Building tree-ring chronologies and Program ARSTAN

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Building tree-ring chronologies and Program ARSTAN

  1. 1. READING Dendroclimatology Brian LuckmanEncyclopedia of Quaternary Sciences, 2006
  2. 2. READINGA conceptual linear aggregate model for tree rings Ed Cook Methods of Dendrochronology, 1990
  3. 3. “ We’re all restless teens when some authority figure with an overhead projector starts yammering. ” Noel Murray The A.V. Club
  4. 4. observation
  5. 5. The Central England Temperature record is the longest instrumental record of temperature in the world. Degrees celsius relative to the long-term meanSource: Parker et al., Journal of Climate, 1992
  6. 6. Source: Goddard Institute for Space Studies, NASA
  7. 7. The number of climate stations recording air temperature falls off rapidly prior to AD 1900. Number of stations in the Northern HemisphereSource: Jones et al., Journal of Geophysical Research, 2012
  8. 8. anecdote < observations <<
  9. 9. CLIMATE HISTORY OF NORTH AMERICA Younger Demise of Laurentide Dryas Ice Sheet 20 16 12 8 4 0 THOUSANDS OF YEARS AGO Final Drainage of Lake AgassizLAST GLACIAL MODERN MAXIMUM OBSERVATIONS
  10. 10. CLIMATE PROXIES ice cores tree rings lake sediments speleothems corals
  11. 11. PALEOCLIMATOLOGYthe study of climate prior to the period of instrumental measurement
  12. 12. Source: Tim Shanahan, University of Texas at Austin
  13. 13. Source: Geological Survey of Canada
  14. 14. Source: LACCORE, University of Minnesota
  15. 15. OBSERVATIONS PROXIES MODELS
  16. 16. READING Holocene perspectives on future climate change Ray BradleyNatural Climate Variability and Global Warming, 2008
  17. 17. “ Tree-ring analysis is one of the most powerful tools available for the study of environmental change and the identification of fundamental relationships ” between tree growth and climate. Ed Cook and Neil Pederson Lamont-Doherty Earth Observatory
  18. 18. “ RINGS ” IN THE BRANCHES OF SAWED TREES SHOWTHE NUMBER OF YEARS AND, ACCORDING TO THEIR THICKNESS, THE YEARS WHICH WERE MORE OR LESS DRY. Leonardo da Vinci
  19. 19. “ The trees composing the forest rejoice and lament with its successes and failures and carry year by year something of its story in ” their annual rings. A. E. Douglass University of Arizona
  20. 20. 755 m3/s847 m 3/s809 m 3/s770 m 3/s823 m 3/s787 m 3/s901 m3/s 3
  21. 21. 5COMPONENTSOF DENDROCLIMATOLOGY
  22. 22. ACTIVITY TIMELINE
  23. 23. ACTIVITY TIMELINEObtaining tree-ring data
  24. 24. ACTIVITY TIMELINE Understanding COFECHA
  25. 25. ACTIVITY TIMELINE The physical basis for dendroclimatology
  26. 26. ACTIVITY TIMELINE Using ARSTAN
  27. 27. ACTIVITY TIMELINE Assessing chronology quality
  28. 28. Source: Baillie (1982)
  29. 29. ?1Where (and how) can we obtain tree-ring data?
  30. 30. DOWNLOAD THESE DATA Bear Canyon West (NM586) Rito de los Frijoles (NM501)Fenton Lake Recollection (NM587) Baca (NM558) Los Alamos (NM044) Abouselman Spring (NM555) Los Alamos (NM046) Cat Mesa (NM556)
  31. 31. h p://www.ldeo.columbia.edu/tree-ring-laboratory/resources/so ware
  32. 32. www.twi er.com/sco stgeorge
  33. 33. ?2 How can we tell if publicly-availableringwidth measurements are good quality?
  34. 34. COFECHA
  35. 35. “ The main purpose of Program COFECHA is the identification of [tree-ring] data that should be reexamined for possible error. ” Richard Holmes Tree-Ring Bulletin 1983
  36. 36. READING Computer-assisted quality controlin tree-ring dating and measurement Richard Holmes Tree-Ring Bulletin, 1983
  37. 37. COFECHA ‘master’ chronology
  38. 38. CAT011COFECHA ‘master’ chronology
  39. 39. CAT011COFECHA ‘master’ chronology
  40. 40. CAT011COFECHA ‘master’ chronology
  41. 41. CAT011COFECHA ‘master’ chronology
  42. 42. EXERCISE Use COFECHA to confirm that our sets ofringwidth measurements are dated correctly.
  43. 43. QUALITY CONTROL THESE DATA Bear Canyon West (NM586) Rito de los Frijoles (NM501)Fenton Lake Recollection (NM587) Baca (NM558) Los Alamos (NM044) Abouselman Spring (NM555) Los Alamos (NM046) Cat Mesa (NM556)
  44. 44. 755 m3/s847 m 3/s809 m 3/s770 m 3/s823 m 3/s787 m 3/s901 m3/s 3
  45. 45. ?3Why should tree-ring variables be connected to climate?
  46. 46. Pinus spp.Source: Paul Schulte
  47. 47. TEMPERATUREhigh growthlow growth cold hot
  48. 48. TEMPERATUREhigh growth frozen water low photosynthetic rate shorter growing seasonlow growth cold hot
  49. 49. TEMPERATUREhigh growth low photosynthetic rate higher evaporationlow growth cold hot
  50. 50. “The growth of trees is undoubtably controlled more bythe movement of water than by the movement of anyother single substance.” Hal Fri s Tree Rings and Climate
  51. 51. WATERhigh growthlow growth dry wet
  52. 52. WATERhigh growth reduced cell division reduced cell expansion C02 starvationlow growth dry wet
  53. 53. WATERhigh growth flooding anoxic conditionslow growth dry wet
  54. 54. Climate acts to synchronize growth ratesat the level of the cell, the tree, the forest and beyond.
  55. 55. READINGHow well understood are the processes that create dendroclimatic records? Eugene Vaganov, Kevin Anchukaitis and Michael Evans Dendroclimatology, 2012
  56. 56. EARLYWOOD AND LATEWOOD WIDTH MAXIMUM LATEWOOD DENSITY VESSEL SIZE AND DISTRIBUTION WOOD BIOCHEMISTRY STABLE ISOTOPES TOTAL RING WIDTH
  57. 57. Tree-ring display at elementary school Photograph:Tom Swetnam
  58. 58. ?4How do we extract climate information from a (complex) set of tree-ring measurements?
  59. 59. TREE-RING WIDTH DATApith bark
  60. 60. “ THERMOMETERS ” TREES ARE NOT OR RAIN GAUGES. Keith Briffa and colleagues
  61. 61. Ed Cook Lamont-Doherty Earth Observatory
  62. 62. THE PRINCIPLE OFAGGREGATE TREE GROWTH
  63. 63. READINGThe decomposition of tree-ring series for environmental studies Ed Cook Tree-Ring Bulletin, 1987
  64. 64. THE PRINCIPLE OF AGGREGATE TREE GROWTHRt = At + Ct + δD1t + δD2t + Et
  65. 65. THE PRINCIPLE OF AGGREGATE TREE GROWTHRt = At + Ct + δD1t + δD2t + Et disturbance within the forest
  66. 66. THE PRINCIPLE OF AGGREGATE TREE GROWTHRt = At + Ct + δD1t + δD2t + Et disturbance from outside the forest
  67. 67. THE PRINCIPLE OF AGGREGATE TREE GROWTHRt = At + Ct + δD1t + δD2t + Et random processes not accounted by other sources
  68. 68. SIGNAL vs. NOISE
  69. 69. THE PRINCIPLE OF AGGREGATE TREE GROWTHRt = At + Ct + δD1t + δD2t + Et
  70. 70. ation replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replication replic
  71. 71. never trust one tree
  72. 72. THE PRINCIPLE OF REPLICATIONMaking measurements from (i) more than one radius pertree and (ii) more than one tree per site maximizes theenvironmental signal and minimizes the amount ofenvironmental ‘noise’.
  73. 73. Etcan be assumed to be uncorrelated within and between trees in a stand.
  74. 74. δD1t Endogenous (‘originating within’) disturbances will be randomevents in both space and time, if the stand of trees is large enough.
  75. 75. δD2to en is shared by most or all trees within a stand, butmay not be shared by all forest stands within a region.
  76. 76. THE PRINCIPLE OF AGGREGATE TREE GROWTHRt = At + Ct + δD1t + δD2t + Et size-related growth trend caused by physiological aging
  77. 77. Ring width Tree age
  78. 78. Atdoes not have a universal or predictable shape.
  79. 79. “ At should be thought of as a nonstationary, stochastic process which may, as a special case, be modeled as a determinstic process. ” Ed Cook Tree-Ring Bulletin 1987
  80. 80. STANDARDIZATION
  81. 81. READINGStandardization of tree-ring data Ray Bradley Paleoclimatology, 1999
  82. 82. “ Growth functions are removed by fi ing a curve to the data and dividing each measured ring-width value by the "expected" value on the growth curve. ” Ray Bradley Paleoclimatology 1999
  83. 83. (A) the ‘raw’ring-width data
  84. 84. (B) the ‘detrending’ curve
  85. 85. DIVIDE(A) the raw ring-width databy (B) the ‘detrending’ curve
  86. 86. the ‘detrended’ ring-width index
  87. 87. “ Standardization transforms the non-stationary ring-widths in a new series of stationary, relative tree-ring indices that have a defined mean of 1.0 and a constant variance. ” Ed Cook Tree-Ring Bulletin 1987
  88. 88. ARSTAN autoregressive standardization
  89. 89. PROGRAM ARSTAN PRODUCES CHRONOLOGIES FROM TREE-RING MEASUREMENT SERIESBY DETRENDING AND INDEXING (STANDARDIZING) THE SERIES, THEN APPLYING A ROBUST ESTIMATIONOF THE MEAN VALUE FUNCTION TO REMOVE EFFECTS OF ENDOGENOUS STAND DISTURBANCES.
  90. 90. DIVIDE(A) the raw ring-width databy (B) the ‘detrending’ curve
  91. 91. ARSTAN OUTPUT
  92. 92. CHRONOLOGY
  93. 93. EXERCISE Use several different detrending methodsto standardize our set of ring-width chronologies and let’s see what happens!
  94. 94. STANDARDIZE THESE DATA Bear Canyon West (NM586) Rito de los Frijoles (NM501)Fenton Lake Recollection (NM587) Baca (NM558) Los Alamos (NM044) Abouselman Spring (NM555) Los Alamos (NM046) Cat Mesa (NM556)
  95. 95. USE FOUR DIFFERENT APPROACHES TO STANDARDIZATION Horizontal line Negative exponential curve 20-yr flexible splineFlexible spline set to 67% of series length
  96. 96. How should we plot our results?
  97. 97. DPL : YUX
  98. 98. WHO WILL FIND THE FIRST “DIRTY DOG”?Source: Terry Bain
  99. 99. USE FOUR DIFFERENT APPROACHES TO STANDARDIZATION Horizontal line Negative exponential curve 20-yr flexible splineFlexible spline set to 67% of series length
  100. 100. Pinus longaeva in western North America show a recent increase in growth even without any standardization to remove age-effects.Source: Saltzer et al., Proceedings of the National Academy of Sciences, 2009
  101. 101. READING Recent unprecedented tree-ring growth in bristlecone pine at thehighest elevations and possible causes Ma hew Salzer, Malcolm Hughes, Andy Bunn and Kurt Kipfmeuller Proceedings of the National Academy of Sciences, 2009
  102. 102. THE ‘SEGMENT-LENGTH’CURSE
  103. 103. READING The ‘segment-length curse’ in longtree-ring chronology development for palaeoclimatic reconstructionEd Cook, Keith Briffa, David Meko, Donald Graybill and Gary Funkhouser The Holocene, 1995
  104. 104. The maximum wavelength of recoverable climatic information is related to the lengths of the individual tree-ring series used to construct the millennia-long chronology.
  105. 105. LOWEST RESOLVABLE FREQUENCY3/n when n represents the average lengths of segments that make up the chronology
  106. 106. the ‘detrended’ ring-width index
  107. 107. Autocorrelation describes the correlation of atime series with its own past and future values.
  108. 108. Ringwidth lag-0 autocorrelation 3 2 1 0 -1 -2 -31900 1920 1940 1960 1980 2000 Year (A.D.)
  109. 109. Ringwidth lag-1 autocorrelation 3 2 1 0 -1 -2 -31900 1920 1940 1960 1980 2000 Year (A.D.)
  110. 110. Ringwidth lag-2 autocorrelation 3 2 1 0 -1 -2 -31900 1920 1940 1960 1980 2000 Year (A.D.)
  111. 111. Ringwidth lag-3 autocorrelation 3 2 1 0 -1 -2 -31900 1920 1940 1960 1980 2000 Year (A.D.)
  112. 112. covariance product of the standard deviationAutocorrelation
  113. 113. The spectrum of a time series describesthe distribution of variance of the seriesas a function of frequency.
  114. 114. yellowviolet blue green orange red
  115. 115. yellow violet blue green orange red short longwavelengths wavelengths
  116. 116. yellowviolet blue green orange red Fast slowchanges changes
  117. 117. 4 example of a ‘white’ time series32 10-1-2-3200 250 300 350 400 450 500
  118. 118. 4 example of a ‘red’ time series32 10-1-2-3200 250 300 350 400 450 500
  119. 119. 4 example of a ‘blue’ time series32 10-1-2-3200 250 300 350 400 450 500
  120. 120. “White”
  121. 121. “Red”
  122. 122. “Blue”
  123. 123. IF a time series (of length N) issignificantly autocorrelated, then: The series is not random in time Each observation is not independent from other observations The number of independant observations is fewer than N
  124. 124. STANDARDRESIDUAL
  125. 125. CAT MESA, NEW MEXICO‘Standard’ chronology‘Residual’ chronology
  126. 126. ?YEAH, BUTWHICH CHRONOLOGY SHOULD I USE?
  127. 127. PDO index Mexican PDSI3 102 5 10 0-1 -5-2-3 -10 1900 1920 1940 1960 1980 2000
  128. 128. The “effective sample size” is an estimate ofthe “real” number of observations a eradjusting for the effects of autocorrelation.
  129. 129. sample size effective sample size first-order autocorrelationEffective sample size
  130. 130. ?5How can we determine if the common signal embedded within a set of tree-ring measurements is strong or weak?
  131. 131. ?To what degree does the chronology represent the hypothetical population chronology?
  132. 132. r btThe mean inter-series correlation calculatedbetween all possible pairs of indexed series drawn from different trees.
  133. 133. How self-similar is this set of detrended ring-width measurements?
  134. 134. How self-similar is this set of detrended ring-width measurements? r = 0.51 bt
  135. 135. EPSquantifies the degree to which this particular chronology portrays the hypothetically perfect chronology.
  136. 136. EPS = f(rbt, t)
  137. 137. CAT MESA, NEW MEXICO‘Standard’ chronology‘Residual’ chronology
  138. 138. READING On the average value of correlated time series, with applications indendroclimatology and hydrometeorology Tom Wigley, Keith Briffa and Phil Jones Journal of Climate and Applied Meteorology, 1984
  139. 139. READINGMillennial precipitation reconstructionfor the Jemez Mountains, New Mexico, reveals changing drought signalRamzi Touchan, Connie Woodhouse, Dave Meko and Craig Allen International Journal of Climatology, 2011
  140. 140. PRODUCE CHRONOLOGIES FOR THESE DATA Bear Canyon West (NM586) Rito de los Frijoles (NM501)Fenton Lake Recollection (NM587) Baca (NM558) Los Alamos (NM044) Abouselman Spring (NM555) Los Alamos (NM046) Cat Mesa (NM556)

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