April 2015 Global Surface (Land+Ocean) and Lower Troposphere Temperature Anomaly & Model-Data Difference 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 April 2015 and HADCRUT4 and NCDC through March 2015—and of the two suppliers of satellite-based lower troposphere temperature data (RSS and UAH) through April 2015.


I’m still using Release 5.6 of the UAH lower troposphere temperature anomalies for this post, because Release 6.0 is only in beta form.  I’ve included the UAH release 6.0 data as a supplemental graph, though.

I’ve eliminated the graphs of the long-term running trends (see sample here from the last update).  I’ve been presenting them for a few years and I can’t recall one comment about them.  I replaced them with a model-data comparison which shows the growing difference between model simulations of global surface temperatures and observations.

For discussions of the annual GISS and NCDC data for 2014, see the posts:

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

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 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?


Introduction: The GISS Land Ocean Temperature Index (LOTI) data is a product of the Goddard Institute for Space Studies.  Starting with their April 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 April 2015 GISS global temperature anomaly is +0.75 deg C.  It dropped (about -0.10 deg C) since March 2015.


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 likely impacted the 2014 rankings.


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 March 2015 NCDC global land plus sea surface temperature anomaly was +0.85 deg C.  See Figure 2. It was basically unchanged (an increase of +0.01 deg C) since February 2015.


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


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 March 2015 HADCRUT4 global temperature anomaly is +0.68 deg C. See Figure 3.  It rose (about +0.03 deg C) since February 2015.


Figure 3 – HADCRUT4


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 April 2015 UAH (Release 5.6) lower troposphere temperature anomaly is +0.16 deg C.  It dropped (a decrease of about -0.09 deg C) since March 2015.

04 UAH TLT R5.6

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


UAH recently released a beta version of Release 6.0 of their atmospheric temperature data. Those enhancements lowered the warming rates of their lower troposphere temperature data.  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. When the release 6.0 becomes the official dataset from UAH, we’ll include it in the comparisons shown later in this post. The UAH lower troposphere Release 6.0 beta data for April 2015 are here.

Update:  The April 2015 UAH (Release 6.0 beta) lower troposphere temperature anomaly is +0.06 deg C.  It dropped (a decrease of about -0.08 deg C) since March 2015.

04 Supplement UAH TLT R6.0

Figure 4 Supplement – UAH Lower Troposphere Temperature (TLT) Anomaly Data – Release 6.0 Beta


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 April 2015 RSS lower troposphere temperature anomaly is +0.17 deg C.  It dropped (a decrease of about -0.08 deg C) since March 2015.


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



There is a noticeable difference between the RSS and UAH lower troposphere temperature anomaly data. Dr. Roy Spencer discussed this in his November 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.

Roy Spencer has recently updated his discussion on the RSS and UAH differences in the post Why Do Different Satellite Datasets Produce Different Global Temperature Trends?


Note:  I’m still using the UAH Release 5.6 data in these comparisons, until the Release 6.0 data become “official”.

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 6 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. 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 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 halt in warming.

Figure 8 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.

06 Comparison 1979 Start

Figure 6 – Comparison Starting in 1979


07 Comparison 1998 Start

Figure 7 – Comparison Starting in 1998


08 Comparison 2001 Start

Figure 8 – Comparison Starting in 2001

Note also that the graphs list the trends of the CMIP5 multi-model mean (historic and RCP8.5 forcings), which are the climate models used by the IPCC for their 5th Assessment Report.

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.


Figure 9 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.

09 Averages

Figure 9 – 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 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, dropping slowly throughout most of that region, until the El Niño of 2009/10, when the surface temperatures of that 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.


Considering the uptick in surface temperatures this year (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, GISS Land-Ocean Temperature Index (LOTI) data are 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 data and model outputs have been smoothed with 61-month filters to reduce the monthly variations. Also, the anomalies for the data and model outputs have been referenced to the period of 1880 to 2013 so not to bias the results.

10 Model-Data 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 data 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 GISS global Land-Ocean Temperature Index (LOTI) 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 near full term of the data (1880 to 2013) were used as the reference period.

In this example, we’re illustrating the model-data differences in the monthly surface temperature anomalies. Also included in red is the difference smoothed with a 61-month running mean filter.

11 Model-Data Difference

Figure 11

The greatest difference between models and data occurs in the 1880s.  The 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 1940. 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 nearer to a zero difference until the 1990s, 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 since about 1910…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.


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.


75 thoughts on “April 2015 Global Surface (Land+Ocean) and Lower Troposphere Temperature Anomaly & Model-Data Difference Update

  1. I am curious if there is any hypothesis that might explain one of the striking differences in the visual characteristics between the GISS/NCDC/HADCRUT graphs versus the UAH/RSS graphs. The specific visual difference is that GISS/NCDC/HADCRUT seem to display some sort of periodic spikes in the record starting about 1990 whereas UAH/RSS do not. While I am nto a statistician, it seems to me that either the spikes should be present and therefore it is a feature that suggests something not being detected by UAH/RSS or the spikes represent an effect of some potentially identifiable specific problem in the measurement or adjustments that form the basis of GISS/NCDC/HADCRUT. For example, could there be some known physical phenomenon that matches the points in time when the spikes occur? If so is that phenomenon one that ought to leave a real temperature signature? Or is it a phenomenon that is adding noise to the measurement/adjustments that needs to be factored out? Could the same phenomenon also account for the recent increased difference between the GISS/NCDC/HADCRUT plots and the UAH/RSS plots?
    Unfortunately all I have to offer are questions. But I find the similarity of each of those groupings of plots within each group but the difference between the two groupings to be something that may be of significance to understand.

    • Thanks for pointing that out. Another possibly related feature that I noticed was that all the surface-based graphs showed a sharp temperature rise over the last three years, bringing the temperature close to the 1998 maximum, while the two satellite-based graphs are flat and at a much lower level.

      • Paris… Manipulation of data to give meaning to the power grab that is Paris.. I have heard no rational explanation for this mysterious warming when global trends are now downward and have been since 2002

  2. You might not have noticed this, but the 12 month running average of GISS is at 0.72, which is the record maximum for the entire instrument record, and probably for the last millennium. This is the second month in a row at this level, and the fourth month in a row at a new record level. Besides the recent spate of record high 12 month running averages, maximum records for GISS were set ending in May and June of 2010 (both at +0.68) , ending in May through Sept of 2007 (all at +0.67) , and ending in August of 1998 ( at +0.65).
    HADCRUT and NOAA set 12 month records last month, and when the values for HADCRUT and NOAA come out in a week or two, they are also likely to stay at record highs.
    So we are in the hottest year of our lives, all of us.

      • Speak for yourself — I was 81 last month, but I am getting younger by the day since I started on a regimen on Carnosine and Sero Vital as recommended by Dr. OZ. In about 20 years I am scheduled to be a teen-ager again.

    • When the land based temperature reporting system can tell me what the temperature is 534 KM due west of Easter Island to the nearest .1 Deg C, I’ll start believing what they publish.

      • Do they have classes in bad analogies, or did you develop this skill all on your own.
        For your analogy to have any merit, rbabcock would have had to declare that he doesn’t believe in temperature.
        Instead he just pointed out that there is no reason to believe that they are capable of measuring the current temperature of the entire earth with the accuracy being claimed.
        Now do you want to try to embarrass yourself, or have you reached your daily limit?

    • on the basis that diurnal variation is around 10 degrees in the UK, I possibly feel the benefit of this warming for around a couple of hours around lunchtime. ….. I make sure I stay in the shade then !

    • IFF you believe all the data manipulations are valid. While the 60s and 70s were cool, and the 80s and 90s were warm, I personally remember the 50s as warmer, and right now we are having cold and rain in San Jose Calif. that is very unseasonably cold and wet. Also there is record cold and snow all over the globe. So IMHO, the temperature data are shown to be crap by the reality on the ground. Satellite data too short to mean much other than no change in 15 years, and land data so discontinuous and fudge and splice artifact filled as to be a fantasy.
      Kind of like Texas where they are having loads of flooding… in an official drought… The methods are junk, the cold and water are real.

      • Actually, the drought in Texas is now considered over, at least in most of the state. As of yesterday, no place in Texas is considered to be in a condition of exceptional drought (the worst category) though drought conditions persist in some places. It’s a wonderful change from the past several years.
        That said, I am in general agreement with your comment.

    • Pippen Kool
      You say

      You might not have noticed this, but the 12 month running average of GISS is at 0.72, which is the record maximum for the entire instrument record, and probably for the last millennium. This is the second month in a row at this level, and the fourth month in a row at a new record level. Besides the recent spate of record high 12 month running averages, maximum records for GISS were set ending in May and June of 2010 (both at +0.68) , ending in May through Sept of 2007 (all at +0.67) , and ending in August of 1998 ( at +0.65).
      HADCRUT and NOAA set 12 month records last month, and when the values for HADCRUT and NOAA come out in a week or two, they are also likely to stay at record highs.
      So we are in the hottest year of our lives, all of us.

      This is “the hottest year of our lives”? Nonsense!
      You have ignored the changes GISS keeps making to the data as reported by Bob Tisdale in his above article
      These graphs show how GISS makes “the hottest year of our lives”.

      • Such a cool complicated conspiracy.
        There are the people working on GISS, the people working on NOAA, the people working on HADCRUT, and they all have to coordinate their little dirty collusion so the data sets match so well. It is also important to not let the people at RSS and UAH (who are also secretly working together again) know what they are doing. I bet you stay up each night wondering, “when are they going to start adding 0.11 rather than 0.10 to the false temperature record.”
        The other thing that must be hard is to hire all these people, many on a short term basis because they are postdocs and graduate students, and just hope that there is not an honest one amongst them.
        As I said, such a cool complicated conspiracy.

      • Pippen,
        You have read the climate gate emails, have you.
        The existence of this conspiracy has been proven.

      • Pippen, you don’t need a “conspiracy” to be mistaken in how to adjust, or how much to adjust for a satellite’s problems. Nor do you need to be a conspirator to think that the difference between the data and what you expect must indicate something wrong with the data, simply an overconfidence in a theory. While Trenberth’s complaint in the climategate emails about the “travesty” that the missing oceanic heat could not be found is very well known, for some reason the immediately following statement that “something” must be “wrong” with the data continues to be ignored.
        Many theorists hate to think their pet theory may have problems, especially if it is either their baby, or if they “grew up” using it in their profession. Kuhn has written extensively about this. In other fields such as cosmology and astrophysics very similarly structured debates that continue between theorists who place mathematical elegance above empirical observation and empiricists who prefer to deal directly with observation and are prone to treat them as uncontaminated truth. Both approaches are fantasy rather than science.

      • Pippen Kool
        The only “conspiracy” exists in your imagination.
        There is confirmation bias and mutual support.
        If you want facts (which would be a first) then read this.
        The actions to prevent their dirty little secrets being published were defensiveness.

      • Mods
        My reply to the false claim that I suggested a “conspiracy” is in the ‘bin’. I ask that it be found and posted as a matter of urgency because it refutes a false accusation that I said what I did not.

      • The latest temperature anomaly, relative to the 1998 high, as eyeballed from the graphs:
        GISS -0.02
        NCDC -0.02
        HadCRUT -0.07
        average land-based -0.04
        UAH(5.6) -0.48
        UAH (6.0) -0.69
        RSS -0.68
        average satellite -0.68 or using older UAH -0.58
        The difference between the land-based and satellite measures is greater than the global warming from 1979 to the present. Something’s badly wrong.

    • Pippen K sez:
      As I said, such a cool complicated conspiracy.
      So, didn’t read the article, did you?
      So we are in the hottest year of our lives, all of us.
      And I’m the tallest I’ve ever been, at 6’2″. At this rate, I will be 8’4″ in 12 more years.

    • Really Pippen. 0.7 degrees C. Well I reckon I am a lot older than you and I remember it being warmer and colder in my life time, and even the available data from Environment Canada confirms that … though it may be adjusted and it isn’t up to date (unless you want to buy it). But the it doesn’t look like much to worry about. A little less warm, a little less cold. Averages are pretty much meaningless without context. Remember, 50% of the people in a room are below the average of the people in the room – no matter what you are measuring.
      Grand Forks, BC 49 N
      Alert, Nunavit 82 N

    • I was modeled to be 30 ft tall by time I was 40 based on my growth rate between 14 and 18. I haven’t grown since I was 18 but I’m still 95% certain that the hiatus in growth will continue any day now and I will be exactly 30 ft tall by time I’m 40.

  3. Clive Best has a new post up showing the changes in land temperatures over some time in the NCDC’s GHCN database (used by everyone producing surface temperature data).
    GHCN V1 Raw land temp increase from 1880 extended to 2014 about 0.65C.
    New adjusted GHCN V3 land temp increase from 1880 to 2014 about 1.2C.

  4. The really interesting bit is the complete divergence between the “Independent” satellite based measurement services and the Land based services.

  5. Yet in spite of all this temperature stability, the ice at both poles continues to melt at an irregular, but undeniably accelerating pace.
    Antarctic: “”We are starting to lose more ice at a faster rate; we’re accelerating,” says Helen Fricker, a climate scientist at University of California, San Diego’s Scripps Institution of Oceanography. In fact, she says the rate of shrinking has increased by 70 percent over the past decade.”
    Arctic: “There was less ice in the Arctic this winter than in any other winter during the satellite era, National Oceanic and Atmospheric Administration scientists said on Tuesday.”

  6. As I understand it the greenhouse effect of CO2 is a logarithmic curve. As the CO2 in the atmosphere increases it has less and less of an effect on temperature. But when I look at the climate model projections I don’t see that. I’m curious as to why that is.

      • Those are not actual “data”, but a tendentious construct. HadCRU may well have been constructed to reverse engineer a rapidly rising log curve.

        Quite possibly. You read my remarks, you know I’m no fan of HadCRUT4 or GISS LOTI. However, note that I’m addressing:

        As the CO2 in the atmosphere increases it has less and less of an effect on temperature. But when I look at the climate model projections I don’t see that.

        The point of my graph above in this context isn’t so much HadCRUT4 (although the fit is very reasonable) as it is to show what a log curve of CO_2 concentration might look like. CO_2 is increasing nonlinearly in time (and from maybe 1953 to the present my CO_2 curve precisely matches Mauna Loa), and taking the log of that concentration means that the resulting function is not a log function of time. It goes up faster than a log of time, because CO_2 isn’t linear in time.
        The CMIP5 results, of course, aren’t generated at all like this. They assume some roughly similar CO_2-only forcing, they assume that aerosols have a significant cooling effect, they assume lots of water vapor positive feedback on top of the CO_2-only forcing and aerosol cooling, and then they implement this in a Navier-Stokes approximate computation on a very coarse-grained grid using initial conditions that they just more or less make up, since we don’t have any measurements at anything like even the coarse grained resolution needed to start the computation with real numbers. They then run the result forward in time umpty years, tweak the initial conditions, and do it again, repeat as long as you have funding. Then they take this ensemble of “Monte Carlo samples” of possible cartoon future climates on an imaginary Earth-like planet, average it, and use the result as the “prediction” of the model. Finally, all of the models in CMIP5 have the results they produce like this indifferently averaged together (regardless of their accuracy, their reasonableness, whether or not the envelope of model predictions are even significantly constraining) to produce the “Multimodel Ensemble Mean” which Bob plots above.
        This is actually another possible answer to the question. Given the complexity of the models and the statistical inanity of just building a superaverage of 36-odd disparate non-independent models’ results that are themselves averaged over an ensemble of chaotic possible future climates from an arbitrary initial condition that is almost nothing like the actual state of the Earth at any time in its climate history, it is remarkable that they do, in the end, produce a mean temperature that vaguely resembles a log curve of the concentration of CO_2, plus a lot of bounces here and there. Bounces so extreme, in fact, that no single model trajectory looks anything at all like the climate of the Earth!
        That’s really the clincher, right there. The CMIP5 MME mean is a superaverage of model results not one of which resembles the actual climate trajectory of the Earth, most of which are obviously and egregiously incorrect and have obviously bad physics in them because they have the wrong e.g. autocorrelation times and spectrum and because they exhibit fluctuations that are many times too large, even when averaged per model.
        That’s one of many reasons I don’t like one thing Bob does above — smooth the CMIP5 MME mean and any or all of the temperature estimates. The MME mean is already hypersmoothed — it is the superaverage of many averages of many meaningless “possible” climate trajectories for a cartoon planet evaluated at an absurdly inadequate resolution and with the wrong physical parameterization. Smoothing it again over time just adds insult to injury and disguises the fact that it has the wrong short-term regression timescale for fluctuations away from the supposedly “stable” local climate.

    • http://www.phy.duke.edu/~rgb/Toft-CO2-PDO.jpg
      Maybe you should try fitting the data to the functional form instead of “looking” at it without any idea of what either parameter is doing? Because a log curve fits the last 165 years of temperature data very reasonably, although it is clearly not the only thing going on.

      • Those are not actual “data”, but a tendentious construct. HadCRU may well have been constructed to reverse engineer a rapidly rising log curve.
        In the real world, the 20-year warm interval c. 1977-96 was probably scarcely warmer, if at all, than the 26-year warm interval c. 1918-44, separated by the 31-year cooler interval of 1945-76. Since around 1997, we’ve been in a flat to cooling interval.

      • CO2 having nothing to do with it. Why don’t you show a graph of CO2 alone versus the temperature and the PDO alone versus the temperature to give a true representation.

      • The reality of CO2 versus temperature when CO2 is alone.
        [Note 1: since 1880 the only one period where Global Mean Temperature and CO2 content of the air increased simultaneously has been 1978-1997. From 1910 to 1940, the Global Mean Temperature increased at about the same rate as over 1978-1997, while CO2 anthropic emissions were almost negligible. Over 1950-1978 while CO2 anthropic emissions increased rapidly the Global Mean Temperature dropped. From Vostok and other ice cores we know that it’s the increase of the temperature that drives the subsequent increase of the CO2 content of the air, thanks to ocean out-gassing, and not the opposite. The same process is still at work nowadays]

    • Not at all. At a single frequency, the response to increasing CO2 in logarithmic. However, in the atmosphere, you need to consider the entire band of absorbing frequencies. When that is done, the effect becomes almost linear. If you simply take the first derivative over a simple doubling from 300 ppm to 600 ppm, the difference between the actual effect and the derivative (a perfectly linear fit) is too small to care about.

  7. Any chart that shows it is warmer today then it was in the 1930’s is a lie. From that simple fact, we are all arguing and discussing government provided lies, trying to ferret out some facts. You won’t find any, of course.
    Then we have hairyflesh and pip doing their usual trolling routine… At least we get some entertainment out of them.

  8. Pippen Kool: “So we are in the hottest year of our lives, all of us.”
    I thought the mean surface temperature in 2014 was about 14.6C.
    What’s ‘hot’ about that? It’s not even warm.

  9. Pippen Kool: “So we are in the hottest year of our lives, all of us.”
    It is the May long week-end, the week-end in which the bedding plants and garden is supposed to go in.
    Well, not this year since snow is forecast on Sunday.
    2 years ago was the latest snow-melt on record where I live – going back to 1883.
    I think we should just throw out all the adjusted temperature data because the real backyard climate tells the truth.

    • I just ran my heater again a few mornings ago in southern Kansas. I don’t ever remember turning the heat on past May 1st. And like a fool, I transplanted my summer garden around April 20th because this is historically the time when it is completely safe to do so and have the plants thrive immediately with monrning lows in the lower to mid 60s F. After a few mornings dipping down below 47 F the plants aren’t doing so well and I’ll probably need to transplant again. Oh well, at least I’ll still have a great spring crop going into June.
      I’m still not sure what to believe, my lying eyes or Pippen Kool’s parroting memes.

    • Global temperature was the perfect scam until the satellite data divergence showed there was something amiss. The AGW industry is clinging to SST as their last hope for data manipulation to support the “cause”.
      Although this strategy is totally dependent on a gullible public not realizing that the bogeyman of CO2 forcing can’t impact ocean temp.
      While amusing on some level, what this movement is doing to science is unconscionable.

  10. Thanks, Bob.
    Another wide angle view at the global temperature data sets, with useful comparisons.
    This hiatus, pause, whatever, is still going on. El Niño is ongoing and now forecast to last through 2015. Is El Niño all that is keeping the Earth from cooling?

  11. Bob
    Any Comment on the NCEP CFSR/cfsv2 temp data last 10 years.. Other links show the previous years, but there is a definite leveling off with el nino spikes followed by bigger downturns, which I think we are about to see again ( spike then bigger fall)
    We have this link on our site
    constructed by Dr Ryan Maue that is quite extensive and is in real time. Color me partial, but I believe it to be the gold standard ( anything Ryan develops usually is)

  12. Global temperature trends (LOTI)…
    GISS data published 2008:
    1901–1950: +0.15°F per decade
    1951–2000: +0.16°F per decade
    1901–2007: +0.12°F per decade
    GISS data published 2015:
    1901–1950: +0.17°F per decade
    1951–2000: +0.19°F per decade
    2001–2007: +0.14°F per decade

    • Regardless of if you think the data is being tampered with, your table shows that it’s going up.

      • The data is “going up.” Acute observation. Note, however, there are two tables covering the same period, and the second shows the data is going up faster. And yet, neither table shows that the rate of increase has substantially gone up in correlation with increasing CO2.
        Consider an influential interval from the newer data. During the ‘scary’ 15 years from the chill of the 1970s through 1990, when CO2 levels were substantially rising and correlation implied causation, the data was going up 0.37° per 10 years. It continued going up as politically-influential industries were built around the fear of consequences. But if CO2 is truly the primary driver of increasing average temperature, the upward trend should not have fallen to pre-1950 levels. And it has.
        So, though the data is going up, there is nevertheless a pause in the increased rate attributed to CO2. The claim by the purveyors of panic that the science was settled was false, and the insistence by Lewandowsky and Oreskes that scientists should not seek a cause for “the pause” is a cry of desperation from ideologues.

    • Oops, mistyped that last line in the 2015 data. Should be:
      1901–2007: +0.14°F per decade

    • Yeah, it really is at some point a bit of a joke, isn’t it? In every other field of human endeavor, errors in historical data tend to be fairly balanced, with people making an error one way about as often as they do the other way. Only in climate science are all corrections certain to add to the present and subtract from the past, even though the only credible systematic bias in the data (the UHI effect) should do exactly the opposite.
      For a very brief time it looked like they were going to be constrained by the failure of the lower troposphere to reflect the supposed rise in average surface temperatures. Silly me. They simple started to “cool” the past temperature record, since they could no longer “warm” the current temperature record with their adjustments. Now the real problem is keeping up with CMIP5, as the credibility of the models is hanging by a thread even with devoted warmists. But to keep up with the models, they have to warm the present, and there they do indeed run square into the LTT satellite record.
      So the LTT satellite record is seriously divergent from the model predictions, the surface temperature is marginally divergent from model predictions and also marginally divergent from the LTT, so that they can — barely — claim that the models aren’t crap.
      Somewhere on the planet Earth, I’m sure 2014 was “the hottest year on record”. But not where I live. Not where a whole lot of people live. For many people, 2014 was really pretty damn cold, especially in the Eastern US. But if you remove the many, many “corrections” to the temperature record that have been quietly thrown on to systematically increase the apparent warming and make it agree better with models “proving” catastrophe the game would already be over, as the overall average temperatures would be an easy 0.2 to 0.3 C less especially in the late 20th and early 21st centuries.
      Personally, I totally believe that humans have increased the average CO_2 content of the atmosphere by burning coal, because that’s entirely consistent with the data and many side observations e.g. radioactive ratios. I can read and understand (well enough to derive it myself if need be) the physics associated with the GHE. I am reasonably certain that the increase in CO_2 has caused some warming over the last 200+ years, relative to what we might have seen without it, in a probabilistic sense since the Earth’s climate is chaotic and one cannot really compare “with” and “without” any parametric perturbation in anything but a stochastic way.
      I am actually more certain that the net effect of the additional CO_2, temperature increase included, has been overwhelmingly beneficial to mankind (so far). There are many, many papers and studies of the positive effects of CO_2 in controlled (actual) greenhouse studies. Plants raised with elevated CO_2 grow faster, have smaller stoma, are more drought resistant and robust, and produce more of everything in a growing season. The difference is predictable. Somewhere between 10 and 15% of the crop production of the world can therefore be attributed to the extra 100 ppm (plus) added to the atmosphere by humans since the industrial revolution began. This means that roughly one billion people will eat food tonight that can be directly attributed to the rise in CO_2.
      Then, there is the direct, unbelievably overwhelming benefit of the industrial revolution itself, which was driven by the availability of cheap, reliable power generated by coal. If somebody were to turn off all of the electricity generated by coal, all over the world, tomorrow, then you would get to see what a catastrophe really looks like. This is not entirely implausible. If the Sun were to pop out a Carrington Event:
      (Coronal Mass Ejection of really significant strength) that hit the Earth square on, it could conceivably blow just about every transformer in the power transmission grid and potentially trash the windings of every generator on the planet, much like the electromagnetic pulse of a nuclear bomb but worldwide. Such an event might well cause the deaths of several billion people worldwide, especially if it struck in the middle of a particularly cold NH winter.
      It is by no means clear that we have yet reached the optimum CO_2 level for life on the planet. 500 ppm or 600 ppm might be even better than 400 ppm, from the point of view of pure biology and human advantage. Or not. But we are probably going to find out.

      • Even the adjusted numbers don’t justify the fear-mongering propaganda.
        Looking at NCDC’s average annual temperatures for the 13 Western United States plus Michigan, Minnesota, the Dakotas, Kansas, Nebraska, Oklahoma, and Texas, all but California have trended cooler since 2000. South Dakota is dropping most precipitously at -1.3°F/decade, followed by Minnesota at -1.2°, North Dakota at -1.1°, Nebraska at -0.9°, and Alaska at -0.8°.
        California’s annual temperature trend was also down until 2014, which, at almost 2° warmer than the previous record in 1934, knocked it up to +0.5°/decade. Is California’s heating the beginning of a trend that will warm the hearts of doomsday prognosticators?
        Looking at station data, I see locations from San Diego to Mt Shasta have all-time records for 2014’s average temperature. One with an especially complete daily record is San Diego IAP and, sure enough, averages for the first 132 days this year are warmer than last. Looking dire!
        But looking deeper it’s clear the trend is not toward immolation. Seasonal trends tell a different story. There are no new extreme high temp records, and May-June-July averages continue a decline.
        The recent great spike in California’s average annual temperatures is due entirely to warmer winters. Could this be a feature of the “warm blob?”
        Given the current unseasonable cold and wet that began this month, pathological prognosticators may be disappointed.

  13. rgbatduke says
    Somewhere on the planet Earth, I’m sure 2014 was “the hottest year on record
    You are absolutely right!!
    The area covered by the ‘Central England Temperature’ was the hottest in a series going back to 1659

    • 1659 began, in the LIA time frame,thus it WILL among the lowest point of the last 355 years.

      • Yes – quite!
        But not only was 2014 warmer than 1659, it was also warmer than the other 354 intervening years

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