Bob Tisdale writes:
I’ve been holding off telling you about my most recent post in hopes that GISS would continue with their warmest-year nonsense. And they did.
Using correlation maps, animations, graphs and a youtube video, the post shows how leftover warm water from an El Nino gets spun up into the Kuroshio-Oyashio Extension (KOE) where it continues to release heat during the La Nina. The KOE correlates with the Northern Hemisphere warming during an La Nina, and one of the datasets used for the graphs and correlation maps is GISTEMP LOTI.
The ENSO-Related Variations In Kuroshio-Oyashio Extension (KOE) SST Anomalies And Their Impact On Northern Hemisphere Temperatures
Guest post by Bob Tisdale
OVERVIEW
This post provides brief background information about the Kuroshio-Oyashio Extension (KOE), and discusses the relationship between NINO3.4 SST anomalies and the SST anomalies of the KOE following major El Niño events. Using correlation maps the post also illustrates the possible impacts of the KOE Sea Surface Temperature (SST) anomalies on North Atlantic SST anomalies, Combined Land and Ocean Surface Temperature anomalies, and Lower Troposphere Temperature anomalies.
INTRODUCTION
The Kuroshio Current and Oyashio Current are located in the western North Pacific Ocean. The Kuroshio Current is the western boundary current of the North Pacific Subtropical Gyre. Its counterpart in the North Atlantic Ocean is the well-known Gulf Stream. The Kuroshio Current carries warm tropical waters northward from the North Equatorial Current to the east coast of Japan. The East Kamchatka Current and the Oyashio Current are the western boundary currents of the Western Subarctic Gyre. The East Kamchatka Current is renamed the Oyashio Current south of the Bussol Strait (which is located about half way between Hokkaido and the Kamchatka Peninsula). They carry cold subarctic waters south to the east coast of Japan. The Kuroshio and Oyashio currents meet and form the North Pacific Current that runs from west to east across the North Pacific at mid latitudes. The Qiu, (2001) paper Kuroshio and Oyashio Currents. In Encyclopedia of Ocean Sciences, (Academic Press, pp. 1413-1425) provides a detailed but easily readable description of the two currents. Figure 1, from Qiu (2001), illustrates the general locations and paths of the Kuroshio and Oyashio Currents.
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Figure 1
As noted above, the Kuroshio and Oyashio Currents collide East of Japan and form the western portion of the North Pacific Current. These waters are often referred to as the Kuroshio-Oyashio Extension or the KOE. For the purpose of this post, I’ve used the coordinates of 30N-45N, 150E-150W for the Kuroshio-Oyashio Extension, Figure 2.
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Figure 2
CORRELATION WITH NORTHERN HEMISPHERE TEMPERATURES
Sea Surface Temperature (SST) anomalies for much of the North Atlantic warm (cool) when the Kuroshio-Oyashio Extension SST anomalies warm (cool). This can be seen in the correlation map of annual (January to December) Kuroshio-Oyashio Extension SST anomalies and annual North Atlantic SST anomalies, Figure 3.
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Figure 3
And, as shown in Figures 4 (RSS) and 5 (UAH), annual TLT anomalies for much of the Northern Hemisphere correlate with the annual SST anomalies of the Kuroshio-Oyashio Extension.
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Figure 4
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Figure 5
The same thing holds true for combined land plus sea surface temperature datasets such as the GISS Land-Ocean Temperature Index (LOTI) data for the Northern Hemisphere, Figure 6. Much of the Northern Hemisphere GISS LOTI data warms (cools) as KOE SST anomalies warm (cool). (Also note the differences in the North Atlantic correlations in Figures 3 and 6. They’re based on the same SST dataset, so why are there differences? GISS deletes SST data from areas with seasonal sea ice and extends land surface data out over the oceans with its 1200km radius smoothing. Refer to GISS Deletes Arctic And Southern Ocean Sea Surface Temperature Data.)
http://i54.tinypic.com/303llxg.jpg
Figure 6
WHEN DOES THE KOE WARM?
As we’ve seen in past posts, the East Indian and West Pacific Oceans warm in response to El Niño events and then during the subsequent La Nina events. As part of the East Indian-West Pacific subset, the Kuroshio-Oyashio Extension warms significantly during La Niña events. Animation 1 is taken from the videos in the post La Niña Is Not The Opposite Of El Niño – The Videos. It presents the 1997/98 El Niño followed by the 1998 through 2001 La Niña. Each map represents the average SST anomalies for a 12-month period and is followed by the next 12-month period in sequence. Using 12-month averages eliminates the seasonal and weather noise. The effect is similar to smoothing data in a time-series graph with a 12-month running-average filter. Note how the Kuroshio-Oyashio Extension warms significantly during the La Niña event and how the warming persists for the entire term of the La Niña.
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Animation 1
Note in Animation 1 that the SST anomalies of the Kuroshio-Oyashio Extension were cool during the 1997/98 El Niño. The KOE actually started with depressed SST anomalies, and they did not drop significantly during the 1997/98 El Niño. Refer to Figure 7. On the other hand, the KOE SST anomalies did rise significantly during the transition from the El Niño to the La Niña in 1998. The other major El Niño event that wasn’t impacted by the aerosols of an explosive volcanic eruption was the 1986/87/88 event. The SST anomalies of the Kuroshio-Oyashio Extension cooled during the 1986/87/88 El Niño, but also rose significantly during the 1988/89 La Nina. We’ll take a closer look at that event later in the post.
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Figure 7
This response of the Kuroshio-Oyashio Extension to El Niño and La Niña events is easier to see if the NINO3.4 SST anomalies are inverted, Figure 8. That is, the Kuroshio-Oyashio Extension warms much more during the 1998/99/00/01 La Niña event than it cools during the 1997/98 El Niño. But could the significant drop in the Kuroshio-Oyashio Extension during the 1986/87/88 El Niño impact the global response to that El Niño? Again, we’ll examine that later in the post.
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Figure 8
WHY DOES THE KOE WARM DURING LA NIÑA EVENTS?
Let’s start with the El Niño. During an El Niño event, a significant volume of warm water from the west Pacific Warm Pool travels east to the central and eastern equatorial Pacific, where it releases heat primarily through evaporation. And most of the warm water from the Pacific Warm Pool water comes from below the surface. There is “leftover” warm water when the La Niña forms, and a portion of this leftover warm water is returned to the western tropical Pacific at approximately 10 deg N latitude. Video 1 illustrates global Sea Level Residuals from January 1998 to June 2001. It captures the 1998/99/00/01 La Niña in its entirety. The video was taken from the JPL video “tpglobal.mpeg”. The phenomenon shown carrying warm waters from east to west in the tropical Pacific at approximately 10 deg N is called a slow-moving Rossby Wave.
Video 1
Link to Video 1:
http://www.youtube.com/watch?v=MF5vZErQ6HM
Unfortunately, the video “tpglobal.mpeg” is no longer available at the JPL VIDEOS web page, but for those who would like to watch the entire video, I uploaded it to YouTube as Sea Surface Height Animation 1992 to 2002 – JPL Video tpglobal.mpg.
In Video 1, the warm “leftover” warm water from the 1997/98 El Niño is clearly carried as far west as the Philippines. Shortly thereafter Kuroshio-Oyashio Extension sea level residuals rise and remain elevated for the duration of the La Niña.
In addition, there are other factors that add to and maintain the elevated SST anomalies in the Kuroshio-Oyashio Extension during the La Niña. As shown in Animation 1 (the gif animation, not the video), Sea Surface Temperature anomalies outside of the tropical Pacific rise in response to the El Niño. The changes occur first in the Atlantic, then Indian, and finally the west Pacific. Sea Surface Temperature anomalies rise as changes in atmospheric circulation caused by the El Niño make their way eastward around the globe to the western Pacific. Then, during the La Niña, the opposite occurs for much of the globe. But in the tropical Pacific, the trade winds strengthen and the North and South Equatorial Currents return warm “leftover” surface waters from the El Niño to the west. So the western Pacific is warmed cumulatively by the El Niño and then by the La Niña. In the northwest Pacific, the Kuroshio Current carries the leftover warm water up to the Kuroshio-Oyashio Extension.
Additionally, the increased strength of the trade winds during the La Niña also reduces cloud cover over the tropical Pacific, which increases the amount of Downward Shortwave Radiation (visible light) there. The increased Downward Shortwave Radiation warms the tropical Pacific. The warmed water is carried to the west by the Equatorial Currents and the North Pacific Gyre spins the warmed water up to the Kuroshio-Oyashio Extension.
WHY IS THIS IMPORTANT?
In the post “RSS MSU TLT Time-Latitude Plots…Show Climate Responses That Cannot Be Easily Illustrated With Time-Series Graphs Alone”, I illustrated that the RSS Lower Troposphere Temperature (TLT) anomalies of Southern Hemisphere and of the Tropics (70S-20N) followed the basic variations in NINO3.4 SST anomalies, Figure 9. This is how one would expect TLT anomalies to respond to El Niño and La Niña events. El Niño events cause the TLT anomalies to rise because they release more heat than normal to the atmosphere, and La Niña events cause TLT anomalies to fall because the tropical Pacific is releasing less heat than normal.
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Figure 9
But the TLT anomalies of the Northern Hemisphere north of 20N, Figure 10, appear to rise in a step after the 1997/98 El Niño. That is, there is very little response to the 1998 through 2001 La Niña. It appears as though a secondary source of heat is maintaining the Northern Hemisphere TLT anomalies at elevated levels.
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Figure 10
A similar upward step can be seen in the GISS Land-Ocean Temperature anomaly index (LOTI) for the latitudes of 20N-65N, Figure 11. (North of 65N the GISS data is biased by their deleting Sea Surface Temperature data and replacing it with land surface data with a higher trend. Again, refer to GISS Deletes Arctic And Southern Ocean Sea Surface Temperature Data.)
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Figure 11
And a similar upward step is visible in the North Atlantic SST anomaly data, Figure 12.
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Figure 12
The North Atlantic SST anomalies, the Lower Troposphere Temperature( TLT) anomalies of the Northern Hemisphere north of 20N, and the Northern Hemisphere Land-Ocean Temperature anomalies (20N-65N) all rise in response to the 1997/98 El Niño, but fail to respond fully to the 1998/99/00/01 La Niña. The similarity of the curves can be seen in Figure 13.
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Figure 13
The correlation maps in Figures 3 through 6 show that a portion of the warming of the Northern Hemisphere north of 20N should be a response to the elevated Kuroshio-Oyashio SST anomalies during the 1998 through 2001 La Niña. To further illustrate this relationship, Figure 14 compares the KOE SST anomalies (not scaled) to the three datasets shown in Figure 13. I did not scale the Kuroshio-Oyashio SST anomalies because I wanted to illustrate the differences in the magnitudes of the variations. The variations in Kuroshio-Oyashio SST anomalies are clearly far greater than the variations of the other three datasets in Figure 14. In fact, the KOE SST anomaly variations are about 40% to 50% of the variations in NINO3.4 SST anomalies (refer back to Figures 7 and 8).
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Figure 14
Figure 15 presents the same datasets as Figure 14, but in Figure 15, the Kuroshio-Oyashio Extension SST anomalies have been scaled. Keep in mind that the three Northern Hemisphere temperature anomaly datasets rise first in response to the El Niño.
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Figure 15
It appears the warming of the Kuroshio-Oyashio Extension during the 1998/99/00/01 La Niña and its interaction with the other datasets could explain a portion of the trend in Northern Hemisphere SST anomalies, TLT anomalies, and Land-Ocean temperature anomalies since 1995. The warming of the Kuroshio-Oyashio Extension during that La Niña counteracts the normal cooling effects of the La Niña and prevents the temperature anomalies for the three datasets shown in Figures 13, 14, and 15 from responding fully to the La Niña.
THE 1986/87/88 EL NIÑO & 1988/89 LA NIÑA
There is a similar effect during the 1988/89 La Niña. That is, Northern Hemisphere temperature anomalies do not drop as one would expect during a La Niña. But the response during the 1986/87/88 El Niño may help to confirm the impact of the Kuroshio-Oyashio Extension on Northern Hemisphere temperatures.
Figure 16 compares scaled NINO3.4 SST anomalies for the period of 1985 through 1994 to the same datasets used in Figures 13: North Atlantic SST anomalies, the Lower Troposphere Temperature (TLT) anomalies of the Northern Hemisphere north of 20N, and the GISS Northern Hemisphere Land-Ocean Temperature anomalies (20N-65N). Once again, the Northern Hemisphere datasets rise in response to the El Niño event, but don’t drop in response to the La Niña. Note also that the North Atlantic SST anomalies lag the NINO3.4 SST by more than 6 months during the ramp-up phase, but the lag in the Northern Hemisphere TLT and Surface Temperature datasets is excessive, about 18 months. Why?
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Figure 16
Could the dip in the Kuroshio-Oyashio Extension SST anomalies during the 1986/87/88 El Niño have counteracted their responses to the El Niño? Refer to Figure 17. It compares Kuroshio-Oyashio Extension SST anomalies (not scaled) to the North Atlantic and Northern Hemisphere datasets. The drop in KOE SST anomalies is significant in 1986/87/88.
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Figure 17
And in Figure 18, the Kiroshio-Oyashio SST anomalies have been scaled. The North Atlantic SST anomalies rise in response to the 1986/87/88 El Niño as noted earlier. The timing of the rises in the KOE data and the GISS LOTI data are very similar. But the rise in the TLT anomalies north of 20N precedes the rise in the KOE data. If the dip in KOE SST anomalies were the only factor preventing the TLT anomalies from rising in response to the El Niño, shouldn’t we expect the TLT anomalies to lag the rise in the KOE data? Or are the TLT anomalies responding to the rise in North Atlantic SST anomalies?
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Figure 18
If we replace the RSS TLT data with TLT data from UAH, Figure 19, the lag decreases between the North Atlantic SST anomalies and the TLT anomalies north of 20N.
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Figure 19
CLOSING
An El Niño event releases vast amounts of warm water from below the surface of the west Pacific Warm Pool. But the end of an El Niño event does not mean all of that warm water suddenly disappears. The warm water sloshes back to the western tropical Pacific during the La Niña. And some of that warm water is spun up into the Kuoshio-Oyashio Extension where it continues to release heat.
Kuroshio-Oyashio Extension SST anomalies rose significantly during the La Niña events of 1988/89 and 1998/99/00/01. These warmings appear to have counteracted the effects of those La Niña events on North Atlantic SST anomalies, and on Lower Troposphere Temperature anomalies north of 20N, and on combined Land-Ocean temperature anomalies of the Northern Hemisphere between the latitudes of 20N-65N. During the 1997/98 El Niño, the drop in Kuroshio-Oyashio Extension SST anomalies was very small and the KOE does not appear to have had a noticeable impact on the effects of that El Niño. On the other hand, the Kuroshio-Oyashio Extension SST anomalies did drop significantly during the 1986/87/88 El Niño and they appear to have suppressed the effects of that El Niño on Northern Hemisphere temperature anomalies. But why did the Kuroshio-Oyashio Extension SST anomalies drop significantly during the 1986/87/88 El Niño but not during the 1997/98 El Niño? Differences in Sea Level Pressure?
SOURCE
Data for graphs are available through, and the correlation and anomaly maps were downloaded from, the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere
Posted by Bob Tisdale at 6:39 AM
R. Gates says:
December 13, 2010 at 1:44 pm
………..
Hey Gates
I hope you’ve looked at links I posted for you . Here is another one (relevant to ENSO)
http://www.vukcevic.talktalk.net/LFC20.htm
R. Gates says:
December 13, 2010 at 1:44 pm (Edit)
rbateman says:
December 13, 2010 at 11:29 am
I would venture that, as the Earth’s oceans stopped warming due to decreased input, the oceans have gone into osmosing thier heat out to the atmosphere. Eventually, though, the well runs dry.
___
Except for the fact that ENSO is never-ending cycle, and SW solar radiation continues to fall on the oceans around the world, including the equatorial pacific, and we’d better hope the “well never runs dry” or we’ll be back to the snowball earth. Bob’s notion has merit in showing a delay in the release of heat during the occilation of the ENSO cycle, but since the process has gone on, and will continue to go for a very long time, there surely won’t be any “well running dry.”
Over the last few hundred years, when the sunspot number is above ~40, the oceans gain energy. When it’s below ~40, they lose energy. This is obviously dependent on cloud cover, but there does seem to be a sufficient linkage between solar activity and cloud cover that the relationship holds quite well.
http://tallbloke.wordpress.com/2010/07/21/nailing-the-solar-activity-global-temperature-divergence-lie/
The biggest problem for the alarmists is the FAILURE TO WARM for 12 years.
Weather 2010 is very slightly warmer than 1998 is unimportant.
The fastest warming during 1978 to 1998 is only 1.2 ° C per century.
[#Least squares trend line; slope = 0.0123219 per year or 1.2 C per century]
http://www.woodfortrees.org/plot/gistemp/from:19 78/to:1998/plot/gistemp/from:1978/to:1998/trend
Whether CO2 is causing the slight warming is a moot point but who cares ?
There is a 30 year warming and a 30 year cooling cycle overlaying the trend of about 1/2 ° C per century. As of 1998 the earth was at the top of the sine wave and the climate scientists freaked out.
I can’t blame them with 20 years of warming just happening I might have been concerned too. Since there has been 12 years of FAILURE TO WARM because the ocean cycles [PDO and ADO] are turning negative and of course it isn’t warming.
With a 60 year cycle and the last cooling cycle starting about 1940 another was due around 1998 and it ARRIVED ON SCHEDULE.
Mojib Latif said essentially the same thing and almost got kicked off the CAGW team.
He predicted 10 to 20 more years of sideways of cooling temperatures. It will be another 10 years before it gets as warm as today.
The big picture is that the cooling cycle which happens every 60 years makes the overall warming very mild about 1/2 ° C per century. Whether it is caused by CO2 or not is a scientific curiosity but not important.
Here is an article from the university of Alaska [ which Sara Palin didn’t write] which explains this more fully and is easy reading.
http://people.iarc.uaf.edu/~sakasofu/pdf/two_nat ural_components_recent_climate_change.pdf
I have studied this theory and tried to find holes it for 2 years and can’t do it.
What do you say?
Bob Tisdale says:
“Snow, if your readers doubt my work, they can reproduce it and find alternate explanations. In lieu of that, they ask for credentials, which is a standard redirection/misdirection practice, like asking if a blog post has been peer reviewed.”
don’t be so defensive. It’s a reasonable question. When I started reading some of your elaborate postings my first reaction was to ask myself what your background was. Were you an academic or a blogologist? I don’t dismiss your efforts because you’re not a career academic and I don’t blindly (or at all) accept the work of someone who has a few letters after their name. (I’ve seen enough rubbish go into PhD theses and “peer-review” publications to double check everything I can) but it is useful when checking someone’s work to know what background they have.
“if your readers doubt my work…” where’s the if ? We’re skeptics , right?
Expect your work to be doubted , explanations requested and criticisms made. That’s not an insult, it’s complement.
BTW, I think it would be useful if you added a reference to where you get your data under each of your graphs. You were kind enough to point me to your source for nino3.4 the other day but I should not need to ask.
I wonder why there is no blue in middle and western norway. Middle of norway i know is at least 1.5-2c below normal, while i would expect western norway to be around same..
This map is just a damn lie like usual..
Paul Vaughan says:
December 13, 2010 at 11:01 am
Does anyone know of a link to the current Indian Ocean Dipole anomaly?
http://www.bom.gov.au/climate/enso/indices.shtml
Select it from the pull down box.
P. Solar: Thanks again for the comparison.
You wrote, “Unfortunately Bob does not give references for his data but I’m guessing I have similar data sources to his figure 9 above.”
The source (KNMI Climate Explorer) is noted at the end of the post. Sorry about not listing all the datasets there. But the RSS and UAH TLT and GISTEMP LOTI data are identified in the post and graphs. And I did actually note the SST dataset (Reynolds OI.v2) as the dataset in Figure 7.
You wrote, “1984-85 , G resolves the cyclic nature of both data sets much better in this area compared to rm where the plot is just a noisy trough.”
To me the top graph, the gaussian filtered data, appears noisier.
You wrote, “1993-95 , again G resolves clearly defines cycles in both whereas rm suggests that the correlation has totally broken down (presumably the reason Bob chose to cut-off his plot pre-95 to “hide the decline” in the relationship 😉 ).”
I cut the data off before 1995 in Figures 9 to 15 because I was illustrating the effects of the 1997/98 El Niño and 1998-01 La Niña. The period of 1991 through 1995 appears in the Figures 16 through 19. There no intent to hide anything. The impacts of Mount Pinatubo are well understood, and I would expect the correlation to break down.
You wrote, “1980 cf 83 , the magnitude of the two peaks are nearly the same in G plot, whereas rm shows a distinct decline. “
And the reason for the decline is the eruption of El Chichon in 1982.
You wrote, “I showed in the previous thread that this is not just “different”, the gaussian is the one correctly following the trends in the data and , where there is a difference it is the running mean that is corrupting the filtered plot.”
Since the gaussian filter failed to capture the effect of El Chichon, I would question the statement that “the gaussian is the one correctly following the trends in the data.”
P. Solar, keeping in mind the strong annual temporal mode, try repeat 1 year smoothing with end-correction as a superior alternative to gaussian filters. If you aim to persuade Bob to change tactics, you might (a) take into consideration the effect of integrating over harmonics and (b) consider ease of implementation & interpretation, particularly for lay members of the audience wishing to reproduce Bob’s work independently. I would also encourage you to acknowledge that simple boxcar kernels (which Bob currently uses) have some properties that are indispensable for certain types of analysis. I suggest that you run some experiments with sinusoidal waves. Roll the boxcar bandwidth and note the effect of smoothing over harmonics. You might rediscover from scratch the motivator of FFT. If, however, you use “fancier” kernels, you might miss the discovery. I’ll agree that Bob doesn’t need boxcar harmonic properties for the types of analyses he’s running. I would encourage Bob to try repeat 1 year smoothing with end-correction.
Bob Tisdale said
“Because this post was another in a long series of posts that have shown that the multiyear aftereffects of ENSO are responsible for much of the rise in global temperatures during the satellite era. It may be the warmest year, but claiming or inferring the high temperature anomalies are the result of AGW is nonsense. ”
You are coming around to my favorite theory.
See my last post for more info.
The warming of the last 120 years can be approximated by a 1/2 ° C warming and a 60 year sine wave. The sine wave crested in 1998 so the climate scientists became concerned. They were looking backwards at 20 years of warming at 1.2 ° C per year.
The sine wave seems to be caused by ENSO and it was positive in 78 – 98, plus sunspots were historically high, and there was a monster El Nino. Since 1998 was the top of the sine wave it failed to warm for 12 years, and will actually cool for 10 or 20 more.
Ask Mojib Latif of NASA, when he said what I just said he was roundly beat up by his fellow warmists.
The periodic 30 year cooling cycles limit warming to very low levels.
Here is a study which explains it better than I can.
http://people.iarc.uaf.edu/~sakasofu/pdf/two_natural_components_recent_climate_change.pdf
Since 1998 there has been a failure to warm for 10 years
I’m growing curious about the role of the Indian Ocean Dipole.
Bob is making an important contribution by drawing attention to KOE phasing. This will help people (a) see beyond AMO and (b) overcome misunderstandings of PDO.
Elaboration:
Ocean surfaces have no problem dropping in temperature over so little as a season. The lag relative to diurnally-averaged air is on the order of a few months, not decades. Careful reconsideration of AMOC’s supposed dominance in multidecadal NH variations is warranted.
I encourage readers to consider that the origin of the low frequency ENSO component is related to spatial modes, most notably the distribution of continents (insolation, thermal properties, maritime-continental contrasts), and the timing of SOI relative to dominant temporal modes, most notably the year (e.g. hemispheric summers/winters on a north-south asymmetric globe).
Seeing beyond AMO:
Carefully compare:
A) http://icecap.us/images/uploads/AMOTEMPS.jpg
B) Figure 10:
Carvalho, L.M.V.; Tsonis, A.A.; Jones, C.; Rocha, H.R.; & Polito, P.S. (2007). Anti-persistence in the global temperature anomaly field. Nonlinear Processes in Geophysics 14, 723-733.
http://www.uwm.edu/~aatsonis/npg-14-723-2007.pdf
http://www.icess.ucsb.edu/gem/papers/npg-14-723-2007.pdf
It’s not just the North Atlantic. AMO has simply attracted the lion’s share of attention to date. Widespread failure to realize the fundamental difference between PDO & Pacific SST, among other things, is interfering with sensible mainstream conceptualization of multidecadal terrestrial oscillations.
The time is ripe for serious climate data explorers to pioneer a shift towards multiscale hypercomplex factor analysis (using up to 4 dimensions with 3 adjacent derivatives, depending on the application).
For a sample of what awaits discovery, see the following:
Schwing, F.B.; Jiang, J.; & Mendelssohn, R. (2003). Coherency of multi-scale abrupt changes between the NAO, NPI, and PDO. Geophysical Research Letters 30(7), 1406. doi:10.1029/2002GL016535.
http://www.spaceweather.ac.cn/publication/jgrs/2003/Geophysical_Research_Letters/2002GL016535.pdf
Anyone carefully looking more deeply will discover that the preceding paper only hits the tip of an iceberg. Past evolution of multiscale spatiotemporal coupling matrices can be estimated from historical records.
When assumptions of randomness fail, Simpson’s Paradox has SHARP teeth & a NASTY bite.
I suggest blinking between the upper & lower panels of figure 6 here:
Trenberth, K.E. (2010). Changes in precipitation with climate change.
http://www.cgd.ucar.edu/cas/Trenberth/trenberth.papers/ClimateChangeWaterCycle-rev.pdf
Temperature/precipitation relations are nonlinear, reversing sign seasonally over large portions of the globe. The timing of ENSO (& other modes like IOD) relative to semi-annual hemispheric summers/winters can stimulate/suppress the hydrologic cycle over extensive regions, so patterns of seasonal persistence of interannual variations are not irrelevant. In order to become truly credible, climate models will have to be capable of reproducing EOP (Earth orientation parameters). Modelers will need to gain a deeper appreciation for the roles of spatiotemporal heterogeneity, scale, & aggregation criteria.
Even if there are endless miles to go in developing a consensus about what belongs in our catalog of important terrestrial frequencies (beyond the simple day & year), sensible people will be able to agree that changes in the frequency of a dominant factor have an effect on beats with other oscillatory factors. Beats change in proportion to the rate of change of the frequency (alternatively viewed as cycle acceleration) of a dominant factor, regardless of whether important nonstationary factors have been recognized or not. (This isn’t just about nonstationary temporal modes; it’s also about nonstationary spatial modes.)
Something to think about:
White, W.B.; & Liu, Z. (2008). Non-linear alignment of El Nino to the 11-yr solar cycle. Geophysical Research Letters 35, L19607. doi:10.1029/2008GL034831.
https://www.cfa.harvard.edu/~wsoon/RoddamNarasimha-SolarENSOISM-09-d/WhiteLiu08-SolarHarmonics+ENSO.pdf
While the authors neglect crucial factors, their work is a stimulating contribution.
–
Nonstationary terrestrial solar thermal tides (cloud/circulation modulated) exhibit:
1) spatial components including:
a. continental-maritime contrast.
b. polar-equatorial contrast.
c. north-south asymmetry (due to the distribution of continents).
d. response to topography.
2) a temporal component related to solar cycle acceleration (alternatively viewed as rate of change of solar cycle length).
This occurs on a framework of stationary diurnal & annual thermal tides and stationary lunisolar gravitational tides.
—
Selected highlights from recent solar-terrestrial relations research:
Vaguely-stated scientific claim (that might cause an auditor to look up the reference):
“The present results are complementary to earlier work (S12), in that both argue that the 11 year solar cycle stimulates ENSO-like variability through dynamically coupled feedbacks.”
Meehl, G.A.; Arblaster, J.M.; Matthes, K.; Sassi, F.; van Loon, H. (2009). Supporting online material for: Amplifying the Pacific climate system response to a small 11-year solar cycle forcing.
http://www.sciencemag.org/content/suppl/2009/08/27/325.5944.1114.DC1/Meehl.SOM.pdf
Useful but unsatisfying (missing key ingredients) elaboration in laymanese:
http://www2.ucar.edu/news/851/scientists-uncover-solar-cycle-stratosphere-and-ocean-connections
Pictures are worth 1000 words:
1) Figure 7:
Meehl, G.A.; & Hu, A. (2006). Megadroughts in the Indian Monsoon Region and Southwest North America and a mechanism for associated multidecadal Pacific sea surface temperature anomalies. Journal of Climate 19, 1604-1623.
http://journals.ametsoc.org/doi/pdf/10.1175/JCLI3675.1
2) Figure 1c:
Meehl, G.A.; Arblaster, J.M.; Branstator, G.; & van Loon, H. (2008). A coupled air-sea response mechanism to solar forcing in the Pacific Region. Journal of Climate 21, 2883-2897.
http://www.cawcr.gov.au/staff/jma/meehl_solar_coldeventlike_2008.pdf
3) Figure 1:
Roy, I; & Haigh, J.D. (2010). Solar cycle signals in sea level pressure and sea surface temperature. Atmospheric Chemistry and Physics 10, 3147-3153.
http://www.atmos-chem-phys.org/10/3147/2010/acp-10-3147-2010.pdf
Generalized conclusion: “The SLP signal in mid-latitudes varies in phase with solar activity […]”
Their speculation about the driver: “[…] changes in the stratosphere resulting in expansion of the Hadley cell and poleward shift of the subtropical jets […] consistent with observational studies […] which have indicated an expansion of the zonal mean Hadley cell, and poleward shift of the Ferrel cell, at solar maxima.”
Highlight: “A large response is found in the Pacific in boreal winter: a positive anomaly […] of up to 5 hPa […] in the Bay of Alaska […] We identify solar cycle signals in the North Pacific in 155 years of sea level pressure […] data. In SLP we find in the North Pacific a weakening of the Aleutian Low and a northward shift of the Hawaiian High in response to higher solar activity, confirming the results of previous authors using different techniques. […] This pattern is robust to the inclusion or not of the ENSO index as an independent index in the regression analysis.”
[Regional geography: nonstationary jet deflector waving downstream hydrologic & thermal variations; curved mountainous Pacific Northwest coast of N. America, dominant westerlies, orographic lift (think clouds) across vertical temperature profile that intersects freezing level for substantial portion of year, strong seasonal precipitation pattern, seasonally-reversing temperature-precipitation relationship (Jan-Feb at sea-level), intermittent powerful winter arctic outflow, seasonal diurnal mountain cold air drainage, strong interannual variations in TMin follow interannual NPI (an index of North Pacific pressure) even more closely than they follow SOI (i.e. ENSO), abrupt spatial gradient (proceeding from coast inland).]
—
Speculation:
Related interannual variations like GLAAM, LOD’ (not to be confused with LOD), & ENSO and multidecadal variations (which perhaps need less misleading definitions & names than PDO & AMO) are not independent of solar cycle acceleration & lunisolar tides.
I encourage exploration of (a) SAM/SOI coupling, (b) aa-conditioned NPI/aa interannual coupling, (c) LOD-conditioned NPI/AO/NAO coupling, (d) LOD’ [not to be confused with LOD] in relation to wavelet-estimated solar cycle acceleration using high-resolution wavelets (e.g. complex Mexican Hat), & (e) the integral of IOD (Indian Ocean Dipole). (At present I have neither the time nor the funding to explain further…)
Best Regards.
keith at hastings uk says: “Took me a while to realise that R Gates is picturing temp rises over centuries…”
I don’t interpret it that way. R. Gates’s comment could be taken as decadal or multidecadal as well. And my point was, there are multiyear aftereffects that contribute greatly to the long-term trends.
“Ocean surfaces have no problem dropping in temperature over so little as a season.”
Right, because ocean surface temperatures are a function of wind velocity (and dorection), not heat content of the water. Example: Dunk your arm in water. Notice how it feels. Place your arm in front of a fan. Notice how it feels now. The surface cooled down pretty quickly, didn’t it?
Show me a tropical SST anomaly and I will show you a corresponding trade wind anomaly.
That is why it is so counterintuitive for many to get their heads around that La Nina is when the ocean surface is cold but the ocean is actually heating up. El Nino is when the surface is warm but the water is cooling off.
Hey Bob, I just tried to post this on your site, not sure if it went through, so I will leave a copy here.
🙂
Bob, another paper that you may find very interesting. These guys, from model runs, say the atmosphere/clouds could be key to ENSO
http://users.monash.edu.au/~dietmard/papers/dommenget.slab.elnino.grl.pdf
Enjoy!
H/T to some guy on Real Climate
Is the observation of temperatures in these charts been seen as the release of accumulated heat as efficient cooling of warmer waters? or Is the observation of temperatures been seen as warmer waters accumulating more heat while warming cooler waters? The latter sounds daft to me too, sorry if I’m a bit off on the Climate relativistic terminology, (some of it still doesn’t make sense to me yet).
e.g. “More warming” & “Warmer temperatures” mean cold or colder temperatures have slightly increased but are still relatively cold, and “less warming”,” drop in Warmer temperatures” means cold or colder temperatures have slightly decreased but are still relatively cold or colder.
And I’ve noticed this year has been quoted as the warmest of relativistic cold global temperatures with the decrease of warming of the regional relativistic cold temperatures being colder.
but as I’m forced to study why crazy organizations and governments from around the world are meeting to raise the cost of living through carbon taxes and markets to force the natural evolution of cleaner energy by making us fund the useless current clean energy companies who sell us back the energy at a higher price, Excuse me for getting lost and asking questions.
r-mean , gaussian comparison
Bob Tisdale wrote:
>>
You wrote, “1984-85 , G resolves the cyclic nature of both data sets much better in this area compared to rm where the plot is just a noisy trough.”
To me the top graph, the gaussian filtered data, appears noisier.
>>
That’s not “noise” that’s the signal ! The gaussian filter resolves annual variations that are often lost behind the distortions of rm and the h.f. (monthly) changes that it is not removing properly. All the squiggly bits on the peaks of TLT 1994,95 and the troughs of 2004,06 should not be there. That is what you are supposed to have removed with the 13 month filter.
Bob:
>>
You wrote, “I showed in the previous thread that this is not just “different”, the gaussian is the one correctly following the trends in the data and , where there is a difference it is the running mean that is corrupting the filtered plot.”
There no intent to hide anything. The impacts of Mount Pinatubo are well understood, and I would expect the correlation to break down.
>>
Well you are at least partly wrong in that “expectation” which is why better filtering would help and why expectations should not affect what you plot. As I pointed out the correlation is still visible in the G filtered data despite the strong impact of Pinatubo. I think that strengthens the link you are proposing. Don’t let expectation get in the way of the data.
Bob:
>>
Since the gaussian filter failed to capture the effect of El Chichon, I would question the statement that “the gaussian is the one correctly following the trends in the data.”
>>
That statement is made on the basis of comparing the two plots to the original unfiltered data as I did in the previous thread. Again you are clouding your thinking with expectations. You cannot say the filter is bad because you expect the data to “capture the effect of El Chichon” . You could say that you can see El C in the original data but not in the smoothed plot , but you don’t say that and I don’t think that is the case.
gaussian, running mean, original data
Your comment proves my point nicely. The rm at the second peak is dragged down prematurely by the later drop in the data (because rm gives equal weight to the whole window of data). This attenuated the peak which you then believe to be reduced because it fits your expectation of an effect. In the original data this is not the case.
The poor response of the running mean filter had shown a non existent attenuation that you have falsely attributed to a physical effect.
This is precisely what I have been trying to warn you can happen.
Thanks for listening 😉
TheTempestSpark says:
>>
Is the observation of temperatures in these charts been seen as the release of accumulated heat as efficient cooling of warmer waters? or Is the observation of temperatures been seen as warmer waters accumulating more heat while warming cooler waters? >>
I think the general thrust is that gobal surface temps (land and sea) can be warmed by large quantities of heat coming from deeper in the oceans. Thus it is not justified to attribute all global warming to increasing GH effect that is amplified by an unproven and unwarrented positive feedback called “climate sensitivity” in the models.
Climate models that do not take account of ocean currents are political tools not scientific tools.
P. Solar says:
December 13, 2010 at 11:59 pm
I think the general thrust is that gobal surface temps (land and sea) can be warmed by large quantities of heat coming from deeper in the oceans.
Which was put there by the unusually active C20th sun, on the quiet, without the surface temperature (or our sst thermometers) noticing too much.
This is the key to ‘global warming’ IMO.
http://tallbloke.wordpress.com/2010/07/21/nailing-the-solar-activity-global-temperature-divergence-lie/
tallbloke, I think you should be careful about drawing that sort of conclusion so lightly. If you play with enough data, cropping, integrating, arbitrarily subtracting you can always find some vague “correlation”. That’s a lot of what has been going on in mainstream modeling.
I don’t find you presentation any more convincing than CO2 AGW.
You call sunspot area a “proxy” (huh) for heat but then subtract the mean from your integral. You chose to split your two temperature trends where it best fits you curve rather than where it best fits the temp itself.
You may not realise it but this is just selective reasoning.
My gut feeling is that solar activity is a major factor but your presentation does not strike me as anything other than twisting the data to fit an initial idea.
I certainly don’t think you can claim to be “nailing the lie” with that kind of analysis.
Bob, all.
if anyone wants to try a gaussian filter here is a quick lash up script I was using to create the plots I linked above. Provide the raw data file as an argument on the command calling the script.
#!/bin/awk -f
# check whether data is continuous !!
BEGIN {twt=m=ln=-1;w=6;
sigma=2;s2=2*sigma*sigma;
gw=3*sigma;
for (gtwt=j=0;j<=gw;j++) {gtwt+=gwt[-j]=gwt[j]=exp(-j*j/s2)};
gtwt+=gtwt-gwt[0];
for (j=-gw;j<=gw;j++) {gwt[j]/=gtwt};
for (twt=j=0;j<=w;j++) {twt+=wt[-j]=wt[j]=1};
twt+=twt-wt[0];
# uncomment following for improved rm filter (as used by M.O. Hadley)
# wt[-w]=wt[w]=0.5;twt--;
for (j=-w;jrmfile;
print "# ",gsfile >gsfile;
}
{
xdata[++ln]=$1;
ydata[ln]=$2;
if ((NR>w+w)&&(NR<last))
{
m=g=0;
for (j=-2*w;j<=0;j++) {m+=ydata[ln+j]*wt[j+w]}
for (j=-2*gw;j> rmfile;
print xdata[ln-gw],g >> gsfile;
}
else
{
# print $1,$2;
}
}
END { print "#window widths = "w,gw",done"}
P. Solar says: “The poor response of the running mean filter had shown a non existent attenuation that you have falsely attributed to a physical effect.”
It’s not a nonexistent attenuation. Let’s look again at the two comparison graphs you had presented earlier.
http://i55.tinypic.com/ftqbf6.jpg
About that graph, you wrote, “1980 cf 83 , the magnitude of the two peaks are nearly the same in G plot, whereas rm shows a distinct decline.”
The raw data shows that the TLT anomalies were in fact attenuated.
http://i55.tinypic.com/152m6mc.jpg
P. Solar, we could debate the differences in appearances for weeks to come. But the bottom line is I will continue to use running-mean filters. The differences between the gaussian and running-mean filters you presented here…
http://i56.tinypic.com/2958yg1.png
…would have little impact on the very rough wiggle matching I perform. Example:
http://i54.tinypic.com/25hl2tz.jpg
Keep in mind, the vast majority of the readers here and at my blog are non-technical people. A running-average and its use as a filter are concepts that are reasonably easy to grasp, and to reproduce if they wanted. Gaussian filters, on the other hand, are not a simple concept. I receive suggestions all of the time, (Bob, you should use wavelet analysis, you should standardize the data, etc.) but the average reader here and at my blog may have difficulty with them, so I don’t use them.
You wrote, “don’t be so defensive.”
I wasn’t being defensive. My comment was a response to the comments at Snow’s blog. They read, “Who is Bob Tisdale? A blogger. What are his qualifications? Zero as far as I can make out. I don’t think he’s even a weatherman. Just a blogger. Why should we care what he thinks? We shouldn’t,” and “So he’s an unqualified nobody and you can’t understand what he says. Why do you post anything about him then?” and the like.
Detailed background information would not change their opinions. Snow’s blog is inhabited by AGW proponents.
My unwillingness to provide background information is a matter of privacy and my attempt to maintain a level of it. That’s all.
Regards
Paul Vaughan says to P. Solar: If you aim to persuade Bob to change tactics…”
Isn’t going to happen. In my reply to P. Solar above, I noted: Keep in mind, the vast majority of the readers here and at my blog are non-technical people. A running-average and its use as a filter are concepts that are reasonably easy to grasp, and to reproduce if they wanted. Gaussian filters, on the other hand, are not a simple concept.
Bob, if you want to see how it compares to the original data why don’t you just refer to the graph I posted. The one you link under it is clearly a different dataset. (Again better referencing of your sources would be useful.)
The post above marked December 13, 2010 at 11:21 pm shows all three : rm , gaussian and raw . There you can see the exact effects I pointed out: on the TLT data set used the running mean artificially reduces the second peak, the gaussian shows it correctly.
If you have another dataset that has different peaks that is IRRELEVANT.
If you don’t want to see that , go look at any other graph except the one I posted, it is you democratic right. But as I said before, if you use gaussian you will probably find better correlation and more evidence of what you are putting forwards.
>>
A running-average and its use as a filter are concepts that are reasonably easy to grasp, and to reproduce if they wanted. Gaussian filters, on the other hand, are not a simple concept.
>>
as you can see from the script I posted it is no more complicated than a weighted mean. Call it a weighted mean, it’s less frightening. If your target audience is that dumb it will not make any difference anyway. Your graphs are labeled “smoothed w/13 month filter”. That description would equally apply to both cases.
I really don’t give a damn what filter you use, it’s your blog, you’re the man.
I thought it would be useful for you to know how rm distorts data but you seem intent not to see it. That’s fine with me.
Since your audience here is less dumbed-down, I think it is relevant to this thread. It was certainly enlightening to me to have a concrete example of how one can fall into the trap.
Happy data digging.
P. Solar says:
December 14, 2010 at 2:28 am
You call sunspot area a “proxy” (huh) for heat but then subtract the mean from your integral. You chose to split your two temperature trends where it best fits you curve rather than where it best fits the temp itself.
Constructive criticism is always welcome, and spurs me to better explain what I’ve found out.
I’m not calling sunspot area itself a proxy for heat. What the legend under the graph says, (which isn’t as legible as it might be) is that the cumulative count of sunspot area (or sunspot number works too) departing from the long term mean (which happens to coincide with the ocean equilibrium figure derived by other means also), is a good proxy for retained heat of insolation, or to put it another way, Ocean Heat Content.
If you look again at the split in the temp curve where I ran the linear trends to and from, you can see that the oceanic oscillations responsible for the low temps around 1910 andf the high temps around 1940 more or less cancel each other out to leave the longer term underlying trends inflecting where I split them. This coincides with the longer term trend inflection point in the sunspot area count too. I suspect in retrospect, we’ll get the same sort of trend inflection around the modern period too. The Big El Nino’s of 1998 and 2010 will lift the temperature curve before the big drop, currently presaged by the fall in global and especially atlantic OHC.
I agree more supporting data is needed, and that is why I threw it out there for comment, to see what others had found too.
Thanks for taking an interest.
P. Solar says:
December 13, 2010 at 11:21 pm
http://i55.tinypic.com/ftqbf6.jpg
There no intent to hide anything. The impacts of Mount Pinatubo are well understood, and I would expect the correlation to break down.
I have also found the effect of Pinatubo in a plot I recently made of the SOI subtracted from the longwave radiation flux as compared to detrended global temperature:
http://tallbloke.files.wordpress.com/2010/12/olr-soivst.jpg
However, the effect of Pinatubo was augmented by a concurrent drop in solar activity, and I’ve been working on a way to represent that too. More on my blog soon.
I won’t go into it more here since it’s OT but you are misunderstanding what the term proxy means. Check it out.