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

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

IPCC Consensus: CO2 Fertilization

WUE

NPP

WUE =GPP

ET

NPP = GPP - R

N & P

Land Surface Models-> Coupled to Climate Models

Other approaches

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 cycleSynthesis

Unifying principles for ecosystem modellers

# 1: Observations, Models and Understanding: Integration of empirical science and modellingbetters 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.

eMAST Observations and Models

Models

OzFluxCO2 and water fluxes

Plot NetworksVegetation Observations

via AeKos and Others

AusCoverRemote Sensing –

Satellite, in-situ & Obs.

Bureau of Meteorology and

Geoscience Australia

Land Surface Models

SoilsProperties of soil

dap.nci.org.augeonetwork

TERN TDDPtern.org.au

RDSI VM’s raijin@nciINTERSECT

NeCTAR

PALSEVALUATION

NeCTARVirtualLabs

eMAST Delivers in 2014-2015 : 1 of 3Simple 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

eMAST Delivers in 2014-2015 : 2 of 3Observation 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

eMAST Delivers in 2014-2015: 3 of 3NEXT 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

Climate and Bioclimate data Res. 0.01 degrees (nominally 1km) T, P, R + and 50 + indices

: 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

Big Data : eMAST’s collections

10

100

1000

10000 5419

1928

326176 140

Dat

a V

olu

me

s (T

B)

Scientific Data for Research (NCI RDSI node)

by 2015

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

ObservationsThe 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 DBExample One

eMAST : Data assimilation

Example Two

eMAST : Data assimilationCollaborative ‘Community’ approach: Work with international experts (Fox –NEON and Hoar – NCAR) and local champions Renzullo (CSIRO) and Evans. Opento community participation (Wang, Haverd and Trudinger CSIRO)

Data assimilation: NEON Leaf Carbon

Fox et al. 2012

Data assimilation: NEON Leaf Carbon

Fox et al. 2012

Ecosystem Production in Space and TimeExample 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

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 TimeePiSaT

Choose the ePiSaT model fromemast.org.au

TDDP orSPEDDEXES

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 TimeePiSaT

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.

Some thoughts on data sharing…