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When is enough enough? Tom Wigley and the quantitative analysis of proxy climate records.

When is enough enough? Tom Wigley and the quantitative analysis of proxy climate records. Malcolm K. Hughes Laboratory of Tree-Ring Research University of Arizona. Work funded by NOAA, NSF and NPS Global Change Program (now USGS). http://www.ltrr.arizona.edu/people/8. Overview.

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When is enough enough? Tom Wigley and the quantitative analysis of proxy climate records.

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  1. When is enough enough? Tom Wigley and the quantitative analysis of proxy climate records. Malcolm K. Hughes Laboratory of Tree-Ring Research University of Arizona Work funded by NOAA, NSF and NPS Global Change Program (now USGS). http://www.ltrr.arizona.edu/people/8

  2. Overview • Introduction to tree rings • Examples of use • Intensive • Extensive • The emergent nature of climate signal in tree rings • Wigley et al., 1984 and its significance • Final thoughts

  3. Climate-sensitive Tree Rings • The common climate signal shared by trees of one species at a location is extracted as a chronology, the mean of manydated and detrendedmeasurement series from individual cores or radii. • Networks of these are then used in reconstructing past climate. Only a few examples will be given here.

  4. Seeking Climate Signal Usually choose trees subject to a common limiting factor – for example: • If seeking moisturesignal, sample near lower distributional limit of species in dry region. • If seeking temperature signal, sample near upper or northern distributional limit of species in regions with cool, moist summers.

  5. Identifying Climate Signal • Then isolate climate signal: • Remove non-climatic variability in tree-ring properties, for example those related to tree size or cambial age. • Construct and test an appropriate model for converting tree-ring measurements into, for example, summer temperature or hydrologic year total precipitation.

  6. Intensive example • Drought-limited bristlecone pine at Methuselah Walk, White Mountains, California

  7. 285 samples of mean length 748 years – living trees and relict timber. • more than 10 series present for each year from 6000 B.C. • no series shorter than 500 years. Hughes and Funkhouser, 1998

  8. 8000 yrs of moisture availability history from White Mts., CA. Blue – instrumental NV div.3 ppt Red - from MWK tree rings Hughes and Funkhouser, 1998 NV Div.3 Jul-Jun precip. (50-yr smoothed)

  9. SBB SBB SBB SBB Such a “regime shift” likely linked to ocean… Dry Wet Wet Cooler Warmer Century to multi-century scale.

  10. La Niña More likely In fact, these records co-vary in physically reasonably proportions with sea surface temp-erature records far away in the central tropical Pacific. Our explanation – a shift in mid-millennium away from La Niña to El Niño – type conditions. Coral record from Cobb et al., 2001, Mindanao, Stott. Comparison by Graham, Hughes et al., 2007

  11. Extensive example • Spatiotemporal patterns at hemispheric scale – Mann et al., 2008 and in review.

  12. Color shows start year of each of the >1000 records used Mann et al., 2008 PNAS and in review.

  13. IPCC AR4 WkGp 1 Chapter 6

  14. With W/O anthropogenic forcings Compared with model runs:

  15. The emergent nature of climate signal in tree rings.

  16. 3. The idea of a target data set. 3. The idea of a target data set. 3. The idea of a target data set. 3. The idea of a target data set. 3. The idea of a target data set. Northern Yakutia samples starting after AD 850 Northern Yakutia samples starting after AD 850 Northern Yakutia samples starting after AD 850 Northern Yakutia samples starting after AD 850 Northern Yakutia samples starting after AD 850 • A simulation starts from the characteristics of an actual data set to be simulated, the target data set. These characteristics are: • sample structure: • the number of samples, • their start or end dates, • the number of years in each sample. • These give the number of samples covering each year (sample depth), and the age of their cambium (the tissue producing wood) in that year. • b) Individual sample properties: • years covered; • correlation with climate signal; • non-climatic persistence (time-series model); • age trend. • A simulation starts from the characteristics of an actual data set to be simulated, the target data set. These characteristics are: • sample structure: • the number of samples, • their start or end dates, • the number of years in each sample. • These give the number of samples covering each year (sample depth), and the age of their cambium (the tissue producing wood) in that year. • b) Individual sample properties: • years covered; • correlation with climate signal; • non-climatic persistence (time-series model); • age trend. • A simulation starts from the characteristics of an actual data set to be simulated, the target data set. These characteristics are: • sample structure: • the number of samples, • their start or end dates, • the number of years in each sample. • These give the number of samples covering each year (sample depth), and the age of their cambium (the tissue producing wood) in that year. • b) Individual sample properties: • years covered; • correlation with climate signal; • non-climatic persistence (time-series model); • age trend. • A simulation starts from the characteristics of an actual data set to be simulated, the target data set. These characteristics are: • sample structure: • the number of samples, • their start or end dates, • the number of years in each sample. • These give the number of samples covering each year (sample depth), and the age of their cambium (the tissue producing wood) in that year. • b) Individual sample properties: • years covered; • correlation with climate signal; • non-climatic persistence (time-series model); • age trend. • A simulation starts from the characteristics of an actual data set to be simulated, the target data set. These characteristics are: • sample structure: • the number of samples, • their start or end dates, • the number of years in each sample. • These give the number of samples covering each year (sample depth), and the age of their cambium (the tissue producing wood) in that year. • b) Individual sample properties: • years covered; • correlation with climate signal; • non-climatic persistence (time-series model); • age trend. Each horizontal line shows the time covered by a single sample in this actual case of 136 samples from a region in North-east Eurasia. Each horizontal line shows the time covered by a single sample in this actual case of 136 samples from a region in North-east Eurasia. Each horizontal line shows the time covered by a single sample in this actual case of 136 samples from a region in North-east Eurasia. Each horizontal line shows the time covered by a single sample in this actual case of 136 samples from a region in North-east Eurasia. Each horizontal line shows the time covered by a single sample in this actual case of 136 samples from a region in North-east Eurasia. The MATLAB code of SYNCHRON can be used to specify fixed parameters (most commonly the number of samples, and the sample structure) or to draw them randomly from the same distribution as that of the target data. By repeated re-sampling of these distributions, it is possible to explore the influence of various aspects of samplestructure and individual sample properties on the performance of standardization methods in conserving climate signal The MATLAB code of SYNCHRON can be used to specify fixed parameters (most commonly the number of samples, and the sample structure) or to draw them randomly from the same distribution as that of the target data. By repeated re-sampling of these distributions, it is possible to explore the influence of various aspects of samplestructure and individual sample properties on the performance of standardization methods in conserving climate signal The MATLAB code of SYNCHRON can be used to specify fixed parameters (most commonly the number of samples, and the sample structure) or to draw them randomly from the same distribution as that of the target data. By repeated re-sampling of these distributions, it is possible to explore the influence of various aspects of samplestructure and individual sample properties on the performance of standardization methods in conserving climate signal The MATLAB code of SYNCHRON can be used to specify fixed parameters (most commonly the number of samples, and the sample structure) or to draw them randomly from the same distribution as that of the target data. By repeated re-sampling of these distributions, it is possible to explore the influence of various aspects of samplestructure and individual sample properties on the performance of standardization methods in conserving climate signal The MATLAB code of SYNCHRON can be used to specify fixed parameters (most commonly the number of samples, and the sample structure) or to draw them randomly from the same distribution as that of the target data. By repeated re-sampling of these distributions, it is possible to explore the influence of various aspects of samplestructure and individual sample properties on the performance of standardization methods in conserving climate signal Each horizontal line shows the time covered by a single sample, in this case for 136 samples from Northern Yakutia Northern Yakutia samples starting after AD 850 Northern Yakutia samples starting after AD 850 Each horizontal line shows the time covered by a single sample in this actual case of 136 samples from a region in North-east Eurasia.

  17. Wigley et al., 1984 and its significance. • How well does the average of a finite number (N) of time series represent the population average; • How well will a subset of series represent the N-series average?

  18. Wigley et al., 1984 and its significance. • They derived and tested formulae for: • The correlation between the N-series average and the population (Expressed Population Signal – EPS). • The correlation coefficient between the average of N time series and the the average of n such series (Subsample Signal Strength).

  19. Wigley et al., 1984 and its significance.

  20. Number of Citations of Wigley et al., 1984 314 (315) total 21 climate observation related 10 fish, molluscs, corals 19 tree-ring isotopes (all since 1997) 264 other tree ring

  21. Final thoughts • By providing a sound basis for useful statistics, Tom and his colleagues have provided an invaluable tool for assessing the adequacy of replication for proxy climate records based on emergent phenomena. • The citation count will continue to grow!

  22. Many thanks Tom!

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