Comparing remotely-sensed and in-situ
data on response of forest growth to climate
variability and change in European Russia.
Malcolm K. Hughes (1), Alexander Olchev
(2), and Andy Bunn (3)
(1) Laboratory of Tree-Ring Research, University of Arizona,
Tucson AZ, USA, (2) Severtsov Institute of Ecology and
Evolution, Moscow, Russia, (3) Western Washington
University, Bellingham, WA, USA
No part of this presentation may be reproduced or distributed
Background
• Our objective is to place satellite observations of
forest growth in European Russia in a longer-term
context, so as to better understand the nature and
causes of inter-annual to multidecadal variability.
• The main questions to be addressed are:
– Are the remotely-sensed measures of vegetation
growth and tree-ring derived measures of vegetation
growth consistent with one another?
– Can their similarities and inconsistencies be
explained on a quantitative basis?
– To what extent do they capture the same or
complementary aspects of forest growth?
Background
• Further questions include:
– What is the role of climate variability in causing the
individual and common patterns of spatiotemporal
variability in the remotely-sensed and tree-ring data?
– How typical have the past 30 years been of the
previous 100 years?
– Have relationships between climate and forest growth
changed in recent years, or are they within the range
of variability of the last 100 years?
– To what extent can the relationships between forest
growth and climate as derived from these datasets
help constrain expectations of near future change in
forest growth?
415 trees sampled from summer 2009, and 391 from 2010,
two cores per tree, presently being processed. All sites except
Lapland now sampled.
For example at
location #1…..
Back to 1714
N = 30 in 20th
Century
Back to 1787
N = 25 in 20th
Century
CBZ Pine mean ring width +/- 1SE
CLIMATIC CORRELATIONS
CBZ spruce correlations
0.4
0.2
0.0
-0.2
p
g
Se
g
Au
Se
Ju
Ju
l
n
Ju
n
ay
M
Ap
b
ar
M
Fe
ov
D
ec
Ja
n
P
P
N
O
ct
p
P
P
g
Se
l
Au
Ju
P
n
P
ay
P
M
P
r
Significant at 95%
temperature
-0.4
Ju
Coefficients
precipitation
Month
Temperature
CBZ
Precipitation
significant precipitation
0.4
0.2
pine correlations
significant temperature
0.0
-0.2
Month
p
l
Au
Ju
Fe
b
M
ar
Ap
r
M
ay
ov
D
ec
Ja
n
P
P
N
ct
p
O
P
g
Se
P
Au
l
Ju
P
P
n
Ju
P
M
ay
-0.4
P
Coefficients
Both species show
significant
correlations with
summer
temperatures of the
year prior to growth.
They differ in
spruce correlating
with January and
current August
temperature and
pine having
precipitation
correlations of
opposing signs in
spring of the year of
growth.
RESPONSE FUNCTIONS
CBZ Spruce Response Function
precipitation
Coefficients
-0.2
Se
p
Month
Ju
n
Ju
l
Au
g
n
Ju
P l
Au
P g
Se
p
P
O
c
P t
N
o
P v
D
ec
Ja
n
Fe
b
M
ar
Ap
r
M
ay
P
Ju
P
P
M
ay
-0.4
CBZ Pine Response Function
0.4
0.2
0.0
-0.2
Se
p
M
ar
Fe
b
ec
Ja
n
Month
Ap
r
M
ay
Ju
n
Ju
l
Au
g
P
D
ov
ct
O
N
P
P
Se
p
P
l
Au
g
P
Ju
P
P
Ju
n
-0.4
M
ay
Coefficients
- Shows no
significant change in
climate response in
trees in satellite area
0.0
temperature
- Gives a more
conservative view of
climatic relationship
- Analogous to partial
regression
coefficients
0.2
P
- A form of
orthogonalized
regression
0.4
EVOLUTIVE RESPONSE FUNCTIONS
- Conservative appraisal of climatic relationship
- Calculated first for the period 1892 through 1959 and then adding a
year and recalculating until 2008 is reached.
- No striking change in climate response in these trees in the satellite
era.
Final words
• This is a very preliminary report and most
sample analysis will take place this winter,
with analysis of the tree-ring data, satellite
observations, meteorological data, fluxtower results and simulation modeling to
occur in the year commencing Summer
2011
Thanks to:
• NASA LCLUC program
• Field work: Elena Novenko, Chris Baisan, Mark
Losleben, Katya Kuznetsova, Logan Berner and
others.
• Access to sites: colleagues at the Biosphere
Reserves.
• Lab work: Mark Losleben and Jim Burns.
• Remote sensing data analyses: Logan Berner.
• Help with presentation: Mark Losleben.