RA 1 – In Progress

Valley 800x250

RA 1.2 (Jan – Dec 2013) Update

Standard Information for all Climate Datasets

  • Spatial resolution: 0.05 x 0.05 degree latitude/longitude (approx. 5km) grid covering all of NZ (11491 gridpoints, aka the ‘Virtual Climate Data’ grid – see here);
  • Temporal resolution: Highest resolution is daily; but monthly, annual or multi-year mean data can be produced;
  • Data period: Continuous daily data over the period 1971-2099;
  • Data format: NetCDF files (but we can generate ASCII data files, if required).

Existing climate datasets on VCS grid (available now)

  • Bias-corrected and downscaled RCM data: Daily temperature and precipitation data, using the NIWA Regional Climate Model (RCM) and Sea Surface Temperature (SST) from four CMIP3 AOGCMs (HadCM3-MOHC, ECHAM5-MPI, CM2.1-GFDL, MK3.5-CSIRO), run for emission scenario A2 (with some A1B and B1 runs)10-12;
  • Downscaled RCM data (no bias correction): Daily surface data using the same GCM boundary conditions as above for three SRES scenarios (A2, A1B and B1), for RH, solar radiation and surface winds.

New Bodeker Scientific Work (data available in 2013)

  • Pattern-scaling approach: First a pattern-scaling approach will be used to simulate low resolution fields of daily maximum/minimum temperatures, precipitation, humidity and radiation at the spatial resolution of the AOGCM data on which the training is performed. Once trained, predictor time series produced from the MAGICC simple climate model will be used to generate fields of these climate variables for the New Zealand region for a wide range of plausible GHG and aerosol emissions scenarios – at the least all SRES and RCP emissions scenarios;
  • Emulating RCM temperature data: Using bias-corrected RCM data above, plus simulated or actual AOGCM data from at least three CMIP5 models, plus historical VCS daily climate data, generate daily maximum/minimum temperature and precipitation data corresponding to the range of GHG and aerosol emissions scenarios used in the pattern-scaling approach. The end product will be a large ensemble of projections of these climate variables through the 21st century at 0.05°x0.05° resolution.

New NIWA Work (data available in 2013/14)

  • Complete bias-corrections: Complete the RCM bias-correction work for three SRES emission scenarios (A2, A1B and B1);
  • Complete downloading and formatting of CMIP5 GCM data: Needed for re-do of RCM work (see below) and for use in RA2 Marine Case Study;
  • Complete documentation: Science papers are being written on the bias correction methodology11, and the climate change trends12 observed in the data;
  • Begin work using CMIP5 data (for 2014): Start work on updating the bias-corrected RCM temperature and rainfall data and on the uncorrected other RCM variables, but using boundary conditions from CMIP5 models and RCPs.


  1. Drost F, Renwick J, Bhaskaran B, Oliver H, McGregor J 2007. Simulation of New Zealand’s climate using a high-resolution nested regional climate model. International Journal of Climatology 27: 1153–1169.
  2. Huntingford C, Cox PM 2000. An analogue model to derive additional climate change scenarios from existing GCM simulations, Climate Dynamics 16: 575–586.
  3. Mitchell JFB, Johns TC, Eagles M, Ingram WJ, Davis RA 1999. Towards the construction of climate change scenarios, Climatic Change 41: 547–581.
  4. Mullan AB, Wratt DS, Renwick JA 2002. Transient model scenarios of climate changes for New Zealand. Weather and Climate 21: 3–34.
  5. Kennett, E.J. and Buonomo. 2006. Methodologies of pattern scaling across the full range of RT2A GCM ensemble members, report from ENSEMBLE-based Predictions of Climate Changes and their Impacts.
  6. Ruosteenoja K, Tuomenvirta H, Jylhä K 2007. GCM-based regional temperature and precipitation change estimates for Europe under four SRES scenarios applying a super-ensemble pattern-scaling method. Climatic Change 81: 193–208.
  7. Santer B, Wigley T, Schlesinger M, Mitchell JFB 1990. Developing climate scenarios from equilibrium GCM results. Tech. Rep. 47. Max-Planck-Institut für Meteorologie.
  8. Otto FEL, Massey N, van Oldenborgh GJ, Jones RG, Allen MR 2012. Reconciling two approaches to attribution of the 2010 Russian heat wave, Geophysical Research Letters 39, L04702, doi:10.1029/2011GL050422.
  9. Pall P, Aina T, Stone D, Stott P, Nozawa T, Hilberts A, Lohmann D, Allen M 2011. Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000. Nature 470: 382–385. doi:10.1038/nature09762.
  10. Ackerley, D., S. Dean, A. Sood, and A. B. Mullan, 2012: Regional climate modeling in NZ: Comparison to gridded and satellte observations. Wea. Clim., 32 (1), 3{22).
  11. Sood, A., 2013, Improved Bias Corrected and Downscaled Regional Climate Model Data for Climate Impact Studies. Part 1 Validation and Assessment for New Zealand. Draft
  12. Sood, A., 2013, Improved Bias Corrected and Downscaled Regional Climate Model Data for Climate Impact Studies. Part 2 Regional transient climate change signal in the 21st century. Draft

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