Plant response to climate change along the forest-tundra ecotone in northeastern Siberia
Logan T. Berner, Pieter S.A. Beck, Andrew G. Bunn, and Scott J. Goetz
Supporting information
Comparison of gridded climate data sets with station observations
In an effort to ensure that we used climate data sets that were accurate in our region of interest,
we compared a number of products with temperature and precipitation measurements from 12
climate stations spread across the Kolyma Basin (Table S2). The climate station observations
were from the US National Oceanic and Atmospheric Administration's (NOAA) Global
Historical Climatology Network (GHCN) (Peterson and Vose 1997). We included all stations in
the Kolyma Basin with continuous records that were at least 50 years long. The gridded climate
data sets that we examined included the University of East Anglia Climate Research Unit's (CRU
3.10.01) (Mitchell and Jones 2005) temperature and precipitation data sets, as well as
precipitation data from the German Meteorological Service Global Precipitation Climatology
Center's (GPCC 6) Full Data Reanalysis (Schneider et al. 2011) and from the US National Center
for Environmental Prediction's Climate Forecast System Reanalysis (CFSR) (Saha et al. 2010).
We chose to use only data from 1938 onward due to the paucity of climate stations in this region
before that time.
Before comparing the GHCN observations with the modeled outputs, we first screened
the observations for potential errors. For each climate station we plotted time series of monthly
mean temperature and total precipitation to check for inconsistencies in the data. Inconsistencies
generally took the form of anomalous 1-year spikes that were evident at only one station. After
visually examining the data we chose to removed data points if the absolute value of the z-score
was >3, assuming that these values resulted from poor data quality.
After screening the GHCN observations, we compared estimates of annual precipitation
(Pa) and mean annual temperature (Ta) from the station measurements and the gridded data sets.
We used the coordinates from each station to extract monthly time series of either temperature or
precipitation from the gridded data sets and then derived annual means or sums for both the
observations and the modeled data. We subset these annual data to include only years for which
a station had observations from every month. We calculated the variance for the observed and
modeled annual time series and then used F-tests to check for statistical differences in variance
between data sets. We also used Pearson's correlations to assess the degree of association
between the observed and modeled annual time series.
The analysis revealed good agreement between estimates of Ta derived from station
observations and from CRU. The modeled values tended to have lower interannual variance than
the station observations, though F-tests indicated that the variance was significantly different at
only two of the 12 stations (Table S3). The correlations between observed and modeled values
ranged from r=0.75 to r=0.99, and averaged (±sd) r=0.93±0.07 (n=12). We opted to use the CRU
temperature data set in subsequent analyses because, on the whole, it agreed well with the station
measurements.
The three precipitation reanalysis data sets that we examined ranged considerably in
agreement with station observations. The GPCC precipitation model showed the strongest
agreement (mean r=0.88±0.11), followed by CFSR (r=0.71±0.09) and then CRU (r=0.63±0.27)
(Table S4). Interannual variance in the GPCC model was not systematically biased and the Ftests showed no significant differences in variance between modeled and observed values.
Interannual variance in CFSR data tended to be much higher than station observations, while
variance CRU data was not systematically biased. Since the GPCC precipitation data set agreed
well with station observations and outperformed both CRU and CFSR data sets, we chose to use
it in subsequent analyses.
Supporting tables
Table S1.
Locations of tree-ring sampling sites, along with sample sizes and a summary of
larch ages and growth characteristics. Cambial age denotes the number of years of growth at
breast height; BAI40-80 yrs denotes the average basal area increment (cm yr-1) when trees were 4080 years old; EPS is the expressed population signal; ISC is the inter-series correlation; AC1 is
the is the autocorrelation lagged one year.
Site
PAR
DVN
ROD
NSS
NES
YRI
SUL
GRS
TRL
Latitude
Longitude
68.5325
68.6694
68.7275
68.7398
68.7443
68.7480
68.7483
68.7486
69.2008
160.1984
159.0758
161.5516
161.3928
161.4093
161.4243
161.3906
161.3780
161.4408
Number of
trees (cores)
13 (21)
9 (16)
13 (20)
12 (23)
13 (23)
10 (19)
14 (25)
13 (25)
7 (12)
Cambial Age
avg (min,max)
98 (59,180)
112 (86,168)
107 (84,156)
155 (107,176)
153 (89,188)
234 (165,290)
172 (120,267)
140 (68,203)
105 (49,187)
BAI40-80 yrs
avg±sd
3.73±0.60
4.20±1.06
2.54±0.84
4.33±0.64
4.23±0.81
0.85±0.16
1.91±0.31
3.21±0.50
1.08±0.19
EPS
ISC
AC1
0.93
0.93
0.95
0.94
0.93
0.94
0.95
0.97
0.83
0.56
0.60
0.60
0.58
0.52
0.61
0.56
0.70
0.46
0.44
0.54
0.48
0.29
0.46
0.58
0.52
0.34
0.40
Table S2
Summary of climate stations in the Kolyma Basin that have data archived in the
Global Historical Climatology Network and which have temperature and precipitation records
longer than 50 years. Stations are ordered by decreasing latitude. Tree cover data are from
Ranson et al. (2011), mean summer temperature (Ts) from CRU 3.10.01, and annual precipitation
(Pa) from GPCC 6.
Station
Buhta Ambarcik
Cherskij
Ostrovnoe
Crednekolymsk
Ust Oloj
Zyrjanka
Omolon
Korkodon
Kedon
Sejmchan
Susuman
Srednikan
Lat.
Lon.
69.62
68.75
68.12
67.45
66.55
65.73
65.23
64.75
64.00
62.92
62.78
62.45
162.30
161.28
164.17
153.72
159.42
150.90
160.53
153.97
158.92
152.42
148.17
152.32
Elev.
(m)
23
28
94
21
127
43
264
98
683
205
655
260
Reliable
Record
1949-2009
1941-2011
1937-2009
1936-2011
1943-2011
1936-2011
1945-2011
1936-2011
1946-2000
1942-2011
1937-2009
1955-2011
Ts (˚C)
(mean ±sd)
6.0±1.7
10.6±2.3
11.5±1.3
12.2±1.4
11.5±1.0
14.0±1.1
11.8±1.1
13.4±1.0
9.4±1.1
13.8±1.0
11.8±1.0
14.1±1.2
Pa (mm yr-1)
(mean ±sd)
150±58
173±49
208±55
210±52
249±52
260±56
238±51
246±51
248±53
293±58
262±47
424±79
Tree Cover
(%)
0
25
5
36
15
53
24
57
0
59
27
34
Table S3
Comparison mean annual temperature derived from climate station observations
and CRU 3.10.01 data for Kolyma Basin. We used F-tests to check for differences in variance
between observations and modeled values, and then used Pearson's correlations to assess to
strength of the associations.
Station
Buhta Ambarcik
Cherskij
Ostrovnoe
Crednekolymsk
Ust Oloj
Zyrjanka
Omolon
Korkodon
Kedon
Sejmchan
Susuman
Srednikan
N
Years
40
14
64
69
62
71
52
63
47
67
70
52
Variance
Station Model
1.185
1.284
1.473
1.19
1.101
0.942
1.781
0.971
0.956
1.201
1.243
1.439
0.859
0.625
0.926
0.863
1.061
0.940
0.949
0.890
0.629
1.046
0.715
1.033
F-Test
F
P-value
1.380
0.319
2.056
0.207
1.589
0.068
1.379
0.188
1.038
0.885
1.002
0.993
1.876
0.027
1.091
0.733
1.520
0.160
1.148
0.577
1.738
0.023
1.393
0.240
Correlation
r
P-value
0.943
<0.001
0.748
0.002
0.914
<0.001
0.955
<0.001
0.993
<0.001
0.991
<0.001
0.925
<0.001
0.972
<0.001
0.908
<0.001
0.989
<0.001
0.885
<0.001
0.944
<0.001
Table S4
Comparison annual precipitation derived from climate station observations and
three reanalysis data sets for Kolyma Basin. We used F-tests to check for differences in variance
between observations and modeled values, and then used Pearson's correlations to assess to
strength of the associations.
Model
Station
GPCC 6
Buhta
Ambarcik
Cherskij
Ostrovnoe
Crednekolymsk
Ust Oloj
Zyrjanka
Omolon
Korkodon
Kedon
Sejmchan
Susuman
Srednikan
Buhta
Ambarcik
Cherskij
Ostrovnoe
Crednekolymsk
Ust Oloj
Zyrjanka
Omolon
Korkodon
Kedon
Sejmchan
Susuman
Srednikan
Buhta
Ambarcik
Cherskij
Ostrovnoe
Crednekolymsk
Ust Oloj
Zyrjanka
Omolon
Korkodon
Kedon
Sejmchan
Susuman
Srednikan
CRU
3.10
CFSR 1
N
Years
Variance
Station Model
F-Test
F
P-value
Correlation
r
P-value
35
46
62
63
56
63
55
57
45
59
63
47
19.378
12.793
17.757
18.822
18.842
21.935
17.321
16.297
12.841
23.677
15.779
41.638
17.705
12.449
12.616
18.772
22.088
25.539
14.918
19.330
13.283
28.422
21.400
33.417
1.094
1.028
1.407
1.003
0.853
0.859
1.161
0.843
0.967
0.833
0.737
1.246
0.794
0.928
0.185
0.992
0.558
0.551
0.585
0.525
0.911
0.489
0.233
0.459
0.997
0.959
0.768
0.897
0.936
0.896
0.843
0.877
0.923
0.919
0.579
0.964
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
35
46
62
63
56
63
55
57
45
59
63
47
19.378
12.793
17.757
18.822
18.842
21.935
17.321
16.297
12.841
23.677
15.779
41.638
5.212
6.935
7.171
16.244
18.573
24.596
10.940
21.728
26.779
26.843
18.742
29.714
3.718
1.845
2.476
1.159
1.014
0.892
1.583
0.750
0.480
0.882
0.842
1.401
<0.001
0.043
0.001
0.564
0.958
0.653
0.094
0.285
0.017
0.634
0.500
0.256
0.332
0.313
0.150
0.833
0.846
0.883
0.693
0.803
0.497
0.908
0.441
0.853
0.052
0.034
0.245
<0.001
<0.001
<0.001
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
23
16
25
24
24
26
27
27
17
26
26
24
20.41
11.835
10.678
18.418
17.047
34.079
17.089
16.242
20.557
19.433
12.878
36.860
36.668
34.183
23.825
54.118
27.372
60.405
25.979
41.184
34.398
38.18
75.442
39.000
0.557
0.346
0.448
0.340
0.623
0.564
0.658
0.394
0.598
0.509
0.171
0.945
0.177
0.048
0.055
0.012
0.263
0.159
0.292
0.021
0.313
0.098
<0.001
0.894
0.580
0.791
0.764
0.843
0.776
0.637
0.734
0.761
0.782
0.645
0.640
0.605
0.004
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.002
Supporting figures
Fig. S1 Map of climatology stations (Global Historical Climatology Network) in the Kolyma
Basin with records longer than 50 years. Details about the climate stations are given in Table S2.
Fig. S2 Spatial averages (±sd) of mean June-August NDVI (GIMMS 3g) based on larch tree
cover for each year from 1982-2010. There was a positive trend (P<0.05; NDVI decade-1) in
NDVI for each tree cover class: no cover (0-1%, τ=0.52, β=0.002±0.0004), low (1-30%, τ=0.47,
β=0.002±0.0004) and moderate (30-84%, τ=0.57, β=0.002±0.0004). The NDVI time series in
areas of high and low tree cover were strongly correlated (r=0.81 P<0.001, n=28).
Fig. S3 Gini coefficients derived from the Cajander larch mean ring-width index chronology (RWIavg) for
21-year intervals extending from 1901 to 2007. There was a significant positive trend in the Gini
coefficient magnitude over this period (τ=0.50, P<0.001), signifying increased dissimilarity among
RWIavg measurements during successive periods.
Fig. S4 Time series of Cajander larch basal area increment (mean±sd) for each of the nine treering sites in our study. The series were smoothed using a five-year average and are ordered (from
top left to bottom right) by increasing average tree age.