Can we define climate by means of an ensemble? A tale of time scales of convergence
- 1Institute for Cross-Disciplinary Physics and Complex Systems (Palma, Spain)
- 2HUN-REN Institute for Nuclear Research (Debrecen, Hungary)
Abstract
It is not really questioned today that climate can be described in theory by an ensemble of trajectories differing in their initial conditions, which is then translated to numerical ensembles in climate models. It is also widely accepted that any evolution observed within a few decades after initialization is not yet relevant to climate. Evolution at a later stage, instead, is then used to characterize climate and its change, under the implicit assumption that slower processes do not considerably contribute to differences between ensemble members, allowing internal variability of climate to be identified with these differences. However, a careful justification for this practice is as yet lacking. In particular, a definition of climate in support of this practice is outstanding, including the identification of the kind of time scales at play through providing an argumentation for their relevance. Our study aims at filling this gap. After pointing out that the most important criterion for a definition of climate is the uniqueness of the probability measure on which the definition relies, we first recall the naive proposal to represent such a probability measure by the distribution of ensemble members that has, loosely speaking, converged to the natural probability measure of the so-called snapshot or pullback attractor of the dynamics. We then consider the time scales of convergence and refine the proposal by taking a probability measure that is conditional on the (possibly time-evolving) state of modes characterized by convergence time scales longer than the horizon of a particular study. We design an ensemble simulation initialization scheme for studying convergence time scales and uniqueness of ensembles in Earth system models.
Versions
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BibTeX
@article{drotos2026define,
title = {{Can we define climate by means of an ensemble? A tale of time scales of convergence}},
author = {Gábor Drótos and Tamás Bódai},
journal = {Earth System Dynamics},
year = {2026},
month = oct,
doi = {10.5194/esd-17-1529-2026},
url = {https://esd.copernicus.org/articles/17/1529/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Can we define climate by means of an ensemble? A tale of time scales of convergence
AU - Gábor Drótos
AU - Tamás Bódai
PY - 2026
DA - 2026/10/09
JO - Earth System Dynamics
DO - 10.5194/esd-17-1529-2026
UR - https://esd.copernicus.org/articles/17/1529/2026/
AB - It is not really questioned today that climate can be described in theory by an ensemble of trajectories differing in their initial conditions, which is then translated to numerical ensembles in climate models. It is also widely accepted that any evolution observed within a few decades after initialization is not yet relevant to climate. Evolution at a later stage, instead, is then used to characterize climate and its change, under the implicit assumption that slower processes do not considerably contribute to differences between ensemble members, allowing internal variability of climate to be identified with these differences. However, a careful justification for this practice is as yet lacking. In particular, a definition of climate in support of this practice is outstanding, including the identification of the kind of time scales at play through providing an argumentation for their relevance. Our study aims at filling this gap. After pointing out that the most important criterion for a definition of climate is the uniqueness of the probability measure on which the definition relies, we first recall the naive proposal to represent such a probability measure by the distribution of ensemble members that has, loosely speaking, converged to the natural probability measure of the so-called snapshot or pullback attractor of the dynamics. We then consider the time scales of convergence and refine the proposal by taking a probability measure that is conditional on the (possibly time-evolving) state of modes characterized by convergence time scales longer than the horizon of a particular study. We design an ensemble simulation initialization scheme for studying convergence time scales and uniqueness of ensembles in Earth system models.
SN - 2190-4987
ER -