The influence of anthropogenic surface processes and inhomogeneities on gridded global climate data

1. The influence of anthropogenic surface processes and inhomogeneities on gridded global climate data Ross McKitrick Department of Economics University of Guelph Guelph ON Canada Presentation to the American Chemical Society Denver CO via Webinar August 28 2011

2. Surface Climate Data • The “global temperature” ross.mckitrick.weebly.com

3. 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 ross.mckitrick.weebly.com

4. 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 ross.mckitrick.weebly.com

5. 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. • rossmckitrick.weebly.com ross.mckitrick.weebly.com

6. Core Methodology • There is a spatial pattern of warming and cooling trends since 1980 • Climate models predict the pattern as a response to GHG’s, solar changes, etc. • The predicted pattern is uncorrelated with spatial 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 ross.mckitrick.weebly.com

7. Core Methodology • There is a spatial pattern of warming and cooling trends since 1980 • Climate models predict the pattern as a response to GHG’s, solar changes, etc. • The predicted pattern is uncorrelated with spatial 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 ross.mckitrick.weebly.com

8. Core Methodology • There is a spatial pattern of warming and cooling trends since 1980 • Climate models predict the pattern as a response to GHG’s, solar changes, etc. • The predicted pattern is uncorrelated with spatial 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 ross.mckitrick.weebly.com

9. Core Methodology • There is a spatial pattern of warming and cooling trends since 1980 • Climate models predict the pattern as a response to GHG’s, solar changes, etc. • The predicted pattern is uncorrelated with spatial 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 ross.mckitrick.weebly.com

10. 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 ross.mckitrick.weebly.com

11. 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 ross.mckitrick.weebly.com

12. 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. ross.mckitrick.weebly.com

13. GHCN sample 1885 • Locations of weather stations ross.mckitrick.weebly.com

14. GHCN sample 1925 • Locations of weather stations ross.mckitrick.weebly.com

15. GHCN sample 1945 ross.mckitrick.weebly.com

16. GHCN sample 1965 • Locations of weather stations ross.mckitrick.weebly.com

17. GHCN sample 1985 • Locations of weather stations ross.mckitrick.weebly.com

18. GHCN sample 2005 • Locations of weather stations ross.mckitrick.weebly.com

19. GHCNsamplesize overtime ross.mckitrick.weebly.com

20. GHCN fraction of sample fromurban airports ross.mckitrick.weebly.com

21. “Climate” data: the record as if the land surface was never modified and equipment never varied Temp data from cities adjustment algorithm “True” record + = ross.mckitrick.weebly.com

22. Structure of data set • Cross-sectional • Observational unit is a 5ox5o grid cell • Dependent variable is 1979-2002 trend ross.mckitrick.weebly.com

23. Measurement Model Where qi = observed climatic trend oC/decade Ti = “true” trend f (Si) = surface processes like urbanization and agriculture g (Ii) = data inhomogeneities ross.mckitrick.weebly.com

24. For gridcell i • Ti(ideal temperature trend) represented by • TROPi = trend in troposphere over same gridcell as measured by satellites ross.mckitrick.weebly.com

25. 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 ross.mckitrick.weebly.com

26. 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) ross.mckitrick.weebly.com

27. Regression equation Surface proc. Inhom. • GLS with clustering-robust std error matrix ross.mckitrick.weebly.com

28. 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 ross.mckitrick.weebly.com

29. 2007 Results Probability that effects are zero: • Joint P = 0.0000 (7x10-14) ross.mckitrick.weebly.com

30. Specification tests • Bootstrap resampling • Remove outliers, re-estimate • RESET test • Cross-validation tests • Hausman endogeneity test (P = 0.9962) ross.mckitrick.weebly.com

31. 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/decade MSU average: 0.23 Adjusted average surface trend: 0.17 ross.mckitrick.weebly.com

32. 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 ross.mckitrick.weebly.com

33. IPCC Report • I obtained correlation fields between gridded temperatures and AO, ENSO and PDO ross.mckitrick.weebly.com

34. 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) ross.mckitrick.weebly.com

35. 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) ross.mckitrick.weebly.com

36. 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) ross.mckitrick.weebly.com

37. 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) ross.mckitrick.weebly.com

38. 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 ross.mckitrick.weebly.com

39. 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 ross.mckitrick.weebly.com

40. 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 looks correlated with socioeconomic data, yet is uncontaminated by construction, so effects must be a fluke ross.mckitrick.weebly.com

41. 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 ross.mckitrick.weebly.com

42. 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 ross.mckitrick.weebly.com

43. 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 ross.mckitrick.weebly.com

44. Data variations • Surface • Observed: CRU, CRU2v, CRU3v • Modeled: GISS-E; GCM average • Troposphere • Observed: UAH, RSS • Modeled: GISS-E; GCM average ross.mckitrick.weebly.com

45. Spatial Autocorrelation Tests OBSERVED: SAC DISAPPEARS MODELS: SAC REMAINS ross.mckitrick.weebly.com

46. Estimation with SAC model ross.mckitrick.weebly.com

47. Estimation with SAC model MODELS: INSIGNIFICANT OBSERVATIONS: SIGNIFICANT ross.mckitrick.weebly.com

48. 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. ross.mckitrick.weebly.com

49. Results ross.mckitrick.weebly.com

50. Results 1 = climate model reproduces observed effect, 0 = failure to do so ross.mckitrick.weebly.com