DATA PRODUCTION

Climate impact indicators
Global essential climate variables/indicators used
  • Daily precipitation sums
  • Daily mean air temperature (also upper air levels)
Regional/local indicators used and produced
  • Daily precipitation sums and derived indices
  • Daily maximum air temperature and derived indices
  • Daily minimum air temperature and derived indices
Global data to regional/local scale

Step 1: Download of relevant variables of all CMIP5 GCM’s for the baseline period and the periods 2040 to 2060 (possibly also for 2080-2100) and for the RCP’s 2.6 and 8.5 on a daily time step. Besides surface variables, those of principal levels, as 850 hPa, 700 hPa, 500 hPa and 250 hPa are also considered for download. Evaluation of the GCM’s concerning their plausibility for the point-to-point downscaling of air temperature and precipitation related variables.

Step 2: Regridding of all relevant GCM variable fields to the same geographical resolution, e.g. with cdo-routines or GDAL.

Step 3: Selection of GCM’s relevant predictor variables. Selection criteria will encompass plausibility of grid values when compared to in-situ stations and satellite derived estimations like CHIRPS, TMPA or GPM-imerg.

Step 4: Statistical downscaling. Several methods are considered for statistical downscaling, as canonical correlation and principal components, but also direct point to point downscaling and correlations to the global circulation. A decisive factor will be the predictor variables and the perimeter considering these. Important details‒as glacier recession‒will not be forgotten.

Step 5: Interpolation to a regular grid: The downscaled variables are interpolated to a regular grid. It is foreseen to use a Kriging‒or similar‒interpolation. Based on the gridded variables the gridded indicators (indices) are calculated.

Step 6: Database management and showcases: The gridded indicators will be managed in a dedicated database. Moreover, showcases for demonstration and promotion purposes are prepared. If ever possible, corresponding hydrological indices of SMHI’s HYPE-model and meteorological and hydrological seasonal forecasts are also part of the database and the showcases.

 
Meteodat GmbH

Servicio Nacional de Meteorología e Hidrología del Perú

Instituto de Hidrología, Meteorología y Estudios Ambientales

Poster displayed at the Kick–Off meeting, 7/8 September 2017, Norrköping, Sweden