Bias-corrected data sets of climate model outputs at uniform space–time resolution for land surface modelling over Amazonia

Sanaz Moghim, Shawna L. McKnight, Ke Zhang, Ardeshir M. Ebtehaj, Ryan G. Knox, Rafael L. Bras, Paul R. Moorcroft, Jingfeng Wang

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Developing high-quality long-term data sets at uniform space–time resolution is essential for improved climate studies. This article processes the outputs from two global and regional climate models, the Community Climate System Model (CCSM3) and the Regional Climate Model driven by the Hadley Centre Coupled Model (RegCM3). The results are bias-corrected time series of atmospheric variables corresponding to Intergovernmental Panel on Climate Change (IPCC's) historical (20C3M) and future (A2) climate scenarios over the Amazon Basin. We use a series of simple but effective interpolation approaches to produce hourly climate data sets at 1° by 1° grid cells. A quantile-based mapping approach is used to reduce the biases of temperature and precipitation in CCSM3 and RegCM3. Adjustments are also made on specific humidity and downwelling longwave radiation to avoid inconsistency between those variables and bias-corrected temperature values. We also interpolated an already bias-corrected Parallel Climate Model data set (PCM1) from 3-hourly to the hourly resolution. The final climate data sets can be used as forcing of ecosystem and hydrologic models to study climate changes and impact assessments over the Amazon Basin.

Original languageEnglish (US)
Pages (from-to)621-636
Number of pages16
JournalInternational Journal of Climatology
Issue number2
StatePublished - Feb 1 2017

Bibliographical note

Publisher Copyright:
© 2016 Royal Meteorological Society


  • Amazon Basin
  • General Circulation Models
  • bias correction
  • climate change
  • climate data sets
  • climate simulations


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