Dr. George HurttProfessor & Research Director

Science Team Leader

DEPARTMENT of GEOGRAPHICAL SCIENCES

Joint Global Carbon Cycle Center

Biomass Needs for Global Climate Modeling

September 25, 2018

DECK (entry card for CMIP)i. AMIP simulation (~1979-2014)ii. Pre-industrial control

simulationiii. 1%/yr CO2 increase iv. Abrupt 4xCO2 run

CMIP6 Historical Simulation (entry card for CMIP6) v. Historical simulation using

CMIP6 forcings (1850-2014)

Note: The themes in the outer circle of the figure might be slightly revised at the end of the MIP endorsement process

(DECK & CMIP6 Historical Simulation to be run for each model configuration used in the subsequent CMIP6-Endorsed MIPs)

Ongoing Diagnosis, Evaluation, and Characterization of Klima(DECK) Experiments

CMIP6-Endorsed Model Intercomparison Projects (MIPs)

Eyring 2015Meehl et al 2014

5-6 May, 2016 | Washington, D.C.

11

Consumer Goods Forum

x Achieve zero net deforestation by 2020, through the responsible sourcing of key commodities - soy, palm oil, paper and pulp and beef.

GEF Integrated Approach Pilot on Commodity Supply Chains

x Conservation and maintenance of globally significant biodiversity, ecosystems goods and services.

x Bring 23 million ha of land under sustainable management practices.

x Mitigate 80 million metric tonnes CO2e of GHG emissions through support for transformational shifts towards low-emission and resilient development paths, reduced deforestation and resilient supply chains.

Bonn Challenge x Restore 150 million hectares of the world’s deforested and degraded lands by 2020.

New York Declaration on Forests

x Restore 350 million hectares by 2030.

Initiative 20x20 x Bring 20 million hectares of land in Latin America and the Caribbean into restoration by 2020.

Africa Forest Landscape Restoration Initiative (AFR100)

x Restore 100 million hectares of degraded and deforested landscapes in Africa by 2030.

EverGreen Agriculture Partnership

x A shared vision of agricultural systems that can sustain a productive green cover on the land throughout the year, for the benefit of the land and livelihoods of smallholder farmers around the world

Afforestation/Reforestation Context

© Crown copyright Met Office

N. Hemi model

spread: factor 4 tropics model

spread: factor 2

Model spread in biomass

540 ± 220 PgC

Global soil and biomass carbon stores

Anav et al, 2013

Resolution/Heterogeneity

Mearns (Pers. Comm.)

Fisher et al. 2008

7

Averaged Not Averaged

Aver

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Not

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Structural Inputs

Structural InputsAveraged Not Averaged

Aver

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Heterogen. Env., Non-linear G, Fusion

9

Model too coarse for env. gradient

Data too coarse for env. gradient

Efficient solution

Hurtt et al. 2010

Duncanson et al. 2012

Hurtt et al. 2018

Huang et al 2015

Medvigy et al. 2012

Hurtt et al. 2018

Hurtt et al. 2018

Hurtt et al. 2018

Hurtt et al. 2018

Hurtt et al. 2018

GEDI- The Global Ecosystem Dynamics Investigation R.Dubayah 1

Science Approach and Data ProductsProduct Description

Level 1 Geolocated Waveforms

Level 2

Canopy Height/Profile Metrics• RH metrics• Canopy top height• Ground elevation• Canopy cover and cover profile• LAI and LAI profile

Level 3 Gridded Footprint Metrics

Level 4 Biomass

Level 4

Demonstrative Products• Ecosystem model outputs• Enhanced height/biomass using

fusion with Tandem X & Landsat• Habitat model outputs

Select References• Hurtt et al. (2018). Beyond MRV: High resolution forest carbon modeling for planning. ERL-

Submitted.• Huang et al. (2017). County-scale biomass map comparison: a case study for Sonoma, California.

Carbon Management 77(1) 1-19.• Hurtt et al. (2016). The Impact of Fine-Scale Disturbances on the Predictability of Vegetation

Dynamics and Carbon FluX. PLOS ONE 11(4):1-11.• Huang et al. (2015). Local discrepancies in continental scale biomass maps: a case study over forested

and non-forested landscapes in Maryland, USA. Carbon Balance and Management 10(1) 1-16.• Hurtt et al. (2014). NASA Carbon Monitoring System: Prototype Monitoring, Reporting, and

Verification. NASA Tech Report.• Meehl et al (2014). Climate Model Intercomparisons: Preparing for the Next Phase. EOS 95(9):77-78• Dubayah et al. (2010). Estimation of tropical forest height and biomass dynamics using lidar remote

sensing at La Selva, Costa Rica. JGR 115(G2):G00E09.• Hurtt et al. (2010). Linking models and data on vegetation structure. JGR 115.• Fisher et al. (2008) Clustered disturbances lead to bias in large scale estimates based on forest sample

plots. Ecol. Letters 11:554-563• Thomas et al. (2008). Using lidar data and a height-structured ecosystem model to estimate forest

carbon stocks and fluxes over mountainous terrain. Can J For Res. 34:S351-S363.• Hurtt et al. (2004). Beyond Potential Vegetation: Combining Lidar data and a height structured model

for carbon studies. Ecological Applications 14(3) 873-883.• Drake et al. (2002). Estimation of tropical forest structural characteristics using large footprint lidar.

RSE 79:305-319.