Towards a more biologically-meaningful climate characterization: Heterogeneity in space and time at multiple scales
Danielle S. Christianson1, Cari G. Kaufman2, Lara M. Kueppers3, and John Harte1
(1) Energy and Resources Group, University of California, Berkeley (2) Statistics, University of California, Berkeley (3) Lawrence Berkeley National Laboratory, Berkeley, California
5
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rea .001 R =0
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all months, 2400-3500m
R2=0.49
all months, all elevations
R2=0.77
1
4
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spatial standard deviation
6 8
10
p<0
2
0
0
15
20
25
30
-5
spatial mean
0
5
10
15
20
25 30
spatial mean
20
25 30
spatial mean
16
3.5
Growing Degree Months Annual Average Temp
3.0
15
(a proxy for frost)
2.5
10
-5
0
5
2 Spatial heterogeneity (SD) in monthly
10
15 20
20
25 30
25
30
35
40
0
45
1
2
3
4
5
3 Bioclimatic metrics:
MAX temp increases at fine scales
but decreases at coarse scales.
monthly MIN temp remains constant (inset)
MAX temp Warmest month: opposite trends
MIN temp Coolest month: uncertain
Few strong trends at either scale
250
M
VE
R
0
p=
.91
0
R=
2
I
SEK
100
9
0.9
2=
R
.04
150 200
RE
VL
A-Forest
R2=0.86
Soil Degree Days
Growing Season Temp
2
e
cr
0
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4R
.00
0
p=
in
spatial standard deviation
4
3 5
1
MIN Monthly Temperature
Coolest Month = October
-5
7L
0.2
=
sl
20
25 30
spatial mean
0
700
900
1100 1300
spatial mean
8
10
12
14
spatial mean
Daily MAX at SEKI - July
-5
-5
Warmest Month
0
M
.23
=0
ope
15
500
Daily MAX at SEKI - August
spatial standard deviation
3 4
5
in
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a
200
250
spatial mean
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5
10
15
20
25 30
spatial mean
0
5
10
15
20
25 30
spatial mean
0
2
2
10
2
0
R=
R
.06
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p<
0.0
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15 20 25 30
spatial mean
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10
spatial standard deviation
3 4
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2
pe=
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t
KI
SE
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R2
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0.0
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p<
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Negative SEKI trend may result from low sample size (n=4):
Daily MAX underlying August means have positive trend.
Daily and monthly MAX in July show positive trend.
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-5
R
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R2
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slope =0.11
R2=0.26
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Less change
with higher
shrub density
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RMBL
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REV
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More change in
montane vs. alpine meadow
-U
V
E
100
MAX Monthly Temperature
Warmest Month = July
1
More change in forest vs. alpine
spatial standard deviation
2
3 4
5
slope=0.08
R2=0.42
constant heterogeneity
1
slope=0.18
R2=0.50
Avg Min Monthly Temp
0
A-Alpine
4
A-Forest
Similar slope in
Sierra vs. Rockies forests
3
0
spatial standard deviation
1
0
Average Maximum Monthly Temperature: May - Oct
0
50
2
BL
.99
.90
RM
R2
=0
A-Alpine
R2=0.91
5
5-cm soil vs. 2-m air
Snowmelt Julian Date
R2
=0
slope=0.008
R2=0.04
15
2.0
10
10
1.5
5
1.5
5
0
1.0
0
-5
0.5
Average April Temp
0
-5
25 30
spatial mean
13 14
15 20 25 30
spatial mean
20
15
12
10
10
11
5
0
0
5
0
1
1
0
0
-5
-5
spatial standard deviation
2
3 4
5
spatial standard deviation
3 4
5
2
constant heterogeneity
MIN Monthly Temperature
Coolest Month = October
1
spatial standard deviation
2
3 4
5
2
1
Avg Min Monthly Temp
SEKI-A
slope=0.16
R2=0.64
spatial standard deviation
3 4
5
96
Maximum Monthly Temperature
Maximum Monthly Temperature
3
spatial standard deviation
dec
Stronger monthly trends occur over larger spatiotemporal extents:
We expect reduced heterogeneity at coarse scales due to uneven
latitudinal warming.
spatial standard deviation
10
1 5
20
spatial standard deviation
4
3 5
SEKI
n=6
2010
MAX Monthly Temperature
Warmest Month = July
slo
methods
For each temperature metric, we calculate a
value for each sensor location or gridcell
within the study extent.
Then, we find the spatial mean and the spatial
standard deviation (sd) of the metric (e.g.,
for each day, we calculate the spatial mean
& sd of daily max temp — each data point
is the spatial mean and sd for a single day).
We use standard deviation as a measure
of heterogeneity and natural climate
variation as a proxy for future warming.
By regressing the mean against the standard
deviation, we can assess how spatial
heterogeneity may change with warming.
(SD) increases in daily MAX temp but
remains constant for daily MIN temp
daily MAX: warmer days = more heterogeneity
daily MIN: constant heterogeneity (not shown)
Complex canopy = more change (we suspect)
spatial standard deviation
3 4
5
questions
1 How might heterogeneity change at fine
spatial and temporal scales?
Does heterogeneity change differently at
fine vs. coarse spatial scales:
2 for monthly SDM metrics?
3 for SDM bioclimatic metrics?
1 Fine scales: Spatial heterogeneity
1895
2
If future fine-scale climate overlaps with a
species’ climate niche then the species may
persist. If not, the species may go locally extinct.
SEKI-A Western CA Sierra Nevada: Montane conifer forest (2500m)
21 shielded air temp sensors at 2m, hourly: 2010-2013*
(*snow-free days only)
PRISM (2400-3500m)
1
climate metric (e.g., temperature)
SEKI Western CA Sierra Nevada: Montane conifer forest (2500m)
98 soil temp sensors at 5cm, every 4 hours: 2010-2013*
0
future fine-scale climate
REV Western CO Rockies: Shrub subalpine meadows
3 sites: L (2770m), M (2940m), U (3190m)
5 soil temp sensors / site at 12cm, every 2 hours: 1996-1998*
spatial standard deviation
4
3 5
spatial mean
RMBL Western CO Rockies: Montane meadow (2920m)
15 soil temp sensors at 12cm, every 2 hours: 1991-1997*
2
decreasing spatial
heterogeneity
species
absent
A (ATWE) Eastern CO Rockies
3 sites: Alpine meadow (3540m)
Treeline krumholtz (3430m)
Forest - subalpine conifer (3060m)
5 soil temp sensors / site at 5-10cm, every15 minutes: 2010-2013*
1
constant spatial
heterogeneity
species at risk
Average Maximum Monthly Temperature: May - Oct
0
species
present
increasing spatial
heterogeneity
species persists
PRISM Continental USA: 30 second gridcells (2400-3500m)
LT71m: Interpolated air temp at 2m, monthly: 1895-2010
coarse scale: 800m grain over 1000s km extent
current
fine-scale
climate within
gridcell
spatial standard deviation
occurences
gridcell mean shifts in
wamer future
coarse-scale
climate sources
vs.
Future change in fine-scale heterogeneity
matters to species:
temporal metric
fine-scale
fine scale: 1m grain over 10-100s m extent
Many species respond to climate at spatial
scales finer than ~1km SDM gridcell
resolution and at temporal scales finer
than commonly used monthly and
seasonal bioclimatic metrics [1-2].
Findings: Change in future montane heterogeneity likely to differ at coarse and fine scales
spatial scale
the problem
Coarse spatial and temporal climate data is
used to project future species range shifts
in Species Distribution Models (SDMs).
-5
0
5
10
15
20
25 30
spatial mean
0
5
10
15
20
25
30
spatial mean
-5
0
5
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25 30
spatial mean
references
1) Dobrowski S.Z. 2011. Global Change Biology 17 (2), 1022-1035.
2) Potter K.A. et al 2013. Global Change Biology 19 (10), 2932-2939.
acknowledgements
ATWE: Thanks to the ATWE team, especially E. Brown, A. Moyes, and C. Castanha. This material is based upon work supported by the U.S. Department of Energy, Office of Science,
Office of Biological and Environmental Research, under Award Number DE-FG02-07ER64457. See Reinharte et al 2011, Tree Physiology 31, 615-625.
REV: Thanks to J. A. Dunne for sharing data. See Dunne, J.A., et al 2003, Ecological Monographs 73 (1), 69-86.
RMBL: Thanks to many previous Harte Lab members for collecting this data. NSF. See Harte J., et al 1995, Ecological Applications 5 (1), 132-150.
SEKI(A): Thanks to many field and lab assistants, USGS WERC (N. Stephenson, A. Das, P. van Mantgem, K. Bollins), and SEKI NP. USGS, NPS, NPS-CCESU, NSF-GFRP, BASC, Philomathia.
PRISM: PRISM Climate Group, Oregon State University. See Daly C., et al, 2000, Transactions of the ASAE-American Society of Agricultural Engineers 43, 1957-1962.