decadal prediction of sustainable agricultural and forest management - earth system prediction...

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


PD: Quinn Thomas, Virginia TechFunded through interagency Decadal and Regional Climate Prediction Using Earth System Models (EaSM) Program


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


• 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


• What matters more for climate: species, location, or intensity of a forest management project?

Led by: Ben Ahlswede (Virginia Tech)


• 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


△℃

Summer Surface

temperatures

Shift to broadleaf trees Lower LAI (2) is cooler than higher LAI (4)


△℃

Summer Surface

temperatures

Shift to broadleaf increased albedo Decreasing LAI increases albedo

Establishing pine trees on cropland decreases albedo

△Albedo

Summer albedo



△℃

Summer Surface

temperatures



△℃

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


• 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


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)


decadal prediction of sustainable agricultural and forest management - earth system prediction...

Environment