Post on 08-Jan-2017
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
Decadal prediction of sustainable agricultural and forest management - Earth system prediction differs from climate prediction
PD: Quinn Thomas, Virginia TechFunded 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
Carbon storageCrop/forest yields
Model response
Parameter uncertainty
Structural uncertainty
Ecological uncertaintyVariation in management implementation
CropManagement
in CESM(NCAR)
Forestmanagement
in CESM(Virginia Tech)
Management alternatives
Key areas of ecological uncertainty
Nitrogen export(Cornell University)
Soil microbial dynamics
(U of New Hampshire)
Plant acclimationto temperature
(Purdue University)
Natural variability simulations
(NCAR)
Model response simulations
(Team)
Scenario forcing simulations
(NCAR)
Earth systemprediction
CropManagement
in CESM(NCAR)
Forestmanagement
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 systemprediction
Chapin et al. 2008
(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
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 scale1.25° long. 0.9375° lat.~100 km 100 km
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 scale1.25° long. 0.9375° lat.~100 km 100 km
Large focus on development and evaluation of CLM 5.0
(an open access, community resource)
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
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)
Key caveats: • Results depend on height of cover crop
• Leaf Area Index an assumed value (4 m2 m-2)• Greenhouse gases not simulated
Examples from project
• What matters more for climate: species, location, or intensity of a forest management project?
Led by: Ben Ahlswede (Virginia Tech)
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
Establish pine trees (LAI = 4) on cropland
△℃
Summer Surface
temperatures
Shift to broadleaf trees
Establish pine trees (LAI = 4) on cropland
△℃
Summer Surface
temperatures
Shift to broadleaf trees Lower LAI (2) is cooler than higher LAI (4)
Establish pine trees (LAI = 4) on cropland
△℃
Summer Surface
temperatures
Shift to broadleaf increased albedo Decreasing LAI increases albedo
Establishing pine trees on cropland decreases albedo
△Albedo
Summer albedo
Shift to broadleaf trees Lower LAI (2) is cooler than higher LAI (4)
Establish pine trees (LAI = 4) on cropland
△℃
Summer Surface
temperatures
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
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)
Cool grownWarm grownHot grown
Leaf temperature (°C)
Proc
ess
rate
Response can shift with acclimationPhotosynthesis and leaf respiration
Smith and Dukes (2013) Global Change Biology
Smith, NG et al. (In Review)
Acclimation – No Acclimation
△℃
Acclimation Photosynthesis Transpiration(Latent heat flux)
Surfacetemperatures
Acclimation increases photosynthesis, but varies by plant type
Smith and Dukes (In Review)
Carbon storageCrop/forest yields
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