September 2014 Global Surface (Land+Ocean) and Lower Troposphere Temperature Anomaly Update

Guest Post by Bob Tisdale

This post provides an update of the data for the three primary suppliers of global land+ocean surface temperature data—GISS through September 2014 and HADCRUT4 and NCDC through August 2014—and of the two suppliers of satellite-based lower troposphere temperature data (RSS and UAH) through September 2014.

The three surface data suppliers have been claiming record high monthly values recently. This is, in part, due to the record high sea surface temperatures in the North Pacific, which impact the global data because of the size of the North Pacific and the intensity of the weather-related warming there. For further information about the unusual warming of the North Pacific, see the post On The Recent Record-High Global Sea Surface Temperatures – The Wheres and Whys. I will discuss that North Pacific hotspot (a.k.a. the blob) again in an upcoming post.

The other factors that contributed to the recent record highs are the updates to the GISS Land-Ocean Temperature Index (LOTI) and UKMO HADCRUT4 data. There has not been a noticeable shift in the NCDC global land+ocean data, as there has been with the GISS and UKMO data, so one would suspect that the changes were not caused by updates to NOAA’s GHCN data. We briefly discussed the changes to the GISS data in the post GISS Tweaks the Short-Term Global Temperature Trend Upwards. The impacts of the adjustments to the HADCRUT4 data were discussed in the recent post at WattsUpWithThat by Werner Brozek, Walter Dnes, and blogger “Just The Facts”.

Whether the data suppliers are aware of this, the adjustments to the data, which always increase the warming rate, appear odd to outsiders. The data suppliers seem to be forcing the warming to appear when there has not been any surface warming.

Additionally, not too surprisingly, when data suppliers have been proclaiming the monthly record highs, they have failed to acknowledge the reasons for them: (1) a naturally occurring and persistent blocking high that caused an unusual warming of the North Pacific and (2) adjustments to the data. When the public must investigate and then discover the reasons for the record highs, the data suppliers lose credibility—they create skeptics. The problem, very few people investigate. Most of the public simply take it for granted that the record highs have something to do with manmade global warming caused by the emissions of greenhouse gases.

Initial Notes: GISS LOTI, and the two lower troposphere temperature datasets are for the most recent month. The HADCRUT4 and NCDC data lag one month.

This post contains graphs of running trends in global surface temperature anomalies for periods of 13+ and 17+ years using GISS global (land+ocean) surface temperature data. They indicate that we have not seen a warming halt (based on 13-years+ trends) this long since the late-1970s or a warming slowdown (based on 17-years+ trends) since about 1980. I used to rotate the data suppliers for this portion of the update, also using NCDC and HADCRUT. With the data from those two suppliers normally lagging by a month in the updates, I’ve standardized on GISS for this portion.

Much of the following text is boilerplate. It is intended for those new to the presentation of global surface temperature anomaly data.

Most of the update graphs in the following start in 1979. That’s a commonly used start year for global temperature products because many of the satellite-based temperature datasets start then.

We discussed why the three suppliers of surface temperature data use different base years for anomalies in the post Why Aren’t Global Surface Temperature Data Produced in Absolute Form?

GISS LAND OCEAN TEMPERATURE INDEX (LOTI)

Introduction: The GISS Land Ocean Temperature Index (LOTI) data is a product of the Goddard Institute for Space Studies. Starting with their January 2013 update, GISS LOTI uses NCDC ERSST.v3b sea surface temperature data. The impact of the recent change in sea surface temperature datasets is discussed here. GISS adjusts GHCN and other land surface temperature data via a number of methods and infills missing data using 1200km smoothing. Refer to the GISS description here. Unlike the UK Met Office and NCDC products, GISS masks sea surface temperature data at the poles where seasonal sea ice exists, and they extend land surface temperature data out over the oceans in those locations. Refer to the discussions here and here. GISS uses the base years of 1951-1980 as the reference period for anomalies. The data source is here.

Update: The September 2014 GISS global temperature anomaly is +0.77 deg C. It increased (cycled upwards) a good amount (an increase of about +0.08 deg C) since August 2014.

01 GISS LOTI

Figure 1 – GISS Land-Ocean Temperature Index

Note: There have been recent changes to the GISS land-ocean temperature index data. They have a noticeable impact on the short-term (1998 to present) trend as discussed in the post GISS Tweaks the Short-Term Global Temperature Trend Upwards. The causes of the changes are unclear at present, but they will likely affect the 2014 rankings at year end.

NCDC GLOBAL SURFACE TEMPERATURE ANOMALIES (LAGS ONE MONTH)

Introduction: The NOAA Global (Land and Ocean) Surface Temperature Anomaly dataset is a product of the National Climatic Data Center (NCDC). NCDC merges their Extended Reconstructed Sea Surface Temperature version 3b (ERSST.v3b) with the Global Historical Climatology Network-Monthly (GHCN-M) version 3.2.0 for land surface air temperatures. NOAA infills missing data for both land and sea surface temperature datasets using methods presented in Smith et al (2008). Keep in mind, when reading Smith et al (2008), that the NCDC removed the satellite-based sea surface temperature data because it changed the annual global temperature rankings. Since most of Smith et al (2008) was about the satellite-based data and the benefits of incorporating it into the reconstruction, one might consider that the NCDC temperature product is no longer supported by a peer-reviewed paper.

The NCDC data source is through their Global Surface Temperature Anomalies webpage. Click on the link to Anomalies and Index Data.)

Update (Lags One Month): The August 2014 NCDC global land plus sea surface temperature anomaly was +0.75 deg C. See Figure 2. It rose sharply (an increase of +0.10 deg C) since July 2014.

02 NCDC

Figure 2 – NCDC Global (Land and Ocean) Surface Temperature Anomalies

UK MET OFFICE HADCRUT4 (LAGS ONE MONTH)

Introduction: The UK Met Office HADCRUT4 dataset merges CRUTEM4 land-surface air temperature dataset and the HadSST3 sea-surface temperature (SST) dataset. 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 NCDC products, missing data is 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 not included in the global average value of HADCRUT4. The HADCRUT4 dataset is described in the Morice et al (2012) paper here. The CRUTEM4 data is described in Jones et al (2012) here. And the HadSST3 data 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 data source is here.

Update (Lags One Month): The August 2013 HADCRUT4 global temperature anomaly is +0.67 deg C. See Figure 3. It too increased sharply (about +0.13 deg C) since July 2014.

03 HADCRUT4

Figure 3 – HADCRUT4

UAH LOWER TROPOSPHERE TEMPERATURE ANOMALY DATA (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. The level nearest to the surface of the Earth is the lower troposphere. The lower troposphere temperature data 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 lower troposphere temperature data are calculated from a series of satellites with overlapping operation periods, not from a single satellite. The monthly UAH lower troposphere temperature data is the product of the Earth System Science Center of the University of Alabama in Huntsville (UAH). UAH provides the data broken down into numerous subsets. See the webpage here. The UAH lower troposphere temperature data 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 data updates a few days before the release at the UAH website. Those posts are also cross posted at WattsUpWithThat. UAH uses the base years of 1981-2010 for anomalies. The UAH lower troposphere temperature data are for the latitudes of 85S to 85N, which represent more than 99% of the surface of the globe.

Update: The September 2014 UAH lower troposphere temperature anomaly is +0.30 deg C. It rose (an increase of about +0.10 deg C) since August 2014.

04 UAH TLT

Figure 4 – UAH Lower Troposphere Temperature (TLT) Anomaly Data

RSS LOWER TROPOSPHERE TEMPERATURE ANOMALY DATA (RSS TLT)

Like the UAH lower troposphere temperature data, Remote Sensing Systems (RSS) calculates lower troposphere temperature anomalies from microwave sounding units aboard a series of NOAA satellites. RSS describes their data at the Upper Air Temperature webpage. The RSS data are 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 data in various subsets. The land+ocean TLT data are here. Curiously, on that webpage, RSS lists the data 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.

Update: The September 2014 RSS lower troposphere temperature anomaly is +0.21 deg C. It showed a very slight warming (an increase of about +0.01 deg C) since August 2014.

05 RSS TLT

Figure 5 – RSS Lower Troposphere Temperature (TLT) Anomaly Data

A QUICK NOTE ABOUT THE DIFFERENCE BETWEEN RSS AND UAH TLT DATA

There is a noticeable difference between the RSS and UAH lower troposphere temperature anomaly data. Dr. Roy Spencer discussed this in his September 2011 blog post On the Divergence Between the UAH and RSS Global Temperature Records. In summary, John Christy and Roy Spencer believe the divergence is caused by the use of data from different satellites. UAH has used the NASA Aqua AMSU satellite in recent years, while as Dr. Spencer writes:

…RSS is still using the old NOAA-15 satellite which has a decaying orbit, to which they are then applying a diurnal cycle drift correction based upon a climate model, which does not quite match reality.

I updated the graphs in Roy Spencer’s post in On the Differences and Similarities between Global Surface Temperature and Lower Troposphere Temperature Anomaly Datasets.

While the two lower troposphere temperature datasets are different in recent years, UAH believes their data are correct, and, likewise, RSS believes their TLT data are correct. Does the UAH data have a warming bias in recent years or does the RSS data have cooling bias? Until the two suppliers can account for and agree on the differences, both are available for presentation.

In a more recent blog post, Roy Spencer has advised that the UAH lower troposphere Version 6 will be released soon and that it will reduce the difference between the UAH and RSS data.

13-YEARS+ (165-MONTH) RUNNING TRENDS

As noted in my post Open Letter to the Royal Meteorological Society Regarding Dr. Trenberth’s Article “Has Global Warming Stalled?”, Kevin Trenberth of NCAR presented 10-year period-averaged temperatures in his article for the Royal Meteorological Society. He was attempting to show that the recent halt in global warming since 2001 was not unusual. Kevin Trenberth conveniently overlooked the fact that, based on his selected start year of 2001, the halt at that time had lasted 12+ years, not 10.

The period from January 2001 to September 2014 is now 165-months long—more than 13 years. Refer to the following graph of running 165-month trends from January 1880 to September 2014, using the GISS LOTI global temperature anomaly product.

An explanation of what’s being presented in Figure 6: The last data point in the graph is the linear trend (in deg C per decade) from January 2001 to September 2014. It is basically zero (about +0.02 deg C/Decade). That, of course, indicates global surface temperatures have not warmed to any great extent during the most recent 165-month period. Working back in time, the data point immediately before the last one represents the linear trend for the 165-month period of December 2000 to August 2014, and the data point before it shows the trend in deg C per decade for November 2000 to July 2014, and so on.

06 165-Month GISS Trends

Figure 6 – 165-Month Linear Trends

The highest recent rate of warming based on its linear trend occurred during the 165-month period that ended about 2004, but warming trends have dropped drastically since then. There was a similar drop in the 1940s, and as you’ll recall, global surface temperatures remained relatively flat from the mid-1940s to the mid-1970s. Also note that the mid-1970s was the last time there had been a 161-month period without global warming—before recently.

17-YEARS+ (208-Month) RUNNING TRENDS

In his RMS article, Kevin Trenberth also conveniently overlooked the fact that the discussions about the warming halt are now for a time period of about 16 years, not 10 years—ever since David Rose’s DailyMail article titled “Global warming stopped 16 years ago, reveals Met Office report quietly released… and here is the chart to prove it”. In my response to Trenberth’s article, I updated David Rose’s graph, noting that surface temperatures in April 2013 were basically the same as they were in September 1997. We’ll use September 1997 as the start month for the running 17-year trends. The period is now 208-months long. The following graph is similar to the one above, except that it’s presenting running trends for 208-month periods.

07 208-Month GISS Trends

Figure 7 – 208-Month Linear Trends

The last time global surface temperatures warmed at this low a rate for a 208-month period was the late 1970s, or about 1980. Also note that the sharp decline is similar to the drop in the 1940s, and, again, as you’ll recall, global surface temperatures remained relatively flat from the mid-1940s to the mid-1970s.

The most widely used metric of global warming—global surface temperatures—indicates that the rate of global warming has slowed drastically and that the duration of the halt in global warming is unusual during a period when global surface temperatures are allegedly being warmed from the hypothetical impacts of manmade greenhouse gases.

COMPARISONS

The GISS, HADCRUT4 and NCDC global surface temperature anomalies and the RSS and UAH lower troposphere temperature anomalies are compared in the next three time-series graphs. Figure 8 compares the five global temperature anomaly products starting in 1979. Again, due to the timing of this post, the HADCRUT4 and NCDC data lag the UAH, RSS and GISS products by a month. The graph also includes the linear trends. Because the three surface temperature datasets share common source data, (GISS and NCDC also use the same sea surface temperature data) it should come as no surprise that they are so similar. For those wanting a closer look at the more recent wiggles and trends, Figure 9 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 halt in warming.

Figure 10 starts in 2001, which was the year Kevin Trenberth chose for the start of the warming halt 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.

08 Comparison Starting 1979

Figure 8 – Comparison Starting in 1979

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09 Comparison Starting 1998

Figure 9 – Comparison Starting in 1998

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10 Comparison Starting 2001

Figure 10 – Comparison Starting in 2001

For those who want to get a rough idea of the impacts of the adjustments to the GISS and HADCRUT4 warming rates, refer to the July update—a month before those adjustments took effect.

AVERAGE

Figure 11 presents the average of the GISS, HADCRUT and NCDC land plus sea surface temperature anomaly products and the average of the RSS and UAH lower troposphere temperature data. Again because the HADCRUT4 and NCDC data lag one month in this update, the most current average only includes the GISS product.

11 Averages

Figure 11 – Average of Global Land+Sea Surface Temperature Anomaly Products

The flatness of the data since 2001 is very obvious, as is the fact that surface temperatures have rarely risen above those created by the 1997/98 El Niño in the surface temperature data. There is a very simple reason for this: the 1997/98 El Niño released enough sunlight-created warm water from beneath the surface of the tropical Pacific to permanently raise the temperature of about 66% of the surface of the global oceans by almost 0.2 deg C. Sea surface temperatures for that portion of the global oceans remained relatively flat until the El Niño of 2009/10, when the surface temperatures of the portion of the global oceans shifted slightly higher again. Prior to that, it was the 1986/87/88 El Niño that caused surface temperatures to shift upwards. If these naturally occurring upward shifts in surface temperatures are new to you, please see the illustrated essay “The Manmade Global Warming Challenge” (42mb) for an introduction.

MONTHLY SEA SURFACE TEMPERATURE UPDATE

The most recent sea surface temperature update can be found here. The satellite-enhanced sea surface temperature data (Reynolds OI.2) are presented in global, hemispheric and ocean-basin bases. We discussed the recent record-high global sea surface temperatures and the reasons for them in the post On The Recent Record-High Global Sea Surface Temperatures – The Wheres and Whys.

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45 thoughts on “September 2014 Global Surface (Land+Ocean) and Lower Troposphere Temperature Anomaly Update

  1. Bob,
    Great work as always.
    You make the point that adjustments that always seem to result in increases in temps seems a bit puzzling to outsiders.
    Please explain why it should not be a bit puzzling. I know you have addressed this other times, but it seems puzzling that the only form of so-called global warming that exists is the sort that requires a person to adjust the record to find it.

    • There was a NOAA graph available online years back that showed the difference between the adjusted data and the raw data for the period 1900–1998. The graph of difference was basically zero from 1900-1960, then from 1960–1998 it was a straight line whose slope just happened to be exactly the amount of warming the models predicted. Seeing that graph convinced me that no matter what excuse was made for “harmonization” it was really blatant data fraud to advance a political agenda.

      There is a similar problem with ARGO where after a brief period of no warming they were all recalibrated up. Nothing produced by any “scientist” who advocates for “climate change” should be taken seriously.

      Sadly the same can be said for most Euro-american science. Physics & astrophysics (see article on this site), nutrition, heart disease, etc.., doubt anything thing you hear from science today, especially if it is funded by the government.

      • (My graph didn’t come through) The first graph of at the link above shows an almost perfect correlation between USHCN temperature “adjustments” and CO2 levels. It’s kind of damning.

      • Bob you wrote: ” Much of the following text is boilerplate. It is intended for those new to the presentation of global surface temperature anomaly data.”

        It is a good idea to keep repeating this material, but in addition to the “boilerplate” you’ve included,perhaps it is time to include in every post, a graph that shows the net “adjustments” made to the record by the various scorekeepers.

        What I’d like to see is a graph of the total anomaly from say 1880 to 2000 as it has drifted over the years under the influence of successive “adjustments”. Put that graph for each scorekeeper in every single post. That would tell the story of the adjustments in a way that would be hard to ignore.

        It is worth remembering that the temperature chart used by Hansen for the lower 48, for years showed very little warming.
        http://www.giss.nasa.gov/research/briefs/hansen_07/
        He only began vigorously “adjusting” in 1999, and hey presto, the U.S. record suddenly matched the aggressive warming he’d found globally.

  2. Thanks Bob.

    I will mention that the weather appears about to change in the great State of Washington. Saturday was a bit wet. Today, Sunday, is more dry, but moisture is to return late afternoon or evening. I’m off for a day of trail work in the Cascade Mtns. – west of Snoqualmie Pass. Thus, rain or not rain is a bit personal.
    We have had a warm and dry period.

    Again, thanks.

  3. Introducing a New Atmospheric El Nino Southern Oscillation Index, its Current State, and Upcoming Winter Implications « WSI Blog

    http://www.wsi.com/blog/energy/introducing-a-new-atmospheric-el-nino-southern-oscillation-index-its-current-state-and-upcoming-winter-implications/

    Bottom Line: A newly derived atmospheric ENSO index suggests that this winter will be more “El Nino” like, favoring Gulf of Alaska-Northeast Pacific trough dominance, and potentially a warm upper-level ridge over North America. This is an exact opposite scenario that was observed last winter, which is indicative of a milder winter may be at hand. Now there are times where the U.S. can still experience cold air intrusions in such a base state. However, the chances for a persistently cold winter diminish in such a low-frequency base state.

    • It appears they have correlated the Pacific North American index to ENSO El Nino/La Nina-leaning states and have indexed the two together. It would certainly make sense that PNA “patterns” (IE mostly positive or mostly negative) seen in the PNA index develop depending on the atmospheric/oceanic bridge that exists between equatorial trade wind induced SST metrics and the North Pacific warm pool and its atmospheric twin that develops above it. That bridge is how the PNA “feels” predominating ENSO conditions.

  4. The most significant weather pattern variation at play here relates to the “blob” of warm water in the North Pacific. I am talking about the Pacific/North American index of high and low pressure systems teleconnected to that oceanic “blob”, specifically the position and strength of the paired high and low pressure systems riding across the North American continental jet stream. Its reach is massive, whether or not it is in its positive phase (in one now though sliding step-wise out of it) or negative phase (which means I have to stock up on wood), causing weather pattern variations that are fairly predictable across the entire width and depth of the North American continent. Look there for causes of these temperature swings above and below the climate average. CO2 is not at play here in the least bit.

    http://www.cpc.ncep.noaa.gov/data/teledoc/pna.shtml

    • Pamela,

      Quite right to focus on jet stream behaviour.

      That behaviour is indeed affected by ocean temperatures towards the equator.

      Meanwhile, I think the state of the jets is also being affected from the poles by solar effects but we are near solar maximum (albeit lower than recent maxima) so I don’t expect large solar induced effects until we move closer to the next minimum.

      If the USA gets a milder winter this cominmg season due to a shift in the jets then I expect Western Europe to get a colder winter than the one experienced last year.

      It’s a matter of the position of the peaks and troughs of the more meridional jets observed since 2000 which I ascribe to solar variability.

      • Stephen we have gone round and round about this. The warm pool came from somewhere which set up the PNA situation resulting in warm dry coastal weather and a subsequent deep loop into the Midwest and Atlantic states. You would have to trace that pool back to its origins, correlating your solar indices all along the way. I think you have it backwards. You miss the all important layering, peeling away layers, and oceanic currents that dance with the atmosphere. Of the two, the oceans are by far the stronger player.

  5. I cranked out two posts this morning, with a total of more than 2 dozen graphs: this post and the sea surface temperature update for September:
    http://bobtisdale.wordpress.com/2014/10/12/september-2014-sea-surface-temperature-sst-anomaly-update/

    With the NOAA NOMADS servers off line, I used the Reynolds OI.v2 SSTa data available through the KNMI Climate Explorer, with 1981-2010 as the base years. The more recent base years have reduced some of the seasonal variations in the basin subsets. I may stick to the KNMI Climate Explorer as the source for those updates.

    • I looked at that PNA index and sure enough it has been in positive territory August through September, leading to warm and dry conditions along the Western states. It appears to be stepping down out of it though.

  6. Looking at the recent trends in conjunction with the subcycles over the decades, it would not be out-of-pattern for 2015 to show a 0.2C drop across the board. While the warmist could reasonably claim such a drop has no long-term significance – referencing the above noted short-term cyclicity, such an “insignificant” drop could cause a great disturbance in the Force (of CAGW).

    We are told that temperatures are NOT undergoing a pause, really, that they are still going up, you just have to choose a longer view. I’ll buy that natural variation can give us the “pause” if you accept the first part of the rise was not CO2 based but positive-natural and that the later portion of the pause is now negative-natural. But a real drop in 2015 would give the fear that recovery from the short-term cycle would be less than total. That, in other words, we are in a longterm down phase, and that much of the 1975 – 2014 rise was actually just a long term up phase.

    I see 2015 as a pivotal year. We had a very late spring in 2014 and cool summer. An early winter, a cold winter, may be enough to kick 2015 in its frozen gonads.

    I hope so. Not because I am unaware or insensitive to the pain from incoming cold, but because something needs to happen to put the brakes on the IPCC and CAGW, anti-capitalist, anti-cheap energy hysteria.

  7. “The September 2014 GISS global temperature anomaly is +0.77 deg C. It increased (cycled upwards) a good amount (an increase of about +0.08 deg C) since August 2014.” I don’t like temperature anomalies because I am living in a world of real temperatures and real measurements. The hottest month on the globe is July. So the globe is cooling now. You can say that it is the hottest Sept ever measured. But the difference to Sep 2013 is 0,05°C. This is smaller than the estimated error of 0.1 °C.
    Local temperature anomalies are also misleading. For instance, in Southern Germany the anomaly for Sep was 1.1°C, while the real mean temperature was 13.8 °C. I liked that temperature, although it was warmer than during the reference period. The weather was fine.

  8. I am very disappointed by this article. Two years ago Roy Spenser published a graph showing the 44 climate models for the years 1980 to 2012 and the two satellite temperature records RSS and UAH.

    Why hasn’t this graph been updated to 2014?

    It is the most conclusive rejection of the climate models possible. 100% of the climate models agree, the data is wrong. And the discrepancy should be getting worse over time. Two years of additional data would be helpful.

      • I am disappointed because none of your graphs have the same PR value as a graph showing that the models are complete BS.

        I am sure that all of your graphs have tremendous scientific value. Nonetheless, none of them have any value trying to convince the average global warming supporter that the theory championed by his side does not agree with reality. For your average leftist global warming supporter, most of your graphs wont be understood, will not be considered relevant to the discussion, or will be attacked as the product of a fringe lunatic who can not be taken seriously.

        Can you imagine turning this article into a 60 second TV piece?

        I could do 60 seconds of TV on 2 graphs: how the climate models don’t represent reality and how all of the warming in the 20th century is the result of data adjustments. Everyone can understand these two points.

        Updating these two graphs monthly would be a tremendous public service even if it would have zero scientific value.

        There is no better comeback to “97% of scientists agree that global warming is true” than “97% of the climate models agree that the data is wrong” combined with a simple graph that anyone can understand.

        The other comment that I support enthusiastically is to update the graph that shows 100% of the 20th century warming is due to data adjustments. That graph is as good as showing the models are inconsistent with the data. It really makes people wonder what is going on.

    • I don’t quite understand your desire to overtly “reject” something unrelated to the focus of both the title and content related to SST updates. If the central question posed by Bob was to reject models or show data manipulation, then yes, his writing here would certainly not answer such a focus. But it seems to me that his title and the body of his work are cohesive. I usually prefer to see a post that is directly related to its title. You do not?

      • Bob Tisdale is obviously making a tremendous effort to keep the SST temperature record up to date and I certainly applaud him for that effort. At the same time your point about sticking to one topic is also correct.

        Nonetheless, the main battle being fought on this issue is political and not scientific. The Global Warming Theory is being used by politicians to produce incredible amounts of bad policy. Sites like WUWT, with real scientists interested in discovering the truth of climate science, have been instrumental in undermining the Global Warming hoax. However the effect could be much larger by hammering away on a few very simple points: the pause (being handled well), the complete lack of value in the current climate models (not so well), and the data adjustment being equal to the entire temperature increase (not so well).

        Maybe Bob’s article is not the appropriate spot to keep this info. Hopefully some scientist who is able to produce this info on a quarterly basis will see the value in keeping it readily available.

        Just as WUWT has a reference page for solar activity, maybe a reference page on these three topics for members of the press to view would be very helpful.

        A reference to the Heartland policy paper Merchants of Smear would round out the offering and provide the press with an incentive to cover the skeptical side of the argument.

  9. ˙ʇd°ɔuoɔ p°u!ɟ°p ʎl°s!ɔ°ɹd ʇnq ‘ʎɹɐɹʇ!qɹɐ uɐ s!ᴉ sʇɔ°ɾqo ¶u!uu!ds ɹoɟ °loԀ ɥʇnoS puɐ °loԀ ɥʇɹoN °ɥʇ llɐɔ °ʍ ʇɐɥM

    sıɥʇ ʇnoqɐ ʍoɥ ‘ɯɐd ıɥ

    ======================

    poɯ ~ ¡ʇᴉ doʇs ‘ʞO]

    • eɔɐldƃguoɹʍ ʇnq ‘ʇɥgıɹ ʇnoqɐ ʇno eɯɐɔ ʇı

      ===========================

      [ Ⓝⓞ ⓜⓞⓡⓔ, ⓟⓛⓔⓐⓢⓔ, ⓞⓡ ⓘⓣ ⓦⓘⓛⓛ ⓑⓔ ⓢⓝⓘⓟⓟⓔⓓ.
      ~ ⓜⓞⓓ. ]

      • If comments like this keep being let thru, I’m just gonna have to turn the music up louder.
        So take that !!

  10. Bob Tisdale writes

    “Additionally, not too surprisingly, when data suppliers have been proclaiming the monthly record highs, they have failed to acknowledge the reasons for them: (1) a naturally occurring and persistent blocking high that caused an unusual warming of the North Pacific and (2) adjustments to the data. When the public must investigate and then discover the reasons for the record highs, the data suppliers lose credibility—they create skeptics. The problem, very few people investigate. Most of the public simply take it for granted that the record highs have something to do with manmade global warming caused by the emissions of greenhouse gases.”

    That’s poor reasoning Bob.

    Reason (1): a naturally occurring and persistent blocking high that caused an unusual warming of the North Pacific.

    This is essentially a random factor. It could happen at any time. Therefore in a long run of data (decades) such events will occur from time to time and produce a warming spike. It is the long term trend that is important. Your figure 11 is useful as it shows the average land and sea surface temperatures across the data sets and it can be seen that the current spike is from a higher base than the big 1998 El Nino spike.

    Reason (2): adjustments to the data

    For this to be a major factor you would need to clearly state what the level and sign of the adjustments are and show that they are producing a change which would be significant over time. So for example NASA GISS LOTI has a 5 year running mean which is now about 0.2 C above the level it was 20 years ago. Can you show that the adjustments to that data set are significant in terms of the 0.2 C warming ?

    Your conclusion from the reasons is :

    “When the public must investigate and then discover the reasons for the record highs, the data suppliers lose credibility—they create skeptics. The problem, very few people investigate. Most of the public simply take it for granted that the record highs have something to do with manmade global warming caused by the emissions of greenhouse gases.”

    No – the job of skeptics appears to be, whatever the data, to try and show that humans are not the cause of the observed warming. Scientists will view properly recorded data presented correctly that shows record highs as showing …. record highs.

    If warming does resume (and there are early signs it may be doing so), it will be very interesting to see what the skeptic community does in response.

    Finally, you keep talking about the warm Pacific as a primary reason for the recent warm months globally. The Indian Ocean is warm, so is much of the Atlantic and especially the North Atlantic and ice free Arctic ocean:

  11. One thing I am curious about in Figures 9 or 10, Is there an inflection point in the various data sets around 2005-2006?

    Comparing Fig 8 with Fig 9, RSS obviously goes negative after some date. Seems the others will too. Where does the data say the inflection point is? GISS, CRU, and NOAA likely know the answer to that question from their own internal analyses of the data sets.

  12. If global warming and temperature “corrections” continue at this pace, we can expect the hottest year ever recorded to accompany ice sheets returning to asia and north america.

  13. Here’s something interesting. The Net Radiation Budget from the CERES satellite. Net radiation imbalance is ZERO.

    The ARGO floats are recording about 0.5 W/m2 of energy accumulation so they have now started calling it the CERES + ARGO energy imbalance around 0.5 W/m2 but CERES has nothing.

    From Norman Loeb, the principal investigator for CERES from a presentation in March 2014.

    And another updated to April 2014.

    • So what is the integral of the net, since these are rates, not absolute amounts?

      These are some of the most interesting graphs ever presented on WUWT, frankly. We are constantly told that there is an “easily observed” radiation imbalance, and that it can be fractionated right down to where they can “see” the positive feedback from water vapor, but this curve suggests that nothing like this is true.

      Here is what I read off of these curves:

      a) There is no linear trend statistically distinguishable from zero. One doesn’t even need to fit the data, it is obvious. In fact, zero linear trend is an excellent fit.

      b) There is no evidence whatsoever for a CO_2 driven alteration of the net, in spite of the fact that CO_2 concentrations probably changed by between 5 and 10% over the interval.

      c) There is no possible evidence for positive feedback from water vapor, as there is no evidence of the direct response that would be amplified by positive feedback.

      d) There is strong evidence for natural variation of both incoming and outgoing radiation rates (and, of course, the net). Basically 100% of the variation visible in the curves has little possible explanation outside of a weakly driven damping response to small variations in incident solar. However, in order to make proper sense of this, one has to integrate the first curve self-consistently against the second curve with a (fit) exponential kernel, math that might be interpreted as:

      “Find the cumulated heat from the incoming radiation balanced by the cumulated heat loss from the outgoing radiation, with a suitable non-Markovian exponential lag indicating the residence time of the heat in the system.”

      From this one might be able to make some statements about the lagged causal linkage between the visible variations in the incoming radiation and the response of the system to those variations, and hence use fluctuation-dissipation to arrive at some really important and useful information about correlation times and dissipation pathways of the overall system, targets that models would have to hit in order to be realistic.

      Finally:

      e) There is almost no lag visible between incoming fluctuations and outgoing correlated responses. This means that the residence time for any additional heat is very short. The system is very stable, with strong negative feedbacks so that any increase in input is almost instantly balanced by an increase in output to keep the system remarkably stable.

      rgb

    • Not sure I understand this Bill Illis.
      It seems as though you are saying that there is no satellite measured increase in greenhouse effect from increasing CO2. That seems like a big claim.

      A small measured increase in greenhouse effect from increasing CO2 is understandable. But evidence that CO2 has no further effect (saturation)? Gosh, have I understood this correctly?

      And would you mind making a full post on what these and the ARGO floats are saying? It sounds interesting.

  14. rgb said:

    “There is almost no lag visible between incoming fluctuations and outgoing correlated responses. This means that the residence time for any additional heat is very short. The system is very stable, with strong negative feedbacks so that any increase in input is almost instantly balanced by an increase in output to keep the system remarkably stable.”

    You need my hypotheses to explain that.

    Everyone else seems to think that GHGs make the convective circulation faster but that cannot be right if GHGs allow radiation to leak out to space from within the atmosphere.

    The key to understanding is to recognise adiabatic warming on descent as the parameter that changes in response to the presence of GHGs.

    More radiation to space from within the atmosphere cools the starting point for the descent so that the parcel of air is less warm than it otherwise would have been when it reaches the surface. That is potentially a surface cooling effect.

    More DWIR then negates the convective imbalance.

    Rather than reducing the cooling rate of the surface DWIR just compensates for the surface cooling arising from the effect of GHGs higher up in the atmosphere radiating directly to space.

    The relevant variable is the rate of warming during adiabatic descent.

  15. Well.
    The chart looks like “the Pause” is over.
    The temperatures are ready for the next take off.
    Strung buy!

  16. NOAA says the September 2014 global temperature was the highest ever, but the other data sets don’t. I’m wondering why the difference,

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