TERN eMAST : Observations and terrestrial ecosystem models : Terrestrial Ecosystem Modelling and Scaling Infrastructure : ecosystem Modelling And Scaling infrasTructure (eMAST)
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The document presents an overview of ecosystem modeling and scaling infrastructure (EMAST) focused on the impacts of rising CO2 on terrestrial ecosystems. It emphasizes the importance of integrating observations, models, and policies for effective ecosystem management and introduces various tools and models developed under EMAST to enhance research and data accessibility. The document highlights collaborative efforts, data assimilation techniques, and the innovative approaches being used in ecosystem science to inform future modeling work.
TERN eMAST : Observations and terrestrial ecosystem models : Terrestrial Ecosystem Modelling and Scaling Infrastructure : ecosystem Modelling And Scaling infrasTructure (eMAST)
- 1. ecosystem Modelling And Scaling infrasTructure (eMAST) Observations and terrestrial ecosystem models Presentation by Brad Evans based on contributions by Colin Prentice, Michael Hutchinson, Gab Abramowitz, Ben Evans, Rhys Whitley, Daisy Duursman, Tim Pugh, Julie Pauwels
- 3. Research domain: Impacts of rising CO2 Thus the ecosystem modeller seeks to: 1. Understand the effects of CO2 increases on ecosystems 2. Quantify negative feedbacks – the impact of rising CO2, land surface warming and extreme events on ecosystems 6CO2 + 6H20 C6H12O6 + 6O2 light energy chlorophyll + nutrients
- 4. IPCC Consensus: CO2 Fertilization WUE NPP WUE = GPP ET NPP = GPP - R N & P Land Surface Models -> Coupled to Climate Models Other approaches
- 5. Observations , models and policy… (1) MORE Observations (2) BETTER models are developed (3) Models evaluated against observations (4) EVEN BETTER Models (5) BETTER Policy A viscous cycle Synthesis
- 6. Unifying principles for ecosystem modellers # 1: Observations, Models and Understanding: Integration of empirical science and modelling betters scientific understanding. # 2: Transparency, Evaluation, Confidence : Reproducible models, evaluated with observations, enhance model efficacy. # 3: Innovation, Standards, Simplicity: Continuous innovation, use standards, mitigate unnecessary complexity.
- 7. eMAST Observations and Models Models OzFlux CO2 and water fluxes Plot Networks Vegetation Observations via AeKos and Others AusCover Remote Sensing – Satellite, in-situ & Obs. Bureau of Meteorology and Geoscience Australia Land Surface Models Soils Properties of soil dap.nci.org.au geonetwork TERN TDDP tern.org.au RDSI VM’s raijin@nci INTERSECT NeCTAR PALS EVALUATION NeCTAR Virtual Labs
- 8. eMAST Delivers in 2014-2015 : 1 of 3 Simple land surface process models • eMAST R-Package: MQ & ANU Bioclimate indices and surface processes • eMAST Earth System Model Connex (C++ & FORTRAN): MQ & ANU Bioclimate indices and surface processes coupled to ACCESS and other Earth System Models • ePiSaT R-Package: Continental Gross Primary Production (data model fusion) • Community R-Packages: Hutchinson Drought & BoM Heatwave – in kind from Ivan Hanigan (ANU) • pyeMAST: Python version of eMAST tools including big data services (connectivity with SPEDDEXES). Statistical land surface models • Data Assimilation: Ensemble Kalman Filter coupled to process based land surface model (Renzullo, CSIRO) • Fubaar: Machine learning land surface model (in-kind MQ – Keenan) Open Source ! Tools
- 9. eMAST Delivers in 2014-2015 : 2 of 3 Observation assimilation into Models • eMAST Ecosystem Model Parameters Database (EMP DB). • NCAR’s Data Assimilation Research Testbed (DART) • DART-CESM : In collaboration with NEON, Inc. (USA) • DART-CABLE : In collaboration with the NCI, NCAR and CSIRO • Assimilation of : fluxes, leaf properties, plot network observations Modelled Data discovery and ACCESS Tools • SPEDDEXES: A community based solution to (a) publishing big data (b) sharing big data (c ) discovering big data and (d) programmatic access to big data on Australia’s eResearch infrastructure. • SPEDDEXES@NeCTAR-VL’s: Collaborative extension of the SPEDDEXES tools to the NeCTAR Virtual Laboratories – embedding in the Climate and Weather Laboratory Benchmarking and Evaluation • eMAST@PALS : Development of the PALS system for eMAST and TERN data streams • eMAST BENCH : International collaboration on benchmarking Tools
- 10. eMAST Delivers in 2014-2015: 3 of 3 NEXT Generation of Ecosystem Models • ARC DP on Australian Tropical Savanna’s : Past Present and Future: Enhancing ecosystem models for Tropical Savanna’s • ARC DP on the Next Generation of Ecosystem Models: Using plant trait observations to inform a new approach to ecosystem modelling. • GePiSaT: Global version of the ePiSaT model (eMAST and Imperial College of London) • CAMELS: Coupling ACCESS with Models of Ecosystems and the Land Surface: Next generation approach to ecosystem and land surface modelling Datasets from eMAST • ANUClimate: A extension of past methods for gridding Climate and Weather for the Australian continent . • eMAST Bioclimate • eMAST Land Surface Modelling Tools & Data
- 11. Climate and Bioclimate data Res. 0.01 degrees (nominally 1km) T, P, R + and 50 + indices
- 12. : New approach for Big Data It is no longer practical, let alone affordable, to continue to do data-intensive ecosystem science in the copy-and-work paradigm, a new approach to working with Big Data is required. Think about network data access, not file downloads … Cross-disciplinary use of file formats and services … Open-source server technology and file formats … Work with big data in a high performance facility
- 13. Big Data : eMAST’s collections 10 100 1000 10000 5419 1928 326 176 140 DataVolumes(TB) Scientific Data for Research (NCI RDSI node) by 2015
- 14. Three eMAST projects 1. Observations: The Ecosystem Model Parameters Database 2. Models: Ecosystem Production in Space and Time 3. Observations in Models: CABLE-DART Data assimilation on the NCI
- 15. Observations The Ecosystem Model Parameters Database • Originally setup to generate continental scale surfaces of leaf properties (nitrogen, phosphorus etc) using ANN’s • Adapted in April 2014 for use with Data assimilation • Focal point for ecosystem scientists and plot networks to contribute observations for use in models EMP DB Example One
- 18. eMAST : Data assimilation Example Two
- 19. eMAST : Data assimilation Collaborative ‘Community’ approach: Work with international experts (Fox – NEON and Hoar – NCAR) and local champions Renzullo (CSIRO) and Evans. Open to community participation (Wang, Haverd and Trudinger CSIRO)
- 20. Data assimilation: NEON Leaf Carbon Fox et al. 2012
- 21. Data assimilation: NEON Leaf Carbon Fox et al. 2012
- 22. Ecosystem Production in Space and Time Example Three ePiSaT Data filtering: Removal of outliers etc.. Gap filling of PAR (PPFD) for GPP 1 3 1R = Assimilation Amax = - 2 Efficiency Φ = 2 2 3 Amax * FC = Rectangular Hyperbole 3 parameter 1 2 3 Respiration Quantum R - Φ I Amax +Φ I
- 23. How does gross primary productivity (GPP) vary in space and time across Australia? How can we ‘simply’ estimate GPP across Australia? What data does TERN provide that might be useful for addressing this research question? Ecosystem Production in Space and Time ePiSaT
- 24. Choose the ePiSaT model from emast.org.au TDDP or SPEDDEXES Obtain OzFlux data via the TERN/ OzFlux portals Run the ePiSaT model – generate estimates of ecosystem parameters, evaluate them Obtain climate (eMAST) and satellite data (AusCover) to scale the ePiSaT parameters Produce continental scale estimates of GPP and evaluate them Ecosystem Production in Space and Time ePiSaT
- 25. This project is supported by the Australian National Data Service (ANDS). ANDS is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative. For more information visit the ANDS website ands.org.au and Research Data Australia services.ands.org.au.
- 26. Some thoughts on data sharing…
Editor's Notes
- #13: Opensource solution - Open servers technology, open data formats. NetCDF - cross-diciplinary use i.e. meteorology, hydrology & modellers - climate, land surface and remote sensing. Value added services such as aggregation services