UPDATE: See the revision to and discussion of Figure 2.
This is not the monthly sea surface temperature update. See the post September 2012 Sea Surface Temperature (SST) Anomaly Update.
CMIP3 (IPCC AR4) HINDCASTS/PROJECTIONS
I included the CMIP3 (IPCC AR4) multi–model mean outputs (hindcasts/projections) in a monthly sea surface temperature anomaly update for the first time six months ago in March 2012 Sea Surface Temperature (SST) Anomaly Update – A New Look. It was suggested that the model-data comparison should not serve as the monthly update, so I’ve provided it separately. I’ll try to update the model-data comparison every six months or so.
The graphs include the multi-model mean of the CMIP3 hindcasts/projections for sea surface temperatures, presenting them in comparisons to the observed data. The observed and modeled linear trends are also shown. This is done for the global, hemispheric and ocean basin sea surface temperature anomalies. As you will recall, CMIP3 is the climate model archive used by the IPCC for its 4thAssessment Report (AR4).
The multi-model mean and linear trends of the CMIP3 model simulation data definitely make the graphs busier. Refer to the Global sea surface temperature anomaly graph. We added the smoothed data (13-month running-average filter) on a trial basis a few months ago, and readers requested that we keep the smoothed data. On some occasions, the trend lines may obscure the most recent changes in the dataset.
(1) Global Sea Surface Temperature Anomalies
Modeled versus observed correlations with time from 1982 to 2011 are shown in the following two maps. The scale below each map is correlation coefficient, not temperature. A positive correlation coefficient of 1.0 (hot pink) would indicate an area warmed linearly from 1982 to 2011, while a negative correlation coefficient of -1.0 (purple) would indicate an area cooled linearly over that period. Basically, what the maps are showing are the modeled and observed warming and cooling trend patterns. There are no similarities. Keep those two images in mind the next time you see a peer-reviewed paper that projects regional climate on decadal or multidecadal bases. The modelers have no hope of doing so unless they can predict ENSO and its impacts on regional sea surface and land surface temperatures. They simulate both poorly.
(2) Modeled and Observed Correlations With Time (REVISED: I altered the title block.)
UPDATE: I was so interested in illustrating the differences in the spatial patterns between the models and observations that I inadvertently used correlation maps. If I had I presented the maps of regression against time, I would have been presenting the actual linear trends. They still show the models do not properly present the spatial patterns for how the oceans warmed (or cooled in the case of the data) over the past 30 years. That, of course, will clearly impact any attempts to use models for projecting regional land surface climate on decadal and multidecadal bases, because where the oceans warm or cool dictates regional climate on land.
(2 Revised) Modeled and Observed Trends
NOTES ABOUT THE MODEL-OBSERVATION COMPARISONS
The model-observations comparisons serve as updates to two of my favorite posts: Satellite-Era Sea Surface Temperature Versus IPCC Hindcast/Projections Part 1 and Part 2. Refer to those posts for the discussions of the monumental differences between the models and observations. They are also presented in my first book If the IPCC was Selling Manmade Global Warming as a Product, Would the FTC Stop their Deceptive Ads?, in Section 8. A few model-data comparisons were also provided in my new book Who Turned on the Heat? – The Unsuspected Global Warming Culprit, El Niño-Southern Oscillation. More on that later.
The multi-model mean are not expected to present the year-to-year variations in sea surface temperature associated with the El Niño-Southern Oscillation (ENSO). Some of the models simulate ENSO; others don’t. The models that do attempt to simulate ENSO do a poor job of it. (This is documented in numerous peer-reviewed papers. Refer to the post Guilyardi et al (2009) “Understanding El Niño in Ocean-Atmosphere General Circulation Models: progress and challenges”) Each model produces ENSO events on its own schedule; that is, the modeled ENSO events do not reproduce the observed frequency, duration, and magnitude of El Niño and La Niña events. Since the multi-model mean presents the average of all of those modeled out-of-synch ENSO signals, they are smoothed out. For this reason, we are only concerned with the disparity in the modeled and observed trends.
And as shown above, the difference between the linear trends on a global basis is quite large. The model simulations hindcast/project a global sea surface temperature anomaly warming rate that is about 80% higher than the observed rate. Depending on the subset, the models perform better and worse. For example, the model-simulated rate of warming for Northern Hemisphere sea surface temperature anomalies is only about 24% higher than observed, while in the Southern Hemisphere, the models say the sea surface temperatures should be warming at a rate that is more than 2.5 times faster than the observed rate.
Keep in mind, the global oceans represent about 70% of the surface area of the globe, and the climate models show no skill at being able to simulate their warming. Global sea surface temperatures have warmed over the past 30+ years in response to ENSO events, not anthropogenic greenhouse gases. This was presented and discussed in detail in my recent book titled Who Turned on the Heat? – The Unsuspected Global Warming Culprit, El Niño-Southern Oscillation and in a good number of posts at my blog.
NOTE: CMIP5-based sea surface temperature outputs had been available through the KNMI Climate Explorer. I was hoping to use it in this post. It, unfortunately, was removed from the KNMI Climate Explorer. Hopefully it will return in the near future so that I can include it in the next update, to serve as a preview of how badly the newest models simulate sea surface temperatures in advance of the IPCC’s upcoming 5thAssessment Report.
The MONTHLY graphs illustrate raw monthly OI.v2 sea surface temperature anomaly data from November 1981 to March 2012, as it is presented by the NOAA NOMADS website linked at the end of the post. I’ve added the 13-month running-average filter to smooth out the seasonal variations. The trends are based on the raw data, not the smoothed data.
Last, the differences between models and observations are not discussed throughout the rest of the post. Feel free, however, to comment on the disparity between the models and the observations.
NINO3.4, INDIVIDUAL OCEAN BASIN AND HEMISPHERIC SEA SURFACE TEMPERATURE COMPARISONS
(3) NINO3.4 Sea Surface Temperature Anomalies
(4) Northern Hemisphere Sea Surface Temperature (SST) Anomalies
(5) Southern Hemisphere Sea Surface Temperature (SST) Anomalies
(6) North Atlantic Sea Surface Temperature (SST) Anomalies
(0 to 70N, 80W to 0)
(7) South Atlantic Sea Surface Temperature (SST) Anomalies
(60S to 0, 70W to 20E)
(8) North Pacific Sea Surface Temperature (SST) Anomalies
(0 to 65N, 100E to 90W)
(9) South Pacific Sea Surface Temperature (SST) Anomalies
(60S to 0, 120E to 70W)
(10) Indian Ocean Sea Surface Temperature (SST) Anomalies
(60S to 30N, 20E to 120E)
(11) Arctic Ocean Sea Surface Temperature (SST) Anomalies
(65N to 90N)
(12) Southern Ocean Sea Surface Temperature (SST) Anomalies
INTERESTED IN LEARNING HOW WE KNOW MOTHER NATURE, NOT GREENHOUSE GASES, WARMED THE GLOBAL OCEANS OVER THE PAST 30 YEARS?
The sea surface temperature record indicates El Niño and La Niña events are responsible for the warming of global sea surface temperature anomalies over the past 30 years, not manmade greenhouse gases. I’ve searched sea surface temperature records for more than 4 years, and I can find no evidence of an anthropogenic greenhouse gas component. That is, the warming of the global oceans has been caused by Mother Nature, not anthropogenic greenhouse gases.
I’ve recently published my e-book (pdf) about the phenomena called El Niño and La Niña. It’s titled Who Turned on the Heat? with the subtitle The Unsuspected Global Warming Culprit, El Niño Southern Oscillation. It is intended for persons (with or without technical backgrounds) interested in learning about El Niño and La Niña events and in understanding the natural causes of the warming of our global oceans for the past 30 years. Because land surface air temperatures simply exaggerate the natural warming of the global oceans over annual and multidecadal time periods, the vast majority of the warming taking place on land is natural as well. The book is the product of years of research of the satellite-era sea surface temperature data that’s available to the public via the internet. It presents how the data accounts for its warming—and there are no indications the warming was caused by manmade greenhouse gases. None at all.
Who Turned on the Heat? was introduced in the blog post Everything You Ever Wanted to Know about El Niño and La Niña… …Well Just about Everything. The Updated Free Preview includes the Table of Contents; the Introduction; the beginning of Section 1, with the cartoon-like illustrations; the discussion About the Cover; and the Closing.
Please buy a copy. (Paypal or Credit/Debit Card). It’s only US$8.00.
You’re probably asking yourself why you should spend $8.00 for a book written by an independent climate researcher. There aren’t many independent researchers investigating El Niño-Southern Oscillation or its long-term impacts on global surface temperatures. In fact, if you were to perform a Google image search of NINO3.4 sea surface temperature anomalies, the vast majority of the graphs and images are from my blog posts. Try it. Cut and paste NINO3.4 sea surface temperature anomaliesinto Google. Click over to images and start counting the number of times you see Bob Tisdale.
By independent I mean I am not employed in a research or academic position; I’m not obligated to publish results that encourage future funding for my research—that is, my research is not agenda-driven. I’m a retiree, a pensioner. The only funding I receive is from book sales and donations at my blog. Also, I’m independent inasmuch as I’m not tied to consensus opinions so that my findings will pass through the gauntlet of peer-review gatekeepers. Truth be told, it’s unlikely the results of my research would pass through that gauntlet because the satellite-era sea surface temperature data contradicts the tenets of the consensus.
The Reynolds Optimally Interpolated Sea Surface Temperature Data (OISST) are available through the NOAA National Operational Model Archive & Distribution System (NOMADS).
The CMIP3 Sea Surface Temperature simulation outputs (identified as TOS, assumedly for Temperature of the Ocean Surface) are available through the KNMI Climate Explorer Monthly CMIP3+ scenario runs webpage. The correlation maps are available through the KNMI Climate Explorer as well.
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If nothing else, the Modelers should give you the credit for your independent comparisons and greatly assisting in the ‘development’ of their newer Mods. But for some reason I don’t think that they’re listening/watching anything outside their own ‘Fantasyland’. The differences just at the poles speak terabites about what they thought was going to happen, have you gotten even a hint that they’re adjusting anything, are they still trying to force the world into their square peghole?
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Great post Bob. Reblogged it for you at The Real World
Bob – FYI
At Bishop Hill this morning
“Matt Ridley on the IPCC ”
Oct 9, 2012
Matt Ridley: The Perils Of Confirmation Bias. Read it at GWPF
[updated at 12.25pm to prevent confusion]
Ridley:”The modus operandi of the Intergovernmental Panel on Climate Change (IPPC) has been to accumulate evidence to champion rather than challenge a hypothesis, namely that rising carbon dioxide levels will in future cause dangerous climate change. A good example is the IPCC’s claim that only models that incorporate high-sensitivity carbon dioxide-induced warming countered by aerosol induced cooling can match (or “hindcast”) the recent upward progress of global average temperatures. The problem with this is that different models use different values of assumed cooling from aerosols. That is to say, the cooling effect of aerosols has been picked so that it fills the gap between observed and expected warming. The modellers are therefore in effect saying: we observe warming of X, we predicted warming of X+5, so there must have been cooling of 5, therefore our prediction is correct.”
Read more here http://www.thegwpf.org/matt-ridley-the-perils-of-confirmation-bias/
I referenced your latest piece here and gave the link in a brief comment.
Pascvaks: Thanks for the link to Bishop Hill, and thanks for the link there.
Good day, If you’d recall my last post via weather .com they predicted an above average October and November for much of the Midwest/ East coast of the US. Well I just saw this video and was amused by it so I thought I’d share. http://www.weather.com/weather/videos/news-41/top-stories-169/science-behind-the-temperature-drop-31565
Thanks, Richard. It’s that last little remaining chunk of Arctic sea ice and the drought that caused the record low temps.
Bob, I was reading an old post on GISS ocean data on the effect of replacing SSTs with land temperatures:
You conclude that the process raises the trend some 40%.
Using the GISS SST series
I calculated a trend of 0.08°C/decade, 1979 to present. For comparison, the UAH ocean-only trend is 0.12°C/decade, as can be found here:
It would appear that UAH, which doesn’t do the replacing, is actually higher than GISS.
DB says: “It would appear that UAH, which doesn’t do the replacing, is actually higher than GISS.”
The GISS data you linked is marked incorrectly. It is SST anomalies, not “Mean Surface Air Temperature over Ocean Areas”. It does not include land air surface temperature data in areas where there is seasonal and permanent sea ice, which is what my post was about. In effect, the GISS data you linked is close to only being sea surface temperature data from 60S-65N. The TLT data over the ocean includes areas with sea ice. It also includes “bleed over” of TLT over land, especially over the Arctic Ocean. In other words, there’s no way to truly isolate the land and oceans with TLT data.
I guess my question was why is the UAH TLT ocean trend 50% greater than the Reynolds SST trend? Is this something to be expected? Is the bleed over that great considering the vast size of the oceans?
DB: Attached is a gif animation that compares TLT and SST trends from 1982 to 2011. Sorry, the KNMI Climate Explorer won’t allow me to mask the land data for the trend maps. Note how Arctic TLT is exaggerated due to polar amplification. Also keep in mind, TLT exaggerates all variations including the volcano dips and that it responds differently to ENSO than SST.
There is a new paper (Nuccitelli et al.) promoted at SkS, which claims that there is no hiatus in OHC during the last decade. Actually they claim that if anything, it has been accelerating. Their data seems bogus to me (comparing to NODC for example), could you dig in to it and perhaps make a post?
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juakola: I just happen to be working on an OHC post. Though it was not intended as a response to Nuccitelli et al, I started working on it before that SkS post appeared, it should squash their nonsense.
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