170 Years of Earth Surface Temperature Data Show No Evidence of Significant Warming

Author: Thomas K. Bjorklund, University of Houston, Dept. of Earth and Atmospheric Sciences

October16, 2019

Key Points

  1. From 1850 to the present, the noise-corrected, average warming of the surface of the earth is less than 0.07 degrees C per decade.

2. The rate of warming of the surface of the earth does not correlate with the rate of increase of fossil fuel emissions of CO2 into the atmosphere.

  1. Recent increases in surface temperatures reflect 40 years of increasing intensities of the El Nino Southern Oscillation climate pattern.


This study investigates the relationships between surface temperatures from 1850 to the present and reported long-range temperature predictions of global warming. A crucial component of this analysis is the calculation of an estimate of the warming curve of the surface of the earth. The calculation removes errors in temperature measurements and fluctuations due to short-duration weather events from the recorded data. The results show the average rate of warming of the surface of earth for the past 170 years is less than 0.07 degrees C per decade. The rate of warming of the surface of the earth does not correlate with the rate of increase of CO2 in the atmosphere. The perceived threat of excessive future global temperatures may stem from misinterpretation of 40 years of increasing intensities of the El Nino Southern Oscillation (ENSO) climate pattern in the eastern Pacific Ocean. ENSO activity culminated in 2016 with the highest surface temperature anomaly ever recorded. The rate of warming of the earth’s surface has dropped 41 percent since 2006.

Section 1


The results of this study suggest the present movement to curtail global warming may by premature. Both the highest ever recorded warming currents in the Pacific Ocean and technologically advanced methods to collect ocean temperature data from earth orbiting satellites coincidently began in the late 1970s. This study describes how newly acquired high-resolution temperature data and Pacific Ocean transient warming events may have convolved to result in long-range temperature predictions that are too high.

HadCRUT4 Monthly Temperature Anomalies

This analysis uses the HadCRUT. version of monthly medians of the global time series of temperature anomalies, Column 2, 1850/01 to 2019/08 (Morice, C. P., et. al. 2012). The NASA Goddard Institute for Space Studies data set of global-mean annual land and sea surface temperature anomalies, 1880 to 2018, was also analyzed using the methodology described in this report. The results are essentially the same as the results from the HadCRUT4 data set analyses. The HadCRUT4 data analysis was used for this report because the time series is longer, and the monthly global temperature anomalies are easier to import to Excel.

Only in recent years have high-resolution satellites provided simultaneously observed data on properties of the land, ocean and atmosphere (Palmer, P.I., 2018). NOAA-6 was launched in December 1979 and NOAA-7 was launched in 1981. Both were equipped with microwave radiometry devices (Microwave Sounding Unit-MSU) to precisely monitor sea-surface temperature anomalies over the eastern Pacific Ocean and the areas of ENSO activity (Spencer, et al., 1990). These satellites were among the first to use this technology.

The initial analyses of the high-resolution satellite data yielded a remarkable result. Spencer, et al. (1990), concluded the following: “The period of analysis (1979–84) reveals that Northern and Southern hemispheric tropospheric temperature anomalies (from the six-year mean) are positively correlated on multi-seasonal time scales but negatively correlated on shorter time scales. The 1983 ENSO dominates the record, with early 1983 zonally averaged tropical temperatures up to 0.6 degrees C warmer than the average of the remaining years. These natural variations are much larger than that expected of greenhouse enhancements and so it is likely that a considerably longer period of satellite record must accumulate for any longer-term trends to be revealed”.

Karl, et al. (2015) claim that the past 18 years of stable global temperatures is due to the use of biased ocean buoy-based data. Karl, et al. state that a “bias correction involved calculating the average difference between collocated buoy and ship SSTs. The average difference globally was −0.12°C, a correction that is applied to the buoy SSTs at every grid cell in ERSST version 4.” This analysis is not consistent with the interpretation of the past 18-year pause in global warming. The discussion below of the first derivative of a temperature anomaly trendline shows the rate of increase of relatively stable and nearly noise-free temperatures peaked in 2006 and has since declined in rate of increase to the present.

The following is a summary of conclusions by Karl, et al. (2015) (called K15 below) by Mckitrick (2015): “All the underlying data (NMAT, ship, buoy, etc.) have inherent problems and many teams have struggled with how to work with them over the years. The HadNMAT2 data are sparse and incomplete. K15 take the position that forcing the ship data to line up with this dataset makes them more reliable. This is not a position other teams have adopted, including the group that developed the HadNMAT2 data itself. It is very odd that a cooling adjustment to SST records in 1998-2000 should have such a big effect on the global trend, namely wiping out a hiatus that is seen in so many other data sets, especially since other teams have not found reason to make such an adjustment. The outlier results in the K15 data might mean everyone else is missing something, or it might simply mean that the new K15 adjustments are invalid.”

Mears and Wentz (2016) discuss adjustments to satellite data and their new dataset, which “shows substantially increased global-scale warming relative to the previous version of the dataset, particularly after 1998. The new dataset shows more warming than most other middle tropospheric data records constructed from the same set of satellites.” The discussion below shows the warming curve of the earth has been decreasing in rate of increase of slope since July 1988; that is, the curve has been concave downward. Based on this observation alone, their new dataset should not show “substantially increased global-scale warming.”

Analysis of Temperature Anomalies

All temperature measurements used in this study are calculated temperature anomalies and not absolute temperatures. A temperature anomaly is the difference of the absolute measured temperature from a baseline average temperature; in this case, the average annual mean temperature from 1961 to 1990. This conversion process is intended to minimize the effects on temperatures related to the location of the measurement station (e.g., in a valley or on a mountain top) and result in better recognition of regional temperature trends.

In Figure 1, the black curve is a plot of monthly mean surface temperature anomalies. The jagged character of the black temperature anomaly curve is data noise (inaccuracies in measurements and random, short term weather events). The red curve is an Excel sixth-degree polynomial best fit trendline of the temperature anomalies. The curve-fitting process removes high-frequency noise. The green curve, a first derivative of the trendline, is the single most important curve derived from the global monthly mean temperature anomalies. The curve is a time-series of the month-to-month differences in mean surface temperatures in units of degrees Celsius change per month. These very small numbers are multiplied by 120 to convert the units to degrees per decade (left vertical axis of the graph). Degrees per decade is a measure of the rate at which the earth’s surface is cooling or warming; it is sometimes referred to as the warming (or cooling) curve of the surface of the earth. The green curve temperature values are similar in magnitude to noise-free earth surface temperature estimates determined by University of Alabama in Huntsville for single points (Christy, J. R. May 8, 2019). The green curve has not previously been reported and is critical to analyzing long-term temperature trends.


Figure 1. The black curve is the HadCRUT4 time series of the mean monthly global land and sea surface temperature anomalies, 1850-present). Anomalies are deviations from the 1961-1990 annual mean temperatures in degrees Celsius. The red curve is the trendline of the HadCRUT4 data set, an Excel sixth-degree polynomial best fit of the temperature anomalies. The green curve is the first derivative of the trendline converted from units of degrees C per month to degrees C per decade, that is; the slope of the trendline curve.

In a recent talk, John Christy (May 8, 2019), director of the Earth System Science Center at the University of Alabama in Huntsville, reported estimates of noise-free earth warming in 1994 and 2017 of 0.09 and 0.095 degrees C per decade, respectively. The 2017 average value for the green curve is 0.154: this value is 0.059 degrees per decade higher than the UAH estimate. The latest value in August 2019 for the green curve is 0.125 degrees C per decade. The average degrees C per decade value of earth warming based on the green curve over 2,032 months since 1850 is 0.068 degrees C per decade. The average rate of warming from 1850 through 1979, to the beginning of the most recent El Nino Southern Oscillation (ENSO), is 0.038 degrees C per decade.

A warming rate of 0.038 degrees C per decade would need to significantly increase or decrease to support a prediction of a long-term change in the earth’s surface temperature. If the earth’s surface temperature increased continuously starting today at a rate of 0.038 degrees C per decade, in 100 years the increase in the earth’s temperature would be only 0.4 degrees C., which is not indicative of a global warming threat to humankind.

The 0.038 degrees C per decade estimate is likely beyond the accuracy of the temperature measurements from 1850 to 1979. Recent statistical analyses conclude that 95% uncertainties of global annual mean surface temperatures range between 0.05 degrees C to 0.15 degrees C over the past 140 years; that is, 95 measurements out 100 are expected to be within the range of uncertainty estimates (Lenssen, N. J. L., et al. 2019). Very little measurable warming of the surface of the earth has occurred from 1850 to 1979.

In Figure 2, the green curve is the warming curve; that is, a time series of the rate of change of the temperature of the surface of the earth in degrees per decade. The blue curve is a time series of the concentration of fossil fuel emissions of CO2 in parts per million in the atmosphere. The green curve is generally level from 1900 to 1979 and then rises slightly due to lower frequency noise remaining in the temperature anomalies from 40 years of ENSO activity. The warming curve declined since early 2000 to the present. The concentration of CO2 increased steadily from 1943 to 2019. There is no correlation between a rising CO2 concentration in the atmosphere and a relatively stable, low rate of warming of the surface of the earth from 1943 to 2019.


Figure 2. The green curve is the first derivative of the trendline converted to units of degrees C per decade, that is, the rate of change of the surface temperature of the earth. See Figure 1 for the same curve along with the temperature anomalies curve and the trendline curve. The blue dotted curve showing total parts per million CO2 emissions from fossil fuels in the atmosphere is modified from Boden, T. A., et al. (2017); the time frame shows only emissions since 1900, and the total reported million metric tons of carbon are converted to parts per million CO2 for the graph. The single blue point is the most recent report from NOAA of an average 414.7 parts per million CO2 in May 2019 (Research News. June 4, 2019). There is no correlation between the curves.

In Figure 3, the December 1979 temperature spike (Point A) is associated with a weak El Nino event. During the following 39 years, five strong to very strong intensity El Nino events are recorded; the last one, in 2015-2016, the highest intensity El Nino ever recorded (Goldengate Weather Services. (2019). The highest ever mean global monthly temperature anomaly of 1.111 degrees C was recorded in February 2016. Since then, monthly global temperature anomalies declined 35 percent to a temperature of 0.724 degrees C in August 2019 as the El Nino decreased in intensity.


Figure 3. An enlarged portion of Figure 1 from 1963 to 2019 with modified vertical scales to emphasize important changes in the shape of the green curve.

Points A, B and C mark very significant changes in the shape of the green warming curve (values on left vertical axis).

  1. The green curve values increased each month from 0.085 degrees C per decade in December 1979 (Point A) to 0.136 degrees C per decade in July 1988 (Point B); this is a 60 percent increase in rate of warming in nearly 9 years. The warming curve is concave upward. Point A marks a weak El Nino and the beginning of increasing ENSO intensity.

  2. From July 1988 to September 2006, the rate of warming increased from 0.136 degrees C per decade to 0.211 degrees per decade (Point C); this is a 55 percent increase in 18 years but about one-half the total rate of the previous 9 years because of a decrease in the rate of increase each month. The July 1988 point on the x-axis is an inflexion point at which the warming curve becomes concave downward.

  3. September 2006 (Point C) marks a very strong El Nino and the peak of the nearly 40-year ENSO transient warming trend, imparting a lazy S shape to the green curve. The rate of warming has declined every month since peaking at 0.211 degrees per decade in September 2006 to 0.125 in August 2019; this is a 41 percent decrease in 13 years.

Section 2

Truth and Consequences

The “hockey stick graph”, which had been cited by the media frequently as evidence for out-of-control global warming over the past 20 years, is not supported by the current temperature record (Mann, M., Bradley, R. and Hughes, M. 1998). The graph is no longer seen in the print media.

None of 102 climate models of the mid-troposphere mean temperature comes close enough to predicting future temperatures to warrant changes in environmental policies. The models start in the 1970s at the beginning of a time period that culminated in the strongest ENSO ever recorded and by 2015, less than 40 years, the average predicted temperature of all the models is nearly 2.4 times greater than the observed global tropospheric temperature anomaly in 2015 (Christy, J. R. May 8, 2019). The true story of global climate change has yet to be written.

The peak surface warming during the ENSO was 0.211 degrees C per decade in September 2006. The highest global mean surface temperature ever recorded was 1.111 degrees C in February 2016; these occurrences are possibly related to the increased quality and density of ocean temperature data from the two, earth orbiting MSU satellites described previously. Earlier large intensity ENSO events may not have been recognized due to the absence of advanced satellite coverage over oceans.

The use of a temperature trendline to remove high frequency noise did not eliminate the transient effects of the longer wavelength components of ENSO warming over the past 40 years; so, estimates of the rate of warming for that period in this study still include background noise from the ENSO. A noise-free signal for the past 40 years probably lies closer to 0.038 degrees C per decade, the average rate of warming from 1850 to the beginning of the ENSO in 1979 than the average rate from 1979 to the present, 0.168 C degrees per decade. The higher number includes uncorrected residual ENSO effects.

Foster and Rahmstorf (2011) used average annual temperatures from five data sets to estimate average earth warming rates from 1979 to 2010. Noise removed from the raw mean annual temperature data is attributed to ENSO activities, volcanic eruptions and solar variations. The result is said to be a noise-adjusted temperature anomaly curve. The average warming rate of the five data sets over 32 years is 0.16 degrees C per decade compared to 0.17 degrees C per decade determined by this study from 384 monthly points derived from the derivative of the temperature trendline. Foster and Rahmstorf (2011) assume the warming trend is linear based on one averaged estimate, and their data cover only 32 years. Thirty years is generally considered to be a minimum period to define one point on a trend. This 32-year time period includes the highest intensity ENSO ever recorded and is not long enough to define a trend. The warming curve in this study is curvilinear over nearly 170 years (green curve on Figures 1 and 3) and is defined by 2,032 monthly points derived from the temperature trendline derivative. From 1979 to 2010, the rate of warming ranges from 0.08 to 0.20 degrees C per decade. The warming trend is not linear.

The perceived threat of excessive future temperatures may stem from an underestimation of the unusually large effects of the recent ENSO on natural global temperature increases. Nearly 40 years of natural, transient warming from the largest ENSO ever recorded may have been misinterpreted to include warming due to anthropogenic activities. There is no evidence of a significant anthropogenic contribution to surface temperatures measured over the last 40 years.

Caltech recently announced the start of a 5-year project with several other research centers to build a new climate model “from the ground up” (Perkins, R. 2018). During these five years, the world’s understanding of the causes of climate change should be greatly improved.

The scientific goal must be to narrow the range of uncertainty of predictions with better data and better models until human intervention makes sense. We have the time to get it right. A rational environmental protection program and a vibrant economy can co-exist. The challenge is to allow scientists the time and freedom to work without interference from special interests.

Acknowledgments and Data

All the raw data used in this study can be downloaded from the HadCRUT4 and NOAA websites. http://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/series_format.html



  1. Boden, T.A., Marland, G., and Andres, R.J. (2017). National CO2 Emissions from Fossil-Fuel Burning, Cement Manufacture, and Gas Flaring: 1751-2014, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, doi:10.3334/CDIAC/00001_V2017.

  2. Christy, J. R., May 8, 2019. The Tropical Skies Falsifying Climate Alarm. Press Release, Global Warming Policy Foundation. https://www.thegwpf.org/content/uploads/2019/05/JohnChristy-Parliament.pdf

  3. Foster, G. and Rahmstorf, S., 2011. Environ. Res. Lett. 6 044022

  4. Golden Gate Weather Services, Apr-May-Jun 2019. El Niño and La Niña Years and Intensities. https://ggweather.com/enso/oni.htm

  5. HadCrut4 dataset. http://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/series_format.html

  6. Karl, T. R., Arguez, A., Huang, B., Lawrimore, J. H., McMahon, J. R., Menne, M. J., et al.

Science 26 June 2015. Vol. 348 no. 6242 pp. 1469-1472. http://www.sciencemag.org/content/348/6242/1469.full

  1. Mann, M., Bradley, R. and Hughes, M. (1998). Global-scale temperature patterns and climate forcing over the past six centuries. Nature, Volume 392, Issue 6678, pp. 779-787.

  2. Mckitrick, R. Department of Economics, University of Guelph. http://www.rossmckitrick.com/uploads/4/8/0/8/4808045/mckitrick_comms_on_karl2015_r1.pdf, A First Look at ‘Possible artifacts of data biases in the recent global surface warming hiatus’ by Karl et al., Science 4 June 2015

  3. Mears, C. and Wentz, F. (2016). Sensitivity of satellite-derived tropospheric
    temperature trends to the diurnal cycle adjustment. J. Climate. doi:10.1175/JCLID-
    15-0744.1. http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-15-0744.1?af=R

  4. Morice, C. P., Kennedy, J. J., Rayner, N. A., Jones, P. D., (2012). Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 dataset. Journal of Geophysical Research, 117, D08101, doi:10.1029/2011JD017187.

11. Lenssen, N. J. L., Schmidt, G. A., Hansen, J. E., Menne, M. J., Persin, A., Ruedy, R, et al. (2019). Improvements in the GISTEMP Uncertainty Model. Journal of Geophysical Research: Atmospheres, 124, 6307–6326. https://doi.org/10. 1029/2018JD029522

  1. NOAA Research News: https://research.noaa.gov/article/ArtMID/587/ArticleID/2461/Carbon-dioxide-levels-hit-record-peak-in-May June 4, 2019.

  2. Palmer, P. I. (2018). The role of satellite observations in understanding the impact of El Nino on the carbon cycle: current capabilities and future opportunities. Phil. Trans. R. Soc. B 373: 20170407. https://royalsocietypublishing.org/doi/10.1098/rstb.2017.0407.

  3. Perkins, R. (2018). https://www.caltech.edu/about/news/new-climate-model-be-built-ground-84636

  4. Spencer, R. W., Christy, J. R. and Grody, N. C. (1990). Global Atmospheric Temperature Monitoring with Satellite Microwave Measurements: Method and Results 1979–84. Journal of Climate, Vol. 3, No. 10 (October) pp. 1111-1128. Published by American Meteorological Society.