Late 20th century divergence in MXD: 20 years on

Late 20th century
divergence in
maximum
latewood density:
20 years on
Tim Osborn
Climatic Research Unit, School of Environmental Sciences, UEA
WorldDendro, June 2018, Bhutan

The “Schweingruber” network
• Almost 400 sites
• Each with about 25 tree cores
• Typical lifespan 200 annual
rings, some go back before
1600 AD
• Almost 2 million rings
measured for width (TRW) and
maximum latewood density
(MXD) (and other parameters)
Updated from Briffa, Osborn, Schweingruber et al. (2002) Holocene

• 1998, Nature
• >450 citations
“During the second
half of the twentieth
century, the decadal-
scale trends in wood
density and summer
temperatures have
increasingly diverged
as wood density has
progressively fallen”

What does MXD
divergence look like?

MXD
Temperature
MXD minus Temperature
Example: 1 site

MXD
Temperature
Example: N Siberia

MXD
Temperature
Example: High latitudes

Relevant papers
• D’Arrigo et al. (2008)
On the ‘Divergence Problem’ in Northern Forests… Glob. Planet. Change
• Stine & Huybers (2014)
Arctic tree rings as recorders of variations in light availability. Nature Comms.
• Björklund et al. (2017)
Cell size & wall dimensions drive distinct variability of earlyw’d & latew’d density… New Phyt.

Relevant papers & potential explanations
• Exceeding optimal/threshold temperature
– Unlikely: Briffa, Osborn & Schweingruber (2004) Fig. 6c
• Increasing drought stress
– Not widespread: Barichivich et al. (2014)
• Snowmelt and seasonality changes
– Maybe local: Vaganov et al. (1999); Barichivich et al. (2014)
and other climatic possibilities
• Reduced light availability (tropospheric aerosol pollution)
– Evidence especially for 1955-1975 period: Stine & Huybers (2014)
• Increased UV due to stratospheric ozone depletion
– Tentative: Briffa, Osborn & Schweingruber (2004) Fig. 7b
and other widespread anthropogenic influences
• Tree-ring standardization
• Tree-ring measurement artefacts

Relevant papers & potential explanations
• Exceeding optimal/threshold temperature
– Unlikely: Briffa, Osborn & Schweingruber (2004) Fig. 6c
• Increasing drought stress
– Not widespread: Barichivich et al. (2014)
• Snowmelt and seasonality changes
– Maybe local: Vaganov et al. (1999); Barichivich et al. (2014)
and other climatic possibilities
• Reduced light availability (tropospheric aerosol pollution)
– Evidence esp. for 1955-1975: Stine & Hubers (2014)
• Increased UV due to stratospheric ozone depletion
– Tentative: Briffa, Osborn & Schweingruber (2004) Fig. 7b
and other widespread anthropogenic influences
• Tree-ring standardization
• Tree-ring measurement artefacts

Does divergence depend
on how you measure it?
Does divergence depend
on tree-ring
standardisation?

MXD
Temperature
Example: High latitudes
Measuring divergence: slope of the trend in the
difference over 1950-end, computed at each site

Divergence metric:
1950-1994 slope of normalized
MXD minus normalized T
Temperature data: CRUTEM1
Season: Apr-Sep
Tree-ring data: MXD original
Hugershoff standardized
chronologies from Schweingruber

Divergence metric:
MXD minus normalized T
Temperature data: CRUTEM4v
Season: Apr-Sep
Updating temperature data
slightly strengthens overall
divergence

Divergence metric:
MXD minus MXD predicted by
regression with T
Season: Apr-Sep
Using regression to estimate
divergence reduces number of
strong (+ and -) divergent slopes,
increases number of moderate
negative ones, & changes pattern

Divergence metric:
regression with T
Season: Season with highest r
Using a locally “optimal” season
affects the pattern (esp. N Eurasia)
but not the overall distribution

Divergence metric:
regression with T
Tree-ring data: MXD Hugershoff
standardized chronologies from
CRUST
Shift distribution towards more
negative divergence slopes:
Schweingruber’s standardization
had fit & removed some changes
in slope of measured data

Divergence metric:
regression with T
Tree-ring data: MXD Signal-Free
chronologies from CRUST
Signal-free standardization does
not remove divergence
Pattern changes once more

Divergence metric:
regression with T
100-yr Spline standardized
chronologies from CRUST
Spline standardization strengthens
divergence

Divergence metric:
regression with T
RCS standardized chronologies
from CRUST
“Regional Curve Standardisation”
doesn’t alter the picture much
Caveat: data are not good for site-by-
site RCS (few have subfossil trees)

If divergence is linked to light
limitation, divergence should
occur more at sites with the
most summer cloud cover

If divergence is linked to light
limitation, MXD should be
correlated with cloud cover
and divergence should be
stronger at sites with greater
sensitivity (negative
correlation) to cloud cover

Correlations between MXD and
year-to-year variations in cloud
cover during the growing season
Caveat:
Cloud cover observations are
poor. Here I use CRU TS, which
combines cloud cover, sunshine
and empirical estimate from
diurnal temperature range

Could divergence be an
artefact of the
Schweingruber MXD
dataset?

Schneider et al. (2015) Geophys. Res. Lett.
Schneider record, reduced post-1960 divergence
Our reconstruction, with post-1960
divergence

Adapted from Fig.1 of Björklund et al. (2017) New Phytologist 216, 728
x-ray photo of
tree sample
wood
density from
x-ray photo

Strong relationship between
Maximum Latewood and Mean Latewood Density

Ratio of Maximum to Mean Latewood Density,
plotted against Latewood Width
90th percentile
Median
10th percentile
Aliasing: potentially missing the maximum density
when latewood is very narrow (< 0.04 mm)

Rings with very narrow latewood, and potentially aliased MXD,
become more frequent during the divergence period

Conclusions
Maximum Latewood Density (MXD) divergence in the Schweingruber network
• There may be a small contribution from aliasing due to recent very
narrow rings
• Does not appear to be a tree-ring standardization phenomenon
– Though this can affect the spatial pattern
• Reduced light availability (tropospheric aerosol pollution) is currently
the most promising explanation but needs more evaluation
– Stine & Huybers (2014) found support from spatial correlation between
pattern of divergence and pattern of light limitation
– But the divergence pattern is sensitive to period of analysis and to choice of
standardization
– Also, temporal correlation between MXD and cloud cover variations is weak
and poorly correlated with the spatial pattern of divergence
• Multiple factors likely to be important

Late 20th century divergence in MXD: 20 years on

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