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Wood density provides new opportunities for reconstructing past temperature variability from southeastern Australian trees

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Abstract and Figures

Tree-ring based climate reconstructions have been critical for understanding past variability and recent trends in climate worldwide, but they are scarce in Australia. This is particularly the case for temperature: only one tree-ring width based temperature reconstruction – based on Huon Pine trees from Mt Read, Tasmania – exists for Australia. Here, we investigate whether additional tree-ring parameters derived from Athrotaxis cupressoides trees growing in the same region have potential to provide robust proxy records of past temperature variability.
(a) Reconstruction of January T max based on ring width (TRW) and mean density of 726 Athrotaxis cupressoides (this study), (b) the Mt Read reconstruction of summer (Nov-­-Apr) T mean 727 based on TRW of Huon Pine (Lagarostrobos franklinii; from Cook et al. 2000; and (c) the wavelet 728 coherence between the two reconstructions. Note: y-­-axes are not directly comparable between (a) 729 and (b). Thin black lines indicate reconstructed temperatures, thin blue line in (a) is the observed 730 JanT max , light grey band in (a) represents the prediction interval (calculated with the predict function 731 in R; R Core Team, 2015). Thick black lines represent 50-­-yr Loess smoothing curves of reconstructed 732 (black) and observed (blue) temperatures. The red extension of the reconstruction in (a) shows the 733 Loess smoothing curve if the 2004-­-2010 period is excluded from the reconstruction. Horizontal 734 dashed lines represent the 1530-­-2010 mean of reconstructed (a) January T max (19.2°C) from PLMC 735 and (b) summer T mean (15°C) from Mt Read. Blue horizontal bars and vertical shading indicate 736 periods when both reconstructions show below average temperatures (Loess curve below the long-­737 term mean). Orange horizontal bars and vertical shading indicate periods when both reconstructions 738 show above average temperatures (Loess curve above the long-­-term mean). Black contours in (c) 739 indicate the 5% significance level. The white shaded area in (c) is the cone of influence, indicating 740 regions subject to edge effects. 741 [2 column widths] 742 743
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Wood$density$provides$new$opportunities$for$
reconstructing$past$temperature$variability$from$
southeastern$Australian$trees$
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2.3$Climate$Data$.A.'
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2.5$Temperature$reconstruction$.XO'
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2&='2"$%'Q%=-"$'>#34'9%34'GD55-&3'2&='"2KK-='b3v.c'_5-=#G3%5'<25#29"-$'3%'3-$3'J%5'2&6'"2KK-='.XZ'
5-"23#%&$4#_$'9-3>--&'34-'_5%a6'$-5#-$'2&='G"#Q23-)'.XX'
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"29%523%56h5-$%D5G-$h$%J3>25-c'D$#&K'$_"#3T_-5#%='b.P7O`.PYP'2&='.PNO`7O.O'?);)c'G2"#9523#%&T.PO'
<-5#J#G23#%&'3-$3$'G%QQ%&"6'D$-='#&'=-&=5%G"#Q23%"%K6'bR5#33$0'.PZNe'?%%@'2&='12#5#D@$3#$0'.PPOc)'.P.'
C>%'5#K%5%D$'3-$3$'%J'J#30'34-'5-=DG3#%&'%J'-55%5'b8;c'2&='34-'G%-JJ#G#-&3'%J'-JJ#G#-&G6'b?;c'>-5-'D$-='.P7'
3%'-<2"D23-'34-'$@#""'%J'34-'Q%=-"$)'8;'-$3#Q23-$'34-'K%%=&-$$'%J'J#3'9-3>--&'34-'%9$-5<-='2&='.PA'
5-G%&$35DG3-='<2"D-$'#&'34-'<-5#J#G23#%&'_-5#%=0'D$#&K'34-'Q-2&'%J'%9$-5<-='<2"D-$'#&'34-'G2"#9523#%&'.PE'
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%9$-5<-='<2"D-$'#&'34-'<-5#J#G23#%&'_-5#%='2$'2'9-&G4Q25@'b?%%@'-3'2")0'.PPPc)'F%$#3#<-'<2"D-$'%J'-2G4'.PN'
%J'34-$-'$323#$3#G$'#&=#G23-'3423'2'Q%=-"'#$'$@#"JD"'#&'5-G%&$35DG3#&K'%9$-5<-='<25#23#%&'#&'34-'G"#Q23-'.PZ'
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5-$#=D2"$'%J'34-'J#&2"'5-G%&$35DG3#%&'Q%=-"'#&'8'A)7)O'b8'?%5-'C-2Q0'7O.Yc)'M-'5-$G2"-='34-'<25#2&G-'7OO'
%J'34-'5-G%&$35DG3-='3-Q_-523D5-'=232'3%'Q23G4'34-'<25#2&G-'%J'34-'%9$-5<-='3-Q_-523D5-'=2320'7O.'
>4#G4'2""%>$'G%Q_25#$%&$'9-3>--&'34-'Q2K&#3D=-'%J'-<-&3$'#&'34-'_2$3'2&='34%$-'#&'34-'%9$-5<-='7O7'
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2&='G2"GD"23-='PYg'_5-=#G3#%&'#&3-5<2"$'J%5'34-'"#&-25'Q%=-"$'#&'8'b8'?%5-'C-2Q0'7O.Yc)''7OE'
2.6$Wavelet$Analysis$7OY'
M-'G2"GD"23-='34-'>2<-"-3'$_-G35DQ'%J'34-'Q-2&'=-&$#36'2&='C8M'G45%&%"%K#-$'2&='34-'7ON'
3-Q_-523D5-'5-G%&$35DG3#%&'3%'-a2Q#&-'J5-fD-&G6'=%Q2#&'_5%_-53#-$'2&='4%>'34-6'G42&K-='%<-5'7OZ'
3#Q-'bC%55-&G-'2&='?%Q_%0'.PPXe'433_[hh>>>)#%&)5-$-25G4$6$3-Q$)G%Qhd*UIG5#_3h>2<-"-3c)'M-'7OX'
$-"-G3-='34-'J%""%>#&K'%_3#%&$'J%5'%D5'2&2"6$#$[':%5"-3'2$'34-'Q%34-5'>2<-"-3'JD&G3#%&e'_2='34-'-&=$'7OP'
>#34'W-5%$e'2&='2'.Og'$#K&#J#G2&G-'"-<-"'D$#&K'2'>4#3-T&%#$-'92G@K5%D&='$_-G35DQ)'M-'2"$%'7.O'
G2"GD"23-='34-'>2<-"-3'G%4-5-&G-'9-3>--&'%D5'3-Q_-523D5-'5-G%&$35DG3#%&'2&='34-':3'8-2='7..'
5-G%&$35DG3#%&'%J'$DQQ-5'3-Q_-523D5-$'#&'34-'9#>2<-"-3'_2G@2K-'bj%D4#-50'7O.Ec'#&'8'b8'?%5-'7.7'
C-2Q0'7O.Yc'D$#&K'34-'J%""%>#&K'%_3#%&$[':%5"-3'2$'34-'Q%34-5'>2<-"-3'JD&G3#%&e'_2='34-'-&=$'>#34'7.A'
W-5%$e'2&='2'Yg'$#K&#J#G2&G-'"-<-")'7.E'
3.$Results$7.Y'
3.1$Chronology$and$model$development$7.N'
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J5-fD-&G6'<25#23#%&'#&'%9$-5<-='3-Q_-523D5-'>-""'bR#K)'Nc)']%>-<-50'34-'Q%5-'G%&$-5<23#<-'?;'3-$3'7YO'
>2$'&%3'_2$$-='b?;'s'Oc'J%5'34-'"23-'b.PNO`7O.O'?);)c'<-5#J#G23#%&'_-5#%='bC29"-'.c'=D-'3%'2'Q#$Q23G4'7Y.'
#&'34-'"%>TJ5-fD-&G6'35-&='9-3>--&'34-'Q-2&'=-&$#36'G45%&%"%K6'2&='%9$-5<-='(2&CQ2a'%<-5'34-'"2$3'7Y7'
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3.2$Reconstructed$past$temperature$variability$7XN'
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G%&=#3#%&$'42<-'%GGD55-='#&'34-'_2$3'EXO'6-25$'b-)K)0'Q#='.ZOO$0'"23-'.XOO$'2&='-25"6'.POO$c'2&='34-'7XX'
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2<-52K-'b#)-)0'.X`7On?e'R#K$)'E-'2&='Z2c)'8-=DG-='4#K4TJ5-fD-&G6'<25#29#"#36'2"$%'%GGD55-='_%$3T.PYO'AOE'
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G-&3-&&#2"T$G2"-'bG2)'.7XT6-25'92&=c'<25#2&G-'#&'34-'"2$3'3>%'G-&3D5#-$'bR#K)'E-`Jc0'9D3'34#$'QD$3'9-'AON'
G2D3#%D$"6'#&3-5_5-3-='=D-'3%'_2==#&K'%J'34-'-&='%J'34-'$-5#-$)']#K4'J5-fD-&G6'<25#29#"#36'%9$-5<-='#&'AOZ'
34-'C8MvQ-2&'=-&$#36'5-G%&$35DG3#%&'#$'G%&$#$3-&3'>#34'4#K4TJ5-fD-&G6'<25#29#"#36'-<#=-&3'#&'%&"6'AOX'
34-'Q-2&'=-&$#36'G45%&%"%K6'bR#K'E2`9c0'>4#"-'"%>'bG2)'.7XT6-25'92&=c'J5-fD-&G6'<25#29#"#36'#&'34-'AOP'
5-G%&$35DG3#%&'#$'G%&$#$3-&3'>#34'"%>'J5-fD-&G6'<25#29#"#36'#&'34-'C8M'G45%&%"%K6'bR#K)'EG`=c)''A.O'
*D5'5-G%&$35DG3#%&'%J'-25"6'$DQQ-5'3-Q_-523D5-$'#&'C2$Q2&#2'>2$'_%$#3#<-"6'G%55-"23-='>#34'34-'A..'
IC8'#&3-&$#36'#&=-a'#&'34-'$DQQ-5'$-2$%&'b,-GTR-9c'%&'2&&D2"'3#Q-$G2"-$'b4#K4T_2$$'J#"3-5-=0'7OT6-25'A.7'
S%-$$'GD5<-e'5't'O)AO0'_'t'O)OOPe'.XPO`7OOP'?);)c'2&='2"$%'$4%>$'G%&$#=-529"-'2K5--Q-&3'>#34'34-'A.A'
IC8'#&3-&$#36'#&=-a'%&'=-G2=2"'3#Q-$G2"-$'bR#K)'I.c)'C4-$-'5-"23#%&$4#_$'>-5-'_253#GD"25"6'$35%&K'#&'A.E'
(2&D256'b4#K4'_2$$0'5't'O)EO0'_'s'O)OOO.c)'*D5'5-G%&$35DG3#%&'>2$'2"$%'_%$#3#<-"6'G%55-"23-='%&'2&&D2"'A.Y'
3#Q-$G2"-$'b4#K4T_2$$'J#"3-5-=0'7OT6-25'S%-$$'GD5<-c'`'9D3'$4%>-='"#33"-'2K5--Q-&3'%&'=-G2=2"'A.N'
3#Q-$G2"-$'`'>#34'34-'#&$35DQ-&32"'I!:'#&=-a'bH!Ic'b5't'O)7Z0'_'t'O)OEX0'.PYZ`7OOP'?);)e'R#K)'I.c'2&='A.Z'
2&'#&$35DQ-&3T92$-='5-G%&$35DG3#%&'%J'34-'I!:'bB#$9-G@e'5't'O)7E0'_'t'O)O.0'.XXZ`7OOE'?);)e'R#K)'I.c'A.X'
#&'34-'$DQQ-5'$-2$%&'b,-GTR-9c)''A.P'
4$Discussion$A7O'
4.1$Wood$density$as$a$proxy$for$temperature$in$southeastern$Australia$A7.'
M%%='=-&$#36'G%&32#&$'2'$35%&K'#&3-5T2&&D2"'G"#Q23-'$#K&2"0'9D3'J2#"-='3%'5-_5%=DG-'2'"%&KT3-5Q'A77'
35-&='-<#=-&3'#&'34-'%9$-5<-='3-Q_-523D5-)'C4-'29$-&G-'%J'2'"%&KT3-5Q'35-&='#&'34-'Q-2&'=-&$#36'A7A'
G45%&%"%K6'#$'"25K-"6'2335#9D329"-'3%'2'$-5#-$'%J'345--'6-25$'#&'34-'"2$3'=-G2=-'b7OOY0'7OON0'7OOZc'A7E'
>4-&'Q-2&'=-&$#36'<2"D-$'>-5-'QDG4'4#K4-5'342&'2<-52K-0'2"9-#3'&%3'-a35-Q-)']%>-<-50'#J'Q-2&'A7Y'
=-&$#36'#$'D$-='2$'34-'$%"-'_5-=#G3%5'%J'3-Q_-523D5-0'34-'5-$D"3#&K'5-G%&$35DG3#%&'D&=-5-$3#Q23-$'A7N'
%9$-5<-='3-Q_-523D5-$'#&'34-$-'6-25$'96'p'7n?'bR#K)'Nc'2&='2$'2'5-$D"3'J2#"$'3%'<-5#J6'b?;'s'Oe'C29"-'.c)'A7Z'
dJ'34#$'_-5#%='b#)-)0'7OOE`7O.O'?);)c'#$'-aG"D=-='J5%Q'34-'Q%=-"'G2"#9523#%&'2&='<-5#J#G23#%&0'34-'?;'A7X'
b2&='2""'%34-5'3-$3$'%J'Q%=-"'J#3c'#Q_5%<-'bC29"-'.c'2&='34-'$"%_-'%J'34-'"#&-25'35-&='#&G5-2$-$'J5%Q'A7P'
O)OOE'65T.''b.P7O`7O.O'?);)c'3%'O)O.An?'65T.'b.P7O`7OOA'?);)e'_'t'O)OYe'R#K)'Nc0'G%&J#5Q#&K'3423'34-'AAO'
Q#$Q23G4'9-3>--&'(2&CQ2a'2&='Q-2&'=-&$#36'#&'34-$-'6-25$'#$'_5#&G#_2""6'5-$_%&$#9"-'J%5'&%&TAA.'
<-5#J#G23#%&'b?;'s'Oc'%J'34-'Q-2&'=-&$#36T%&"6'Q%=-")'C4-'-aG"D$#%&'%J'34-'7OOE`7O.O'?);)'_-5#%='AA7'
J5%Q'34-'Q-2&'=-&$#36T%&"6'Q%=-"'#&G5-2$-$'34-'35-&='#&'5-G%&$35DG3-='(2&CQ2a'b3%'O)O.An?'65T.e'AAA'
.P7O`7OOA'?);)c0'9D3'$3#""'D&=-5-$3#Q23-$'34-'35-&='2__25-&3'#&'34-'%9$-5<-='3-Q_-523D5-'=232'AAE'
bO)O7En?'65T.c'%<-5'34-'$2Q-'_-5#%=)'?%&$-fD-&3"60'Q-2&'=-&$#36'2"%&-'2__-25$'3%'9-'D&29"-'3%'AAY'
5%9D$3"6'5-G%&$35DG3'"%&KT3-5Q'<25#29#"#36'#&'%9$-5<-='3-Q_-523D5-$)'AAN'
C4-'5-2$%&'J%5'34-'=#$G5-_2&G6'9-3>--&'34-'Q-2&'=-&$#36'G45%&%"%K6'2&='%9$-5<-='3-Q_-523D5-$'#&'AAZ'
34-$-'_253#GD"25'6-25$'#&'34-'"2$3'=-G2=-'5-Q2#&$'D&G"-25)'d3'#$'_%$$#9"-'3423'34#$'=#$G5-_2&G6'#$'2'AAX'
Q-2$D5-Q-&3'-55%5'%5'%D3"#-50'9D3'D&"#@-"6'9-G2D$-'Q%5-'342&'42"J'%J'34-'$2Q_"-='35--$'-a4#9#3-='AAP'
4#K4'=-&$#36'#&'34-$-'6-25$)'C-Q_-523D5-$'23'34-'&-2596'S#2>-&--'G"#Q23-'$323#%&'25-'$#Q#"25'3%'AEO'
34%$-'%J'34-'K5#==-='3-Q_-523D5-'=232'D$-='3%'<-5#J6'%D5'5-G%&$35DG3#%&'Q%=-"'$%'#3'#$'D&"#@-"6'3423'AE.'
34-'2GGD52G6'%J'34-'K5#==-='=232'#$'34-'G2D$-'%J'34#$'Q#$Q23G4)'d&'2==#3#%&0'34-5-'#$'&%'-<#=-&G-'%J'AE7'
D&D$D2"'G%&=#3#%&$'b=2#"6'"-<-"c'#&'34-'#&$35DQ-&32"'_5-G#_#323#%&'%5'3-Q_-523D5-'5-G%5='3423'Q26'AEA'
4-"_'-a_"2#&'4#K4-5'342&'2<-52K-'5#&K'=-&$#36'#&'34-$-'6-25$)'C4-5-'#$'2"$%'&%'-<#=-&G-'%J'=#$-2$-'&%5'AEE'
42$'34-'$#3-'9--&'5-G-&3"6'=#$3D59-=)'M#34%D3'@&%>#&K'34-'G2D$-'%J'34-'Q#$Q23G4'9-3>--&'%9$-5<-='AEY'
2&='5-G%&$35DG3-='3-Q_-523D5-$'#&'34-'Q%$3'5-G-&3'=-G2=-'>-'QD$3'_5%<#=-'34-'G2<-23'3423'$#Q#"25'AEN'
#$$D-$'42<-'_%$$#9"6'%GGD55-='#&'%34-5'_-5#%=$'%J'34-'5-G%&$35DG3#%&'2&='34-'5-G%&$35DG3#%&'$4%D"='AEZ'
9-'#&3-5_5-3-='>#34'34#$'#&'Q#&=)'''''AEX'
C4-'#&G"D$#%&'%J'C8M'>#34'Q-2&'=-&$#36'2$'2'_5-=#G3%5'%J'3-Q_-523D5-'#Q_5%<-$'34-'G%4-5-&G-'%J'AEP'
34-'"#&-25'35-&='#&'34-'5-G%&$35DG3#%&'3%'3423'%J'34-'%9$-5<-='3-Q_-523D5-'=232'b#)-0'5-G%&$35DG3-='AYO'
35-&='%J'O)O7On?'65T.'G%Q_25-='3%'%9$-5<-='35-&='%J'O)O7An?'65T.e'.P7O`7O.O'?);)ce'4%>-<-50'34-'AY.'
Q#$Q23G4'9-3>--&'34-$-'6-25$'%J'D&D$D2""6'4#K4'Q-2&'=-&$#36'<2"D-$'b"%>'5-G%&$35DG3-='AY7'
3-Q_-523D5-$c'2&='%9$-5<-='3-Q_-523D5-$'#&'34-'"2$3'=-G2=-'5-Q2#&$'%9<#%D$'%&'9%34'34-'#&3-5TAYA'
2&&D2"'2&='"%&K-5T3-5Q'$G2"-'#&'34-'J#&2"'5-G%&$35DG3#%&'bR#K)'Zc)'C4-'-aG"D$#%&'%J'34-'7OOE`7O.O'?);)'AYE'
_-5#%='Q%=-"'#Q_5%<-$'Q%=-"'J#3'J%5'34-'C8MTQ-2&'=-&$#36'Q%=-"'bC29"-'.c0'9D3'3-&=$'3%'AYY'
%<-5-$3#Q23-'34-'#&G5-2$#&K'35-&='#&'%9$-5<-='(2&CQ2a'b#)-)0'5-G%&$35DG3-='"#&-25'35-&='%J'O)O7Z°?'65T.'AYN'
%<-5'.P7O`7OOA'?);)c)'H-G2D$-'%J'34#$'2&='9-G2D$-'>-'=%'&%3'@&%>'%J'2'&%&TG"#Q23#G'5-2$%&'J%5'34-'AYZ'
Q#$Q23G4'9-3>--&'%9$-5<-='3-Q_-523D5-$'2&='34-'Q-2&'=-&$#36'G45%&%"%K6'#&'7OOY`7OOZ'?);)0'>-'AYX'
42<-'&%3'-aG"D=-='34-$-'6-25$'J5%Q'34-'J#&2"'C8MvQ-2&'=-&$#36'Q%=-")''AYP'
d&'G%&352$3'3%'$3D=#-$'#&'U%534'!Q-5#G2'2&=';D5%_-'>4-5-'=-&$#36'b_253#GD"25"6'Q2a#QDQ'"23->%%='ANO'
=-&$#360':l,c'#$'_%$#3#<-"6'5-"23-='3%'$DQQ-5'3-Q_-523D5-$0'>-'J%D&='3423'Q-2&'=-&$#36'%J'AN.'
Athrotaxis'#$'&-K23#<-"6'5-"23-='3%'$DQQ-5'3-Q_-523D5-$'#&'C2$Q2&#2'bR#K)'Yc)',5->'-3'2")'b7O.Ac'2"$%'AN7'
J%D&='2'&-K23#<-'5-"23#%&$4#_'9-3>--&'>%%='=-&$#36'bQ-2&'5#&K'=-&$#36'2&='Q-2&'"23->%%='ANA'
=-&$#36c'2&='$DQQ-5'3-Q_-523D5-$'#&']D%&'F#&-'23'"%>'-"-<23#%&$'#&'C2$Q2&#2)'C4-'_46$#%"%K#G2"'ANE'
Q-G42&#$Q'D&=-5"6#&K'34#$'D&D$D2"'&-K23#<-'5-"23#%&$4#_'9-3>--&'3-Q_-523D5-'2&='35--T5#&K'ANY'
=-&$#36'#&'$%Q-'C2$Q2&#2&'G%&#J-5$'42$'&%3'6-3'9--&'#&<-$3#K23-=)']%>-<-50'?MC'2&='34D$'=-&$#36'ANN'
#&'G%&#J-5$'#$'K-&-52""6'G"%$-"6'5-"23-='3%'34-'=D523#%&'2&='523-'%J'G-""'>2""'34#G@-&#&K0'>4#G4'#$'ANZ'
#&J"D-&G-='96'3-Q_-523D5-'bM%=W#G@#0'.PZ.e'LKK"2'-3'2")0'7OO.e'8%$$#'-3'2")0'7OONc'2&='G-""'>2""$'#&'ANX'
G%&#J-5$'42<-'9--&'$4%>&'3%'9-'34#&&-5'>4-&'=#JJ-5-&3#23#%&'%GGD5$'D&=-5'>25Q-5'G%&=#3#%&$'ANP'
b,-&&-0'.PZ.e'!&3%&%<2'2&='I32$%<20'.PPAe'.PPZc)'d3'#$'_%$$#9"-'3423'>25Q-5'Q#=T$DQQ-5'b(2&D256c'AZO'
3-Q_-523D5-$'#&'G-&352"'C2$Q2&#2'G%D"='5-$D"3'#&'34#&&-5'G-""'>2""$'2&='4-&G-'"%>-5'5#&K'=-&$#36)'C4#$'AZ.'
Q-G42&#$Q'42$'2"$%'9--&'#&<%@-='3%'-a_"2#&'34-'&-K23#<-'5-"23#%&$4#_'9-3>--&'3-Q_-523D5-'2&='AZ7'
>%%='=-&$#36'#&']D%&'F#&-0'2"$%'#&'C2$Q2&#2'b,5->'-3'2")'7O.Ac)'d&'G%&352$30'2'&DQ9-5'%J'$3D=#-$'AZA'
92$-='%&'4#K4'-"-<23#%&'U%534-5&']-Q#$_4-5-'$#3-$'42<-'J%D&='3423'"%>'5234-5'342&'4#K4'AZE'
3-Q_-523D5-$'D$D2""6'"#Q#3'G-""'>2""'=-<-"%_Q-&3'2&='35--'K5%>34'=D5#&K'34-'K5%>#&K'$-2$%&'bH5#JJ2'AZY'
-3'2")0'7OO7e'8%$$#'-3'2")0'7OOXc)''AZN'
M-'J%D&='3423'Q-2&'=-&$#36'%J'A.*cupressoides'$4%>$'K5-23-5'<25#29#"#36'23'4#K4'J5-fD-&G#-$'b7`.NTAZZ'
6-25'92&=$e'R#K)'E2`9c'342&'C8M0'9D3'C8M'$4%>$'K5-23-5'<25#29#"#36'23'"%>'J5-fD-&G#-$'bNE`.7XT6-25'AZX'
92&=$c0'_253#GD"25"6'%<-5'34-'"2$3'3>%'G-&3D5#-$'bR#K)'EG`=c)'C4#$'J#&=#&K'#$'G%&$#$3-&3'>#34'%34-5'AZP'
$3D=#-$0'>4#G4'42<-'$4%>&'3423'=-&$#36'K-&-52""6'G%&32#&$'$35%&K-5'#&3-5T2&&D2"'<25#23#%&'2&='AXO'
>-2@-5'"%>TJ5-fD-&G6'<25#23#%&'342&'C8M'bIG4>-#&K5D9-5'-3'2")0'.PZPe',\!55#K%'-3'2")0'.PP7e'R52&@'AX.'
2&=';$_-50'7OOYe'R52&@'-3'2")0'7OOZe'Hk&3K-&'-3'2")0'7O.Oc)'C8M'3-&=$'3%'#&3-K523-'-JJ-G3$'J5%Q'34-'AX7'
_5-<#%D$'6-25\$'G"#Q23#G'2&='-&<#5%&Q-&32"'G%&=#3#%&$0'>4#G4'G2&'"-2='3%'=#JJ-5#&K'$#K&2"T3%T&%#$-'AXA'
523#%$'%&'#&3-5T2&&D2"'2&='QD"3#T=-G2=2"'3#Q-$G2"-$'bR52&@'-3'2")0'7OOZc)']-5-'>-'42<-'G%Q9#&-='34-'AXE'
G%Q_"-Q-&3256'4#K4T'2&='"%>TJ5-fD-&G6'$#K&2"$'#&'Q-2&'=-&$#36'2&='C8M0'5-$_-G3#<-"60'3%'_5%=DG-'AXY'
2'5%9D$3'5-G%&$35DG3#%&'%J'_2$3'3-Q_-523D5-$'bR#K$)'E-'2&='Z2c)'AXN'
4.2$Past$temperature$variability$in$southeastern$Australia$AXZ'
F-5#%=$'%J'"%>-5T342&T2<-52K-'-25"6'$DQQ-5'3-Q_-523D5-'#&'34-'"23-'.XOO$'2&='-25"6'.POO$'#&'%D5'AXX'
5-G%&$35DG3#%&'G%#&G#=-'>#34'34-'G%"=-$3'_-5#%='b.XPO`.PNO'?);)c'#&'34-':3'8-2='5-G%&$35DG3#%&'%J'AXP'
$DQQ-5'Q-2&'3-Q_-523D5-$'bR#K)'Zc'2&='2'K-&-52"'G%%"#&K'35-&='#&'Q%$3'5-K#%&$'%J'34-'>%5"='#&'34-'APO'
"23-'.P34'?-&3D56'bF!j;I'7@'?%&$%53#DQ0'7O.Ac)'I#Q#"25"60'_%$3T.PYO'>25Q#&K'#&'%D5'5-G%&$35DG3#%&'#$'AP.'
G%&$#$3-&3'>#34'34-'>25Q#&K'35-&='#&'34-':3'8-2='C8MT92$-='3-Q_-523D5-'5-G%&$35DG3#%&'bR#K'Ze'AP7'
?%%@'-3'2")0'.PP.e'?%%@'-3'2")0'.PP7e'?%%@'-3'2")0'7OOOc0'2&'%9$-5<-='#&G5-2$-'#&'K5%>34'#&'Halocarpus'APA'
#&'U->'w-2"2&='bB#""2"92'-3'2")0'7O.7c0'2&='95%2=T$G2"-'>25Q#&K'#&'34-'I%D34-5&']-Q#$_4-5-'%<-5'34-'APE'
$2Q-'_-5#%='bF!j;I'7@'?%&$%53#DQ0'7O.Ac)'C4#$'5-G-&3'>25Q'_-5#%='2__-25$'3%'9-'2335#9D3-='3%'2'APY'
=-G5-2$-'#&'34-'J5-fD-&G6'2&='Q2K&#3D=-'%J'G%"='$DQQ-5$'5234-5'342&'2&'#&G5-2$-'#&'34-'J5-fD-&G6'APN'
2&=h%5'Q2K&#3D=-'%J'>25Q'$DQQ-5$'bR#K)'Zc)'C4#$'#$'G%&$#$3-&3'>#34'%9$-5<-='5-G-&3'2&='_5%i-G3-='APZ'
&-25T3-5Q'=-G"#&-$'#&'34-'J5-fD-&G6'%J'G%"='=26$'#&'!D$352"#2'b!"-a2&=-5'2&='!59"2$3-50'7OOPc'2&='APX'
K"%92""6'b1#53Q2&'-3'2")0'7O.Ac)'APP'
8-G-&3'35-&=$'#&'4-Q#$_4-5#G'b#)-)0'34-'I!:c'2&='5-K#%&2"'b#)-)0'34-'IC8c'G#5GD"23#%&'J-23D5-$'25-'"#@-"6'EOO'
3%'42<-'_"26-='2'5%"-'#&'34-'%9$-5<-='#&G5-2$#&K'35-&='#&'$DQQ-5'3-Q_-523D5-$'#&'G-&352"'C2$Q2&#2'EO.'
%<-5'34-'"2$3'42"J'G-&3D56)'d&'34-'!D$352"'$DQQ-50'34-'IC8'#$'D$D2""6'#&'#3$'Q%$3'$%D34-5"6'_%$#3#%&0'EO7'
$%D34'%J'34-'!D$352"#2&'G%&3#&-&3)'d&'34#$'_%$#3#%&0'34-'4#K4T_5-$$D5-'$6$3-Q$'2"%&K'34-'IC8'3-&='3%'EOA'
9"%G@'b%5'>-2@-&c'Q#=T"23#3D=-'J5%&32"'$6$3-Q$'J5%Q'5-2G4#&K'$%D34-2$3-5&'!D$352"#20'95#&K#&K'EOE'
>25Q-5'2&='=5#-5'G%&=#3#%&$'3%'34-'5-K#%&'b!D$352"#2&'HD5-2D'%J':-3-%5%"%K60'7OOXc)'I35%&K-5'EOY'
#&3-&$#3#-$'%J'34-'IC8'42<-'2"$%'9--&'"#&@-='3%'$D__5-$$-='52#&J2""'%<-5'34-'5-K#%&'b:D5_46'2&='EON'
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7OONe'!D$352"#2&'HD5-2D'%J':-3-%5%"%K60'7OOXc)'dQ_%532&3"60'$#&G-'34-'.PPO$'#&'_253#GD"25'34-5-'42$'E7.'
9--&'2&'#&G5-2$#&K'=%Q#&2&G-'2&='$35-&K34-&#&K'%J'34-'_%$#3#<-'_42$-'%J'34-'I!:'bC4%Q_$%&'-3'2")0'E77'
7O..e'R#K'I.c)'C4#$'D&D$D2"'9-42<#%D5'%J'34-'I!:'42$'9--&'#Q_"#G23-='2$'2'=5#<-5'%J'5-G-&3'35-&=$'#&'E7A'
52#&J2""'#&'$-<-52"'5-K#%&$'%J'!D$352"#2'b?2#'2&='?%>2&0'7OONe'C2$G4-33%'2&=';&K"2&=0'7OOPe'R#-55%'2&='E7E'
S-$"#-0'7O.Ae'S2<-&=-5'2&='!99$0'7O.Ae'82D3'-3'2")0'7O.Ee'*\,%&&-""'-3'2")0'7O.Yc)'C4-'$#K&#J#G2&3'E7Y'
G%55-"23#%&$'9-3>--&'%D5'5-G%&$35DG3#%&'2&='34-'I!:'%<-5'34-'"2$3'G-&3D56'$DKK-$3'3423'34-'_-5#%='E7N'
%J'>25Q'-25"6T$DQQ-5'3-Q_-523D5-$'#&'C2$Q2&#2'=D5#&K'34-'"23-'7O34'?-&3D56'#$'2"$%'"#@-"6'=5#<-&'#&'E7Z'
_253'96'34-'#&G5-2$#&K'=%Q#&2&G-'%J'34-'_%$#3#<-'_42$-'%J'34-'I!:'#&'$DQQ-5'2&='2'$D9$-fD-&3'E7X'
>-2@-&#&K'%J'34-'_5-<2#"#&K'>-$3-5"6'>#&=$'%<-5'C2$Q2&#2)''E7P'
Conclusions$$EAO'
*D5'J#&=#&K$'4#K4"#K43'34-'_%3-&3#2"'3%'D$-'34-'G%Q_"-Q-&3256'G"#Q23-'$#K&2"$'#&'C8M'2&='Q-2&'EA.'
=-&$#36'3%'_5%<#=-'2'Q%5-'G%Q_"-3-'_#G3D5-'%J'#&3-5T2&&D2"'3%'G-&3-&&#2"T$G2"-'<25#23#%&'#&'_2$3'EA7'
3-Q_-523D5-$'#&'$%D34-2$3-5&'!D$352"#2)'C4-'5-"23#<-'>-2@&-$$'%J'34-'C8M'$#K&2"'2"%&-'Q-2&30'D&3#"'EAA'
&%>0'#3'42$'&%3'9--&'_%$$#9"-'3%'5-G%&$35DG3'3-Q_-523D5-$'J5%Q'A.*cupressoides)''M%%='=-&$#36'EAE'
_5%<#=-$'&->'%__%53D&#3#-$'J%5'$DGG-$$JD"'3-Q_-523D5-'5-G%&$35DG3#%&$'#&'$%D34-2$3-5&'!D$352"#2'EAY'
J5%Q'35--'$_-G#-$'#&'>4#G4'C8M'#$'%&"6'>-2@"6'G%55-"23-='>#34'3-Q_-523D5-)'H%34'%D5'C8MvQ-2&'EAN'
=-&$#36T92$-='(2&CQ2a'5-G%&$35DG3#%&'2&='34-':3'8-2='C8MT92$-='Q-2&'$DQQ-5'3-Q_-523D5-'EAZ'
5-G%&$35DG3#%&'$4%>'3423'$DQQ-5'3-Q_-523D5-$'#&'C2$Q2&#2'42<-'K-&-52""6'9--&'>25Q-5'#&'34-'"2$3'EAX'
42"J'G-&3D56'342&'#&'34-'_5-<#%D$'J#<-'G-&3D5#-$0'G%#&G#=-&3'>#34'95%2=T$G2"-'3-Q_-523D5-'#&G5-2$-$'EAP'
#&'34-'I%D34-5&'2&='U%534-5&']-Q#$_4-5-$)'M25Q-5'3-Q_-523D5-$'#&'G-&352"'C2$Q2&#2'#&'34-'"2$3'EEO'
42"JTG-&3D56'Q26'9-'5-"23-='3%'$4#J3$'#&'34-'9-42<#%D5'%J'I%D34-5&']-Q#$_4-5-'G#5GD"23#%&'J-23D5-$'EE.'
b#)-)0'34-'#&G5-2$#&K'=%Q#&2&G-'%J'34-'_%$#3#<-'I!:'2&='34-'#&3-&$#J#G23#%&'%J'34-'IC8c)'I#K&#J#G2&3'EE7'
G%55-"23#%&$'9-3>--&'%D5'5-G%&$35DG3#%&'2&='34-$-'G#5GD"23#%&'J-23D5-$'#&'34-'I%D34-5&']-Q#$_4-5-'EEA'
2"$%'$DKK-$3'3423'35--T5#&K'5-G%&$35DG3-='3-Q_-523D5-$'J5%Q'C2$Q2&#2'5-J"-G3'95%2=T$G2"-'G"#Q23-'EEE'
<25#29#"#36'#&'34-'3-Q_-523-'"23#3D=-$'%J'34-'I%D34-5&']-Q#$_4-5-)'EEY'
Acknowledgements$EEN'
C4#$'5-$-25G4'>2$'JD&=-='96'2&'!D$352"#2&'8-$-25G4'?%D&G#"',#$G%<-56'F5%i-G3'K52&3'b,F.7O.OEA7O'EEZ'
3%'F(Hc)'M-'25-'K523-JD"'3%':#G42-"'j%==25='J%5'2$$#$32&G-'#&'_5-_25#&K'G%5-'$2Q_"-$'J%5'2&2"6$#$0'EEX'
IG%33'U#G4%""$'J%5'2$$#$32&G-'#&'_5-_25#&K'2&='2&2"6$#&K'$2Q_"-$0'2&='34-'_253#G#_2&3$'%J'34-'EEP'
,-&=5%G"#Q23%"%K6':2$3-5G"2$$'2$'_253'%J'M%5"=,-&=5%7O.E['!&=-5$'H5D&=#&0'H#&%=',2>2=#0'EYO'
U2342&';&K"#$40':225#3'12"-"2TH5D&=#&0'8%9-53'1-&&-=60'1234-"6&'F25-=-$0'2&=':-5#3a-""'82Q#5-WTEY.'
*""-)'M-'25-'2"$%'K523-JD"'3%'M2$6"',5%$=%>$@6'J%5'_5%<#=#&K'34-'IC8'#&3-&$#36'#&=-a'=232'2&=':253#&'EY7'
B#$9-G@'J%5'_5%<#=#&K'34-'Q%&34"6'#&$35DQ-&3T92$-='5-G%&$35DG3#%&'%J'34-'I!:)'S2Q%&3T,%4-536'EYA'
;2534'*9$-5<23%56'?%&35#9D3#%&'U%)'llll'EYE'
References$EYY'
!"-a2&=-50'S)'B0'!59"2$3-50'():)0'7OOP)'!$$-$$#&K'35-&=$'#&'%9$-5<-='2&='Q%=-""-='G"#Q23-'-a35-Q-$'EYN'
%<-5'!D$352"#2'#&'5-"23#%&'3%'JD3D5-'_5%i-G3#%&$)'d&3)'()'?"#Q23%")'7P0'E.Z`EAY)'EYZ'
=%#[.O).OO7hi%G).ZAO'EYX'
!""-&0'1)0',5->0',):)0',%>&-$0'j):)0';<2&$0'8)0'H2@-50'F)0'j5%$-0':)0'7O.7)'8#&K'>#=340'G"#Q23-'2&='EYP'
>%%='=-&$#36'5-"23#%&$4#_$'#&'3>%'"%&KT"#<-='C2$Q2&#2&'35--'$_-G#-$)',-&=5%G45%&%"%K#2'AO0'ENO'
.NZ`.ZZ)'=%#[433_[hh=a)=%#)%5Kh.O).O.Nhi)=-&=5%)7O.O).7)OON'EN.'
!""-&0'1)0'R52&G-60'8)0':#G42-"0'1)0'UD&-W0':)0'.PPP)'!'$35DG3D52"'3#Q-'$-5#-$'2__5%2G4'3%'34-'EN7'
5-G%&$35DG3#%&'%J'C2$Q2&#2&'Q2a#QDQ'3-Q_-523D5-$)';&<#5%&)':%=-")'I%J3>)'.E0'7N.`7ZE)'ENA'
=%#[.O).O.NhI.ANETX.Y7bPXcOOOZXTE'ENE'
!""-&0'1)()0'?%%@0';)8)0'HDG@"-60'H):)0'S25$-&0'I)])0',5->0',):)0',%>&-$0'j):)0'R52&G-60'8)()0'F-3-5$%&0'ENY'
:)()0'H2@-50'F)()0'7O.E)'?%&3#&D#&K'D_>25='35-&='#&':3'8-2=']D%&'_#&-'5#&K'>#=34$'`'ENN'
C-Q_-523D5-'%5'=#<-5K-&G-x'yD23)'IG#)'8-<)'.O70'AP`YA)'=%#[.O).O.Nhi)fD2$G#5-<)7O.E)OX)OOA'ENZ'
!""-&0'1)()0',5->0',):)0',%>&-$0'j):)0';<2&$0'8)0'?%%@0';)8)0'H2332K"#20':)0'H2@-50'F)()0'7O.A)'!'$35%&K'ENX'
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=%#[.O).OO7hif$)7NAZ'EZO'
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F5-"#Q#&256',-G-Q9-5T(2&D256'#&J"%>'2&='$35-2QJ"%>'5-G%&$35DG3#%&$'J5%Q'35--'5#&K$'J%5'EZ7'
>-$3-5&'C2$Q2&#20'$%D34-2$3-5&'!D$352"#2)'M23-5'8-$%D5)'8-$)'Y.0'YEXZ`YYOA)'EZA'
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$_-G#-$0'C2$Q2&#2)'?"#Q)',6&)'AZ0'.ZPP`.X7.)'=%#[.O).OOZh$OOAX7TO..T.O..TZ'EZZ'
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433_[hh>>>)9%Q)K%<)2Dh>23"h29%D3T>-234-5T2&=TG"#Q23-h2D$352"#2&TG"#Q23-T#&J"D-&G-$)$43Q"'EXA'
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1970#
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1980#
1985#
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1995#
2000#
2005#
2010#
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Year (C.E.)
... To avoid confusion, we henceforth refer to absorbed BI unless explicitly stated otherwise. X-ray studies have shown great potential in the SH (Xiong et al., 1998;Drew et al., 2013;O'Donnell et al., 2016) and we hypothesise that BI, as an X-ray surrogate, will likely prove successful in this region. ...
... This increased ability to capture summer temperatures mimics findings from the NH Fuentes et al., 2018) and suggests similar success could be achieved in the SH, particularly S. A.P. Blake, et al. Dendrochronologia 60 (2020) 125664 from species that have documented relationships between density and temperature such as Anthrotaxis cupressoides (Allen et al., 2012;O'Donnell et al., 2016), Lagarostrobos franklinnii and Phyllocladus aspleniifolius (Allen et al., 2012) from Tasmania and Halocarpus biformis (Xiong et al., 1998) from New Zealand. Further research into the differing ecophysiology of SH and NH conifers and pine trees could clarify some of our other results. ...
... In S. A.P. Blake, et al. Dendrochronologia 60 (2020) 125664 comparison, negative correlations between density parameters and temperature have been recorded in several dendrochronological species in the SH alongside M. colensoi, including Lagarostrobus franklinii, Halocarpus biformis and Athrotaxis cupressoides (Xiong et al., 1998;Allen et al., 2012;Drew et al., 2013;Brookhouse and Graham, 2016;O'Donnell et al., 2016). Earlywood density has also proven to have similar climatic sensitivity to latewood in other SH species as well (Xiong et al., 1998). ...
Article
This study investigates if Blue Intensity (BI) parameters are capable of capturing enhanced climatic signals from a key New Zealand dendrochronological species when compared to ring-width (RW) measurements. Three BI parameters (earlywood mean, latewood mean and maximum latewood) recorded generally superior correlations to temperature than conventional RW measurements and these correlations appeared more stable over time. For all BI parameters, austral summer (Dec-Feb) was identified as the strongest reconstructive window. However, all the BI parameters required greater sample replication than RW to reach widely accepted thresholds of chronology reliability, highlighting the need for further examination into the physiological controls on the BI signal. Despite the need for further research, this pilot study is strongly encouraging for the wider application of BI in Southern Hemisphere dendrochronology.
... Latewood density appears to modestly increase in wet years (Cleaveland, 1986), most likely due to increased deposition in the cell wall, but this has not been explicitly studied. Interestingly, this robust earlywood and latewood dichotomy of Northern Hemisphere conifers does not generally apply to Australasian conifers where earlywood and latewood densities are both negatively influenced by increased temperatures (Drew et al., 2012;O'Donnell et al., 2016). Drew et al. (2012) suggest that for Australasian conifers, increased temperatures reduce the duration of the wall thickening leading to a net decrease in latewood density. ...
... These studies have predominantly been conducted in Australia uncovering significant climate information imprinted in various density and tracheid dimensions, for trees where ring width has been unusable (Allen et al., 2013;Drew et al., 2012). O'Donnell et al. (2016) and Allen et al. (2018) reconstructed temperature, and Allen et al. (2015) explored stream flow in Tasmania using density data. Outside of Australia, SilviScan wood density has been used in reconstructing summer temperature (Wood & Smith, 2015) and partially involved in reconstructing Glacier mass balance (Wood & Smith, 2013) in Canada. ...
Article
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X‐ray microdensitometry on annually resolved tree‐ring samples has gained an exceptional position in last‐millennium paleoclimatology through the maximum latewood density (MXD) parameter, but also increasingly through other density parameters. For 50 years, X‐ray based measurement techniques have been the de facto standard. However, studies report offsets in the mean levels for MXD measurements derived from different laboratories, indicating challenges of accuracy and precision. Moreover, reflected visible light‐based techniques are becoming increasingly popular, and wood anatomical techniques are emerging as a potentially powerful pathway to extract density information at the highest resolution. Here we review the current understanding and merits of wood density for tree‐ring research, associated microdensitometric techniques, and analytical measurement challenges. The review is further complemented with a careful comparison of new measurements derived at 17 laboratories, using several different techniques. The new experiment allowed us to corroborate and refresh “long‐standing wisdom” but also provide new insights. Key outcomes include (i) a demonstration of the need for mass/volume‐based recalibration to accurately estimate average ring density; (ii) a substantiation of systematic differences in MXD measurements that cautions for great care when combining density data sets for climate reconstructions; and (iii) insights into the relevance of analytical measurement resolution in signals derived from tree‐ring density data. Finally, we provide recommendations expected to facilitate futureinter‐comparability and interpretations for global change research.
... Excluding MMWTRL (King Billy pine) and HUP (Kauri), which do not have any significant correlations with temperature in the growing season, all the LWB chronologies express positive correlations with summer and early autumn temperatures. This antithetic behaviour is not a new observation and has been noted by Drew et al. (2012), O'Donnell et al. (2016), and Blake et al. (2020) for latewood anatomical parameters and LWB data, but these new results suggest that this physiological phenomenon is not based on a chance occurrence of a single species and is consistent between several Australasian conifer tree species (pencil pine, Huon pine (low elevation), Kauri, NZ cedar, silver pine, and pink pine). Blake et al. (2020) explained the inverse LWB relationship as a reduction in the duration of secondary cell wall thickening in warmer years. ...
Article
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We evaluate a range of blue intensity (BI) tree-ring parameters in eight conifer species (12 sites) from Tasmania and New Zealand for their dendroclimatic potential, and as surrogate wood anatomical proxies. Using a dataset of ca. 10–15 trees per site, we measured earlywood maximum blue intensity (EWB), latewood minimum blue intensity (LWB), and the associated delta blue intensity (DB) parameter for dendrochronological analysis. No resin extraction was performed, impacting low-frequency trends. Therefore, we focused only on the high-frequency signal by detrending all tree-ring and climate data using a 20-year cubic smoothing spline. All BI parameters express low relative variance and weak signal strength compared to ring width. Correlation analysis and principal component regression experiments identified a weak and variable climate response for most ring-width chronologies. However, for most sites, the EWB data, despite weak signal strength, expressed strong coherence with summer temperatures. Significant correlations for LWB were also noted, but the sign of the relationship for most species is opposite to that reported for all conifer species in the Northern Hemisphere. DB results were mixed but performed better for the Tasmanian sites when combined through principal component regression methods than for New Zealand. Using the full multi-species/parameter network, excellent summer temperature calibration was identified for both Tasmania and New Zealand ranging from 52 % to 78 % explained variance for split periods (1901–1950/1951–1995), with equally robust independent validation (coefficient of efficiency = 0.41 to 0.77). Comparison of the Tasmanian BI reconstruction with a quantitative wood anatomical (QWA) reconstruction shows that these parameters record essentially the same strong high-frequency summer temperature signal. Despite these excellent results, a substantial challenge exists with the capture of potential secular-scale climate trends. Although DB, band-pass, and other signal processing methods may help with this issue, substantially more experimentation is needed in conjunction with comparative analysis with ring density and QWA measurements.
... Moreover, only few of the abovementioned tree-ring parameters, other than TRW, have been examined in the SH, with the exception of density and cellular parameters in Australia & New Zealand (e.g. Allen et al., 2012Allen et al., , 2019Allen et al., ,2018Blake et al., 2020;Drew et al., 2013;O'Donnell et al., 2016) and stable isotopes in South America (e.g. Lavergne et al., 2017;Roig et al., 2006) and in Central Africa (Colombaroli et al., 2016). ...
Article
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Clanwilliam cedar (Widdringtonia cedarbergensis; WICE), a long-lived conifer with distinct tree rings inCape Province, South Africa, has potential to provide a uniquely high-resolution climate proxy for southern Africa. However, the climate signal in WICE tree-ring width (TRW) is weak and the dendroclimatic potential of other WICE tree-ring parameters therefore needs to be explored. Here, we investigate the climatic signal in various tree-ring parameters, including TRW, Minimum Density (MND), Maximum Latewood Density (MXD), Maximum Latewood Blue Intensity (MXBI), and stable carbon and oxygen isotopes (δ¹⁸O and δ¹³C) measured in WICE samples collected in 1978. MND was negatively influenced by early spring (Oct-Nov) precipitation whereas TRW was positively influenced by spring November-December precipitation. MXD was negatively influenced by autumn (April-May) temperature whereas MXBI was not influenced by temperature. Both MXD and MXBI were negatively influenced by Jan-March and Jan-May precipitation respectively. We did not find a significant climate signal in either of the stable isotope time series, which were measured on a limited number of samples. WICE can live to be at least 356 years old and the current TRW chronology extends back to 1564 CE. The development of full-length chronologies of alternative tree-ring parameters, particularly MND, would allow for an annually resolved, multi-century spring precipitation reconstruction for this region in southern Africa, where vulnerability to future climate change is high.
... The two types of information can be matched using the radial growth rate of the tree. In this way, the variations in the core properties can be related to the external (silvicultural and climatic) influences acting on the cambium [125][126][127][128][129][130][131][132][133][134][135][136][137][138][139][140][141]. In addition, cambial process modeling is greatly accelerated by the availability of high-resolution wood property information. ...
Article
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To maximize utilization of our forest resources, detailed knowledge of wood property variation and the impacts this has on end-product performance is required at multiple scales (within and among trees, regionally). As many wood properties are difficult and time-consuming to measure our knowledge regarding their variation is often inadequate as is our understanding of their responses to genetic and silvicultural manipulation. The emergence of many non-destructive evaluation (NDE) methodologies offers the potential to greatly enhance our understanding of the forest resource; however, it is critical to recognize that any technique has its limitations and it is important to select the appropriate technique for a given application. In this review, we will discuss the following technologies for assessing wood properties both in the field: acoustics, Pilodyn, Resistograph and Rigidimeter and the lab: computer tomography (CT) scanning, DiscBot, near infrared (NIR) spectroscopy, radial sample acoustics and SilviScan. We will discuss these techniques, explore their utilization, and list applications that best suit each methodology. As an end goal, NDE technologies will help researchers worldwide characterize wood properties, develop accurate models for prediction, and utilize field equipment that can validate the predictions. The continued advancement of NDE technologies will also allow researchers to better understand the impact on wood properties on product performance.
... Running correlations also show a weakening of the association with temperature at the modern end of the non-signal-free chronologies, although the correlation does remain significantly negative (Figure 7; S3) and there is no significant low frequency modulation (Table 2). O'Donnell et al. (2016) also noted an issue with the last half decade of the density chronology from this same site and found that excluding these years from the model improved the reconstruction. The pattern of difference between early and late period correlations is the same across different standardisations but only differences for ADspl and Medspl are significant (Figure 8, S4). ...
Article
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Temporal stability of the relationship between a potential proxy climate record and the climate record itself is the foundation of palaeoproxy reconstructions of past climate variability. Dendroclimatologists have spent considerable effort exploring the issue of temporal instability of temperature records at high-latitude and −altitude Northern Hemisphere sites. Much of this work has focused on the Divergence Problem in which the modern ends of tree-ring chronologies exhibit pronounced departures from the climate-proxy relationships of preceding decades. However, there has been little scrutiny of how different methods might influence determinations of temporal instability at either the local scale or across broader spatial domains. Here we use four sets of Southern Hemisphere (SH) chronologies and three sets of synthetic data with known interventions to compare four methodologies that have been widely used to assess the temporal stability of relationships between tree-ring series and climate. Our analyses demonstrate that a determination of temporal instability may be partially dependent on method used to examine data, that some methods are more sensitive to standardisation choice than others, and that all methods are better at detecting high- rather than low-frequency instability. In all cases, the relatively modest strength of the relationships between the selected SH ring-width chronologies and temperature is likely to be an issue, especially if changes in trends are of interest. We recommend that robust assessment of temporal instability between tree-ring chronologies and observational climate data should use a range of methods and that unstable temporal relationships across space be carefully considered in the context of large climate field reconstructions.
... What is less clear from instrumental data, particularly for Australia with its limited records (typically < 100 years), is whether such persistent warm conditions have occurred in previous centuries. Over the past two decades, however, significant efforts have extended the annually resolved Australasian temperature record by several hundred years (Cook et al. 2000;2002;PAGES 2K Consortium 2013;Saunders et al. 2013;O'Donnell et al. 2016;Gergis et al. 2016). Without exception, these reconstructions show a strong and 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 A c c e p t e d M a n u s c r i p t 4 These considerable uncertainties underscore the pressing need for further development of climate-sensitive proxies to improve our understanding of historical climate variability and to assess current climate extremes. ...
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Very few annually resolved millennial-length temperature reconstructions exist for the Southern Hemisphere. Here we present four 979-year reconstructions for southeastern Australia for the austral summer months of December–February. Two of the reconstructions are based on the Australian Water Availability Project dataset and two on the Berkeley Earth Surface Temperature dataset. For each climate data set, one reconstruction is based solely on Lagarostrobos franklinii (restricted reconstructions) while the other is based on multiple Tasmanian conifer species (unrestricted reconstructions). Each reconstruction calibrates ~50−60% of the variance in the temperature datasets depending on the number of tree-ring records available for the reconstruction. We found little difference in the temporal variability of the reconstructions, although extremes are amplified in the restricted reconstructions relative to the unrestricted reconstructions. The reconstructions highlight the occurrence of numerous individual years, especially in the 15th−17th Centuries, for which temperatures were comparable with those of the late 20th Century. The 1950−1999 period, however, stands out as the warmest 50-year period on average for the past 979 years, with a sustained shift away from relatively low mean temperatures, the length of which is unique in the 979-year record. The reconstructions are strongly and positively related to temperatures across the southeast of the Australian continent, negatively related to temperatures in the north and northeast of the continent, and uncorrelated with temperatures in the west. The lack of a strong relationship with temperatures across the continent highlights the necessity of a sub-regional focus for Australasian temperature reconstructions.
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We evaluate a range of blue intensity (BI) tree-ring parameters in eight conifer species (12 sites) from Tasmania and New Zealand for their dendroclimatic potential, and as surrogate wood anatomical proxies. Using a dataset of ca. 10–15 trees per site, we measured earlywood maximum blue reflectance intensity (EWB), latewood minimum blue reflectance intensity (LWB) and the associated delta blue intensity (DB) parameter for dendrochronological analysis. No resin extraction was performed, impacting low frequency trends. Therefore, we focused only on the high frequency signal by detrending all tree-ring and climate data using a 20-year cubic smoothing spline. All BI parameters express low relative variance and weak signal strength compared to ring-width. Correlation analysis and principal component regression experiments identified a weak and variable climate response for most ring-width chronologies. However, for most sites, the EWB data, despite weak signal strength, expressed strong calibrations with summer temperatures. Significant correlations for LWB were also noted, but the sign of the relationship for most species is opposite to that reported for all conifer species in the Northern Hemisphere. DB performed well for the Tasmanian sites but explained minimal temperature variance in New Zealand. Using the full multi-species/parameter network, excellent summer temperature calibration was identified for both Tasmania and New Zealand ranging from 52 % to 78 % explained variance, with equally robust independent validation (Coefficient of Efficiency = 0.41 to 0.77). Comparison of the Tasmanian BI reconstruction with a wood anatomical reconstruction shows that these parameters record essentially the same strong high frequency summer temperature signal. Despite these excellent results, a substantial challenge exists with the capture of potential secular scale climate trends. Although DB, band-pass and other signal processing methods may help with this issue, substantially more experimentation is needed in conjunction with comparative analysis with ring density and quantitative WA measurements.
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However, for much of the Southern Hemisphere, the ability to identify spatial patterns of past climatic variability is constrained by the sparse distribution of proxy records. This is particularly true for mainland Australia, where relatively few proxy records are located. In this paper we (1) assess the potential to use existing proxy records in the Australasian region—starting with the only two multi-century tree-ring proxies from mainland Australia—to reveal spatial patterns of past hydroclimatic variability across the western third of the continent, and (2) identify strategic locations to target for the development of new proxy records. We show that the two existing tree-ring records allow robust reconstructions of past hydroclimatic variability over spatially broad areas (i.e. >3◦ ×3◦) in inland north- and south-western Australia. Our results reveal synchronous periods of drought and wet conditions between the inland northern and southern regions of western Australia as well as a generally anti-phase relationship with hydroclimate in eastern Australia over the last two centuries. The inclusion of 174 tree-ring proxy records from Tasmania, New Zealand and Indonesia and a coral record from Queensland did not improve the reconstruction potential over western Australia. However, our findings suggest that the addition of relatively few new proxy records from key locations in western Australia that currently have low reconstruction skill will enable the development of a comprehensive drought atlas for the region, and provide a critical link to the drought atlases of monsoonal Asia and eastern Australia and New Zealand.
Chapter
Recent and projected changes in climate highlight the need to understand and predict human-environment interactions. However the diversity of cultures, livelihoods, and political formations today and in the past indicate that relationships between climate, ecosystems, and societies are likely non-linear, complex, and variable over time. A growing network of multi-millennial, absolutely dated annual tree-ring records from Asia provide climatic context for several important historic events which emphasize the diversity of human-environment interactions. Tree rings are an ideal proxy for understanding these relationships due to their extensive spatial coverage, temporal resolution relevant to human systems, and their ability to integrate climate in ways that relate directly to human livelihoods. Herein, we focus on five examples of human-environment interactions in two climatically and culturally distinct regions of Asia: Monsoon and Arid Central Asia. Over the last three millennia, societies have adjusted to climate variability in diverse and (mal)adaptive ways. In Monsoon Asia, drastic swings in moisture availability, notably megadroughts associated with monsoon failure, interacted with socio-political and technical institutions to spur the disintegration of the fourteenth century Khmer kingdom at Angkor and engender continental scale famine in nineteenth century colonial Asia. In Arid Central Asia, elevated temperatures and moisture were both a boon and a limitation for historic and modern nomadic pastoralists, depending on the historical context in which climatic events occurred. Future efforts to statistically model and predict human-environment relationships over the arc of human history in Asia will need to account for the diversity of economic, political, and cultural features that filter, dampen, and amplify the effects of climate change on society.
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Agricultural production across eastern Australia and New Zealand is highly vulnerable to drought, but there is a dearth of observational drought information prior to CE 1850. Using a comprehensive network of 176 drought-sensitive tree-ring chronologies and one coral series, we report the first Southern Hemisphere gridded drought atlas extending back to CE 1500. The austral summer (December–February) Palmer drought sensitivity index reconstruction accurately reproduces historically documented drought events associated with the first European settlement of Australia in CE 1788, and the leading principal component explains over 50% of the underlying variance. This leading mode of variability is strongly related to the Interdecadal Pacific Oscillation tripole index (IPO), with a strong and robust antiphase correlation between (1) eastern Australia and the New Zealand North Island and (2) the South Island. Reported positive, negative, and neutral phases of the IPO are consistently reconstructed by the drought atlas although the relationship since CE 1976 appears to have weakened.
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This article presents a late summer temperature reconstruction (AD 1675–1980) for the northeastern Mediterranean (NEMED) that is based on a compilation of maximum latewood density tree-ring data from 21 high-elevation sites. This study applied a novel approach by combining individual series from all sites into one NEMED master chronology. This approach retains only the series with a strong and temporally robust common signal and it improves reconstruction length. It further improved the regional character of the reconstruction by using as a target averaged gridded instrumental temperature data from a broad NEMED region (38–45°N, 15–25°E). Cold (e.g. 1740) and warm (e.g. 1945) extreme years and decades in the reconstruction correspond to regional instrumental and reconstructed temperature records. Some extreme periods (e.g. cold 1810s) reflect European-wide or global-scale climate conditions and can be explained by volcanic and solar forcing. Other extremes are strictly regional in scope. For example, 1976 was the coldest NEMED summer over the last 350 years, but was anomalously dry and hot in northwestern Europe and is a strong manifestation of the summer North Atlantic Oscillation (sNAO). The regional NEMED summer reconstruction thus contributes to an improved understanding of regional (e.g. sNAO) vs. global-scale (i.e. external) drivers of past climate variability.
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Traditionally tree ring analysis has been used for dating structures and reconstructing past climates. Recently it has been recognised as a valuable tool in detecting environmental change. This wide-ranging review of current knoledge is organised around six chapters, each of them with from 5-11 sections contributed by different authors: the authors are drawn from all over the world and have knowledge of both Northern and Southern Hemispheres. Appendix A lists tree species known to have been used to build tree-ring chronologies, Appendix B provides a sample site and tree information form for tree ring collections, and Appendix C lists sources of tree ring analytical programs and measuring stages. The consolidated bibliography (pp 351-391) totals about 650 references. The addresses of all authors are given on pp 393-394. -K.Clayton