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EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
EcoTas13 Hutchinson e-MAST ANU
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EcoTas13 Hutchinson e-MAST ANU

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

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

Published in: Technology, News & Politics
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Transcript

  • 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)
  • 9. α vs -μ/σ 1976-2005
  • 10. Power Parameter 1976-2005 Jan, July
  • 11. 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)
  • 12. μ/σ 1976-2005 Jan, July
  • 13. Mean daily rain mm/day 1976-2005 Jan, July
  • 14. 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)
  • 15. 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
  • 16. 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
  • 17. 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
  • 18. Daily rainfall 5 Jan 1970
  • 19. Daily rainfall 5 Jan 1970
  • 20. ANUClimate - Interrogation of Elevation Dependent Climate Surfaces
  • 21. Monthly Mean Daily Maximum Temperature for 2001-2010
  • 22. Daily Maximum Temperature over NE Qld on 12/02/1999 Temperature (C) High : 28.7 Low : 19.0
  • 23. Daily Rainfall over NE Qld on 12/02/1999 Rainfall (mm) High : 460 Low : 113
  • 24. 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
  • 25. 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
  • 26. 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
  • 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

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