- Explore how crop and forest management influences decadal scale climate predictions - Improve the representation of managed ecosystems in Earth system models - Specific focus on institutional strengths: soil carbon dynamics, pine plantation forestry, plant physiology under warming temperatures, forest nitrogen cycling - Evaluate and reduce uncertainty associated with ecological processes in climate predictions

1. Decadal prediction of sustainable agricultural
and forest management - Earth system
prediction differs from climate prediction
R. Quinn Thomas (Virginia Tech)
Gordon Bonan (NCAR)
Christine Goodale (Cornell University)
Jed Sparks (Cornell University)
Jeffrey Dukes (Purdue University)
Serita Frey (U of New Hampshire)
Stewart Grandy (U of New Hampshire)
Thomas Fox (Virginia Tech)
Harold Burkhart (Virginia Tech)
Danica Lombardozzi (NCAR)
William Wieder (NCAR)
Susan Cheng (Cornell)
Nicholas Smith (Purdue, LBNL)
Benjamin Ahlswede (Virginia Tech)
Joshua Rady (Virginia Tech)
Emily Kyker-Snowman (U of New
Hampshire)
USDA-NIFA Project 2015-67003-23485

2. Decadal prediction of sustainable agricultural and forest management -
Earth system prediction differs from climate prediction
PD: Quinn Thomas, Virginia Tech
Funded through interagency Decadal and Regional Climate Prediction Using Earth System Models (EaSM) Program
USDA-NIFA Project 2015-67003-23485
Objectives
Approach Impacts
- Explore how crop and forest management
influences decadal scale climate predictions
- Improve the representation of managed
ecosystems in Earth system models
- Specific focus on institutional strengths:
soil carbon dynamics, pine plantation
forestry, plant physiology under warming
temperatures, forest nitrogen cycling
- Evaluate and reduce uncertainty associated with
ecological processes in climate predictions
- Integrated effort involving climate modelers,
ecosystem scientists, plant physiologists, soil
scientists, and foresters.
- New field measurements and synthesis of existing
datasets for parameterization and evaluation of an
Earth system model
- Development and application of the Community
Earth System Model
- Crop and forest management strategies that
maximize climate benefits
- Earth system modeling tool available to the
community to predict crop and timber
production in a changing environment
- Capacity building through connecting and
training scientists to work at the interface of
managed ecosystems and climate sciences

3. Carbon storage
Crop/forest yields
Model response
Parameter
uncertainty
Structural
uncertainty
Ecological uncertainty
Variation in management implementation

4. Crop
Management
in CESM
(NCAR)
Forest
management
in CESM
(Virginia Tech)
Management
alternatives
Key areas of
ecological
uncertainty
Nitrogen export
(Cornell University)
Soil microbial
dynamics
(U of New Hampshire)
Plant acclimation
to temperature
(Purdue University)
Natural variability
simulations
(NCAR)
Model response
simulations
(Team)
Scenario forcing
simulations
(NCAR)
Earth system
prediction

5. Crop
Management
in CESM
(NCAR)
Forest
management
in CESM
(Virginia Tech)
Management
alternatives
Key areas of
ecological
uncertainty
Nitrogen export
(Cornell University)
Soil microbial
dynamics
(U of New Hampshire)
Plant temperature
acclimation
(Purdue University)
Natural variability
simulations
(NCAR)
Model response
simulations
(Team)
Scenario forcing
simulations
(NCAR)
Earth system
prediction

6. Chapin et al. 2008

7. (IPCC 2007)
Earth system models
Earth system models use mathematical
formulas to simulate the physical,
chemical, and biological processes that
drive Earth’s atmosphere, hydrosphere,
biosphere, and geosphere
A typical Earth system model consists
of coupled models of the atmosphere,
ocean, sea ice, and land
Land is represented by its ecosystems,
watersheds, people, and
socioeconomic drivers of
environmental change
The model provides a comprehensive
understanding of the processes by
which people and ecosystems feed
back, adapt to, and mitigate global
environmental change

8. Surface energy fluxes Hydrology Biogeochemistry
Landscape dynamics
The Community Land Model
Fluxes of energy, water,
CO2, CH4, BVOCs, and
reactive N and the
processes that control
these fluxes in a
changing environment
Temporal scale
 30-minute coupling with
atmosphere
 Seasonal-to-interannual
(phenology)
 Decadal-to-century (disturbance,
land use, succession)
 Paleoclimate (biogeography)
Spatial scale
1.25° long.  0.9375° lat.
~100 km  100 km

9. Surface energy fluxes Hydrology Biogeochemistry
Landscape dynamics
The Community Land Model
Fluxes of energy, water,
CO2, CH4, BVOCs, and
reactive N and the
processes that control
these fluxes in a
changing environment
Temporal scale
 30-minute coupling with
atmosphere
 Seasonal-to-interannual
(phenology)
 Decadal-to-century (disturbance,
land use, succession)
 Paleoclimate (biogeography)
Spatial scale
1.25° long.  0.9375° lat.
~100 km  100 km
Large focus on development and evaluation of
CLM 5.0
(an open access, community resource)

10. Examples from project
• How can cover crops impact climate?
• What matters more for climate: species,
location, or intensity of a forest management
project?
• How does the acclimation of photosynthesis
and respiration to warming temperatures
influence climate?
Focus on idealized simulations to explore sensitivity of
temperature to these biogeophysical land surface processes

11. Examples from project
• How can cover crops impact climate?
- Increased LAI 0 from 4
outside of growing
season for all crops
- Focus on winter
(December-January-
February) responses
Led by: Danica Lombardozzi (NCAR)

15. Key caveats:
• Results depend on height of cover crop
• Leaf Area Index an assumed value (4 m2 m-2)
• Greenhouse gases not simulated

16. Examples from project
• What matters more for climate: species,
location, or intensity of a forest management
project?
Led by: Ben Ahlswede (Virginia Tech)

17. Examples from project
• What matters more for climate: species,
location, or intensity of a forest management
project?
Standardizes for LAI across tree types and location

18. Establish pine trees (LAI = 4) on cropland
△℃
Summer
Surface
temperatures

19. Shift to broadleaf trees
Establish pine trees (LAI = 4) on cropland
△℃
Summer
Surface
temperatures

20. Shift to broadleaf trees Lower LAI (2) is cooler than higher LAI (4)
Establish pine trees (LAI = 4) on cropland
△℃
Summer
Surface
temperatures

21. Shift to broadleaf increased albedo Decreasing LAI increases albedo
Establishing pine trees on cropland decreases albedo
△
Albedo
Summer
albedo

22. Shift to broadleaf trees Lower LAI (2) is cooler than higher LAI (4)
Establish pine trees (LAI = 4) on cropland
△℃
Summer
Surface
temperatures

23. Shift to broadleaf trees Lower LAI (2) is cooler than higher LAI (4)
Establish pine trees (LAI = 4) on cropland
△℃
Summer
Surface
temperatures
Key caveats:
• Greenhouse gases not simulated
• Assumes grid-cell is entirely the plant type
• Shift from crop to trees, other studies shift from bare
ground to trees

24. Examples from project
• How does the acclimation of photosynthesis
and respiration to warming temperatures
influence climate?
- Used experimental data
to parameterize
acclimation
- Simulated climate with
and without acclimation
Led by: Nick Smith (Purdue, now LBNL)

25. Processrate
Leaf temperature (°C)
Cool grown
Warm grown
Hot grown
Response can shift with acclimation
Photosynthesis and leaf respiration
Smith and Dukes (2013) Global Change Biology

26. -90
<60°S
-1.0
-0.5
0.0
0.5
1.0
-90-4504590
<60°S
60°S-20°S
20°S-20°N
20°N-60°N
>60°N
1.0
4590
20°N-60°N
>60°N
Smith, NG et al. (In Review)
Acclimation – No Acclimation
△℃
Acclimation Photosynthesis
Transpiration
(Latent heat flux)
Surface
temperatures

27. Acclimation increases photosynthesis,
but varies by plant type
0
50
100
150
200
Jmax(µmolm-2
s-1
)
C3 Annual (a)
Ta=15°C
Ta=20°C
Ta=25°C
Ta=30°C
Ta=35°C
0
10
20
30
40
50
60
70
C3 Perennial (b)
0
50
100
150
200
250
C4 Annual (c)
0 10 20 30 40 50
0
50
100
150
200
C4 Perennial (d)
0 10 20 30 40 50
0
50
100
150
200
Tropical (e)
15 20 25 30 35
0
50
100
150
200
250 (f)C3 Annual
C3 Perennial
C4 Annual
C4 Perennial
Tropical
Leaf temperature (°C) Smith and Dukes (In Review)

28. Carbon storage
Crop/forest yields
-1.0
-0.5
0.0
0.5
1.0
-90-4504
<60°S
60°S-20°S
20°S-20°N
20°N-60°N
MAM
*
*
-1.0
-0.5
0.0
0.5
1.0
-90-4504590
<60°S
60°S-20°S
20°S-20°N
20°N-60°N
>60°N
JJA
*
*
-1.0
-0.5
0.0
0.5
1.0
-90-4504590
-180 -90 0 90 180
<60°S
60°S-20°S
20°S-20°N
20°N-60°N
>60°N
-0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5
SON
∆SAT (°C)
*

29. Decadal prediction of sustainable agricultural
and forest management - Earth system
prediction differs from climate prediction
R. Quinn Thomas (Virginia Tech)
Gordon Bonan (NCAR)
Christine Goodale (Cornell University)
Jed Sparks (Cornell University)
Jeffrey Dukes (Purdue University)
Serita Frey (U of New Hampshire)
Stewart Grandy (U of New Hampshire)
Thomas Fox (Virginia Tech)
Harold Burkhart (Virginia Tech)
Danica Lombardozzi (NCAR)
William Wieder (NCAR)
Susan Cheng (Cornell)
Nicholas Smith (Purdue, LBNL)
Benjamin Ahlswede (Virginia Tech)
Joshua Rady (Virginia Tech)
Emily Kyker-Snowman (U of New
Hampshire)
USDA-NIFA Project 2015-67003-23485