A Study on Reducing Uncertainties in Climate Change Projections and Decadal Forecasts(III)

Project Details


Climate change is a pressing issue that the global society is facing. Accurate decadal predictions and projection are the corner stone for mitigation planning of climate change problems. The main purpose of this study is to find a better way to use the Coupled Model Intercomparison Project, Phase 5(CMIP5) climate change scenario runs to reduce uncertainties in climate change projection than just use the mean and spread of Multi-Model Ensembles (MME). In the past two years, we applies pattern stability analysis, rank histogram calibration, and perfect model approach to global tropical sea surface temperature and global mean surface temperature from both observed and model runs to calibrated the 5-95 uncertainty ranges of CMIP5 RCP scenarios MME climate change projections. The results showed that the use of rank histogram calibration to constrained MME (i.e., filtered MME using perfect model approach) indeed could effectively reduce the 5-95 uncertainty ranges of MME climate change projections. In the following year, our study will focus on three aspects. The first is to find a proper way to choose filtering conditions with perfect model approach. The second is to extend the calibration process from individual reference spatial pattern to individual grid scale. The third is to explore the relation between perfect model filtering process and emergent constraints. Through this study, we hope to improve our capability in climate change projections.
Effective start/end date1/08/1831/07/19

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 8 - Decent Work and Economic Growth
  • SDG 13 - Climate Action
  • SDG 17 - Partnerships for the Goals


  • Multi-Model Ensembles
  • Climate change projections
  • Pattern stability analysis
  • Rank histogram calibration
  • Perfect model approach.


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