Why does the signal-to-noise paradox exist  in seasonal climate predictability?

Yashas, Shivamurthy; Saha, Subodh Kumar; Pokhrel, Samir; Konwar, Mahen; Utkarsh, Verma

doi:10.5194/gmd-19-4817-2026

Articles | Volume 19, issue 11

https://doi.org/10.5194/gmd-19-4817-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Collection:

The global hydrological model WaterGAP (GMD/NHESS)

https://doi.org/10.5194/gmd-19-4817-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 11

Model evaluation paper

|

05 Jun 2026

Model evaluation paper |

| 05 Jun 2026

Why does the signal-to-noise paradox exist in seasonal climate predictability?

Shivamurthy Yashas, Subodh Kumar Saha, Samir Pokhrel, Mahen Konwar, and Verma Utkarsh

Supplement

https://doi.org/10.5194/gmd-19-4817-2026-supplement

Data sets

Hybrid gridded demographic data for the world, 1950-2020 R. Adler et al. https://doi.org/10.5281/zenodo.3768003

Why does the signal-to-noise paradox exist in seasonal climate predictability? Subodh Kumar Saha 0.5281/zenodo.13166897

Model code and software

Codes used in the preparation of the article "Why does the signal-to-noise paradox exist in seasonal climate predictability?'' S. Yashas https://doi.org/10.5281/zenodo.15369106

Short summary

This study highlights challenges in estimating seasonal climate predictability using the perfect model framework, which assumes only initial conditions cause error. We show that forecasts can exceed the predicted limit, known as the Potential Predictability Limit (PPL), due to model imperfections in simulating physical processes. A new method is proposed to estimate PPL more accurately and avoid such paradoxes.