Ross McKitrick Department of Economics University of Guelph Guelph ON Canada Presentation to the

The influence of anthropogenic surface processes and

inhomogeneities on gridded global climate data

Ross McKitrickDepartment of EconomicsUniversity of GuelphGuelph ON Canada

Presentation to the American Chemical SocietyDenver CO via WebinarAugust 28 2011

ross.mckitrick.weebly.com 2

Surface Climate Data The “global temperature”


Summary Climate data is the output of a model

Raw data: daily T-Min and T-Max readings from inhabited places

This isn’t what the climate analyst is interested in: it must be converted into “climate data” using a statistical adjustment model.

How do we know the adjustment model “works”?

Many papers merely describe the adjustment steps in enthusiastic detail

I have focused on devising statistical tests of the results


Conclusions Based on analysis of multiple data sets, and after addressing a

long list of statistical rebuttals, I find the evidence convincing that:

The adjustment models are inadequate

The resulting climate record over land is contaminated with patterns of socioeconomic development

This adds a net warming bias to the global trend and may lead to misattribution of spatial patterns to greenhouse gases

A valid empirical model of the spatial pattern of observed warming must include anthropogenic surface processes


Papers McKitrick, Ross and Patrick J. Michaels (2004). “A Test of Corrections for

Extraneous Signals in Gridded Surface Temperature Data” Climate Research 26 pp. 159-173.

McKitrick, Ross R. and Patrick J. Michaels. (2007) “Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded surface climate data.” Journal of Geophysical Research-Atmospheres 112, D24S09, doi:10.1029/2007JD008465.

McKitrick, Ross R. and Nicolas Nierenberg (2010) “Socioeconomic Patterns in Climate Data.” Journal of Economic and Social Measurement, 35(3,4) pp. 149-175. DOI 10.3233/JEM-2010-0336.

McKitrick, Ross R. (2010) “Atmospheric Oscillations do not Explain the Temperature-Industrialization Correlation.” Statistics, Politics and Policy, Vol 1 No. 1, July 2010.

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Core Methodology There is a spatial pattern of warming and

cooling trends since 1980

Climate models predict the pattern as a response toGHG’s, solar changes, etc.

The predicted pattern is uncorrelated withspatial pattern of socioeconomic development

But raw weather data is known to be correlated with socioeconomic development

The adjustment models are supposed to remove these effects.

Therefore: If the adjustments are adequate, the climate data should be uncorrelated with socioeconomic patterns


Core Methodology Hypothesis:

{spatial pattern of trends in surface climate data} is uncorrelated with

{spatial pattern of socioeconomic development}

In a series of papers I have shown that this hypothesis is strongly rejected


Sources of climate data CRU, NOAA, NASA all produce “global climate

data” products

All rely on same underlying archive Global Historical Climatology Network (run by NOAA)

The 3 data products are very similar since they all use the same input data and similar, though not identical, averaging methods


Sources of observational error: Changing sample size Changing sample locations Build up of surrounding landscape Equipment changes Poor quality control Local air pollution Waste heat from buildings and traffic, etc.


GHCN sample 1885

Locations of weather stations


GHCN sample 1925



GHCN sample 1945


GHCN sample 1965



GHCN sample 1985



GHCN sample 2005



GHCNsamplesize overtime


GHCN fraction of sample fromurban airports


“Climate” data: the record as if the land surface was never modified and equipment never varied

Temp data from cities adjustment algorithm “True” record

+ =


Structure of data set Cross-sectional Observational unit is a 5ox5o grid cell Dependent variable is 1979-2002 trend


Measurement Model

Where i = observed climatic trend oC/decade

Ti = “true” trend

f (Si) = surface processes like urbanization and agriculture

g (Ii) = data inhomogeneities

)()( iiii IgSfT


For gridcell i Ti (ideal temperature trend) represented by

TROPi = trend in troposphere over same gridcell as measured by satellites

iiiiii WATERDSLPDRYPRESSTROPT 543210

iABSLAT6


For gridcell i Surface processes f (Si) measured by

pi = % growth in population density

mi = % growth in real average income

yi = % growth in real national GDP

ci = % growth in national coal consumption


For gridcell i Inhomogeneities g (Ii) measured by

gi = GDP density (GDP per square km)

ei = availability of educated workers (sum of literacy + postsecondary education)

xi = rate of missing observations (# missing months in cell)


Regression equation

Surface proc. Inhom.

GLS with clustering-robust std error matrix

iiiiii WATERDSLPDRYPRESSTROP 543210 iABSLAT6

iiiiiiii uxgecymp 13121110987


First pair of studies: McKitrick and Michaels (2004)

Tested 218 raw series and corresponding CRU gridded data Both exhibited significant imprint of socioeconomic data with v.

similar coefficients ‘Adjustment’ hypothesis rejected at high confidence level

McKitrick and Michaels (2007) Complete sample of (available) surface grid cells ‘Independence’ hypothesis again rejected at high confidence

level

Both studies: nonclimatic signals likely add up to a net warming bias in global average


2007 Results

Variable SURF trop 0.8625

(8.61) slp 0.0043

(0.99) dry 0.4901

(0.09) dslp -0.0004

(-0.07) water -0.0284

(-1.34) abslat 0.0006

(0.49) g 0.0434

(3.38) e -0.0027 (-5.11)

x 0.0041 (1.66)

p 0.3831 (2.70)

m 0.4075 (2.37)

y -0.3032 (-2.19)

c 0.0059 (3.25)

_cons -4.0368 (-0.92)

N 440 R2 0.53 ll 139.01

P(I) 0.0000 P(S) 0.0005

P(all) 0.0000

Probability that effects are zero:

Joint P = 0.0000 (7x10-14)

Effect on Trend of Doubling Level

0.383 0.407

-0.303

0.006

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

Population Density Real Average Income Real National GDP National Coal Use

deg

C


Specification tests Bootstrap resampling Remove outliers, re-estimate RESET test Cross-validation tests Hausman endogeneity test (P = 0.9962)


Generating ‘clean’ trends Set GDP density and education to US levels Set all other surface and inhomogeneity effects to 0 Use model coeff’s to generate adjusted predicted values

Observed average surface trend: 0.30 oC/decadeMSU average: 0.23Adjusted average surface trend: 0.17


IPCC Report

How did the IPCC deal with this?

IPCC AR4 page 244:

McKitrick and Michaels (2004) and De Laat and Maurellis (2006) attempted to demonstrate that geographical patterns of warming trends over land are strongly correlated with geographical patterns of industrial and socioeconomic development, implying that urbanisation and related land surface changes have caused much of the observed warming. However, the locations of greatest socioeconomic development are also those that have been most warmed by atmospheric circulation changes (Sections 3.2.2.7 and 3.6.4), which exhibit large-scale coherence. Hence, the correlation of warming with industrial and socioeconomic development ceases to be statistically significant.

No supporting citation given


IPCC Report

I obtained correlation fields between gridded temperatures and AO, ENSO and PDO


IPCC Report

I augmented data sets for M&M 2004 and M&M 2007 with circulation terms

2004 Model: Circulation index effects are insignificant Including them anyway does not remove the significance of the conclusions

2007 Model Circulation index effects are jointly barely significant Including them increases size and significance of socioecononomic terms

Conclusion: IPCC claim is false.

(McKitrick 2010, Statistics Politics and Policy July 2010)


IPCC Report







Schmidt (2009) “Spurious correlation between recent warming and indices of local economic activity.” International Journal of Climatology 10.1002/joc.1831

3 arguments against our findings

surface temperature field exhibits spatial autocorrelation (SAC) so results are insignificant

Use of RSS satellite series rather than UAH series removes significance of results

Data generated by climate model yields apparent correlations with socioeconomic data, yet is uncontaminated by construction, so effects must be a fluke






Data generated by climate model yields apparent correlations with socioeconomic data, yet is uncontaminated by construction, so effects must be a fluke






Data generated by climate model looks correlated with socioeconomic data, yet is uncontaminated by construction, so effects must be a fluke


McKitrick & Nierenberg“Socioeconomic patterns in climate data” J Econ Soc Measurement 2010

Responses Schmidt did not actually test SAC. We do, and show that while

depvar is AC’d, regression residuals are not, as long as socioecon variables are included in model.

Use of RSS data diminishes individual significance but effect due to a small number of outliers. Once these removed, RSS yields strongest results of all data sets

Model-based data cannot replicate observed patterns; predicts opposite signs


Data variations Surface

Observed: CRU, CRU2v, CRU3v Modeled: GISS-E; GCM average

Troposphere Observed: UAH, RSS Modeled: GISS-E; GCM average


Spatial Autocorrelation TestsOBSERVED: SAC DISAPPEARS

MODELS: SAC REMAINS


Estimation with SAC model


Estimation with SAC model

OBSERVATIONS:

SIGNIFICANT

MODELS:

INSIGNIFICANT


GCM Counterfactual Schmidt 2009, p.2:

There is a relatively easy way to assess whether there is any true significance to these correlations. We can take fully consistent model simulations for the same period and calculate the distribution of the analogous correlations. Those simulations contain no unaccounted-for processes (by definition!) but plenty of internal variability, locally important forcings and spatial correlation. If the distribution encompasses the observed correlations, then the null hypothesis (that there is no contamination) cannot be rejected.


Results


Results

1 = climate model reproduces observed effect,

0 = failure to do so


Filtering results on surface data Set GDP density and education to US levels Set all other surface and inhomogeneity effects to 0 Use model coeff’s to generate adjusted predicted values



This method should not reduce mean trend in GISS data


Conclusions In general, I reject the null hypothesis that

adjustment models yield “climate” data

socioeconomic patterns are highly significant across wide variety of specifications and data combinations

socioeconomic data are necessary for well-specified error term

This suggests a causal interpretation of the regression results


Responses to critiques: IPCC claim that the results were statistically

insignificant & due to natural circulation patterns was a fabrication

The claim was both unsubstantiated and untrue

Various critiques have not held up SAC is not a source of bias Results hold up across numerous data sets Climate models cannot reproduce results


Thank you

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Ross McKitrick Department of Economics University of Guelph Guelph ON Canada Presentation to the

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