period. While there are some differences between land surface temperature and land TLT anomalies during the year from July 1998 to June 1999 in Figure 8, most of the additional warming appears to take place in the sea surface temperature data:

– In the Western North Atlantic,

– In the tropical Eastern South Atlantic in the Benguela and South Equatorial Currents, and

– In the Antarctic Circumpolar Current (ACC) south of South Africa and the western Indian Ocean.

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Figure 8

NEAR EAST-NEAR WEST SEA SURFACE AND SEA TLT COMPARISON

So far we’ve compared the average of GISTEMP LOTI and NCDC combined surface data to the average of RSS and UAH TLT anomalies. Unfortunately, the RSS TLT data through the KNMI Climate Explorer is not divided into land and sea TLT subsets. Luckily, the UAH data is, so we’ll have to use it alone in the following comparison.

Figure 9 illustrates the Near East-Near West sea surface and scaled sea TLT data (UAH only) anomalies. The scaled sea TLT linear trend is only 0.27 deg C per Century, while the sea surface temperature (SST) trend is approximately 4 times higher at 1.1 deg C per Century. Much of the divergence occurs during the 1998/99/00/01 La Niña. Then, from 2001 to present, the SST anomalies have climbed higher while the sea TLT anomalies remained relatively flat.

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Figure 9

Consider now that the Near East-Near West sea surface area represents approximately 63% of the total surface area for this part of the globe. The divergence between sea surface and sea TLT anomalies for this part of the globe, therefore, represents a significant portion of the overall global difference.

A DISCUSSION OF ENSO EVENTS

During an El Niño, warm water from the surface and from up to 300 meters below the surface of the Pacific Warm Pool sloshes to the east and spreads across the surface of the eastern tropical Pacific. This raises SST there. The rise in SST in the eastern Tropical Pacific causes more moisture than normal to be pumped into the atmosphere through evaporation, which is how the ocean releases most of its heat to the atmosphere. The warm and moist air rises in a process known as convection, and the air cools as it rises. The cooler air can’t hold as much water, so the moisture condenses and it rains, releasing the heat into the atmosphere. (During a La Niña, the waters in the eastern tropical Pacific are cooler than normal, and less heat than normal is released to the atmosphere.)

The warmed air is transported to the east and poleward by atmospheric circulation. This can be seen in the animations of TLT anomalies in the two right-hand cells of Animation 2.

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Animation 2

Note: Each of the cells in this animation represents a 12-month average of the temperature anomalies. Again, the surface data are on the left and the TLT data are on the right. The first cell represents the period of Jun 1996 to May 1997. It is followed by the next 12-month period, July 1996 to June 1997, and so on, in sequence. The use of 12-month averages minimizes weather and seasonal noise. I’ve included the eastern Pacific (the Far West sector) in this animation to show the timing of the El Niño in the eastern tropical Pacific and the transition to La Niña. Last, the animation lasts for 36 frames, from the period of July 1996 – June 1997 to the period of June 1999 – May 2000. This captures the evolution and decay of the 1997/98 El Niño plus the first few months of the 1998/99/00/01 La Niña.

It is very apparent in the two left-hand cells of Animation 2 that sea surface temperatures in the tropical North Atlantic rise in response to the El Niño. For a detailed discussion of how this occurs, refer to Wang (2005), “ENSO, Atlantic Climate Variability, And The Walker And Hadley Circulation.” Wang (2005) link:

http://www.aoml.noaa.gov/phod/docs/Wang_Hadley_Camera.pdf

Wang (2005) states, “The Walker and Hadley circulations can serve as a ‘tropospheric bridge’ for transferring the Pacific El Niño SST anomalies to the Atlantic sector and inducing the TNA SST anomalies just at the time of year when the warm pool is developing.”

The warm pool being discussed in Wang (2005) is the Western Hemisphere Warm Pool, which is made up of eastern North Pacific west of Central America, the Gulf of Mexico, the Caribbean and the western tropical North Atlantic. The acronym TNA stands for Tropical North Atlantic.

Wang (2005) continues, “The Hadley weakening results in less subsidence over the subtropical North Atlantic, an associated breakdown of the anticyclone, and a weakening of the NE trades in the TNA. The wind weakening leads to less evaporative surface cooling and entrainment of colder water from below the shallow mixed layer, resulting in positive SST anomalies.”

In other words, an El Niño causes changes in atmospheric circulation which causes a weakening of the trade winds in the tropical North Atlantic. In turn, the weaker trade winds cause less evaporation, resulting in warmer SST. The weakening of the trade winds also causes less cool water from below the surface to be drawn to the surface.

Wang (2005) concludes that paragraph on page 27 with, “The opposite is presumed to occur during boreal winters with unusually cool SSTs in the equatorial Pacific,” meaning that it is assumed the opposite takes place during a La Niña.

Animation 2 shows the differences and similarities between the patterns of surface and TLT anomalies during the evolution and decay of an El Niño. The surface temperature anomalies are responding to changes in atmospheric circulation, while the TLT anomalies are responding to the heat released from the oceans.

For a further discussion of how these patterns are created, refer to Trenberth et al (2002) “Evolution of El Nino–Southern Oscillation and global atmospheric surface temperatures”:

http://www.cgd.ucar.edu/cas/papers/2000JD000298.pdf

Trenberth et al (2002) use a time sequence of correlation maps, their Figure 8, to illustrate the evolution of ENSO events. They write in paragraph 29, “To explore where the heat in the oceans and its effects on the atmosphere actually occur, we have computed correlation and regression maps for several fields at various lags, a subset of which are given here.” And they continue, “This sequence reveals the mean evolution of ENSO and is quite coherent and traceable from about -12- to +12-months lag with N3.4, but becomes quite weak at larger lags. The panels presented in Figure 8 and the [delta] 4-month sequence are chosen to illustrate the evolution in the fewest panels.”

CORRELATION MAP ANIMATIONS

Animating correlation maps of NINO3.4 SST anomalies with surface and TLT anomalies does help to illustrate the evolution and decay in global temperatures in response to ENSO. The areas where there are differences between the surface and TLT datasets also become apparent. And although we are discussing the Near East and Near West sector, we’ll start with an animation of global maps. Keep in mind that the RSS does not present data north of 82.5N or south of 70S, where GISS deletes SST data in these areas and extends land surface data out over the oceans.

Animation 3 shows maps of the correlation of December NINO3.4 SST anomalies with GISS LOTI global surface temperature anomalies in the left-hand cell and RSS global TLT anomalies on the right. GISS data was included because they use satellite-based Reynolds OI.v2 SST data during this period. December NINO3.4 SST anomalies were used because El Niño events normally peak in November-December-January. The animation starts with zero-month lag and continues monthly through a 12-month lag. Like the other animations, there are differences and similarities between the surface and TLT datasets. What caught my eye was the significant positive correlation in the North Atlantic during lags 10 through 12, where they did not appear in the TLT data.

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Animation 3

Since the animation can’t be stopped, I’ve included the frames with the 10-, 11-, and 12-month lags as Figures 10 through 12.

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Figure 10

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Figure 11

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Figure 12

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I decided to take the animation of the correlation maps one step further. I’ve presented the Atlantic Ocean SST data only in Animation 4, using Reynolds OI.v2 SST data. The correlation maps also use 3-month averages of NINO3.4 and Atlantic SST data. I found the animation quite interesting. During the early lag months, warm waters created by the El Niño appear to migrate from the tropical South Atlantic to the tropical North Atlantic. The warm waters in the tropical North Atlantic then appear to follow ocean currents west into the Caribbean and Gulf of Mexico, before entering the Gulf Stream. Starting around the 9-month lag, warm waters migrate south from the southern tip of Greenland and the Davis Strait. Is that from glacial ice melt in Greenland and Arctic sea ice melt, with the melt caused by the El Niño? They’re correlated with NINO3.4 SST anomalies.

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Animation 4

That would be a good lead-in animation for a new post on the AMO.

CLOSING

A significant portion of the difference between global surface temperature and TLT anomalies occurs in the North Atlantic Ocean. And the additional warming that takes place appears to be caused by ocean processes correlated with ENSO. Why don’t the TLT anomalies respond to these additional ocean processes?

SOURCE

The data and maps presented in this post are available through the KNMI Climate Explorer:

http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

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74 thoughts on “On The Differences Between Surface and Satellite Datasets

  1. “A significant portion of the difference between global surface temperature and TLT anomalies occurs in the North Atlantic Ocean. And the additional warming that takes place appears to be caused by ocean processes correlated with ENSO. Why don’t the TLT anomalies respond to these additional ocean processes?”
    Well maybe there is also a top down solar induced component to consider as well ?
    The net outcome at any one time being the result of the interplay between the bottom up oceanic forcing and the top down solar forcing.

  2. I am not sure about correctness of scaling down the TLT record to 1997/98 El Nino peak, and then comparing subsequent trends. Maybe cutting off all El Nino phases above certain ENSO index limits and comparing the rest? You did it once before.
    But it is true that TLT responds to El Nino much more than surface record, and thus comparing (especially short period) trends might yield weird results.
    I compared once CRU/HadSST record with UAH and obtained similar results:
    http://i48.tinypic.com/2w55c2s.jpg
    Europe, Africa, Central Asia and tropical oceans ran significantly warmer than TLT. Maybe it is because of using SST instead of MAT, which were told to be diverging also in the last paper from Ross McKittrick.

  3. Not to get off-topic so early, but did Hal Lewis just resign and call AGW a “scam?”
    REPLY: Yes, and it always helps to scan the front page of WUWT, we covered this Friday – Anthony

  4. You say that the divergence between TLT and SST appears to be correlated with a failure of the SST to properly respond to the 99/00/01 La Nina. Has it occurred to you that an alternate, and perhaps better, explanation is that the SST data over-responded to the 1998 “super El Nino”?
    Don’t forget that you have an artificial vertical translation in the data, apparently chosen so that the first year or so correlates well. This is the common practice: align unmatched time series in common units for comparison so that their initial points correspond so that the “drift” is obvious. But what if one begins the time series at an anomalous point? By all accounts, 1998 was anomalous. The match looks pretty good through 1998, so the “initial point” in your procedure really is the 3 year period 1995-1998 in your Figure 1, before the subsequent drift.
    What if you selected the vertical offset in that graph to minimize the squared or total absolute difference between the graphs? I contend that you would see an extremely good fit on 2000-2009. The mismatch appears to be caused by a regime shift in one or both data sets at about 1999-2000. You attribute this to the overriding climatic system during that period. I think it is more likely due to something happening in one of the data sets on one side or the other of that shift. I would place my money on the guilty set being SST and further on the data prior to 1999 being the culprit.
    It would be interesting to see an analysis designed to discern whether something anomalous was occurring in the actual empirically collected data (and whether this was an artifact of collection or an actual reflection of climate behavior) or whether it was introduced by “analysis”.

  5. Bob
    The global temperatures to 1880 are flawed enough, but we simply mustn’t place any scientific credence whatsoever on the merits of sea surface temperatures going back to the same date that are purported to be accurate to fractions of a degree (top graph).
    They were carried out in a very ad hoc and random fashion over a minsicule part of the ocean and we (scientists and Governments) simply shouldn’t be drawing any conclusions from them at all.
    Someone will also have to convince me that the Satellite temperature record has the accuracy claimed for it if the woeful example of the sea level one is any guide.
    tonyb

  6. tonyb: You wrote, “The global temperatures to 1880 are flawed enough, but we simply mustn’t place any scientific credence whatsoever on the merits of sea surface temperatures going back to the same date that are purported to be accurate to fractions of a degree (top graph).”
    The data in my post start in 1995.

  7. tonyb says:
    October 10, 2010 at 12:11 pm
    “They were carried out in a very ad hoc and random fashion over a minsicule part of the ocean and we (scientists and Governments) simply shouldn’t be drawing any conclusions from them at all.”
    Shipping routes, particularly international, followed ‘trade’ winds in the days of sail so temp measurements then tended to be taken within them. Since the advent of motorised shipping, both routes and traffic have increased but the early routes are still covered by virtue of shipping density. This link from the International Maritime Organisation carries some info that may be of relevence to any attempted analysis over a long time period;
    http://www.imo.org/safety/mainframe.asp?topic_id=770

  8. Juraj V. says: “I am not sure about correctness of scaling down the TLT record to 1997/98 El Nino peak, and then comparing subsequent trends.”
    All I’m doing is showing that the surface data and TLT data responded differently to the 1997/98 El Nino. Christy has stated that global TLT variability is greater than surface temperature variability and has provided a scaling factor for the global data for the term of the data. Each of the parts of the globe used in this post responded differently so I’ve set the scaling factor accordingly.

  9. Bob
    Between writing and pressing ‘send’ the words ‘your top graph’ disappeared in relation to ‘global temperatures since 1880’. I was referring only to the idea of historic data-as shown in that top graph- being good enough to take seriously. I wasn’t referring to any other part of your post
    tonyb

  10. R. Craigen says: “Don’t forget that you have an artificial vertical translation in the data, apparently chosen so that the first year or so correlates well. This is the common practice: align unmatched time series in common units for comparison so that their initial points correspond so that the “drift” is obvious. But what if one begins the time series at an anomalous point?”
    The intent of the post was illustrate that the aftereffects of the TLT and surface data to the 1997/98 El Nino were different and to determine where the differences occurred.

  11. So Bob, much of the global surface temp anomaly seems to be generated by periodically reduced wind speed in the tropical Atlantic Ocean. And this is a response to the El Nino- La Nina cycle. Do I have that right? The differences in temperature between the lower troposphere and the surface in areas outside the near-east and near-west appear to be relatively minor.
    I would have thought that global warming due to greenhouse gases would actually act in a global manner, not just over several mostest sized regions in one sector. Is there any chance that there is a linkage between the increased atmospheric CO2 concentration and what we see in the above graphs and animations? I note that the explanations given by Bob Tisdale do not refer to any “greenhouse gases”. So would the 1998 and subsequent warming in these regions have occurred in the absence of elevated greenhouse gas concentrations?

  12. The error propagation for the non-satellite measurements is handled in a fashion that is wildly at variance with the use of the instruments involved if you’re looking for the engineering quantity GMST. It’s fine for an ad hoc ballpark-type estimate, but then you shouldn’t be performing comparisons with a competent dataset with true error measurements.
    What I mean is:
    Given a well-maintained, zero UHI surface station, you still have very little certainty that this site is in any way representative of your gridcell. And, regardless, the error for this measurement is not the 0.1C listed on the instrument. The listed error is for the actual measurement of the temperature immediately surrounding the thermometer. By switching to the anomaly method, we’re able to ‘not care’ that the instrument reads an average 2C higher than the actual gridcell temperature. But that doesn’t make the instrumental error and the ‘gridcell anomlay measurement error’ identical.
    Shorter version:
    Ground measurements (as performed) are effectively proxies, not measurements.

  13. “That would be a good lead-in animation for a new post on the AMO.”
    Indeed. Let me point out that 1995 is considered by Bill Gray et al as the start of the current multi-decadal active Atlantic tropical storm period. Thank you.
    I don’t know of a tropical storm related event to couple to “upward shift in the surface data in 1998.”
    Now let me go back and read the whole post more closely. 🙂

  14. Rob R says: “So would the 1998 and subsequent warming in these regions have occurred in the absence of elevated greenhouse gas concentrations?”
    There is no evidence that greenhouse gases have any measureable effect on tropical Pacific OHC, which is what fuels El Nino events. The 1997/98 El Nino was fueled by the warm waters created during the 1995/96 La Nina:
    http://i36.tinypic.com/eqwdvl.png
    Prior to the 1995/96 La Nina, tropical Pacific OHC had been declining since the late 1970s. Graph is from this post:
    http://bobtisdale.blogspot.com/2009/09/enso-dominates-nodc-ocean-heat-content.html
    And this post showed that some of the warming of the North Atlantic after the 1997/98 El Nino could be attributed to the transfer of warm waters created during the El Nino from the South Atlantic to the North Atlantic and the migration of that warm water northwards into the Gulf Stream. There also appears to be warm waters created by excessive sea ice and glacial melt in the northern North Atlantic that is caused by and correlates with the El Nino. The remainder of the additional warming in the North Atlantic could then be attributed to the Atlantic Multidecadal Oscillation.

  15. Magisterial as always, thanks Dr Tisdale!
    I am intrigued by the plots of surface temperature minus TLT (figs 2 and 7). They show quite a smooth curve with less fluctuation than either component separately. The global surface minus TLT curve in fig. 2 shows an apparent downturn in the last 3 years or so, from about 2007, more weakly evident in fig. 7 also (near east and west).
    One can conjecture that this surface minus TLT curve might represent an underlying curve of climatic variation with an atmospheric “lag” stripped from it. The idea is that when the oceans are losing heat as part of a climatic cycle prior to a temperature downturn, this heat will reside for a while in the troposphere, as an inertial heat lag. Thus stripping TLT from surface temperatures gives the underlying picture.
    Following from this there is another reason why the surface minus TLT curves are interesting, particularly the suggestive downward inflection in 2007. It relates to a possible link between solar – planetary orbit coupling and the PDO-AMO cycles.
    A paper by Sidorenkov, Wilson and Khlystov 2010 presents data on solar inertial movement due to gravitational interaction with the Jovian planets (Jupiter, Saturn, Neptune) and evidence that these control length of day (LOD) of the earth. Furthermore, the oscillation of solar inertia caused by relative planetary orbits shows a striking correlation with the PDO.
    http://meetingorganizer.copernicus.org/EPSC2010/EPSC2010-21.pdf
    Here is an extract:
    Ian Wilson et al. (2008) presented evidence that claimed that changes in the Sun’s equatorial rotation rate are synchronized with changes in the Sun’s orbital motion about the barycentre of the Solar System. This paper showed that the recent maximum asymmetries in the Solar motion about the barycentre have occurred in the years 1865, 1900, 1934, 1970 and 2007. These years closely match the points of inflection in the Earth’s LOD.
    What jumps out from this is that the years 1865, 1900, 1934, 1970 and 2007 correspond reasonably well to the global temperature inflections of the PDO-AMO oceanic cycle. The latest inflection- 2007 – is the one that one could tenuously read into figures 2 and 7 (surface temp minus TLT).
    BTW why indeed would the Atlantic be so different to the rest? Perhaps as a relatively new ocean (the “new kid on the block”) it is a bit of a backwater to global circulation patterns established before there was an Atlantic – the North is more isolated than the south. (Notwithstanding the fact that the Norwegian Sea is a globally significant site of downwelling for the THC.)

  16. Bob,
    there was a La Nina in 2008 (obvious in Fig1). This doesn’t produce the same sudden shift in anomoly difference graph as the 1998-2001 La Nina (Fig2). Any thoughts why the processes you describe that produce the difference in measured surface and TLT temperature are not at work in 2008? Do you think once the global temp peak from the most recent El Nino that the surface and TLT lines will come together again and the anomaly will be back to zero? It looks like that’s where the data is heading.

  17. phlogiston says: “BTW why indeed would the Atlantic be so different to the rest?”
    The South Atlantic is the only ocean that transports heat toward the equator, and to the North Atlantic. All of the others transport heat poleward.

  18. It has been surmised that the troposphere is supposed to warm at a slightly faster rate than the surface. There is a tropical troposphere hotspot in the climate models that makes the troposphere warm at a little faster rate than the surface.
    But I’m not sure this is really what the theory is based on – maybe the climate models predict it – but theoritically the +3.7 watts/m2 increase in energy levels in the troposphere is supposed to translate into +3.0C of warming in the troposphere (including feedbacks) and then the surface is supposed to warm by the same +3.0C (the lapse rate is supposed to remain more-or-less constant).
    So there is a contradiction – the climate models predict more warming in the troposphere but the theory is not really based on that – it is based on a similar warming between the troposphere and the surface.
    Just another one of those little glossed-over and purposely ignored issues in this very unusual scientific field which has lots of them.
    I’d like to see the analysis without the scaling. Then we can talk about how the troposphere as measured by the satellites is not warming as fast as the surface temperature record. Then we can talk about UHI and the probably-unjustified-surface-temperature-record-adjustments (especially back to 1880) and then the ENSO’s impact on the Atlantic (which is the best part of Bob’s great analysis above – graphically the link is clear and I haven’t seen it shown so clearly before).
    Thanks Bob.

  19. HR says: “Any thoughts why the processes you describe that produce the difference in measured surface and TLT temperature are not at work in 2008?”
    The 2007/08 La Nina was strong enough to cause a rise in the East Indian and West Pacific Oceans, which should be the cause of the divergence at that time in Figure 5.
    But the 2006/07 El Nino was rather weak.
    http://i55.tinypic.com/iwn96d.jpg

  20. Bill Illis says: “the ENSO’s impact on the Atlantic (which is the best part…”
    Agreed.
    The original draft of this post was so long I had to break it down into two posts and those included more than 20 graphs and animations each. I concluded that (1) no one was going to read a post that long, and (2) I really wanted to illustrate the effect in the Atlantic correlation maps so I deleted everything that didn’t lead to it.

  21. There is more microstructure to be found in the 1998 global hot year. If one takes the data as reliable, then many questions can be deduced and few have answers. For example, as a lag exercise, subtract 1997 monthly temperatures from 1998 monthly temperatures to see if there is a pattern. There usually is, in the examples I have worked, but the difference graph is often cyclic with a 3 month period. What does that mean? Dunno. Possibly data adjustment.
    Example from BOM land temperatures, West Australia:
    http://i260.photobucket.com/albums/ii14/sherro_2008/MeekaJ.jpg
    Here is the same exercise for latitude bands globally. UAH ocean data:
    http://i260.photobucket.com/albums/ii14/sherro_2008/uahocean.jpg
    More quarterly cycles.
    The main fundamental question I’d like to ask is: Is the heat reflected by temperatures in 1998 internal and conserved (say, within the oceans) or is it external from a source outside the earth?
    It is hard to make a case for CO2 as relevant. There are many sites where there is no evidence of a 1998 hot year.

  22. Having seem far too many asymmetric “corrections” being made to data recently, my initial reaction is to ask whether these increasing differences are also shown in the raw data.
    It seems adjustments all too often result by regarding any downward trend as an anomaly that needs to be corrected whereas upwards trends are readily accepted as being normal. The more the temperatures fail to rise the more they get massaged.
    While short-term lags and differences between SST and TLT may be a real effect it does not seem probably that SSTs will be constantly jacking up and an ever widening difference between the two records can develop. Unfortunately this study is limited to less than one solar cycle so it seems difficult to call anything a “trend” when the primary forcing is going through a cyclic change.
    Noting that regions >60 N/S are excluded here due to concerns over the treatment of that data and presumably issues with the scarcity of real data sites, isn’t the first step in any such treatment to asses whether the data is of a quality that merits such an effort.
    In view of recent revelations about apparently spurious and non reproducible “adjustments” in NZ land data amongst others I’m unwilling to give any credibility to any data set where raw data is not provided and adjustments clearly documented. I don’t see evidence that that was done here.
    Maybe that was done but not stated but some clarification would probably be a necessary precursor to such a detailed analysis.

  23. The post represents an incredible amount of work.
    Taking a stab at answering the question Why don’t the TLT anomalies respond to these additional ocean processes? , I think it is because the atmosphere, more precisely the lower troposphere, is encompassing several things not in the analyses. These probably include the relatively rapid movement from sea to land to sea on a continual basis, large scale warming and cooling of air masses over continents, large scale blocking patterns, rapid movement of cold air in jet streams, and possibly other sources of heat input . . ., for example:
    In the last animation (#4) and beginning at about Lag = 6, there is a positive anomaly in the Atlantic west of Portugal, Spain, and France. At first appearance it is small but seems to expand to the NW and then expand and connect to others to make a large horseshoe-shape at Lag =10 and 11. This could be warm water coming from the Mediterranean Sea and upun encountering the topography of the sea floor moving to the surface.
    http://clasticdetritus.com/2007/12/16/sea-floor-sunday-7-bathymetry-of-northeastern-atlantic-ocean/
    There seems to be a spurt of warmth out of the Gulf of Mexico that completes and puts a curl on the bottom of the horseshoe shape and both the tropical Atlantic and the Gulf of Mexico take up a lot of energy every summer.
    So, the point is there are things going on “in addition to” the processes you have described. I don’t mean that as a criticism, only that maybe you are hoping for a clearer picture than is possible without including other things.

  24. Bob Tisdale says:
    October 10, 2010 at 4:54 pm
    phlogiston says: “BTW why indeed would the Atlantic be so different to the rest?”
    The South Atlantic is the only ocean that transports heat toward the equator, and to the North Atlantic. All of the others transport heat poleward.
    I guess it could be a reaction to the south flowing North Atlantic deep water. According to http://en.wikipedia.org/wiki/Gulf_Stream there is a current starting from south of the equator sucked across the equator into the gulf of Mexico to balance the gulf stream heading north east.

  25. The difference between the different sets has been a serious source of frustration for me. This is a very good analysis of where the sources differ. While some may consider this nit-picking, reliable data is critical for determining what is going on.
    For better or for worse I have been working on and have just completed an alternate solution to this problem. I had previously decided to build a merged set of data for my own use and to make it available for all to use.
    The article with the details and the file are up on my site.
    John Kehr

  26. If the North Atlantic ocean warms how does this affect global homogenized temperature data? Hansen doesn’t use SST, does he? How would this North Atlantic rise translate over the entire 3 parts you’ve broken the earth into?

  27. Los Ninos says:
    October 10, 2010 at 2:40 pm
    Has anybody seen a 900 pound gorilla? It might even be wearing a tutu…
    Is this a reference to CO2, perchance? If so, it is the wrong way around – you (the models, that is) would expect tropospheric warming – the so-called hot spot – to be ahead of surface warming.
    But hot spot there is not – apparently a cold spot instead.

  28. phlogiston,
    NO. Only an idiot would think CO2 drives temperature trends and or is responsible for most of the warming.
    I would never make the assumption that warm and cool cycles negate each other, why should they be in balance?

  29. LightRain says: “Hansen doesn’t use SST, does he?”
    GISS uses HADISST data from Jan 1880 to Nov 1981 and Reynolds OI.v2 SST data from Dec 1981 to present.

  30. This will happen if the mixing of the ocean and the atmosphere is reduced, and it probably is, compared to a few dacades back. It will also happen when GHG increase, and they did. So why a larger difference in North Atlantic Ocean? Because both of the above mechanisms have changed more there the last decades.
    It makes no sense to first do a scaling and then compare trends, never do that.
    Anyway, comparing surface and TLT trends using absolute values doesn’t make sense either because the atmosphere is much colder than the surface so if they both change 1 K the relative change of the atmosphere is larger that the relative change of the surface.

  31. Quick note (from a power trader):
    Alternating positive and negative correlations along the Norwegian coast, comes from the fact that a low pressure during summer months generally gives cooler than normal temperatures while a low during winter time gives warmer than normal temperatures.
    This means that the same low pressure causing negative correlations towards temperature at lower latitudes, is gives positive correlations at higher latitudes (in the winter). This might be very confusing when using correlations on temperature alone.

  32. P. Solar says: “Having seem far too many asymmetric ‘corrections’ being made to data recently, my initial reaction is to ask whether these increasing differences are also shown in the raw data. “
    The only thing I’ve done to the data is to scale it and to shift it up or down, and those adjustments are listed in the title blocks of the graphs. Then the data were smoothed to reduce the noise. Here’s a sample of the individual datasets that went into Figure 1 without the scaling:
    http://i56.tinypic.com/2lswnpf.jpg
    You wrote, “While short-term lags and differences between SST and TLT may be a real effect it does not seem probably that SSTs will be constantly jacking up and an ever widening difference between the two records can develop”
    There are also differences between the land surface and land TLT data, but I elected not to show it. This post worked its way to the SST vs sea TLT to bring the post to the correlation maps and the differences in the SST and TLT data there.

  33. John F. Hultquist says: “In the last animation (#4) and beginning at about Lag = 6, there is a positive anomaly in the Atlantic west of Portugal, Spain, and France. At first appearance it is small but seems to expand to the NW and then expand and connect to others to make a large horseshoe-shape at Lag =10 and 11. This could be warm water coming from the Mediterranean Sea and upun encountering the topography of the sea floor moving to the surface.”
    I elected not to speculate on what initiates the warmer waters west of Portugal, Spain, and France at Lag 6. There could be many reasons for it, including the one you suggested.
    Regards

  34. Bob Tisdale: I’m not an expert on your area or your coverage of it but this is the best post of yours I can recall. The insightfully chosen target and thorough data set are most impressive.

  35. The only thing I’ve done to the data is to scale it and to shift it up or down, and those adjustments are listed in the title blocks of the graphs. Then the data were smoothed to reduce the noise. Here’s a sample of the individual datasets that went into Figure 1 without the scaling:
    http://i56.tinypic.com/2lswnpf.jpg
    Thanks, I was not suggesting you had adjusted the data other than how you had clearly documented. What I am wary of is GISS & NCDC pre-processing the data in ways that do not correspond to documented “homogenisation” techniques they declare and CRU “we lost the experimental data”.
    There was also the recent Phil Jones paper where he wanted to “correct” the post war cooling in SST on the basis of some rather speculative and poorly quantified arguments about collection methods.
    Unless the dataset can be verified I’m not sure of the significance that can attributed to the kind of analysis that you have done. Is it a reflection of a geophysical process or an artifact of some asymmetrical pre-processing?
    As a scientist I prefer the unscaled graph you link in your reply. This indicates that TLT is more sensitive to short term change. It also shows reduced amplitude and peak spreading in the SST which is consistent with the much greater thermal mass of the oceans. In scaling the TLT response you seem to be implying that is a response to the same forcing which is not entirely true.
    TLT will be heated from the SST signal below (itself a mix of deep ocean currents and solar input with implied spreading and an additional phase shift), solar from above and to a lesser extent by the effects of water vapour variations in both directions.
    After scaling and subtracting , what does the remaining signal represent? The fact that you are scaling them suggests the idea that they “should” be the same and hence need adjusting. Is that a conscious intent?
    As a general point of method I would suggest not smoothing the data until the final stage, for visualising only. All filters introduce frequency and phase distortions , distorting both data sets before processing (as it seems you do) will introduce compounded artifacts and mask real information. Even a simple mean is very clumsy frequency filter with an aweful frequency profile.
    I don’t wish to destroy you great effort but it is too easy once it’s reduced to an animated plot not to realise some of what you see may be nothing to do with the data you started with and to what extent that data was already processed.

  36. Unless I’m missing something significant in your method I don’t see how you justify this scaling.
    In the eastern segment you have a scaled TLT slope of 0.0053 and a scaling factor of 0.72. Well 0.0053/0.72 = 0.00736 ie almost exactly the SST slope.
    It can also be seen that apart from the magnitude of the Nino spike the two datasets correspond better in both slope and magnitude of response to other features before the scaling. The scaling seems arbitrary and generally counter productive.
    Similarly in the Near E/W segment: 0.0047/0.68 = 0.069, at least nearer to the SST slope and again other features fit better before scaling.
    I think any such scaling is hard to justify. Maybe at least it should be trying equate the area under each peak and not the peak excursion. The ocean clearly spreads the peak so this should not be ignored. But any such move seems to imply HUGE assumptions as to the cause and is very hard to justify.
    I have similar reservations about the shift, although it’s effect is smaller. In all cases it offsets later features that seem more in sync before the shift (especially far west). This probably increases deviations in the differences outside the initial spike.
    What effect does all this have on the time progression and the heat flow this is infered to show?

  37. Aside from previous comments, the one feature that stands out like a sore thumb in all these plots is the far east region fails to drop circa 2008 , whereas TLT does and all other areas drop in both TLT and SST.
    Is this a data error, a correction , a real effect? Worth investigating perhaps.

  38. lgl says: “This will happen if the mixing of the ocean and the atmosphere is reduced, and it probably is, compared to a few dacades back. It will also happen when GHG increase, and they did.”
    Please link papers that confirm your statements. The “probably is” is telling.

  39. P. Solar says: “Aside from previous comments, the one feature that stands out like a sore thumb in all these plots is the far east region fails to drop circa 2008 , whereas TLT does and all other areas drop in both TLT and SST. ”
    I agree it needs further investigation, but off the top of my head I would attribute this to the fact that the East Indian and West Pacific Oceans (which make up about 75% of the data for that area) can warm during both El Nino and La Nina events, depending on the significance of both. Refer to the following:
    http://bobtisdale.blogspot.com/2009/12/more-detail-on-multiyear-aftereffects.html

  40. P. Solar says: “As a general point of method I would suggest not smoothing the data until the final stage, for visualising only. All filters introduce frequency and phase distortions , distorting both data sets before processing (as it seems you do) will introduce compounded artifacts and mask real information.”
    I make the adjustments to the raw data and then smooth it.
    With respect to the scaling, it was a shortcut. I used the scaling to show that the surface data does not respond proportionately to the La Niña events. This is most evident in the “Near East-Near West” data. To this end, I probably should have removed the linear effects of ENSO from the surface and TLT data individually by subtracting scaled and lagged NINO3.4 SST anomalies from them. The result would have been similar; that is, the surface data would have diverged from the TLT data during the 1998/99/00/01 La Niña.
    I may re-issue the post, eliminating the scaling and then removing the linear effects of ENSO in the “Near East-Near West” data, and in the SST vs Sea TLT comparison. It won’t alter the outcome. The ocean area with the greatest divergence should still be the North Atlantic. So I’ll still conclude the post with the two gif animations.
    Regards

  41. Just to put a little different graphical look at some of the points Bob has raised here, at least with respect to the Ocean SSTs (and not the lower troposphere temperature trends) …
    Last week, Global SSTs dropped to 0.1C (versus the 1971 to 2000 average), down from 0.35C at the beginning of the year. Both hemispheres fell.
    Here is the weekly Nino 3.4 index back to 1990. It was -1.84C last week and is forecast to fall to -2.0C by December – but I’m predicting a little more decline than that. I’ve got an El Nino developing in late 2011.
    http://img143.imageshack.us/img143/8480/weeklyensotempsoct6.png
    Here is the weekly AMO index which is falling quite rapidly now in a lagged response to the Nino 3.4 – I’ve got it falling below Zero by January. Note there was definitely a switch upward in about 1995.
    http://img545.imageshack.us/img545/8329/weeklyamooct6.png
    Putting them both together, we see that the AMO does have some type of lagged response to the Nino 3.4 although it doesn’t always do so.
    http://img87.imageshack.us/img87/6707/weeklyensoamooct6.png
    Now Global SSTs since 1990 and how the regressions indicate the ENSO and AMO affect Global SSTs. We are definitely on a rapid descent right now. Together, the ENSO and the AMO seem to be a big driver of Global SSTs (although there are periods of divergence, mainly on the down cycles).
    http://img258.imageshack.us/img258/4264/weeklyensoamoglobalssts.png

  42. Yet to observe: How much will the next SH summer warm the La Nina equatorial waters. Is it the Sun strong enough to fulfill its duty? 🙂

  43. Bill Illis / Bob Tisdale
    Have either of you investigated a possible Southern Ocean/ Southern Atlantic /North Atlantic SST / Arctic possible connection where colder or warmer phases of the Southern Ocean SST ultimately affect the North Atlantic SST and the AMO[ and hence local global temperatures]on a lagged 5-10 year basis like perhaps the warming of 97/98 El Nino did about 9 years later to the North Atlantic and the Arctic . I am referring to the kind of periodic cold water upswelling at certain points in the Southern Oceans which gets ultimately into the deep currents that come up the Atlantic Ocean as Prof. Gray proposed. The Southern Ocean STT’s have been dropping since the 1990’s and there is 5-10 years of colder SST that is now in the coveyor belt?

  44. Bob Tisdale
    To expand on my previous post and using your August 2010 Southern Ocean SST Anomalies graph as noted below , if one were to equate the just passed year 2008/2009 with the year 2000 on your graph, the past cooling of the planet in January 2008 could be the result cold water mixing that was inserted into the ocean coveyor belt in the Southern Oceans back in 1999/2000. By the same reasoning , it also shows that there may not be any sustained Atlantic cooling until after 2o15 due to the latest Southern Ocean cooling which started about 2006 and has been on going since. It does show some cooling in 2011 and then again warmer for 3-4 years. Your thoughts?
    http://i32.tinypic.com/2vjxtp1.png

  45. matt v. says: “Have either of you investigated a possible Southern Ocean/ Southern Atlantic /North Atlantic SST / Arctic possible connection where colder or warmer phases of the Southern Ocean SST ultimately affect the North Atlantic SST and the AMO[ and hence local global temperatures]on a lagged 5-10 year basis like perhaps the warming of 97/98 El Nino did about 9 years later to the North Atlantic and the Arctic .”
    Unfortunately, there is no “real” long-term Southern Ocean SST data. It really starts with the satellite era. Even as late as 2000, there were very few in situ readings in that part of the world:
    http://i37.tinypic.com/x1wtvm.jpg
    The HADISST and ERSST.v3b data for the Southern Ocean is basically infilled make-believe data before then.
    The maps above are from this post:
    http://bobtisdale.blogspot.com/2010/08/on-liu-and-curry-2010-accelerated.html

  46. Maybe something to do with continental-maritime contrasts and zonal vs. meridional dominance of global winds on decadal timescales. (Bear in mind the [sign-reversing] role of the O degree C isotherm in temperature-precipitation relations [relates to point raised by Håvard Hvarnes – also observed in North American Pacific NorthWest], the nonlinear relationship of wind with evaporation, COWL in the northern hemisphere, and NH nonlinearities pointed out by W. Hsieh.) Coupling & decoupling patterns of interannual components of NPI, AO/NAO/NAM, & AAO/SAM on decadal timescales might be an interesting place to start digging deeper…

  47. Paul Vaughan says:
    October 11, 2010 at 8:09 pm
    “”Maybe something to do with continental-maritime contrasts and zonal vs. meridional dominance of global winds on decadal timescales.””
    To paraphrase I would say that the net effects of the long term trends of global circulation patterns on the lee side of the Rockies and Andes mountains cause the net effect of producing the temperature shifts seen in this study.
    That the driver of these shifts between meridional and zonal flow patterns is the declinational tidal effects of the moon shifting through, not only it’s 27.32 day period patterns, but the longer term 18.6 year angle at culmination pattern.
    The 800 billion ton elephant in the room is the MOON.

  48. Bob Tisdale says:
    October 11, 2010 at 7:18 am
    Yes I should have omitted ‘probably’. Trade winds below normal is reduced mixing.

  49. lgl says: “Trade winds below normal is reduced mixing.”
    So let me see if I understand what you’re saying. You’re saying that if trade winds over the tropical North Atlantic drop during El Nino events, and if the frequency and magnitude of El Nino events have increased, then your assumption is that greenhouse gases have caused an increase in the frequency and magnitude of El Nino events and the reduction in trade winds and mixing in the tropical North Atlantic.
    Unfortunately, the long-term trend in NINO3.4 SST anomalies is flat:
    http://i33.tinypic.com/2s9ud6q.jpg
    And if we smooth NINO3.4 SST anomalies with a 121-month filter, we can see that there are decadal and multidecadal variations:
    http://i43.tinypic.com/33agh3c.jpg
    Hmm. Looking at that graph, I wonder why studies about the frequency and magnitude of El Nino events always seem to start in 1950.

  50. Bob, taking my above comment a step further, I would be interested in seeing a comparison of the climatologies (i.e. the annual “normal” cycles) for the series under comparison. There are both advantages & disadvantages to working with anomalies. Given that (a) you are pointing to the North Atlantic [in particular], (b) AO variability spikes mid-winter, (c) annual cycles often arise in anomalies, & (d) spatial [& spatiotemporal] averages are a function of aggregation criteria, I would be inclined to run diagnostics to investigate the role of [particularly] the ’98 event on various calibrations [with particular concern about nonlinear amplification, particularly any occurring in winter, and what statisticians call “leverage”].

  51. Bob Tisdale wrote: “[…] if we smooth NINO3.4 SST anomalies with a 121-month filter, we can see that there are decadal and multidecadal variations: http://i43.tinypic.com/33agh3c.jpg
    Bob, I want to suggest an alternate means of isolating decadal (terrestrial) variability: repeat-smoothing over the (dominant) annual cycle, with end-correction. Different methods have different advantages & disadvantages. An advantage of the method I’m suggesting is that it preserves the location of major events (with minimal temporal shift even under heavy smoothing). Another advantage: This is very handy for isolating interannual features (via subtraction – results compare favorably with other methods of isolating interannual variability).

  52. BobTisdale
    You said
    “Unfortunately, there is no “real” long-term Southern Ocean SST data. It really starts with the satellite era. Even as late as 2000, there were very few in situ readings in that part of the world:”
    Are we not making similar observations in other areas of climate science based on satellite data mostly. Setting this point aside , do you not think cold water could be injected into the deep ocean conveyor belt at the South Atlantic and Southern Oceans interface just like warm water is from the 1997/1998 El Nino warming but with a 5-10 year lag before it gets to the North Atlantic and the Arctic waters? Where is the current cooling of the South Atlantic coming from?
    http://i36.tinypic.com/287ejkg.jpg
    http://i48.tinypic.com/16ow3l.jpg

  53. Paul Vaughan says: “Bob, taking my above comment a step further, I would be interested in seeing a comparison of the climatologies (i.e. the annual “normal” cycles) for the series under comparison.”
    The SST datasets and the RSS TLT data are available in absolute temperatures, but the GISS and NCDC Land Surface Data and the UAH TLT data are only available in anomalies.

  54. This article seems to be an adaptation of what is on Bob Tisdale’s web site. I looked at it but he does not take comments so this comment refers to both presentations. First thing I noticed on his web site is that he does not correctly interpret what TLT tells us. Starting from the left side he complains that the record is contaminated by the effect of volcanic eruptions and thus does not correctly reflect the climate. I had the same problem when I started to interpret the record and had to look into it. There is a section in my book where I proved that what is attributed to Pinatubo cooling is nothing more than a normal la Nina cooling and belongs to ENSO. I have more about it including figures in the new edition that is coming out. El Chichon caused no cooling whatsoever because it erupted when an El Nino was just beginning and there was no convenient La Nina handy to ascribe to it. That whole story of volcanic cooling started off with Self et. al. who had a long article in Fire and Mud – the big Pinatubo book. They were wrong but their work has been endlessly copied by subsequent authors. Volcanic cooling, even by Krakatao, has actually never been observed because it stays in the stratosphere and never descends to us. And now that we have cleared the air of volcanic smoke, let us look at the full satellite record. The entire section to the left of the 1998 is a series of peaks and valleys – El Nino peaks and La Nina valleyes in between. It must not be averaged in with what follows. This is another stupidity that comes to us from the prevailing practice of eliminating irregularities to show long term trends. It destroys real information and results in wrong conclusions about what is in the record. The center line of these oscillations is very nearly horizontal, meaning that there was no warming during this period. But this period is the eighties and nineties, the so-called “late twentieth century warming” which turns out to be a non-existent warming. But wait a minute, didn’t Hansen testify in 1988 that warming had started? He sure did, and now it turns out that his testimony was dead wrong. But lets go on. Next comes the giant super El Nino of 1998. It is sui generis and must not be averaged in with anything. The peaks to the left of it belong to ENSO but it is not a part of that company. I interpret it as caused by a rare storm surge in the Indo-Pacific region that dumped a large amount of warm water at the beginning of the equatorial countercurrent. That current starts near the Philippines and New Guinea and ends at the South American coast. It is the route all El Ninos take across the ocean and that interloper just squeezed itself in between two of these regular El Ninos. (Nino 3.4, by the way, just happens to be astride that countercurrent.) What followed after the super El Nino was unusual: the next regular El Nino peak rose 0.2 degrees higher than any before it and what is more, the La Nina that should have followed it was suppressed. The result was a long high temperature platform I call the twenty-first century high. It is very likely due to the large amount of warm water that came over with the super El Nino. Temperature stagnated for six years and then dropped when the 2008 La Nina arrived. I knew then that the oscillatory climate of the eighties and nineties had returned and I was right: the 201o El Nino followed and the next La Nina is already on the way. The twenty-first century high forms a high temperature platform with a mean approximately one third of a degree higher than the center line of the eighties and nineties. The changeover took only four years. Since this temperature has been stable except for the new ENSO oscillations we might regard this state of climate as a climate change for now. I thought the influence of warm water would eventually subside but it is already twelve years since it arrived and if you extend the mean you see that it neatly bisects the line connecting the bottom of the 2008 La Nina and the tip of the 2010 El Nino. A linear trend since about 2002, for sure, but who knows for how long. The warming it brought is responsible for the first decade of the century being the warmest on record. No wonder, since one third of a degree is half the temperature rise assigned to the entire twentieth century by IPCC. This is real warming but not of the anthropogenic kind. And neither is arctic warming. Its cause turned out to be warm water brought north by ocean currents and not some nebulous arctic amplification of the greenhouse effect.

  55. Arno Arrak says: “This article seems to be an adaptation of what is on Bob Tisdale’s web site. I looked at it but he does not take comments so this comment refers to both presentations.”
    It’s the same post here and at my blog, and my blog does allow comments.
    You continued, “There is a section in my book where I proved that what is attributed to Pinatubo cooling is nothing more than a normal la Nina cooling and belongs to ENSO.”
    There is no evidence of a La Nina during 1991/92/93/94 in the COADS SST data, or in the satellite-based SST datasets, or in the direct measurements from the TAO Project buoys, or in the SOI data, or in eastern tropical Pacific precipitation data, yet global temperatures and global TLT anomalies dipped and rebounded, starting within months of the eruption of Mount Pinatubo. That period has been called the longest El Nino during the satellite era. There are numerous studies about it.
    You wrote, “That whole story of volcanic cooling started off with Self et. al. who had a long article in Fire and Mud – the big Pinatubo book.”
    Actually, the effects of volcanic aerosols have been studied since well before Mount Pinatubo. Self was co-author of a paper on the VEI back in 1982. Before that there were studies by Humphreys in 1940, a number of papers by Wexler in the 1950s, Lamb in the 1970s, etc.

  56. matt v says: “Where is the current cooling of the South Atlantic coming from?”
    I don’t know. A natural cycle? Without long-term surface and subsurface data for the Southern Ocean, it’s impossible to say.
    Are variations in Southern Ocean SST anomalies precursors for the variability of the remaining oceans, working northward? Looking at the two Southern Ocean graphs you linked, it could be argued that all Southern Ocean SST anomaly datasets show an increase in the 1960s, well before the 1976 Pacific Climate Shift and before the 1975 trough of the AMO. The Southern Ocean SST anomalies appear to have peaked in the 1980s or 1990s, depending on the dataset, and Global temperatures have flattened since the late 1990s. In those two instances, one could argue that the Southern Ocean is setting the pace, and that global temperatures respond a decade or so later, but that’s less than a cycle. Will it continue? Dunno.

  57. Bob Tisdale wrote: “The SST datasets and the RSS TLT data are available in absolute temperatures, but the GISS and NCDC Land Surface Data and the UAH TLT data are only available in anomalies.”
    GISS & NCDC don’t give the climatologies?

    One more thought: diurnal patterns. Is there some difference in the timing of observations (during the day) that are used to construct daily summaries (for the different series)? For example, when I looked into TMin & TMax some time ago for a few sites, I found all sorts of interesting differences (and those are just temporally-crude constructs based on only 2 measurements per day). There is also the distribution of monitoring in space. Spatiotemporal aggregation criteria affect pattern, often in profound ways (“Simpson’s Paradox”) — a major blind spot on most radars in this forum (to date…)

  58. Bob, I see constant references to NINO3.4 which causes me to wonder if that is the same NINO as El Nino; or izzat some satellite or satellite instrument that I’m not aware of ? I need an encyclopedia of these texting words.
    George

  59. George E. Smith says: “Bob, I see constant references to NINO3.4 which causes me to wonder if that is the same NINO as El Nino; or izzat some satellite or satellite instrument that I’m not aware of ?”
    In the post and in my comments, “NINO3.4” was always followed by “SST anomalies”. NINO3.4 SST anomalies are the sea surface temperature anomalies of the NINO3.4 region, which has the coordinates of 5S-5N, 170W-120W. See map here:
    http://oi39.tinypic.com/6hujv6.jpg

  60. Bob, I just checked at KNMI Climate Explorer.
    The following are available for both NCDC & GISS:
    a) raw data.
    b) climatologies.
    c) anomalies.
    As I suspected might be the case, the climatologies (for the different series) differ — [certainly not a surprise].

    This climate science mainstream convention of working with anomalies comes with pitfalls. I would not recommend using them exclusively, even though they have clear utility for some purposes. Using them exclusively will leave blindspots on radars. [Plain & simple: Aggregation criteria affect spatiotemporal pattern.]
    Related suggestion:
    Be careful interpreting Bob’s ENSO/Atlantic correlation lag movie. The first (not the last) thing I would do is see how the movie changes when anomalies are swapped for 1-year-smoothed raw data. I would recommend exceptional interpretive caution [& extensive post-analysis diagnostics] for any cross-correlation analyses based on anomalies for a short record including ’98 leveraging.

    To get away from widespread nonsensical interpretation of lags in climate science, there is a need for new algorithms hybridizing traditional cross-correlation analysis with cross-wavelet methods in the windowed complex domain. [Bear in mind the orthogonality of rate of change and the effect of integrating over dominant harmonics (like the day & year).]
    My instinct is that such algorithms may clearly elucidate the cumulative effect (on decadal timescales) of interannual-timescale regional/hemispheric coupling/decoupling. A good 60 to 75% of the variance is interannual & spatially turbulent, so global linear methods in the non-complex domain will NEVER (with absolute certainty) finish the job.
    A good example is Bob’s oft-cited ENSO/PDO relationship, which only does HALF the job – i.e. it only explains 50% of the variance. Is the other half somehow dismissible as “unimportant”? Most certainly not. So is Bob wrong? That’s not what I’m saying. My criticism in this & preceding paragraphs is not of Bob, but of conventional mainstream climate science statistical methodologies that require absolutely untenable assumptions.
    In short: Climate science is missing the spatiotemporal constraints on decadal variations due to a failure to avoid the hazards of Simpson’s Paradox. (For anyone who doesn’t realize: This is a very serious mistake [that is hammered like thunder by Stat 101 instructors].)

  61. Paul Vaughan says: “Bob, I just checked at KNMI Climate Explorer. The following are available for both NCDC & GISS: a) raw data. b) climatologies. c) anomalies. As I suspected might be the case, the climatologies (for the different series) differ — [certainly not a surprise]. This climate science mainstream convention of working with anomalies comes with pitfalls.”
    Paul, the raw NCDC and GISS data are anomalies or didn’t you bother to look?

  62. Thanks for pointing that out Bob. I’m surprised to see such a mistake on the otherwise impressive KNMI Climate Explorer site.
    I linked from KNMI to the “authoritative sites” for NCDC & GISS. I didn’t find any climatologies or links to climatologies. Absolutely ridiculous. Unconditionally unacceptable. These folks need to be pressed. They cannot provide a simple list of 12 numbers? It might be amusing to hear the flimsy attempted-justifications. Note to everyone in the community: Press them on this. If they are truly refusing to provide a list of 12 averages, their position is absolutely indefensible & unconditionally inexcusable, no matter what twisted “logic” might be administratively shoveled.

  63. Paul Vaughan says: “My criticism in this & preceding paragraphs is not of Bob…”
    It’s not? Maybe you need to take another look at what you wrote, Paul.
    You wrote in the preceding: “Be careful interpreting Bob’s ENSO/Atlantic correlation lag movie. The first (not the last) thing I would do is see how the movie changes when anomalies are swapped for 1-year-smoothed raw data.”
    Hmm. That sounds very much like criticism of my work, Paul. Writing, “first (not the last) thing I would do is see how the movie changes,” indicates you’re criticizing without any basis whatsoever. In other words, you’re speculating that the method you described presents a different result. Do you know whether I’ve looked into other ways of presenting the animation, Paul? No.
    BTW, feel free to create the animation you suggest, with its limitations, and post it with your interpretation and your explanation of why your animation is better. I believe you’ll discover it’s not.
    And you wrote, “A good example is Bob’s oft-cited ENSO/PDO relationship, which only does HALF the job – i.e. it only explains 50% of the variance. Is the other half somehow dismissible as ‘unimportant’?”
    Nice caps on the word “HALF”, Paul. Did you think those who read your comment would miss the meaning of it, so you have to shout about it, especially when you expand on it in the next sentence? Not sure what you’re up to with all of the caps and italics, but apparently you felt they were necessary.
    And the part of your comment I quoted sounds like a criticism of my work, too. If you’re not aware, I’ve also illustrated that the difference between ENSO and the PDO appears to be a function of Sea Level Pressure. Or are you overlooking that post that I’ve linked here at WUWT multiple times for some other reason?
    And you wrote, “This climate science mainstream convention of working with anomalies comes with pitfalls. I would not recommend using them exclusively, even though they have clear utility for some purposes.”
    And that sounds like an unfounded criticism, because, as I replied above, global surface temperature data by GISS, Hadley Centre and NCDC are only presented in anomalies. That fact is actually pretty difficult to miss. I believe you could even find papers by them that describe why they only present global temperatures as anomalies, if you’d bother to look.
    There is, however, if you’re not aware, an absolute Land Surface Temperature dataset. It’s identified on the KNMI Climate Explorer as “1948-now: CPC GHCN/CAMS t2m analysis (land)”. I posted about it back in March:
    http://bobtisdale.blogspot.com/2010/03/absolute-land-surface-temperature.html
    Feel free to create your own merged absolute global temperature dataset, using the GCHN/CAMS data and the SST dataset of your choice. (Hint: Don’t use HADSST2 since it’s only presented in anomalies.) But in order to do any wiggle matching, you’re go9ing to have to smooth it with a 12-month filter and then you’ve got the same data as anomaly data except the absolute data has been shifted up a few degrees. And if you’re not going to smooth it, then you have to be careful how you account for and interpret the seasonal component. Also, you need to understand the shortcomings of that dataset, and there are many.
    BTW, Paul, does this reply give you the impression that I am not pleased with your comment? It should.

  64. Paul Vaughan wrote, “Thanks for pointing that out Bob. I’m surprised to see such a mistake on the otherwise impressive KNMI Climate Explorer site.”
    There’s no mistake, Paul. The GISS and NCDC data are only presented as anomalies.

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