Biomass Needs for Global Climate Modeling

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1 Biomass Needs for Global Climate Modeling Joint Global Carbon Cycle Center DEPARTMENT of GEOGRAPHICAL SCIENCES September 25, 2018 Dr. George Hurtt Professor & Research Director Science Team Leader

2 CMIP6-Endorsed Model Intercomparison Projects (MIPs) Ongoing Diagnosis, Evaluation, and Characterization of Klima (DECK) Experiments DECK (entry card for CMIP) i. AMIP simulation (~ ) ii. Pre-industrial control simulation iii. 1%/yr CO 2 increase iv. Abrupt 4xCO 2 run CMIP6 Historical Simulation (entry card for CMIP6) v. Historical simulation using CMIP6 forcings ( ) (DECK & CMIP6 Historical Simulation to be run for each model configuration used in the subsequent CMIP6-Endorsed MIPs) Note: The themes in the outer circle of the figure might be slightly revised at the end of the MIP endorsement process Eyring 2015 Meehl et al 2014

3 Afforestation/Reforestation Context 5-6 May, 2016 Washington, D.C. Consumer Goods Forum GEF Integrated Approach Pilot on Commodity Supply Chains Bonn Challenge New York Declaration on Forests Initiative 20x20 Africa Forest Landscape Restoration Initiative (AFR100) EverGreen Agriculture Partnership Achieve zero net deforestation by 2020, through the responsible sourcing of key commodities - soy, palm oil, paper and pulp and beef. Conservation and maintenance of globally significant biodiversity, ecosystems goods and services. Bring 23 million ha of land under sustainable management practices. 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. Restore 150 million hectares of the world s deforested and degraded lands by Restore 350 million hectares by Bring 20 million hectares of land in Latin America and the Caribbean into restoration by Restore 100 million hectares of degraded and deforested landscapes in Africa by 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

4 Global soil and biomass carbon stores Model spread in biomass 540 ± 220 PgC N. Hemi model spread: factor 4 tropics model spread: factor 2 Crown copyright Met Anav et al, 2013

5 Resolution/Heterogeneity Mearns (Pers. Comm.)

6 Fisher et al. 2008

7 Structural Inputs Averaged Not Averaged Environmental Inputs Not Averaged Averaged 7

8 Structural Inputs Averaged Not Averaged Environmental Inputs Not Averaged Averaged

9 Heterogen. Env., Non-linear G, Fusion Efficient solution 9 Hurtt et al Data too coarse for env. gradient Model too coarse for env. gradient

10 Duncanson et al. 2012

11 Hurtt et al. 2018

12 Huang et al 2015

13 Medvigy et al. 2012

14 Hurtt et al. 2018

15 Hurtt et al. 2018

16 Hurtt et al. 2018

17 Hurtt et al. 2018

18 Hurtt et al. 2018

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20 Science Approach and Data Products Product Level 1 Level 2 Description Geolocated Waveforms Canopy Height/Profile Metrics RH metrics Canopy top height Ground elevation Canopy cover and cover profile LAI and LAI profile Level 3 Level 4 Gridded Footprint Metrics Biomass Level 4 Demonstrative Products Ecosystem model outputs Enhanced height/biomass using fusion with Tandem X & Landsat Habitat model outputs GEDI- The Global Ecosystem Dynamics Investigation R.Dubayah 1

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22 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) 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) 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: 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) Drake et al. (2002). Estimation of tropical forest structural characteristics using large footprint lidar. RSE 79: