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

eMAST : Data assimilation

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

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?

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?

What eMAST is delivering

High-resolution data products: climate, canopy conductance, water use, primary productionTools for interpolation, downscaling, upscaling, hindcasting, forecastingA state-of-the-art data assimilation system for ecosystem model optimizationSoftware for model evaluation (based on PALS)Top-level ecosystem drivers and targets for models

http://www.tern.org.au/e-MAST-Data-Products-pg26355.html

ANUClimate A NEW approach to interpolating our national network0.01 degree climate surfaces

Who? Professor Mike Hutchinson (ANU)

Climate data sets (1 km)Tmin Tmax vp P pan

evap.wet days

solar rad.

wind speed

daily1970-2011

✔ ✔ ✔ ✔

monthly 1970-2011

✔ ✔ ✔ ✔ ✔ ✔

mean monthly

✔ ✔ ✔ ✔ ✔ ✔ ✔

When? Delivery timeline…

30 Nov 2013

Data starts propagating to RDSI*ADVANCED USER ACCESSDOI’s NOT YET AVAILABLE = NO PUBLISH

*Currently experiencing delays in RDSI allocation – delays in the Raijin cloud roll out etc…

RDSI opendap netCDF CF & Metadata store complete= public release

24 Dec 2013

Complete set of Climate andBioclimatic data available on RDSI

31 Jan 2013

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 (non-anomaly) and current anomaly approach

• Coastal proximity: A new ‘proximity to coast’ modifier captures marine perturbation of climate

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 !

ANUClimate

How is it done?ANUClimate

AMOS 2014

Bioclimate data sets (1 km T, P and R)

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?

User workflow: ePiSaT GPP

Choose the ePiSaT model from the TERN

portal

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

http://episat-software.blogspot.com.au/

OzFlux

ePiSaT : Flux tower scaling

OzFlux: Flux partitioning

Data filtering: Removal of outliers etc.. Gap filling of PAR (PPFD) for GPP

1

3

1R =

Assimilation

Amax = - 2

Efficiency

Φ =

2

2

3Amax *FC =

Rectangular Hyperbole

3 parameter

1 2 3

Respiration

Quantum

R -Φ I

Amax +Φ I

ePiSaT v 1.0 : Tower GPP

Amax *GPP = I

Amax + C

Where: Amax is the maximum rate of carboxylation, I is PAR (PPFD) and C = parameter 3 from the rectangular hyperbola described in the previous slide

ePiSaT v 1.0 : Map GPP

fAPAR *I* LUE GPP =

Where: fAPAR is the fraction of absorbed photosynthetic active radiation, I is PAR (PPFD) and LUE is light use efficiency derived from the relationship of Tower GPP (previous slide) and fAPAR and I.

ePiSaT v 2.0 : Map GPP

fAPAR *I* LUE*WUE*TrangeGPP =

Where: fAPAR is the fraction of absorbed photosynthetic active radiation, I is PAR (PPFD) and LUE is light use efficiency derived from the relationship of Tower GPP (previous slide) and fAPAR and I. WUE and Trange are derived similarly.

ePiSaT : Partitioning evaluation

ePiSaT : Partitioning evaluation

from Gab Abramowitz (UNSW)

Model data evaluation

Plant trait surfaces• Leaf nitrogen• Leaf phosphorus• Specific leaf area• Leaf area• Maximum plant height• Photosynthesis per leaf

area• Photosynthesis per leaf

dry mass• Leaf stomatal

conductance

Dr. Rhys Whitley

Plant trait surfaces

NEON & TERN

TERN Data Discovery Portal

Summary: Data-model fusion toolsData assimilation collaboration with NEON and NCAR, CSIRO, Macquarie University and the Australian National University- ACEAS workshop on data assimilation early 2014

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’

-ACEAS worskshop on SPEDDEXES

Protocol for the Analysis of Land Surface Models (PALS) for evaluation of data and models

The future of eMASTContinue delivery of our key datasets through the RDSI, Data Discovery, Visualization & Exploitation… consolidation of our tools and porting them to Raijin.