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.
http://i52.tinypic.com/2uorrsp.jpg
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.
http://i52.tinypic.com/2uyiqs7.jpg
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.
http://i51.tinypic.com/291m7nq.jpg
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.
http://i52.tinypic.com/142t7q.jpg
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.
http://i52.tinypic.com/xp0ec0.jpg
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.
http://i51.tinypic.com/mj5rwy.jpg
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.
http://i52.tinypic.com/23l0ais.jpg
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.
http://i52.tinypic.com/2gtai6d.jpg
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
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.
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.
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.
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.
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.
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
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?
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.
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?
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.
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.
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.
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.
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
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.
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.
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?
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.
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.
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
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
Håvard Hvarnes: Thanks for the insight.
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
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? 🙂
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?