peter huybers

Reconstructing Northern Hemisphere temperature over the past millennium: 

Reconstructing Northern Hemisphere temperature over the past millennium Peter Huybers WHOI/Harvard

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The hockey stick (IPCC, 2001) Millennial Northern Hemisphere (NH) temperature reconstruction (blue) and instrumental data (red) from AD 1000 to 1999, adapted from Mann et al. (1999).

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Media coverage 'The [hockey stick] has often been trotted out as the clinching proof of anthropogenic warming. Skeptics have never liked this chart, which necessarily relies on statistics to infer historical temperature from tree rings. ... [McIntyre and McKitrick] allege that Dr. Mann’s statistics are inaccurate, and that the hockey stick is a mere statistical artifact.' -The Economist, Feb, 2005.

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Proxy cross-correlation with instrumental temperatures

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Leading principal component using different normalizations MM05 MBH98

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Two normalizations of the Sheep Mountain bristlecone pine record, CA534 and#x18;and#x13; MM05 MBH98 =7 =0.1

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Variance and normalization conventions + MBH98 x MM05 . This study

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Leading principal components and the straight average MM05 MBH98 This study

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Normalize all paleo temperature records to zero mean and unit variance between 1820 (the shortest record) and 1980. Weight records according to spatial distributionand#x18;and#x13; and then compute the average. Adjust mean and variance of the average proxy record to that of the instrumental record and correct for noise bias. Uncertainty scales as N-1/2. A simple method to estimate past temperatures

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Re-reconstruction of temperatures bias corrected average PCA method (Mann et al, 1998)

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'It is also likely that, in the Northern Hemisphere, the 1990s was the warmest decade and 1998 the warmest year' over the last 1,000 years. ---IPCC, 2001 'Likely' is defined as a 60%-99% chance. More rigorous methods are necessary Convert proxy variability into temperature Proxy type and location b) Seasonal dependence c) Non-temperature dependencies d) Age-model uncertainties 2. Estimate average Northern Hemisphere temperatures a) Spatial covariability b) Spatially dependent noise c) Multi-resolution temperature signals

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Estimating Northern Hemisphere temperature is not at the cutting edge of mathematics or statistics, but has important societal implications. Placing current warming trends in a longer term perspective requires a more rigorous statistical approach.

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Comparison of bias in reconstruction techniques cross-correlation weighted average PCA or simple average Bias corrected average

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Leading principal components and the straight average MM05 MBH98

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Monte Carlo results using synthetic records (following MM05) Non-centered PCA typically gives hockey sticks