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Multi-Scale Synthesis and Terrestrial Model Intercomparison Project – A Systematic Approach for Evaluating Land-Atmosphere Flux Estimates February 4 th,

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Presentation on theme: "Multi-Scale Synthesis and Terrestrial Model Intercomparison Project – A Systematic Approach for Evaluating Land-Atmosphere Flux Estimates February 4 th,"— Presentation transcript:

1 Multi-Scale Synthesis and Terrestrial Model Intercomparison Project – A Systematic Approach for Evaluating Land-Atmosphere Flux Estimates February 4 th, 2013 NACP All Investigators Meeting, Albuquerque, NM Deborah Huntzinger C. Schwalm, A. Michalak, W. Post, K. Schaefer, A. Jacobson. Y. Wei, R. Cook, & MsTMIP Participants

2 Future projections depend, in part, on ability to model land-atmosphere carbon exchange Huntzinger et al. (2012) Ecological Modeling Friedlingstein et al. 2006

3 Land surface Models Policy and management choices Input data Initial conditions Parameter values Assumptions Process inclusion & formulation Understanding of system /

4 Input data Initial conditions Parameter values Assumptions Process inclusion & formulation How do intermodel differences influence variability or uncertainty in model results? Parametric uncertainty Structural uncertainty: In order to quantify, need: Large community of models Strict simulation protocol

5 Multi-scale Synthesis & Terrestrial Model Intercomparison Project (MsTMIP) Unique in several ways: Two spatial scales: Global (0.5° by 0.5°); North America (0.25° by 0.25°); Two distinct sets of standardized environmental input data – Climate, land cover & land-use/land-cover change history, phenology, atmospheric CO 2, nitrogen deposition rates, soil, C3/C4 grass, major crops Includes over 20 different TBMs 110-year simulation period ( ) 10 different simulations model to assess sensitivity to different forcing factors Evaluation of model performance against available observations (benchmarking)

6 OrderDomainCodeClimateLULUCAtm. CO 2 Nitrogen 1 Global RG1Constant 2SG1 Time-varying (CRU+NCEP) 3SG2 Time- varying (Hurtt) 4SG3 Time- varying 5BG1 Time-varying Reference simulations  spin-up run out to 2010 Sensitivity simulations  turn one variable component on at a time to systematically test the impact of climate variability, CO 2 fertilization, nitrogen limitation, and land cover / land-use change on carbon exchange. Baseline simulations  model’s best estimate of net land-atmosphere carbon flux (everything turned on) MsTMIP Simulations: Global Start with steady-state initial conditions Start monthly output Start 3-hourly output Stop Changing land-use, land-cover, CO 2 concentrations, nitrogen deposition rates, etc.

7 MsTMIP experimental design represents a set of collective hypotheses: – Strict protocol isolate sources of differences – Similar structural characteristics similar estimates of fluxes, carbon pools, etc. – Sensitivity to forcing factors will differ among models

8 NACP Regional Interim Synthesis vs. MsTMIP Mean GPP for North America ( ) 5 models (CLM, DLEM, LPJ, ORCHIDEE, VEGAS) Range Interquartile range Median Huntzinger et al., (2012) GMD in prep. Does strict protocol help to isolate sources if different in model output?

9 MsTMIP models Steady-state results 10 models GPP varies by factor of 2 in tropics Soil carbon pool size in NHL ranges from 5 – 60 kg C m -2 Total living biomass varies by factor of 3.5 in tropics Range Interquartile range Median Huntzinger et al., (2012) GMD in prep.

10 “Best estimate” ( ) 9 models ( BIOME-BGC, CLM, CLM4ViC, DLEM, LPJ, ORCHIDEE, TRIPLEX-GHGm, VEGAS, VISIT ) Total living biomass Range Interquartile range Median

11 75% 90% 95% “Hot spots” of interannual variability (IAV) ( ) Map highlights areas where the models show the greatest degree of interannual variability (IAV)

12 Compare simulated GPP to other GPP products: MODIS-GPP (Zhao and Running, 2010) MPI-BGC (Jung et al., 2011)

13 MsTMIP experimental design represents a set of collective hypotheses: – Strict protocol isolate sources of differences – Similar structural characteristics similar estimates of fluxes, carbon pools, etc. – Sensitivity to forcing factors will differ among models Need to identify models that share similar characteristics

14 Visualizing model structural differences using dendrograms Huntzinger et al., (2012) GMD in prep.

15 Do models with similar structural characteristics will have similar estimates of flux? Overall model structural differences Mean global GPP ( )

16 Model sensitivity to different environmental drivers Global Net GPP

17 Change in GPP (relative to SS) with each simulation Nitrogen dynamics Time-varying atmospheric CO 2 Time-varying climate Land-use, land-cover change history Dynamic Land Ecosystem Model (DLEM)

18 Additive change in GPP attributed to different forcing factors (DLEM) LULCC Climate Atm. CO 2 N-cycling

19 Model sensitivity to different environmental drivers ( )

20 Summary and what’s next We can evaluate model results in a way that was not possible with the NACP regional synthesis activity: – Attribute inter-model variability to structural differences – Quantify sensitivity of models (and their estimates) to forcing factors Model-data evaluation (benchmarking) is currently underway. Will evaluate model performance as a function of: – Domain (Site, North America, Global) – Spatial and temporal resolution of driver data MsTMIP workshop following meeting

21 Acknowledgements Funding for MsTMIP: – NASA Terrestrial Ecology Program Grant No. NNX10AG01A – NOAA Data/model output management and processing – MAST-DC and ORNL DAAC MsTMIP modeling teams: – John Kim (BIOMAP); Weile Wang (Biome-BGC ); Altaf Arain (CLASS-CTEM-N+); Dan Hayes (CLM and TEM6); Mayoi Huang (CLM4-VIC); Hanqin Tian (DLEM); Dan Riccuito (GTEC); Tom Hilinksi (IRC/DayCent5); Atul Jain (ISAM); Ben Poulter (LPJ); Dominique Bachelet (MC1); Josh Fisher (JULES, ORCHIDEE, SIB3, Shushi Peng and Gwenaelle Berthier (ORCHIDEE); Kevin Schaefer (SiBCASA); Rob Braswell (SIPNET); Chanqhui Peng (TRIPLEX-GHG); Ning Zeng (VEGAS); Akihiko Ito (VISIT)


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