Environmental Sciences
Department of Geography
University of Idaho, Moscow, ID 83844-3021
• In spring and fall, positive relationships
occurred between T and NPP
3. Study region: Ecosystems of the mountainous West
JanMarMay Jul Sep Nov JanMarMay Jul Sep Nov JanMarMay Jul Sep Nov
N Rockies
Precip (mm)
300
75 cells
1232 cells
1931 cells
749 cells
34 cells
200
100
0
40
30
20
10
0
-10
JanMarMay Jul Sep Nov JanMarMay Jul Sep Nov JanMarMay Jul Sep Nov JanMarMay Jul Sep Nov JanMarMay Jul Sep Nov
S Rockies
Precip (mm)
300
566 cells
1218 cells
300 cells
200
100
0
40
30
20
10
0
-10
Precip (mm)
Sierra Nevada
JanMarMay Jul Sep Nov JanMarMay Jul Sep Nov JanMarMay Jul Sep Nov
300
23 cells
229 cells
359 cells
168 cells
35 cells
200
100
0
40
30
20
10
0
-10
JanMarMay Jul Sep Nov JanMarMay Jul Sep Nov JanMarMay Jul Sep Nov JanMarMay Jul Sep Nov JanMarMay Jul Sep Nov
AZ Mtns
• Here we used 22 years of satellite-derived NPP at 8-km spatial resolution to
investigate interannual variability of NPP and temperature drivers. Resulting
relationships between NPP and temperature were then used to predict changes in
NPP in response to future warming
Figure 1. Ecoregions of study: Cascade Mountains
(dark blue), Northern Rocky Mountains (light blue),
Southern Rocky Mountains (green), Sierra Nevada
(brown), Arizona Mountains (red).
• Climate inputs include PRISM temperature and precipitation and NCEP PAR
297 cells
1194 cells
40
30
20
10
0
-10
196 cells
200
100
0
JanMarMay Jul Sep Nov JanMarMay Jul Sep Nov JanMarMay Jul Sep Nov JanMarMay Jul Sep Nov
100
50
0
150
100
50
0
150
11
13
110 Slope = 9.7
100 R = 0.87
90
80
70
60
50
40
6 7 8 9 10 11 12 13
70 Slope = 7.6
60 R = 0.83
50
40
30
20
4
5
6
Cascades
8
9
9
60 Slope = 7.5
50 R = 0.76
40
30
20
10
2
3
4
12
80 Slope = 10.5
70 R = 0.77
60
50
40
30
4
5
6
7
2
8
2
140 Slope = 9.6
110 Slope = 10.2
130 R = 0.75
100 R = 0.95
120
90
110
80
100
70
90
60
80
50
10 11 12 13 14 15 16
8 9 10 11 12 13 14
2
35 Slope = -0.3
R2 = 0.01
30
25
20
20 21 22 23 24 25 26 27
2
50 Slope = -0.3
R = 0.01
45
80 Slope = 8.5
70 R = 0.92
60
50
40
30
5 6 7 8
11
6
7
30 Slope = 2.8
25 R = 0.53
20
15
10
5
0
-2
0
2
13
14
8
2
9 10 11
1
2
3
4
5
80 Slope = 5.3
R = 0.53
70
2
60
14
15
16
Temperature (deg C)
9
10
11
3
4
5
6
7
2
30
50
40
13
2
2
20
10
2
50
40
30
12
9
60 Slope = 6.7
50 R = 0.63
40
30
20
10
1 2 3 4
6
50 Slope = 5.1
R = 0.64
40
70 Slope = 4.7
R = 0.48
60
2
40
30 Slope = 4.2
25 R = 0.57
20
15
10
5
0
-2 -1 0 1
2
5
2
2
35
30
16 17 18 19 20 21 22
7
2
7
70 Slope = 7.0
65 R = 0.82
60
55
50
45
40
8
9
10
4
N Rockies
10
2
12
0-800 m
12
125
120
800-1600 m
Slope2 = -0.2
R = 0.00
85
80
115
75
110
105
16
70
65
15
95
90
85
80
75
70
17
17
18
19
20
Slope2 = -2.2
R = 0.21
18
19
20
21
22
1600-2400 m
Slope
= 0.1
R2 = 0.00
16
17
18
19
20
110
Slope = -5.0
105
R = 0.38
100
95
90
85
80
16.5 17.5 18.5 19.5 20.5
2
100
50
0
150
!NPP/!T
(g C m-2 mon-1)/(deg C)
0-800 m
800-1600 m
1600-2400 m
2400-3200 m
0
-10
-20
Jan FebMarAprMayJun Jul Aug Sep Oct Nov Dec
Month
Cascades
100
50
0
150
100
50
0
N Rockies
S Rockies
Sierra Nevada
AZ Mtns
5
4
3
2
CCSM B1
CCSM A2
GFCM B1
GFCM A2
CCSM B1
CCSM A2
GFCM B1
GFCM A2
CCSM B1
CCSM A2
GFCM B1
GFCM A2
0
CCSM B1
CCSM A2
GFCM B1
GFCM A2
1
CCSM B1
CCSM A2
GFCM B1
GFCM A2
Projected !T (deg C)
6
Cascades
N Rockies
S Rockies
Sierra Nevada
AZ Mtns
17.6%
21.9%
19.3%
20.3%
-2.4%
100
50
0
• In the Arizona Mountains, NPP will increase slightly -50
or decrease, depending on scenario, as a result of the
smaller spring sensitivity of NPP to T and greater Figure 5. (top) Projected (2070-2099) temperature
change. (bottom) Estimated change in NPP. Number
negative response in summer
at top indicate multimodel mean change in NPP.
• The nature of the influence of temperature on NPP differed based on ecoregion, elevation,
and month
• Spring and fall increases in NPP in response to warming will be somewhat offset by
summer decreases
• Based on scenarios of future warming, we predict substantial NPP increases in the
Cascades, Rockies, and Sierra Nevada
• NPP in the Arizona Mountains will increase only slightly or decrease
• Future changes in other atmospheric variables, such as precipitation or CO2, are not
considered here and may affect estimated changes in NPP
• Other climate change effects such as shifts in species distributions or changes in forest
disturbance regimes will also influence forest functioning
6
95
90
Slope2 = -1.7
R = 0.46
90
85
85
80
80
75
20 21 22 23 24 25 26
75
70
16
30
25
60
50
Slope2 = -3.6
R = 0.57
20
15
10
29
Slope2 = -2.2
R = 0.43
18
20
22
24
Slope2 = -7.2
R = 0.71
40
30
31
32
33
34
30
20
24
25
26
27
28
29
2400-3200 m
3200-4000 m
75
Slope = -1.4
R = 0.18
70
65
60
55
50
13 14 15 16 17 18 19
2
85
Slope2 = -2.0
R = 0.22
80
75
70
14
95
90
85
80
75
70
65
17
S Rockies
2
3200-4000 m
NPP (gC m-2 mon-1)
15
2400-3200 m
2
Sierra Nevada
14
70 Slope = 6.7
60 R = 0.77
50
40
30
20
5
6
7
Jan FebMarAprMayJun Jul Aug Sep Oct Nov Dec
Month
AZ Mtns
20
10
• In the Cascades, Rockies, and Sierra Nevada, NPP
will increase 18-22% in response to warming
(Figure 5, bottom)
July
1600-2400 m
2
0-800 m
800-1600 m
1600-2400 m
2400-3200 m
3200-4000 m
-5
7. Conclusions
AZ Mtns
Cascades
N Rockies
110 Slope = 9.6
100 R = 0.70
90
80
70
60
50
9
10
11
80 Slope = 5.9
75 R = 0.71
70
65
60
55
50
45
7
8
9
S Rockies
Sierra Nevada
NPP (gC m-2 mon-1)
800-1600 m
2
Sierra Nevada
0
Figure 2. Mean monthly precipitation (bars),
temperature (blue), and NPP (red).
May
0-800 m
160 Slope = 11.4
140 R = 0.71
120
100
80
60
10 11 12 13
Jan FebMarAprMayJun Jul Aug Sep Oct Nov Dec
Month
5
150
• For each month/ecoregion/elevation, we minimized the effect of PPT
by selecting only years within a narrow range of monthly Wε
• Scatter plots of T versus NPP for those years (Figure 3) revealed
• in spring, a positive relationship between T, NPP except at the
lowest elevations of the Arizona Mountains ecoregion
• in summer, a switch to either a negative relationship between T,
NPP or no relationship for the northernmost or highest areas
AZ Mtns
• We used the Normalized Difference Vegetation Index (NDVI) from Advanced
Very High Resolution Radiometer (AVHRR) instruments
• 1982-2003, monthly resolution, 8-km spatial resolution
• corrected by NASA (C. J. Tucker and others) to minimize undesirable artifacts
14 cells
150
4. The variable influence of T on NPP
• The Carnegie-Ames-Stanford Approach (CASA) light-use efficiency model was used
to compute monthly NPP
• NPP = PAR × fPAR × ε* × Tε × Wε
• PAR = photosynthetically active radiation (W m-2)
• fPAR = fraction of PAR absorbed by canopy
• ε* = maximum light-use efficiency (gC/W)
• Tε is a temperature down-regulator that reduces potential NPP in conditions that
are too hot or too cold
• Wε is a soil moisture down-regulator that reduces potential NPP in suboptimal
conditions
• Wε is a function of actual and potential evapotranspiration and therefore
governed by precipitation, past soil moisture, and temperature
Precip (mm)
300
-5
10
CCSM B1
CCSM A2
GFCM B1
GFCM A2
40
30
20
10
0
-10
0
CCSM B1
CCSM A2
GFCM B1
GFCM A2
3200-4000 m
Jan FebMarAprMayJun Jul Aug Sep Oct Nov Dec
Month
0-800 m
800-1600 m
1600-2400 m
2400-3200 m
3200-4000 m
5
CCSM B1
CCSM A2
GFCM B1
GFCM A2
2400-3200 m
365 cells
0
-5
N Rockies
10
CCSM B1
CCSM A2
GFCM B1
GFCM A2
1600-2400 m
1285 cells
100
0
CCSM B1
CCSM A2
GFCM B1
GFCM A2
800-1600 m
1338 cells
200
1600-2400 m
2400-3200 m
3200-4000 m
5
NPP (gC m-2 mon-1)
Cascades
300
S Rockies
• Most ecoregions will have greatest warming in
spring/summer/fall (data not shown)
NPP (gC m-2 mon-1)
0-800 m
Ecoregions
Jan FebMarAprMayJun Jul Aug Sep Oct Nov Dec
Month
• In the mountainous ecosystems of the western
United States, warming from 2-5°C was predicted
based on two climate models and two emissions
scenarios (Figure 5, top)
NPP (gC m-2 mon-1)
• We hypothesize that future warming will modify plant growth differently in
different regions depending on the current climate conditions
• areas where growth is limited by low soil moisture and high evaporative
demand will experience reductions in growth
• areas where growth is limited by low temperatures will likely experience
increases in growth
• these areas will be defined not only by different regions with different climates
but also by elevation
-5
10
6. Future warming and NPP
NPP (gC m-2 mon-1)
• Global ecosystem models predict a range of NPP responses to future warming,
both positive and negative, depending on model and region (Cramer et al. 2001,
Cao and Woodward 1998)
0
Figure 4. ΔNPP/ΔT by month and elevation bin for each
ecoregion. Note change in y scale for Arizona Mountains.
NPP (gC m-2 mon-1)
• Past studies have documented historical increases in net primary production
(NPP; the net amount of carbon fixed by plants from the atmosphere) due to
climate change (summarized by Boisvenue and Running 2006)
Precip (mm)
• Climate variability and change affect plant growth through modifications to
physiology, soil moisture, evaporative demand, and growing season length
Temperature (deg C) Temperature (deg C) Temperature (deg C) Temperature (deg C) Temperature (deg C)
• We studied five mountainous ecosystems in the West (Figure 1)
• Mean temperature (T), precipitation (PPT), and NPP for these ecoregions
stratified by elevation (Figure 2) revealed:
• cooler conditions at higher elevations and more northern latitudes,
though reduction amounts varied by ecoregion
• more PPT in winter in general with the exception of the Arizona
Mountains and Southern Rockies ecoregions
• summertime peaks in NPP driven by T and solar radiation
• reductions in NPP at higher elevations, except
• in the Arizona Mountains ecoregion, NPP increased at higher
elevations due to cooler and wetter
• peak NPP occurred at middle elevations in the Southern Rockies
0-800 m
800-1600 m
1600-2400 m
5
Projected !NPP (g C m-2 yr-1)
1. Introduction: Warming expected to modify plant growth
2. Methods: NDVI and the CASA light-use efficiency model
• In summer, warmer conditions reduced
NPP, especially in Arizona Mountains
Cascades
10
!NPP/!T
(g C m-2 mon-1)/(deg C)
Arjan Meddens
!NPP/!T
(g C m-2 mon-1)/(deg C)
• We computed the slope of each regression
line in Figure 3 (ΔNPP/ΔT) for each month
(Figure 4)
!NPP/!T
(g C m-2 mon-1)/(deg C)
Jeffrey A. Hicke
5. ΔNPP/ΔT varies over a year
!NPP/!T
(g C m-2 mon-1)/(deg C)
Climate Influences on Plant Growth: Using Satellite
Observations to Predict Future Patterns
75
70
65
60
55
50
14
15
16
17
18
18
19
20
Slope2 = -1.7
R = 0.27
16
18
20
105
100
95
90
85
80
75
9.5
Slope
= 2.1
R2 = 0.05
10.5 11.5 12.5 13.5
22
2
Temperature (deg C)
Slope2 = -1.2
R = 0.07
10.5 11.5 12.5 13.5
50
Slope = -0.4
48
R = 0.05
46
44
42
40
38
10 11 12 13 14 15 16
2
2
10
11
30
28
26
24
22
20
18
16
12
13
Slope2 = -0.0
R = 0.00
6
Acknowledgments. Funding was provided by the USGS through the Western Mountain Initiative and the NSF-Idaho EPSCoR Program
and the National Science Foundation.
Slope2 = -3.6
R = 0.26
2
80
120
Slope = -13.5
Slope = -19.3
110
R = 0.82
R = 0.70
70
100
60
90
80
50
70
40
60
30
50
18.5 19.5 20.5 21.5 22.5 15.5 16.5 17.5 18.5 19.5
Figure 3. T versus NPP for selected years for May (left) and July (right).
44
42
40
38
36
34
9.5
115
105
Slope = -1.8
100
R = 0.10
110
95
105
90
100
85
95
80
90
75
21
12.5 13.5 14.5 15.5 16.5
9
Slope2 = -4.3
R = 0.20
7
8
9
10
11
References
Boisvenue, C., and S. W. Running. 2006. Impacts of climate change on natural forest productivity - evidence since the middle of the
20th century. Global Change Biology 12:862-882.
Cao, M. K., and F. I. Woodward. 1998. Dynamic responses of terrestrial ecosystem carbon cycling to global climate change. Nature
393:249-252.
Cramer, W., A. Bondeau, F. I. Woodward, I. C. Prentice, R. A. Betts, V. Brovkin, P. M. Cox, V. Fisher, J. A. Foley, A. D. Friend, C.
Kucharik, M. R. Lomas, N. Ramankutty, S. Sitch, B. Smith, A. White, and C. Young-Molling. 2001. Global response of terrestrial
ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Global Change
Biology 7:357-373.