ANU's Mike Hutchinson presentation on e_MAST and ANU Climate at EcoTas13 in November 2013.

1. Topographic-dependent modelling
of surface climate for earth system
modelling and assessment
Michael Hutchinson, Jennifer Kesteven, Tingbao Xu
Australian National University

2. e-MAST’s objectives
DEVELOP research infrastructure to integrate
TERN (and external) data streams
ENABLE benchmarking, evaluation, optimization
of ecosystem models
SUPPORT ecosystem science, impact assessment
and management

3. What e-MAST will provide
Top-level drivers and targets (from TERN and
elsewhere) for models
Software for benchmarking (based on PALS)
Data-assimilation for optimization
Tools for interpolation, downscaling, upscaling,
hindcasting, forecasting
High-resolution products: climate, canopy
conductance, water use, primary production

4. Climate data sets (1 km)
Tmin
Tmax
vp
Precip
daily
✔
1970-2011
✔
✔
✔
monthly
✔
1970-2011
✔
✔
✔
✔
monthly
mean
pan
evap
wet
days
✔
✔
✔
✔
✔
✔
solar
rad
wind
speed
✔
✔

5. High-resolution climate surfaces

6. Daily Rainfall Data Network

7. Anomaly-based daily interpolation
Background field can be calibrated on full historical data
Can be extended to sites with modest numbers of records –
beyond what is available day by day
Topographic dependence can be (largely) incorporated into the
background field parameters
Anomalies from the background field have broader scale spatial
patterns, with little or no dependence on topography – supports
day by day interpolation from limited numbers of sites
How to do this for daily rainfall?

8. Censored power of normal distribution
Rainα = μ + σz
α
0.3 – 0.9
z
standard normal variable,
μ/σ
-3.0 to 2.0
z ≥ -μ/σ
P(W) = Φ(μ/σ)

10. α vs -μ/σ
1976-2005

11. Power Parameter 1976-2005 Jan, July

12. Parameterisation
Two parameters – calibrated on a monthly basis:
Mean daily rainfall = f(μ/σ).σ2
(σ ranges from 5 to 6)
P(W) = Φ(μ/σ)
(μ/σ ranges from -3.0 to 2.0)

13. μ/σ
1976-2005 Jan, July

14. Mean daily rain mm/day 1976-2005 Jan, July

15. Regression extension of short period records –
for 1976-2005
6400 stations with at least 20 years of record
Additional 3200 stations with at least 10 years of record
Without regression
RMSE = 20%
With regression
RMSE = 10%
Cross validation RMSE of interpolated long period stns = 15%
Cross validation MAE of interpolated long period stns = 7%
(3172 stations, at least 28 years of record)

16. Defining the anomalies
For positive rainfall – the z value of the underlying normal
distribution - z = (Rainα - μ)/σ
For zero rainfall – invent a latent negative anomaly by placing the
normalised value “mid-way” in the zero (dry day) probability region

17. Interpolation of anomalies
Adaptive thin plate smoothing spline interpolation of anomalies
More knots for positive rainfall, fewer for latent negatives:
– up to 5000 for positives (amounts)
– 1500 for negatives (occurrence)
Tune the placement and relative weighting of the latent negatives
to minimise the RMS of cross validated normalised rainfall values
Placement: 0.25, weighting: 4.0
Monitor cross validation of occurrence structure
Monitor goodness of fit – amounts and occurrence

18. Statistics for 6 Representative Days
Statistic
Cross Validation
Residuals of Fit
RMS of normalised
values
0.223
0.300
MAE (mm)
1.43
0.940
RMS (mm)
3.62
2.25
MAE of positive rain
(mm)
2.9
1.80
Class average of
occurrence
82.2%
90.6%
Kappa statistic of
occurrence
0.668
0.810

19. Daily rainfall 5 Jan 1970

20. Daily rainfall 5 Jan 1970

21. ANUClimate - Interrogation of Elevation Dependent Climate Surfaces

22. Monthly Mean Daily Maximum Temperature for 2001-2010

23. Daily Maximum Temperature over NE Qld on 12/02/1999
Temperature (C)
High : 28.7
Low : 19.0

24. Daily Rainfall over NE Qld on 12/02/1999
Rainfall (mm)
High : 460
Low : 113

25. Conclusion
Censored square of normal distribution provides a stable
parameterisation of the background daily rainfall distribution
Provides stable assessment of residual interpolation statistics
The anomalies, for both positive and zero rainfall, can be
effectively interpolated by a TPS with adaptive complexity
Possible to incorporate additional fine scale predictors – radar,
cloud data, etc
Cross validation and goodness of fit statistics show modest, but
significant, improvements over some existing methods
Further assessment of accuracy, and of the tuning of the adaptive
interpolation procedure, is in progress

26. Conclusion
Censored square of normal distribution provides a
stable parameterisation of the background daily
rainfall distribution
Censored square of normal distribution a model
“Spread” fluxes across the landscape via a model
Connect observed [CO2] and streamflow to modelled CO2
flux and runoff
Compute data-model comparison statistics
Derive re-analysis products
Downscale climate drivers to any point
Downscale climate change scenarios to a grid

27. Tools
Connect inputs and targets via a model
“Spread” fluxes across the landscape via a model
Connect observed [CO2] and streamflow to
modelled CO2 flux and runoff
Compute data-model comparison statistics
Derive re-analysis products
Downscale climate drivers to any point
Downscale climate change scenarios to a grid

28. Tools
Connect inputs and targets via a model
“Spread” fluxes across the landscape via a model
Connect observed [CO2] and streamflow to
modelled CO2 flux and runoff
Compute data-model comparison statistics
Derive re-analysis products
Downscale climate drivers to any point
Downscale climate change scenarios to a grid

29. Tools
Connect inputs and targets via a model
“Spread” fluxes across the landscape via a model
Connect observed [CO2] and streamflow to
modelled CO2 flux and runoff
Compute data-model comparison statistics
Derive re-analysis products
Downscale climate drivers to any point
Downscale climate change scenarios to a grid

30. Tools
Connect inputs and targets via a model
“Spread” fluxes across the landscape via a model
Connect observed [CO2] and streamflow to
modelled CO2 flux and runoff
Compute data-model comparison statistics
Derive re-analysis products
Downscale climate drivers to any point
Downscale climate change scenarios to a grid

31. Tools
Connect inputs and targets via a model
“Spread” fluxes across the landscape via a model
Connect observed [CO2] and streamflow to
modelled CO2 flux and runoff
Compute data-model comparison statistics
Derive re-analysis products
Downscale climate drivers to any point
Downscale climate change scenarios to a grid