John Nielsen-Gammon Comments Regarding Climate Models And The Process Of El Niño-Southern Oscillation


I can see no basis for John Nielsen-Gammon’s attempt to attribute the record high temperatures in Texas to the hypothesis of Anthropogenic Global Warming. It appears that Nielsen-Gammon, like the Intergovernmental Panel on Climate Change (IPCC), relies on climate models to conclude that most of the rise in Surface Temperatures, globally and regionally, is caused by anthropogenic greenhouse gases. Unfortunately, their reliance on models to support that hypothesis is unfounded. The climate models show little to no skill at hindcasting past global and regional natural variations in Sea Surface Temperature, which, through coupled ocean-atmospheric processes, would have impacts on the temperature and drought in Texas. Since the climate models are incapable of replicating the natural modes of multiyear and multidecadal variability in Sea Surface Temperatures, the models are of little value as tools to determine if the warming could be attributed to manmade or natural causes, and they are of little value as tools to project future climate on global or regional bases.

And based on John Nielsen-Gammon’s comment about El Niño-Southern Oscillation (ENSO), it appears he has overlooked the significant contribution ENSO can make to the multiyear and multidecadal variations in Global Sea Surface Temperature anomalies, which are so obvious during the satellite-era of Sea Surface Temperature observations.


Roger Pielke Sr., has published at his blog a series of emails between he and John Nielsen-Gammon. Roger’s post is dated November 10, 2011 and is titled John Nielsen-Gammon and I Continue Our Discission. Pielke Sr.’s initial post on this topic, dated November 4, 2011, is titled NBC Nightly News Regarding The Recent October Snowstorm And A Quote From John Nielsen-Gammon. In it, Pielke Sr. refers to Nielsen-Gammon’s September 9, 2011 blog post at the Houston Chronicle website titled Texas Drought and Global Warming. All three posts are worth a read and provide the fuel for this post.

In one of the emails reproduced in his recent post, Roger Pielke Sr. provided Nielsen-Gammon with a link to my November 4, 2011 post An Initial Look At The Hindcasts Of The NCAR CCSM4 Coupled Climate Model. (Please read this post also, if you haven’t done so already. It shows how poorly the recent version of the NCAR CCSM coupled climate model replicates the surface temperatures from 1900 to 2005.) And Nielsen-Gammon’s response to it included:

“When driven by observed oceanic variability, the models do a great job simulating the atmospheric response.  With the present drought, it’s not a matter of predicting the oceans and atmosphere.  We know the present ocean temperature patterns, so we can estimate their contribution very well from both observations and models.  The models’ difficulty in simulating the statistics of ENSO itself is a red herring.”

First, I have no basis from which to dispute Nielsen-Gammon’s opening sentence of, “When driven by observed oceanic variability, the models do a great job simulating the atmospheric response”.  I have not investigated how well the models actually perform this function. But that’s neither here nor there. Why? Well, if the hindcast and projected representations of sea surface temperatures created by the models are not realistic, then the atmospheric response to the modeled oceanic variability would also fail to be realistic.

Second, Nielsen-Gammon wrote, “We know the present ocean temperature patterns, so we can estimate their contribution very well from both observations and models.” Nielsen-Gammon’s sentence does not state that the models provide a reasonable representation of ocean variability. So the fact that Nielsen-Gammon can estimate the oceanic contributions from observations AND from models is immaterial. The models are so far from reality, they have little value as climate hindcasting, or projection, or attribution tools, as stated previously.

Also, if you’re new to the subject of climate change, always keep in mind, when you read a climate change post like John Nielsen-Gammon’s, where the author constantly refers to models and model-based studies (in an attempt to add credibility to the post?), that it may not be the same climate model being referred to. Models have strengths and weaknesses, and climate scientists use different models for different studies. Depending on the coupled ocean-atmosphere process being studied, even if one organization’s model is used, model parameters may be set differently, they may be initialized differently, they may use different forcings, etc. So, while two model-based climate studies may use the same model, the model runs used to study the atmospheric response to the Atlantic Multidecadal Oscillation, for example, may not incorporate the same forcings that are used to hindcast past climate and project future climate. In fact, there are model-based studies where observed Sea Surface Temperature data are used to force the climate models.


In addition to the post linked earlier in which I compared climate model outputs to observed data, I have also illustrated and discussed in detail the differences between the observed sea surface temperature anomalies and those hindcast/projected by climate models in the two posts titled:

Part 1 – Satellite-Era Sea Surface Temperature Versus IPCC Hindcast/Projections


Part 2 – Satellite-Era Sea Surface Temperature Versus IPCC Hindcast/Projections.

In those posts, I showed the very obvious differences between observed Sea Surface Temperature data and the model mean of the climate models used in the IPCC AR4 on global and ocean-basin bases, during the satellite-era of sea surface temperature measurement, 1982 to present. Here are a few examples:

Figure 1 is a time-series graph of the satellite-based observations of Global Sea Surface Temperatures versus the model mean of the hindcasts/projections made by the climate models used in the IPCC AR4. It shows how poorly the linear trend of the model mean compares to the trend for the measured Global Sea Surface Temperature anomalies. The models overestimate the warming by approximately 50%.

Figure 1

Figure 2 compares the linear trends for the observations and the model mean of the IPCC AR4 hindcasts/projections of Sea Surface Temperatures on a zonal mean basis. That is, it compares, for the period of January 1982 to February 2011, the modeled and observed linear trends, in 5-degree-latitude bands (80S-75S, then 75S-70S, etc., from pole to pole) from the Southern Ocean around Antarctica north through to the Arctic Ocean. It clearly shows that, in the models, the tropics warm faster than at higher latitudes, where in reality, that is clearly not the case. This implies that the models do an extremely poor job of simulating how the oceans distribute warm water from the tropics toward the poles. Extremely poor.

Figure 2

In those two posts, I not only illustrate the failings of the models on a Global basis, but I also illustrate them on an ocean-basin basis: North and South Pacific, East and West Pacific, North and South Atlantic and Indian Ocean. There are no subsets of the models that come close to the observations on a time-series basis and on a zonal-mean basis.


John Nielsen-Gammon notes in his article, after he changed attribution from “greenhouse gases” to “global warming”, that:

The IPCC has not estimated the total century-scale contribution to global warming from anthropogenic greenhouse gases, but has said that the warming since 1950 was probably mostly anthropogenic.  So it seems reasonable to estimate that somewhere around two-thirds of the century-scale trend is due to anthropogenic greenhouse gas increases. That is to say, the summer temperatures would have been about one or one and a half degrees cooler one half to one degree cooler without the increases in CO2 and other greenhouse gases. [John Nielsen-Gammon’s boldface and strikes.]

I cannot see how Nielsen-Gammon can make that claim when the IPCC’s model depictions of sea surface temperature variability over the past 30 years, which are coupled to global and regional variations in temperature and precipitation, differ so greatly from the observations. I truly cannot. The models are so different from observations that they have no value as an attribution tool. None whatsoever.


The last sentence in the first quote from John Nielsen-Gammon above reads, “The models’ difficulty in simulating the statistics of ENSO itself is a red herring.” As a reference, Animation 1, is the El Niño-Southern Oscillation (ENSO)-related comparison from my post that Roger Pielke Sr. linked for Nielsen-Gammon (An Initial Look At The Hindcasts Of The NCAR CCSM4 Coupled Climate Model).It shows how poorly the models hindcast the frequency, magnitude, and trend of ENSO events. In that post, I explained why the failure of climate models to reproduce the frequency and magnitude of ENSO events was important. Yet John Nielsen-Gammon characterized my illustrations and discussion as a “red herring”.

Animation 1

Here’s what I wrote, in part, about Animation 1:

The first thing that’s obviously different is that the frequency and magnitude of El Niño and La Niña events of the individual ensemble members do not come close to matching those observed in the instrument temperature record. Should they? Yes. During a given time period, it is the frequency and magnitude of ENSO events that determines how often and how much heat is released by the tropical Pacific into the atmosphere during El Niño events, how much Downward Shortwave Radiation (visible sunlight) is made available to warm “and recharge” the tropical Pacific during La Niña events, and how much heat is transported poleward in the atmosphere and oceans, some of it for secondary release from the oceans during some La Niña events. If the models do not provide a reasonable facsimile of the strength and frequency of El Niño and La Niña events during given epochs, the modelers have no means of reproducing the true causes of the multiyear/multidecade rises and falls of the surface temperature anomalies. The frequency and magnitude of El Niño and La Niña events contribute to the long-term rises and falls in global surface temperature.

My illustrations and discussions of ENSO in that post are not intended to divert anyone’s attention from the actual cause of the rise in global temperatures, which is what I assume John Nielsen-Gammon intended with the “red herring” remark. The frequency and magnitude of ENSO events are the very obvious cause of the rise in Sea Surface Temperatures during the satellite era. And that isn’t a far-fetched hypothesis; that is precisely the tale told by the sea surface temperature data itself. One simply has to divide the data into logical subsets to illustrate it, and it is so obvious once you know it exists that it is hard to believe that it continues to be overlooked by some members of the climate science community.

Recently I started including two illustrations of ENSO’s effect on Sea Surface Temperatures in each of my monthly Sea Surface Temperature anomaly updates. (Example post: October 2011 Sea Surface Temperature (SST) Anomaly Update) Refer to the graphs of the “volcano-adjusted” East Pacific Sea Surface Temperature anomalies and of the Sea Surface Temperature anomalies for the Rest of the World. I’ve reposted them here as Figures 3 and 4, respectively.

Note Regarding Volcano Adjustment: I described the method used to determine the volcano adjustment in the post Sea Surface Temperature Anomalies – East Pacific Versus The Rest Of The World, where I first illustrated these two datasets. The description reads:

To determine the scaling factor for the volcanic aerosol proxy, I used a linear regression software tool (Analyse-it for Excel) with global SST anomalies as the dependent variable and GISS Stratospheric Aerosol Optical Thickness data (ASCII data) as the independent variable. The scaling factor determined was 1.431. This equals a global SST anomaly impact of approximately 0.2 deg C for the 1991 Mount Pinatubo eruption.

Back to the discussion of the volcano-adjusted East Pacific and Rest-of-the-World data: Let’s discuss the East Pacific data first. As you’ll quickly note in Figure 3, based on the linear trend produced by EXCEL, there has been no rise in the Sea Surface anomalies for the volcano-adjusted East Pacific Ocean Sea Surface Temperature anomaly data, pole to pole, or the coordinates of 90S-90N, 180-80W, for about the past 30 years. The El Niño events and La Niña events dominate the year-to-year variations, as one would expect, but the overall trend is slightly negative. The East Pacific Ocean dataset represents about 33% of the surface area of the global oceans, and there hasn’t been a rise in sea surface temperature anomalies there for three decades.

Figure 3

Since we’ve already established that Global Sea Surface Temperature observations have risen during that period (Refer back to the observation-based data in Figure 1), that means the Rest-of-the-World data is responsible for the rise in global Sea Surface Temperature anomalies. But as you’ll note in Figure 4, the volcano-adjusted Sea Surface Temperature anomalies for the Rest of the World (90S-90N, 80W-180) rise in very clear steps, and that those rises are in response to the significant 1986/87/88 and 1997/98 El Niño/La Niña events. (It also appears as though the Sea Surface Temperature anomalies of this dataset are making another upward shift in response to the 2009/10 El Niño and 2010/11 La Niña.) And between those steps, the Rest-of-the World Sea Surface Temperature anomalies remain relatively flat. How flat will be illustrated shortly.

Figure 4

Note: The periods used for the average Rest-Of-The-World Sea Surface Temperature anomalies between the significant El Niño events of 1982/83, 1986/87/88, 1997/98, and 2009/10 are determined as follows. Using the NOAA Oceanic Nino Index(ONI) for the official months of those El Niño events, I shifted (lagged) those El Niño periods by six months to accommodate the lag between NINO3.4 SST anomalies and the response of the Rest-Of-The-World Sea Surface Temperature anomalies, then deleted the Rest-Of-The-World data that corresponds to those significant El Niño events. I then averaged the Rest-Of-The-World SST anomalies between those El Niño-related gaps.

I have in numerous posts discussed, illustrated, and animated the variables associated with the coupled ocean-atmosphere process of El Niño-Southern Oscillation (ENSO) that cause these apparent upward shifts in the Rest-of-the-World Sea Surface Temperature anomalies. My first posts on this were in January 2009. The most recent ones are from the July 2011: ENSO Indices Do Not Represent The Process Of ENSO Or Its Impact On Global Temperature and Supplement To “ENSO Indices Do Not Represent The Process Of ENSO Or Its Impact On Global Temperature”.Those two posts were written at an introductory level for those who aren’t familiar with the process of the El Niño-Southern Oscillation (ENSO). In the initial post, I further illustrated the actual linear trends of the Rest-of-the-World data between the significant ENSO events, reproduced here as Figure 5. They are indeed flat.

Figure 5

And in the supplemental post, I further subdivided the Rest-of-the-World Sea Surface Temperature data into two more subsets. The first to be illustrated, Figure 6, covers the South Atlantic, Indian and West Pacific Oceans. As shown, Sea Surface Temperature anomalies decay between the significant ENSO events, just as one would expect.

Figure 6

And for the North Atlantic, Figure 7, which is impacted by another mode of natural variability called the Atlantic Multidecadal Oscillation (AMO), the linear trends between those significant ENSO events are slightly positive, also as one would expect. And the short-term ENSO-induced upward shifts are plainly visible in Figure 7 and are responsible for a significant portion of the rise in North Atlantic Sea Surface Temperature anomalies over the past 30 years.

Figure 7


This post clearly illustrates that John Nielsen-Gammon failed to consider that climate models prepared for the Intergovernmental Panel on Climate Change (IPCC) AR4 have little to no basis in reality. When one considers the significant differences between the observed Sea Surface Temperature anomaly variations and those hindcast/projected by climate models, the models provide no support for his conclusion that most of the rise in Surface Temperatures, globally and regionally, was caused by anthropogenic greenhouse gases.

This post also clearly illustrated that “The models’ difficulty in simulating the statistics of ENSO itself is”…NOT…“a red herring.” The process of the El Niño-Southern Oscillation was responsible for most of the rise in global sea surface temperature anomalies over the past thirty years.


For the sources of data presented in this post, refer to the linked posts from which the graphs were borrowed.

ABOUT: Bob Tisdale – Climate Observations

About Bob Tisdale

Research interest: the long-term aftereffects of El Niño and La Nina events on global sea surface temperature and ocean heat content. Author of the ebook Who Turned on the Heat? and regular contributor at WattsUpWithThat.
This entry was posted in CAGW Proponent Arguments, El Nino-La Nina Processes, Model-Data Comparison SST, Natural Warming. Bookmark the permalink.

18 Responses to John Nielsen-Gammon Comments Regarding Climate Models And The Process Of El Niño-Southern Oscillation

  1. Pascvaks says:

    There are those who cannot see and those who will not see. So too, there are those who cannot understand and those who will not understand. In my humble and lowly opinion, your explaination of the facts as we currently “know” them is totally sufficient to draw the conclusions you have. Nielson-Gammon, et al, cannot or will not see or understand, and I fear their problem has nothing to do with the “science” of the matter.

  2. Pingback: The Texas ENSO Bassmaster Classic | Watts Up With That?

  3. Pingback: Bob Tisdale’s Response To The E-Mail Interaction Between John Neilsen-Gammon And I | Climate Science: Roger Pielke Sr.

  4. Pingback: The Texas ENSO Bassmaster Classic | TaJnB | TheAverageJoeNewsBlogg

  5. jae3 says:

    Ha, ha, Johnnie, you finally lost your “cool” and showed your true bias. Yeah, you tipped your dumb side!

  6. Joe Lalonde says:


    Have you looked at the salt changes that have been increasing in the last 4 decades?
    More dense at the equatorial region and fresher in the higher latitudes.

    Click to access curryetal_nature2003.pdf

    That is also where the strongest centrifugal force is located.

    Click to access world-calculations.pdf

    Click to access world-calculations-2.pdf

  7. Michael Hart says:

    5 Years-David Bowie 1972
    One of his finest creations

    “Pushing thru the market square, so many mothers sighing
    News had just come over, we had five years left to cry in
    News guy wept and told us, earth was really dying
    Cried so much his face was wet…..”

    and later

    “….We’ve got five years, stuck on my eyes
    Five years, what a surprise
    We’ve got five years, my brain hurts a lot
    Five years, that’s all we’ve got
    We’ve got five years, what a surprise…..”

  8. Michael Hart says:

    sorry! wrong column
    It was meant for “Finally Some Good News”

  9. Bob Tisdale says:

    Joe Lalonde says: “Have you looked at the salt changes that have been increasing in the last 4 decades”

    Sorry. I have not had the chance to explore that data, yet. Thanks for the link.

  10. John N-G says:

    Bob – You could have saved yourself a lot of trouble if you’d checked with me to see if you’d interpreted my comments correctly, as Roger had the courtesy to do. Contact me by email if you’d like to set the record straight.

  11. Bob Tisdale says:

    John N-G: Thanks for responding to my post. Preparing it was little trouble. I’d already read your “Texas Drought and Global Warming” post at Climate Abyss (I lurk there) so I knew your opinions about the topics you addressed in it. And I used illustrations from past posts, so it really wasn’t any trouble at all. Some of my comments on blog threads at WattsUpWithThat take more time to prepare.

    And I will apologize if I’ve come across to you as discourteous for not discussing my opinions with you beforehand. But I responded to something written in a blog post with a blog post. It was clear to me then, and it still is, that your comment, “The models’ difficulty in simulating the statistics of ENSO itself is a red herring”, was a response to my post about the NCAR CCSM4 model. The thing is, John, I’ve well documented the multiyear impacts of significant ENSO events on global SST, TLT, and combined Land+Sea Surface Temperature anomalies like GISS LOTI and the effects those events have on decadal and multidecadal trends. Most of the rise in those Global temperature anomaly datasets over the past 30 years can be explained by ENSO, with a little help from the AMO.

    Glad to see Southeast Texas got some rain today.



  12. John N-G says:

    Bob –
    Thanks for your response. To clarify, my estimation of the portion of global warming due to increased CO2 and other Tyndall gases was not based on models, but was based on a simple acceptance of the IPCC estimate as the best available value. The IPCC estimate in turn is based upon a variety of evidence, including models. I don’t interpret your attribution of recent warming as being due to ENSO as directly contradicting the observations of Tyndall gases as the largest external forcing. If your hypothesis about ENSO is correct, the positive forcing from Tyndall gases must have been cancelled by negative forcing from other mechanisms (the most likely candidate being aerosols).
    This brings us to an interesting but little-appreciated aspect of attribution: it’s not purely additive. For example, if the observed rise in global temperatures over the past half-century was 0.5C, the contribution due to ENSO was +0.5C, the contribution due to Tyndall gases was +0.4C, and the contribution due to aerosols was -0.4C, both you and I would be simultaneously correct. That’s why I say that the contribution due to ENSO is a red herring when discussing the contribution due to Tyndall gases.
    The other two sentences in the paragraph you highlighted were referring to Wang et al. (2009) and conclusions drawn therefrom, which is based on atmosphere-only simulations.
    College Station picked up 0.28″. We’re now running 20% for month-to-date. Oh boy. Maybe Monday or Tuesday we’ll get something decent.
    – John

  13. Bob Tisdale says:

    John N-G: Thanks for the reply. I understand, but don’t agree with, your explanation of how you came up with ENSO being a red herring. Your example assumes, correct me if I’m wrong, that greenhouse gases have a measurable impact on Sea Surface Temperatures and that the effect is offset by aerosols.

    Let’s look at another line of evidence of a hole in the AGW hypothesis as it relates to sea surface temperature data. Bear with me for a few minutes. I got a little wordy. I prepared the following graphs earlier this year, but never got around to writing a post about them.

    A number of years ago someone (assumedly an AGW skeptic) performed a multiple linear regression analysis with the GISS (dts) land surface temperature dataset as the dependent variable and the GISS forcings as the independent variables. If you recall, the GISS dts was the dataset with the 1200km smoothing created by Hansen and Lebedeff back in 1987. It was created because there were no spatially complete SST datasets at that time, so they extended Land Surface Temperature out over the oceans by 1200 km and used land surface data from islands to help the coverage.

    Click to access 1987_Hansen_Lebedeff.pdf

    The GISS dTs data is still available through, and updated by, GISS.

    And the blogger (I don’t recall if this took place at Climate Audit or as a post at John Daly’s website) created a simple regression analysis-based model that resembled the GISS dTs data remarkably well. So I went through the same exercise, and here are the results:

    The fit is really remarkable. (If I were to add NINO3.4 SST anomalies as an independent variable, the model would fit even better, with a correlation coefficient of about 0.89. Pretty good.) That regression-based model even includes the mid-20th century flattening and decline in surface temperatures.

    Let’s drop back to 1970s, ‘80s and ‘90s, early on in the development of the AGW hypothesis and computer models. I would have to assume that linear regression analyses were used to supplement climate models. Main frame time was, and still is, expensive. Using the coefficients created by the linear regression, the scientists could create a simple regression-based model similar to the one shown above, and they could also compare the global warming potential of the forcings:

    And of course, Greenhouse Gases were the dominant forcing with that simple model. So, back in the old days, the coefficients based on the multiple linear regression analysis would make sense for a starting point, and then the models could help to work out the details.

    The fit of the regression analysis-based model, of course, generated lots of comments around the skeptical climate change blogs: the forcings datasets were manufactured to recreate the temperature anomaly curve, etc. Those arguments can still be found in comments to this day around the blogosphere.

    As you may be aware, my interest is Sea Surface Temperature data. So I went through the same process, but I used HADISST data (the long-term SST dataset used in the GISS Land-Ocean Temperature Index) as the dependent variable in the regression analysis, and I eliminated two of the GISS forcings from the independent variables (Land Use and Snow Albedo), since they don’t make sense with the SST data. Here’s the comparison graph of the linear regression-based model and the Global HADISST data:

    The fit isn’t bad with the HADISST data, but it’s not as good as the other one of the GISS dTs data with all of the forcings. (I haven’t attempted to determine which of the two excluded forcings helps to give the better fit in the late 1930s and early 40s.) But note the coefficients in the model equation. Here’s a graph of the global warming potential for those forcings:

    They would be considered wrong by most, if not all, climate scientists. The coefficient for the Greenhouse Gases is relatively small and it’s negative.

    But, going back to the early days of AGW hypothesis development, let’s assume only SST data was available. If the linear regression analysis of SST data showed the above results, would the AGW research have even continued? Of course it would have, but adjustments would have been made to something.

    And as a reference, I’ve added the AR4 Model Mean Hindcast/Projection for SST in the following graph. The simple model based on the linear regression fits better than the AR4 model mean. No surprise there:

    There are too many inconsistencies with climate science, John. A simple model based on linear regression analysis using the GISS climate forcings supports the hypothesis of greenhouse gas-driven AGW of land surface temperatures, but not of sea surface temperatures. Were you aware of that?

    Climate models do a very poor job of hindcasting Sea Surface Temperatures during the satellite era and since 1900. I have illustrated this in numerous posts, and I have another one I hope to post today using 17-year and 30-year trends. The models don’t look too good. The fact that the models cannot replicate Sea Surface Temperature data on long-term and short-term bases and on global, hemispheric, and ocean-basin bases should call into question not only their ability to project future climate, but also the hypothesis of anthropogenic global warming as it relates to Sea Surface Temperature.



  14. John N-G says:

    “Your example assumes, correct me if I’m wrong, that greenhouse gases have a measurable impact on Sea Surface Temperatures…”

    No. My example is purely to illustrate that an important role for greenhouse gases is not excluded by your hypothesis of an important role for oceanic variability, thus discussion of oceanic variability by itself is not directly relevant to the problem of determining the effect of greenhouse gases. My example does not assume a particular provenance for the numbers, whether it be measurements, theoretical calculations, or model simulations. It certainly does not rely on multiple linear regression using strongly correlated “independent” variables.

  15. Bob Tisdale says:

    John N-G: Please correct me if I’m wrong, but your example has to assume that Greenhouse Gases have a measurable effect on Sea Surface Temperature. And while you don’t assume particular provenance for any numbers, the observed rise in sea surface temperature can be explained without Greenhouse gases and the climate model portrayal of sea surface temperature variability on any time scale bears no relationship to the observed variations in sea surface temperature, so I can’t see how that greenhouse gas assumption could be justified. And nowhere in my last reply to you did I imply that you were basing your example on linear regression analysis. I look at that as a curiosity, which is why the presentation was as a series of suppositions.


  16. John N-G says:

    I think we’re down to semantics. An effect can be important, and its magnitude estimable, without it being directly measurable. By linear regression, I was referring to your counterexample. Thanks for the opportunities to respond; Happy Thanksgiving!

  17. Bob Tisdale says:

    John N-G: Here’s a hot-off-the-press post that helps to illustrate and reinforce my earlier comment that the climate model portrayal of sea surface temperature variability on any time scale bears no relationship to the observed variations in sea surface temperature:

    I hope you and yours enjoyed your Thanksgiving holiday.


  18. Pingback: Interpret nielsen | Myutopianlife

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