Are land + sea averages meaningful ?


Several of the major datasets that claim to represent “global average surface temperature” are directly or effectively averaging land air temperatures with sea surface temperatures. These are typically derived by weighting a global land average and global SST average according to the 30:70 land-sea geographical surface area ratio. However, there is very little consideration of whether such a result has any physical meaning and what, if anything, it means.

One thing it does not represent is a metric of global surface heat content. However, this is ( often implicitly ) one of the most common uses for such data.

Temperatures don’t add !

In technical terms, temperature is not an extensive quantity. That is illustrated by the fact that if you have one bucket of water at 30 degrees Celsius and you add another bucket of water at 30 degrees Celsius, you do not end up with water at 60 deg. C.

Energy is an extensive property: if you have a volume of water with a thermal energy of 4000 megajoules and you add a second similar volume you will have twice the thermal energy. The average energy per unit area can be compared to the radiative energy budget per unit area.

The ratio of temperature to thermal energy is not the same for all materials, it varies greatly depending on the physical properties of the substance. It also depends on the amount of a substance present, ie the mass. In physics and materials science, it is often most convenient to study “specific heat capacity”, that is the change in energy content per unit mass, per degree change in temperature. It is thus a property of each type of material, independent of any particular object.

In S.I. ( Système Internationale ) units this is measured in joule / kilogram / kelvin or J/kg/K . The kelvin is the same size as one degree C. and is interchangeable in this context. Some examples for common materials :

Material S.H.C.
Fresh Water 4.19
Sea Water ( 2 deg. C ) 3.93
Mercury 0.14
Dry Air 1.01
Stone 0.84
Dry Earth 1.26
Clay 0.92
Tar 1.47
Concrete 0.75

Table 1. Specific heat capacity of various materials in J/kg/K

So one could consider temperature change as a “proxy” for change in thermal energy for equivalent VOLUMES of the SAME material. In this context one could calculate an ‘average change in temperature’ for that medium and use it infer a change in thermal energy, which can be related to incoming and outgoing radiation, for example. If this is a surface temperature ( eg SST ) this implies assuming that the surface represents the temperature of a certain depth of water and that this representative depth remains about the same over regions that are being averaged, in order to respect the “volume” condition above. That is somewhat questionable for the ocean ‘mixed layer’ but may provide a crude energy proxy.

However, it is immediately clear that one cannot start adding or averaging air and SST, or land and sea temperatures. They are not compatible media. It is like asking what is the average of an apple and an orange: it has no physical meaning. It certainly can not be the basis of an energy budget calculation, since it is no longer a measure of the change in thermal energy.

As seen from the above figures: air, stone and earth will change temperature about four times as much as water in response to the same energy input.

No one would think of trying average temperature records in deg. Fahrenheit with records in deg. C, yet, for some reason, mixing land and sea data does not seem to raise any eyebrows.

Rate of change in global temperature datasets

Figure 1. Comparing the rate of change of temperature in land and sea datasets ( 30 month low-pass gaussian filter ).

Figure 1 shows the rate of change in two SST datasets and the BEST land dataset scaled down by a factor of two. They are all reasonably close with this scaling factor. The large peak in ICOADS data is a recognised sampling issue due to changes in shipping routes and sampling methods during and after WWII. The UK Met Office processed HadISST dataset aims to remove this bias.

The rate of change of near surface land air temperature as estimated in the Berkeley “BEST” dataset is very similar to the rate of change in the sea surface temperature record, except that it shows twice the rate of change.

Sea water has a specific heat capacity about 4 times that of rock. This means that rock will change in temperature four times more than water for the same change in thermal energy, for example from incoming solar radiation.

Since soil, in general, is a mix of fine particles of rock and organic material with a significant water content. The two temperatures records are consistent with the notion of considering land as ‘moist rock’. This also partly explains the much larger temperature swings in desert regions: the temperature of dry sand will change four times faster than ocean water and be twice as volatile as non-desert land regions.

This also underlines why is it inappropriate to average land and sea temperatures as is done in several recognised global temperature records such as HadCRUT4 ( a bastard mix of HadSST3 and CRUTem4 ) as well as GISS-LOTI and the new BEST land and sea averages.

It is a classic case of ‘apples and oranges’. If you take the average of an apple and an orange, the answer is a fruit salad. It is not a useful quantity for physics based calculations such as earth energy budget and the impact of a radiative “forcings”.

The difference in heat capacity will skew the data in favour of the land air temperatures which vary more rapidly and will thus give an erroneous basis for making energy based calculations. In addition land air temperatures are also compromised by urban heat island and other biases, so these will be effectively doubled before contaminating the global land + sea record.

In this sense the satellite data provide a more physically consistent global average because they are measuring a more consistent medium. If the aim is to do radiation based energy calculations it is probably more meaningful to use SST as the calorimeter.

Climate sensitivity is defined as the ∆rad , ∆T ratio, usually in the context of a linear approximation to the Planck feedback which is valid over relatively small deviations in the circa 300K temperature range. Other feedbacks are seen a perturbations that either add or subtract from the dominant Planck radiative feedback. All this and even the far more complex general circulation climate models are basically energy balance calculations. The conservation of energy is one of the defining axioms of physics. A fundamental test of any theory or equation is whether it respects the conservation of energy.

Horizontal heat transfer ensures that land temperature is constrained by ocean heat capacity: the thermal anchor of the climate system. It is well known that temperatures in coastal regions are stabilised by the proximity of sea/ocean and the centre of continents show greater extremes of diurnal and annual variation. However, land near-surface temperature remains more volatile than SST and analysis of climate models shows that they display greater climate sensitivity over land, and produce a different lapse rate.[1] IF that can be taken as being reliable.

In this context, temperature rise is the final result of all inputs, “forcings” and feedbacks many of which may be different over land. Heat capacity and available moisture both play an important role. Obviously these two factors are related. Using a non-thermodynamically relevant “average” temperature from two different ecologies with different climate sensitivities and lapse rates to produce an ‘average’ CS also seems open to bias.


Temperatures are not abstract statistics, their physical meaning needs to be considered when choosing how to process them. Using land + sea global average temperature datasets, biased by giving undue weight to the more volatile land-based temperatures, will produce physically incorrect results.

Most climate data are not just dimensionless numbers. Any processing should be considered in the context of the physical quantities that they represent. If temperature or temperature anomaly is being considered as an energy proxy for energy based calculations this should be explicitly stated and any biases that this may introduce should be discussed.

The physical significance, validity and limitations of “average” land + sea temperatures should be considered where they are used. This is rarely, if ever, done.


A typical equation for the definition of the settled change in temperature in response to a change in radiative ‘forcing’ F has the form:

∆F = λ * ∆T + ∆N ; where ∆N is the change in top-of-atmosphere radiation.

λ is the reciprocal of climate sensitivity ( CS ) . A more realistic model to asses the effect of differing responses would be :

∆F = α * λland * ∆Tland + (1 – α) * λsea * ∆Tsea + ∆N

Here alpha represents the geographic proportion of land area and is what is usually taken to weight the land and sea mean temperatures into a single “mean temperature”. Land temperatures will change by a greater magnitude due to the larger CS as indicated in the model runs in Geoffroy at al [1]

Due to it’s lesser heat capacity, land will equilibrate faster than the oceans. In this intermediate period there will be horizontal heat transfer from land to sea to redress the imbalance. This extra heat flux will somewhat increase the ocean temperature response thus increasing the effective transient climate sensitivity ( TCS ). The opposite will apply to land.

After hundreds of years, a dynamic equilibrium will establish where the horizontal flux balances the different responses of the two media. Land will heat more but is constrained by the ocean response.

The details of how this will level out is not trivial and will depend on arguments of heat capacity, lapse rate, moisture content and the mechanics of the horizontal heat transfer.

It is the λ * ∆T product ( a heat flux term ) that is being averaged, not temperature itself. That is as it should be to retain a physically meaningful calculation, so if temperatures are to be added ( or averaged ) they should be weighted not only by the land area but by the ratio λland / λsea. This respects the scientific requirement to be working in extensive properties, not an intensive one and restores the physical meaning to the resulting “global mean temperature”. It does not mean abandoning the GMST index, simply applying a correct weighting to account for the different media in a similar way to what is already done to account for land area.

According to the model runs this lies between 1.4 and 1.9 . Not dissimilar to the crude factor of 2 scaling of BEST land and SST shown in figure 1.

This implies that the classic 30/70% weighting of land and sea averages should probably be more like 15/85% or 20/80%.


The data used in figure 1 can be obtained from KNMI climate explorer:

The values of specific heat capacity shown in table 1 are provided by the Engineering Toolbox:

The 3-sigma gaussian filter is a standard filter available on most data processing packages. A description and graph of the frequency response is provided accompanied by a script to apply this filter at the following link:

Geoffroy et al 2015 : “Land-sea warming contrast: the role of the horizontal energy transport” [ paywalled ]

Triple running mean filter

The following script will call a simple running mean three times with appropriate window size to do effect triple running mean, as described in the article (as amended for the asymmetric kernel to minimise negative leakage):

It requires the runmean.awk script found here:

Select code with mouse to copy elsewhere.


# call runmean.awk three times to compose triple running mean
# usage: ./ file window_len   ; default window is 12 data point

if [ "x$1" == "x"  ]; then echo "$0 : err no file name,   usage: $0 filename "
 exit 1;
else fn=$1

if [ "x$2" == "x"  ]; then win=12; else win=$2; fi

#win2=`awk "BEGIN{print $win/ 1.3371}"`
#win3=`awk "BEGIN{print $win/ 1.3371/ 1.3371}"`

# asymmetric stages with following window ratios minimise neg. lobe in freq. response
k=1.15; k2=1.58

win2=`awk "BEGIN{print $win/ "$k"}"`
win3=`awk "BEGIN{print $win/ "$k2" }"`

outfile=`echo $fn | awk '{ print substr($1,1,length($1)-4) }'`

echo  "# triple running mean :  $win  $win2  $win3 " > $outfile

# echo $fn; echo $win; echo $win2; echo $win3

cat $fn | ./runmean.awk - $win |  ./runmean.awk - $win2 |  ./runmean.awk - $win3 >> $outfile

#echo "# cat $fn | ./runmean.awk - $win |  ./runmean.awk - $win2 |  ./runmean.awk - $win3 >> $outfile"

echo "# triple running mean :  $win  $win2  $win3 "
echo "# outfile = "$outfile;

On inappropriate use of least squares regression

Figure 1 showing conventional and inverse ‘ordinary least squares’ fits to some real, observed climate variables.

Ordinary least squares regression ( OLS ) is a very useful technique, widely used in almost all branches of science. The principal is to adjust one or more fitting parameters to attain the best fit of a model function, according to the criterion of minimising the sum of the squared deviations of the data from the model.

It is usually one of the first techniques that is taught in schools for analysing experimental data. It is also a technique that is misapplied almost as often as it is used correctly.

It can be shown, that under certain conditions, the least squares fit is the best estimation of the true relationship that can be derived from the available data. In statistics this is often called the ‘best, unbiased linear estimator’ of the slope. (Those, who enjoy contrived acronyms, abbreviate this to “BLUE”.)

It is a fundamental assumption of this technique that the ordinate variable ( x-axis ) has negligible error: it is a “controlled variable”. It is the deviations of the dependant variable ( y-azix ) that are minimised. In the case of fitting a straight line to the data, it has been known since at least 1878 that this technique will under-estimate the slope if there is measurement or other errors in the x-variable. (R. J. Adcock) [0]

There are two main conditions for this result to be an accurate estimation of the slope. One is that the deviations of the data from the true relationship are ‘normally’ or gaussian distributed. That is to say that they are of a random nature. This condition can be violated by significant periodic components in the data or excessive number of out-lying data points. The latter may often occur when only a small number of data points is available and the noise, even if random in nature, is not sufficiently sampled to average out.

The other main condition is that there be negligible error ( or non-linear variability ) in the x variable. If this condition is not met, the OLS result derived from the data will almost always under-estimate the slope of the true relationship. This effect is sometimes referred to as regression dilution. The degree by which the slope is under-estimated is determined by the nature of the x and y errors but most strongly by those in x since they are required to be negligible for OLS to give the best estimation.

In this discussion, “errors” can be understood to be both observational inaccuracies and any variability due to some factor other than the supposed linear relationship that it is sought to determine by regression of the two variables.

In certain circumstances regression dilution can be corrected for, but in order to do so, some knowledge of the nature and size of the both x and y errors has to be known. Typically this is not the case beyond knowing whether the x variable is a ‘controlled variable’ with negligible error, although several techniques have been developed to estimate the error in the estimation of the slope [7].

A controlled variable can usually be attained in a controlled experiment, or when studying a time series, provided that the date and time of observations have been recorded and documented in a precise and consistent manner. It is typically not the case when both sets of data are observations of different variables, as is the case when comparing two quantities in climatology.

One way to demonstrate the problem is to invert the x and y axes and repeat the OLS fit. If the result were valid, irrespective of orientation, the first slope would be the reciprocal of second one. However, this is only the case when there is very small errors in both variables, ie. the data is highly correlated ( grouped closely around a straight line ). In the case of one controlled variable and one error prone variable, the inverted result will be incorrect. In the case of two datasets containing observational error, both results will be wrong and the correct result will generally lie somewhere in between.

Another way to check the result is by examining the cross-correlation between the residual and the independent variable ie. ( model – y ) vs x , then repeat for incrementally larger values of the fitted ratio. Depending on the nature of the data, it will often be obvious that the OLS result does not produce the minimum residual between the ordinate and the regressor, ie. it does not optimally account for co-variability of the two quantities.

In the latter situation, the two regression fits can be taken as bounding the likely true value but some knowledge of the relative errors is needed to decide where in that range the best estimation lies. There are a number of techniques such as bisecting the angle, taking the geometric mean (square root of the product), or some other average, but ultimately, they are no more objective unless driven by some knowledge of the relative errors. Clearly bisection would not be correct if one variable had low error, since the true slope would then be close to the OLS fit done with that quantity on the x-axis.

Figure 2. A typical example of linear regression of two noisy variables produced from synthetic randomised data. The true known slope used in generating the data is seen in between the two regression results. ( Click to enlarge graph and access code to reproduce data and graph. )

Figure 2b. A typical example of correct application of linear regression to data with negligible x-errors. The regressed slope is very close to the true value, so close as to be indistinguishable visually. ( Click to enlarge )

The larger the x-errors, the greater the skew in the distribution and the greater the dilution effect.

An Illustration: the Spencer simple model.

The following case is used to illustrate the issue with ‘climate-like’ data. However, it should be emphasised that the problem is an objective mathematical one, the principal of which is independent of any particular test data used. Whether the following model is an accurate representation of climate ( it is not claimed to be ) has no bearing on the regression problem.

In a short article on his site Dr. Roy Spencer provided a simple, single-slab ocean, climate model with a predetermined feedback variable built into it. He observed that attempting to derive the climate sensitivity in the usual way consistently under-estimated the know feedback used to generate the data.

By specifying that sensitivity (with a total feedback parameter) in the model, one can see how an analysis of simulated satellite data will yield observations that routinely suggest a more sensitive climate system (lower feedback parameter) than was actually specified in the model run.

And if our climate system generates the illusion that it is sensitive, climate modelers will develop models that are also sensitive, and the more sensitive the climate model, the more global warming it will predict from adding greenhouse gasses to the atmosphere.

This is a very important observation. Regressing noisy radiative flux change against noisy temperature anomalies does consistently produce incorrectly high estimations of climate sensitivity. However, it is not an illusion created by the climate system, it is an illusion created by the incorrect application of OLS regression. When there are errors on both variables, the OLS slope is no longer an accurate estimation of the underlying linear relationship being sought.

Dr Spencer was kind enough to provide an implementation of the simple model in the form of a spread sheet download so that others may experiment and verify the effect.

To demonstrate this problem, the spreadsheet provided was modified to duplicate the dRad vs dTemp graph but with the axes inverted, ie. using exactly the same data for each run but additionally displaying it the other way around. Thus the ‘trend line’ provided by the spreadsheet is calculated with the variables inverted. No changes were made to the model.

Three values for the predetermined feedback variable were used in turn. Two values: 0.9 and 1.9 that Roy Spencer suggests represent the range of IPCC values and 5.0 which he proposes as a value closer to that which he has derived from satellite observational data.

Here is a snap-shot of the spreadsheet showing a table of results from nine runs for each feedback parameter value. Both the conventional and the inverted regression slopes and their geometric mean have been tabulated.

Figure 3. Snap-shot of spreadsheet, click to enlarge.

Firstly this confirms Roy Spencer’s observation that the regression of dRad against dTemp consistently and significantly under-estimates the feedback parameter used to create the data in the first place (and hence over-estimates climate sensitivity of the model). In this limited test, error is between a third and a half of the correct value. There is only one value of the conventional least squares slope that is greater than the respective feedback parameter value.

Secondly, it is noted that the geometric mean of the two OLS regressions does provide a reasonably close to the true feedback parameter, for the value derived from satellite observations. Variations are fairly evenly spread either side: the mean is only slightly higher than the true value and the standard deviation is about 9% of the mean.

However, for the two lower feedback values, representing the IPCC range of climate sensitivities, while the usual OLS regression is substantially less than the true value, the geometric mean over-estimates and does not provide a reliable correction over the range of feedbacks.

All the feedbacks represent a net negative feedback ( otherwise the climate system would be fundamentally unstable ). However, the IPCC range of values represents less negative feedbacks, thus a less stable climate. This can be seen reflected in the degree of variability in data plotted in the spreadsheet. The standard deviations of the slopes are also somewhat higher. This can be expected with less feedback controlling variations.

It can be concluded that the ratio of the proportional variability in the two quantities changes as a function of the degree of feedback in the system. The geometric mean of the two slopes does not provide a good estimation of the true feedback for the less stable configurations which have greater variability. This is in agreement with Isobe et al 1990 [7] which considers the merits of several regression methods.

The simple model helps to see how this relates to Rad / Temp scatter plots and climate sensitivity. However, the problem of regression dilution is a totally general mathematical result and can be reproduced from two series having a linear relationship with added random changes, as shown above.

What the papers say

A quick review of several recent papers on the problems of estimating climate sensitivity shows a general lack of appreciation of the regression dilution problem.

Dessler 2010 b [1] :

Estimates of Earth’s climate sensitivity are uncertain, largely because of uncertainty in the long-term cloud feedback.

Spencer & Braswell 2011 [2] :

Abstract: The sensitivity of the climate system to an imposed radiative imbalance remains the largest source of uncertainty in projections of future anthropogenic climate change.

There seems to be agreement that this is the key problem in assessing future climate trends. However, many authors seem unaware of the regression problem and much published work on this issue seems to rely heavily on the false assumption that OLS regression of dRad against dTemp can be used to correctly determine this ratio, and hence various sensitivities and feedbacks.

Trenberth 2010 [3] :

To assess climate sensitivity from Earth radiation observations of limited duration and observed sea surface temperatures (SSTs) requires a closed and therefore global domain, equilibrium between the fields, and robust methods of dealing with noise. Noise arises from natural variability in the atmosphere and observational noise in precessing satellite observations.

Whether or not the results provide meaningful insight depends critically on assumptions, methods and the time scales ….

Indeed so, unfortunately he then goes on to contradict earlier work by Lindzen and Choi that did address the OLS problem including a detailed statistical analysis comparing their results, by relying on inappropriate use of regression. Certainly not an example of the “robust methods” he is calling for.

Figure 4. Excerpt from Lindzen & Choio 2011, figure 7, showing consistent under-estimation of the slope by OLS regression ( black line ).

Spencer and Braswell 2011 [2]

As shown by SB10, the presence of any time-varying radiative forcing decorrelates the co-variations between radiative flux and temperature. Low correlations lead to regression-diagnosed feedback parameters biased toward zero, which corresponds to a borderline unstable climate system.

This is an important paper highlighting the need to take account of the lagged response of the climate during regression to avoid the decorrelating effect of delays in the response. However, it does not deal with the further attenuation due to regression dilution. It is ultimately still based on regression of two error laden-variables and thus does not recognise regression dilution that is also present in this situation. Thus it is likely that this paper is still over-estimating sensitivity.

Dessler 2011 [4] :

Using a more realistic value of σ(dF_ocean)/σ(dR_cloud) = 20, regression of TOA flux vs. dTs yields a slope that is within 0.4% of lamba.

Then in the conclusion of the paper, emphasis added:

Rather, the evolution of the surface and atmosphere during ENSO variations are dominated by oceanic heat transport. This means in turn that regressions of TOA fluxes vs. δTs can be used to accurately estimate climate sensitivity or the magnitude of climate feedbacks.

Also from a previous paper:

Dessler 2010 b [1]

The impact of a spurious long-term trend in either dRall-sky or dRclear-sky is estimated by adding in a trend of T0.5 W/m 2/ decade into the CERES data. This changes the calculated feedback by T0.18 W/m2/K. Adding these errors in quadrature yields a total uncertainty of 0.74 and 0.77 W/m2/K in the calculations, using the ECMWF and MERRA reanalyses, respectively. Other sources of uncertainty are negligible.

The author was apparently unaware that the inaccuracy of regressing two uncontrolled variables is a major source of uncertainty and error.

Lindzen & Choi 2011 [5]

[Our] new method does moderately well in distinguishing positive from negative feedbacks and in quantifying negative feedbacks. In contrast, we show that simple regression methods used by several existing papers generally exaggerate positive feedbacks and even show positive feedbacks when actual feedbacks are negative.

… but we see clearly that the simple regression always under-estimates negative feedbacks and exaggerates positive feedbacks.

Here the authors have clearly noted that there is a problem with the regression based techniques and go into quite some detail in quantifying the problem, though they do not explicitly identify it as being due to the presence of uncertainty in the x-variable distorting the regression results.

The L&C papers, to their credit, recognise that regression based methods on poorly correlated data seriously under-estimates the slope and utilise techniques to more correctly determine the ratio. They show probability density graphs from Monte Carlo tests to compare the two methods.

It seems the latter authors are exceptional in looking at the sensitivity question without relying on inappropriate use of linear regression. It is certainly part of the reason that their results are considerably lower than almost all other authors on this subject.

Forster & Gregory 2006 [8]

For less than perfectly correlated data, OLS regression of Q-N against δTs will tend to underestimate Y values and therefore overestimate the equilibrium climate sensitivity (see Isobe et al. 1990).

Another important reason for adopting our regression model was to reinforce the main conclusion of the paper: the suggestion of a relatively small equilibrium climate sensitivity. To show the robustness of this conclusion, we deliberately adopted the regression model that gave the highest climate sensitivity (smallest Y value). It has been suggested that a technique based on total least squares regression or bisector least squares regression gives a better fit, when errors in the data are uncharacterized (Isobe et al. 1990). For example, for 1985–96 both of these methods suggest YNET of around 3.5 +/- 2.0 W m2 K-1 ( a 0.7–2.4-K equilibrium surface temperature increase for 2 ϫ CO2 ), and this should be compared to our 1.0–3.6-K range quoted in the conclusions of the paper.

Here, the authors explicitly state the regression problem and its effect on the results of their study on sensitivity. However, when writing in 2005, they apparently feared that it would impede the acceptance of what was already a low value of climate sensitivity if they presented the mathematically more accurate, but lower figures.

It is interesting to note that Roy Spencer, in a non peer reviewed article, found an very similar figure of 3.66 W/m2/K by comparing ERBE data to MSU derived temperatures following Mt Pinatubo. [10]

So Forster and Gregory felt constrained to bury their best estimation of climate sensitivity, and the discussion of the regression problem in an appendix. In view of the ‘gatekeeper’ activities revealed in the Climategate emails, this may have been a wise judgement in 2005.

Now, ten years after the publication of F&G 2006, proper application of the best mathematical techniques available to correct this systematic over-estimation of climate sensitivity is long overdue.

A more recent study Lewis & Curry 2014 [11] used a different method of identifying changes between selected periods and thus is not affected by regression issues. This method also found lower values of climate sensitivity.



Inappropriate use of linear regression can produce spurious and significantly low estimations of the true slope of a linear relationship if both variables have significant measurement error or other perturbing factors.

This is precisely the case when attempting to regress modelled or observed radiative flux against surface temperatures in order to estimate sensitivity of the climate system.

In the sense that this regression is conventionally done in climatology, it will under-estimate the net feedback factor (often denoted as ‘lambda’). Since climate sensitivity is defined as the reciprocal of this term, this results in an over-estimation of climate sensitivity.

If an incorrect evaluation of climate sensitivity from observations is used as a basis for the choice of parametrised inputs to climate models, the resulting model will be over sensitive and produce exaggerated warming. Similarly faulty analyses of their output will further inflate the apparent model sensitivity.

This situation may account for the difference between regression-based estimations of climate sensitivity and those produced by other methods. Many techniques to reduce this effect are available in the broader scientific literature, thought there is no single, generally applicable solution to the problem.

Those using linear regression to assess climate sensitivity need to account for this significant source of error when supplying uncertainly values in published estimations of climate sensitivity or take steps to address this issue.

The decorrelation due to simultaneous presence of both the in-phase and orthogonal climate reactions, as noted by Spencer et al, also needs to be accounted for to get the most accurate information from the available data. One possible approach to this is detailed here:

A mathematical explanation of the origin of regression dilution is provided here:
On the origins of regression dilution



 [1] Dessler 2010 b “A Determination of the Cloud Feedback from Climate Variations over the Past Decade”

 [2] Spencer and Braswell 2011: “On the Misdiagnosis of Surface Temperature Feedbacks from Variations in Earth’s Radiant Energy Balance”

 [3] Trenberth et al 2010 “Relationships between tropical sea surface temperature and top‐of‐atmosphere radiation”

 [4] Dessler 2011
“Cloud variations and the Earth’s energy budget”

 [5] Lindzen & Choi 2001 “On the Observational Determination of Climate Sensitivity and Its Implications”

 [6] Nic Lewis : “A Sensitive Matter: How The IPCC Buried Evidence Showing Good News About Global Warming ”

 [7] Isobe et al 1990 “Linear Regression in Astronomy I”

 [8] Forster & Gregory 2006
“The Climate Sensitivity and Its Components Diagnosed from Earth Radiation Budget Data”

Adcock, R.J., 1878. A Problem in Least Squares. The Analyst, 5, 53-54.

 [0] Quirino Paris 2004 : “Robust Estimators of Errors-In-Variables Models Part I”

 [10] R Spencer “Revisiting the Pinatubo Eruption as a Test of Climate Sensitivity”

 [11]Lewis & Curry 2014
“The implications for climate sensitivity of AR5 forcing and heat uptake estimates”