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GPP/RE discussion

GPP/RE discussion. Who am I: Ankur Desai National Center for Atmospheric Research What we’re doing Initial results What we have What should we do Plan of action. What we’re doing.

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GPP/RE discussion

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  1. GPP/RE discussion • Who am I: • Ankur Desai • National Center for Atmospheric Research • What we’re doing • Initial results • What we have • What should we do • Plan of action

  2. What we’re doing • Using same dataset from NEE gap filling (12 site-years, 51 scenarios/site) to comparing GPP/RE across methods • 10 of 15 methods produce GPP/RE • Neural networks and tables do not? • 9 of these analyzed so far • With variants, currently at 19 analyzed • Unlike NEE, no benchmark • However, BETHY model is part of group • Can use BETHY as benchmark • Because I’m lazy

  3. What we’re doing • Hypotheses: • Intramethod variability in GPP/RE < Intermethod variability for any site • i.e., insensitive to most gaps • Site GPP/RE estimates vary < 20% across methods • Using BETHY as model benchmark, mean of other methods is similar to BETHY GPP/RE • More sophisticated methods have less difference to BETHY GPP/RE than simpler methods • Variability in GPP/RE < GPP or RE • Others?

  4. What we’re doing

  5. Initial results • To date, all datasets have been processed and put in common binary format • Daily and annual sums of GPP,RE computed • Mean and variance across 51 replicates computed and across methods computed • Diagnostic plots made • Box plots look at GPP/RE across methods (letters) , replicates (gray bars), mean and st.dev (+) across methods and range (box) • Colors delineate method type

  6. Initial results • Other plots • GPP/RE plots • Cumulative 2-week smoothed GPP (negative values) and RE (positive values) to look at when methods diverge for a site • Line is method mean, shadow is variance across replicates • Similar plots made for growing season (mid-May to mid-Sept) and dormant season (all other months) • Benchmark plots look at similar data as percent different from BETHY full run

  7. Initial results (annual)

  8. Initial results (annual)

  9. Initial results (annual)

  10. Initial results (cumulative)

  11. Initial results (cumulative)

  12. Initial results (cumulative)

  13. Initial results (cumulative)

  14. Initial results (summer)

  15. Initial results (summer)

  16. Initial results (dormant season)

  17. Initial results (dormant season)

  18. What do we have • A lot of messy plots! • Data reduction is hard (5 GB of data!) • Tables might work better? • Have not done hypothesis testing yet • Generally looks like intermethod var. is higher than 20% in some cases and biases in some methods compared to BETHY • Intramethod var. << intermethod var. • Will we be able to tease mechanistic reasons for method differences from this analysis? • Can we make any recommendations?

  19. GPP table

  20. RE table

  21. What should we do • Other hypotheses • Other kinds of benchmarks/models • Other kinds of comparisons • Artificial data? • Technical approach • Different kinds of figures • Different analysis techniques / stats • Philosophical questions • Is this manuscript worthy?

  22. Plan of action • Get all data! • mixed gap runs - late oct • no il1, it3_2001 • Jens to give Dave BETHY data (all sites), Dave to corrupt, Antje to gapify (10 mixed scenarios 35% missing + 0% missing) - next 3-4 mos • 10 Mixed gaps only + r0 • Fill and decompose as you would when publishing GPP/RE for your sites - final sets Spring 07 • Run other benchmarks, tests if needed • Running corrupt data through methods • Seasonal diurnal plots • ANOVA of GPP or RE for site x method • Find independent data (chamber, inventory, etc…) • Share data • Discuss - Desai to create wiki • Write a manuscript - Dec. • Delegate tasks

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