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Decadal prediction of sustainable agricultural and forest management - Earth system prediction differs from climate prediction
The document outlines a project focused on decadal predictions of sustainable agricultural and forest management using earth system models, emphasizing the need to improve representations of managed ecosystems. It details the collaborative efforts of researchers from various institutions to explore the impacts of crop and forest management on climate predictions, integrating new field data and synthesis of existing datasets. Key themes include assessing ecological uncertainties, effective management alternatives, and the role of plant acclimation under changing temperatures.
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Decadal prediction of sustainable agricultural and forest management - Earth system prediction differs from climate prediction
- 1. Decadal prediction ofsustainable 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 ofsustainable 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 Modelresponse Parameter uncertainty Structural uncertainty Ecological uncertainty Variation in management implementation
- 4. Crop Management in CESM (NCAR) Forest management in CESM (VirginiaTech) 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 (VirginiaTech) 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 systemmodels 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 fluxesHydrology 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 fluxesHydrology 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: • Resultsdepend 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 broadleaftrees Establish pine trees (LAI = 4) on cropland △℃ Summer Surface temperatures
- 20. Shift to broadleaftrees Lower LAI (2) is cooler than higher LAI (4) Establish pine trees (LAI = 4) on cropland △℃ Summer Surface temperatures
- 21. Shift to broadleafincreased albedo Decreasing LAI increases albedo Establishing pine trees on cropland decreases albedo △ Albedo Summer albedo
- 22. Shift to broadleaftrees Lower LAI (2) is cooler than higher LAI (4) Establish pine trees (LAI = 4) on cropland △℃ Summer Surface temperatures
- 23. Shift to broadleaftrees 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) Coolgrown 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 etal. (In Review) Acclimation – No Acclimation △℃ Acclimation Photosynthesis Transpiration (Latent heat flux) Surface temperatures
- 27. Acclimation increases photosynthesis, butvaries 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 ofsustainable 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
Editor's Notes
- #13 This is the forced change in LAI. I modified the input land surface properties to add this LAI after the growing season ends. Note: Plotting grid-averaged changes in LAI, includes both crop and non-crop land types Largest LAI changes are in the regions with senses crop areas
- #14 Albedo decreases, significant in the same region where T increases This is likely driving the changes in patterns
- #15 The resulting change in temperature, significant over the region where LAI changes were largest. Some other changes likely driven by changes in circulation patterns
- #16 The resulting change in temperature, significant over the region where LAI changes were largest. Some other changes likely driven by changes in circulation patterns
- #19 Broadleaf trees have higher albedo and more latent heat flux Higher LAI have lower albedo and XXXX latent heat flux
- #20 Broadleaf trees have higher albedo and more latent heat flux Higher LAI have XXXXX albedo and XXXX latent heat flux
- #21 Broadleaf trees have higher albedo and more latent heat flux Higher LAI have XXXXX albedo and XXXX latent heat flux
- #22 Broadleaf trees have higher albedo and more latent heat flux Higher LAI have XXXXX albedo and XXXX latent heat flux
- #23 Broadleaf trees have higher albedo and more latent heat flux Higher LAI have XXXXX albedo and XXXX latent heat flux
- #24 Broadleaf trees have higher albedo and more latent heat flux Higher LAI have XXXXX albedo and XXXX latent heat flux
- #27 Present day simulation
- #28 Legend: The instantaneous temperature response of Jmax (µmol m-2 s-1) at acclimated temperatures (Ta) of 15 (blue solid), 20 (green short dashed), 25 (gold dotted), 30 (orange dot-dashed), and 35°C (red long dashed) in (a) non-tropical C3 annual, (b) non-tropical C3 perennial, (c) non-tropical C4 annual, (d) non-tropical C3 perennial, and (e) tropical species. Curves were drawn using least squared mean parameters from the mixed-model analysis of variance. Black dots indicate mean Jmax at leaf temperature equal to Ta. Error bars represent standard errors of the mean. Panel (f) shows the data from the black dots in panels (a-e) plotted on the same y-axis. In panel (f), non-tropical C3 annual, non-tropical C3 perennial, non-tropical C4 annual, non-tropical C3 perennial, and tropical species are indicated with pink solid, red short dashed, light blue dotted, blue dash-dot, and green long dashed lines, respectively. Take home: plants grown at warmer temperatures generally have greater photosynthesis (follow black dots); however the increase is greatest for annual (ALL CROPS) and C4 species for light-limited photosynthesis (i.e., Jmax; shown here). We are finding that plants grown at warmer temperatures allocate more N to leaves, which is then allocated within the leaf to the most limiting photosynthetic processes. As such, we are developing plant-type specific, allocation-driven formulas for CLM. From: Smith and Dukes (submitted to GCB)