Attributing correlation skill of dynamical GCM precipitation  forecasts to statistical ENSO teleconnection  using a set-theory-based approach

Zhao, Tongtiegang; Chen, Haoling; Shao, Quanxi; Tu, Tongbi; Tian, Yu; Chen, Xiaohong

doi:https://doi.org/10.5194/hess-25-5717-2021

Articles | Volume 25, issue 11

https://doi.org/10.5194/hess-25-5717-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-25-5717-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 11

Research article

|

08 Nov 2021

Research article |

| 08 Nov 2021

Attributing correlation skill of dynamical GCM precipitation forecasts to statistical ENSO teleconnection using a set-theory-based approach

Tongtiegang Zhao, Haoling Chen, Quanxi Shao, Tongbi Tu, Yu Tian, and Xiaohong Chen

Supplement

https://doi.org/10.5194/hess-25-5717-2021-supplement

Data sets

Climate Indices: Monthly Atmospheric and Ocean Time-Series NOAA https://psl.noaa.gov/data/climateindices/list/

Model code and software

Models NMME IRI https://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/

Short summary

This paper develops a novel approach to attributing correlation skill of dynamical GCM forecasts to statistical El Niño–Southern Oscillation (ENSO) teleconnection using the coefficient of determination. Three cases of attribution are effectively facilitated, which are significantly positive anomaly correlation attributable to positive ENSO teleconnection, attributable to negative ENSO teleconnection and not attributable to ENSO teleconnection.