arXiv subjects

David Saint-Martin

Publications and source records attributed to David Saint-Martin.

6 recordsLinked to original sources

arXiv2026

Global kilometer-scale climate simulations: new opportunities for climate services

Climate services are essential for risk management and economic planning. This paper presents a century-long simulation performed with the ARP-GEM global atmosphere model at 2.6 km horizontal resolution. This simulation was specifically designed to complement and extend the set of simulations used in the latest version of the French climate services. A key feature is its global coverage at kilometer-scale resolution, enabling the representation of phenomena that depend on such fine scales. This is especially relevant for French climate services, as many processes - particularly over islands and resolution-sensitive regions - are currently overlooked in global simulations. Additionally, France's overseas territories are globally dispersed, and this approach allows their representation within a single framework. A limitation of this simulation is the use of prescribed sea surface temperatures due to the lack of ocean coupling; this will be addressed in future work. The present study demonstrates the feasibility of this approach and highlights the benefits of this new generation of climate modeling for climate services.

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arXiv2025

Improvement of a neural network convection scheme by including triggering and evaluation in present and future climates

In this study, we improve a neural network (NN) parameterization of deep convection in the global atmosphere model ARP-GEM. To take into account the sporadic nature of convection, we develop a NN parameterization that includes a triggering mechanism that can detect whether deep convection is active or not within a grid-cell. This new data-driven parameterization outperforms the existing NN parameterization in present climate when replacing the original deep convection scheme of ARP-GEM. Online simulations with the NN parameterization run without stability issues. Then, this NN parameterization is evaluated online in a warmer climate. We confirm that using relative humidity instead of the specific total humidity as input for the NN (trained with present data) improves the performance and generalization in warmer climate. Finally, we perform the training of the NN parameterization with data from a warmer climate and this configuration get similar results when used in simulations in present or warmer climates.

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arXiv2025

Global Kilometer-Scale Simulations with ARP-GEM2: Effect of Parameterized Convection and Calibration

The objective of this paper is twofold. First, it documents the second version of the global atmospheric model ARP-GEM and its calibration at kilometer-scale resolution. The model is currently able to run simulations at a resolution of up to 1.3 km. Second, this paper focus on multi-year global atmospheric simulations at a 2.6 km resolution with and without parameterized convection and associated calibration. Simulations without deep convection tend to be similar to those with infinite, or at least large, entrainment values. Consistently, entrainment and detrainment are used as primary drivers for the gradual reduction of convection as resolution increases. The results indicate that, with this hydrostatic model, parameterized convection still plays a significant role in the correct representation of the mean state at the kilometer scale. Additionally, they suggest some added value of high resolution in representing climate variability. However, a compromise between the adequate representation of the mean state and variability is necessary, as both are differently favored by the degree of parameterized convection. Finally, it is likely that even higher resolutions are necessary to achieve an unequivocal added value.

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arXiv2024

Online Test of a Neural Network Deep Convection Parameterization in ARP-GEM1

In this study, we present the integration of a neural network-based parameterization into the global atmospheric model ARP-GEM1, leveraging the Python interface of the OASIS coupler. This approach facilitates the exchange of fields between the Fortran-based ARP-GEM1 model and a Python component responsible for neural network inference. As a proof-of-concept experiment, we trained a neural network to emulate the deep convection parameterization of ARP-GEM1. Using the flexible Fortran/Python interface, we have successfully replaced ARP-GEM1's deep convection scheme with a neural network emulator. To assess the performance of the neural network deep convection scheme, we have run a 5-years ARP-GEM1 simulation using the neural network emulator. The evaluation of averaged fields showed good agreement with output from an ARP-GEM1 simulation using the physics-based deep convection scheme. The Python component was deployed on a separate partition from the general circulation model, using GPUs to increase inference speed of the neural network.

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arXiv2024

The ARP-GEM1 Global Atmosphere Model. Part I: Description and Speed-up Analysis

This is the first part of a series of two articles describing the ARP-GEM global atmosphere model version 1 and its evaluation in simulations from 55 km to 6 km resolutions. This article provides a complete description of ARP-GEM1, focusing on its new physical parameterizations and acceleration factors aimed at improving computational efficiency. ARP-GEM1 is approximately 15 times faster than its base model version, ARPEGE-Climat v6.3, while maintaining accurate simulations and enhancing model performance, as shown in Part II.This significant acceleration results from a combination of optimizations, rather than a single factor, with each factor's contribution quantified. Additionally, a detailed decomposition of the model's speedup per component is presented. Streamlining the model reduces computational costs, enhances transparency and portability, and maximizes the benefits of future advanced computing technologies. The results presented here suggest that kilometer-scale global climate simulations should become feasible in the near future.

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arXiv2024

The ARP-GEM1 Global Atmosphere Model. Part II: Multiscale Evaluation up to 6 km

This is the second part of a series of two articles focused on the development and evaluation of the ARP-GEM1 global atmosphere model. The first paper introduced the model's new physics and speedup improvements. In this second part, we evaluate ARP-GEM1 through a set of 30-year prescribed sea-surface-temperature simulations at 55, 25, 12.6, and 6.3 km resolutions. The model demonstrates reliability in representing global climate metrics, comparable to the top-performing CMIP6 models, while maintaining high computational efficiency at all resolutions. These simulations further demonstrate the feasibility of O(10)-km climate simulations, and highlight their added value, particularly for capturing phenomena such as cyclones. Ultimately, these exploratory simulations should be considered an intermediate step toward the development and tuning of even higher-resolution, convection-permitting kilometer-scale configurations.

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