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.)
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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
Why let actual measured temperatures get in the way of conjecture? Where’s the money in that?
Thanks, Anthony.
lol wut?
I see a lot of text but the title “Tisdale K.O.e’s GISS’s latest “warmest-year nonsense”” doesn’t bear on it at all.
It’s like you’ve plonked an essay on here and just given it a “GISS has been disproved” title.
REPLY: Warmists like yourself who snipe from shadows really don’t have any sense of humor do they? – A
I have been in South America in November for vacations:
In Peru, I was shivering with cold. In Chile the nights were very cool. I had never experienced such a cold November in Argentina (I am Argentinian) . South America have experienced this year one of the coldest winters of the last years, with thousands of dead fishes in the rivers of Bolivia and snow in Brazil.
Now I am in Norway, which has experienced one of the coldest Novembers of the last century and December continues to be very cold.
We all know what has happened last week in UK, in France (and in almost all Europe) and today in Turkey. And also today in Buenos Aires, Argentina, the temperature is of 20C when normally should be 30C at this time of the year. Could you explain me where is that excess of heat that GISS talk about??
What is the relationship between the headline and the discussion?
Facts, facts, facts,….
if they don’t match the reality you want manufacture them…. Briffa, Mann, Hansen, and the IPCC doesn’t seem to want to learn… push the crap, get it out to create fear and get the one world control system in place… its all about control and the agenda…
the sorry state of our scientific community… GISS how pathetic…
Joe Romm is already getting dramatic regarding heat. To me it looks like the heat is concentrated at the north pole where they don have readings so they use ultra hig speculation to fill in space.
Really important article Bob.
The Kuroshio Extension should be added into the list of important ocean cycles.
I just added the data to a few of my reconstructions and it makes a significant improvement. It even enhances the stats on the ENSO component which is perhaps unexpected. I’ll have to play around with it a little more, test a few lags etc., but this could be a big finding.
ps: the global warming residuals fall considerably on the RSS and UAH satellite reconstructions so there is pretty small warming left.
Figures 14 & 17 make a good point. Very nice work, Bob.
Brilliant, Important, Brilliant and Important, Thanks Bob!!
K.R. Frank
Bill Illis: I’d be interested in seeing your reconstructions when you’re done.
Rather obsure post. Why don’t the conclusions follow from the data in a clear, easy to follow argument?
A good summary explaining why and how lag effects can operate in connection with ENSO effects on the atmosphere.
It also confirms the importance of integrating ALL the ocean cycles at any given moment before discussing the anticipated effect on tropospheric temperatures.
The oceans clearly control air temperatures and can be invoked to explain ALL observed tropospheric temperature changes without needing any input from GHG changes.
The next big question is as to how solar variability can be linked in mechanistically with SSTs to achieve observed climate cycles over centuries such as the cycling from MWP to LIA to date.
Recent climate changes having been seen to occur in parallel with the changes in solar variability should give us useful pointers over the coming years.
Does the sun change the tropospheric pressure distribution to alter global cloudiness and albedo thereby skewing the balance between El Nino and La Nina for centuries at a time?
This is excellent and quite informative. One general question however: Isn’t this sort of residual heat from an El Nino always present when averaged over many decades such that the effect would be averaged in and included in prior temperature readings? In short, if the decade of 2010-2019 (which will very likely include several El Nino/La Nina cycles) is warmer than the decade of 2000-2009, (which was undiputably the warmest on instrument record) doesn’t this sort of residual El Nino effect not really matter in the long run if we are looking at a warming trend over a longer period of time?
Looking athe the GISS year Anomoly map, my area is swathed in yellow, no two ways about it – they have as us a positive .2 c degree anomoly. I’ve done the maths from the BOM in Australia for my Area and we are 3 whole degrees cooler this year than last – how the hell does tha make a positive anomoly? THey are lying – pure and simple. Everyone shivvering their arses off around the world knows it.
With apologies to carollers around the globe:
Hark! The climate modellers sing
Climate change is happening;
Funds must now collected be
From the rich,(that’s you and me.)
Joyful, all ye nations, rise
Solve the problem in the skies
With the Cancun host proclaim
Earth will no more be the same
Hark! The climate modellers sing
Climate change is happening.
Let’s all sign treaties that bind
We’re all doomed unless we find
Consensus at the Cancun talks;
They will fail if China balks.
The Third World is the victim here;
Western guilt’s too much to bear.
Polar bears are on thin ice
Can’t you see this isn’t nice?
Hark! The climate modellers sing
Climate change is happening
Temps are rising, so’s the sea.
The Maldives? They’ll no longer be.
Floods and droughts; more sun; more snow,
All these things just go to show
Just how bad the world has got
The climate is completely shot
All mankind must take the blame
It’s got much worse since they came.
Hark! The climate modellers sing
Climate change is happening
Mother Earth must be adored
Eden, now, must be restored
Western life will kill us all
Burning all the evil oil
CO2’s a poisonous gas
Can’t you see? You silly ass!
Save the planet while you can
Fossil fuels aren’t hard to ban
Hark! The climate modellers sing
Climate change is happening
Onion says:
December 12, 2010 at 3:09 pm
It’s like you’ve plonked an essay on here and just given it a “GISS has been disproved” title.
It’s like you just plonked a comment here and thought it washed away what the post says.
Do you post comments under another name also?
This doesn’t make any sense. You guys (Bob & Anthony) are way off base with this one. You have 21 charts that do nothing to advance the cause that this is not the warmest year on record. Conversely, you seem to be arguing that it *is* the warmest year on record but that you have a reasonable explanation.
Supppose there weren’t leftover warm water from an el nino year…. is it still the warmest year on record? If I believed the temperature “measurements”, then, yes. See what I mean? The cause for it being so is irrelevant to whether it is or not. I’m guessing the GISS is lying somehow. I would change the title.
This makes excellent sense when using anomalies that average the whole globe. I am correct in my understanding that the stronger the El Nino the stronger the residual warming effects in the Western Pacific and this in turn tips the whole global average? If so, this is quite an important observation.
JDN says: “This doesn’t make any sense. You guys (Bob & Anthony) are way off base with this one. You have 21 charts that do nothing to advance the cause that this is not the warmest year on record.”
The post deals with attribution, not with whether or not this year is warmest. As I noted on the “Hansen feels the need to explain why GISS is high in the midst of frigid air” thread…
http://wattsupwiththat.com/2010/12/12/hansen-feels-the-need-to-explain-why-giss-is-high-in-the-midst-of-frigid-air/#comment-549207
…
Hansen writes, “The warmth of 2010 is especially noteworthy, given the strong La Nina that developed in the second half of 2010.”
But what he fails to tell you is what happens to the leftover warm water from the El Nino during the transition to La Nina. It gets returned to the West Pacific by a slow-moving Rossby wave and spun up into the Kuroshio-Oyashio Extension (KOE) in the northwest North Pacific where it continues to release heat during the La Nina.
http://bobtisdale.blogspot.com/2010/12/enso-related-variations-in-kuroshio.html
######
And that post is the one Anthony co-posted here.
Regards
R. Gates says: “This is excellent and quite informative. One general question however: Isn’t this sort of residual heat from an El Nino always present when averaged over many decades such that the effect would be averaged in and included in prior temperature readings? In short, if the decade of 2010-2019 (which will very likely include several El Nino/La Nina cycles) is warmer than the decade of 2000-2009, (which was undiputably the warmest on instrument record) doesn’t this sort of residual El Nino effect not really matter in the long run if we are looking at a warming trend over a longer period of time?”
It’s the residual effect that’s creating the trend.
Since the North Atlantic SST anomalies and the Northern Hemisphere TLT anomalies (north of 20N) and GISS LOTI (20N-65N) do not respond to the La Nina events discussed in the post, those datasets in effect rise in steps and they should make yet another step up after the 2009/10 El Nino & 2010/11 La Nina “cycle” is concluded. In other words, the reason the decade from the late 1980s to the late 1990s is warmer than the one before it is due to the response to the 1986/87/88 El Nino and 1988/89 La Nina. Same thing holds true for the period after the 1997/98 El Niño and 1998-2001 La Niña. It’s warmer than the period before it due the upward step.
Keep in mind that the East Indian and West Pacific Oceans (60S-65N, 80E-180, or about 25% of the global ocean surface area) also make similar upward steps. That’s one of the subjects I’ve repeatedly posted about for almost two years.
BillD says: “Why don’t the conclusions follow from the data in a clear, easy to follow argument?”
Please clarify, so that I can improve my future posts,
Bob, just a preliminary look but the Kuroshio will certainly be added to my reconstructions.
Not a huge improvement, but more than enough to include. I’ve got the coefficient at 0.09 to 0.10 (which means it has an impact of +/-0.1C or so) and no lag seems to be required. [By contrast the ENSO is +/- 0.2C so that makes it half as important as the ENSO which makes it one of three or four most important ocean cycles in my opinion]. In addition, it bumped up the coefficients for the ENSO and their significance level which is also important].
It explains a certain amount of the extra warming in the the 2000s, the 1940s, and the relative cooling in the mid-1980s, and the early 1900s.
Just RSS here. I want to look into a few more things before going any farther.
http://img543.imageshack.us/img543/4809/rssmodelnov10.png
Global Warming/CO2 residuals. Down quite a bit from before.
http://img823.imageshack.us/img823/9959/rsswarmingnov10.png
Good work.
The gist of Tisdales post seems to be that Hansen didn’t mention the remnants of the El Nino warm pool in his analysis. The anomaly map that accompanied Hansen’s article did show that the ocean in that area was abnormally warm, with small portions in the 1 to 2 degree range.
Hansen highlighted the 10C anomaly in the Hudson’s bay area due to the absence of sea ice, and gave a possible explanation of why Europe turned out to be so cold. This was relevant and interesting.
Even with the residual warm pool left over after the El Nino subsided, the La Nina would not be expected to influence the global average temperature as strongly as an El Nino situation, which provides a much larger area of warm water at the surface of the Southern Pacific. So it is perfectly legitimate for Hansen to point out that the El Nino has subsided.
None of this contradicts Hansen’s analysis which indicates that 2010 will be close to a record warm year.
Bob – I have to explain ENSO to you. But first, where did you get that subtropical countercurrent? That’s a new one for me – my chart does not have it. Couple of things I’ve noticed with your article. First, figure 9 compares RSS TLT (70S-20N) and Nino 3.4 and they convey basically the same information except for a noticeable and variable time shift. That should be no wonder because Nino3.4 is part of that TLT. I am not exactly sure why they use it because it encompasses a vastly greater and more heterogeneous area than Nino3.4 does. That Nino just sits right smack in the middle of the equatorial countercurrent, watches El Nino waves go by, and is good at it. That is because all ENSO waves use the equatorial countercurrent to get to South America. The ENSO oscillation itself is created by trade winds that push the equatorial currents west until they are stopped by the Philippines and by New Guinea. The water has been warmed by being pushed across the tropical ocean and feeds the Indo-Pacific Warm Pool. Some of it does leak between the islands into the Indian Ocean but eventually an El Nino wave builds up and crosses the ocean in the opposite direction using the equatorial countercurrent. The timing of these El Nino waves is determined by wave resonance and depends upon the dimensions of the ocean basin itself. Once it gets to South America it runs ashore at the equator and spreads out north and south. This spread-out water now warms the air, warm air rises, interferes with trades, mixes with global circulation, and raises global temperature by half a degree. But any wave that runs ashore must also retreat. When the El Nino wave retreats water level behind it drops by half a meter or more, cold water from below wells up to fill the space, and a La Nina has started. As much as the El Nino raised the global temperature the La Nina will now lower it. Since we are dealing with a resonant oscillation the time series of temperature should look like a sinusoid. Some parts of the satellite record in the eighties and nineties are almost but not quite like that because of numerous interferences due to the long path length involved. The most prominent is the 1998 super El Nino that does not even belong to ENSO. I looked for historical records of ENSO and found a version of HadCRUT3 that shows them back to 1850. It has bimonthly resolution but they have processed it so that both peaks and valleys are pointed, not rounded as they should be. It was handy for locating the timing of volcanic eruptions but there must be data somewhere that are not processed by people who do not understand what they are doing. What I have described is the outline of a dynamical theory of ENSO that for reasons unknown no one else had thought of. Some came close – whoever located NINO3.4 in the middle of the equatorial countercurrent could have taken the next step simply by observing the time lag involved and realizing that a huge water mass was in motion. The total mass of water taking part in the ENSO oscillation is so large that it will cause a periodic change of the angular momentum of the earth. This can be observed by measuring atmospheric shear at high altitude and was already known in 1984 although they did not understand the cause.
Bob Tisdale says:
December 12, 2010 at 5:47 pm
R. Gates says: “This is excellent and quite informative. One general question however: Isn’t this sort of residual heat from an El Nino always present when averaged over many decades such that the effect would be averaged in and included in prior temperature readings? In short, if the decade of 2010-2019 (which will very likely include several El Nino/La Nina cycles) is warmer than the decade of 2000-2009, (which was undiputably the warmest on instrument record) doesn’t this sort of residual El Nino effect not really matter in the long run if we are looking at a warming trend over a longer period of time?”
It’s the residual effect that’s creating the trend.
______
Thanks for your response, but in the long run, if this continues decade after decade, isn’t heat just heat…regardless of whether is comes from residual effects of ENSO or whatever. I mean, if in the next 100 years, we get 8 out of 10 decades that are warmer than the previous, then that is essentially what GCM’s are predicting from AGW, such that if you could trace some of that heat from residual ENSO effects, who cares? ENSO redistributes heat but does not create it, since it all begins as solar, and so even if we get some of the excess heat from AGW altering the the ENSO cycle and if the effect is delayed, then the effect would be exactly what you’re observing, (i.e. the heat will eventually show up in some measurement period). Residual El Nino heat is still heat and if the trend is greater amounts of residual heat, no matter when it’s measured, we still have an uptrend.
“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?”
Quite possibly, or even pressures aloft. I would be willing to bet that if you looked at jet stream maps of the periods involved, they would be quite different. Persistent regions of higher/lower pressure might have been displaced slightly in the later event compared to the earlier events so wind patterns might have been much different. Slack winds over the region would certainly cause sea surface temperatures to rise as, after all, sea surface temperature anomalies are often more of a wind proxy than anything else. A little bit of wind can cool the surface of a “hot blob of water” down to “normal” even though the underlying water is still warmer than normal.
Onion says:
December 12, 2010 at 3:09 pm
If anyone else comments about the title not matching the essay I’ll pull my hair out.
Peal back your blindfold Onion, K.O.e’s as in Kuroshio-Oyashio Extension (KOE)
Geddid?
Hanson defended his data with drivvel that showed he didn’t understand why 2010 temperatures are what they are. Bob just told him and some of us learned a lot.
Obviously some didn’t.
I think I have found the source of all that global warming on the ground here in Wallowa County. Was wondering how a cold Pacific could have so much water vapor coming out of it.
The mighty conveyor built speaks volumes don’t it.
Bob Tisdale says:
December 12, 2010 at 5:49 pm
BillD says: “Why don’t the conclusions follow from the data in a clear, easy to follow argument?”
Please clarify, so that I can improve my future posts,
=================================
I think pare it down, some, Bob. It is a difficult read. For us laymen (really for us all), less is more.
And make more concise conclusions at the end, rather than ending in a rhetorical question.
Those are my suggestions.
All the best,
Chris
R. Gates says:
“Thanks for your response, but in the long run, if this continues decade after decade, isn’t heat just heat…regardless of whether is comes from residual effects of ENSO or whatever. I mean, if in the next 100 years, we get 8 out of 10 decades that are warmer than the previous, then that is essentially what GCM’s are predicting from AGW, such that if you could trace some of that heat from residual ENSO effects, who cares?”
=================================
Reposted for sorry effect.
Preposterous, ridiculous, juvenile, CIRCULAR REASONING.
Chris
Norfolk, VA, USA
Well, global hot spots are heat shedding mechanisms, more than a sign of global warming. It’s the overall heat balance that’s important and the GISS “average global temperature anomaly” is largely meaningless, given the huge thermal capacity of the oceans (1100 x the atmosphere) and latent heat and humidity effects. There’s just no place to stick a big thermometer to get a single measurement that represents the equilibrium temperature of the earth.
Great post, Bob.
Well, maybe not one, but maybe a dozen, in the abyssal ocean might do it.
Your animation captures nicely the effect of El Nino/La Ninas on the pumping of warm water northward and reminds me of the PDO. During the PDO cool phase, the area near the KOE develops warm anomalies and there are fewer El Ninos and more La Ninas. During the PDO warm phase as we have been in for most of the last 30+ years, there are fewer La Ninas and more El Ninos and the KOE areas anomalously cool.
However I am not sure how directly the KOE oscillations account for warming trends , other than its connections to the PDO and its impact on Arctic ice thinning , that blocks less heat escaping from the ocean and the resultant warming of recorded temperatures, which is not the same as a warming planet. Ironically while they measure warmest temperatures, the oceans are releasing more heat which in the long run will cool the oceans.
Has anyone had a chance to read this paper?
http://journals.ametsoc.org/doi/abs/10.1175/2010JCLI3682.1
Warming of Global Abyssal and Deep Southern Ocean Waters Between the 1990s and 2000s: Contributions to Global Heat and Sea Level Rise Budgets
Sarah G. Purkey and Gregory C. Johnson
Just answer this simple question: did more energy arrive on earth than left earth in 2010? I don’t care what the temperature was – did more energy arrive than left? Who can answer that? Anyone? Come on – it can’t be that hard. Nobody knows?
I didn’t think so.
I’m new, a layman and I’m really having trouble understanding how this concludes to match the title. Could you PLEASE edit the article to end with a statement, instead of a question?
Good stuff Bob!
Now if I had the time I would have a look at how the KOE ties in with the PDO – have a look here: http://130.207.67.194/npgo/enso.html
Cheers mate!
@-jorgekafkazar
“There’s just no place to stick a big thermometer to get a single measurement that represents the equilibrium temperature of the earth. ”
Given the massively dominant role of the oceans as a reservoir of the thermal energy the volume change from thermal expansion and addition of land-based ice is a good global measure of changing thermal content.
Sea level rise is a big thermometer that rises in proportion to the extra energy absorbed.
Just a guess.
Current Pacific situation according to some of my research is very reminiscent of a period 1900 to 1915 when all pointers are converging to a low value, when there was substantial dip in the
global temperatures .
Some time ago while looking through the sea level anomaly maps at this site
http://bulletin.aviso.oceanobs.com/html/produits/aviso/welcome_uk.php3
I noticed a phenomenon that I found quite puzzling. If you peruse a selection of the daily SLA maps you’ll see several persistent bands, one nearly circumnavigating Antarctica, one running from the mid Atlantic states of the US on a vector toward Britain, and one running east from Japan about in the area described as the “mixed water region” in Bob’s figure 1. What distinguishes them is a consistent pattern of nearly maximum negative anomaly trend with nodes of near maximum positive anomaly trend interspersed. I haven’t examined every one of the daily maps available, but each that I have has the pattern present to some extent, irrespective of year, month or season. Given the locations involved it seems to suggest that when currents with widely variant temperatures blend that, rather than being well mixed, the different temp waters maintain the differential for some considerable length of time. The phenomenon is so consistent that it even appears in somewhat muted form on a map which averages anomaly trends over decades.
http://www.aviso.oceanobs.com/en/news/ocean-indicators/mean-sea-level/
I’ve tried on several occasions to find further information on this phenomenon, but it’s rather difficult to craft a search enquiry that is on point for this. Does anyone know if this has been discussed or named in the oceanographic literature, or know of a link where this is covered?
Jim Steele says: “Ironically while they measure warmest temperatures, the oceans are releasing more heat which in the long run will cool the oceans.”
And ocean heat content (0-700meters) in some ocean basins has been dropping for the past few years:
http://bobtisdale.blogspot.com/2010/10/update-and-changes-to-nodc-ocean-heat.html
Jim Steele: Oops. Sorry. Somehow I snipped the “m” in your first name.
[I have corrected the spelling …. bl57~mod]
Has the residual area of warming always been captured in the global temperature record since 1860?
Moderator
I am absolutely not a warmist but I can’t see any humour in the the title being mis-connected to the essay. It doesn’t bother me one little bit and I find Bob’s works far more plausible than anything I have seen from the warming crew here and the ‘Team’ (example. The Dessler Paper, the worst scientific paper I have ever read and I’ve read a few in my long life).
[REPLY: Note the very first part:
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.
Quoting Bob to create the title is valid. .. bl57~mod]
R. Gates says: “Thanks for your response, but in the long run, if this continues decade after decade, isn’t heat just heat…regardless of whether is comes from residual effects of ENSO or whatever. I mean, if in the next 100 years, we get 8 out of 10 decades that are warmer than the previous, then that is essentially what GCM’s are predicting from AGW, such that if you could trace some of that heat from residual ENSO effects, who cares?”
The creators of the GCMs should care and so should you, because it means GCMs and their creators incorrectly attributed the source of the global warming. Also, there’s no indication it will continue decade after decade. It didn’t continue from 1945 to 1975.
You added, “ENSO redistributes heat but does not create it…
El Niño events discharge heat and La Niña events redistribute it. La Niña events also recharge the heat released during the El Niño in part or in whole.
You continued, “…since it all begins as solar, and so even if we get some of the excess heat from AGW altering the the ENSO cycle…”
There is no evidence that the ENSO cycle has been altered by AGW and there’s no evidence that AGW has had a measurable impact on OHC over the past 55 years. Refer to:
http://bobtisdale.blogspot.com/2009/09/enso-dominates-nodc-ocean-heat-content.html
And:
http://bobtisdale.blogspot.com/2009/10/north-atlantic-ocean-heat-content-0-700.html
And:
http://bobtisdale.blogspot.com/2009/12/north-pacific-ocean-heat-content-shift.html
Onion says:
December 12, 2010 at 3:09 pm
lol wut?
I see a lot of text but the title “Tisdale K.O.e’s GISS’s latest “warmest-year nonsense”” doesn’t bear on it at all.
It’s like you’ve plonked an essay on here and just given it a “GISS has been disproved” title.
Just for you, because we care.
This graph is cumulative in that it takes the December anomaly of each data set and sets it to zero. The rise or fall compared with the December anomaly for each month thereafter is added to the preceding month/months. This tells you what months are deflating or inflating the year average. This is not a moving average and does not give you the average. It shows that from September GISS data has been inflating the year average whilst UAH data has been deflating the year average.
http://i599.photobucket.com/albums/tt74/MartinGAtkins/Cum-GISS-UAH.png
Arno Arrak says: “Bob – I have to explain ENSO to you. But first, where did you get that subtropical countercurrent? ”
That’s as far as I read, Arno. Or maybe you should read my posts that explain ENSO in detail. If you had you wouldn’t have been surprised by the Rossby wave. Start here:
http://bobtisdale.blogspot.com/2010/08/introduction-to-enso-amo-and-pdo-part-1.html
There are links to other posts included.
I first illustrated the Rossby Wave in the December 18, 2008 post:
http://bobtisdale.blogspot.com/2010/08/introduction-to-enso-amo-and-pdo-part-1.html
Bob I wonder if I’m barking up the wrong tree here.
If I look at your fig. 2 side by side with a chart of the Thermocline Circulation, the Kuroshio and Oyashio Currents look trapped, almost like an eddy.
If no one will put people out of there misery I will: it’s a pun, OK?
KOed = knocked-out, also KOE = Kuroshio-Oyashio Extension. Bob hasn’t knocked-out the GISS warmest alarmism he’s Kuroshio-Oyashio’d it. It doesn’t, as has been noted, rebut it, it just explains it.
eadler says: “None of this contradicts Hansen’s analysis which indicates that 2010 will be close to a record warm year.”
It wasn’t intended to contradict. This post is, in effect, a discussion of attribution.
You wrote, “Hansen highlighted the 10C anomaly in the Hudson’s bay area due to the absence of sea ice, and gave a possible explanation of why Europe turned out to be so cold. This was relevant and interesting.”
How would he know what the SST anomalies are in the Hudson Bay or in Baffin Bay? They aren’t represented by GISS data. GISS deletes SST anomaly data in areas where there’s seasonal sea ice and extends land surface data (with its greater variability and higher trends) out over the oceans and those bays.
http://bobtisdale.blogspot.com/2010/05/giss-deletes-arctic-and-southern-ocean.html
R. Gates:
“….we still have an uptrend….”
Yes and even if you accept that the GISTEMP/HadCRUT record is free of jiggery-pokery, then that uptrend for the period since c.1950 for which the IPCC proclaims, with tangled logic, that human GHG emissions (mainly CO2) are over 90% likely to be over 50% responsible (GHG emissions prior to c. 1940 being irrelevant), then the most those emissions can be said to be responsible for is about 1.2°C/century and falling due to its logarithmically declining nature.
As others have commented, on the evidence of over 60 years, without the initial ‘C’, alleged AGW is unexceptional.
http://wattsupwiththat.files.wordpress.com/2009/04/lanser_holocene_figure5.png
The title is maybe playful for insiders, not for people that came here for a couple of minutes.
Arno Arrak: I went back and read your comment. You’re missing a few things. I know, it’s tough to include all aspects of ENSO in a comment. Here are a few that you missed. La Nina events return the leftover heat from the El Nino back to the western tropical Pacific and they redistribute it as well, as was illustrated in this post. And if the redistribution to the KOE could be considered a part of the La Nina then your statement, “As much as the El Nino raised the global temperature the La Nina will now lower it,” is incorrect. It is also incorrect when you consider the cumulative effect that El Nino/La Nina events have on the SST anomalies of the East Indian and West Pacific Oceans; that is, the SST anomalies of the East Indian and West Pacific Oceans are wamed by both the El Nino and La Nina events. I’ve written dozens of posts on that subject over the past two years. In other words, El Nino and La Nina events do not have an equal and opposite effect on Global Temperatures. That is a myth that needs to be ended.
Your comment also missed a discussion of the recharge that takes place during the La Nina, and this is accomplished by the increased strength of the trade winds reducing cloud cover, which allows more downward shortwave radiation to warm the tropical Pacific east of the western Pacific Warm Pool.
Alex says:
December 13, 2010 at 1:57 am
The title is playful for all those with a sense of humour and an eye for detail. True for regulars and passers by alike.
ad says: “If no one will put people out of there misery I will: it’s a pun, OK?
“KOed = knocked-out, also KOE = Kuroshio-Oyashio Extension. Bob hasn’t knocked-out the GISS warmest alarmism he’s Kuroshio-Oyashio’d it. It doesn’t, as has been noted, rebut it, it just explains it.”
I liked Anthony’s double entendre.
I can’t help but wonder if there are not two other rare cases that have the possibility to cause major step changes. The usual La Nina is strong trades and low clouds. The usual El Nino is slack trades with increased clouds. What would happen in the other two cases? By this I mean strong trades with clouds, for example. This might reduce the surface temperatures AND reduce the absorption of solar energy resulting in a step down of oceanic surface temperatures for a considerable period. The other case of slack trades and less clouds would result in a step up as equatorial waters would absorb greater than normal energy.
I wonder … did the 1993 El Nino event experience clearer than normal skies and so absorb a lot more energy than normal and so set the stage for what is called “the great climate shift of 1975”? In other words, if the ’83 El Nino saw clearer conditions than previous events, maybe that was what really “tipped” things into a long term warmer regime. Wondering if all it takes is a “sunnier than normal” El Nino or “cloudier than normal” La Nina to tip things in one direction or the other.
“The usual La Nina is strong trades and low clouds”
Meant “few” clouds (in a relative sense).
“Njorway says:
December 12, 2010 at 3:18 pm
I have been in South America in November for vacations:
In Peru, I was shivering with cold. In Chile the nights were very cool. I had never experienced such a cold November in Argentina (I am Argentinian) . South America have experienced this year one of the coldest winters of the last years, with thousands of dead fishes in the rivers of Bolivia and snow in Brazil.
Now I am in Norway, which has experienced one of the coldest Novembers of the last century and December continues to be very cold.
We all know what has happened last week in UK, in France (and in almost all Europe) and today in Turkey. And also today in Buenos Aires, Argentina, the temperature is of 20C when normally should be 30C at this time of the year. Could you explain me where is that excess of heat that GISS talk about??”
Similarly here in Australia over most of he country below average temps and abov e average rains for months and months on end…Australia’s temps have taken a huge “southward” nose dive for most this year!…and the trend looks set to continue for a long time yet.
“did the 1993 El Nino event experience clearer than normal skies”
Sheesh, meant 1983, not 1993.
Baa Humbug says: “If I look at your fig. 2 side by side with a chart of the Thermocline Circulation, the Kuroshio and Oyashio Currents look trapped, almost like an eddy.”
It’s not an eddy. The KOE is the western portion of the North Pacific Current, which stretches from Japan to west coast of the U.S., where it splits into the California and Alaska Currents. As soon as the La Nina subsides, most (but not all) of the elevated SST anomalies there drop.
The appearance of it being trapped may be due to the persistence of the ENSO pattern. There are a number of factors that contribute to the persistence of the elevated SST anomalies in the KOE, one being Sea Level Pressure, another bsing the reemergence mechanism. I have a quick post on that phenomenon here:
http://bobtisdale.blogspot.com/2009/06/reemergence-mechanism.html
Reemergence was one of the factors Newman et al (2003) used to conclude that the PDO is dependent on ENSO on all timescales. Link to Newman et al:
http://www.cdc.noaa.gov/people/gilbert.p.compo/Newmanetal2003.pdf
crosspatch says: “I can’t help but wonder if there are not two other rare cases that have the possibility to cause major step changes. The usual La Nina is strong trades and low clouds. The usual El Nino is slack trades with increased clouds. What would happen in the other two cases?”
During the El Nino, the increase in total cloud amount over the central and eastern tropical Pacific results from the relocation of the warm water from the PWP. The convection and clouds stay with the warm water. The decrease in cloud amount versus “normal”, as you’ve noted, has to do with the increased strength of the trade winds pushing the clouds out of the way during the La Nina. For the other conditions to exist, they really wouldn’t be ENSO related.
Hi Bob,
I’m curious. You had an excellent post, so I decided to share it on a site called http://www.theenvironmentsite.org/forum/climate-change-forum/ they are wondering what credentials you have. Unfortunately, I can’t find any. Can you help on this? I would greatly appreciate it.
-Snow
Thnku Bob the links are very informative, though I have to read these things more than once.
p.s. before spending further $9, give me a yell, I gained lots of practice chasing down papers during the Citizens Audit Project. A free copy of Hanawa Sugimoto is HERE
If you look at today’s equatorial Pacific conditions, Ocean SSTs are -1.6C. Just a year ago, it was +1.8C. Where did all that warm water go.
Some of it released its heat during 2010 and cooled off. Some of it was pushed through the Indonesian Islands into the Indian Ocean. Some of it was pushed down at New Guinea to about 250 metres depth and is now moving east and is at 165E.
And some of it filters north into the Kuroshio. (I think some of it flows underneath the surface initially but that is for another day).
The Kuroshio is just like the Gulf Stream. The same processes drive both currents – its just that the equatorial Pacific has more oscillation of warm and cold than the equatorial Atlantic has. So the Gulf Stream is always warmer while the Kuroshio is warm and cold.
Bob has been looking for the sources of the lag and step impacts from the ENSO. He has now found one of the sources – the Kuroshio. The warm or cool water actually extends down to about 400 metres and extends into a wide area which means it is carrying alot of energy with it.
You can see the lag impacts of last year’s El Nino right now in the today’s SST map in the Kuroshio area.
http://www.osdpd.noaa.gov/data/sst/anomaly/2010/anomnight.12.13.2010.gif
You can see how the global ocean currents operate in this animation of the last 30 days from the US Navy. The Kuroshio and its tie-in to the ENSO circulation is plainly evident.
http://www7320.nrlssc.navy.mil/global_nlom32/navo/WHOSP1_nlomw12930doper.gif
I think my other PC is a closet warmist – it crashes when I show the expanded version of this post ! Anyone else have the same problem ?
Old machine running Chrome under XP, but this laptop’s the same and seems OK…
Keep up the good work !
Snowlover123 says: “I’m curious. You had an excellent post, so I decided to share it on a site called http://www.theenvironmentsite.org/forum/climate-change-forum/ they are wondering what credentials you have.”
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.
Regards
crosspatch asked: “did the 1983 [corrected per your note] El Nino event experience clearer than normal skies”
The El Chichon eruption occurred during the development of the 1982/83 El Nino. A couple of years ago, I found a few papers that speculated about the effect the volcanic eruption had on the El Nino event itself, but they admitted it was speculation and that there was no way to document or estimate it.
The year isn’t over yet. Wait until NASA gives us the adjusted temps for this year and gives us the adjusted temps for the previous years. It will all become apparent then.
Bill Illis (December 12, 2010 at 6:44 pm)
Thanks for the preview.
I’ve been pondering, with no real answers yet, how to somehow come up with a factor that accounts for the response of the East Indian and West Pacific Oceans. As you’re aware, that portion of the global oceans can rise cumulatively in response to both El Nino and La Nina events if the significant El Nino is followed by the La Nina. The effect would not take place with the lesser El Nino events like the ones in 2002/03 and 2004/05. It might be a factor of the temperature transition from the peak of the El Nino to the low of the La Nina. There are also decay rates to consider and “gyre mixing” that would have to be considered. Whatever, it wouldn’t be simple.
“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?”
Different winters.
Bob Tisdale says:
December 13, 2010 at 7:37 am
The El Chichon eruption occurred during the development of the 1982/83 El Nino. A couple of years ago, I found a few papers that speculated about the effect the volcanic eruption had on the El Nino event itself, but they admitted it was speculation and that there was no way to document or estimate it.
Hi Bob, thanks for another excellent post. I have a new post up that might shed light on the effect of volcanoes (I know I know 😉
http://tallbloke.wordpress.com/2010/12/13/working-out-where-the-energy-goes-part-1/
I’d appreciate your input, because I need to solve a mystery about OLR data sets and you may be able to help.
@Bob Tisdale
But what he fails to tell you is what happens to the leftover warm water from the El Nino during the transition to La Nina. It gets returned to the West Pacific by a slow-moving Rossby wave and spun up into the Kuroshio-Oyashio Extension (KOE) in the northwest North Pacific where it continues to release heat during the La Nina.
Why is this important to the discussion of global warming or the warmest year “nonsense”?
dp says:
December 12, 2010 at 11:34 pm
Just answer this simple question: did more energy arrive on earth than left earth in 2010? I don’t care what the temperature was – did more energy arrive than left? Who can answer that? Anyone? Come on – it can’t be that hard. Nobody knows?
I didn’t think so.
==========================
The answer is we don’t know! – and we will never really ‘know’. Which is precisely why the AGW shambles is so long lasting. Make an observation, pose a theory, and wait for somebody to disprove it. The AGW theory may as well be based on something else, like cow dung, or sea algae, or people with false teeth (steradent produces CO2 I believe?).
This post is very good though, clearly showing lag effects and ‘reasons’ for potential localised oceanic ‘heat’ retention. It is exactly this kind of information that the likes of Hansen and Schmidt (so tenpting to spell it wrong!) will NOT publish.
Bob Tisdale says:
December 13, 2010 at 1:19 am
The creators of the GCMs should care and so should you, because it means GCMs and their creators incorrectly attributed the source of the global warming.
______
ENSO cycles can’t be a long-term source of global warming. They do not create any heat, but only redistrubute it from ocean back to the atmosphere. We all know (or should know) that El Nino events can easily be seen in the long term temperature record as short-term signal that rides on on the longer term trend along with the 11+ year solar cycle. While I’ll think your delayed heat release scenario in the ENSO seems quite plausible, it doesn’t seem to be something that could be “mistaken” for a much longer term warning signal. If such as mistake is being made by GCM’s, it wouldn’t be the delayed El Nino heat release, but more likely the longer term PDO which aligns much better with the longer term signal seen in the records.
Dr, Bob T.
Great job,
I got the impression that the sign of the PDO becomes increasingly certain noise of ENSO conditions.
4. Pacific decadal variability and secular trend
Decadal variability is captured by two modes in this
analysis: the fourth (Pan Pacific) and the sixth (North
Pacific) leading modes. The related PCs (Fig. 2) exhibit
variations on long time scales (longer than ENSO’s),
capturing the climate shifts in the twentieth century:
PC4 has the 1920s shift, while the shifts from the 1940s
and mid-1970s are manifest in PC6. These shifts have
been noted in the context of Pacific SST variability by
Mantua et al. (1997) and Minobe (1997).
________
a. Pan-Pacific mode
b. North Pacific mode
http://www.atmos.umd.edu/~bguan/download/index.php?Guan&Nigam_2009.pdf
in,
http://bobtisdale.blogspot.com/2010_11_01_archive.html
Abraços
Does anyone know of a link to the current Indian Ocean Dipole anomaly?…
R. Gates says:
December 13, 2010 at 8:50 am
We all know (or should know) that El Nino events can easily be seen in the long term temperature record as short-term signal that rides on on the longer term trend
Keep up at the back there!
R. Gates says: “ENSO cycles can’t be a long-term source of global warming.”
The warm water (to depths of 300 meters) released from the Pacific Warm Pool during an El Nino doesn’t just disappear when the El Nino is done. I’ve illustrated the residual effects of that warm water after it returns to the West Pacific in this post and in at least a dozen others for the past two years. So I don’t accept your explanation when, using SST data, subsurface temperature profiles, sea level data, ocean heat content data, etc., I have shown that there are multiyear aftereffects of ENSO and that these multiyear aftereffects are responsible for most of the rise on global sea surface temperatures during the satellite era. And since land surface temperatures are along for the ride, the multiyear aftereffects of ENSO would explain much of the LST warming as well.
Fernando (Yes, we have no bananas) says: “Dr, Bob T.”
There’s no Dr in front of my name. Thanks, though.
I found Guan and Nigam 2008 and 2009 to be very informative, as you are aware.
http://bobtisdale.blogspot.com/2010/11/guan-and-nigam-2008-and-2009.html
I’m hoping to do a follow-up post on them and another paper that takes a different approach.
Paul Vaughan says: Does anyone know of a link to the current Indian Ocean Dipole anomaly?…
It’s in the KNMI Climate Explore Monthly Cimate Indices page:
http://climexp.knmi.nl/selectindex.cgi?someone@somewhere
JDN says: “Why is this important to the discussion of global warming or the warmest year “nonsense”?”
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.
@dp says:
December 12, 2010 at 11:34 pm
“Just answer this simple question: did more energy arrive on earth than left earth in 2010? I don’t care what the temperature was – did more energy arrive than left? Who can answer that? Anyone? Come on – it can’t be that hard. Nobody knows? I didn’t think so.”
Try doing a 27day averaged plot for 10-20yrs with plasma temperature and flow speed here http://omniweb.gsfc.nasa.gov/form/dx1.html
There is a recovery since the lows of last year.
I’m more interested in the + 4.5 temp anomaly sitting right above Canada… +4.5… Really??? And is that being bled over to push the world temp series to achieve the “warmest year” title, a la Steig’s warmer Antarctica?
Re: Bob Tisdale
Thanks Bob, but KNMI does not provide the current Indian Ocean Dipole anomaly.
–
Does anyone know of a link to the current Indian Ocean Dipole anomaly?
How is it going the Ap index?….
Bob Tisdale says:
December 13, 2010 at 9:58 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.
I agree that it wouldn’t be ENSO related, it could be simply some fluke weather condition. What I was thinking about was if you do have some unusual condition, say a large mass of cold Antarctic air moves unusually far North (as we saw happen in South America this past summer) over the equatorial Pacific, it could to my mind result in a step change by altering the heat absorbed or dumped during one of the cycles.
But who knows, maybe it would be self correcting. Maybe these conditions occur because of a surplus or deficit of heat in the first place and altering the rate of change simply alters the duration of the event, I don’t know.
Thanks Bob, and also R Gates, for giving me lots to mull over.
Took me a while to realise that R Gates is picturing temp rises over centuries, so that heat storage and release cycles over lesser time periods would be averaged out (but I think thats a little simplistic ‘cos in a complex system one mayn’t “get back” to a similar state); whereas Bob T is concentrating on illuminating effects on the satellite records, which records are quite short. 30 years or so. So, a degree of cross purposes.
Happy to be corrected, or even KO’ed.
JDN says:
December 13, 2010 at 8:13 am
Why is this important to the discussion of global warming or the warmest year “nonsense”?
The change in the ENSO from a eastern pacific warm phase to a eastern pacific cold phase is accompanied by a change in the the polar and pacific jet streams from a fairly stable configuration to a variable one. The current configuration of the pacific jet stream is similar to a warm phase ENSO pattern and weather or local climatology for north america is also similar.
http://www.opc.ncep.noaa.gov/shtml/ppbe50.gif
This has been the configuration of the pacific jet stream for the last several weeks but it is about to change.
http://www.opc.ncep.noaa.gov/shtml/ppbm50.gif
This predicted configuration is similar to the configuration of the pacific jet stream during part of the month of November and low pressure weather systems move heat in the form of water vapor parallel to the path of a jet stream. During the month of November some storms tracked from the NW pacific, ~140 deg E/~45 deg N, NE to the Bearing Sea & Alaska and then east into Canada causing tempture anomalies in areas that don’t usually see many warm low pressure systems.
In addition, the variable pacific jet stream changes the typical storm track on the eastern side of the continent. Instead of moving out over the Atlantic and then turning NE the storms typically turn NE while still over land and pass through the Hudsons and Baffin bay areas. The counter clockwise rotation of the anticyclones pulled in warm moist air from the NW Atlantic causing temperature anomalies in those areas.
The November weather pattern left it’s signature on sea ice area in the Bearing straight, Hudsons & Baffin bay, and the SW & SE coasts of Greenland.
Temperatures in the Hudsons & Baffin bay areas are now normal. There is no ‘heat source’ in these areas as was claimed. The heat source was and is the NW Pacific and the NW Atlantic. Using the difference of two averages of averages of sensible air temperature at the surface doesn’t really say anything about weather, local climatology or climate. Sea ice area isn’t any better as it’s sea ice mass that is of thermodynamic interest. Locating heat sources, heat sinks and heat flows does.
Bob,
If I have not committed a gross error.
You are correct. Something smells fishy (%&*#).
corr Nov NINO3.4 with Nov UAH MSU Tlt anomaly 2000:2010 p<10% (eps: colour, B/W pdf: colour, B/W)
http://img841.imageshack.us/img841/3840/correlationuauuahxnio34.png
Statistically, there is almost certainly a significant connection in the map (pfield < 0.1%).
corr Nov NINO3.4 with Nov GISS 250 T2m/SST anom 2000:2010 p<10%
http://img130.imageshack.us/img130/4596/correlationgissnio34.png
Statistically, there is almost certainly a significant connection in the map (pfield < 0.1%)
Enneagram…..(aka Adolfo)…..abraços
Plot omni2_27day data from 19700101 to 20101131
http://img543.imageshack.us/img543/1827/enegran.png
“if you do have some unusual condition, say a large mass of cold Antarctic air moves unusually far North (as we saw happen in South America this past summer) over the equatorial Pacific.”
Not so unusual. More meridional jets are occurring in both hemispheres and seem to be common to all global cooling periods including that of the mid 20th century and the LIA.
In the process the boundaries between air masses are greatly elongated for more total cloudiness and a higher global albedo enhanced further by greater reflectance from more equatorward clouds.
Thus less energy getting into the oceans and probably a tendency to skew ENSO in favour of La Nina for as long as the more meridional (or equatorward) jets continue.
Prime candidate as the cause is the reduced level of solar activity.
“Maybe these conditions occur because of a surplus or deficit of heat in the first place .”
I suggest such conditions arise as the jets are bounced about between a variable flow of energy from oceans to air and a variable flow of energy from air to space. The former being a product of internal ocean cycles and the latter being a product of multidecadal or even multicentennial solar cycles (the climate effects from one cycle to the next being too small to measure at present).
I found this paper interesting, especially after page 280 or so. Seems Yousef was correct in his guess that a new Gleissberg cycle was about to start, but maybe missed it by one solar cycle. All the indications are there. He has some interesting correlations with SST and these cycles, too.
http://www.virtualacademia.com/pdf/cli267_293.pdf
Hi all,
just to illustrate my point in an earlier thread about use of running mean , I reworked one of Bob’s graphs once with rm and again with gaussian filter. Both filter work on a 13 month window.
The difference it not huge but some interesting features can be seen by comparing the two.
Firstly, there is quite a bit of high frequency noise left by the rm filter. As I previously showed this is a distortion, the gaussian follows the trends in the data rm often gets it wrong. It should not be there, it is selective and and depends heavily on the surrounding data as to whether it inverts small peaks, misses them of creates false features.
Before looking at dates , please DO note that this is just playing with pictures to illustrate the different filter behaviour. I have given ZERO thought to any physical meaning of the graph or it’s differences. My sole point here is about data processing using Bob’s work as an example. That said, the are relevant to Bob’s work and to this discussion. They have implications to wider climate data presentations because they are very commonly used.
Unfortunately Bob does not give references for his data but I’m guessing I have similar data sources to his figure 9 above. At least the Nino3.4 should be the same since he gave me a pointer to that last time.
Some intersting features emerge:
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.
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 😉 ). In the post pinotubo period there is a disruption but the correlation can still be seem to be present in the gaussion smoothing. Spurious distortion hides this relationship in the rm plots.
1977 , the single “event” in the rm plot actually resolves as two adjacent peaks with G.
1980 cf 83 , the magnitude of the two peaks are nearly the same in G plot, whereas rm shows a distinct decline.
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.
I would suggest from this evidence that using better data processing would probably better expose the correlation between that two sets of data and avoid the need to crop off annoying bits that don’t seem to match. (Sorry to labour the point but I don’t like selective science, it smacks too much of Mann et al. If there is a divergence , lets see it not hide it).
It would be interesting to see some of the other numerous plots here reworked with a gaussian filter (for example) to see whether the relationship Bob is putting forward is more clearly demonstrated.
Just posted this as a comment on Sciencemag.org. It’s awaiting moderator approval 😉
Enneagram says:
December 13, 2010 at 11:20 am
How is it going the Ap index?….
Ap’s gone to sleep, NFC1 does its stuff
Hmm, seems to have lost the image somewhere in the mix.
http://i55.tinypic.com/ftqbf6.jpg
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.”
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.
Sure, the ocean heat content doesn’t make a mark on the solar activity level. I don’t know what term to use really. ‘Bellweather’ seems a bit quaint. I think that your point is a minor quibble rather than a fatal flaw in my hypothesis though. The main point is that sunspot area and number are a proxy for solar activity level and it’s effect on ocean heat content, which is amplified by the amount calculated by Nir Shaviv in his JGR paper ‘Using the oceans as a calorimeter’.
A jargon note for P. Solar:
A running mean has a growing window anchored at the left end of a time series whereas the window of a moving average has constant width and floats with the window center. Misuse of the term “running mean”, as if it were synonymous with “moving average”, is widespread, so often one has to infer from context what is meant.
P. Solar says: “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.”
Because this graph…
http://i55.tinypic.com/ftqbf6.jpg
…is not raw data. And this graph…
http://i56.tinypic.com/2958yg1.png
…does not compare TLT data to NINO3.4 SST anomalies, which is the topic of discussion.
You wrote, “Again better referencing of your sources would be useful.”
The TLT data source (UAH) is shown on my graph. Is it shown on yours or mentioned in your discussion? The NINO3.4 SST anomalies are HADISST. What NINO3.4 SST anomaly dataset did you use? HADSST2? ONI? ERSST.v3b? Kaplan? It’s not Reynolds OI.v2 because it starts too early.
And here’s the same graph that I posted above, but I replaced the UAH data with RSS TLT anomalies. My same comment applies to it as well:
http://i55.tinypic.com/2mocxp1.jpg
The closing sentences of Bob’s article: “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?”
Ulric Lyons has offered a suggestion (“Different winters.”)
Interannual NPI was phased a little differently relative to SOI & PWP for the 2 events (including winter), but I wonder if Ulric is thinking more of AO/NAO than NPI? Some clarification might help. I do note a phase reversal in interannual solar-terrestrial coupling (using either aa or SW) beginning about the time of the ’98 El Nino. Perhaps Ulric is hinting at that?
A simpler possibility?
http://i29.tinypic.com/rwp8ut.png
Further to my last comment, I think this is what was on Bob’s mind when he proposed SLP:
http://i47.tinypic.com/29uvpe9.png
Bob, in pondering the answer to your question, you might want to dig out Thompson et al.’s (2009) COWL (Cold Oceans Warm Land) article. There are some parallels. I wonder if they are aware of the wave & mode switching that goes on between AO/NAO, NPI, & AAO/SAM. If they are not, this could be substantial.
Just a quick question from a layman with a limited understanding of the mechanics of ENSO events.
The sub-surface in the western pacific is now showing signs of warming bringing with it the prospect of a return to neutral or possibly even El-Nino conditions next year.
This made me wonder whether the cooler SST’s making their way East to West from the current La-Nina could eventually downwell in the Western Pacific preventing a transition back to El-Nino and thus the potential for back-to-back La Nina events?
What follows from this is what impact back to back La-Ninas might have on the KOE.
Bob, this is a note to confirm that T_ENSO & COWL are not nonlinearly independent. COWL is a mash-up of AO, NAO, NPI, plus a bunch of other NAM stuff and so it fits the framework outlined by Schwing+ (2003). Perhaps Thompson+ (2009) only checked for linear orthogonality without so much as a glance at the complex plane. Piers Corbyn must be LHFAO. You could potentially write the blogging article of your blogging career if you look into this carefully, but you’ll have to switch to using repeat 1 year smoothing because wide boxcars simply cannot render the patterns discernible for a lay audience. Best Regards.
Bob Tisdale:
>>
http://i56.tinypic.com/2958yg1.png>
…does not compare TLT data to NINO3.4 SST anomalies, which is the topic of discussion.
>>
No, it shows what I said it shows: that when you compare the two filters to the raw data you see that your use of running mean incorrectly attenuates the 1983 peak, and not by a small amount.
You stated that the gaussian was not a better filter because it “failed to capture the El Chincon” effects.
You erroneously concluded the rm to be better because you “expected” to see the second peak smaller. You did not check to see if this was actually present in the data and now I point it out to you, you seem to steadfastly refuse to accept the point.
You are throwing sand in the air linking other graphs and arguing about datasets. The one graph proves the point. The rest is a distraction.
So having established that your data processing is corrupting the very peaks you are trying to compare, I suggest it may be better using a different filter.
You are free to ignore that suggestion but do not try to make out I am incorrect.
David W: Figure 7, above, compares KOE and NINO3.4 SST anomalies since 1995. The data have been smoothed in it, and because of the smoothing, we lose the fact that there was a relaxation of La Nina conditions after the 2007/08 La Nina. That is, if we look at the same data without the smoothing…
http://i52.tinypic.com/f9qck1.jpg
…we can see that NINO3.4 SST anomalies actually rose to “zero” in the first half of 2008 before taking another swing down. The drop in NINO3.4 SST anomalies during the second half of 2008 didn’t register as an “official” La Nina, but KOE SST anomalies remained elevated. In fact, they didn’t drop until the the switch to the 2009/10 El Nino.
Paul Vaughan: Thanks for your research and insights into COWL. It sounds like you were examining the Thompson et al data. Did you notice how their “ENSO fit” data was biased up in early years compared to raw HADSST2 CTI data?
http://i38.tinypic.com/10rikb4.png
Meaning when they subtract the “ENSO fit” data from their global temp data, it will add to the global trend. I showed a comparison in my post on Thompson et al but no one picked up on it. It’s a small bias but it exists.
http://bobtisdale.blogspot.com/2009/09/thompson-et-al-2009-high-tech-wiggle.html
Since Thompson is using the same process in a 2010 paper to show how well climate models reproduce the 20th century temperature record, I may have to revisit that data. That 2010 paper is not worth a separate post, but I’m sure I can wiggle it into another as a “Why is this important?” side note.
Regards
“The data have been smoothed in it, and because of the smoothing, we lose the fact that there was a relaxation of La Nina conditions after the 2007/08 La Nina. ”
Yes this is a known issue with some smoothing techniques but not all. nino3.4 2008 peak-a-boo
P. Solar says: “You stated that the gaussian was not a better filter because it ‘failed to capture the El Chincon’ effects,” and, “You erroneously concluded the rm to be better because you ‘expected’ to see the second peak smaller.”
Because it is smaller, which was why I presented the two graphs that compared “raw” scaled NINO3.4 SST anomalies to global TLT anomalies in these two graphs:
http://i55.tinypic.com/152m6mc.jpg
And:
http://i55.tinypic.com/2mocxp1.jpg
You added, “You are throwing sand in the air linking other graphs and arguing about datasets. The one graph proves the point. The rest is a distraction.”
The reason I asked you which NINO3.4 dataset you used was so that I could use exactly the same data and present to you that the El Chichon eruption had in fact suppressed the global TLT anomalies, unlike your presentation of it. And you still haven’t identified the NINO3.4 data you used.
P. Solar says: “Yes this is a known issue with some smoothing techniques but not all. nino3.4 2008 peak-a-boo”
That’s right. The gaussian filter leaves in the seasonal component, while the running mean minimzes it.
Regards
Bob, yes I noticed (by comparing integrals of T_ENSO & CTI — makes it crystal clear). They’ve subtracted a global summary from CTI. (According to their reasoning this eliminates a bias-step in the CTI record.)
There are much bigger issues with the work.
The concept “COWL” was a valuable contribution and the authors are clearly very intelligent, but I cannot turn a blind eye to their foolhardy promotion of absolutely untenable assumptions. That the coupling is nonlinear is undeniable (and yet their decompositions are linear). The authors should at least acknowledge that multiscale hypercomplex factor analysis is necessary in such a context.
–
I sincerely hope readers see the connection between KOE & COWL. [If not, please see my notes above in which I link to Carvalho+ (2007).]
A key in identifying relatively elusive spatiotemporal signals is to pin down spatial modes that are somewhat stationary due to the lay of topography relative to dominant flows. Such a focal point exists in the North American Pacific Northwest (Northeast Pacific Ocean). Anyone understanding Schwing+ (2003) will be better positioned to make sense of the mid-20th-century discontinuity in the following article:
Courtillot, V.; LeMouel, J.L.; Blanter, E.; & Shnirman, M. (2010). Evolution of seasonal temperature disturbances and solar forcing in the US North Pacific. Journal of Atmospheric and Solar-Terrestrial Physics 72, 83-89.
Cautionary Note: Don’t uncritically accept the single ~1958 date given by Schwing+ (2003) without pursuing more detailed data exploration. (There is multivariate switching on a number of spatiotemporal scales. Seasons & regions are not irrelevant due to factors such as north-south asymmetry.)
–
Thanks Bob, Anthony, & Moderators.
Bob, the nino3.4 is the same one you indicated to me in to other thread so that should match what you use. I even posted a snippet of numbers and you confirmed it was the right data, so I don’t think there should be any confusion there.
“That’s right. The gaussian filter leaves in the seasonal component, while the running mean minimzes it.”
If you run a 13 month filter you will not get a cut off at a frequency of one year whatever filter you choose. For memory have a look at the two profiles in the article I linked before:
http://homepages.inf.ed.ac.uk/rbf/HIPR2/gsmooth.htm
13 months is the window not the cut-off and in the case of running mean you will get a load of harmonics still getting through as well. (see summer 1999)
It is true that the 13m rm is reducing the amplitude more than 13m gaussian but that does not in itself mean it is better filtering the data. I have already noted drawn attention to the small quantity of monthly noise that can be seen in parts of the rm output. You will see that G does not have that even thought the cut-off is lower. It is a very smooth curve, no h.f. component visible. Also note the way some rm peaks are skewed left or right when surrounding data is higher one side than the other (summer 2002).
In the last comparative plot I posted you can see the winter trough of 2001 and 2004 actually ends up as a slight peak in the rm, so it is not that it is taking out this feature better it’s actually causing a spurious increase as a result of the peaks either side.
Again, referring to the article, you will see that G freq response cuts off earlier than rm so you may want to compare to a wider window if you need to eliminate the annual signal.
Play with the script I posted above keeping gw=3 sigma but increase the value of sigma. A value of three gives amplitudes close to your rm without the defects I noted.
If you want to eliminate the annual component you need a heavier filter. The following uses w=12 (ie 25m window) and sigma=8 in the script I posted.
Here it is even clearer that rm is letting stuff through, skewing peaks and even inverting some of the smaller peaks.
This plot actually shows your correlation quite well.
regards.
P. Solar, the filters aren’t “inverting” peaks. As I explained in an earlier thread, that’s not a “problem” with the filter, but rather with interpretation. To get around the centering issue, a 12 month boxcar (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) can be adjusted to a 13 month kernel with weights 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5. It makes sense to design a kernel that is mindful of dominant temporal modes of variation — in this case the terrestrial year. Your gaussian kernel might be a great option in contexts where no dominant periods exist, but it complicates interpretation of climate data. In practice “anomalies” do not have the properties that they have in theory. Repeat application of a 1 year smoother works well for many climate time series, but for some, 6 month smoothers make sense (e.g. for some equatorial series). For a time series like SOI, try 3 month, then 6 month, then 12 month. (You will note that repeat 3 month smoothing has the peak-&-trough location-preserving properties that you seem to be advocating. Repetition has the effect of creating a triangle rather than a bell.)
Thanks for you enlightened comments Paul.
It was careless of me to say inverting peaks. What I meant was showing a trough/peak where the original data shows a peak/trough. The effect, as I am sure you are aware is dependent on the relation of surrounding peaks and the width of the kernel (window) used.
You will note that the adjusted rm you suggest was included in the script I posted and documented with a comment. Since that modified kernel puts less weight on the outlying points (the root of all these issues in rm ) it does “help”. It does not really get around the problem. It is just one very crude concession to the idea of using a weighted mean. Gaussian is one case of a thoughtfully designed weighted mean with a clean monotonic frequency response.
Repeating a narrower window is often a good option if, like here, the window width is interacting with prominent features of the dataset. It also helps reduce the h.f. components getting through.
>> For a time series like SOI, try 3 month, then 6 month, then 12 month.
Hmm, it may “work well” but it would be less obvious to say what you had actually done to the data. Back of envelop sketching suggests it may have a frequency response a bit like a wobbly bell shape. It would be interesting to see calculated plot of its response.
>> Your gaussian kernel might be a great option in contexts where no dominant periods exist, but it complicates interpretation of climate data.
I’m not promoting gaussian as the holy grail of filters , it seemed better than box car running mean in this case and helped show some of the distortions produces by rm.
ANY filter complicates interpretation if it is used without thought and a fair degree of understanding. That is what I was getting at. I don’t see that the results of gaussian are more complicated to interpret than a box-car or repeated running mean. In fact the simpler form of the frequency response should make it more predictable.
Having identified a correlation Bob is looking at time lags. In the example we have been discussing here, the lag is different before and after 1998. In investigating what this means it seems fairly important that the filter is not bending and shifting the primary peaks as the 13m box-car is doing.
The results are not hugely different but removing some of the obvious defects should help a more precise analysis of the lag between the various cycles.
The bottom line, as you are well aware, is that this is not a trivial subject and awareness of all these issues is important in interpreting “features” we perceive in filtered data to be sure that they are real and not artifacts of the filtering process or being corrupted and distorted by it.
regards.
P. Solar, I can agree that nonsensical notions about smoothing & lags abound in all forums devoted to the discussion of climate variations.
–
P. Solar wrote, “Repeating a narrower window is often a good option if, like here, the window width is interacting with prominent features of the dataset.”
Good to see this enlightened comment. Certainly the mainstream has gotten caught up in the notion that anomalies have some ideal properties which they absolutely do not have in practice.
–
My attention to this thread has expired. Thanks to all who have participated. I look forward to Bob’s future posts.
Just for the record , in case anyone is interested, here is the frequency response of the combined 3 month, 6 month, 12 month running mean filter. (unscaled x-axis)
triple-sync
So my intuitive guess was correct, it is quite similar to the form a gaussian plus a small amount of ripple in the stop band.
Running this filter in three passes probably allows use of a narrower total window for a result similar to the gaussian. This may be an advantage where one wishes to run as near to the end of the data as possible (eg. as will global mean temps perhaps) a the cost of having run three filters instead of one.
Man, my gut feeling was a lot close than I realised.
Here is the gaussian overlayed with the tripled up running mean Paul suggested above. It is uncannly close to the gaussian apart from the ripple.
triple-sync vs gaussian
Paul Vaughan says:
>>It makes sense to design a kernel that is mindful of dominant temporal modes of variation — in this case the terrestrial year. Your gaussian kernel might be a great option in contexts where no dominant periods exist, but it complicates interpretation of climate data.
>>
So in conclusion to this discussion on filters, I agree, it makes sense to design a kernel. That seems to be a point that is almost universally ignored. Not just by amateurs but by major climate data organisations and university professors !
If you want to remove a 12 month signal you don’t start with a 12 month (or 13 month) window and a crappy filter which lets through significant amounts of what you imagine you have filtered out and moves peaks around and replaces some smaller peaks with troughs.
Designing a filter means at least considering what the frequency response looks like and deciding if it is suitable. This you do _before_ looking at the data that comes out the other end, deciding it looks like what you’d expect and not going any further in determining what you are doing to the data.
It’s interesting that you suggest my gaussian is not suitable for climate data yet propose a triple running mean that has a very similar response except for retaining some of the defects of a simple running mean.
You don’t explain how the gaussian is not suitable or how it “complicates” interpretation.
The only down side I see to G is that it requires a wider window, so data cannot be filtered quite so close to the start and end . If this is a problem the triple rm may be a good second best since the defects in the filter are not huge.
Interestingly enough the KNMI service where Bob sources much of his data used to use gaussian until they noticed it was exaggerating the post 2000 cooling. (Never got noticed while it was exaggerating the 1990s warming !) . In fact this was not due to the gaussian filter itself but was due to the fact that they were using a partially filled window at the end of the data.
Clearly this has no mathematical validity at all and is such a stupid thing for a professional body to do it merely underlines how little thought or understanding goes into much of what is presented a climate science even from such august bodies.
The other problem with the proposed 3,6,12 month running mean is that only the first can be done without a time shift. Introducing two shifts at different stages of the multipass filter will have some odd effects on how it distorts the data. The result will not be as clean or simple as the comparison I posted.
Also the start and end sections that don’t have a complete window will be additive in multiple runs so the only advantage it has over gaussian is largely lost.
If Paul Vaughan is using that sort of technique I can see why he has doubts (expressed elsewhere) about the use of looking at phase lags in climate data.
In conclusion, the idea that people have that running mean is easy to understand is only based on their lack of understanding. Though it is simple to implement, understanding the resulting effects is far from simple and not good when you do understand.
Sticking with a bad filter because other, less well informed people think they understand it, seems like a pretty bad way to go.
RUNNING MEAN MUST DIE.
Hadcru and NCDC are in. NCDC and GISS are in good agreement. It’s obvious that Hadcru is the “odd man out” this november and this year, not GISS: http://img684.imageshack.us/img684/1617/gissncdchadcruthadcrut3.jpg