EcoTas13 BradEvans e-MAST
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TERN's e-MAST Director Brad Evans's presentation on e_MAST at EcoTas13 in November 2013.
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EcoTas13 BradEvans e-MAST
- 1. ecosystem Modelling And Scaling infrasTructure (eMAST) - Where models and data become one Presentation by Brad Evans based on contributions by Colin Prentice, Michael Hutchinson, Gab Abramowitz, Ben Evans, Rhys Whitley, Julie Pauwels
- 2. eMAST’s objectives 2013-2015 DELIVER research data infrastructure to integrate TERN (and other) data streams on the National Computing Infrastructure ENABLE data assimilation, model evaluation and accreditation and ecosystem model optimization DRIVE advances in ecosystem science, impact assessment and land management
- 3. Driving science questions CARBON: How much CO2 is exchanged? How much carbon can be stored and where? WATER: What drives water use by ecosystems, and runoff in rivers? CLIMATE CHANGE: How does it change the rules? LAND MANAGEMENT: What will work, in a changing climate?
- 4. More driving science questions FIRE: What are the risks? How can they be mitigated? CLIMATE FEEDBACKS: How will ecosystem changes influence the exchanges of carbon, water and energy with the atmosphere? BIODIVERSITY: What species are threatened? Where are likely refugia? Is there a tipping point?
- 5. Our target market Ecosystem scientists Australian Government e.g. BoM, DCCEE, SEWPAC Natural resource managers Conservation organizations Answering questions at all scales Addressing tradeoffs – carbon, water, biodiversity
- 6. What eMAST is delivering High-resolution data products: climate, canopy conductance, water use, primary production Tools for interpolation, downscaling, upscaling, hindcasting, forecasting A state-of-the-art data assimilation system for ecosystem model optimization Software for model evaluation (based on PALS) Top-level ecosystem drivers and targets for models
- 9. ANUClimate A NEW approach to interpolating our national network 0.01 degree climate surfaces Who? Professor Mike Hutchinson (ANU)
- 10. Climate data sets (1 km) Tmin Tmax vp P daily ✔ 1970-2011 ✔ ✔ ✔ monthly ✔ 1970-2011 ✔ ✔ ✔ ✔ mean monthly pan evap. wet days ✔ ✔ ✔ ✔ ✔ ✔ solar rad. wind speed ✔ ✔
- 11. ANUClimate When? Delivery timeline… Complete set of Climate and Bioclimatic data available on RDSI RDSI opendap netCDF CF & Metadata store complete = public release Data starts propagating to RDSI* ADVANCED USER ACCESS DOI’s NOT YET AVAILABLE = NO PUBLISH 30 Nov 2013 24 Dec 2013 31 Jan 2013 *Currently experiencing delays in RDSI allocation – delays in the Raijin cloud roll out etc…
- 12. ANUClimate What is different? • Improved ‘background-anomaly-interpolation’ approach • • • Temperature and both positive and zero rainfall can be effectively interpolated by the thin plate splines method - with adaptive capacity ! Monthly means, topographically corrected yield influence of atmospheric processes and terrain Significant improvement over both direct (nonanomaly) and current anomaly approach • Coastal proximity: A new ‘proximity to coast’ modifier captures marine perturbation of climate
- 13. ANUClimate What can we expect? • Temperature estimates improved by around 25% compared to Jones et al. 2009 (RMSE cross validation) • Precipitation estimates a modest, but significant, improvement (7-15% RMSE cross validation) The model makes no further improvement on accuracy beyond the 1km mark !
- 14. ANUClimate How is it done?
- 15. AMOS 2014
- 16. Bioclimate data sets (1 km T, P and R)
- 17. ecosystem Production in Space and Time: ePiSaT eMAST: How does gross primary productivity (GPP) vary in space and time across Australia? Colin: How can we ‘simply’ estimate GPP across Australia? What data does TERN provide that might be useful for addressing this research question?
- 18. User workflow: ePiSaT GPP Choose the ePiSaT model from the TERN portal Produce continental scale estimates of GPP and evaluate them Obtain OzFlux data via the TERN/ OzFlux portals Obtain climate (eMAST) and satellite data (AusCover) to scale the ePiSaT parameters Run the ePiSaT model – generate estimates of ecosystem parameters, evaluate them
- 20. OzFlux
- 21. ePiSaT : Flux tower scaling
- 22. OzFlux: Flux partitioning 1 Respiration R= 1 Data filtering: Removal of outliers etc.. Gap filling of PAR (PPFD) for GPP Amax = - 2 Quantum 3 Rectangular Hyperbole 3 parameter 1 Assimilation 2 2 Efficiency 2 Φ= 3 3 FC = R - Amax * Φ I Amax +Φ I
- 23. ePiSaT : Partitioning evaluation
- 24. ePiSaT : Partitioning evaluation
- 25. Model data evaluation from Gab Abramowitz (UNSW)
- 29. NEON, TERN and ????
- 30. Supersites: the Australian Supersite Network (ASN)
- 33. LTERN
- 34. Summary: Data-model fusion tools Data assimilation collaboration with NEON and NCAR, CSIRO, Macquarie University and the Australian National University eMAST : An R-Package ‘emast’ for the computation and visualization of bioclimatic indices ePiSaT : Collaboration with OzFlux and AusCover to model Gross Primary Production across the landscape, another R-Package ‘ePiSaT’. Protocol for the Analysis of Land Surface Models (PALS) for evaluation of data and models Downscale climate change scenarios
- 35. Status of the facility Delivery of our key datasets through the RDSI, Data Discovery, Visualization & Exploitation… consolidation of our tools and porting them to Raijin.
- 36. eMAST in 2014 Delivery of our key datasets through the RDSI, Data Discovery, Visualization & Exploitation… consolidation of our tools and porting them to Raijin.