Efficient ensemble estimation using an MCMC sampler: a reconstruction of the Mediterranean low-frequency variability combining observed and simulated sea level
- 1Université Grenoble Alpes (Saint-Martin-d'Hères, France)
Abstract
The skill of climate projections depends on the ability of models to reproduce the long-term and low-frequency variability of the system. It is thus important that low-frequency model statistics can be checked against observations. In this paper, a method is proposed to estimate directly the low-frequency component of the ocean variability from native observations using statistics from a prior long-term simulation. It is designed to account for possible model biases and to provide an estimate of the correction required to fit observations. The result is obtained by an MCMC sampler (modified to include localization of the model covariance), which provides an ensemble description of the solution, so that uncertainties can be properly assessed using independent data. This algorithm is shown well suited to work with MPI and GPUs, and efficient enough to solve large-size problems (about 10 8 variables and 10 7 observations). The approach is illustrated by the reconstruction of the low-frequency variability of the Mediterranean sea level, using statistics from a 1/12° resolution ensemble model simulation. The resulting ensemble is assessed against independent observations (by cross-validation), showing good reliability (flat rank histogram). The method also produces a consistent estimate of the model bias and of the observation error variance (mainly representativity error), while the missing prior ensemble variance is shown to be less controlable by the observations, and thus rather computed as a diagnostic. Overall, this application shows the importance of reliable model statistics, and thus the importance of enhancing model simulations to represent all main sources of uncertainty.
Versions
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BibTeX
@article{brankart2026efficient,
title = {{Efficient ensemble estimation using an MCMC sampler: a reconstruction of the Mediterranean low-frequency variability combining observed and simulated sea level}},
author = {Jean-Michel Brankart and Damien Héron and Lisa Weiss and Thierry Penduff and Pierre Brasseur},
journal = {Ocean Science},
year = {2026},
month = oct,
doi = {10.5194/os-22-3207-2026},
url = {https://hal.science/hal-05805156v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Efficient ensemble estimation using an MCMC sampler: a reconstruction of the Mediterranean low-frequency variability combining observed and simulated sea level
AU - Jean-Michel Brankart
AU - Damien Héron
AU - Lisa Weiss
AU - Thierry Penduff
AU - Pierre Brasseur
PY - 2026
DA - 2026/10/19
JO - Ocean Science
DO - 10.5194/os-22-3207-2026
UR - https://hal.science/hal-05805156v1
AB - The skill of climate projections depends on the ability of models to reproduce the long-term and low-frequency variability of the system. It is thus important that low-frequency model statistics can be checked against observations. In this paper, a method is proposed to estimate directly the low-frequency component of the ocean variability from native observations using statistics from a prior long-term simulation. It is designed to account for possible model biases and to provide an estimate of the correction required to fit observations. The result is obtained by an MCMC sampler (modified to include localization of the model covariance), which provides an ensemble description of the solution, so that uncertainties can be properly assessed using independent data. This algorithm is shown well suited to work with MPI and GPUs, and efficient enough to solve large-size problems (about 10 8 variables and 10 7 observations). The approach is illustrated by the reconstruction of the low-frequency variability of the Mediterranean sea level, using statistics from a 1/12° resolution ensemble model simulation. The resulting ensemble is assessed against independent observations (by cross-validation), showing good reliability (flat rank histogram). The method also produces a consistent estimate of the model bias and of the observation error variance (mainly representativity error), while the missing prior ensemble variance is shown to be less controlable by the observations, and thus rather computed as a diagnostic. Overall, this application shows the importance of reliable model statistics, and thus the importance of enhancing model simulations to represent all main sources of uncertainty.
SN - 1812-0784
SN - 1812-0792
ER -