Articles | Volume 28, issue 4
Nonlin. Processes Geophys., 28, 501–532, 2021
https://doi.org/10.5194/npg-28-501-2021
Nonlin. Processes Geophys., 28, 501–532, 2021
https://doi.org/10.5194/npg-28-501-2021
Research article
14 Oct 2021
Research article | 14 Oct 2021

Identification of linear response functions from arbitrary perturbation experiments in the presence of noise – Part 1: Method development and toy model demonstration

Guilherme L. Torres Mendonça et al.

Related authors

Identification of linear response functions from arbitrary perturbation experiments in the presence of noise – Part 2: Application to the land carbon cycle in the MPI Earth System Model
Guilherme L. Torres Mendonça, Julia Pongratz, and Christian H. Reick
Nonlin. Processes Geophys., 28, 533–564, https://doi.org/10.5194/npg-28-533-2021,https://doi.org/10.5194/npg-28-533-2021, 2021
Short summary

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Solid earth, continental surface, biogeochemistry | Techniques: Theory
Identification of linear response functions from arbitrary perturbation experiments in the presence of noise – Part 2: Application to the land carbon cycle in the MPI Earth System Model
Guilherme L. Torres Mendonça, Julia Pongratz, and Christian H. Reick
Nonlin. Processes Geophys., 28, 533–564, https://doi.org/10.5194/npg-28-533-2021,https://doi.org/10.5194/npg-28-533-2021, 2021
Short summary
An enhanced correlation identification algorithm and its application on spread spectrum induced polarization data
Siming He, Jian Guan, Xiu Ji, Hang Xu, and Yi Wang
Nonlin. Processes Geophys., 28, 247–256, https://doi.org/10.5194/npg-28-247-2021,https://doi.org/10.5194/npg-28-247-2021, 2021
Short summary

Cited articles

Abraham, R. and Marsden, J. E.: Foundations of Mechanics, 2nd edn., Benjamin, New York, NY, USA, 1982. a
Aengenheyster, M., Feng, Q. Y., van der Ploeg, F., and Dijkstra, H. A.: The point of no return for climate action: effects of climate uncertainty and risk tolerance, Earth Syst. Dynam., 9, 1085–1095, https://doi.org/10.5194/esd-9-1085-2018, 2018. a, b, c
Anderssen, R. S. and Bloomfield, P.: Numerical differentiation procedures for non-exact data, Numer. Math., 22, 157–182, 1974. a
Åström, K. J. and Eykhoff, P.: System identification – a survey, Automatica, 7, 123–162, 1971. a
Bakushinskii, A.: Remarks on choosing a regularization parameter using the quasi-optimality and ratio criterion, Comp. Math. Math. Phys.+, 24, 181–182, 1984. a
Short summary
Linear response functions are a powerful tool to both predict and investigate the dynamics of a system when subjected to small perturbations. In practice, these functions must often be derived from perturbation experiment data. Nevertheless, current methods for this identification require a tailored perturbation experiment, often with many realizations. We present a method that instead derives these functions from a single realization of an experiment driven by any type of perturbation.