SUPPLEMENTARY INFORMATION, Moberg et al.
Supplementary Note 1
PROXY DATA SELECTION
We strived at using records with complete data in the 2000-year reconstruction
period to avoid jumpwise inhomogeneities caused by a time-varying number of
proxy series. Such long records are scarce; in particular the available records end
in various years in the 20th century, wherefore it is impossible to reconstruct
temperatures up to the end of the last century using only proxy data. Records that
have been interpreted as reflecting annual mean temperatures were preferred, but
a strict application of such a criterion would have led to a too limited amount of
data. Therefore, data representing seasonal (mainly summer) temperatures were
also allowed. This is not necessarily a large problem, as summer temperatures
averaged over the entire hemisphere are strongly correlated with hemispheric
annual mean temperatures1, but it could be serious as differential trends in
summer and winter temperatures in the last millennium have been observed at
least for some locations in China and Europe2. In any case, all previous NH
temperature reconstructions also suffer from a warm-season bias2, and the
problem seems unavoidable until more winter-sensitive proxies have been
developed.
LOW-RESOLUTION PROXY DATA
The following eleven proxy data series (numbered as in Fig. 1 and Table 1) were
used as input to the low-frequency component of the reconstruction. Time series
plots of each series is shown in Fig. S1.
1. A stacked ice melt record derived from three ice cores from Agasssiz Ice cap,
Ellesmere Island, Canada3-4. It shows changes in percent summer melt reflecting
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SUPPLEMENTARY INFORMATION, Moberg et al.
summer temperatures. We used an arithmetic average of data from the files:
ftp://ftp.ngdc.noaa.gov/paleo/icecore/polar/agassiz/pcmelt77_5yr.txt
ftp://ftp.ngdc.noaa.gov/paleo/icecore/polar/agassiz/pcmelt84_5yr.txt
ftp://ftp.ngdc.noaa.gov/paleo/icecore/polar/agassiz/pcmelt87_5yr.txt
2. Annual temperature reconstruction based on ice borehole temperatures from
the Greenland Ice Core Project (GRIP) borehole3. Data source: digitized from Fig.
4B in ref. 5.
3. Summer temperature reconstruction derived from pollen composition in annually
laminated sediments from Conroy Lake in the eastern US6. We used Conroy Lake
data from: ftp://ftp.ngdc.noaa.gov/paleo/pollen/recons/liadata.txt
4. Spring temperature reconstruction estimated from variations in the Mg/Ca
composition in fossil shells from Chesapeake Bay in the eastern U.S. 7. Data
source: ftp://ftp.ngdc.noaa.gov/paleo/contributions_by_author/cronin2003/cronin2003.txt
5-6. Two annual sea surface temperature (SST) reconstructions, from the
Sargasso Sea8 and the Caribbean Sea9 respectively, estimated from the 18O ratio
in planctonic foraminifera. Data sources:
ftp://ftp.ngdc.noaa.gov/paleo/contributions_by_author/keigwin1996/fig4bdata
ftp://ftp.ngdc.noaa.gov/paleo/contributions_by_author/nyberg2002/nyberg2002.txt
7. Summer temperature reconstruction based on diatom composition in sediments
from Lake Tsuolbmajavri in northern Finland10. Data source: Atte Korhola and Jan
Weckström (personal communication).
8. Annual mean temperature reconstruction based on a stalagmite 18O record
from cave Søylegrotta, near Mo i Rana in northern Norway11. Data source: SteinErik Lauritzen (personal communication).
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SUPPLEMENTARY INFORMATION, Moberg et al.
9. Summer temperature reconstruction for the Beijing region, China, based on
observed correlation between stalagmite layer thickness (from the Shihua cave)
and temperature12. Data source:
ftp://ftp.ngdc.noaa.gov/paleo/speleothem/china/shihua_tan2003.txt
10. A Chinese multi-proxy composite regional annual mean temperature
reconstruction13 based on 9 individual records of various origins. We used the
“Weighted” version in ref. 13. Data source:
ftp://ftp.ngdc.noaa.gov/paleo/contributions_by_author/yang2002/china_temp.txt
11. A combination of two marine sediment records from the Arabian Sea 14-15 in
which the percentage of the foraminifera Globigerina bulloides reflects the extent of
ocean up-welling, which is determined by the strength of monsoons, which in turn
indirectly reflect both summer and winter large-scale temperature changes
through the differential seasonal heating and cooling of the Asian continent and
surrounding oceans16. We used data from Core 723A15 for the early years up to
1390 A.D. and data from Core RC273014 from 1391 to 1986 A.D. (c.f. Fig. 3c in ref.
15). Although this record reflects temperatures only indirectly, it was included to
improve the balance in the geographical distribution of proxy sites. Data sources:
ftp://ftp.ngdc.noaa.gov/paleo/contributions_by_author/anderson2002/anderson2002.txt
ftp://ftp.ngdc.noaa.gov/paleo/contributions_by_author/gupta2003/gupta2003.txt
TREE-RING DATA
Seven tree-ring temperature reconstructions (labeled as in Fig. 1) were used as
input to the high-frequency component of the reconstruction; four from northern
Eurasia and three from the western U.S. Their time series are plotted in Fig. S2.
The northern Eurasian records are obtained from conifer trees (Pinus silvestris
from northern Sweden17 (D in Fig. 1), Larix siberica from the Yamal Peninsula18
(E), Larix gmelinii from the Taimyr Peninsula19 (F) and Larix cajanderi Mayr from
the Indigirka River region in north-eastern Yakutia20 (G)) growing close to the
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SUPPLEMENTARY INFORMATION, Moberg et al.
northern tree line limit where summer temperature is the major growth-limiting
factor. The western U.S. records (A-C) are derived from Bristlecone Pine (Pinus
longaeva) growing in Nevada and California (Indian Garden, Methuselah Walk,
White Mountain Master; obtained from the International Tree Ring Data Bank,
http://www.ngdc.noaa.gov/paleo/treering.html)
and are primarily interpreted as reflecting
annual precipitation variability21. It has also been suggested that their signal at
decadal and longer timescales is sensitive to variability in warm-season
temperatures22 although this relationship is questionable21. We performed
correlation analysis between the average of the three western U.S. tree-ring series
and with temperatures averaged for the region 30-45N, 100-130W, and found a
correlation of –0.57 for 10-year averages of annual mean temperatures (–0.22 at
annual resolution). This is weaker than for the three northern Eurasian series from
Torneträsk, Yamal and Taimyr, which after averaging to one regional series have a
correlation of +0.79 with 10-year annual mean temperatures (+0.35 at annual
resolution) in the region 60-75N, 5-130E, but still strong enough to suggest a
negative response of tree-ring growth to annual mean temperatures at decadal
timescales; i.e. tree growth is limited by dry and warm conditions and favoured by
moist and cold conditions.
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We gratefully acknowledge Håkan Grudd, Rashit Hantemirov, Atte Korhola, Stein-Erik Lauritzen,
Mukhtar Naurzbaev, Eugene Vaganov and Jan Weckström for contributing with their proxy data
series.
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