Parameterizing ecosystem biogeochemistry using physical rules | 9am PT May 21, 2024

[Grigory Bronevetsky]

Modeling Talks

Parameterizing ecosystem biogeochemistry using physical rules Jinyun Tang, Berkeley Lab Tues, May 21 | 9:00 am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/parameterizing-ecosystem-biogeochemistry-using-physical-rules Abstract: To effectively combat climate change, we need accurate predictions of how terrestrial ecosystems respond to environmental shifts. However, existing ecosystem biogeochemical models are likely not up to this task. We argue this is due to their insufficient account of scale coherence in the parameterization of biogeochemical processes. This insufficiency is manifested by their wide use of empirical relationships and the careless adoption of the multiplicative model and the law of the minimum, all of which disrupt the information flow between interacting entities essential for the functioning of ecosystem biogeochemistry. We contend that incorporating more physical rules into the parameterization will enable models to better resolve the scale coherence among biogeochemical processes. This will lead to a deeper understanding of ecosystem biogeochemistry, better-constrained model structures, and reduced model sensitivity to parametric uncertainty. We demonstrate the advantage of physical rules using three examples and provide guidance to help other researchers build a more solid foundation for ecosystem biogeochemical models used in predicting ecosystem-climate feedback.     Bio: Dr. Jinyun Tang is a staff scientist in the Earth and Environmental Sciences Division at Lawrence Berkeley National Laboratory. He obtained his Ph.D. in Atmospheric Sciences from Purdue University, joined LBNL as a postdoctoral researcher in 2011, and has remained there ever since. His research encompasses various aspects of land surface modeling, focusing on developing theories, algorithms, and numerical codes that simulate and analyze climate-ecosystem feedback. Some of his landmark theoretical works include the equilibrium chemistry approximation theory for biogeochemistry and ecology, the chemical kinetics theory for temperature-dependent biochemical reactions, and the reaction-based theory for upscaling soil moisture dependence of biochemical reactions. Dr. Tang has been heavily involved in the development of the Community Land Model (versions 4.5 and 5.0) and the Department of Energy’s Energy Exascale Earth System Model. Currently, he is working on reformulating biogeochemical processes using physical rules derived from first principles, a new approach to improving the rigor and predictability of ecosystem-climate interactions. He now leads the development of EcoSIM for BioEPIC (the Biological and Environmental Program Integration Center) at LBNL, a new numerical code that uses physical rules to mechanistically integrate the interactions between plants, microbes, water, soil physics and chemistry, as well as ecosystem management and disturbances. More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home

[Grigory Bronevetsky]

Video Recording: https://youtu.be/F2meQ83j56s

Slides: https://docs.google.com/presentation/d/1fsXnRq7d_EDlXlXZ5fkaNJ8pqQENMnOE/edit?usp=sharing&ouid=110584304018462442605&rtpof=true&sd=true

Summary:

• Focus: modeling ecosystem response and interaction with the climate

  • Critical for understanding the overall climate

  • Needed to plan climate mitigation/carbon storage technologies

• Challenge: predict land carbon cycle

  • Difficulties

    • Atmospheric model inaccuracy

    • Initial and boundary conditions inaccurate

    • Land model parameters are not well constrained

    • Land model implementations flawed

    • Uncertain land model structure

  • Symptom: land model parameterizations vary significantly across simulations

• Types of ecosystem models

  • Multiplicative model: 

    • Outcome scaled by multiple sub-systems 

    • Multiply together terms that model the sub-systems

  • Min model: 

    • Outcome is bottlenecked by multiple sub-systems

    • Min constraints from all sub-systems

  • Good: simple and easy to use

  • Bad: non-causal, inaccurate

• Scale hierarchy in ecosystem modeling

  • Time: 10-3s - 109s

  • Space: 10-3m - 106m

  • Empirical biogeochemical models operate at the 101s x 101m scale

  • Missing key details, invariants of biochemical processes at finer scales

  • Argument: models founded at the finest scale will be more robust, causal and accurate

• Approach to bring in constraints from fine scale:

  • Apply first principles/rules: conservation relationships, key diffusion, kinetic dynamics

  • Construct mechanistically: causal interactions of elementary units

• Example: soil resistance for land-atmosphere volatile tracer exchange

  • Gas exchange between soil and atmosphere

  • Empirical approximations based on fitting curves to observational data

  • Derived fundamental differential equation based on gas flow laws and hydraulics

  • Error in measurement and limits in capturing experimental conditions makes empirical data fitting infeasible

  • A first principles model can account for all confounding factors

• Example: affinity for plant-microbial substrate intake

  • Usually calibrated as a single constant

  • But actually a variable that depends on environment and spans > 3 orders of magnitude

  • Derived a first-principles model founded on individual bacteria -> microbial colonies -> soil layer + diffusion theory across scales

  • Can predict how soil respiration responds to soil properties without model calibration

• Multi-nutrient regulated biological growth

  • Liebig’s law (law of minimum): growth of organisms depends (bounded by) on the nutrient that is least supplied

  • Very commonly used model of how nutrients regulate organism growth

  • Approach: a more first-principles model

    • Law of mass action to conserve mass/charge

    • Chemicals combine via multiple models: SU, Additive, LLM

    • Use experimental relationships between pairs of chemicals and organism growth

    • Parameterized with data from algal and plant growth experiments

    • Lieblig’s law is often a poor model for this data: very inconsistent values for the same parameters

    • The additive model is quite generalizable

  • First principles models are more generalizable and applicable across different ecosystems