- Year Funded: 2012
- Principal Investigators: Kumar, Arun (NOAA/CPC)
- Co-Principal Investigators: Hoerling, Martin (NOAA/ESRL); Hurrell, James (NCAR), Quan, Xiaowei (University of Colorado)
- Program: MAPP Funded Project
- Climate Prediction Task Force (2012-2015)
We propose to develop and validate a new method for predicting North American (NA) decadal climate. The approach will involve integrating knowledge of the statistics of internal atmospheric decadal climate variability in NA climate with a) the estimate of the North American signal associated with the external radiative forcings, and b) the estimate of the North American response to trajectories of boundary forcings that are initialized from the observed state of the climate system (e.g., sea surface temperature) which may differ from the boundary forcings consistent with the external signal alone.
Three sets of information will be utilized in generating the decadal predictions: (i) an estimate of the North American decadal signal associated with the external radiative forcing based on the uninitialized CMIP5 simulations, (ii) an estimate of the internal component of SST (and the corresponding NA response) for the next decade based on the initialized decadal predictions, and finally (iii) an estimate of the uncertainty in the decadal means of the North American climate due to the atmospheric internal variability, and adding that to the estimate of NA decadal signal estimated as part of steps one and two. This procedure will be repeated for each decade spanning the 1980-2010 period.
This proposal builds upon our prior research that led to the first, experimental probabilistic forecast of North American decadal climate for 2011-2020 (but using uninitialized methods alone). Finally, thru an integration of uninitialized and initialized approaches, this project will also produce a probabilistic decadal forecast for North America for the independent period of 2015-2024, and will be accompanied by an estimate of skill based on the decadal hindcasts for 1980-2004 period. We will also provide a comparison of the skill of decadal predictions based on our approach with that from the CMIP5 uninitialized and initialized decadal predictions.