Guest post by Bob Tisdale
In this post, I divide the globe (60S-60N) into two subsets and remove the linear effects of ENSO and volcanic eruptions from GISS Land-Ocean Temperature Index data since 1982. This is done using common methods. I further adjust the data to account for secondary ENSO-related processes. The Sea Surface Temperature subsets used for these adjustments are identified. The processes are briefly discussed, supported by links to past posts, and the data are presented that support the existence of these secondary effects. An additional volcanic aerosol refinement that increases the global trend is made. The bottom line is, the GISS LOTI and Reynolds OI.v2 SST data indicates that natural variables could be responsible for approximately 85% of the rise in global surface temperature since 1982. I’ll be the first to point out that I qualified my last sentence with the word “could”. This post illustrates a story presented by the data, nothing more. But this basic evaluation indicates these secondary effects of ENSO require further research.
This post continues with the two-year series of posts that basically illustrate that the effects of El Niño-Southern Oscillation (ENSO) cannot be accounted for using a single index like a commonly used SST-based dataset such as NINO3.4, or CTI, or MEI. These indices represent only the sea surface temperature of the central and eastern equatorial Pacific (that’s modified in the case of the MEI). They do not represent the process of ENSO. They do not account for the warm water that is returned to the western Pacific and redistributed during the La Niña. This post provides further evidence of those effects.
This post is long but I elected not to divide it in two. It’s 6,000 words or 13 single-spaced pages in length. It includes 32 Figures, a gif animation, and a video. So there’s a lot to digest. I tried to anticipate questions and answer them.
REMOVING THE LINEAR EFFECTS OF ENSO AND VOLCANIC AEROSOLS HELP TO SHOW THE TIMING OF THE WARMING
Many papers and blog posts that attempt to prove the existence of anthropogenic global warming remove the obvious linear effects of El Niño-Southern Oscillation (ENSO) events and of stratospheric aerosols discharged by explosive volcanic eruptions. An example is Thompson et al (2009) “Identifying Signatures of Natural Climate Variability in Time Series of Global-Mean Surface Temperature: Methodology and Insights”… http://www.atmos.colostate.edu/ao/ThompsonPapers/ThompsonWallaceJonesKennedy_JClimate2009.pdf
…and its companion paper Fyfe et al (2010), “Comparing Variability and Trends in Observed and Modelled Global-Mean Surface Temperature.”
http://www.atmos.colostate.edu/ao/ThompsonPapers/FyfeGillettThompson_GRL2010.pdf
Let’s run through the process using GISS Land-Ocean Temperature (LOTI) data. That’s their global temperature anomaly dataset with the 1200km radius smoothing. A known problem with that dataset is that GISS Deletes Arctic And Southern Ocean Sea Surface Temperature (SST) Data. Since that creates a bias, we’ll delete the GISS LOTI data where they extend land surface data (with its higher variability) out over the oceans. That is, we’ll confine the data used in this post to 60S-60N.
Someone is bound to complain that I’ve deleted the Arctic data from the GISS LOTI data and that the Arctic is warming much faster than lower latitudes. Keep in mind that the Arctic is amplifying the effects of the rise in temperature at lower latitudes. This is the basis of the concept of polar amplification. If the vast majority of the change in temperature at the lower latitudes is natural, the same would hold true for the Arctic. Regardless, these latitudes were also chosen because the effects I want to illustrate with this post are relatively easy to display using them.
Back to the data: since GISS switches sources for their Sea Surface Temperature data from HADISST to Reynolds OI.v2 data in December 1981, we’ll look at the LOTI data starting in 1982. Smith and Reynolds (2004) Improved Extended Reconstruction of SST (1854-1997)] states the following about the OI.v2 SST data: “Although the NOAA OI analysis contains some noise due to its use of different data types and bias corrections for satellite data, it is dominated by satellite data and gives a good estimate of the truth.”
The truth is a nice place to start.
And we’ll smooth the monthly data with a 13-month running-average filter to lessen noise and season variations.
Figure 1 shows monthly GISS LOTI data (60S-60N), from March 1982 to November 2010, compared to NINO3.4 SST anomalies. The NINO3.4 data represent the Sea Surface Temperature of a region in the central equatorial Pacific bound by the coordinates of 5S-5N, 170W-120W. NINO3.4 SST anomalies are a commonly used proxy for the strength and frequency of El Niño and La Niña events, also known as ENSO. (And for those new to ENSO, refer to An Introduction To ENSO, AMO, and PDO – Part 1.) Note also that the NINO3.4 data has been scaled (multiplied by a factor of 0.16) so that the rises of the two datasets are about the same during the evolution of the 1997/98 El Niño. The NINO3.4 SST anomalies have also been shifted down 0.01 deg C and moved back in time by 3 months (lagged) to align the leading edges of the two datasets at that time. (The data in the graph starts in March 1982 because of the 3-month lag in the NINO3.4 data.) I chose the 1997/98 El Niño because that event wasn’t opposed by a volcanic eruption and it was large enough to overwhelm the background noise. As you can see, the wiggles of lesser El Niño events after 2000 don’t match as well.
http://i53.tinypic.com/deuxdz.jpg
Figure 1
Many of the large year-to-year changes in global temperatures are removed when we subtract the scaled NINO3.4 data from the GISS Global (60S-60N) LOTI data. Refer to the “olive drab” curve in Figure 2. Since the NINO3.4 data has a negative trend since 1982, we increase the trend in the GISS LOTI data by subtracting it. Also note how the ENSO-adjusted GISS LOTI data has “flattened” after 1998. Without the volcano-related dip and rebound starting in 1991, the period from 1988 to 1998 would also be relatively flat. It appears as though the ENSO-adjusted GISS LOTI rose in two steps since 1982. Let’s remove the cooling effects of the El Chichon and Mount Pinatubo eruptions to see if that holds true. We’ll use a GISS dataset that represents Stratospheric Aerosols. (ASCII data) Like the ENSO Proxy, we’ll scale the data and lag it. The estimated range of the impact of Mount Pinatubo on Global Temperatures varies from 0.2 to 0.5 deg C, depending on the study, so we’ll use approximately 0.35 deg C to account for its effect. Visually, that scaling appears right.
http://i51.tinypic.com/8vyd54.jpg
Figure 2
Figure 3 illustrates the GISS Land-Ocean Temperature Index (LOTI) anomaly data with the linear effects of ENSO events and the effects of large volcanic eruptions removed. Also illustrated is the linear trend. I’ve included the linear trend line to illustrate the effect the straight line has on the appearance of the data. The trend line gives the misleading impression that there has been a constant but noisy rise in global temperatures.
http://i55.tinypic.com/zur19i.jpg
Figure 3
During the discussion of Figure 2, I noted that the data appeared to flatten after 1998. The upward steps in the data can be illustrated if we present the period average temperature anomalies for the three periods of 1982 to 1987, 1988 to 1997, and 1998 to 2010.
http://i55.tinypic.com/m96sly.jpg
Figure 4
WHAT COINCIDES WITH THE UPWARD STEPS?
The timings of those upward steps coincide with the transitions from the large El Niño to La Niña events that took place in 1988 and 1998. This can be seen in Figure 5, which includes the adjusted GISS LOTI data. The other dataset is scaled NINO3.4 SST anomalies that have been inverted (multiplied by a negative number). Figure 5 is a gif animation, and in it, the NINO3.4 data shifts up and down. That was done to show how precisely the upward steps in the adjusted GISS data coincide with ENSO transitions. The adjusted GISS data trails the NINO3.4 data by a month or two. And the scales are correct for both upward steps.
http://i54.tinypic.com/2i8b02b.jpg
Figure 5
But how can ENSO be impacting global temperature data if we’ve subtracted the scaled NINO3.4 anomaly data from the Global (60S-60N) GISS LOTI anomalies?
LA NIÑA EVENTS ARE NOT THE OPPOSITE OF EL NIÑO EVENTS
The assumption made when we removed the linear effects of ENSO (discussion of Figure 1) was that La Niña events were the opposite of El Niño events. But they are not. (This is the same incorrect assumption made by papers like Thompson et al 2009). This post is very long and to adequately describe how La Niña events are not the opposite of El Niño events would make it much longer. So it will be best to provide links to earlier detailed discussions on this topic.
Refer to:
More Detail On The Multiyear Aftereffects Of ENSO – Part 1 – El Nino Events Warm The Oceans
And:
More Detail On The Multiyear Aftereffects Of ENSO – Part 2 – La Nina Events Recharge The Heat Released By El Nino Events AND…During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents.
And:
I provide a relatively brief description in the following section.
WHY ENSO INDICES LIKE NINO3.4 SST ANOMALIES DO NOT ACCOUNT FOR THE PROCESS OF ENSO
ENSO is a process, and ENSO indices such as NINO3.4 SST anomalies, the Cold Tongue Index (CTI), or the Multivariate ENSO Index (MEI) do not account for that process.
El Niño description: A reduction in the strength of the Pacific trade winds triggers an El Niño. A number of interrelated events then take place. Huge amounts of warm water from the surface and, more importantly, from below the surface of the western tropical Pacific (the Pacific Warm Pool) slosh east during an El Niño and are spread across the surface of the central and eastern equatorial Pacific. The increased area of warm water on the surface allows the tropical Pacific Ocean to discharge more heat than normal into the atmosphere through evaporation. That, combined with the change in location of the convection, cause drastic changes in global atmospheric circulation patterns. As a result, global temperatures vary. And most parts of the globe outside of the central and eastern tropical Pacific warm during an El Niño. The changes in atmospheric circulation work their way eastward–over the Americas, the Atlantic, Europe and Africa, the Indian Ocean and Asia. Eventually, the changes reach the western Pacific, but by that time, the El Niño is transitioning to a La Niña.
Refer again to the NINO3.4 SST anomaly data in Figure 1. A La Niña event, based on the temperature values on a graph, appears to be an El Niño of the opposite sign, and for some regional responses in temperature and precipitation that is true. But as noted before the use of NINO3.4 SST anomalies or other ENSO indices does not capture the fact that ENSO is a process. Those indices fail to account for the relocation and redistribution of huge amounts of warm water.
In the description of the El Niño, I noted that huge amounts of warm water from the surface and below the surface of the West Pacific Warm Pool had sloshed east during an El Niño. What happens to all of that warm water from below the surface of the Pacific Warm Pool that had been spread across the surface of the central and eastern tropical Pacific during the El Niño? Before the El Niño, it was below the surface and not included in the measured global surface temperature anomalies. During the El Niño, some of the warm water that had been below the surface is now on the surface of the central and eastern tropical Pacific and included in the measured global temperature. In response, surface temperatures there rose. The ENSO index captures that part of the process and only that part.
But during the La Niña, what happens to the warm water? It wasn’t all “used up” by the El Niño. And what happens to all of the subsurface warm water that had shifted east during the El Niño and had remained below the surface. It doesn’t simply disappear during the La Niña. Answering those questions explains why La Niña events are not the opposite of El Niño events, and why an ENSO index does not capture the aftereffects of an ENSO event.
The leftover warm water returns to the western Pacific. This is accomplished in a few ways. One is through a phenomenon called a slow-moving Rossby Wave. This can be seen in Video 1. It illustrates global Sea Level Residuals from January 1998 to June 2001 and captures the 1998/99/00/01 La Niña in its entirety. The video was taken from the JPL video “tpglobal.mpeg”. The slow moving Rossby wave is shown as the westward moving band of elevated sea level at about 10N. Watch the effect it has on western Pacific Sea Level Residuals when it reaches there.
The second way that the leftover warm water is carried to the Western Pacific is through a strengthening of the trade winds. During a La Niña event, trade winds strengthen above their “normal” levels and the ocean currents carry the warm water back to the west and then poleward.
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 Sea Surface Temperature 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.
There are a number of things to note in Animation 1. First, the El Niño and La Niña events cause changes in the sea surface temperatures in the central and eastern tropical Pacific. The NINO3.4 SST anomalies used in this post are a measure of that variation in the central equatorial Pacific, and only that variation. Second, during the El Niño, note how the sea surface temperatures warm first in the Atlantic, then in the Indian Ocean, and then in the western Pacific. The warming is caused by changes in atmospheric circulation. And by the time these changes in atmospheric circulation make their way east to the western Pacific and it starts to warm there, the El Niño is transitioning to La Niña. Third, note how the sea surface temperature anomalies in the Western Pacific (and East Indian Ocean) continue to rise as the La Niña event strengthens. Fourth, note how the SST anomalies remain elevated in the East Indian and West Pacific Oceans during the entire term of the 1998/99/00/01 La Niña.
http://i53.tinypic.com/etb58j.jpg
Animation 1
The Sea Surface Temperatures of the East Indian and West Pacific Oceans remain elevated during the La Nina because the stronger trade winds reduce cloud cover. The reduction in cloud cover allows more Shortwave Radiation (visible light) to provide additional warming of the tropical Pacific waters east of the Pacific Warm Pool. The ocean currents carry this sunlight-warmed water to the west and then poleward.
DIVIDING THE GLOBE IN TWO HELPS IDENTIFY THE REASONS FOR THE UPWARD STEPS IN THE GISS LOTI DATA
To help illustrate the reasons for the upward shifts in the ENSO- and Volcano-adjusted GISS LOTI data (Figure 4), let’s divide the data into two subsets split at 20N. Refer to Figure 6.
http://i52.tinypic.com/jjpl5c.jpg
Figure 6
First we’ll look at the Northern Hemisphere GISS LOTI anomaly data, north of 20N. It has a relatively high linear trend since 1982, about 2.8 deg C/Century. Part is due to the additional variability of the North Atlantic. To compound that, these latitudes have a relatively high land surface area, and land surface temperatures vary much more than sea surface temperatures. The land surface area of the Northern Hemisphere latitudes of 20N-60N is about 45% of the total surface area, but the land surface in the tropical and Southern Hemisphere latitudes of 60S-20N is only 17%.
http://i53.tinypic.com/20mdjc.jpg
Figure 7
The dataset shown in Figure 7 has not been adjusted for ENSO or volcanic eruptions. Let’s correct first for ENSO, then for the volcanic eruptions, using the same methods we did for the Global (60S-60N) data. Figures 8 and 9 illustrate the interim steps and the required scaling factors, and Figure 10 illustrates the result.
http://i53.tinypic.com/15odzkh.jpg
Figure 8
http://i52.tinypic.com/34jdp94.jpg
Figure 9
http://i55.tinypic.com/vmx18x.jpg
Figure 10
The Northern Hemisphere data still has a relatively high trend, approximately 2.2 deg C/Century. But what causes the additional variability if we’ve removed the effects of ENSO and volcanic eruptions? The additional variations are often described as noise, but they have sources.
THE KUROSHIO-OYASHIO EXTENSION HOLDS THE ANSWER
There is a strong ENSO-related warming of the Kuroshio-Oyashio Extension that occurs during La Niña events. This was discussed and illustrated in my recent post The ENSO-Related Variations In Kuroshio-Oyashio Extension (KOE) SST Anomalies And Their Impact On Northern Hemisphere Temperatures. That secondary warming can be used to explain a major portion of the year-to-year variability in Northern Hemisphere land and sea surface temperature. And, along with ENSO, it helps to explain nearly all of the variations in the Northern Hemisphere (20N-60N) GISS LOTI data, including the rising trend. Figure 11 illustrates the location of the KOE dataset used in this post (30N-45N, 150E-150W).
http://i52.tinypic.com/14twvox.jpg
Figure 11
The GISS LOTI anomalies for much of the Northern Hemisphere 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 Northern Hemisphere (0-90N) GISS LOTI data, Figure 12. Also note the correlation with the North Atlantic.
http://i54.tinypic.com/303llxg.jpg
Figure 12
As mentioned above, the secondary warming of the Kuroshio-Oyashio Extension was discussed in detail in my recent post The ENSO-Related Variations In Kuroshio-Oyashio Extension (KOE) SST Anomalies And Their Impact On Northern Hemisphere Temperatures. A quick description of the process: During a La Niña event, leftover warm water from the El Niño is returned to the Western Pacific and spun poleward by the North and South Pacific gyres. Much of that warm water finds its way to the Kuroshio-Oyashio Extension, where it apparently impacts atmospheric circulation.
The agreement between the variations in KOE SST anomalies and the adjusted Northern Hemisphere GISS LOTI anomalies is shown in Figure 13. I find that match quite remarkable. The additional spike (highlighted in blue) in the KOE data that starts in 1990 is out of place. It will make itself known later in this post. The other thing to note is the scaling factor required to align the two datasets in Figure 13. The scaling factor of 0.7 is very high. We’ll discuss this later in the post.
http://i51.tinypic.com/2j15udv.jpg
Figure 13
Some might think the agreement between those datasets is a lucky coincidence. Of course, the agreement between the adjusted LOTI data and the unadjusted KOE data in Figure 13 is based solely on the lags and scaling factors I used. But the scaling and lags were established logically. Eyeballing the data, the scaling factors appear to be correct. And as we shall see, using the same methods, the results are very similar for the data that covers the Tropics and Southern Hemisphere.
LET’S LOOK AT THE TROPICS AND SOUTHERN HEMISPHERE
The Southern Hemisphere and Tropics dataset includes the GISS LOTI data from 60S-20N, Figure 14. This subset has a relatively low trend, approximately 1 deg C/Century. Some of this is related to the amount of continental land mass. For these latitudes, land represents only about 17% of the surface area. The Southern Ocean (90S-60S), which is outside of the latitudes portrayed in the post, also impacts the Southern Hemisphere data. And since the Southern Ocean SST anomaly trend over this period is negative, its interaction with the Southern Hemisphere oceans lowers the trend of the dataset.
http://i54.tinypic.com/eiqtsy.jpg
Figure 14
And again, using the same methods, we’ll adjust for ENSO, then volcanic eruptions, Figures 15 and 16, and present the results, Figure 17. Refer to Figures 15 and 16 for the scaling factors.
http://i56.tinypic.com/e5nxg1.jpg
Figure 15
http://i56.tinypic.com/2vx28tt.jpg
Figure 16
http://i53.tinypic.com/2cqy0s3.jpg
Figure 17
As shown in Figure 17, removing the effects of the volcanoes has once again lowered the trend, and removing the ENSO data reduced the year-to-year variations.
Now we need a dataset for these latitudes to illustrate the secondary warming due to the leftover warm water from El Niño events and use it to account for the adjusted GISS LOTI data for the latitudes of 60S-20N.
THE SOUTH PACFIC CONVERGENCE ZONE (SPCZ) EXTENSION SST ANOMALY DATA AND CORRELATION MAP ARE REVEALING
The KOE was used in the discussion of the Northern Hemisphere data, so it seems logical that a similar area exists in the South Pacific. And for this discussion, we’ll designate that area as the South Pacific Convergence Zone (SPCZ) Extension. The SPCZ Extension data will be the SST anomalies of the area east of Australia (35S-20S, 160E-150W). As shown in Figure 18, it had a relatively high SST anomaly at the peak of the 1998/99 portion of the 1998 through 2001 La Niña.
http://i56.tinypic.com/2zhpb92.jpg
Figure 18
The SST anomalies for SPCZ Extension are shown in Figure 19.
http://i51.tinypic.com/2dinmgw.jpg
Figure 19
Like the KOE Extension data, the SST anomalies of the SPCZ Extension warm greatly during transitions from El Niño to La Niña events and appear to shift upward at those times. Refer to Figure 20.
http://i54.tinypic.com/35mf4v8.jpg
Figure 20
Creating the correlation map of annual (January to December) SPCZ Extension SST anomalies and annual Tropical and Southern Hemisphere (90S-20N) GISS LOTI data was eye-opening. It appears the SPCZ data is a good proxy for those areas in the western tropical Pacific and southwest Pacific that warm during La Niña events. It would also appear to show the effects those western Pacific areas have on the rest of the globe. As we can see in Figure 21, when the SPCZ Extension warms (cools) many areas throughout the tropics and Southern Hemisphere warm (cool). But as illustrated in Figure 20, the warming that occurs during La Niña events is not counteracted by the cooling during El Niño events. This causes the data to rise in steps during the La Niña events.
http://i56.tinypic.com/30d9xt0.jpg
Figure 21
Does the correlation map indicate that the upward shifts in the SPCZ Extension data also exist in the tropical and Southern Hemisphere GISS LOTI data? My understanding of correlation maps is that they emphasize the larger events in the data, and if we refer again to Figure 20, the larger events are those that occur during these upward shifts. We can also confirm this by comparing the respective time-series graphs.
Figure 22 illustrates the adjusted GISS LOTI data for the Tropics and Southern Hemisphere north of 60S. Also shown are scaled (0.25) SPCZ Extension SST anomalies. There are minor divergences from time to time, but in general the two curves agree surprisingly well.
http://i53.tinypic.com/6tmj9y.jpg
Figure 22
WHAT’S THE BOTTOM LINE?
What do the curves and linear trends of the adjusted GISS LOTI data look like if the KOE and SPCZ Extension data are removed? And what happens when you combine the two results to form a global dataset with all of the adjustments? Let’s take a look. The Northern Hemisphere GISS LOTI data (20N-60N) that’s been adjusted for ENSO and volcanic aerosols and the KOE SST anomalies is shown in Figure 23. Recall the divergence circled in blue in Figure 13; that’s the cause of the significant additional dip in 1990. Other than that, this was not a bad first attempt with scaling factors. But notice how small the trend is, 0.13 deg C/Century. If that dip was removed, the trend would be even lower.
http://i51.tinypic.com/jze2o9.jpg
Figure 23
The Tropical and Southern Hemisphere GISS LOTI data (60S-20N) with the ENSO, Volcano, and SPCZ Extension adjustments is shown in Figure 24. The trend is basically flat. This dataset appears noisy, but look at the temperature scale. The range is only one-quarter of one used in Figure 23.
http://i53.tinypic.com/67i7iu.jpg
Figure 24
We can combine the Northern Hemisphere data (20N-60N) with the Tropical and Southern Hemisphere data (60S-20N) using a weighted average. (The latitudes of 20N-60N represent approximately 29% of the surface area between 60S-60N.) Figure 25 shows the result. The linear trend is basically flat at 0.06 deg C/Century. The saw-tooth pattern is interesting, but…
http://i53.tinypic.com/29cw9dc.jpg
Figure 25
Due to the timing, the saw-tooth pattern appears to indicate that there was a lagged (repeated) volcano signal in the data. Refer to Figure 26. The reason I say repeated is that originally when the volcanic signal was removed, the Aerosol Optical Depth data was lagged 3 months and the leading edges of the data aligned well in Figures 9 and 16. The volcano signals in Figures 25 and 26, assuming those spikes are volcano signals, are lagged 9 months. The additional signal may also simply mean the Sato Mean Optical Thickness data doesn’t account perfectly for the decay of the volcano signal and that an additional adjustment is required.
http://i51.tinypic.com/6rjxpg.jpg
Figure 26
So let’s make the secondary volcano correction, refer to Figure 27. That will raise the linear trend of the adjusted GISS LOTI data.
http://i54.tinypic.com/2a8s204.jpg
Figure 27
After all of the adjustments are made, there is a small trend, about 0.24 deg C/Century. Compared to the original, unadjusted data, Figure 28, the trend of the adjusted data is only about 15% of the original GISS LOTI data for 60S-60N.
http://i56.tinypic.com/wnxa9.jpg
Figure 28
This makes perfect sense since there is little to no evidence of an anthropogenic global warming effect on global Ocean Heat Content (OHC) data. All one needs to do is divide the global oceans into tropical and extratropical subsets per ocean basin. Then it’s relatively easy to determine that ENSO, changes in Sea Level Pressure, and AMO/AMOC are responsible for that vast majority of the rise in OHC since 1955. Refer to:
A. ENSO Dominates NODC Ocean Heat Content (0-700 Meters) Data
B. North Pacific Ocean Heat Content Shift In The Late 1980s
C. North Atlantic Ocean Heat Content (0-700 Meters) Is Governed By Natural Variables
SHOULDN’T THE KUROSHIO-OYASHIO EXTENSION AND SPCZ EXTENSION DATA BE DETRENDED?
In this post and in The ENSO-Related Variations In Kuroshio-Oyashio Extension (KOE) SST Anomalies And Their Impact On Northern Hemisphere Temperatures, we illustrated that the Kuroshio-Oyashio Extension and South Pacific Convergence Zone Extension SST anomalies rise in steps during La Niña events. Since those upward steps are clearly responses to ENSO, there should be no need to detrend those datasets.
A NOTE ABOUT THE ATLANTIC MULTIDECADAL OSCILLATION
There is a natural variable I did not account for in this post, and it is the Atlantic Multidecadal Oscillation, or AMO. I did not remove its impacts on the Northern Hemisphere data. For those new to the AMO, refer to An Introduction To ENSO, AMO, and PDO — Part 2.
As noted in that post, RealClimate defines the Atlantic Multidecadal Oscillation (“AMO”) as, “A multidecadal (50-80 year timescale) pattern of North Atlantic ocean-atmosphere variability whose existence has been argued for based on statistical analyses of observational and proxy climate data, and coupled Atmosphere-Ocean General Circulation Model (“AOGCM”) simulations. This pattern is believed to describe some of the observed early 20th century (1920s-1930s) high-latitude Northern Hemisphere warming and some, but not all, of the high-latitude warming observed in the late 20th century. The term was introduced in a summary by Kerr (2000) of a study by Delworth and Mann (2000).”
I could have accounted for the AMO before removing the impacts of ENSO and the volcanic eruptions. But I chose to leave it in so that I could include the impact of the KOE on the North Atlantic.
As shown in Figure 29, the trend of the North Atlantic SST anomalies between 20N-60N is 70% higher than the North Pacific SST anomalies trend. By accounting for that additional “some, but not all” trend from the AMO, the scaling factor required to align the KOE dataset with the North Hemisphere data would drop.
http://i52.tinypic.com/x4lx5t.jpg
Figure 29
THE KOE SCALING IS TOO HIGH
The scaling factor for the Kuroshio-Oyashio Extension data in Figure 13 was 0.7. To some, it would not seem likely that the secondary warming of the KOE could raise temperatures for the Northern Hemisphere (20N-60N) that high, especially when one considers the multiplier for the SPCZ Extension was 0.25 in Figure 22.
First: Let’s consider the known effects of an El Niño event. When surface temperatures around the globe warm in response to an El Niño, most of those areas warm due to changes in atmospheric circulation. That is, they do not rise because the heat released into the atmosphere is warming the land and sea surfaces. The following is an example I often use. During an El Niño, the tropical North Atlantic warms even though it is separated from the Pacific by the Americas. The tropical North Atlantic warms during the El Niño because the El Niño causes a weakening of the North Atlantic trade winds. With the decrease in Atlantic trade wind strength there is less evaporation, and if there is less evaporation, sea surface temperatures rise. There is also less upwelling of cool water from below the surface when the trade winds weaken. This also causes sea surface temperatures to rise.
Therefore, it is through teleconnections or atmospheric bridges, not the direct transfer of heat, that the KOE would impact the areas of the Northern Hemisphere.
Second: There is a second western boundary current extension in the Northern Hemisphere, and it is the Gulf Stream Extension in the North Atlantic. For this quick discussion, we’ll define the Gulf Stream Extension by the coordinates of 35N-45N, 75W-30W. The map in Figure 30 is a correlation map and it shows that when the Gulf Stream Extension warms (cools) there are many parts of the Northern Hemisphere that warm (cool). And note that the eastern tropical Pacific is negatively correlated, indicating that these areas warm during La Niña events.
http://i52.tinypic.com/x1alur.jpg
Figure 30
Scroll back up to Animation 1. It also shows the parallel warming of the Gulf Stream Extension with the KOE.
But do the SST anomalies of the Gulf Stream Extension cool during El Niño events? As shown in Figure 31, the SST anomaly variations of the Gulf Stream Extension and the Kuroshio-Oyashio Extension are very similar. Both datasets can warm significantly during La Niña events but they do not drop proportionally during El Niño events. In an earlier linked post, I described the process that causes the KOE to warm, but I have not found a paper that describes the warming of the Gulf Stream Extension at those times. Why does the Gulf Stream Extension respond differently to El Niño and La Niña events? Like the KOE, is the warm water created during an El Niño also carried north by the Gulf Stream during the following La Niña? Do the changes in atmospheric circulation caused by the La Niña add to the warming? During the La Niña, does an increase in the strength of the North Atlantic trade winds also reduce cloud cover over the tropical North Atlantic? Does the warm water created by the decrease in cloud cover and resulting increase in sunlight then get transported to the Gulf Stream Extension? There are too many unanswered questions for me to use the Gulf Stream Extension data in this post.
http://i52.tinypic.com/4g6i9w.jpg
Figure 31
But, the parallel warming of the KOE and the Gulf Stream Extension during the transitions from El Niño to La Niña events would help to reduce the KOE scaling factor required to explain the step changes in the adjusted GISS LOTI data.
WHAT ABOUT SOLAR?
If we scale sunspot numbers so that the variations from solar minimum to maximum represent about a 0.1 deg change in temperature, and if we lag the sunspot data 6 years, it compares well visually with the adjusted GISS LOTI data. Refer to Figure 32. Someone with additional data processing tools could duplicate the steps taken in this post and confirm how well the two curves align.
http://i51.tinypic.com/23jsjo1.jpg
Figure 32
WHAT FUELS THE EL NIÑO EVENTS?
The warm water created during the previous La Niña(s) via the increase in Downward Shortwave Radiation (visible light) fuels El Niño events. This was discussed in More Detail On The Multiyear Aftereffects Of ENSO – Part 2 – La Nina Events Recharge The Heat Released By El Nino Events AND… …During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents.
CAN THE THIS TYPE OF EVALUATION BE EXTENDED BACK IN TIME?
I would not expect that what was presented in this post could be extended back in time. The Pacific climate shifted in 1976/77. In the abstract of Trenberth et al (2002), they write, “The 1976/1977 climate shift and the effects of two major volcanic eruptions in the past 2 decades are reflected in different evolution of ENSO events. At the surface, for 1979–1998 the warming in the central equatorial Pacific develops from the west and progresses eastward, while for 1950–1978 the anomalous warming begins along the coast of South America and spreads westward. The eastern Pacific south of the equator warms 4–8 months later for 1979–1998 but cools from 1950 to 1978.”
The way ENSO events interacted with the Kuroshio-Oyashsio Extension and the SPCZ Extension also appear different before and after 1979 in the correlation and regression analyses presented in that paper. Link to Trenberth et al (2002):
http://www.cgd.ucar.edu/cas/papers/2000JD000298.pdf
SOURCES
Most of the data used in this post are available through the KNMI Climate Explorer Monthly observations webpage. GISS LOTI is identified there in the second field under “Temperature” as “1880-now anomalies: GISS”, with the “1200km” radius smoothing. The Reynolds OI.v2 is listed under SST as “1982-now: 1° Reynolds OI v2 SST”. The coordinates used are identified in the text and/or on the graphs.
And if you want to attempt to duplicate my results but have never used the KNMI Climate Explorer, refer to the post Very Basic Introduction To The KNMI Climate Explorer for a place to start.
The dataset used to simulate the impacts of the volcanic eruptions is available through GISS:
http://data.giss.nasa.gov/modelforce/strataer/tau_line.txt
The Sunspot data is available through the KNMI Climate Explorer Monthly climate indices webpage. Refer to the Sunspots (1749-now, SIDC) field under the heading of “Sun”.
CLOSING REMARKS
This was a very basic attempt to approximate the effects of natural variables on global temperatures, using scaling and lags that were eye-balled. Sometimes basic things work well, and in this case, they appear to have done that. The similarities between the adjusted GISS LOTI datasets and the respective KOE and SPCZ Extension data were remarkable. While those similarities and the correlation maps do not prove the KOE and SPCZ Extension SST anomalies cause those addition rises in surface temperature, they imply that natural factors are causing the upward steps in global temperatures illustrated in Figure 4.
After some preliminary discussions, I divided the global (60S-60N) GISS LOTI data into two sections. The linear impacts of ENSO and volcanic eruptions were then removed from those subsets. The processes that cause the Sea Surface Temperatures in two parts of the Pacific to warm greatly during La Niña events were discussed. The unadjusted SST anomalies of the KOE and the SPCZ Extension were then compared to their respective adjusted GISS LOTI anomalies. The related curves were surprisingly similar. After removing the impacts of the KOE and the SPCZ Extension from the related GISS LOTI data, the linear trends dropped significantly. When the two GISS LOTI datasets were again combined, we had removed approximately 85% of what some consider to be the “anthropogenic global warming signal.”
This post differs from studies such as Thompson et al (2009). Thompson et al assumed that the ENSO proxy accounts for all of the processes within the Pacific that take place during ENSO events. In reality, NINO3.4 SST anomalies (or the CTI SST anomalies they used) can only account for the linear responses to the changes in equatorial Pacific SST anomalies. NINO3.4 SST anomalies cannot be assumed to account for the ENSO processes that take place within the Pacific or the aftereffects of those processes. What I presented in this post was a simple way to view those aftereffects within the Pacific and the global responses to them.
In short, I presented a story told by the GISS Land-Ocean Temperature Index and Reynolds OI.v2 SST data between the latitudes of 60S to 60N.
Discover more from Watts Up With That?
Subscribe to get the latest posts sent to your email.
John says:
January 10, 2011 at 2:24 pm
“The debate is about whether the increase in warming is small, medium or (unlikely in my mind) large….”
But if it can be small, it can be very very small. So small that it cannot be measured…..
R Gates
I think you need to take a good look in a mirror that reflects your own image.
You have assumed that warming in the arctic has a largely anthropogenic cause. That assumption is not settled science. If the warming from 60N to 60S is largely from natural sources, and if the same warming bleeds northward into the arctic, also by natural processes, then the albedo feedback you refer to will naturally kick in. So if Bob Tisdales interpretation is largely correct then a substantial part of the Arctic warming is likely to be a response to the same ENSO related processes.
Bob, “rectangular boxes” seem to be used to define areas of sea surface for temperature measurement in many research papers. I hesitate to raise this, but it seems to me that the some of these processes that you are describing could be captured better if the shapes of the relevant oceanic areas being measured were alowed to have curved margins.
Anyway this is great work and is a pleasure to read.
Eventually I see this line of enquiry as being very useful helping inform us on the real sensitivity of climate to anthropogenic greenhouse gases. Ultimately I suspect it will also have significant implications for analyses of changes in total oceanic heat content. Its hard to see how the oceans can be warming dramatically due to anthropogenic causes if the sea surface temperature (controlled for ENSO, ENSO afteraffects etc) is actually relatively stable.
R. Gates says:
January 10, 2011 at 2:25 pm
“Polar amplification is thought to result primarily from positive feedbacks from the retreat of ice and snow.”
And the retreat of ice and snow is due to warm air/water originating elsewhere.
Hi Bob,
Thanks for another great post. I really like the way you’ve worked the solar data into this. It’s very interesting to note the spikes in the adjusted temperature data lining up post-minimum on the lagged solar cycles. It will be interesting to see what happens next. If that pattern recurs and we get a spike around 2013, my guess is we are in for a big long fall in temp before that happens.
Last week I stuck my neck out and predicted -0.32C +/- 0.05C on Dr Roy’s metric by September this year. Interesting times.
I really admire R.Gates. Perhaps he would like to participate in the bet at No Tricks Zone.
Another couple of hours reading Bob’s post leaves me in awe.
R. Gates says: “This would imply some kind of causal linkage to the temperatures at lower latitudes with those at the pole in the process of polar amplification. This is definitely NOT what is implied by the causal definition of polar amplification.”
Let’s look at the data. Go to the GISS map-making webpage and ask for maps of the GISS LOTI Annual TRENDS from 1910 to 1945:
http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2010&month_last=11&sat=4&sst=1&type=trends&mean_gen=0112&year1=1910&year2=1945&base1=1951&base2=1980&radius=1200&pol=reg
And from 1945 to 1975:
http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2010&month_last=11&sat=4&sst=1&type=trends&mean_gen=0112&year1=1945&year2=1975&base1=1951&base2=1980&radius=1200&pol=reg
And 1975 to 2009:
http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2010&month_last=11&sat=4&sst=1&type=trends&mean_gen=0112&year1=1975&year2=2009&base1=1951&base2=1980&radius=1200&pol=reg
That’s two warming periods and a cooling period. Each time the map is presented, scroll down to the Zonal Mean plots and download the data for the trends in the zonal mean data for those periods. Here, I’ll save you some time:
http://i56.tinypic.com/29yihz6.jpg
The first thing you’ll notice is that the warming trend in the Arctic was greater during the earlier warming period than the latter. Kind of makes one wonder about the supposed AGW impact during the latter warming period. Note also that the Arctic is amplifying the cooling during the cooling decades from 1945 to 1975. To me these two things indicate that the Arctic is simply exaggerating (amplifying) the trends at lower latitudes, and that the Arctic trends are not based on the greater Greenhouse Gas concentration.
The ocean does not create its own heat. Heat is transferred from deeper to colder water under the influence of winds and currents. So the surface temperature can be changed by internal variation.
Surface temperature is an imperfect gauge of whether the earth has been warmed by an imbalance between incoming radiation from the sun, and outgoing radiation, because of the role of ocean currents in the distribution of heat between deeper and surface waters. The total change in ocean heat, including deep water regions is the best gauge of global warming.
The fact is that the oceans have been gaining heat since 1970:
http://www.skepticalscience.com/How-do-we-know-global-warming-is-still-happening.html
http://www.skepticalscience.com/images/Total-Heat-Content.gif
Measurements of the radiational imbalance are converging on 0.8 to 0.9W/M^2.
http://www.skepticalscience.com/How-we-know-global-warming-is-happening-Part-2.html
I would like to see an explanation of how the ENSO cycle, and other ocean current phenomena, can result in a spontaneous increase in energy entering the earth atmosphere system. If the external GHG forcing accounted for only 15% the warming, as Tisdale is claiming, it would seem to me that the system must have an enormous positive feed back so that it becomes extremely unstable. This would seem to me a cause for alarm.
Really great material Bob. I support the conclusions.
Just a couple of points. The Kuroshio-Oyasho extension does not need to be detrended. Check the data back to before 1900, there is just a very small trend in the Kuroshio.
Taking your idea on the original Kuroshio post, I had been working on the general idea – that there are areas in the oceans which seem to be the most active in exchanging energy with the atmosphere. Those areas are the ENSO, the Kuroshio, the Gulf-Stream equivalent area of the Kuroshio, the Equatorial Atlantic (which has some type of oscillation like the ENSO) (and these two replace the AMO), the Agulhus current off South Africa, the Brazil-Malvinus Confluence closer to Antarctica. I guess you added the southern Pacific version of the Kuroshio which makes sense to me.
All these areas share common latitudes and common land boundary confinements. These are the areas where the ocean currents are the strongest and the most confined. Ocean energy is moving hard and moving fast.
I couldn’t get it to work the way I wanted though. Maybe you can try adding a few other areas using this idea.
Great stuff.
OT but hilarious and a little bit scary
Check this out – are you on the green hit list? Put your site and personal details in and find out what they are saying about you!
http://www.twawki.com/?p=10535
REPLY: heh, that’s interesting, I’m going to look further. -Anthony
Bob, thanks for this wonderfully illuminating piece. I’m looking forward to the RSS discussion. I’ve little to add or detract to your piece in that the subject remains out of my purview. Perhaps that will change. I would caution you that you’re running close to the correlation/causation difficulty, but you’ve made a remarkable case in my estimation.
Great job! Keep it up!
“The Arctic is the front-line of AGW, and to attempt to remove the front-line (and the wealth of data and real world effects found there) is to attempt to redefine the battle in terms that are both illogical and in my opinion…not founded on science.”
The GISS data for the Arctic is made up and we all know this and does not show what the nearby instruments there are actually showing for most regions. Weather patterns have warm and cold anomalies within hundreds of miles, this man-made data is very poor at best. During persistent positive/negative NAO and AO phases many regions stay warm or cold throughout. (compared to average) If one station is used that is warm during the postive phase and is painted over 250km/1200km this gives a warm bias to regions that are cold during this phase. Many regions are cold or warm with these changing phases so odd stations can be cherry picked. This is overall both illogical and not founded on science so it is discarded. If AGW driving the climate is not found using the majority of ocean it won’t be found by using the Arctic too.
This excellent presentation by Bob demonstrates what a number of scientists have thought occurred, but had difficulty in demonstrating it. We can go back number of issues and not surprising when LWR can’t warm a small volume of water and especially not the deep ocean when compared to SWR. The difference in the role between LWR and SWR on a volume of the ocean is like comparing a teaspoon of water with a swimming pool.
The swimming pool can change temperatures up to 2c and back down again, how many teaspoons of water at 0.1c warmer added are going to require to increase the swimming pool 1c? There are so many needed that when this amount is achieved, far more energy will have already been lost to the atmosphere with the natural day and night cycle of the sun with evaporative cooling. Now we have a comparable estimate of how LWR is compared to SWR in climate. The oceans don’t freeze because the SWR controls the energy in it and LWR has no measurable affect compared with it’s many orders times greater brother.
Paul Deacon says:
R Gates – as you refer to “the polar regions”, why are all your other comments confined exclusively to the Arctic? It is not clear to me why the Arctic region has become the poster child for the Warmists, other than that nobody lives there, it has few weather stations, and polar bears look cute in photos. If in practice a warmer (less cold) Arctic coincides with cooling in the world’s main population centres (NE USA, NW Europe, N India, N China), as appears to have happened in the early part of the current NH winter, it is not clear to me why a warming Arctic should be cause for AGW alarm (although it could perhaps in a crude/tabloid sense be used as cause for Global Cooling alarm).
And at a general level, I don’t see anything inherently wrong with Bob Tisdale isolating parts of datasets in order to ask questions. It’s the sort of thing climate scientists do all the time, isn’t it?
All the best.
____
By every global climate model the Arctic would be the first to see the most visible signs of warming. From lower year-to-year sea ice to melting permafrost, the warming would begin to change the entire region. For those, like myself, who are not “true believers” in AGW, but, only sit at “more likely than not” that it is occurring, then, like looking down a long train tunnel for the headlight of the train long before you hear the whistle, it makes sense to study the changes going on in the Arctic. Anyway, that’s why I (and many other “warmists”) pay attention to the Arctic. But there is another reason the climate experts do, and that’s because the Arctic climate is tightly connected to the rest of the weather in the N. Hemisphere, as we see with this winter (and last’s) round of extremely negative Arctic Oscillation index. When the conditions are right atmospherically for the Arctic “freezer door” to be left open, it can mean misery for the populated areas further south. Along with the negative AO index, we’ve seen an increased frequency of the the Arctic Dipole Anomaly, whereby the deep polar closed low that normally keeps the Arctic air contained is split into a pressure zones on both sides of the pole (i.e. a dipole) creating zonal winds across the Arctic shunt both cold air (and ice) more vigorously out of the Arctic. This also has effects on population centers further south.
It is interesting to me that AGW skeptics only pay attention to the Arctic when some short term event (like a small bump up in sea ice) appears to go against a longer term trend. Arctic sea ice “recovered” (if you want to use that term very loosely) in 2008 and 2009 from the extreme low seen in 2007, and skeptics were certain that the trend would continue up in 2010. They were of course disappointed, as the power of the long term down trend intruded on their plans.
As a matter of pure research, I don’t have any problem with Bob doing whatever he wants to do to isolate causes (or parts of causes) with his use of the data and study whatever he wants, but I would question the value of any climate study that does not take into consideration the changes observed in the Arctic.
Interesting post.
Who is paying you for all this work you are doing?
Central England sure doesn’t feel any excess heat anymore:
http://hadobs.metoffice.com/hadcet/cetml1659on.dat
Year mean 1659: 8.83 °C
Year mean 1676: 8.83 °C
Year mean 1754: 8.83 °C
Year mean 1902: 8.83 °C
Year mean 1956: 8.83 °C
Year mean 2010: 8.83 °C
John says: “The debate is about whether the increase in warming is small, medium or (unlikely in my mind) large, and then whether we should be further bankrupting already bankrupt societies to only slightly affect that rate of warming, whatever it is.”
This post showed that the AGW component could be small. So I don’t understand the rest of your comment.
harvey says: “Who is paying you for all this work you are doing?”
I don’t receive any funding, if that’s your question. I’m retired and do this to keep myself busy. I get a few donations from time to time. The tips link is at the bottom of my webpage, BTW:
http://bobtisdale.blogspot.com/
Bob:
A marvelous post. As noted by some, it will take hours to completely digest.
Having done a lot of Xbar and Rbar charts for SPC (statistical process control) and being greatly steeped in the business of “statistical significance” it appears to me that despite the “linear fit” upward trends, the “noise” of the data would be strong enough to make useful conclusions on “trends” impossible.
Have you run any S.D. (Standard Deviation) tests on the data sets?
Thanks!
Max
Hello Bob,
Did I get that correct; it WAS you who recently posted at WUWT to the effect, that Clausius-Clapeyron, predicts a 7% increase in atmospheric water content for a one deg C Temperature rise; as found experimentally by Wentz et al. I think I originally mistakenly blamed Bill Illis for posting that.
George
eadler says: “I admit that I haven’t followed the details of Tisdale’s above post. From what I have read of it, it seems to be take most of the observed sea surface temperature variations out of the global surface temperature variations between 60S and 60N, and then come to the conclusion that there has been no global warming of the surface. Since the sea surface represents most of the surface of the globe, my reaction is ‘duh!’.”
Apparently you missed much of the content of the post since you admit you “haven’t followed the details of Tisdale’s above post.” In other words, maybe you should have read the entire post before you react with “duh”.
You continued, “In fact if one looks at the heat balance of the oceans down to 2000M, which cannot be driven to first order by surface related ocean cycles, we come to the conclusion that the earth is warming due to radiative imbalance. This is estimated at about 0.8W/M^2.”
If greenhouse gases were having an impact on the OHC to depths of 2000 meters, eadler, they would have to have had an impact on the upper 700meters. Greenhouse gases couldn’t bypass the upper 700 meters to warm the 700 to 2000 meter depths.
If you’re not aware (and apparently you’re not since you didn’t bother to read this post), I’ve written three posts that illustrate that most if not all of the rise in OHC from 1955 to present (0-700meters) can be explained as responses to natural variables. Let me copy what was written. Maybe you won’t overlook it this time.
Just below Figure 28 I wrote: This makes perfect sense since there is little to no evidence of an anthropogenic global warming effect on global Ocean Heat Content (OHC) data. All one needs to do is divide the global oceans into tropical and extratropical subsets per ocean basin. Then it’s relatively easy to determine that ENSO, changes in Sea Level Pressure, and AMO/AMOC are responsible for that vast majority of the rise in OHC since 1955.
Here are the links again, eadler:
http://bobtisdale.blogspot.com/2009/09/enso-dominates-nodc-ocean-heat-content.html
AND:
http://bobtisdale.blogspot.com/2009/12/north-pacific-ocean-heat-content-shift.html
AND:
http://bobtisdale.blogspot.com/2009/10/north-atlantic-ocean-heat-content-0-700.html
This should also serve as a reply to your later comment in which you linked the posts from skepticalscience.
R. Gates:
You say “I would question the value of any climate study that does not take into consideration the changes observed in the Arctic.”
Taking these these changes into consideration doesn’t make any difference. Bob Tisdale would have obtained the same results had he used the GISS met station air temperature series, which include both the Arctic and the Antarctic (and also doesn’t use SSTs) instead of the 60N-60S GISS LOTI series.
“”””” R. Gates says:
January 10, 2011 at 2:25 pm
Bob Tisdale says:
January 10, 2011 at 1:40 pm
R. Gates says: “Indeed, someone IS bound to complain, for your basic definition of polar amplfication of AGW is quite incorrect.”
The RealClimate post on Polar Amplification…
http://www.realclimate.org/index.php/archives/2006/01/polar-amplification/
…begins with the statement, “’Polar amplification’ usually refers to greater climate change near the pole compared to the rest of the hemisphere or globe in response to a change in global climate forcing, such as the concentration of greenhouse gases (GHGs) or solar output (see e.g. Moritz et al 2002).” “””””
Well it seems to me, that incoming solar energy is concentrated in the mid latitude regions (tropics, and Temperate) simly because most of the sunlight lands there. So those places reach higher Temperatures compared to the poles, which receive relatively little sunlight.
As a result there is a natural Temperature gradient from equator to poles, and a natural flow of heat in that direction.
Because the polar Temperatures are so low, the cooling by radiation is very much slower than in the tropics, since the radiation goes at T^4.
So you have excess heat being transported towards the poles; where the loss mechanisms are quite poor. It stands to reason that Temperature increases will be larger at the poles than at the equator. It also takes less heat to increase the Temperature at low Temperatures (since the heat loss rate is much lower).
So this is a simple picture, and nothing that could be called an “Amplification” is occurring. It’s a non l;inear process.
Rob R says: “Bob, ‘rectangular boxes’ seem to be used to define areas of sea surface for temperature measurement in many research papers. I hesitate to raise this, but it seems to me that the some of these processes that you are describing could be captured better if the shapes of the relevant oceanic areas being measured were alowed to have curved margins.”
I use the KNMI Climate Explorer as a data source, and it is a coordinate-based system, meaning I have to enter the coordinates to retrieve the data. The easiest way (not necessarily the best) is to use the rectangles and rely on Climate Explorer to provide a weighted average of the data based on those coordinates.
George E. Smith says: “Did I get that correct; it WAS you who recently posted at WUWT to the effect, that Clausius-Clapeyron, predicts a 7% increase in atmospheric water content for a one deg C Temperature rise; as found experimentally by Wentz et al.”
It wasn’t me, George>
Max Hugoson says: “Have you run any S.D. (Standard Deviation) tests on the data sets?”
Nope.
Bob – excellent!
Suggest you download the data sources, segregate and save them to an available (Anthony, can you oblige?) server. Mosh has suggested this in the past, and I strongly recommend it. THE DATA HAS A BAD HABIT OF CHANGING ON YOU!
R. Gates is very exercised about your exclusion of the Arctic. I believe that Bob’s approach is appropriate, and that he has responded appropriately.
It seems, Mr. Gates, that we should be concerned about the MECHANISMS of the first order cause of the warming, not the “first (region) to see the most visible signs of warming”, nor the feedbacks (second and third order impacts) that it may engender.
For us to consider the arctic warming to be serious, we need to understand what causes the arctic warming. Mr. Gates may be absolutely correct that when the arctic is warmed there results some (serious) additional warming: however, if that warming is not caused overwhelmingly by anthropogenic sources, then – at the very least – we can halt the alarmist cry over CO2 emissions and start the important work of science.
Remember, the GCMs (and their approach to the Aerosol/GHG balance) are predicated on the confident statement that absent CO2, there is not an identified mechanism that could have caused the warming over the past 30 years. Bob has advanced a plausible mechanism for a fairly large component of that warming (in the geographic areas that count. Interestingly enough, the greater the feedback at the Poles, the more comprehensively Bob’s mechanism explains the entire temperature rise!)
As an inventor of a computer model, I would focus on the first order issues first, and suggest that the second order issues are better resolved when the principal components of the model are better understood.