Climate Criminal Of The Day – John Abraham

Posted on June 20, 2015 by Tony Heller

ScreenHunter_9549 Jun. 17 21.12

Wood for Trees: Interactive Graphs

For climate criminal John Abraham, “off the chart” means “average with a slight downwards trend”

First Five Months Of 2015 Were The Hottest Ever Recorded

June 16, 2015 | by Aamna Mohdin

The year 2015 is set to be a record-breaker, according to NASA’s latest global temperature data. This year’s temperature is 0.71°C (1.3°F) above the long-term average, and the first five months have been the hottest ever recorded.  NASA’s annual temperatures show a slight variation, where some years are cooler than others, but as John Abraham for The Guardian reports, “2015 is so far this year, simply off the chart.” Abraham suggests that the recent record-breaking temperatures put global warming critics in a difficult position—the evidence is simply not on their side.

First Five Months Of 2015 Were The Hottest Ever Recorded | IFLScience

About Tony Heller

Just having fun
This entry was posted in Uncategorized. Bookmark the permalink.

24 Responses to Climate Criminal Of The Day – John Abraham

  1. rah says:
    June 20, 2015 at 12:06 pm

    Yep, Global Warming moved to India! It really does get around now doesn’t it? From California, to Alaska, to India. In fact the only place it seems to hang out all the time these days is in the deep oceans and at NASA GISS! Where will it show up next?

    Reply
  2. Gail Combs says:
    June 20, 2015 at 3:56 pm

    John Abraham is a real loser. I tangled with him over at WUWT. He could not reason his way out of a paper bag. An Arogant no-nothing academic of the worst sort. His only goal is to impose HIS thoughts ON YOU. He has spent way too long having students kiss his rosy red rec…um

    Reply
  3. Phillippe Jones says:
    June 20, 2015 at 7:25 pm

    Climate Models have become Political, not scientific tools..

    Reply
  4. ren says:
    June 20, 2015 at 8:03 pm

    Comparison of the lower troposphere temperature and AMO since 2010.
    http://woodfortrees.org/plot/rss/from:2010/plot/esrl-amo/from:2010
    Can be seen that AMO is a cycle independent of global temperature .

    Reply
  5. mpcraig says:
    June 20, 2015 at 10:26 pm

    We tell them there had been no warming for years. They say that’s too short a time to come to any conclusions. Now they say five months of record breaking temperatures is a climate signal and is “evidence” that us critics are “in a difficult position”.

    Am I missing something?

    Reply
    • mpcraig says:
      June 20, 2015 at 10:28 pm

      Sorry, I meant “for 18 years”.

      Reply
    • AndyG55 says:
      June 20, 2015 at 11:22 pm

      Correction…..

      “five months of record breaking temperatures ”

      should read…

      “five months of record breaking adjustments”

      Reply
    • gator69 says:
      June 21, 2015 at 12:38 am

      Phil Jones originally said that if there was no warming for 11 years, it would be problematic for the AGW theory. Then they put wheels on the goalposts.

      Reply
    • Gail Combs says:
      June 21, 2015 at 12:25 pm

      Yes you are missing the fact that the five months are ‘record breaking’ only due to the fraudulent adjustments made.

      For example:
      Tom Nelson shows NOAA lowered past global temperature by more than 4F since 1997.
      http://tomnelson.blogspot.com/2015/02/noaa-settled-science-earth-at-5824f-in.html

      According to Zeke Hausefeather of BEST

      …Most of the stations have changed from using liquid in glass thermometers (LiG) in Stevenson screens to electronic Minimum Maximum Temperature Systems (MMTS) or Automated Surface Observing Systems (ASOS). Observation times have shifted from afternoon to morning at most stations since 1960, as part of an effort by the National Weather Service to improve precipitation measurements.

      All of these changes introduce (non-random) systemic biases into the network. For example, MMTS sensors tend to read maximum daily temperatures about 0.5 C colder than LiG thermometers at the same location. There is a very obvious cooling bias in the record associated with the conversion of most co-op stations from LiG to MMTS in the 1980s….

      Yet when one of these excuses for adjustments are actually looked at by an unbiased observer, any change should be in the OPPOSITE direction!

      The change from Liquid in Glass thermometers to MMTS sensors

      The last couple of days I posted on an 8.5 year side-by-side test conducted by German veteran meteorologist Klaus Hager, see here and here. The test compared traditional glass mercury thermometer measurement stations to the new electronic measurement system, whose implementation began at Germany’s approximately 2000 surface stations in 1985 and concluded around 2000.

      Hager’s test results showed that on average the new electronic measurement system produced warmer temperature readings: a whopping mean of 0.93°C warmer. The question is: Is this detectable in Germany’s temperature dataset? Do we see a temperature jump during the time the new “warmer” system was put into operation (1985 – 2000)? The answer is: absolutely!
      http://notrickszone.com/#sthash.Es2IbMZo.sAqMRsUB.dpbs

      This makes sense since the thermal inertia due to mass alone will make LiG much less sensitive to transitory spikes (Gust of hot air off airport runways) than MMTS sensors.

      So did Zeke Hausefeather lie when he said there was an “a very obvious cooling bias in the record “ and that “MMTS sensors tend to read maximum daily temperatures about 0.5 C colder than LiG thermometers at the same location? “

      I just looked at two studies and things get even murkier.

      Reply
      • AndyG55 says:
        June 21, 2015 at 12:34 pm

        Zeke and Mosher are the Dodgy Bros of climate salesmen.

        Treat anything they say as they were trying to sell you a rusted, clapped out old car

        Reply
  6. Gail Combs says:
    June 21, 2015 at 12:28 pm

    This is going to be long winded but if you read between the lines their ‘adjustments’ are even more bogus than we thought.

    So where did Zeke got his information? Probably from this ‘Official Paper’ by C. B. BAKER AND B. SUN, National Climatic Data Center, Asheville, North Carolina in 2004.
    By reading it carefully you can spot the ‘tricks’ used to get the ‘adjustment’ they want — Cool the past, warm the present.

    ABSTRACT
    A new U.S. Climate Reference Network (USCRN) was officially and nationally commissioned by the Department of Commerce and the National Oceanic and Atmospheric Administration in 2004. During a 1-yr side-by-side field comparison of USCRN temperatures and temperatures measured by a maximum–minimum temperature system (MMTS), analyses of hourly data show that the MMTS temperature performed with biases:
    1) a systematic bias–ambient-temperature-dependent bias and
    2) an ambient-solar-radiation- and ambient-wind-speed-dependent bias. Magnitudes of these two biases ranged from a few tenths of a degree to over 1C compared to the USCRN temperatures. The hourly average temperatures for the USCRN were the dependent variables in the development of two statistical models that remove the biases due to ambient temperature, ambient solar radiation, and ambient wind speed in the MMTS. The model performance was examined, and the results show that the adjusted MMTS data were substantially improved with respect to both systematic bias and the bias associated with ambient solar radiation and ambient wind speed. In addition, the results indicate that the historical temperature datasets prior to the MMTS era need to be further investigated to produce long-term homogenous times series of area-average temperature.

    Adjustments of temperature data in the USHCN dataset have been made to account for systematic biases introduced by changes in the time of observation (Karl et al. 1986), urban heat islands (Karl et al. 1988), changes of station location and station exposure (Karl and Williams 1987), and changes of instruments (Quayle et al. 1991). The magnitudes of these adjustments range from a few tenths of a degree [changes of instruments in Quayle et al. (1991) and urbanization in Karl et al. (1988)] to as high as 2C [time of observations in Karl and Williams (1987)].

    So that gives you an idea of just how large the ‘adjustments’ are. “….as high as 2C [time of observations in Karl and Williams (1987)]” plus what ever the heck else they want to toss in like the 0.5C instrument change.

    TOBS = Time of Observation
    My old comment addresses Tobs HERE and Steve addresses TOBS in another mannerHERE.

    CONTINUED

    Reply
    • Gail Combs says:
      June 21, 2015 at 12:52 pm

      I can not post the second section – GRRRrrrr

      Reply
    • Gail Combs says:
      June 21, 2015 at 12:52 pm

      The paper goes on to say:

      The transition from the liquid-in-glass (LIG) maximum and minimum thermometer in a cotton-region shelter (CRS) to the thermistor-based maximum–minimum temperature systems (MMTSs) in the 1980s was the main bias. The results based on 424 MMTS stations and 675 CRS stations showed that average minimum temperature changes of +0.3C and average maximum temperature changes of -0.4C were introduced (Quayle et al. 1991). At the same time, a side-by-side comparison was conducted that concluded that the MMTS underestimated the maximum temperature by as much as 0.6C but that found virtually no bias for minimum temperature (Wendland and Armstrong 1993).…
      Conclusions
      Although the MMTS temperature records have been officially adjusted for cooler maxima and warmer minima in the USHCN dataset, the MMTS dataset in the United States will require further adjustment. In general, our study infers that the MMTS dataset has warmer maxima and cooler minima compared to the current USCRN air temperature system. Likewise, our conclusion suggests that the LIG temperature records prior to the MMTS also need further investigation because most climate researchers considered the MMTS more accurate than the LIG records in the cotton-region shelter due to possible better ventilation and better solar radiation shielding….

      Reply
    • Gail Combs says:
      June 21, 2015 at 12:59 pm

      The problems in the design
      FIRST

      …temperature signals were averaged over 1-min outputs. An hourly average of all measurement quantities, including temperatures, solar radiation,and ambient wind speed were formed for this study.

      In other words they averaged away the transient temperature spikes that the Min-Max LiG thermometer system would catch and that the current Min-Max system reports as maximum readings.

      Reply
    • Gail Combs says:
      June 21, 2015 at 1:04 pm

      SECOND

      …. experiments were conducted … at the University of Nebraska’s Horticulture Experimental Site …. The site had flat terrain and the grass was mowed regularly to maintain a uniform ground surface. There were no physical obstructions within 25 m of the sensors installed.
      …

      The experiment was conducted under ideal circumstances that do not reflect actual conditions such as airport runsways, multi-lane highways, blacktop parking lots, A/C exhaust, building walls and other causes of temperature excusions.
      The site was Class 2

      So how many US stations meet those requirements? Less than 8%.
      Se the Surface Station Project.

      Reply
    • Gail Combs says:
      June 21, 2015 at 1:14 pm

      . The MMTS bias I model from Eq. (1) can be used to adjust the MMTS temperature records and remove the MMTS systematic bias. We found this bias to be a function of temperature, and its magnitude varied from about -0.4C to -0.2C in our study. The nighttime MMTS temperature records were affected by the MMTS temperature-dependent bias but not by the ambient wind speed. The daytime MMTS temperature records were first adjusted by using the temperature-dependent bias (i.e., the MMTS bias I model), then using a nonlinear regression model (i.e., the MMTS bias II model) associated with the solar radiation and ambient wind speed effects at the observation site. Without the information of site solar radiation and ambient wind speed, the MMTS temperature data cannot be accurately transformed into the current USCRN temperature data….

      This problem howeverwas not unknown. Willis Isbister Milham was talking about the problem in 1918 in his book Meteorology: A Text-book on the Weather, the Causes of Its Changes, and Weather Forecasting

      On page 68 he says a thermometer in a Stevenson screen is correct to within a half degree. It is most in error on still days, hot or cold. “In both cases the indications of the sheltered thermometers are too conservative.”

      And yet the Climastrologists want to adjust all the past data without this critical information.
      CONTINUED

      Reply
  7. Gail Combs says:
    June 21, 2015 at 12:39 pm

    And the situation gets even more complicated:
    SURFACE AIR TEMPERATURE RECORDS BIASED BY SNOW-COVERED SURFACE

    4. CONCLUSIONS
    This study demonstrates that the MMTS shield bias can be seriously elevated by the snow surface and the daytime MMTS shield bias can additively increase by about 1 ° C when the surface is snow covered compared with a non-snow-covered surface (Table I).

    It seems that the sun radiation reflected off the snow surface changes the temperature reading.

    …Although the evolution of both temperature sensors and temperature radiation shields is unavoidable due to gradual improvements in air temperature measurement, the homogeneity adjustment due to the effect of change of instrument (Quayle et al., 1991) is the only adjustment used in the US Historical Climatology Network (USHCN) and the USHCN is a major component of the Global Historical Climate Network (GHCN) (Peterson and Vose, 1998). The statistical study (Quayle et al., 1991) of the effect of an instrument change was accomplished in response to a change from a liquid-in-glass thermometer in a wooden Cotton Region Shelter (CRS) to a thermistor maximum–minimum temperature system (MMTS) at many US surface climate stations. Quayle et al. (1991) used station metadata to determine which US stations remained unchanged with CRSs and which stations switched to an MMTS as their only change. The five most highly correlated nearby CRS stations for each MMTS station were used to create a local CRS temperature time series for each MMTS station. Therefore, after averaging hundreds of time series differences between the MMTS and CRSs, two constant adjustment factors for maximum and minimum temperatures were derived for the average effect due to a combined change of instrumentation and radiation shields.

    Currently, the temperature radiation shield and temperature sensing element of thermometers continue to
    change. In response to a new US Climate Reference Network (CRN), which was nationally commissioned by the National Oceanic and Atmospheric Administration in 2004, Hubbard et al. (2004) promptly investigated the MMTS shield bias compared with an aspirated air temperature radiation shield used in the CRN by using hourly data analysis from a 1-year field side-by-side comparison. [This is the study I analyze above] The magnitude of MMTS shield bias ranges from a few tenths of a degree C to over 1 ° C and is a function of solar radiation and ambient wind speed (Hubbard et al., 2004). The US CRN temperature system consists of a temperature sensor (Thermometric Co., USA) and an aspirated radiation shield (Model 076B, Met One Instruments, Inc., USA). However, the MMTS is non-aspirated and its temperature sensor is shielded from radiation by a multiple-plate, cylindrical, plastic shield. In addition, the solar radiation can reflectively arrive on the body of the MMTS sensor inside the MMTS shield, whereas the shield used in the US CRN is comprised of three concentric cylinders with a double meshed opening at the base of shield. Both the temperature sensor and the radiation shield used in the MMTS and CRN systems were described detail in Hubbard et al. (2004). The solar radiation loading on the MMTS shield is not only determined by the global solar radiation regime (including solar elevation), but also depends upon the radiative properties of the ground surface (Hubbard et al., 2001)…..

    IMPORTANT! Here we have the crux of the matter. I repeat:
    The solar radiation loading on the MMTS shield is not only determined by the global solar radiation regime (including solar elevation), but also depends upon the radiative properties of the ground surface

    And it only gets worse….

    The SR (W m?2 ) and WS (m s?1 ) are respectively the solar radiation and the ambient wind speed at 1.5 m at our experimental site. There are two options for us to use measurement data to derive coefficients of Equation (1): hourly data and minute data. The minute data have 60 times the data volume of the hourly data, but the frequency distribution of solar radiation in the minute data was seriously uneven. For example, the number of minutes when solar radiation was less than 200 W m?2 was approximately equal to the remaining number of minutes when the solar radiation ranged from 200 to 790 W m?2 . Therefore, numeric evaluation has to be conducted for determining either use of hourly data or minute data for Equation (1). This numeric evaluation was conducted by selecting a minimum shield difference between the debiased MMTS and the CRN along with a better non-regression performance.

    Results from 1-year intercomparison indicated that the night-time MMTS shield bias was much less significant than the daytime MMTS shield bias, and it was virtually independent from ambient wind speed in an average sense (Hubbard et al., 2004). However, not only is the MMTS radiation loading dramatically increased in terms of the increase of solar reflectivity of snow-covered surface during daytime, but it is also elevated in terms of infrared emissivity of snow-covered surface during night-time. Therefore, a night-time MMTS shield bias model would be expected for night-time temperatures under conditions of snow-covered ground surface based on the effect of ambient temperature change rate (° C min?1 ). The difference of thermal time constants between the MMTS sensor and the MMTS shield would play a major role for the contribution of infrared radiation effect on the night-time MMTS shield bias if the ambient wind speed influence can be ignored (Hubbard et al., 2004)…..

    CONTINUED

    Reply
  8. Gail Combs says:
    June 21, 2015 at 1:16 pm

    IMPORTANT! Here we have the crux of the matter. I repeat:
    The solar radiation loading on the MMTS shield is not only determined by the global solar radiation regime (including solar elevation), but also depends upon the radiative properties of the ground surface

    And it only gets worse….

    The SR (W m?2 ) and WS (m s?1 ) are respectively the solar radiation and the ambient wind speed at 1.5 m at our experimental site. There are two options for us to use measurement data to derive coefficients of Equation (1): hourly data and minute data. The minute data have 60 times the data volume of the hourly data, but the frequency distribution of solar radiation in the minute data was seriously uneven. For example, the number of minutes when solar radiation was less than 200 W m?2 was approximately equal to the remaining number of minutes when the solar radiation ranged from 200 to 790 W m?2 . Therefore, numeric evaluation has to be conducted for determining either use of hourly data or minute data for Equation (1). This numeric evaluation was conducted by selecting a minimum shield difference between the debiased MMTS and the CRN along with a better non-regression performance.

    Results from 1-year intercomparison indicated that the night-time MMTS shield bias was much less significant than the daytime MMTS shield bias, and it was virtually independent from ambient wind speed in an average sense (Hubbard et al., 2004). However, not only is the MMTS radiation loading dramatically increased in terms of the increase of solar reflectivity of snow-covered surface during daytime, but it is also elevated in terms of infrared emissivity of snow-covered surface during night-time. Therefore, a night-time MMTS shield bias model would be expected for night-time temperatures under conditions of snow-covered ground surface based on the effect of ambient temperature change rate (° C min?1 ). The difference of thermal time constants between the MMTS sensor and the MMTS shield would play a major role for the contribution of infrared radiation effect on the night-time MMTS shield bias if the ambient wind speed influence can be ignored (Hubbard et al., 2004)…..

    CONTINUED

    ………………………………

    And the BIG I GOTTCHA!

    It is interesting to examine visually some of time series of MMTS shield bias relative to the CRN temperature system when the MMTS shield bias is considered as a response of many microclimate factors inside the shield, such as ambient temperature, ambient wind speed, solar radiation, and ambient temperature change rates. Figure 2 shows a typical example for illustrating possible links between the MMTS shield biases and microclimate factors. Clearly, the daytime MMTS shield bias was larger when the solar radiation was larger and ambient wind speed was smaller (Figure 2). The magnitude of MMTS shield bias could reach over 1.5 ° C. When the ambient wind speed was increased, the MMTS shield bias was substantially decreased

    This paper estimate error bands:
    http://www.eike-klima-energie.eu/uploads/media/E___E_algorithm_error_07-Limburg.pdf

    “By knowing this the minimum uncertainty for every annual global mean temperature should be expanded not only to the value described here i.e. with 95 % confidence interval to ± 1.084 °C, but should be at least 3 to 5 times wider. Thus, the average global temperature anomaly for the last 150 years is dissolved in a wide noisy uncertainty band, which is much wider than the whole assumed variation of the 20th century.”

    Other good references:
    http://pugshoes.blogspot.se/2010/10/metrology.html

    http://strata-sphere.com/blog/index.php/archives/11420

    Reply
  9. Gail Combs says:
    June 21, 2015 at 1:19 pm

    So their own experiments show that since radiation from the surroundings and wind, both of which change by the minute, have major effects on the temperature reading, there is no way in Hades to get the accuracy they claim especially in the old data. It just ain’t gonna happen folks.

    Reply

Leave a Reply

Your email address will not be published. Required fields are marked *