Guest Post by Bob Tisdale
This post provides an update of the values for the three primary suppliers of global land+ocean surface temperature reconstructions—GISS through May 2016 and HADCRUT4 and NCEI (formerly NCDC) through April 2016—and of the two suppliers of satellite-based lower troposphere temperature composites (RSS and UAH) through May 2016. It also includes a model-data comparison.
INITIAL NOTES:
We recently discussed and illustrated the impacts of the adjustments to surface temperature data in the posts:
- Do the Adjustments to Sea Surface Temperature Data Lower the Global Warming Rate?
- UPDATED: Do the Adjustments to Land Surface Temperature Data Increase the Reported Global Warming Rate?
- Do the Adjustments to the Global Land+Ocean Surface Temperature Data Always Decrease the Reported Global Warming Rate?
The NOAA NCEI product is the new global land+ocean surface reconstruction with the manufactured warming presented in Karl et al. (2015). For summaries of the oddities found in the new NOAA ERSST.v4 “pause-buster” sea surface temperature data see the posts:
- The Oddities in NOAA’s New “Pause-Buster” Sea Surface Temperature Product – An Overview of Past Posts
- On the Monumental Differences in Warming Rates between Global Sea Surface Temperature Datasets during the NOAA-Picked Global-Warming Hiatus Period of 2000 to 2014
Even though the changes to the ERSST reconstruction since 1998 cannot be justified by the night marine air temperature product that was used as a reference for bias adjustments (See comparison graph here), and even though NOAA appears to have manipulated the parameters (tuning knobs) in their sea surface temperature model to produce high warming rates (See the post here), GISS also switched to the new “pause-buster” NCEI ERSST.v4 sea surface temperature reconstruction with their July 2015 update.
The UKMO also recently made adjustments to their HadCRUT4 product, but they are minor compared to the GISS and NCEI adjustments.
We’re using the UAH lower troposphere temperature anomalies Release 6.5 for this post even though it’s in beta form. And for those who wish to whine about my portrayals of the changes to the UAH and to the GISS and NCEI products, see the post here.
The GISS LOTI surface temperature reconstruction and the two lower troposphere temperature composites are for the most recent month. The HADCRUT4 and NCEI products lag one month.
Much of the following text is boilerplate that has been updated for all products. The boilerplate is intended for those new to the presentation of global surface temperature anomalies.
Most of the graphs in the update start in 1979. That’s a commonly used start year for global temperature products because many of the satellite-based temperature composites start then.
We discussed why the three suppliers of surface temperature products use different base years for anomalies in chapter 1.25 – Many, But Not All, Climate Metrics Are Presented in Anomaly and in Absolute Forms of my free ebook On Global Warming and the Illusion of Control – Part 1 (25MB).
Since the July 2015 update, we’re using the UKMO’s HadCRUT4 reconstruction for the model-data comparisons using 61-month filters.
And I’ve resurrected the model-data 30-year trend comparison using the GISS Land-Ocean Temperature Index (LOTI) data.
GISS LAND OCEAN TEMPERATURE INDEX (LOTI)
Introduction: The GISS Land Ocean Temperature Index (LOTI) reconstruction is a product of the Goddard Institute for Space Studies. Starting with the June 2015 update, GISS LOTI uses the new NOAA Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4), the pause-buster reconstruction, which also infills grids without temperature samples. For land surfaces, GISS adjusts GHCN and other land surface temperature products via a number of methods and infills areas without temperature samples using 1200km smoothing. Refer to the GISS description here. Unlike the UK Met Office and NCEI products, GISS masks sea surface temperature data at the poles, anywhere seasonal sea ice has existed, and they extend land surface temperature data out over the oceans in those locations, regardless of whether or not sea surface temperature observations for the polar oceans are available that month. Refer to the discussions here and here. GISS uses the base years of 1951-1980 as the reference period for anomalies. The values for the GISS product are found here. (I archived the former version here at the WaybackMachine.)
Update: The May 2016 GISS global temperature anomaly is +0.93 deg C. It made another relatively large downtick since April 2016, a -0.16 deg C decrease, which should be a response to the decay of the El Niño. According to the GISS LOTI data, global surface temperature anomalies have dropped about 0.4 deg C since their peak in January February 2016.
Figure 1 – GISS Land-Ocean Temperature Index
NCEI GLOBAL SURFACE TEMPERATURE ANOMALIES (LAGS ONE MONTH)
NOTE: The NCEI only produces the product with the manufactured-warming adjustments presented in the paper Karl et al. (2015). As far as I know, the former version of the reconstruction is no longer available online. For more information on those curious adjustments, see the posts:
- NOAA/NCDC’s new ‘pause-buster’ paper: a laughable attempt to create warming by adjusting past data
- More Curiosities about NOAA’s New “Pause Busting” Sea Surface Temperature Dataset
- Open Letter to Tom Karl of NOAA/NCEI Regarding “Hiatus Busting” Paper
- NOAA Releases New Pause-Buster Global Surface Temperature Data and Immediately Claims Record-High Temps for June 2015 – What a Surprise!
And recently:
- Pause Buster SST Data: Has NOAA Adjusted Away a Relationship between NMAT and SST that the Consensus of CMIP5 Climate Models Indicate Should Exist?
- The Oddities in NOAA’s New “Pause-Buster” Sea Surface Temperature Product – An Overview of Past Posts
- On the Monumental Differences in Warming Rates between Global Sea Surface Temperature Datasets during the NOAA-Picked Global-Warming Hiatus Period of 2000 to 2014
Introduction: The NOAA Global (Land and Ocean) Surface Temperature Anomaly reconstruction is the product of the National Centers for Environmental Information (NCEI), which was formerly known as the National Climatic Data Center (NCDC). NCEI merges their new “pause buster” Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4) with the new Global Historical Climatology Network-Monthly (GHCN-M) version 3.3.0 for land surface air temperatures. The ERSST.v4 sea surface temperature reconstruction infills grids without temperature samples in a given month. NCEI also infills land surface grids using statistical methods, but they do not infill over the polar oceans when sea ice exists. When sea ice exists, NCEI leave a polar ocean grid blank.
The source of the NCEI values is through their Global Surface Temperature Anomalies webpage. Click on the link to Anomalies and Index Data.)
Update (Lags One Month): The April 2016 NCEI global land plus sea surface temperature anomaly was +1.10 deg C. See Figure 2. It dropped noticeably (a decrease of -0.13 deg C) since March 2016.
Figure 2 – NCEI Global (Land and Ocean) Surface Temperature Anomalies
UK MET OFFICE HADCRUT4 (LAGS ONE MONTH)
Introduction: The UK Met Office HADCRUT4 reconstruction merges CRUTEM4 land-surface air temperature product and the HadSST3 sea-surface temperature (SST) reconstruction. CRUTEM4 is the product of the combined efforts of the Met Office Hadley Centre and the Climatic Research Unit at the University of East Anglia. And HadSST3 is a product of the Hadley Centre. Unlike the GISS and NCEI reconstructions, grids without temperature samples for a given month are not infilled in the HADCRUT4 product. That is, if a 5-deg latitude by 5-deg longitude grid does not have a temperature anomaly value in a given month, it is left blank. Blank grids are indirectly assigned the average values for their respective hemispheres before the hemispheric values are merged. The HADCRUT4 reconstruction is described in the Morice et al (2012) paper here. The CRUTEM4 product is described in Jones et al (2012) here. And the HadSST3 reconstruction is presented in the 2-part Kennedy et al (2012) paper here and here. The UKMO uses the base years of 1961-1990 for anomalies. The monthly values of the HADCRUT4 product can be found here.
Update (Lags One Month): The April 2016 HADCRUT4 global temperature anomaly is +0.93 deg C. See Figure 3. It dropped about -0.14 deg C from March to April 2016.
Figure 3 – HADCRUT4
UAH LOWER TROPOSPHERE TEMPERATURE ANOMALY COMPOSITE (UAH TLT)
Special sensors (microwave sounding units) aboard satellites have orbited the Earth since the late 1970s, allowing scientists to calculate the temperatures of the atmosphere at various heights above sea level (lower troposphere, mid troposphere, tropopause and lower stratosphere). The atmospheric temperature values are calculated from a series of satellites with overlapping operation periods, not from a single satellite. Because the atmospheric temperature products rely on numerous satellites, they are known as composites. The level nearest to the surface of the Earth is the lower troposphere. The lower troposphere temperature composite include the altitudes of zero to about 12,500 meters, but are most heavily weighted to the altitudes of less than 3000 meters. See the left-hand cell of the illustration here.
The monthly UAH lower troposphere temperature composite is the product of the Earth System Science Center of the University of Alabama in Huntsville (UAH). UAH provides the lower troposphere temperature anomalies broken down into numerous subsets. See the webpage here. The UAH lower troposphere temperature composite are supported by Christy et al. (2000) MSU Tropospheric Temperatures: Dataset Construction and Radiosonde Comparisons. Additionally, Dr. Roy Spencer of UAH presents at his blog the monthly UAH TLT anomaly updates a few days before the release at the UAH website. Those posts are also regularly cross posted at WattsUpWithThat. UAH uses the base years of 1981-2010 for anomalies. The UAH lower troposphere temperature product is for the latitudes of 85S to 85N, which represent more than 99% of the surface of the globe.
UAH recently released a beta version of Release 6.0 of their atmospheric temperature product. Those enhancements lowered the warming rates of their lower troposphere temperature anomalies. See Dr. Roy Spencer’s blog post Version 6.0 of the UAH Temperature Dataset Released: New LT Trend = +0.11 C/decade and my blog post New UAH Lower Troposphere Temperature Data Show No Global Warming for More Than 18 Years. The UAH lower troposphere anomalies Release 6.5 beta through May 2016 are here.
Update: The May 2016 UAH (Release 6.5 beta) lower troposphere temperature anomaly is +0.55 deg C. It dropped considerably (a decrease of about -0.16 deg C) since April 2016.
Figure 4 – UAH Lower Troposphere Temperature (TLT) Anomaly Composite – Release 6.5 Beta
RSS LOWER TROPOSPHERE TEMPERATURE ANOMALY COMPOSITE (RSS TLT)
Like the UAH lower troposphere temperature product, Remote Sensing Systems (RSS) calculates lower troposphere temperature anomalies from microwave sounding units aboard a series of NOAA satellites. RSS describes their product at the Upper Air Temperature webpage. The RSS product is supported by Mears and Wentz (2009) Construction of the Remote Sensing Systems V3.2 Atmospheric Temperature Records from the MSU and AMSU Microwave Sounders. RSS also presents their lower troposphere temperature composite in various subsets. The land+ocean TLT values are here. Curiously, on that webpage, RSS lists the composite as extending from 82.5S to 82.5N, while on their Upper Air Temperature webpage linked above, they state:
We do not provide monthly means poleward of 82.5 degrees (or south of 70S for TLT) due to difficulties in merging measurements in these regions.
Also see the RSS MSU & AMSU Time Series Trend Browse Tool. RSS uses the base years of 1979 to 1998 for anomalies.
Note: RSS recently release new versions of the mid-troposphere temperature and lower stratosphere temperature (TLS) products. So far, their lower troposphere temperature product has not been updated to this new version.
Update: The May 2016 RSS lower troposphere temperature anomaly is +0.53 deg C. It dropped a whopping amount since April 2016, about -0.23 deg C.
Figure 5 – RSS Lower Troposphere Temperature (TLT) Anomalies
COMPARISONS
The GISS, HADCRUT4 and NCEI global surface temperature anomalies and the RSS and UAH lower troposphere temperature anomalies are compared in the next three time-series graphs. Figure 6 compares the five global temperature anomaly products starting in 1979. Again, due to the timing of this post, the HADCRUT4 and NCEI updates lag the UAH, RSS and GISS products by a month. For those wanting a closer look at the more recent wiggles and trends, Figure 7 starts in 1998, which was the start year used by von Storch et al (2013) Can climate models explain the recent stagnation in global warming? They, of course, found that the CMIP3 (IPCC AR4) and CMIP5 (IPCC AR5) models could NOT explain the recent slowdown in warming, but that was before NOAA manufactured warming with their new ERSST.v4 reconstruction…and before the strong El Niño of 2015/16.
Figure 8 starts in 2001, which was the year Kevin Trenberth chose for the start of the warming slowdown in his RMS article Has Global Warming Stalled?
Because the suppliers all use different base years for calculating anomalies, I’ve referenced them to a common 30-year period: 1981 to 2010. Referring to their discussion under FAQ 9 here, according to NOAA:
This period is used in order to comply with a recommended World Meteorological Organization (WMO) Policy, which suggests using the latest decade for the 30-year average.
The impacts of the unjustifiable adjustments to the ERSST.v4 reconstruction are visible in the two shorter-term comparisons, Figures 7 and 8. That is, the short-term warming rates of the new NCEI and GISS reconstructions are noticeably higher during “the hiatus”. See the June 2015 update for the trends before the adjustments. But the trends of the revised reconstructions still fall short of the modeled warming rates during the hiatus periods.
Figure 6 – Comparison Starting in 1979
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Figure 7 – Comparison Starting in 1998
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Figure 8 – Comparison Starting in 2001
Note also that the graphs list the trends of the CMIP5 multi-model mean (historic through 2005 and RCP8.5 forcings afterwards), which are the climate models used by the IPCC for their 5th Assessment Report. The metric presented for the models is surface temperature, not lower troposphere.
AVERAGE
Figure 9 presents the average of the GISS, HADCRUT and NCEI land plus sea surface temperature anomaly reconstructions and the average of the RSS and UAH lower troposphere temperature composites. Again because the HADCRUT4 and NCEI products lag one month in this update, the most current average only includes the GISS product.
Figure 9 – Average of Global Land+Sea Surface Temperature Anomaly Products
MODEL-DATA COMPARISON & DIFFERENCE
Note: The HADCRUT4 reconstruction is now used in this section. [End note.]
As noted above, the models in this post are represented by the CMIP5 multi-model mean (historic through 2005 and RCP8.5 forcings afterwards), which are the climate models used by the IPCC for their 5th Assessment Report.
Considering the uptick in surface temperatures in 2014, 2015 and now 2016 (see the posts here and here), government agencies that supply global surface temperature products have been touting “record high” combined global land and ocean surface temperatures. Alarmists happily ignore the fact that it is easy to have record high global temperatures in the midst of a hiatus or slowdown in global warming, and they have been using the recent record highs to draw attention away from the growing difference between observed global surface temperatures and the IPCC climate model-based projections of them.
There are a number of ways to present how poorly climate models simulate global surface temperatures. Normally they are compared in a time-series graph. See the example in Figure 10. In that example, the UKMO HadCRUT4 land+ocean surface temperature reconstruction is compared to the multi-model mean of the climate models stored in the CMIP5 archive, which was used by the IPCC for their 5th Assessment Report. The reconstruction and model outputs have been smoothed with 61-month running-mean filters to reduce the monthly variations. The climate science community commonly uses a 5-year running-mean filter (basically the same as a 61-month filter) to minimize the impacts of El Niño and La Niña events, as shown on the GISS webpage here. Using a 5-year running mean filter has been commonplace in global temperature-related studies for decades. (See Figure 13 here from Hansen and Lebedeff 1987 Global Trends of Measured Surface Air Temperature.) Also, the anomalies for the reconstruction and model outputs have been referenced to the period of 1880 to 2013 so not to bias the results. That is, by using the almost the full term of the data, no one with the slightest bit of common sense can claim I’ve cherry picked the base years for anomalies with this comparison.
Figure 10
It’s very hard to overlook the fact that, over the past decade, climate models are simulating way too much warming and are diverging rapidly from reality.
Another way to show how poorly climate models perform is to subtract the observations-based reconstruction from the average of the model outputs (model mean). We first presented and discussed this method using global surface temperatures in absolute form. (See the post On the Elusive Absolute Global Mean Surface Temperature – A Model-Data Comparison.) The graph below shows a model-data difference using anomalies, where the data are represented by the UKMO HadCRUT4 land+ocean surface temperature product and the model simulations of global surface temperature are represented by the multi-model mean of the models stored in the CMIP5 archive. Like Figure 10, to assure that the base years used for anomalies did not bias the graph, the full term of the graph (1880 to 2013) was used as the reference period.
In this example, we’re illustrating the model-data differences smoothed with a 61-month running mean filter. (You’ll notice I’ve eliminated the monthly data from Figure 11. Example here. Alarmists can’t seem to grasp the purpose of the widely used 5-year (61-month) filtering, which as noted above is to minimize the variations due to El Niño and La Niña events and those associated with catastrophic volcanic eruptions.)
Figure 11
The difference now between models and data is almost worst-case, comparable to the difference at about 1910.
There was also a major difference, but of the opposite sign, in the late 1880s. That difference decreases drastically from the 1880s and switches signs by the 1910s. The reason: the models do not properly simulate the observed cooling that takes place at that time. Because the models failed to properly simulate the cooling from the 1880s to the 1910s, they also failed to properly simulate the warming that took place from the 1910s until the 1940s. (See Figure 12 for confirmation.) That explains the long-term decrease in the difference during that period and the switching of signs in the difference once again. The difference cycles back and forth, nearing a zero difference in the 1980s and 90s, indicating the models are tracking observations better (relatively) during that period. And from the 1990s to present, because of the slowdown in warming, the difference has increased to greatest value ever…where the difference indicates the models are showing too much warming.
It’s very easy to see the recent record-high global surface temperatures have had a tiny impact on the difference between models and observations.
See the post On the Use of the Multi-Model Mean for a discussion of its use in model-data comparisons.
MODEL-DATA COMPARISON – 30-YEAR RUNNING TRENDS
Yet another way to show how poorly climate models simulate surface temperatures is to compare 30-year running trends of global surface temperature data and the model-mean of the climate model simulations of it. See Figure 12. First thing to note is that the y-axis is scaled in deg C/decade. In this case, we’re using the global GISS Land-Ocean Temperature Index for the data. For the models, once again we’re using the model-mean of the climate models stored in the CMIP5 archive with historic forcings to 2005 and worst case RCP8.5 forcings since then.
Figure 12
There are numerous things to note in the trend comparison. First, there is a growing divergence between models and data starting in the early 2000s. The continued rise in the model trends indicates global surface warming is supposed to be accelerating, but the data indicate little to no acceleration since then. Second, the plateau in the data warming rates begins in the early 1990s, indicating that there has been very little acceleration of global warming for more than 2 decades. This suggests that there MAY BE a maximum rate at which surface temperatures can warm. Third, note that the observed 30-year trend ending in the mid-1940s is comparable to the recent 30-year trends. (That, of course, is a function of the new NOAA ERSST.v4 data used by GISS.) Fourth, yet that high 30-year warming ending about 1945 occurred without being driven by the forcings the that drive the climate models. That is, the climate models indicate that global surface temperatures should have warmed at about a third that fast if global surface temperatures were dictated by the forcings used to drive the models. In other words, if the models can’t explain the observed 30-year warming ending around 1945, then the warming must have occurred naturally. And that, in turns, generates the question: how much of the current warming occurred naturally. Fifth, the agreement between model and data trends for the 30-year periods ending in the 1960s to about 2000 suggests the models were tuned to that period or at least part of it. Sixth, going back further in time, the models can’t explain the cooling seen during the 30-year periods before the 1920s, which is why they fail to properly simulate the warming in the early 20th Century.
One last note, the monumental difference in modeled and observed warming rates at about 1945 confirms my earlier statement that the models can’t simulate the warming that occurred during the early warming period of the 20th Century.
MONTHLY SEA SURFACE TEMPERATURE UPDATE
The most recent sea surface temperature update can be found here. The satellite-enhanced sea surface temperature composite (Reynolds OI.2) are presented in global, hemispheric and ocean-basin bases.
RECENT RECORD HIGHS
We discussed the recent record-high global sea surface temperatures for 2014 and 2015 and the reasons for them in General Discussions 2 and 3 of my recent free ebook On Global Warming and the Illusion of Control (25MB). The book was introduced in the post here (cross post at WattsUpWithThat is here).
UPDATE
Corrected a few typos.
Bob, I think the peak of the GISS LOTI data was in February, not January. The drop of 0.4 since then is one of the largest 3-month drops ever recorded. In the last 100 years there was larger drops only in in feb-may 1995 (-0.5), dec 1981 – mar 1982 (-0.42), nov 1928 – feb 1929 (-0.48) and sep 1917 – dec 1917 (-0.53).
don’t worry, they haven’t finished their adjustments yet. The next “fix” will take care of that.
Thanks, Espen. I corrected the post.
“occurred without being driven by the forcings the drive the climate models. ”
Should be “THAT drive the climate models” ??
These ‘forcings’ are a bunch of crooks who cook the books 🙂
Thanks, Marcus. I corrected the typo.
Why isn’t any of the evidence of fraudulent climate data and/or conclusions published in peer-reviewed journals? More importantly, why do deniers think it means anything at all without the peer-review process? This information has the same scientific value as Jack and the Beanstalk but convinces deniers anyway. Good thing real global warming is no threat at all, or this would be criminal.
Ever hear of ‘censorship’?
Peer review in climate science is equivalent to the agreement that blood letting was a valid medical treatment just a century or two ago. When you have large unknowns and massive ignorance you will get agreement when that is the only way to get ahead.
we will keep them out somehow, even if we have to redefine what peer review literature is—Phil Jones CRU.
David Seeling,
You not only used the “d” word, but you repeated it. As far as I’m concerned, you just came ever so close to invalidating anything you have to say, going forward. You may have identified yourself as possibly either a know- nothing true believer, a propagandist, or both.
You also alluded to global warming as being no threat and while there is plenty of evidence afloat that could have led you to that conclusion, your remark could also be an ingratiating attempt to provide some measure of cover for yourself, or merely smartalec sarcasm. You then went on to state your belief that some speech should be criminalized.
Your only saving grace so far is that you used your name to post and thus, don’t fall into the category of random anonymous troll.
Welcome to the conversation.
1) Deniers is fighting words, only trolls and the incredibly ignorant use it.
2) A grand total of nobody denies that climate changes. What we disagree with is the claim that global warming is a threat to all. Given the tone of your last sentence, it would appear that you qualify as a denier yourself.
3) The idea that disagreeing with others should be criminal is what totalitarians do. Trying to criminalize dissent is way more offensive than anything we “deniers” might have ever done.
4) Grow up.
Actually, David, since all he is doing is re-graphing accepted and published (except most recent) data and actual output from the climate models, there is nothing in this post that would be publishable. He is simply reporting the facts and noting the temperature trends over various time periods already used by various people in the published literature. He gives references to everything. Why don’t you look them up? I would observe that error bars on a few graphs would be helpful. But what struck me the most, is that even using the worst case RCP8.5 scenario, they are only off by a few tenths of a degree. If you used the RCP6.5, they would be even closer. That is impressive. The problem is always that the models always project an exponential type of acceleration in the future decades that so far has not occurred. So the models from 20 years ago were making even worse projections which have not come true. One of the things the recent pause buster adjustments did (which many in the climate community grumbled about, not just lukewarmers and skeptics) was to make the data look like a single (smaller as I recall) slope so there was not an obvious pause. Interesting that it is so close to the models. Actually, once the temperatures drop from the El nino and especially if there is a La nina, the differences for the near future with projections will be very large.
moderators, I believe my response to David was moderate enough not to have been deletion worthy.
[It just dropped into the queue. Be patient. It was only one of 1,798,000 that needed to be read. .mod]
Basically the analysis above would not stand up under questioning.
For example.
1. Does not supply a copy of data and code so folks can check
2. Doesn’t explain which variables from Model runs he is using to compare with the observations
3. Doesn’t explain his methods.
4. His comparisons look nothing like the work that is properly documented.
its the same boiler plate every month and never any attempt to practice self doubt. In short Bob thinks his “science” is settled.
There are several comparisons he could do for example that demonstrate the Karl corrections are valid,
but Bob can’t even think of what those comparisons are much less do them.
How many comparisons could be (have been) done to show the Karl “corrections” are invalid?
I’d love to hear a thorough response from Bob, explaining the points you have raised in detail. Should be easy for someone who really knows what they are doing to explain such things.
The Peer Review process is broken… I can give you many, many examples of such like the several hundreds of papers that had to be pulled because it was found that they were computer generated gibberish or the guy that had all his papers pulled when it was found he was his own peer reviewer… In Climate “Science” there are a very small group used to peer review articles and any paper submitted that disagrees with the dogma is rejected by reviewers that make their living as AGW proponents….. There are many within the IPCC that have produced opposing reviews of the reports findings that are not included and they have been screaming about their opposing positions being left our of the final reports… You really should sit down sometime and look at the list of people that that actually write the IPCC reports and the numbers of them.. It’s just a handful of scientists … And many of those same scientists are listed in paper after paper as the Peer Reviewers of except-able papers used to bolster their reports…
The only thing that makes a scientific claim settled it the claims ability to make correct predictions… Consistently Correct Predictions… Newton predicted that if you drop an object in a vacuum it would drop on earth at 32f per second per second… That became settle when it was experimentally tested Over and Over and Over and it was a correct prediction every freaking time… So far not a single prediction or claim made by AGW proponents has been correct … At any point you have to change methodology , statistically change past data sets, have more than one predictive model then by definition the science is no where close to settled…
Thanks Bob
Yes, Bob< thanks again. You are giving us a shot at a better World as knowledge leads to adcances. Your generosity with you time and education is inspirational.
Selvishly I enjoy the effect of presenting truth as an alterative to "progressive" State-speech.
It is mid-June and I am still heating my home in the night due to it being below 50 degrees F! NOAA said last month my part of the US was going to be ‘hotter than normal.’ Well, it barely gets up to normal warmth, mostly cold with a stiff wind from the northwest.
Also, the desert Southwest is being shown as having blowtorch type temperatures. May here was quite cool and pleasant due to the dryness. We even had a number of days with highs in the 80s. Today it barely made 100 degrees, no big deal in Yuma County. In fact, its about average. But NCEP has us as part of a giant red blob in the West.
Yeah, and NOAA said my part of the country was going to be cooler than normal, but we are going to hit a 105 and 110 heat index over the next two days.
If the GHE theory is all an illusion/delusion, the rest of it is moot, sea levels, temperatures, ice caps melting, drought, extremes, etc. If climate cause/control/correction cannot be laid at the feet of man all the research and discussions and theories make for interesting hobbies.
Q/A = U * dT runs the climate just like heat through the walls of your house. Q/A = σ * ε * T^4
Is an esoteric unnecessary complication that does not apply/work as advertised.
340 W/m^2 arrive at the ToA, 100 W/m^2 are reflected straight away leaving 240 W/m^2 to continue into the atmosphere (80 W/m^2) and surface (160 W/m^2). In order to maintain the existing thermal equilibrium (not really required) 240 W/m^2 must leave the ToA. Leaving the surface at 1.5 m are: thermals, 17 W/m^2; evapotranspiration, 80 W/m^2; LWIR, 63 W/m^2 totaling 160 W/m^2 plus the atmosphere’s 80 W/m^2 making a grand total of 240 W/m^2 at ToA.
When more energy leaves ToA than enters it, the atmosphere will cool down. When less energy leaves the ToA than enters it, the atmosphere will heat up. The GHE theory postulates that GHGs impede/trap/store the flow of heat leaving the ToA and as a consequence the atmosphere will heat up. Actually if the energy leaving the ToA goes down, say from 240 to 238 W/m^2, the atmosphere will cool per Q/A = U * dT.
The S-B BB temperature corresponding to ToA 240 W/m^2 OLR is 255 K or -18 C. This value is compared to a surface at 1.5 m temperature of 288 K, 15 C. The 33 C higher 1.5 m surface temperature is allegedly attributed to/explained by the GHE theory.
Comparing ToA values to 1.5 m surface values is an incorrect comparison.
The ToA temperature of 255 K should be compared to the ToA surface temperature of 193 K, -80 C, not the 1.5 m above land surface temperature of 288 K, 15 C. The 255 – 193 = 62 difference is explained by the earth’s effective emissivity. The ratio of the ToA observed surface temperature to the S-B BB temperature gives the emissivity: (273-80) / (273 – 18) = .767.
Because the +33 C comparison between ToA 255 K and 1.5 m 288 K is invalid the GHE theory/explanation is an invalid non-solution to a non-problem.
References: ACS Toolkit, Trenberth et. al. 2011 “Atmospheric Moisture Transports …….” Figure 10, IPCC AR5 Annex III
I THINK that Anthony did an analysis of the temperature measuring centres in the US, looking at which were well sited, which suffered from urban heat issues, how well the resulting data was parsed (if this is the right word) to overcome anomalies, etc. Is it possible to create long-term trend data from the well-sited measurement centres and give a monthly update, along with UAH, etc?
Where it around in 1944, here are the news releases that NOAA would be issuing:
Hottest January in history
Third Hottest February in history
Second Hottest March in history
Hottest May in history
Hottest June in history
Hottest July in history
Second Hottest August in history
Hottest September in history
Third Hottest October in history
Unfortunately for many millions of people, climate change was not then (and is not now) the biggest threat to mankind.
“Were” not “Where”
Yes Spidy – there were real threats to humanity in 1944. Lets call them the three stooges – Hitler, Stalin and Mao. Between them they killed about 180 million people.
Now the radical enviros are challenging their record with their false global warming alarmism, which has compromised energy systems and greatly increased energy costs to “fight global warming” – in a world that is (probably) about to cool naturally.
https://wattsupwiththat.com/2016/05/30/may-2016-enso-update-the-201516-el-nino-has-reached-its-end/comment-page-1/#comment-2226677
Posted June 1, 2016:
Hi Bob,
Thank you for your work.
I just plotted UAH Lower Troposphere (UAHLT) global temperature anomalies vs Nino34 anomalies and there appears to be a fair-to-good correlation especially for the 1997-98 and 2015-16 El Nino’s, with a ~4-month lag of UAHLT after Nino34.
https://www.facebook.com/photo.php?fbid=1012901348787427&set=a.1012901982120697.1073741826.100002027142240&type=3&theater
It appears reasonable to conclude that global temperatures will fall steeply for the rest of 2016 and then continue to decline for an equal or greater time, but on a flatter trajectory.
I also predicted net global cooling (defined as colder than +0.2C UAHLT anom) by about mid-2017 based on low solar activity, but hope to be wrong about that. Warm is good, cold is bad – it IS that simple.
Regards to all, Allan
Perhaps also of interest: The difference between 1997/1998 and 2015/2016:
http://www.dh7fb.de/ninos/ninodiff.gif
The difference had a maximum of 0.6K and vanishes fast.
PS: Data from GISS
GISS my @&&. 🙂
It is obvious several of the data bases have been stepped on. Pity there is no unbiased historical record readily available.
@Tom Halla: Tom, as a former computer operator in my younger days, I can tell you that it is pretty much standard procedure for data centers to backup all or most of their important data files to backup media on a regular basis. Files are backed up depending on the data file type and how often they are updated, which can be daily, weekly, monthly or yearly. This is done so that the the files can be restored from the backup medium in case they are lost or damaged for some reason. IF (and that’s a big IF) the GISS data center follows standard procedures, it is possible that the untampered with data MIGHT still be on backup media in their data center. It would depend on how far back in time their library of backups go and if they have backups from before the tampering began or when it began.
If the unfudged temperature data DOES exist on backup media, I would guess that it will take an official investigation by the DOJ (assuming laws were broken here) with help from scientists from outside of NASA to retrieve the unfudged temp data from the backups, graph it, and compare it to the temperature data as it exists today. That (I would think) would show what has been going on with the temperature data.
This of course is all just speculation and wishful thinking on my part based on my past experience. If an investigation if this nature is to take place, it would probably have to be with Trump in the White House in January and be triggered by a petition or request by a group of scientists from outside of NASA/GISS with an explanation stating why the investigation should take place.
I of course would like to see this happen, but I will not be holding my breath waiting for it to happen if it ever does. All we can do is keep our fingers crossed and hope.
“This of course is all just speculation”
Yes, and uninformed speculation. The fact is, GISS does not originate data, they analyse it. They use the adjusted data supplied by NOAA, for land assembled as GHCN adjusted. GHCN also supplies the unadjusted data, which except for additions, is very little changed since it was issued on CD last century. And if you really want to get to the original data, in the uS at least NOAA will show you the original observer forms.
‘It would depend on how far back in time their library of backups go and if they have backups from before the tampering began or when it began.’
. . . and if the hardware that could read the backups still existed.
Nick Stokes wrote: “GHCN also supplies the unadjusted data, which except for additions, is very little changed since it was issued on CD last century.”
“Last century” sounds so ancient. I know they didn’t have CD’s in the 1930’s, so you must mean a more recent time than that.
Thanks for the great update Bob. The Climate Forecast System Reanalysis (CFSR) global temperature anomaly estimates continue to show weak statistical evidence of a slight downward trend for our current century so far (since 2001) as reported by the University of Maine Climate Change Institute (UM CCI):
The preliminary May UM CCI CFSR global temperature anomaly estimate was 0.42C down from 0.56C in April and down from the peak of 0.72C in February (referenced to 1981-2010). So far for June through today (June 14) the month-to-date June average is down to 0.24C and has shown a large drop during the last week apparently caused mainly by recent very cold anomalies in Antarctica.
More details here for monthly CFSR trends:
https://oz4caster.wordpress.com/monthly-trends/
And for the latest daily CFSR trends here:
https://oz4caster.wordpress.com/cfsr/
How low does the anomaly have to drop before the pause resumes?
MarkW, the CFSR estimates show a slight *decline* since 2001, which is lower than a “pause”. However, over the next several years, the CFSR global temperature anomaly will have to stay near the 0.2C to 0.3C range or lower for a pause to extend for many more years. That is certainly not out of the realm of possibilities, but I don’t think anyone can really predict short or long range climate with any kind of confidence now. There are just too many large uncertainties involved in making accurate climate predictions. Maybe in a few hundred years our descendants will have climate models that can make fairly reliable predictions. Obviously, I am not optimistic in that regard.
Here is a rough answer:
***FOR UAH ONLY***
I put the pause at anomaly = 0.14, before it ended. I calculated the degree-months since the end as 3.12 deg-mo. So we need an equal amount of deg-mo. below the pause as above, to pull the LLS line back down to horizontal.
So I get an anomaly of -0.12 for 1 year, or +0.01 for 2 years.
My crystal ball indicates a strong La Nina coming up next. Typically La Nina events are much less intense than El Ninos, but can last 2 years or more. So it is not unreasonable. *Unless* We get a step up in baseline temp like we got after the ’98 event.
{note: the deg-mo. calculation is only approximate, because the LLS algorithm uses the squares of the deviations, which I neglected for simplicity. It still gives us a good idea of what needs to happen}
oz4caster June 14, 2016 at 12:56 pm: “MarkW, the CFSR estimates show a slight *decline* since 2001, which is lower than a “pause”.
We have a “longterm” downtrend from the 1930’s to the present; a 21st century “pause”; and also a slight decline in the trend since 2001 to the present.
The Alarmists keep claiming the Earth’s temperature is going up, up, up. But it isn’t, is it. The Alarmists are indulging in wishful thinking, and bad science propaganda.
Oz4- Sorry for this late entry but after reading all the information that was presented in this post, I want to thank you for the outstanding website you have. Incidentally, there are only 24 people that look at it each day. I am sure they are like me and are interested in the Daily Update in order to get a “heads up” as to what we can expect to see reported by other agencies. Very enlightening.
And if Willis is still watching, I have double checked my daily observations of atmospheric changes that occur around 6am local time. Because the sun seems to be waning, the events that occur are becoming more prominent. It appears that the temperature changes occur shortly after the barometric pressure begins to rise. This indicates that the pressure is being forced to rise by an increase of temperature in the upper atmosphere when the first light appears. RH does lag temperature at this time meaning that the changes in pressure have alot more to do with temperature changes than I expected.
I have also compared Temp and RH. Again pressure seems to be the factor that changes both. But of course after the sun starts warming the lower atmosphere (down here where we can measure it at around 10:30am) the temperature slows it’s rate of increase.
LeeO
Lee, thanks for the compliments. I enjoy tracking the daily CFSR global temperature metrics, which are of course dominated by weather, but can sometimes also hint at climate tendencies. I have been a meteorologist for over 40 years now and the more I learn, the more I realize how extremely complicated weather and climate are. That is why our best weather forecast models cannot predict the weather very well even a week in advance. In my view, both weather and climate are dominated by the amount of sunshine reaching the earth’s surface and how much is reflected and absorbed, and how much is radiated out to space both day and night. The oceans covering over 70% of the earth’s surface are also a huge factor for storing and moving energy, as are water vapor and cloud cover which strongly influence the incoming and outgoing radiation at the earth’s surface. Then there is changing ground cover and associated reflectivity, rainfall and soil moisture, vegetation, air pollution, ice/snow cover to make things even more difficult to predict. In the longer run for climate, continental drift comes into play along with wild cards like intense vulcanism and large meteor strikes, as do orbital mechanics, possible slight solar output variability and very gradual increase over time, possible dust clouds in parts of our path through the galaxy, and probably some other factors I have forgotten or that are at present unknown.
The 30 year running trend gives us a very awkward-looking graph, hard to interpret, how about a simpler 360 month running mean (starting of course, whenever the records began),
Francois, the 30-year running trend provides exactly what I wanted to present.
An epsilon °C difference per ten years on a 30 year moving trend (whatever that is), does not show anything meaningful or does it? Please explain..
Something has changed with the surface data, big time.
Consider the 1998 El Nino. The surface data response is very muted compared to the sat. data. The whole event is only slightly different from the periods before and after. The sat. data shows a fairly dramatic event.
Now consider the current El Nino. The surface data shows the event just as dramatically as the sat. data, which it has never done before. The surface data has always been more steady than the sat. data, that is one of it’s features.
Now all of a sudden, it is not.
Any ideas?
noticed that too and mentioned it somewhere in a comment as well i even asked if Maybe “the blob” was responsible for this huge peak….
however that answer can’t be given yet.
also what i find striking is that even so the “pause” does somehow persist if we compare both El nino spikes: the 1998 el nino and this 2016 el nino: the most recent el nino spikes 0.1-0.12 °C above the 1998 el nino on RSS and UAH. So basicly that would then be consistent with a gentle rise of 0.5-0.6 degrees C per century. which is still consistent with “coming out of the LIA”
The reason why i say this is that the “extra heat region of the blob added up to the nino 1+2 region would make them equivalent to the 1998 values.
of course that’s not an entirely scientifice “correct” approach, but that anomaly did clearly give this el nino a higher baseline to start from
howeven the el nino and the blob seem now both to dissappear….
Tony L- I have noticed several changes in the data collected here in my back yard. The most obvious is how the barometric has settled into operating around 29.92″hg. I am graphing it every 15 minutes. It sometimes becomes straight lines for hours.
Before the Sun spot count dropped, I was able to get correlations between x-rays (detected by GOES satelites) that are produced by flares with small changes in the barometric pressure. All that “noise” has gone away. I have shifted my study to the affects of the solar wind on our atmosphere. Unfortunately again, there has been a calming effect on it too. Fronts can still be seen, but, again they are becoming less and less.
Maybe, the Sun is no longer able to “stir things up” like it used to.
The Arctic was colder during the last super El Nino since the AMO has just recently turned positive. The surface data exploits the warm Arctic to show warming. While this works for them now it will soon start to work against them as the AMO cools and the Arctic also cools.
NASA
May goes down as Earth’s hottest on record:
http://phys.org/news/2016-06-earth-hottest-nasa.html
That NASA speaks in such terms, “hottest on record”, speaks to their fundamental dishonesty regarding climate matters.
So, they filter out the 98 spike and scream alarm at the latest. The satellite data indicates the 98 record spike was broken by a whopping 0.1 C and it is most unlikely that we can measure to this resolution. They should be rejoicing that the world just rid itself of some excess heat
El Nino looks to be a very important mechanism within earth’s thermostat. My bet is that we are in for another decade of stability similar in temp to the last decade. The next 6 months will be most interesting. How low will it go?
The latest monthly update to the NOAA Multivariate ENSO Index was finally released today (June 14) with yesterday’s date on it. It shows the April-May MEI at 1.699, down from 2.070 for March-April, and down from the peak of 2.527 for August-September 2015.
Bob Tisdale, thank you for the update.
The 1997-1998-1999 El Nino/La Nina swing was followed by 15 years with a higher mean temperature than the previous 15 years. What happens following the 2015-2016 El Nino isn’t going to be known for a while (obviously?). I expect that the next 16 years will try our patience.
The scientific case against the models is growing. The case regarding CO2 remains mostly a confusion, though I think the evidence against a “large” CO2 effect is growing faster than the case in favor of a “large” CO2 effect..
“The 1997-1998-1999 El Nino/La Nina swing was followed by 15 years with a higher mean temperature than the previous 15 years.”
True, but the prior 15 years shows a trend of around + 0.3 C whereas the latter was pretty static. In terms of a better understanding on energy flow El Nino is our friend. Things happen so fast and we can now trace heat distribution throughout the ocean surface zone
What a fascinating topic is this 🙂
One possible explanation is the Pinatubo eruption. It cooled the Stratosphere which has allowed the Troposphere to warm. It actually happened before the 1997 El Nino but all the ENSO activity masked the situation.
Keep looking in this direction. Especially difference in effects of effusive versus explosive volcanoes and how that produces net warming and cooling respectively. Then overlay these eruptions with UV and ozone depletion, especially around timing of CFCs, Cl, Br, NOx, SOx. It ties together when considering that UV has the intensity to penetrate oceans by several meters whilst IR does not. About 50 Times more hence why we get sunburn from UV not IR. Lots of charts here but not much WHY being written about the observations or trends being tracked……just sayin, itsall.
This report is about as comprehensive and scientific as possible !
Good job !!
But keep in mind, people down deep (Many American’s leading the way (And I’m not talking about the idiotic leaders, I’m talking about the general public, and those are the idiots following the idiots!) are generally really stupid, and they will never listen to facts and reason, even seeing it with their own eyes !!!
And that “is” the epitome of stupidity…
ScienceOn
“This report is about as comprehensive and scientific as possible !”
Methods? Data? Steve Mosher is right. A true skeptic couldn’t properly test Bob Tisdales claims with the information he has presented. Isn’t demanding this information, and providing it when you publish something, an essential trait of a true skeptic?
I mean, even if you agree with him, how would you test your, or his position, without this information?
In the terrestrial datasets, the 1998 El Nino is much less noticeable compared to the current one. However, in the satellite datasets, both the 1998 and 2015/2016 events are similarly prominent. What’s going on there?
The Arctic has warmed due to the loss of sea ice. This was not the case in the previous El Nino. As the AMO cools over the next decade this warmth will disappear.
What caused loss of sea Ice?
And what causes AMO to cool?
Pika made fools of the climate elite. My money is on the melomy.
NorCal on-the-ground obs regarding La Nina. So here we are, just a couple days before the Solstice. Winter Storm Watch for northeastern mountains, temps well below normal, rain prog’ed all the way down into the Bay Area for tonight through tomorrow. Sensible weather is more like Fall than Summer. On that note, after the current system a major offshore wind event is prog’ed for the weekend and early next week. After that? Maybe another Gulf of Alaska system.