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

Project Details


Climate change is a pressing issue that the global society is facing. Accurate decadal predictions andprojection are the corner stone for mitigation planning of climate change problems. The main purpose ofthis study is to apply pattern stability analysis to both observed and the Coupled Model IntercomparisonProject, Phase 5(CMIP5) decadal forecasts and climate change scenario runs to find a better way to useMulti-Model Ensembles (MME) to reduce uncertainties in decadal forecasts and climate change projectionthan just use the mean and spread of MME. The study is currently in the middle of its first year stage. So farwe had found that there are two extremely stable spatial patterns exist in the global tropical sea surfacetemperature(SST) field, one is associated with the mean state and the other is associated with the meridionalmigration of mean seasonal cycle, that can be used as reference spatial patterns to evaluate and calibrateclimate models’ results. The rank histograms derived from projecting observed and historical runs of CMIP5to these two reference states between 1950 and 1999 indicated that most CMIP5 models hadunderestimation problems and the MME tended to be over-dispersed. Using observed rank histograms tocalibrate CMIP5 RCP8.5 scenario MME, we found that the future warming projection uncertainty is greatlyreduced. Furthermore, the future warming trend not only occurs on global mean state but also on themeridional migration of mean seasonal cycle state. These results suggest that the future warming is a globalphenomenon and the warming will be more severe in northern hemisphere summers than those of thesouthern hemisphere and winters. In the following year, the main goal of this study is trying to extend theapplicability of the above calibration method. There are two foci. One is to find a way to increase thenumber of reference spatial patterns to allow more climate variability to be included in the calibrationprocess. The other is to extend the calibration process from individual reference spatial pattern to individualgrid scale. Through this study, we hope to improve decadal forecasts and climate change projections toallow for more accurate mitigation planning of climate change problem.
Effective start/end date1/08/1731/12/18

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
  • Decadal forecasts
  • Climate change projections
  • Pattern stabilityanalysis
  • Rank histogram calibration


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.