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ANU's Mike Hutchinson presentation on e_MAST and ANU Climate at EcoTas13 in November 2013.
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Topographic-dependent modelling of surface climate for earth system modelling and assessment
Michael Hutchinson, Jennifer Kesteven, Tingbao XuAustralian National University
e-MAST’s objectives
DEVELOP research infrastructure to integrate TERN (and external) data streamsENABLE benchmarking, evaluation, optimization of ecosystem modelsSUPPORT ecosystem science, impact assessment and management
What e-MAST will provide
Top-level drivers and targets (from TERN and elsewhere) for modelsSoftware for benchmarking (based on PALS)Data-assimilation for optimizationTools for interpolation, downscaling, upscaling, hindcasting, forecastingHigh-resolution products: climate, canopy conductance, water use, primary production
Climate data sets (1 km)Tmin Tmax vp Precip pan
evapwet days
solar rad
wind speed
daily1970-2011
✔ ✔ ✔ ✔
monthly 1970-2011
✔ ✔ ✔ ✔ ✔ ✔
monthly mean
✔ ✔ ✔ ✔ ✔ ✔ ✔
High-resolution climate surfaces
Daily Rainfall Data Network
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?
Censored power of normal distribution
Rainα = μ + σz
α 0.3 – 0.9
z standard normal variable, z ≥ -μ/σ
μ/σ -3.0 to 2.0 P(W) = Φ(μ/σ)
α vs -μ/σ 1976-2005
Power Parameter 1976-2005 Jan, July
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)
μ/σ 1976-2005 Jan, July
Mean daily rain mm/day 1976-2005 Jan, July
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)
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
Interpolation of anomaliesAdaptive 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
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
Daily rainfall 5 Jan 1970
Daily rainfall 5 Jan 1970
ANUClimate - Interrogation of Elevation Dependent Climate Surfaces
Monthly Mean Daily Maximum Temperature for 2001-2010
Cairns
N
Low : 19.0
High : 28.7
Temperature (C)
Daily Maximum Temperature over NE Qld on 12/02/1999
Cairns
N
High : 460
Low : 113
Rainfall (mm)
Daily Rainfall over NE Qld on 12/02/1999
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
Conclusion
Censored square of normal distribution provides a stable parameterisation of the background daily rainfall distributionCensored square of normal distribution a model“Spread” fluxes across the landscape via a modelConnect observed [CO2] and streamflow to modelled CO2 flux and runoffCompute data-model comparison statisticsDerive re-analysis productsDownscale climate drivers to any pointDownscale climate change scenarios to a grid
Tools
Connect inputs and targets via a model“Spread” fluxes across the landscape via a modelConnect observed [CO2] and streamflow to modelled CO2 flux and runoff
Compute data-model comparison statisticsDerive re-analysis productsDownscale climate drivers to any pointDownscale climate change scenarios to a grid
Tools
Connect inputs and targets via a model“Spread” fluxes across the landscape via a modelConnect observed [CO2] and streamflow to modelled CO2 flux and runoff
Compute data-model comparison statisticsDerive re-analysis productsDownscale climate drivers to any pointDownscale climate change scenarios to a grid
Tools
Connect inputs and targets via a model“Spread” fluxes across the landscape via a modelConnect observed [CO2] and streamflow to modelled CO2 flux and runoff
Compute data-model comparison statisticsDerive re-analysis productsDownscale climate drivers to any pointDownscale climate change scenarios to a grid
Tools
Connect inputs and targets via a model“Spread” fluxes across the landscape via a modelConnect observed [CO2] and streamflow to modelled CO2 flux and runoff
Compute data-model comparison statisticsDerive re-analysis productsDownscale climate drivers to any pointDownscale climate change scenarios to a grid
Tools
Connect inputs and targets via a model“Spread” fluxes across the landscape via a modelConnect observed [CO2] and streamflow to modelled CO2 flux and runoff
Compute data-model comparison statisticsDerive re-analysis productsDownscale climate drivers to any pointDownscale climate change scenarios to a grid