B1 -Biogeochemical ANL - Townhall V. Rao Kotamarthi.
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B1 -Biogeochemical ANL - Townhall V. Rao Kotamarthi
Introduction Large scale inter-disciplinary research teams from ecology, biology and earth sciences a new level model abstraction needed We will need climate model predictions down to local and regional level (10 km and less grid size). Future versions of climate models will most likely be used as resource management/adaptation/mitigation application. Focus will shift to ecosystems, human impacts, energy/population/urban/agriculture planning. Running ‘extremely’ large number of simulations (ensembles) to understand uncertainty, assist in planning and help evaluate policy options.
Top Question In Biogeochemical ESM –Carbon sequestration strategy for the next century Microbiology and Biology –Characterizing and bounding the coupled climate system Rational approaches (math and algorithmic) –Prioritizing the impacts of climate change/Extreme Weather Events Extreme events Hurricanes etc.,
Top Questions Abrupt Climate Change Issues –Sustainability of the tropical rain forest –Stability of the Polar caps and Greenland ice sheets – Release of methane clathrates –Sustainability of sea life –Sustainability of biofuel production
biogeochemical cycles needed advances integration of models and observations of the carbon cycle (data assimilation with significant satellite and ground based data streams) process-level modeling of biogeochemical cycles across space and time scales (mechanistic models of microbiological and ecological processes). Eg. Individual based models of ecology accurate models of the coupled physical and biogeochemical system robust economic models hindcasting and predicting climate-changing emissions and water resources
Major challenges in understanding biogeochemical cycles –4.1 Integration of models and observations of the carbon cycle –4.2 Process-level modeling of biogeochemical cycles –4.3 Accurate models of the coupled physical and biogeochemical system
Feasible objectives over the next decade –5.1 Integrated models and measurements of biogeochemical cycles Using models to plan and prioritize observations. –5.2 Models for the Earth, environment, and society B6 - Socio-economic Disaggregating economic models to ESM regions –5.3 Better theory for and quantification of uncertainty
Approaches to accelerate development and understanding –Focused model development teams with dedicated resources high-resolution ESMs with massive assimilation of satellite and other data development of hierarchical unit testable model with requirements for accuracy in the ESMs detailed modeling of controlled and modified ecosystems to fit the environmental envelope in which future climate changes will occur (ecosystem succession models) greater scalability and identification of greater degrees of parallelism Development of process scale mechanistic models for biogeochemical, ecological and aerosol processes.
6.2 Applied mathematics and computer science crosscuts new applications of new algorithms in the physical climate model new software architectures and rapid development environments to facilitate code rewrites and refactoring
–6.3High-end simulation force for climate change experiments and analysis rational design and analysis of computer experiments to navigate very large parameter space with very large outputs advances in analysis tools with parallelized capabilities, and the ability to explore the full climate solution space using climate experiments based on data mining, objective and repeatable metrics (e.g. Taylor diagrams), and expert pattern recognition development of individual-based and agent-based methods for coupling of ecosystem demographies increased computational capacity and capability with dedicated cycles for large climate change studies
Outcomes of accelerated development and understanding Asymptotic process uncertainties: These are the errors remaining in the limit of the greatest process fidelity (e.g., incorporation of full-complexity cloud models) based on fundamental theory that can be constrained by observations. These uncertainties are also caused by the interactions of errors among process representations. Asymptotic scale uncertainties: These are the errors in the mean state and uncertainties in its high-order statistics (e.g., extremes) remaining in the limit of highest-possible spatial and temporal resolution. These are due to couplings between the processes, state, and dynamics out of the reach of modern observational systems. Asymptotic state uncertainties: These are the errors in the constituents of the system (the mixture of condensed and gaseous species) remaining in the limit of the most detailed possible constituent treatments. Representative treatments include aerosol modules that track huge ensembles of individual aerosol particles, master chemical mechanisms, etc.
Major risks to accelerated development and understanding lack of sufficient data to constrain key climate processes slow reduction of model uncertainty due to highly intractable or more complicated climate processes Absence of adequate diagnostic frameworks connecting forcing, response, and initial conditions Development of models too complicated for adoption by the academic, impacts, and/or mitigation communities