Related papers: Dynamical Landscape and Multistability of a Climate Model

Dynamical Landscape and Multistability of a Climate Model

URL: http://arxiv.org/abs/2010.10374v2
Date: Fri, 8 Jan 2021 23:24:13 GMT
Title: Dynamical Landscape and Multistability of a Climate Model
Authors: Georgios Margazoglou and Tobias Grafke and Alessandro Laio and Valerio Lucarini
Abstract summary: We find a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
Score: 64.467612647225
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We apply two independent data analysis methodologies to locate stable climate states in an intermediate complexity climate model and analyze their interplay. First, drawing from the theory of quasipotentials, and viewing the state space as an energy landscape with valleys and mountain ridges, we infer the relative likelihood of the identified multistable climate states, and investigate the most likely transition trajectories as well as the expected transition times between them. Second, harnessing techniques from data science, specifically manifold learning, we characterize the data landscape of the simulation output to find climate states and basin boundaries within a fully agnostic and unsupervised framework. Both approaches show remarkable agreement, and reveal, apart from the well known warm and snowball earth states, a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production via the hydrological cycle drastically change the topography of the dynamical landscape of Earth's climate.

Related papers

Robustness of AI-based weather forecasts in a changing climate [1.4779266690741741]
We show that current state-of-the-art machine learning models trained for weather forecasting in present-day climate produce skillful forecasts across different climate states. Despite current limitations, our results suggest that data-driven machine learning models will provide powerful tools for climate science.
arXiv Detail & Related papers (2024-09-27T08:11:49Z)
MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
arXiv Detail & Related papers (2024-08-20T13:45:49Z)
Probabilistic Emulation of a Global Climate Model with Spherical DYffusion [15.460280166612119]
We present the first conditional generative model that produces accurate and physically consistent global climate ensemble simulations. Our model integrates the dynamics-informed diffusion framework (DYffusion) with the Spherical Fourier Neural Operator (SFNO) architecture. The model achieves near gold-standard performance for climate model emulation, outperforming existing approaches and demonstrating promising ensemble skill.
arXiv Detail & Related papers (2024-06-21T00:16:55Z)
Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests [47.285748922842444]
We train a deep neural network to predict a phenological index from meteorological time series. We find that this approach outperforms traditional process-based models.
arXiv Detail & Related papers (2024-01-08T15:29:23Z)
Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
arXiv Detail & Related papers (2022-11-20T06:46:35Z)
Spatiotemporal modeling of European paleoclimate using doubly sparse Gaussian processes [61.31361524229248]
We build on recent scale sparsetemporal GPs to reduce the computational burden. We successfully employ such a doubly sparse GP to construct a probabilistic model of paleoclimate.
arXiv Detail & Related papers (2022-11-15T14:15:04Z)
Accessing the topological Mott insulator in cold atom quantum simulators with realistic Rydberg dressing [58.720142291102135]
We investigate a realistic scenario for the quantum simulation of such systems using cold Rydberg-dressed atoms in optical lattices. We perform a detailed analysis of the phase diagram at half- and incommensurate fillings, in the mean-field approximation. We furthermore study the stability of the phases with respect to temperature within the mean-field approximation.
arXiv Detail & Related papers (2022-03-28T14:55:28Z)
HECT: High-Dimensional Ensemble Consistency Testing for Climate Models [1.7587442088965226]
Climate models play a crucial role in understanding the effect of environmental changes on climate to help mitigate climate risks and inform decisions. Large global climate models such as the Community Earth System Model (CESM), are very complex with millions of lines of code describing interactions of the atmosphere, land, oceans, and ice. Our work uses probabilistics like tree-based algorithms and deep neural networks to perform a statistically rigorous goodness-of-fit test of high-dimensional and man-made data.
arXiv Detail & Related papers (2020-10-08T15:16:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.